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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2106.12340
|
Andreea Iana
|
Andreea Iana, Heiko Paulheim
|
GraphConfRec: A Graph Neural Network-Based Conference Recommender System
|
Accepted at the Joint Conference on Digital Libraries (JCDL 2021)
|
2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2021,
pp. 90-99
|
10.1109/JCDL52503.2021.00021
| null |
cs.IR cs.DL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In today's academic publishing model, especially in Computer Science,
conferences commonly constitute the main platforms for releasing the latest
peer-reviewed advancements in their respective fields. However, choosing a
suitable academic venue for publishing one's research can represent a
challenging task considering the plethora of available conferences,
particularly for those at the start of their academic careers, or for those
seeking to publish outside of their usual domain. In this paper, we propose
GraphConfRec, a conference recommender system which combines SciGraph and graph
neural networks, to infer suggestions based not only on title and abstract, but
also on co-authorship and citation relationships. GraphConfRec achieves a
recall@10 of up to 0.580 and a MAP of up to 0.336 with a graph attention
network-based recommendation model. A user study with 25 subjects supports the
positive results.
|
[
{
"version": "v1",
"created": "Wed, 23 Jun 2021 12:10:40 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Iana",
"Andreea",
""
],
[
"Paulheim",
"Heiko",
""
]
] |
new_dataset
| 0.997538 |
2110.08518
|
Lei Cui
|
Junlong Li, Yiheng Xu, Lei Cui, Furu Wei
|
MarkupLM: Pre-training of Text and Markup Language for Visually-rich
Document Understanding
|
ACL 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Multimodal pre-training with text, layout, and image has made significant
progress for Visually Rich Document Understanding (VRDU), especially the
fixed-layout documents such as scanned document images. While, there are still
a large number of digital documents where the layout information is not fixed
and needs to be interactively and dynamically rendered for visualization,
making existing layout-based pre-training approaches not easy to apply. In this
paper, we propose MarkupLM for document understanding tasks with markup
languages as the backbone, such as HTML/XML-based documents, where text and
markup information is jointly pre-trained. Experiment results show that the
pre-trained MarkupLM significantly outperforms the existing strong baseline
models on several document understanding tasks. The pre-trained model and code
will be publicly available at https://aka.ms/markuplm.
|
[
{
"version": "v1",
"created": "Sat, 16 Oct 2021 09:17:28 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Mar 2022 15:38:07 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Li",
"Junlong",
""
],
[
"Xu",
"Yiheng",
""
],
[
"Cui",
"Lei",
""
],
[
"Wei",
"Furu",
""
]
] |
new_dataset
| 0.982959 |
2111.00585
|
Pulkit Verma
|
Naman Shah, Pulkit Verma, Trevor Angle, Siddharth Srivastava
|
JEDAI: A System for Skill-Aligned Explainable Robot Planning
|
AAAMS 2022 (Demonstration Track)
| null | null | null |
cs.AI cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents JEDAI, an AI system designed for outreach and educational
efforts aimed at non-AI experts. JEDAI features a novel synthesis of research
ideas from integrated task and motion planning and explainable AI. JEDAI helps
users create high-level, intuitive plans while ensuring that they will be
executable by the robot. It also provides users customized explanations about
errors and helps improve their understanding of AI planning as well as the
limits and capabilities of the underlying robot system.
|
[
{
"version": "v1",
"created": "Sun, 31 Oct 2021 20:18:45 GMT"
},
{
"version": "v2",
"created": "Sun, 30 Jan 2022 00:01:10 GMT"
},
{
"version": "v3",
"created": "Fri, 11 Mar 2022 08:26:38 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Shah",
"Naman",
""
],
[
"Verma",
"Pulkit",
""
],
[
"Angle",
"Trevor",
""
],
[
"Srivastava",
"Siddharth",
""
]
] |
new_dataset
| 0.998611 |
2203.01017
|
Ahmed Nassar
|
Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar
|
TableFormer: Table Structure Understanding with Transformers
| null | null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Tables organize valuable content in a concise and compact representation.
This content is extremely valuable for systems such as search engines,
Knowledge Graph's, etc, since they enhance their predictive capabilities.
Unfortunately, tables come in a large variety of shapes and sizes. Furthermore,
they can have complex column/row-header configurations, multiline rows,
different variety of separation lines, missing entries, etc. As such, the
correct identification of the table-structure from an image is a non-trivial
task. In this paper, we present a new table-structure identification model. The
latter improves the latest end-to-end deep learning model (i.e.
encoder-dual-decoder from PubTabNet) in two significant ways. First, we
introduce a new object detection decoder for table-cells. In this way, we can
obtain the content of the table-cells from programmatic PDF's directly from the
PDF source and avoid the training of the custom OCR decoders. This
architectural change leads to more accurate table-content extraction and allows
us to tackle non-english tables. Second, we replace the LSTM decoders with
transformer based decoders. This upgrade improves significantly the previous
state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple
tables and from 88.7% to 95% on complex tables.
|
[
{
"version": "v1",
"created": "Wed, 2 Mar 2022 10:46:24 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Mar 2022 14:03:47 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Nassar",
"Ahmed",
""
],
[
"Livathinos",
"Nikolaos",
""
],
[
"Lysak",
"Maksym",
""
],
[
"Staar",
"Peter",
""
]
] |
new_dataset
| 0.997796 |
2203.05306
|
Lei Fan
|
Lei Fan, Yiwen Ding, Dongdong Fan, Donglin Di, Maurice Pagnucco, Yang
Song
|
GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive
Recognition of Cereal Grains
|
8pages, 6 figures, accepted by CVPR2022 dataset is available at
\url{https://github.com/hellodfan/GrainSpace}
| null | null | null |
cs.CV cs.DB
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Cereal grains are a vital part of human diets and are important commodities
for people's livelihood and international trade. Grain Appearance Inspection
(GAI) serves as one of the crucial steps for the determination of grain quality
and grain stratification for proper circulation, storage and food processing,
etc. GAI is routinely performed manually by qualified inspectors with the aid
of some hand tools. Automated GAI has the benefit of greatly assisting
inspectors with their jobs but has been limited due to the lack of datasets and
clear definitions of the tasks.
In this paper we formulate GAI as three ubiquitous computer vision tasks:
fine-grained recognition, domain adaptation and out-of-distribution
recognition. We present a large-scale and publicly available cereal grains
dataset called GrainSpace. Specifically, we construct three types of device
prototypes for data acquisition, and a total of 5.25 million images determined
by professional inspectors. The grain samples including wheat, maize and rice
are collected from five countries and more than 30 regions. We also develop a
comprehensive benchmark based on semi-supervised learning and self-supervised
learning techniques. To the best of our knowledge, GrainSpace is the first
publicly released dataset for cereal grain inspection.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 11:41:28 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Fan",
"Lei",
""
],
[
"Ding",
"Yiwen",
""
],
[
"Fan",
"Dongdong",
""
],
[
"Di",
"Donglin",
""
],
[
"Pagnucco",
"Maurice",
""
],
[
"Song",
"Yang",
""
]
] |
new_dataset
| 0.999882 |
2203.05321
|
Dan Ruta
|
Dan Ruta, Andrew Gilbert, Pranav Aggarwal, Naveen Marri, Ajinkya Kale,
Jo Briggs, Chris Speed, Hailin Jin, Baldo Faieta, Alex Filipkowski, Zhe Lin,
John Collomosse
|
StyleBabel: Artistic Style Tagging and Captioning
| null | null | null | null |
cs.CV cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present StyleBabel, a unique open access dataset of natural language
captions and free-form tags describing the artistic style of over 135K digital
artworks, collected via a novel participatory method from experts studying at
specialist art and design schools. StyleBabel was collected via an iterative
method, inspired by `Grounded Theory': a qualitative approach that enables
annotation while co-evolving a shared language for fine-grained artistic style
attribute description. We demonstrate several downstream tasks for StyleBabel,
adapting the recent ALADIN architecture for fine-grained style similarity, to
train cross-modal embeddings for: 1) free-form tag generation; 2) natural
language description of artistic style; 3) fine-grained text search of style.
To do so, we extend ALADIN with recent advances in Visual Transformer (ViT) and
cross-modal representation learning, achieving a state of the art accuracy in
fine-grained style retrieval.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 12:15:55 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Mar 2022 08:51:33 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Ruta",
"Dan",
""
],
[
"Gilbert",
"Andrew",
""
],
[
"Aggarwal",
"Pranav",
""
],
[
"Marri",
"Naveen",
""
],
[
"Kale",
"Ajinkya",
""
],
[
"Briggs",
"Jo",
""
],
[
"Speed",
"Chris",
""
],
[
"Jin",
"Hailin",
""
],
[
"Faieta",
"Baldo",
""
],
[
"Filipkowski",
"Alex",
""
],
[
"Lin",
"Zhe",
""
],
[
"Collomosse",
"John",
""
]
] |
new_dataset
| 0.996126 |
2203.05338
|
Yusuke Nagata
|
Yusuke Nagata, Jinki Otao, Daichi Haraguchi, and Seiichi Uchida
|
TrueType Transformer: Character and Font Style Recognition in Outline
Format
|
DAS 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose TrueType Transformer (T3), which can perform character and font
style recognition in an outline format. The outline format, such as TrueType,
represents each character as a sequence of control points of stroke contours
and is frequently used in born-digital documents. T3 is organized by a deep
neural network, so-called Transformer. Transformer is originally proposed for
sequential data, such as text, and therefore appropriate for handling the
outline data. In other words, T3 directly accepts the outline data without
converting it into a bitmap image. Consequently, T3 realizes a
resolution-independent classification. Moreover, since the locations of the
control points represent the fine and local structures of the font style, T3 is
suitable for font style classification, where such structures are very
important. In this paper, we experimentally show the applicability of T3 in
character and font style recognition tasks, while observing how the individual
control points contribute to classification results.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 12:38:13 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Mar 2022 02:03:29 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Nagata",
"Yusuke",
""
],
[
"Otao",
"Jinki",
""
],
[
"Haraguchi",
"Daichi",
""
],
[
"Uchida",
"Seiichi",
""
]
] |
new_dataset
| 0.959887 |
2203.05602
|
Osama Shahin R
|
Rady El Rwelli, Osama R. Shahin, Ahmed I. Taloba
|
Gesture based Arabic Sign Language Recognition for Impaired People based
on Convolution Neural Network
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The Arabic Sign Language has endorsed outstanding research achievements for
identifying gestures and hand signs using the deep learning methodology. The
term "forms of communication" refers to the actions used by hearing-impaired
people to communicate. These actions are difficult for ordinary people to
comprehend. The recognition of Arabic Sign Language (ArSL) has become a
difficult study subject due to variations in Arabic Sign Language (ArSL) from
one territory to another and then within states. The Convolution Neural Network
has been encapsulated in the proposed system which is based on the machine
learning technique. For the recognition of the Arabic Sign Language, the
wearable sensor is utilized. This approach has been used a different system
that could suit all Arabic gestures. This could be used by the impaired people
of the local Arabic community. The research method has been used with
reasonable and moderate accuracy. A deep Convolutional network is initially
developed for feature extraction from the data gathered by the sensing devices.
These sensors can reliably recognize the Arabic sign language's 30 hand sign
letters. The hand movements in the dataset were captured using DG5-V hand
gloves with wearable sensors. For categorization purposes, the CNN technique is
used. The suggested system takes Arabic sign language hand gestures as input
and outputs vocalized speech as output. The results were recognized by 90% of
the people.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 19:36:04 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Rwelli",
"Rady El",
""
],
[
"Shahin",
"Osama R.",
""
],
[
"Taloba",
"Ahmed I.",
""
]
] |
new_dataset
| 0.954581 |
2203.05657
|
Jens Helge Reelfs
|
Jens Helge Reelfs and Oliver Hohlfeld and Niklas Henckell
|
Anonymous Hyperlocal Communities: What do they talk about?
|
Accepted submission to the 12th International Workshop on Location
and the Web (LocWeb'22). To appear in WWW'22 Companion proceedings; 9 pages,
5 figures, 3 tables
| null |
10.1145/3487553.3524644
| null |
cs.SI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
In this paper, we study what users talk about in a plethora of independent
hyperlocal and anonymous online communities in a single country: Saudi Arabia
(KSA). We base this perspective on performing a content classification of the
Jodel network in the KSA. To do so, we first contribute a content
classification schema that assesses both the intent (why) and the topic (what)
of posts. We use the schema to label 15k randomly sampled posts and further
classify the top 1k hashtags. We observe a rich set of benign (yet at times
controversial in conservative regimes) intents and topics that dominantly
address information requests, entertainment, or dating/flirting. By comparing
two large cities (Riyadh and Jeddah), we further show that hyperlocality leads
to shifts in topic popularity between local communities. By evaluating votes
(content appreciation) and replies (reactions), we show that the communities
react differently to different topics; e.g., entertaining posts are much
appreciated through votes, receiving the least replies, while beliefs &
politics receive similarly few replies but are controversially voted.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 21:48:22 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Reelfs",
"Jens Helge",
""
],
[
"Hohlfeld",
"Oliver",
""
],
[
"Henckell",
"Niklas",
""
]
] |
new_dataset
| 0.988423 |
2203.05875
|
Mar\'ia Alejandra Cardoza Cer\'on
|
Maria Alejandra Cardoza Ceron
|
Using Word Embeddings to Analyze Protests News
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The first two tasks of the CLEF 2019 ProtestNews events focused on
distinguishing between protest and non-protest related news articles and
sentences in a binary classification task. Among the submissions, two well
performing models have been chosen in order to replace the existing word
embeddings word2vec and FastTest with ELMo and DistilBERT. Unlike bag of words
or earlier vector approaches, ELMo and DistilBERT represent words as a sequence
of vectors by capturing the meaning based on contextual information in the
text. Without changing the architecture of the original models other than the
word embeddings, the implementation of DistilBERT improved the performance
measured on the F1-Score of 0.66 compared to the FastText implementation.
DistilBERT also outperformed ELMo in both tasks and models. Cleaning the
datasets by removing stopwords and lemmatizing the words has been shown to make
the models more generalizable across different contexts when training on a
dataset with Indian news articles and evaluating the models on a dataset with
news articles from China.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 12:25:59 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Ceron",
"Maria Alejandra Cardoza",
""
]
] |
new_dataset
| 0.981635 |
2203.05908
|
Araceli Morales
|
Araceli Morales, Antonio R. Porras, Marius George Linguraru, Gemma
Piella, Federico M. Sukno
|
BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a 3D face reconstruction system that aims at recovering the 3D
facial geometry of babies from uncalibrated photographs, BabyNet. Since the 3D
facial geometry of babies differs substantially from that of adults,
baby-specific facial reconstruction systems are needed. BabyNet consists of two
stages: 1) a 3D graph convolutional autoencoder learns a latent space of the
baby 3D facial shape; and 2) a 2D encoder that maps photographs to the 3D
latent space based on representative features extracted using transfer
learning. In this way, using the pre-trained 3D decoder, we can recover a 3D
face from 2D images. We evaluate BabyNet and show that 1) methods based on
adult datasets cannot model the 3D facial geometry of babies, which proves the
need for a baby-specific method, and 2) BabyNet outperforms classical
model-fitting methods even when a baby-specific 3D morphable model, such as
BabyFM, is used.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 13:26:25 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Morales",
"Araceli",
""
],
[
"Porras",
"Antonio R.",
""
],
[
"Linguraru",
"Marius George",
""
],
[
"Piella",
"Gemma",
""
],
[
"Sukno",
"Federico M.",
""
]
] |
new_dataset
| 0.992908 |
2203.05928
|
Shiwen Zhang
|
Shiwen Zhang
|
TFCNet: Temporal Fully Connected Networks for Static Unbiased Temporal
Reasoning
|
The code and model will be released once accepted
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Temporal Reasoning is one important functionality for vision intelligence. In
computer vision research community, temporal reasoning is usually studied in
the form of video classification, for which many state-of-the-art Neural
Network structures and dataset benchmarks are proposed in recent years,
especially 3D CNNs and Kinetics. However, some recent works found that current
video classification benchmarks contain strong biases towards static features,
thus cannot accurately reflect the temporal modeling ability. New video
classification benchmarks aiming to eliminate static biases are proposed, with
experiments on these new benchmarks showing that the current clip-based 3D CNNs
are outperformed by RNN structures and recent video transformers.
In this paper, we find that 3D CNNs and their efficient depthwise variants,
when video-level sampling strategy is used, are actually able to beat RNNs and
recent vision transformers by significant margins on static-unbiased temporal
reasoning benchmarks. Further, we propose Temporal Fully Connected Block (TFC
Block), an efficient and effective component, which approximates fully
connected layers along temporal dimension to obtain video-level receptive
field, enhancing the spatiotemporal reasoning ability. With TFC blocks inserted
into Video-level 3D CNNs (V3D), our proposed TFCNets establish new
state-of-the-art results on synthetic temporal reasoning benchmark, CATER, and
real world static-unbiased dataset, Diving48, surpassing all previous methods.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 13:58:05 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Zhang",
"Shiwen",
""
]
] |
new_dataset
| 0.999422 |
2203.05943
|
Dominique Attali
|
Dominique Attali and Andr\'e Lieutier
|
Flat Delaunay Complexes for Homeomorphic Manifold Reconstruction
|
38 pages, 2 figures
| null | null | null |
cs.CG
|
http://creativecommons.org/licenses/by/4.0/
|
Given a smooth submanifold of the Euclidean space, a finite point cloud and a
scale parameter, we introduce a construction which we call the flat Delaunay
complex (FDC). This is a variant of the tangential Delaunay complex (TDC)
introduced by Boissonnat et al.. Building on their work, we provide a short and
direct proof that when the point cloud samples sufficiently nicely the
submanifold and is sufficiently safe (a notion which we define in the paper),
our construction is homeomorphic to the submanifold. Because the proof works
even when data points are noisy, this allows us to propose a perturbation
scheme that takes as input a point cloud sufficiently nice and returns a point
cloud which in addition is sufficiently safe. Equally importantly, our
construction provides the framework underlying a variational formulation of the
reconstruction problem which we present in a companion paper.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 14:25:03 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Attali",
"Dominique",
""
],
[
"Lieutier",
"André",
""
]
] |
new_dataset
| 0.99839 |
2203.05944
|
Kristian Fischer
|
Kristian Fischer, Felix Fleckenstein, Christian Herglotz, Andr\'e Kaup
|
Saliency-Driven Versatile Video Coding for Neural Object Detection
|
5 pages, 3 figures, 2 tables; Originally submitted at IEEE ICASSP
2021
|
IEEE ICASSP 2021
|
10.1109/ICASSP39728.2021.9415048
| null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Saliency-driven image and video coding for humans has gained importance in
the recent past. In this paper, we propose such a saliency-driven coding
framework for the video coding for machines task using the latest video coding
standard Versatile Video Coding (VVC). To determine the salient regions before
encoding, we employ the real-time-capable object detection network You Only
Look Once~(YOLO) in combination with a novel decision criterion. To measure the
coding quality for a machine, the state-of-the-art object segmentation network
Mask R-CNN was applied to the decoded frame. From extensive simulations we find
that, compared to the reference VVC with a constant quality, up to 29 % of
bitrate can be saved with the same detection accuracy at the decoder side by
applying the proposed saliency-driven framework. Besides, we compare YOLO
against other, more traditional saliency detection methods.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 14:27:43 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Fischer",
"Kristian",
""
],
[
"Fleckenstein",
"Felix",
""
],
[
"Herglotz",
"Christian",
""
],
[
"Kaup",
"André",
""
]
] |
new_dataset
| 0.99917 |
2203.05949
|
Rui Liu
|
Yaowei Wang, Zhouxin Yang, Rui Liu, Deng Li, Yuandu Lai, Leyuan Fang,
Yahong Han
|
Peng Cheng Object Detection Benchmark for Smart City
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Object detection is an algorithm that recognizes and locates the objects in
the image and has a wide range of applications in the visual understanding of
complex urban scenes. Existing object detection benchmarks mainly focus on a
single specific scenario and their annotation attributes are not rich enough,
these make the object detection model is not generalized for the smart city
scenes. Considering the diversity and complexity of scenes in intelligent city
governance, we build a large-scale object detection benchmark for the smart
city. Our benchmark contains about 500K images and includes three scenarios:
intelligent transportation, intelligent security, and drones. For the
complexity of the real scene in the smart city, the diversity of weather,
occlusion, and other complex environment diversity attributes of the images in
the three scenes are annotated. The characteristics of the benchmark are
analyzed and extensive experiments of the current state-of-the-art target
detection algorithm are conducted based on our benchmark to show their
performance.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 14:39:48 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Wang",
"Yaowei",
""
],
[
"Yang",
"Zhouxin",
""
],
[
"Liu",
"Rui",
""
],
[
"Li",
"Deng",
""
],
[
"Lai",
"Yuandu",
""
],
[
"Fang",
"Leyuan",
""
],
[
"Han",
"Yahong",
""
]
] |
new_dataset
| 0.99946 |
2203.05967
|
Yi Fung
|
Yi R. Fung and Heng Ji
|
A Weibo Dataset for the 2022 Russo-Ukrainian Crisis
|
Russia-Ukraine Crisis, Weibo Dataset
| null | null | null |
cs.SI cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Online social networks such as Twitter and Weibo play an important role in
how people stay informed and exchange reactions. Each crisis encompasses a new
opportunity to study the portability of models for various tasks (e.g.,
information extraction, complex event understanding, misinformation detection,
etc.), due to differences in domain, entities, and event types. We present the
Russia-Ukraine Crisis Weibo (RUW) dataset, with over 3.5M user posts and
comments in the first release. Our data is available at
https://github.com/yrf1/RussiaUkraine_weibo_dataset.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 19:06:04 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Fung",
"Yi R.",
""
],
[
"Ji",
"Heng",
""
]
] |
new_dataset
| 0.999769 |
2203.06061
|
Weilu Gao
|
Yingheng Tang, Princess Tara Zamani, Ruiyang Chen, Jianzhu Ma, Minghao
Qi, Cunxi Yu, and Weilu Gao
|
Device-system Co-design of Photonic Neuromorphic Processor using
Reinforcement Learning
|
30 pages, 4 figures
| null | null | null |
cs.ET physics.optics
|
http://creativecommons.org/licenses/by/4.0/
|
The incorporation of high-performance optoelectronic devices into photonic
neuromorphic processors can substantially accelerate computationally intensive
operations in machine learning (ML) algorithms. However, the conventional
device design wisdom is disconnected with system optimization. We report a
device-system co-design methodology to optimize a free-space optical general
matrix multiplication (GEMM) hardware accelerator by engineering a spatially
reconfigurable array made from chalcogenide phase change materials. With a
highly-parallelized hardware emulator constructed based on experimental
information, we demonstrate the design of unit device by optimizing GEMM
calculation accuracy via reinforcement learning, including deep Q-learning
neural network, Bayesian optimization, and their cascaded approach, which show
a clear correlation between system performance metrics and physical device
specifications. Furthermore, we employ physics-aware training approaches to
deploy optimized hardware to the tasks of image classification, materials
discovery, and a closed-loop design of optical ML accelerators. The
demonstrated framework offers insights into the co-design of optoelectronic
devices and systems with reduced human-supervision and domain-knowledge
barriers.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 17:18:41 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Tang",
"Yingheng",
""
],
[
"Zamani",
"Princess Tara",
""
],
[
"Chen",
"Ruiyang",
""
],
[
"Ma",
"Jianzhu",
""
],
[
"Qi",
"Minghao",
""
],
[
"Yu",
"Cunxi",
""
],
[
"Gao",
"Weilu",
""
]
] |
new_dataset
| 0.952654 |
2203.06075
|
Micha\"el Cadilhac
|
Corentin Barloy and Micha\"el Cadilhac and Charles Paperman and Thomas
Zeume
|
The Regular Languages of First-Order Logic with One Alternation
|
11 pages + bibliography, submitted to LICS'22
| null | null | null |
cs.LO cs.FL
|
http://creativecommons.org/licenses/by/4.0/
|
The regular languages with a neutral letter expressible in first-order logic
with one alternation are characterized. Specifically, it is shown that if an
arbitrary $\Sigma_2$ formula defines a regular language with a neutral letter,
then there is an equivalent $\Sigma_2$ formula that only uses the order
predicate. This shows that the so-called Central Conjecture of Straubing holds
for $\Sigma_2$ over languages with a neutral letter, the first progress on the
Conjecture in more than 20 years. To show the characterization, lower bounds
against polynomial-size depth-3 Boolean circuits with constant top fan-in are
developed. The heart of the combinatorial argument resides in studying how
positions within a language are determined from one another, a technique of
independent interest.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 16:53:25 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Barloy",
"Corentin",
""
],
[
"Cadilhac",
"Michaël",
""
],
[
"Paperman",
"Charles",
""
],
[
"Zeume",
"Thomas",
""
]
] |
new_dataset
| 0.999626 |
2203.06082
|
Fernando Alonso-Fernandez
|
Pontus Hedman, Vasilios Skepetzis, Kevin Hernandez-Diaz, Josef Bigun,
Fernando Alonso-Fernandez
|
LFW-Beautified: A Dataset of Face Images with Beautification and
Augmented Reality Filters
|
Under consideration at Elsevier Data in Brief
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Selfie images enjoy huge popularity in social media. The same platforms
centered around sharing this type of images offer filters to beautify them or
incorporate augmented reality effects. Studies suggests that filtered images
attract more views and engagement. Selfie images are also in increasing use in
security applications due to mobiles becoming data hubs for many transactions.
Also, video conference applications, boomed during the pandemic, include such
filters.
Such filters may destroy biometric features that would allow person
recognition or even detection of the face itself, even if such commodity
applications are not necessarily used to compromise facial systems. This could
also affect subsequent investigations like crimes in social media, where
automatic analysis is usually necessary given the amount of information posted
in social sites or stored in devices or cloud repositories.
To help in counteracting such issues, we contribute with a database of facial
images that includes several manipulations. It includes image enhancement
filters (which mostly modify contrast and lightning) and augmented reality
filters that incorporate items like animal noses or glasses. Additionally,
images with sunglasses are processed with a reconstruction network trained to
learn to reverse such modifications. This is because obfuscating the eye region
has been observed in the literature to have the highest impact on the accuracy
of face detection or recognition.
We start from the popular Labeled Faces in the Wild (LFW) database, to which
we apply different modifications, generating 8 datasets. Each dataset contains
4,324 images of size 64 x 64, with a total of 34,592 images. The use of a
public and widely employed face dataset allows for replication and comparison.
The created database is available at
https://github.com/HalmstadUniversityBiometrics/LFW-Beautified
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 17:05:10 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Hedman",
"Pontus",
""
],
[
"Skepetzis",
"Vasilios",
""
],
[
"Hernandez-Diaz",
"Kevin",
""
],
[
"Bigun",
"Josef",
""
],
[
"Alonso-Fernandez",
"Fernando",
""
]
] |
new_dataset
| 0.999576 |
2203.06096
|
Federico Tavella
|
Federico Tavella and Viktor Schlegel and Marta Romeo and Aphrodite
Galata and Angelo Cangelosi
|
WLASL-LEX: a Dataset for Recognising Phonological Properties in American
Sign Language
|
Accepted at ACL 2022 main conference
| null | null | null |
cs.CL cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Signed Language Processing (SLP) concerns the automated processing of signed
languages, the main means of communication of Deaf and hearing impaired
individuals. SLP features many different tasks, ranging from sign recognition
to translation and production of signed speech, but has been overlooked by the
NLP community thus far. In this paper, we bring to attention the task of
modelling the phonology of sign languages. We leverage existing resources to
construct a large-scale dataset of American Sign Language signs annotated with
six different phonological properties. We then conduct an extensive empirical
study to investigate whether data-driven end-to-end and feature-based
approaches can be optimised to automatically recognise these properties. We
find that, despite the inherent challenges of the task, graph-based neural
networks that operate over skeleton features extracted from raw videos are able
to succeed at the task to a varying degree. Most importantly, we show that this
performance pertains even on signs unobserved during training.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 17:21:24 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Tavella",
"Federico",
""
],
[
"Schlegel",
"Viktor",
""
],
[
"Romeo",
"Marta",
""
],
[
"Galata",
"Aphrodite",
""
],
[
"Cangelosi",
"Angelo",
""
]
] |
new_dataset
| 0.999853 |
2203.06117
|
Yanqing Zhang
|
Yanqing Zhang, Haoxing Ren, Akshay Sridharan, Brucek Khailany
|
GATSPI: GPU Accelerated Gate-Level Simulation for Power Improvement
| null | null | null | null |
cs.LG cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present GATSPI, a novel GPU accelerated logic gate
simulator that enables ultra-fast power estimation for industry sized ASIC
designs with millions of gates. GATSPI is written in PyTorch with custom CUDA
kernels for ease of coding and maintainability. It achieves simulation kernel
speedup of up to 1668X on a single-GPU system and up to 7412X on a multiple-GPU
system when compared to a commercial gate-level simulator running on a single
CPU core. GATSPI supports a range of simple to complex cell types from an
industry standard cell library and SDF conditional delay statements without
requiring prior calibration runs and produces industry-standard SAIF files from
delay-aware gate-level simulation. Finally, we deploy GATSPI in a
glitch-optimization flow, achieving a 1.4% power saving with a 449X speedup in
turnaround time compared to a similar flow using a commercial simulator.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 17:40:19 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Zhang",
"Yanqing",
""
],
[
"Ren",
"Haoxing",
""
],
[
"Sridharan",
"Akshay",
""
],
[
"Khailany",
"Brucek",
""
]
] |
new_dataset
| 0.999655 |
2203.06143
|
Alexandra Weinberger
|
Oswin Aichholzer and Alfredo Garc\'ia and Javier Tejel and Birgit
Vogtenhuber and Alexandra Weinberger
|
Twisted Ways to Find Plane Structures in Simple Drawings of Complete
Graphs
|
41 pages, 44 figures, this work (without appendix) will be available
in the proceedings of the 38th International Symposium on Computational
Geometry (SoCG 2022)
| null | null | null |
cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Simple drawings are drawings of graphs in which the edges are Jordan arcs and
each pair of edges share at most one point (a proper crossing or a common
endpoint). We introduce a special kind of simple drawings that we call
generalized twisted drawings. A simple drawing is generalized twisted if there
is a point $O$ such that every ray emanating from $O$ crosses every edge of the
drawing at most once and there is a ray emanating from $O$ which crosses every
edge exactly once.
Via this new class of simple drawings, we show that every simple drawing of
the complete graph with $n$ vertices contains $\Omega(n^{\frac{1}{2}})$
pairwise disjoint edges and a plane path of length $\Omega(\frac{\log n }{\log
\log n})$. Both results improve over previously known best lower bounds. On the
way we show several structural results about and properties of generalized
twisted drawings. We further present different characterizations of generalized
twisted drawings, which might be of independent interest.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 18:15:49 GMT"
}
] | 2022-03-14T00:00:00 |
[
[
"Aichholzer",
"Oswin",
""
],
[
"García",
"Alfredo",
""
],
[
"Tejel",
"Javier",
""
],
[
"Vogtenhuber",
"Birgit",
""
],
[
"Weinberger",
"Alexandra",
""
]
] |
new_dataset
| 0.987861 |
2108.04314
|
Fangtian Zhong
|
Fangtian Zhong, Zekai Chen, Minghui Xu, Guoming Zhang, Dongxiao Yu,
Xiuzhen Cheng
|
Malware-on-the-Brain: Illuminating Malware Byte Codes with Images for
Malware Classification
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Malware is a piece of software that was written with the intent of doing harm
to data, devices, or people. Since a number of new malware variants can be
generated by reusing codes, malware attacks can be easily launched and thus
become common in recent years, incurring huge losses in businesses,
governments, financial institutes, health providers, etc. To defeat these
attacks, malware classification is employed, which plays an essential role in
anti-virus products. However, existing works that employ either static analysis
or dynamic analysis have major weaknesses in complicated reverse engineering
and time-consuming tasks. In this paper, we propose a visualized malware
classification framework called VisMal, which provides highly efficient
categorization with acceptable accuracy. VisMal converts malware samples into
images and then applies a contrast-limited adaptive histogram equalization
algorithm to enhance the similarity between malware image regions in the same
family. We provided a proof-of-concept implementation and carried out an
extensive evaluation to verify the performance of our framework. The evaluation
results indicate that VisMal can classify a malware sample within 4.0ms and
have an average accuracy of 96.0%. Moreover, VisMal provides security engineers
with a simple visualization approach to further validate its performance.
|
[
{
"version": "v1",
"created": "Mon, 9 Aug 2021 19:18:56 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Mar 2022 15:27:08 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Mar 2022 23:06:02 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Zhong",
"Fangtian",
""
],
[
"Chen",
"Zekai",
""
],
[
"Xu",
"Minghui",
""
],
[
"Zhang",
"Guoming",
""
],
[
"Yu",
"Dongxiao",
""
],
[
"Cheng",
"Xiuzhen",
""
]
] |
new_dataset
| 0.9997 |
2108.05560
|
Hyungtae Lim
|
Hyungtae Lim, Minho Oh, Hyun Myung
|
Patchwork: Concentric Zone-based Region-wise Ground Segmentation with
Ground Likelihood Estimation Using a 3D LiDAR Sensor
| null | null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Ground segmentation is crucial for terrestrial mobile platforms to perform
navigation or neighboring object recognition. Unfortunately, the ground is not
flat, as it features steep slopes; bumpy roads; or objects, such as curbs,
flower beds, and so forth. To tackle the problem, this paper presents a novel
ground segmentation method called \textit{Patchwork}, which is robust for
addressing the under-segmentation problem and operates at more than 40 Hz. In
this paper, a point cloud is encoded into a Concentric Zone Model-based
representation to assign an appropriate density of cloud points among bins in a
way that is not computationally complex. This is followed by Region-wise Ground
Plane Fitting, which is performed to estimate the partial ground for each bin.
Finally, Ground Likelihood Estimation is introduced to dramatically reduce
false positives. As experimentally verified on SemanticKITTI and rough terrain
datasets, our proposed method yields promising performance compared with the
state-of-the-art methods, showing faster speed compared with existing plane
fitting--based methods. Code is available:
https://github.com/LimHyungTae/patchwork
|
[
{
"version": "v1",
"created": "Thu, 12 Aug 2021 06:52:10 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Mar 2022 11:13:23 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Lim",
"Hyungtae",
""
],
[
"Oh",
"Minho",
""
],
[
"Myung",
"Hyun",
""
]
] |
new_dataset
| 0.999577 |
2110.02498
|
Diqun Yan
|
Jiahao Chen, Diqun Yan
|
Adversarial Attacks on Machinery Fault Diagnosis
|
5 pages, 5 figures. Submitted to Interspeech 2022
| null | null | null |
cs.CR cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Despite the great progress of neural network-based (NN-based) machinery fault
diagnosis methods, their robustness has been largely neglected, for they can be
easily fooled through adding imperceptible perturbation to the input. For fault
diagnosis problems, in this paper, we reformulate various adversarial attacks
and intensively investigate them under untargeted and targeted conditions.
Experimental results on six typical NN-based models show that accuracies of the
models are greatly reduced by adding small perturbations. We further propose a
simple, efficient and universal scheme to protect the victim models. This work
provides an in-depth look at adversarial examples of machinery vibration
signals for developing protection methods against adversarial attack and
improving the robustness of NN-based models.
|
[
{
"version": "v1",
"created": "Wed, 6 Oct 2021 04:26:30 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Mar 2022 08:25:52 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Chen",
"Jiahao",
""
],
[
"Yan",
"Diqun",
""
]
] |
new_dataset
| 0.970182 |
2111.08492
|
Xing Li
|
Xing Li, Qian Huang, Zhijian Wang, Zhenjie Hou, Tianjin Yang
|
SequentialPointNet: A strong frame-level parallel point cloud sequence
network for 3D action recognition
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The point cloud sequence of 3D human actions consists of a set of ordered
point cloud frames. Compared to static point clouds, point cloud sequences have
huge data sizes proportional to the time dimension. Therefore, developing an
efficient and lightweight point cloud sequence model is pivotal for 3D action
recognition. In this paper, we propose a strong frame-level parallel point
cloud sequence network referred to as SequentialPointNet for 3D action
recognition. The key to our approach is to divide the main modeling operations
into frame-level units executed in parallel, which greatly improves the
efficiency of modeling point cloud sequences.Moreover, we propose to flatten
the point cloud sequence into a new point data type named hyperpoint sequence
that preserves the complete spatial structure of each frame. Then, a novel
Hyperpoint-Mixer module is introduced to mix intra-frame spatial features and
inter-frame temporal features of the hyperpoint sequence. By doing so,
SequentialPointNet maximizes the appearance encoding ability and extracts
sufficient motion information for effective human action recognition. Extensive
experiments show that SequentialPointNet achieves up to 10X faster than
existing point cloud sequence models. Additionally, our SequentialPointNet
surpasses state-of-the-art approaches for human action recognition on both
large-scale datasets (i.e., NTU RGB+D 60 and NTU RGB+D 120) and small-scale
datasets (i.e., MSR Action3D and UTD-MHAD).
|
[
{
"version": "v1",
"created": "Tue, 16 Nov 2021 14:13:32 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Mar 2022 13:55:29 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Li",
"Xing",
""
],
[
"Huang",
"Qian",
""
],
[
"Wang",
"Zhijian",
""
],
[
"Hou",
"Zhenjie",
""
],
[
"Yang",
"Tianjin",
""
]
] |
new_dataset
| 0.999445 |
2201.03954
|
Joshua Joseph
|
Kasia S. Chmielinski, Sarah Newman, Matt Taylor, Josh Joseph, Kemi
Thomas, Jessica Yurkofsky, Yue Chelsea Qiu
|
The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate
Harms in Artificial Intelligence
| null | null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
As the production of and reliance on datasets to produce automated
decision-making systems (ADS) increases, so does the need for processes for
evaluating and interrogating the underlying data. After launching the Dataset
Nutrition Label in 2018, the Data Nutrition Project has made significant
updates to the design and purpose of the Label, and is launching an updated
Label in late 2020, which is previewed in this paper. The new Label includes
context-specific Use Cases &Alerts presented through an updated design and user
interface targeted towards the data scientist profile. This paper discusses the
harm and bias from underlying training data that the Label is intended to
mitigate, the current state of the work including new datasets being labeled,
new and existing challenges, and further directions of the work, as well as
Figures previewing the new label.
|
[
{
"version": "v1",
"created": "Mon, 10 Jan 2022 18:43:47 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Mar 2022 17:45:13 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Chmielinski",
"Kasia S.",
""
],
[
"Newman",
"Sarah",
""
],
[
"Taylor",
"Matt",
""
],
[
"Joseph",
"Josh",
""
],
[
"Thomas",
"Kemi",
""
],
[
"Yurkofsky",
"Jessica",
""
],
[
"Qiu",
"Yue Chelsea",
""
]
] |
new_dataset
| 0.994708 |
2201.07048
|
Tianyu Fang
|
Tianyu Fang, Yijie Mao, Shanpu Shen, Zhencai Zhu, Bruno Clerckx
|
Fully Connected Reconfigurable Intelligent Surface Aided Rate-Splitting
Multiple Access for Multi-User Multi-Antenna Transmission
|
6 pages, 5figures, conference
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Rate-splitting multiple access (RSMA) has been recognized as a promising and
powerful multiple access (MA) scheme, non-orthogonal transmission framework and
interference management strategy for 6G. Inspired by the appealing spectral
efficiency gain achieved by RSMA over conventional MA schemes in multi-user
multi-antenna transmission, in this paper we introduce RSMA to reconfigurable
intelligent surface (RIS)-aided multiple-input single-out (MISO) broadcast
channel (BC). To further enhance the spectral efficiency, a more generalized
RIS architecture called fully connected RIS is considered. By jointly
optimizing the scattering matrix of the fully connected RIS and the transmit
beamformers to maximize the sum-rate, we show that the proposed fully connected
RIS aided RSMA transmission scheme significantly improves the spectral
efficiency compared with the conventional single connected RIS schemes and the
schemes without RIS. It acts as a new benchmark for linearly precoded
multi-user multi-antenna networks.
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 15:20:02 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Mar 2022 05:19:47 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Fang",
"Tianyu",
""
],
[
"Mao",
"Yijie",
""
],
[
"Shen",
"Shanpu",
""
],
[
"Zhu",
"Zhencai",
""
],
[
"Clerckx",
"Bruno",
""
]
] |
new_dataset
| 0.977731 |
2203.03560
|
Xudong Zhang
|
Xudong Zhang, Zan Wang, Jingke Zhao, Lanjun Wang
|
Targeted Data Poisoning Attack on News Recommendation System by Content
Perturbation
| null | null | null | null |
cs.CR cs.AI cs.IR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
News Recommendation System(NRS) has become a fundamental technology to many
online news services. Meanwhile, several studies show that recommendation
systems(RS) are vulnerable to data poisoning attacks, and the attackers have
the ability to mislead the system to perform as their desires. A widely studied
attack approach, injecting fake users, can be applied on the NRS when the NRS
is treated the same as the other systems whose items are fixed. However, in the
NRS, as each item (i.e. news) is more informative, we propose a novel approach
to poison the NRS, which is to perturb contents of some browsed news that
results in the manipulation of the rank of the target news. Intuitively, an
attack is useless if it is highly likely to be caught, i.e., exposed. To
address this, we introduce a notion of the exposure risk and propose a novel
problem of attacking a history news dataset by means of perturbations where the
goal is to maximize the manipulation of the target news rank while keeping the
risk of exposure under a given budget. We design a reinforcement learning
framework, called TDP-CP, which contains a two-stage hierarchical model to
reduce the searching space. Meanwhile, influence estimation is also applied to
save the time on retraining the NRS for rewards. We test the performance of
TDP-CP under three NRSs and on different target news. Our experiments show that
TDP-CP can increase the rank of the target news successfully with a limited
exposure budget.
|
[
{
"version": "v1",
"created": "Fri, 4 Mar 2022 16:01:11 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Mar 2022 04:10:12 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Zhang",
"Xudong",
""
],
[
"Wang",
"Zan",
""
],
[
"Zhao",
"Jingke",
""
],
[
"Wang",
"Lanjun",
""
]
] |
new_dataset
| 0.961953 |
2203.05043
|
Alexander Chang
|
Alexander H. Chang, Patricio A. Vela
|
In-Place Rotation for Enhancing Snake-like Robot Mobility
|
8 pages, 5 figures. Submitted to RA-L (IEEE Robotics and Automation
Letters) with IROS 2022 Option
| null | null | null |
cs.RO cs.SY eess.SY
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Gaits engineered for snake-like robots to rotate in-place instrumentally fill
a gap in the set of locomotive gaits that have traditionally prioritized
translation. This paper designs a Turn-in-Place gait and demonstrates the
ability of a shape-centric modeling framework to capture the gait's locomotive
properties. Shape modeling for turning involves a time-varying continuous body
curve described by a standing wave. Presumed viscous robot-ground frictional
interactions lead to body dynamics conditioned on the time-varying shape model.
The dynamic equations describing the Turn-in-Place gait are validated by an
articulated snake-like robot using a physics-based simulator and a physical
robot. The results affirm the shape-centric modeling framework's capacity to
model a variety of snake-like robot gaits with fundamentally different
body-ground contact patterns. As an applied demonstration, example locomotion
scenarios partner the shape-centric Turn-in-Place gait with a Rectilinear gait
for maneuvering through constrained environments based on a multi-modal
locomotive planning strategy. Unified shape-centric modeling facilitates
trajectory planning and tracking for a snake-like robot to successfully
negotiate non-trivial obstacle configurations.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 20:57:20 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Chang",
"Alexander H.",
""
],
[
"Vela",
"Patricio A.",
""
]
] |
new_dataset
| 0.994986 |
2203.05160
|
Subhash Bhagat
|
Subhash Bhagat and Andrzej Pelc
|
Deterministic Rendezvous in Infinite Trees
| null | null | null | null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
The rendezvous task calls for two mobile agents, starting from different
nodes of a network modeled as a graph to meet at the same node. Agents have
different labels which are integers from a set $\{1,\dots,L\}$. They wake up at
possibly different times and move in synchronous rounds. In each round, an
agent can either stay idle or move to an adjacent node. We consider
deterministic rendezvous algorithms. The time of such an algorithm is the
number of rounds since the wakeup of the earlier agent till the meeting. In
this paper we consider rendezvous in infinite trees. Our main goal is to study
the impact of orientation of a tree on the time of rendezvous.
We first design a rendezvous algorithm working for unoriented regular trees,
whose time is in $O(z(D) \log L)$, where $z(D)$ is the size of the ball of
radius $D$, i.e, the number of nodes at distance at most $D$ from a given node.
The algorithm works for arbitrary delay between waking times of agents and does
not require any initial information about parameters $L$ or $D$. Its
disadvantage is its complexity: $z(D)$ is exponential in $D$ for any degree
$d>2$ of the tree. We prove that this high complexity is inevitable:
$\Omega(z(D))$ turns out to be a lower bound on rendezvous time in unoriented
regular trees, even for simultaneous start and even when agents know $L$ and
$D$. Then we turn attention to oriented trees. While for arbitrary delay
between waking times of agents the lower bound $\Omega(z(D))$ still holds, for
simultaneous start the time of rendezvous can be dramatically shortened. We
show that if agents know either a polynomial upper bound on $L$ or a linear
upper bound on $D$, then rendezvous can be accomplished in oriented trees in
time $O(D\log L)$, which is optimal. When no such extra knowledge is available,
we design an algorithm working in time $O(D^2+\log ^2L)$.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 05:17:09 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Bhagat",
"Subhash",
""
],
[
"Pelc",
"Andrzej",
""
]
] |
new_dataset
| 0.996267 |
2203.05173
|
Satyendra Singh Chouhan
|
Sanskar Soni, Satyendra Singh Chouhan, and Santosh Singh Rathore
|
TextConvoNet:A Convolutional Neural Network based Architecture for Text
Classification
| null | null | null | null |
cs.CL cs.AI cs.LG cs.NE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In recent years, deep learning-based models have significantly improved the
Natural Language Processing (NLP) tasks. Specifically, the Convolutional Neural
Network (CNN), initially used for computer vision, has shown remarkable
performance for text data in various NLP problems. Most of the existing
CNN-based models use 1-dimensional convolving filters n-gram detectors), where
each filter specialises in extracting n-grams features of a particular input
word embedding. The input word embeddings, also called sentence matrix, is
treated as a matrix where each row is a word vector. Thus, it allows the model
to apply one-dimensional convolution and only extract n-gram based features
from a sentence matrix. These features can be termed as intra-sentence n-gram
features. To the extent of our knowledge, all the existing CNN models are based
on the aforementioned concept. In this paper, we present a CNN-based
architecture TextConvoNet that not only extracts the intra-sentence n-gram
features but also captures the inter-sentence n-gram features in input text
data. It uses an alternative approach for input matrix representation and
applies a two-dimensional multi-scale convolutional operation on the input. To
evaluate the performance of TextConvoNet, we perform an experimental study on
five text classification datasets. The results are evaluated by using various
performance metrics. The experimental results show that the presented
TextConvoNet outperforms state-of-the-art machine learning and deep learning
models for text classification purposes.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 06:09:56 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Soni",
"Sanskar",
""
],
[
"Chouhan",
"Satyendra Singh",
""
],
[
"Rathore",
"Santosh Singh",
""
]
] |
new_dataset
| 0.990497 |
2203.05199
|
Zuohui Chen
|
Yun Xiang, Qijun Chen, Zhongjin Su, Lu Zhang, Zuohui Chen, Guozhi
Zhou, Zhuping Yao, Qi Xuan, and Yuan Cheng
|
Hyperspectral Imaging for cherry tomato
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cherry tomato (Solanum Lycopersicum) is popular with consumers over the world
due to its special flavor. Soluble solids content (SSC) and firmness are two
key metrics for evaluating the product qualities. In this work, we develop
non-destructive testing techniques for SSC and fruit firmness based on
hyperspectral images and a corresponding deep learning regression model.
Hyperspectral reflectance images of over 200 tomato fruits are derived with
spectrum ranging from 400 to 1000 nm. The acquired hyperspectral images are
corrected and the spectral information is extracted. A novel
one-dimensional(1D) convolutional ResNet (Con1dResNet) based regression model
is prosed and compared with the state of art techniques. Experimental results
show that, with a relatively large number of samples our technique is 26.4\%
better than state of art technique for SSC and 33.7\% for firmness. The results
of this study indicate the application potential of hyperspectral imaging
technique in the SSC and firmness detection, which provides a new option for
non-destructive testing of cherry tomato fruit quality in the future.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 07:21:50 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Xiang",
"Yun",
""
],
[
"Chen",
"Qijun",
""
],
[
"Su",
"Zhongjin",
""
],
[
"Zhang",
"Lu",
""
],
[
"Chen",
"Zuohui",
""
],
[
"Zhou",
"Guozhi",
""
],
[
"Yao",
"Zhuping",
""
],
[
"Xuan",
"Qi",
""
],
[
"Cheng",
"Yuan",
""
]
] |
new_dataset
| 0.999373 |
2203.05215
|
Carlos Diego Nascimento Damasceno
|
Shaghayegh Tavassoli, Carlos Diego Nascimento Damasceno, Mohammad Reza
Mousavi, Ramtin Khosravi
|
A Benchmark for Active Learning of Variability-Intensive Systems
|
5 pages, 3 figures, Paper accepted in the Challenge Cases Track of
the 26th ACM International Systems and Software Product Line Conference (SPLC
2022)
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Behavioral models are the key enablers for behavioral analysis of Software
Product Lines (SPL), including testing and model checking. Active model
learning comes to the rescue when family behavioral models are non-existent or
outdated. A key challenge on active model learning is to detect commonalities
and variability efficiently and combine them into concise family models.
Benchmarks and their associated metrics will play a key role in shaping the
research agenda in this promising field and provide an effective means for
comparing and identifying relative strengths and weaknesses in the forthcoming
techniques. In this challenge, we seek benchmarks to evaluate the efficiency
(e.g., learning time and memory footprint) and effectiveness (e.g., conciseness
and accuracy of family models) of active model learning methods in the software
product line context. These benchmark sets must contain the structural and
behavioral variability models of at least one SPL. Each SPL in a benchmark must
contain products that requires more than one round of model learning with
respect to the basic active learning $L^{*}$ algorithm. Alternatively, tools
supporting the synthesis of artificial benchmark models are also welcome.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 07:47:30 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Tavassoli",
"Shaghayegh",
""
],
[
"Damasceno",
"Carlos Diego Nascimento",
""
],
[
"Mousavi",
"Mohammad Reza",
""
],
[
"Khosravi",
"Ramtin",
""
]
] |
new_dataset
| 0.996926 |
2203.05256
|
Joseph Da Silva
|
Joseph Da Silva
|
Cyber security and the Leviathan
| null | null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dedicated cyber-security functions are common in commercial businesses, who
are confronted by evolving and pervasive threats of data breaches and other
perilous security events. Such businesses are enmeshed with the wider societies
in which they operate. Using data gathered from in-depth, semi-structured
interviews with 15 Chief Information Security Officers, as well as six senior
organisational leaders, we show that the work of political philosopher Thomas
Hobbes, particularly Leviathan, offers a useful lens through which to
understand the context of these functions and of cyber security in Western
society. Our findings indicate that cyber security within these businesses
demonstrates a number of Hobbesian features that are further implicated in, and
provide significant benefits to, the wider Leviathan-esque state. These include
the normalisation of intrusive controls, such as surveillance, and the
stimulation of consumption. We conclude by suggesting implications for
cyber-security practitioners, in particular, the reflexivity that these
perspectives offer, as well as for businesses and other researchers.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 09:35:36 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Da Silva",
"Joseph",
""
]
] |
new_dataset
| 0.999093 |
2203.05301
|
Yun Fan
|
Yun Fan, Hualu Liu
|
Double Constacyclic Codes over Two Finite Commutative Chain Rings
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many kinds of codes which possess two cycle structures over two special
finite commutative chain rings, such as ${\Bbb Z}_2{\Bbb Z}_4$-additive cyclic
codes and quasi-cyclic codes of fractional index etc., were proved
asymptotically good. In this paper we extend the study in two directions: we
consider any two finite commutative chain rings with a surjective homomorphism
from one to the other, and consider double constacyclic structures. We
construct an extensive kind of double constacyclic codes over two finite
commutative chain rings. And, developing a probabilistic method suitable for
quasi-cyclic codes over fields, we prove that the double constacyclic codes
over two finite commutative chain rings are asymptotically good.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 11:29:15 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Fan",
"Yun",
""
],
[
"Liu",
"Hualu",
""
]
] |
new_dataset
| 0.998257 |
2203.05333
|
Desmond Caulley
|
Desmond Caulley, Yufeng Yang, David Anderson
|
EACELEB: An East Asian Language Speaking Celebrity Dataset for Speaker
Recognition
| null | null | null | null |
cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Large datasets are very useful for training speaker recognition systems, and
various research groups have constructed several over the years. Voxceleb is a
large dataset for speaker recognition that is extracted from Youtube videos.
This paper presents an audio-visual method for acquiring audio data from
Youtube given the speaker's name as input. The system follows a pipeline
similar to that of the Voxceleb data acquisition method. However, our work
focuses on fast data acquisition by using face-tracking in subsequent frames
once a face has been detected -- this is preferable over face detection for
every frame considering its computational cost. We show that applying audio
diarization to our data after acquiring it can yield equal error rates
comparable to Voxceleb. A secondary set of experiments showed that we could
further decrease the error rate by fine-tuning a pre-trained x-vector system
with the acquired data. Like Voxceleb, the work here focuses primarily on
developing audio for celebrities. However, unlike Voxceleb, our target audio
data is from celebrities in East Asian countries. Finally, we set up a speaker
verification task to evaluate the accuracy of our acquired data. After
diarization and fine-tuning, we achieved an equal error rate of approximately
4\% across our entire dataset.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 12:29:35 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Caulley",
"Desmond",
""
],
[
"Yang",
"Yufeng",
""
],
[
"Anderson",
"David",
""
]
] |
new_dataset
| 0.999871 |
2203.05367
|
Ishu Gupta
|
Ishu Gupta and Sloni Mittal and Ankit Tiwari and Priya Agarwal and
Ashutosh Kumar Singh
|
TIDF-DLPM: Term and Inverse Document Frequency based Data Leakage
Prevention Model
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Confidentiality of the data is being endangered as it has been categorized
into false categories which might get leaked to an unauthorized party. For this
reason, various organizations are mainly implementing data leakage prevention
systems (DLPs). Firewalls and intrusion detection systems are being outdated
versions of security mechanisms. The data which are being used, in sending
state or are rest are being monitored by DLPs. The confidential data is
prevented with the help of neighboring contexts and contents of DLPs. In this
paper, a semantic-based approach is used to classify data based on the
statistical data leakage prevention model. To detect involved private data,
statistical analysis is being used to contribute secure mechanisms in the
environment of data leakage. The favored Frequency-Inverse Document Frequency
(TF-IDF) is the facts and details recapture function to arrange documents under
particular topics. The results showcase that a similar statistical DLP approach
could appropriately classify documents in case of extent alteration as well as
interchanged documents.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 13:37:48 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Gupta",
"Ishu",
""
],
[
"Mittal",
"Sloni",
""
],
[
"Tiwari",
"Ankit",
""
],
[
"Agarwal",
"Priya",
""
],
[
"Singh",
"Ashutosh Kumar",
""
]
] |
new_dataset
| 0.998242 |
2203.05516
|
Grace Li Zhang
|
Grace Li Zhang and Bing Li and Xing Huang and Xunzhao Yin and Cheng
Zhuo and Masanori Hashimoto and Ulf Schlichtmann
|
VirtualSync+: Timing Optimization with Virtual Synchronization
| null | null | null | null |
cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In digital circuit designs, sequential components such as flip-flops are used
to synchronize signal propagations. Logic computations are aligned at and thus
isolated by flip-flop stages. Although this fully synchronous style can reduce
design efforts significantly, it may affect circuit performance negatively,
because sequential components can only introduce delays into signal
propagations but never accelerate them. In this paper, we propose a new timing
model, VirtualSync+, in which signals, specially those along critical paths,
are allowed to propagate through several sequential stages without flip-flops.
Timing constraints are still satisfied at the boundary of the optimized circuit
to maintain a consistent interface with existing designs. By removing
clock-to-q delays and setup time requirements of flip-flops on critical paths,
the performance of a circuit can be pushed even beyond the limit of traditional
sequential designs. In addition, we further enhance the optimization with
VirtualSync+ by fine-tuning with commercial design tools, e.g., Design Compiler
from Synopsys, to achieve more accurate result. Experimental results
demonstrate that circuit performance can be improved by up to 4% (average 1.5%)
compared with that after extreme retiming and sizing, while the increase of
area is still negligible. This timing performance is enhanced beyond the limit
of traditional sequential designs. It also demonstrates that compared with
those after retiming and sizing, the circuits with VirtualSync+ can achieve
better timing performance under the same area cost or smaller area cost under
the same clock period, respectively.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 18:08:57 GMT"
}
] | 2022-03-11T00:00:00 |
[
[
"Zhang",
"Grace Li",
""
],
[
"Li",
"Bing",
""
],
[
"Huang",
"Xing",
""
],
[
"Yin",
"Xunzhao",
""
],
[
"Zhuo",
"Cheng",
""
],
[
"Hashimoto",
"Masanori",
""
],
[
"Schlichtmann",
"Ulf",
""
]
] |
new_dataset
| 0.999536 |
2009.00951
|
Samuel Blake T
|
Sam Blake
|
Embedded Blockchains: A Synthesis of Blockchains, Spread Spectrum
Watermarking, Perceptual Hashing & Digital Signatures
|
Going in a different direction with this research
| null | null | null |
cs.IT cs.CR cs.MM math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we introduce a scheme for detecting manipulated audio and
video. The scheme is a synthesis of blockchains, encrypted spread spectrum
watermarks, perceptual hashing and digital signatures, which we call an
Embedded Blockchain. Within this scheme, we use the blockchain for its data
structure of a cryptographically linked list, cryptographic hashing for
absolute comparisons, perceptual hashing for flexible comparisons, digital
signatures for proof of ownership, and encrypted spread spectrum watermarking
to embed the blockchain into the background noise of the media. So each media
recording has its own unique blockchain, with each block holding information
describing the media segment. The problem of verifying the integrity of the
media is recast to traversing the blockchain, block-by-block, and
segment-by-segment of the media. If any chain is broken, the difference in the
computed and extracted perceptual hash is used to estimate the level of
manipulation.
|
[
{
"version": "v1",
"created": "Wed, 2 Sep 2020 11:08:43 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Sep 2020 00:46:47 GMT"
},
{
"version": "v3",
"created": "Tue, 17 Nov 2020 01:55:59 GMT"
},
{
"version": "v4",
"created": "Wed, 9 Mar 2022 11:28:01 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Blake",
"Sam",
""
]
] |
new_dataset
| 0.998001 |
2011.11974
|
Yang You
|
Yang You, Wenhai Liu, Yanjie Ze, Yong-Lu Li, Weiming Wang, Cewu Lu
|
UKPGAN: A General Self-Supervised Keypoint Detector
|
Accepted to CVPR2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Keypoint detection is an essential component for the object registration and
alignment. In this work, we reckon keypoint detection as information
compression, and force the model to distill out irrelevant points of an object.
Based on this, we propose UKPGAN, a general self-supervised 3D keypoint
detector where keypoints are detected so that they could reconstruct the
original object shape. Two modules: GAN-based keypoint sparsity control and
salient information distillation modules are proposed to locate those important
keypoints. Extensive experiments show that our keypoints align well with human
annotated keypoint labels, and can be applied to SMPL human bodies under
various non-rigid deformations. Furthermore, our keypoint detector trained on
clean object collections generalizes well to real-world scenarios, thus further
improves geometric registration when combined with off-the-shelf point
descriptors. Repeatability experiments show that our model is stable under both
rigid and non-rigid transformations, with local reference frame estimation. Our
code is available on https://github.com/qq456cvb/UKPGAN.
|
[
{
"version": "v1",
"created": "Tue, 24 Nov 2020 09:08:21 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Jan 2021 04:04:19 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Mar 2022 05:27:26 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"You",
"Yang",
""
],
[
"Liu",
"Wenhai",
""
],
[
"Ze",
"Yanjie",
""
],
[
"Li",
"Yong-Lu",
""
],
[
"Wang",
"Weiming",
""
],
[
"Lu",
"Cewu",
""
]
] |
new_dataset
| 0.999125 |
2105.11888
|
Saman Ahmadi
|
Saman Ahmadi, Guido Tack, Daniel Harabor, Philip Kilby
|
Bi-objective Search with Bi-directional A*
|
16 pages, 4 figures, in Proceedings of The European Symposium on
Algorithms 2021 (ESA21), Changes: including the backward search of BOBA*
| null |
10.4230/LIPIcs.ESA.2021.3
| null |
cs.AI cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Bi-objective search is a well-known algorithmic problem, concerned with
finding a set of optimal solutions in a two-dimensional domain. This problem
has a wide variety of applications such as planning in transport systems or
optimal control in energy systems. Recently, bi-objective A*-based search
(BOA*) has shown state-of-the-art performance in large networks. This paper
develops a bi-directional and parallel variant of BOA*, enriched with several
speed-up heuristics. Our experimental results on 1,000 benchmark cases show
that our bi-directional A* algorithm for bi-objective search (BOBA*) can
optimally solve all of the benchmark cases within the time limit, outperforming
the state of the art BOA*, bi-objective Dijkstra and bi-directional
bi-objective Dijkstra by an average runtime improvement of a factor of five
over all of the benchmark instances.
|
[
{
"version": "v1",
"created": "Tue, 25 May 2021 12:46:25 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Jul 2021 04:32:30 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Ahmadi",
"Saman",
""
],
[
"Tack",
"Guido",
""
],
[
"Harabor",
"Daniel",
""
],
[
"Kilby",
"Philip",
""
]
] |
new_dataset
| 0.979312 |
2106.04284
|
Bernhard Manfred Gruber
|
Bernhard Manfred Gruber, Guilherme Amadio, Jakob Blomer, Alexander
Matthes, Ren\'e Widera, Michael Bussmann
|
LLAMA: The Low-Level Abstraction For Memory Access
|
39 pages, 10 figures, 11 listings
|
Softw Pract Exper. 2022; 1- 27
|
10.1002/spe.3077
| null |
cs.PF
|
http://creativecommons.org/licenses/by/4.0/
|
The performance gap between CPU and memory widens continuously. Choosing the
best memory layout for each hardware architecture is increasingly important as
more and more programs become memory bound. For portable codes that run across
heterogeneous hardware architectures, the choice of the memory layout for data
structures is ideally decoupled from the rest of a program. This can be
accomplished via a zero-runtime-overhead abstraction layer, underneath which
memory layouts can be freely exchanged.
We present the Low-Level Abstraction of Memory Access (LLAMA), a C++ library
that provides such a data structure abstraction layer with example
implementations for multidimensional arrays of nested, structured data. LLAMA
provides fully C++ compliant methods for defining and switching custom memory
layouts for user-defined data types. The library is extensible with third-party
allocators.
Providing two close-to-life examples, we show that the LLAMA-generated AoS
(Array of Structs) and SoA (Struct of Arrays) layouts produce identical code
with the same performance characteristics as manually written data structures.
Integrations into the SPEC CPU\textsuperscript{\textregistered} lbm benchmark
and the particle-in-cell simulation PIConGPU demonstrate LLAMA's abilities in
real-world applications. LLAMA's layout-aware copy routines can significantly
speed up transfer and reshuffling of data between layouts compared with naive
element-wise copying.
LLAMA provides a novel tool for the development of high-performance C++
applications in a heterogeneous environment.
|
[
{
"version": "v1",
"created": "Tue, 8 Jun 2021 12:23:31 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Nov 2021 14:43:40 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Mar 2022 17:20:17 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Gruber",
"Bernhard Manfred",
""
],
[
"Amadio",
"Guilherme",
""
],
[
"Blomer",
"Jakob",
""
],
[
"Matthes",
"Alexander",
""
],
[
"Widera",
"René",
""
],
[
"Bussmann",
"Michael",
""
]
] |
new_dataset
| 0.993395 |
2107.06075
|
Umberto Straccia
|
Giovanni Casini, Umberto Straccia
|
A Rational Entailment for Expressive Description Logics via Description
Logic Programs
| null | null |
10.1007/978-3-030-95070-5_12
| null |
cs.LO cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Lehmann and Magidor's rational closure is acknowledged as a landmark in the
field of non-monotonic logics and it has also been re-formulated in the context
of Description Logics (DLs).
We show here how to model a rational form of entailment for expressive DLs,
such as SROIQ, providing a novel reasoning procedure that compiles a
non-monotone DL knowledge base into a description logic program (dl-program).
|
[
{
"version": "v1",
"created": "Mon, 28 Jun 2021 14:35:42 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Casini",
"Giovanni",
""
],
[
"Straccia",
"Umberto",
""
]
] |
new_dataset
| 0.99855 |
2108.03815
|
Yurong Chen Dr
|
Yurong Chen
|
P-WAE: Generalized Patch-Wasserstein Autoencoder for Anomaly Screening
|
I need to revise the paper
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Anomaly detection plays a pivotal role in numerous real-world scenarios, such
as industrial automation and manufacturing intelligence. Recently, variational
inference-based anomaly analysis has attracted researchers' and developers'
attention. It aims to model the defect-free distribution so that anomalies can
be classified as out-of-distribution samples. Nevertheless, there are two
disturbing factors that need us to prioritize: (i) the simplistic prior latent
distribution inducing limited expressive capability; (ii) the strong
probability distance notion results in collapsed features. In this paper, we
propose a novel Patch-wise Wasserstein AutoEncoder (P-WAE) architecture to
alleviate those challenges. In particular, a patch-wise variational inference
model coupled with solving the jigsaw puzzle is designed, which is a simple yet
effective way to increase the expressiveness of the latent manifold. This makes
using the model on high-dimensional practical data possible. In addition, we
leverage a weaker measure, sliced-Wasserstein distance, to achieve the
equilibrium between the reconstruction fidelity and generalized
representations. Comprehensive experiments, conducted on the MVTec AD dataset,
demonstrate the superior performance of our proposed method.
|
[
{
"version": "v1",
"created": "Mon, 9 Aug 2021 05:31:45 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Sep 2021 06:43:36 GMT"
},
{
"version": "v3",
"created": "Sat, 18 Sep 2021 00:12:19 GMT"
},
{
"version": "v4",
"created": "Wed, 9 Mar 2022 09:12:48 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Chen",
"Yurong",
""
]
] |
new_dataset
| 0.999308 |
2108.04750
|
Songlin Yang
|
Songlin Yang, Kewei Tu
|
Headed-Span-Based Projective Dependency Parsing
|
ACL2022 camera ready
| null | null | null |
cs.CL
|
http://creativecommons.org/publicdomain/zero/1.0/
|
We propose a new method for projective dependency parsing based on headed
spans. In a projective dependency tree, the largest subtree rooted at each word
covers a contiguous sequence (i.e., a span) in the surface order. We call such
a span marked by a root word \textit{headed span}.
A projective dependency tree can be represented as a collection of headed
spans. We decompose the score of a dependency tree into the scores of the
headed spans and design a novel $O(n^3)$ dynamic programming algorithm to
enable global training and exact inference. Our model achieves state-of-the-art
or competitive results on PTB, CTB, and UD. Our code is publicly available at
\url{https://github.com/sustcsonglin/span-based-dependency-parsing}.
|
[
{
"version": "v1",
"created": "Tue, 10 Aug 2021 15:27:47 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Mar 2022 11:09:29 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Yang",
"Songlin",
""
],
[
"Tu",
"Kewei",
""
]
] |
new_dataset
| 0.969088 |
2109.05788
|
Tiange Xiang
|
Tiange Xiang, Yang Song, Chaoyi Zhang, Dongnan Liu, Mei Chen, Fan
Zhang, Heng Huang, Lauren O'Donnell, Weidong Cai
|
DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology
Image Analysis
|
IEEE TMI 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a novel weakly-supervised framework for classifying whole slide
images (WSIs). WSIs, due to their gigapixel resolution, are commonly processed
by patch-wise classification with patch-level labels. However, patch-level
labels require precise annotations, which is expensive and usually unavailable
on clinical data. With image-level labels only, patch-wise classification would
be sub-optimal due to inconsistency between the patch appearance and
image-level label. To address this issue, we posit that WSI analysis can be
effectively conducted by integrating information at both high magnification
(local) and low magnification (regional) levels. We auto-encode the visual
signals in each patch into a latent embedding vector representing local
information, and down-sample the raw WSI to hardware-acceptable thumbnails
representing regional information. The WSI label is then predicted with a
Dual-Stream Network (DSNet), which takes the transformed local patch embeddings
and multi-scale thumbnail images as inputs and can be trained by the
image-level label only. Experiments conducted on two large-scale public
datasets demonstrate that our method outperforms all recent state-of-the-art
weakly-supervised WSI classification methods.
|
[
{
"version": "v1",
"created": "Mon, 13 Sep 2021 09:10:43 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Mar 2022 08:15:53 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Xiang",
"Tiange",
""
],
[
"Song",
"Yang",
""
],
[
"Zhang",
"Chaoyi",
""
],
[
"Liu",
"Dongnan",
""
],
[
"Chen",
"Mei",
""
],
[
"Zhang",
"Fan",
""
],
[
"Huang",
"Heng",
""
],
[
"O'Donnell",
"Lauren",
""
],
[
"Cai",
"Weidong",
""
]
] |
new_dataset
| 0.984964 |
2201.01367
|
Won Kyung Do
|
Won Kyung Do and Monroe Kennedy III
|
DenseTact: Optical Tactile Sensor for Dense Shape Reconstruction
| null | null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Increasing the performance of tactile sensing in robots enables versatile,
in-hand manipulation. Vision-based tactile sensors have been widely used as
rich tactile feedback has been shown to be correlated with increased
performance in manipulation tasks. Existing tactile sensor solutions with high
resolution have limitations that include low accuracy, expensive components, or
lack of scalability. In this paper, an inexpensive, scalable, and compact
tactile sensor with high-resolution surface deformation modeling for surface
reconstruction of the 3D sensor surface is proposed. By measuring the image
from the fisheye camera, it is shown that the sensor can successfully estimate
the surface deformation in real-time (1.8ms) by using deep convolutional neural
networks. This sensor in its design and sensing abilities represents a
significant step toward better object in-hand localization, classification, and
surface estimation all enabled by high-resolution shape reconstruction.
|
[
{
"version": "v1",
"created": "Tue, 4 Jan 2022 22:26:14 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Mar 2022 03:59:31 GMT"
},
{
"version": "v3",
"created": "Wed, 2 Mar 2022 19:04:26 GMT"
},
{
"version": "v4",
"created": "Tue, 8 Mar 2022 20:49:26 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Do",
"Won Kyung",
""
],
[
"Kennedy",
"Monroe",
"III"
]
] |
new_dataset
| 0.996558 |
2201.05671
|
Alberto Sonnino
|
Mathieu Baudet, Alberto Sonnino, Mahimna Kelkar, George Danezis
|
Zef: Low-latency, Scalable, Private Payments
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce Zef, the first Byzantine-Fault Tolerant (BFT) protocol to
support payments in anonymous digital coins at arbitrary scale. Zef follows the
communication and security model of FastPay: both protocols are asynchronous,
low-latency, linearly-scalable, and powered by partially-trusted sharded
authorities. Zef further introduces opaque coins represented as off-chain
certificates that are bound to user accounts. In order to hide the face values
of coins when a payment operation consumes or creates them, Zef uses random
commitments and NIZK proofs. Created coins are made unlinkable using the blind
and randomizable threshold anonymous credentials of Coconut. To control storage
costs associated with coin replay prevention, Zef accounts are designed so that
data can be safely removed once an account is deactivated. Besides the
specifications and a detailed analysis of the protocol, we are making available
an open-source implementation of Zef in Rust. Our extensive benchmarks on AWS
confirm textbook linear scalability and demonstrate a confirmation time under
one second at nominal capacity. Compared to existing anonymous payment systems
based on a blockchain, this represents a latency speedup of three orders of
magnitude, with no theoretical limit on throughput.
|
[
{
"version": "v1",
"created": "Fri, 14 Jan 2022 21:09:11 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jan 2022 11:07:13 GMT"
},
{
"version": "v3",
"created": "Thu, 3 Mar 2022 19:13:30 GMT"
},
{
"version": "v4",
"created": "Tue, 8 Mar 2022 21:37:24 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Baudet",
"Mathieu",
""
],
[
"Sonnino",
"Alberto",
""
],
[
"Kelkar",
"Mahimna",
""
],
[
"Danezis",
"George",
""
]
] |
new_dataset
| 0.997237 |
2203.02225
|
Yucheng Zhou
|
Yucheng Zhou, Tao Shen, Xiubo Geng, Guodong Long, Daxin Jiang
|
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer
for Event-Centric Generation and Classification
|
ACL 2022 camera-ready version
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Generating new events given context with correlated ones plays a crucial role
in many event-centric reasoning tasks. Existing works either limit their scope
to specific scenarios or overlook event-level correlations. In this paper, we
propose to pre-train a general Correlation-aware context-to-Event Transformer
(ClarET) for event-centric reasoning. To achieve this, we propose three novel
event-centric objectives, i.e., whole event recovering, contrastive
event-correlation encoding and prompt-based event locating, which highlight
event-level correlations with effective training. The proposed ClarET is
applicable to a wide range of event-centric reasoning scenarios, considering
its versatility of (i) event-correlation types (e.g., causal, temporal,
contrast), (ii) application formulations (i.e., generation and classification),
and (iii) reasoning types (e.g., abductive, counterfactual and ending
reasoning). Empirical fine-tuning results, as well as zero- and few-shot
learning, on 9 benchmarks (5 generation and 4 classification tasks covering 4
reasoning types with diverse event correlations), verify its effectiveness and
generalization ability.
|
[
{
"version": "v1",
"created": "Fri, 4 Mar 2022 10:11:15 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Mar 2022 08:55:31 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Zhou",
"Yucheng",
""
],
[
"Shen",
"Tao",
""
],
[
"Geng",
"Xiubo",
""
],
[
"Long",
"Guodong",
""
],
[
"Jiang",
"Daxin",
""
]
] |
new_dataset
| 0.999075 |
2203.02797
|
Jiang Shuyu
|
Shuyu Jiang, Dengbiao Tu, Xingshu Chen, Rui Tang, Wenxian Wang,
Haizhou Wang
|
ClueGraphSum: Let Key Clues Guide the Cross-Lingual Abstractive
Summarization
|
12 pages,4 figures
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cross-Lingual Summarization (CLS) is the task to generate a summary in one
language for an article in a different language. Previous studies on CLS mainly
take pipeline methods or train the end-to-end model using the translated
parallel data. However, the quality of generated cross-lingual summaries needs
more further efforts to improve, and the model performance has never been
evaluated on the hand-written CLS dataset. Therefore, we first propose a
clue-guided cross-lingual abstractive summarization method to improve the
quality of cross-lingual summaries, and then construct a novel hand-written CLS
dataset for evaluation. Specifically, we extract keywords, named entities, etc.
of the input article as key clues for summarization and then design a
clue-guided algorithm to transform an article into a graph with less noisy
sentences. One Graph encoder is built to learn sentence semantics and article
structures and one Clue encoder is built to encode and translate key clues,
ensuring the information of important parts are reserved in the generated
summary. These two encoders are connected by one decoder to directly learn
cross-lingual semantics. Experimental results show that our method has stronger
robustness for longer inputs and substantially improves the performance over
the strong baseline, achieving an improvement of 8.55 ROUGE-1
(English-to-Chinese summarization) and 2.13 MoverScore (Chinese-to-English
summarization) scores over the existing SOTA.
|
[
{
"version": "v1",
"created": "Sat, 5 Mar 2022 18:01:11 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Mar 2022 08:01:15 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Jiang",
"Shuyu",
""
],
[
"Tu",
"Dengbiao",
""
],
[
"Chen",
"Xingshu",
""
],
[
"Tang",
"Rui",
""
],
[
"Wang",
"Wenxian",
""
],
[
"Wang",
"Haizhou",
""
]
] |
new_dataset
| 0.998904 |
2203.03175
|
Said Varlioglu
|
Said Varlioglu, Nelly Elsayed, Zag ElSayed, Murat Ozer
|
The Dangerous Combo: Fileless Malware and Cryptojacking
|
9 Pages - Accepted to be published in SoutheastCon 2022 IEEE Region 3
Technical, Professional, and Student Conference. Mobile, Alabama, USA. Mar
31st to Apr 03rd 2022. https://ieeesoutheastcon.org/
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fileless malware and cryptojacking attacks have appeared independently as the
new alarming threats in 2017. After 2020, fileless attacks have been
devastating for victim organizations with low-observable characteristics. Also,
the amount of unauthorized cryptocurrency mining has increased after 2019.
Adversaries have started to merge these two different cyberattacks to gain more
invisibility and profit under "Fileless Cryptojacking." This paper aims to
provide a literature review in academic papers and industry reports for this
new threat. Additionally, we present a new threat hunting-oriented DFIR
approach with the best practices derived from field experience as well as the
literature. Last, this paper reviews the fundamentals of the fileless threat
that can also help ransomware researchers examine similar patterns.
|
[
{
"version": "v1",
"created": "Mon, 7 Mar 2022 07:03:43 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Mar 2022 08:38:04 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Varlioglu",
"Said",
""
],
[
"Elsayed",
"Nelly",
""
],
[
"ElSayed",
"Zag",
""
],
[
"Ozer",
"Murat",
""
]
] |
new_dataset
| 0.995163 |
2203.04142
|
Latif Salum
|
Latif Salum
|
A Reply to "On Salum's Algorithm for X3SAT"
| null | null | null | null |
cs.CC
|
http://creativecommons.org/licenses/by/4.0/
|
This paper is a reply to "On Salum's Algorithm for X3SAT" (arXiv:2104.02886)
|
[
{
"version": "v1",
"created": "Mon, 6 Dec 2021 11:46:20 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Mar 2022 13:21:28 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Salum",
"Latif",
""
]
] |
new_dataset
| 0.95474 |
2203.04358
|
Hemant Surale
|
Hemant Bhaskar Surale, Yu Jiang Tham, Brian A. Smith, Rajan Vaish
|
ARcall: Real-Time AR Communication using Smartphones and Smartglasses
|
19 pages, 6 figures, Augmented Humans 2022 March 13-15th, 2022,
Munich, Germany
| null | null | null |
cs.HC cs.SI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Augmented Reality (AR) smartglasses are increasingly regarded as the next
generation personal computing platform. However, there is a lack of
understanding about how to design communication systems using them. We present
ARcall, a novel Augmented Reality-based real-time communication system that
enables an immersive, delightful, and privacy-preserving experience between a
smartphone user and a smartglasses wearer. ARcall allows a remote friend
(Friend) to send and project AR content to a smartglasses wearer (Wearer). The
ARcall system was designed with the practical limits of existing AR glasses in
mind, including shorter battery life and a reduced field of view. We conduct a
qualitative evaluation of the three main components of ARcall: Drop-In,
ARaction, and Micro-Chat. Our results provide novel insights for building
future AR-based communication methods, including, the importance of context
priming, user control over AR content placement, and the feeling of co-presence
while conversing.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 19:32:16 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Surale",
"Hemant Bhaskar",
""
],
[
"Tham",
"Yu Jiang",
""
],
[
"Smith",
"Brian A.",
""
],
[
"Vaish",
"Rajan",
""
]
] |
new_dataset
| 0.996731 |
2203.04410
|
Rayan El Helou
|
Rayan El Helou, Kiyeob Lee, Dongqi Wu, Le Xie, Srinivas Shakkottai,
Vijay Subramanian
|
OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market
Simulation
| null | null | null | null |
cs.AI cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents OpenGridGym, an open-source Python-based package that
allows for seamless integration of distribution market simulation with
state-of-the-art artificial intelligence (AI) decision-making algorithms. We
present the architecture and design choice for the proposed framework,
elaborate on how users interact with OpenGridGym, and highlight its value by
providing multiple cases to demonstrate its use. Four modules are used in any
simulation: (1) the physical grid, (2) market mechanisms, (3) a set of
trainable agents which interact with the former two modules, and (4)
environment module that connects and coordinates the above three. We provide
templates for each of those four, but they are easily interchangeable with
custom alternatives. Several case studies are presented to illustrate the
capability and potential of this toolkit in helping researchers address key
design and operational questions in distribution electricity markets.
|
[
{
"version": "v1",
"created": "Sun, 6 Mar 2022 07:03:05 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Helou",
"Rayan El",
""
],
[
"Lee",
"Kiyeob",
""
],
[
"Wu",
"Dongqi",
""
],
[
"Xie",
"Le",
""
],
[
"Shakkottai",
"Srinivas",
""
],
[
"Subramanian",
"Vijay",
""
]
] |
new_dataset
| 0.998947 |
2203.04412
|
Maura Pintor
|
Maura Pintor, Daniele Angioni, Angelo Sotgiu, Luca Demetrio, Ambra
Demontis, Battista Biggio, Fabio Roli
|
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness
against Adversarial Patches
| null | null | null | null |
cs.CR cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Adversarial patches are optimized contiguous pixel blocks in an input image
that cause a machine-learning model to misclassify it. However, their
optimization is computationally demanding, and requires careful hyperparameter
tuning, potentially leading to suboptimal robustness evaluations. To overcome
these issues, we propose ImageNet-Patch, a dataset to benchmark
machine-learning models against adversarial patches. It consists of a set of
patches, optimized to generalize across different models, and readily
applicable to ImageNet data after preprocessing them with affine
transformations. This process enables an approximate yet faster robustness
evaluation, leveraging the transferability of adversarial perturbations. We
showcase the usefulness of this dataset by testing the effectiveness of the
computed patches against 127 models. We conclude by discussing how our dataset
could be used as a benchmark for robustness, and how our methodology can be
generalized to other domains. We open source our dataset and evaluation code at
https://github.com/pralab/ImageNet-Patch.
|
[
{
"version": "v1",
"created": "Mon, 7 Mar 2022 17:22:30 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Pintor",
"Maura",
""
],
[
"Angioni",
"Daniele",
""
],
[
"Sotgiu",
"Angelo",
""
],
[
"Demetrio",
"Luca",
""
],
[
"Demontis",
"Ambra",
""
],
[
"Biggio",
"Battista",
""
],
[
"Roli",
"Fabio",
""
]
] |
new_dataset
| 0.999811 |
2203.04440
|
Kshitiz Bansal
|
Kshitiz Bansal, Keshav Rungta, Siyuan Zhu, Dinesh Bharadia
|
Pointillism: Accurate 3D bounding box estimation with multi-radars
|
Accepted in SenSys '20. Dataset has been made publicly available
|
Proceedings of the 18th Conference on Embedded Networked Sensor
Systems. Pages 340-353, 2020
|
10.1145/3384419.3430783
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Autonomous perception requires high-quality environment sensing in the form
of 3D bounding boxes of dynamic objects. The primary sensors used in automotive
systems are light-based cameras and LiDARs. However, they are known to fail in
adverse weather conditions. Radars can potentially solve this problem as they
are barely affected by adverse weather conditions. However, specular
reflections of wireless signals cause poor performance of radar point clouds.
We introduce Pointillism, a system that combines data from multiple spatially
separated radars with an optimal separation to mitigate these problems. We
introduce a novel concept of Cross Potential Point Clouds, which uses the
spatial diversity induced by multiple radars and solves the problem of noise
and sparsity in radar point clouds. Furthermore, we present the design of
RP-net, a novel deep learning architecture, designed explicitly for radar's
sparse data distribution, to enable accurate 3D bounding box estimation. The
spatial techniques designed and proposed in this paper are fundamental to
radars point cloud distribution and would benefit other radar sensing
applications.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 23:09:58 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Bansal",
"Kshitiz",
""
],
[
"Rungta",
"Keshav",
""
],
[
"Zhu",
"Siyuan",
""
],
[
"Bharadia",
"Dinesh",
""
]
] |
new_dataset
| 0.997503 |
2203.04448
|
Jordan Samhi
|
Jordan Samhi, Tegawend\'e F. Bissyand\'e, Jacques Klein
|
TriggerZoo: A Dataset of Android Applications Automatically Infected
with Logic Bombs
|
In the proceedings of the 19th International Conference on Mining
Software Repositories, Data Showcase, (MSR 2022)
| null | null | null |
cs.CR cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many Android apps analyzers rely, among other techniques, on dynamic analysis
to monitor their runtime behavior and detect potential security threats.
However, malicious developers use subtle, though efficient, techniques to
bypass dynamic analyzers. Logic bombs are examples of popular techniques where
the malicious code is triggered only under specific circumstances, challenging
comprehensive dynamic analyses. The research community has proposed various
approaches and tools to detect logic bombs. Unfortunately, rigorous assessment
and fair comparison of state-of-the-art techniques are impossible due to the
lack of ground truth. In this paper, we present TriggerZoo, a new dataset of
406 Android apps containing logic bombs and benign trigger-based behavior that
we release only to the research community using authenticated API. These apps
are real-world apps from Google Play that have been automatically infected by
our tool AndroBomb. The injected pieces of code implementing the logic bombs
cover a large pallet of realistic logic bomb types that we have manually
characterized from a set of real logic bombs. Researchers can exploit this
dataset as ground truth to assess their approaches and provide comparisons
against other tools.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 23:43:26 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Samhi",
"Jordan",
""
],
[
"Bissyandé",
"Tegawendé F.",
""
],
[
"Klein",
"Jacques",
""
]
] |
new_dataset
| 0.999825 |
2203.04472
|
Qige Song
|
Qige Song, Yongzheng Zhang, Linshu Ouyang, Yige Chen
|
BinMLM: Binary Authorship Verification with Flow-aware Mixture-of-Shared
Language Model
|
12 pages, 8 figures, 5 tables, accepted by Research Track of 29th
IEEE International Conference on Software Analysis, Evolution and
Reengineering (SANER 2022), the camera-ready version
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Binary authorship analysis is a significant problem in many software
engineering applications. In this paper, we formulate a binary authorship
verification task to accurately reflect the real-world working process of
software forensic experts. It aims to determine whether an anonymous binary is
developed by a specific programmer with a small set of support samples, and the
actual developer may not belong to the known candidate set but from the wild.
We propose an effective binary authorship verification framework, BinMLM.
BinMLM trains the RNN language model on consecutive opcode traces extracted
from the control-flow-graph (CFG) to characterize the candidate developers'
programming styles. We build a mixture-of-shared architecture with multiple
shared encoders and author-specific gate layers, which can learn the
developers' combination preferences of universal programming patterns and
alleviate the problem of low training resources. Through an optimization
pipeline of external pre-training, joint training, and fine-tuning, our
framework can eliminate additional noise and accurately distill developers'
unique styles. Extensive experiments show that BinMLM achieves promising
results on Google Code Jam (GCJ) and Codeforces datasets with different numbers
of programmers and supporting samples. It significantly outperforms the
baselines built on the state-of-the-art feature set (4.73% to 19.46%
improvement) and remains robust in multi-author collaboration scenarios.
Furthermore, BinMLM can perform organization-level verification on a real-world
APT malware dataset, which can provide valuable auxiliary information for
exploring the group behind the APT attack.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 01:24:23 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Song",
"Qige",
""
],
[
"Zhang",
"Yongzheng",
""
],
[
"Ouyang",
"Linshu",
""
],
[
"Chen",
"Yige",
""
]
] |
new_dataset
| 0.984152 |
2203.04478
|
Rajeev Yasarla
|
Rajeev Yasarla, Renliang Weng, Wongun Choi, Vishal Patel, and Amir
Sadeghian
|
3SD: Self-Supervised Saliency Detection With No Labels
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We present a conceptually simple self-supervised method for saliency
detection. Our method generates and uses pseudo-ground truth labels for
training. The generated pseudo-GT labels don't require any kind of human
annotations (e.g., pixel-wise labels or weak labels like scribbles). Recent
works show that features extracted from classification tasks provide important
saliency cues like structure and semantic information of salient objects in the
image. Our method, called 3SD, exploits this idea by adding a branch for a
self-supervised classification task in parallel with salient object detection,
to obtain class activation maps (CAM maps). These CAM maps along with the edges
of the input image are used to generate the pseudo-GT saliency maps to train
our 3SD network. Specifically, we propose a contrastive learning-based training
on multiple image patches for the classification task. We show the multi-patch
classification with contrastive loss improves the quality of the CAM maps
compared to naive classification on the entire image. Experiments on six
benchmark datasets demonstrate that without any labels, our 3SD method
outperforms all existing weakly supervised and unsupervised methods, and its
performance is on par with the fully-supervised methods. Code is available at
:https://github.com/rajeevyasarla/3SD
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 01:40:28 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Yasarla",
"Rajeev",
""
],
[
"Weng",
"Renliang",
""
],
[
"Choi",
"Wongun",
""
],
[
"Patel",
"Vishal",
""
],
[
"Sadeghian",
"Amir",
""
]
] |
new_dataset
| 0.998826 |
2203.04637
|
Haoyu Liu
|
Haoyu Liu, Yang Liu, Hongkai He and Hangfang Yang
|
LEBP -- Language Expectation & Binding Policy: A Two-Stream Framework
for Embodied Vision-and-Language Interaction Task Learning Agents
|
6 pages
| null | null | null |
cs.AI cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
People always desire an embodied agent that can perform a task by
understanding language instruction. Moreover, they also want to monitor and
expect agents to understand commands the way they expected. But, how to build
such an embodied agent is still unclear. Recently, people can explore this
problem with the Vision-and-Language Interaction benchmark ALFRED, which
requires an agent to perform complicated daily household tasks following
natural language instructions in unseen scenes. In this paper, we propose LEBP
-- Language Expectation and Binding Policy Module to tackle the ALFRED. The
LEBP contains a two-stream process: 1) It first conducts a language expectation
module to generate an expectation describing how to perform tasks by
understanding the language instruction. The expectation consists of a sequence
of sub-steps for the task (e.g., Pick an apple). The expectation allows people
to access and check the understanding results of instructions before the agent
takes actual actions, in case the task might go wrong. 2) Then, it uses the
binding policy module to bind sub-steps in expectation to actual actions to
specific scenarios. Actual actions include navigation and object manipulation.
Experimental results suggest our approach achieves comparable performance to
currently published SOTA methods and can avoid large decay from seen scenarios
to unseen scenarios.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 10:47:10 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Liu",
"Haoyu",
""
],
[
"Liu",
"Yang",
""
],
[
"He",
"Hongkai",
""
],
[
"Yang",
"Hangfang",
""
]
] |
new_dataset
| 0.998222 |
2203.04645
|
Marcos Faundez-Zanuy
|
Elsa Fernandez, Jordi Sole-Casals, Pilar M. Calvo, Marcos
Faundez-Zanuy, Karmele Lopez-de-Ipina
|
HAIDA: Biometric technological therapy tools for neurorehabilitation of
Cognitive Impairment
|
2 pages
|
In: Masia L., Micera S., Akay M., Pons J. (eds) Converging
Clinical and Engineering Research on Neurorehabilitation III. ICNR 2018.
Biosystems & Biorobotics, vol 21. Springer, Cham
|
10.1007/978-3-030-01845-0_148
| null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Dementia, and specially Alzheimer s disease (AD) and Mild Cognitive
Impairment (MCI) are one of the most important diseases suffered by elderly
population. Music therapy is one of the most widely used non-pharmacological
treatment in the field of cognitive impairments, given that music influences
their mood, behavior, the decrease of anxiety, as well as facilitating
reminiscence, emotional expressions and movement. In this work we present
HAIDA, a multi-platform support system for Musical Therapy oriented to
cognitive impairment, which includes not only therapy tools but also
non-invasive biometric analysis, speech, activity and hand activity. At this
moment the system is on use and recording the first sets of data.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 11:13:39 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Fernandez",
"Elsa",
""
],
[
"Sole-Casals",
"Jordi",
""
],
[
"Calvo",
"Pilar M.",
""
],
[
"Faundez-Zanuy",
"Marcos",
""
],
[
"Lopez-de-Ipina",
"Karmele",
""
]
] |
new_dataset
| 0.966185 |
2203.04699
|
Boris Shminke
|
Boris Shminke
|
Gym-saturation: an OpenAI Gym environment for saturation provers
|
6 pages, 1 figure
|
Journal of Open Source Software, 7(71), 3849, 2022
|
10.21105/joss.03849
| null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
`gym-saturation` is an OpenAI Gym environment for reinforcement learning (RL)
agents capable of proving theorems. Currently, only theorems written in a
formal language of the Thousands of Problems for Theorem Provers (TPTP) library
in clausal normal form (CNF) are supported. `gym-saturation` implements the
'given clause' algorithm (similar to the one used in Vampire and E Prover).
Being written in Python, `gym-saturation` was inspired by PyRes. In contrast to
the monolithic architecture of a typical Automated Theorem Prover (ATP),
`gym-saturation` gives different agents opportunities to select clauses
themselves and train from their experience. Combined with a particular agent,
`gym-saturation` can work as an ATP. Even with a non trained agent based on
heuristics, `gym-saturation` can find refutations for 688 (of 8257) CNF
problems from TPTP v7.5.0.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 13:22:15 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Shminke",
"Boris",
""
]
] |
new_dataset
| 0.999503 |
2203.04765
|
V\'ictor Mayoral Vilches
|
V\'ictor Mayoral-Vilches
|
Robot Hacking Manual (RHM)
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Robots are often shipped insecure and in some cases fully unprotected. The
rationale behind is fourfold: first, defensive security mechanisms for robots
are still on their early stages, not covering the complete threat landscape.
Second, the inherent complexity of robotic systems makes their protection
costly, both technically and economically. Third, robot vendors do not
generally take responsibility in a timely manner, extending the zero-days
exposure window (time until mitigation of a zero-day) to several years on
average. Fourth, contrary to the common-sense expectations in 21st century and
similar to Ford in the 1920s with cars, most robot manufacturers oppose or
difficult robot repairs.
The Robot Hacking Manual (RHM) is an introductory series about cybersecurity
for robots, with an attempt to provide comprehensive case studies and
step-by-step tutorials with the intent to raise awareness in the field and
highlight the importance of taking a security-first approach. The material
available here is also a personal learning attempt and it's disconnected from
any particular organization. Content is provided as is and by no means it's
encouraged or promoted the unauthorized tampering of robotic systems or related
technologies.
|
[
{
"version": "v1",
"created": "Fri, 7 Jan 2022 17:34:15 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Mayoral-Vilches",
"Víctor",
""
]
] |
new_dataset
| 0.99271 |
2203.04776
|
Carlos Ganan
|
Antoine d'Estalenx and Carlos H. Ga\~n\'an
|
NURSE: eNd-UseR IoT malware detection tool for Smart homEs
| null | null |
10.1145/3494322.3494340
| null |
cs.CR cs.NI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Traditional techniques to detect malware infections were not meant to be used
by the end-user and current malware removal tools and security software cannot
handle the heterogeneity of IoT devices. In this paper, we design, develop and
evaluate a tool, called NURSE, to fill this information gap, i.e., enabling
end-users to detect IoT-malware infections in their home networks. NURSE
follows a modular approach to analyze IoT traffic as captured by means of an
ARP spoofing technique which does not require any network modification or
specific hardware. Thus, NURSE provides zero-configuration IoT traffic analysis
within everybody's reach. After testing NURSE in 83 different IoT network
scenarios with a wide variety of IoT device types, results show that NURSE
identifies malware-infected IoT devices with high accuracy (86.7%) using device
network behavior and contacted destinations.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 14:52:26 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"d'Estalenx",
"Antoine",
""
],
[
"Gañán",
"Carlos H.",
""
]
] |
new_dataset
| 0.969768 |
2203.04803
|
Roy Friedman
|
Roy Friedman and Or Goaz and Dor Hovav
|
Limited Associativity Caching in the Data Plane
| null | null | null | null |
cs.NI cs.OS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In-network caching promises to improve the performance of networked and edge
applications as it shortens the paths data need to travel. This is by storing
so-called hot items in the network switches on-route between clients who access
the data and the storage servers who maintain it. Since the data flows through
those switches in any case, it is natural to cache hot items there.
Most software-managed caches treat the cache as a fully associative region.
Alas, a fully associative design seems to be at odds with programmable
switches' goal of handling packets in a short bounded amount of time, as well
as their restricted programming model. In this work, we present PKache, a
generic limited associativity cache implementation in the programmable
switches' domain-specific P4 language, and demonstrate its utility by realizing
multiple popular cache management schemes.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 15:32:40 GMT"
}
] | 2022-03-10T00:00:00 |
[
[
"Friedman",
"Roy",
""
],
[
"Goaz",
"Or",
""
],
[
"Hovav",
"Dor",
""
]
] |
new_dataset
| 0.951426 |
1907.04826
|
Holger Dell
|
Holger Dell, John Lapinskas, Kitty Meeks
|
Approximately counting and sampling small witnesses using a colourful
decision oracle
| null | null |
10.1137/1.9781611975994.135
| null |
cs.DS cs.CC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this paper, we design efficient algorithms to approximately count the
number of edges of a given $k$-hypergraph, and to sample an approximately
uniform random edge. The hypergraph is not given explicitly, and can be
accessed only through its colourful independence oracle: The colourful
independence oracle returns yes or no depending on whether a given subset of
the vertices contains an edge that is colourful with respect to a given
vertex-colouring. Our results extend and/or strengthen recent results in the
graph oracle literature due to Beame et al. (ITCS 2018), Dell and Lapinskas
(STOC 2018), and Bhattacharya et al. (ISAAC 2019).
Our results have consequences for approximate counting/sampling: We can turn
certain kinds of decision algorithms into approximate counting/sampling
algorithms without causing much overhead in the running time. We apply this
approximate-counting/sampling-to-decision reduction to key problems in
fine-grained complexity (such as $k$-SUM, $k$-OV and weighted $k$-Clique) and
parameterised complexity (such as induced subgraphs of size $k$ or weight-$k$
solutions to CSPs).
|
[
{
"version": "v1",
"created": "Wed, 10 Jul 2019 17:11:58 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Mar 2022 19:01:01 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Dell",
"Holger",
""
],
[
"Lapinskas",
"John",
""
],
[
"Meeks",
"Kitty",
""
]
] |
new_dataset
| 0.954091 |
2005.04990
|
Luigi De Simone
|
Domenico Cotroneo, Luigi De Simone, Pietro Liguori, Roberto Natella
|
ProFIPy: Programmable Software Fault Injection as-a-Service
|
50th IEEE/IFIP International Conference on Dependable Systems and
Networks (DSN 2020)
| null |
10.1109/DSN48063.2020.00052
| null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present a new fault injection tool (ProFIPy) for Python
software. The tool is designed to be programmable, in order to enable users to
specify their software fault model, using a domain-specific language (DSL) for
fault injection. Moreover, to achieve better usability, ProFIPy is provided as
software-as-a-service and supports the user through the configuration of the
faultload and workload, failure data analysis, and full automation of the
experiments using container-based virtualization and parallelization.
|
[
{
"version": "v1",
"created": "Mon, 11 May 2020 10:45:49 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Cotroneo",
"Domenico",
""
],
[
"De Simone",
"Luigi",
""
],
[
"Liguori",
"Pietro",
""
],
[
"Natella",
"Roberto",
""
]
] |
new_dataset
| 0.999423 |
2005.07997
|
Warut Suksompong
|
Florian Brandl, Felix Brandt, Matthias Greger, Dominik Peters,
Christian Stricker, Warut Suksompong
|
Funding Public Projects: A Case for the Nash Product Rule
| null |
Journal of Mathematical Economics, 99:102585 (2022)
|
10.1016/j.jmateco.2021.102585
| null |
cs.GT econ.TH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study a mechanism design problem where a community of agents wishes to
fund public projects via voluntary monetary contributions by the community
members. This serves as a model for public expenditure without an exogenously
available budget, such as participatory budgeting or voluntary tax programs, as
well as donor coordination when interpreting charities as public projects and
donations as contributions. Our aim is to identify a mutually beneficial
distribution of the individual contributions. In the preference aggregation
problem that we study, agents report linear utility functions over projects
together with the amount of their contributions, and the mechanism determines a
socially optimal distribution of the money. We identify a specific mechanism --
the Nash product rule -- which picks the distribution that maximizes the
product of the agents' utilities. This rule is Pareto efficient, and we prove
that it satisfies attractive incentive properties: it spends each agent's
contribution only on projects the agent finds acceptable, and agents are
strongly incentivized to participate.
|
[
{
"version": "v1",
"created": "Sat, 16 May 2020 14:17:00 GMT"
},
{
"version": "v2",
"created": "Mon, 11 Oct 2021 13:40:39 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Brandl",
"Florian",
""
],
[
"Brandt",
"Felix",
""
],
[
"Greger",
"Matthias",
""
],
[
"Peters",
"Dominik",
""
],
[
"Stricker",
"Christian",
""
],
[
"Suksompong",
"Warut",
""
]
] |
new_dataset
| 0.969149 |
2006.06131
|
Zhiyi Zhang
|
Zhiyi Zhang, Tianyuan Yu, Xinyu Ma, Yu Guan, Philipp Moll, Lixia Zhang
|
Sovereign: User-Controlled Smart Homes
| null |
IEEE Internet of Things Journal, 2022
|
10.1109/JIOT.2022.3144980
| null |
cs.NI cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent years have witnessed the rapid deployment of smart homes; most of them
are controlled by remote servers in the cloud. Such designs raise security and
privacy concerns for end users. In this paper, we describe the design of
Sovereign, a home IoT system framework that provides end users complete control
of their home IoT systems. Sovereign lets home IoT devices and applications
communicate via application-named data and secures data directly. This enables
direct, secure, one-to-one and one-to-many device-to-device communication over
wireless broadcast media. Sovereign utilizes semantic names to construct usable
security solutions. We implement Sovereign as a publish-subscribe-based
development platform together with a prototype home IoT controller. Our
preliminary evaluation shows that Sovereign provides a systematic, easy-to-use
solution to user-controlled, self-contained smart homes running on existing IoT
hardware without imposing noticeable overhead.
|
[
{
"version": "v1",
"created": "Thu, 11 Jun 2020 00:44:09 GMT"
},
{
"version": "v2",
"created": "Fri, 12 Jun 2020 18:38:55 GMT"
},
{
"version": "v3",
"created": "Sun, 1 Aug 2021 20:00:08 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Zhang",
"Zhiyi",
""
],
[
"Yu",
"Tianyuan",
""
],
[
"Ma",
"Xinyu",
""
],
[
"Guan",
"Yu",
""
],
[
"Moll",
"Philipp",
""
],
[
"Zhang",
"Lixia",
""
]
] |
new_dataset
| 0.998603 |
2007.06795
|
Hiram H. L\'opez
|
Taylor Ball, Eduardo Camps, Henry Chimal-Dzul, Delio Jaramillo-Velez,
Hiram H. L\'opez, Nathan Nichols, Matthew Perkins, Ivan Soprunov, German
Vera-Mart\'inez, Gwyn Whieldon
|
Coding theory package for Macaulay2
| null |
J. Softw. Alg. Geom. 11 (2021) 113-122
|
10.2140/jsag.2021.11.113
| null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this Macaulay2 \cite{M2} package we define an object called {\it linear
code}. We implement functions that compute basic parameters and objects
associated with a linear code, such as generator and parity check matrices, the
dual code, length, dimension, and minimum distance, among others. We define an
object {\it evaluation code}, a construction which allows to study linear codes
using tools of algebraic geometry and commutative algebra. We implement
functions to generate important families of linear codes such as Hamming codes,
cyclic codes, Reed--Solomon codes, Reed--Muller codes, Cartesian codes,
monomial--Cartesian codes, and toric codes. In addition, we define functions
for the syndrome decoding algorithm and locally recoverable code construction,
which are important tools in applications of linear codes. The package
\textit{CodingTheory.m2} is available at
\url{https://github.com/Macaulay2/Workshop-2020-Cleveland/tree/CodingTheory/CodingTheory}
|
[
{
"version": "v1",
"created": "Tue, 14 Jul 2020 03:38:29 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Ball",
"Taylor",
""
],
[
"Camps",
"Eduardo",
""
],
[
"Chimal-Dzul",
"Henry",
""
],
[
"Jaramillo-Velez",
"Delio",
""
],
[
"López",
"Hiram H.",
""
],
[
"Nichols",
"Nathan",
""
],
[
"Perkins",
"Matthew",
""
],
[
"Soprunov",
"Ivan",
""
],
[
"Vera-Martínez",
"German",
""
],
[
"Whieldon",
"Gwyn",
""
]
] |
new_dataset
| 0.996934 |
2102.02959
|
Fakrul Islam Tushar
|
Vincent M. D'Anniballe, Fakrul Islam Tushar, Khrystyna Faryna, Songyue
Han, Maciej A. Mazurowski, Geoffrey D. Rubin, Joseph Y. Lo
|
Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text
Reports Using Deep Learning
| null | null | null | null |
cs.AI cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Purpose: To develop high throughput multi-label annotators for body (chest,
abdomen, and pelvis) Computed Tomography (CT) reports that can be applied
across a variety of abnormalities, organs, and disease states.
Approach: We used a dictionary approach to develop rule-based algorithms
(RBA) for extraction of disease labels from radiology text reports. We targeted
three organ systems (lungs/pleura, liver/gallbladder, kidneys/ureters) with
four diseases per system based on their prevalence in our dataset. To expand
the algorithms beyond pre-defined keywords, attention-guided recurrent neural
networks (RNN) were trained using the RBA-extracted labels to classify reports
as being positive for one or more diseases or normal for each organ system.
Confounding effects on model performance were evaluated using random
initialization or pre-trained embedding as well as different sizes of training
datasets. Performance was evaluated using the receiver operating characteristic
(ROC) area under the curve (AUC) against 2,158 manually obtained labels.
Results: Our models extracted disease labels from 261,229 radiology reports
of 112,501 unique subjects. Pre-trained models outperformed random
initialization across all diseases. As the training dataset size was reduced,
performance was robust except for a few diseases with relatively small number
of cases. Pre-trained classification AUCs achieved > 0.95 for all five disease
outcomes across all three organ systems.
Conclusions: Our label-extracting pipeline was able to encompass a variety of
cases and diseases by generalizing beyond strict rules with exceptional
accuracy. This method can be easily adapted to enable automated labeling of
hospital-scale medical data sets for training image-based disease classifiers.
|
[
{
"version": "v1",
"created": "Fri, 5 Feb 2021 02:07:39 GMT"
},
{
"version": "v2",
"created": "Thu, 25 Feb 2021 03:12:11 GMT"
},
{
"version": "v3",
"created": "Sun, 30 May 2021 09:37:37 GMT"
},
{
"version": "v4",
"created": "Sat, 12 Jun 2021 11:38:33 GMT"
},
{
"version": "v5",
"created": "Tue, 8 Mar 2022 02:13:44 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"D'Anniballe",
"Vincent M.",
""
],
[
"Tushar",
"Fakrul Islam",
""
],
[
"Faryna",
"Khrystyna",
""
],
[
"Han",
"Songyue",
""
],
[
"Mazurowski",
"Maciej A.",
""
],
[
"Rubin",
"Geoffrey D.",
""
],
[
"Lo",
"Joseph Y.",
""
]
] |
new_dataset
| 0.996115 |
2106.08074
|
Todd Schmid
|
Todd Schmid (UCL, London, UK), Jurriaan Rot (Radboud University,
Nijmegen, The Netherlands), Alexandra Silva (UCL, London, UK)
|
On Star Expressions and Coalgebraic Completeness Theorems
|
In Proceedings MFPS 2021, arXiv:2112.13746
|
EPTCS 351, 2021, pp. 242-259
|
10.4204/EPTCS.351.15
| null |
cs.LO cs.FL
|
http://creativecommons.org/licenses/by/4.0/
|
An open problem posed by Milner asks for a proof that a certain
axiomatisation, which Milner showed is sound with respect to bisimilarity for
regular expressions, is also complete. One of the main difficulties of the
problem is the lack of a full Kleene theorem, since there are automata that can
not be specified, up to bisimilarity, by an expression. Grabmayer and Fokkink
(2020) characterise those automata that can be expressed by regular expressions
without the constant 1, and use this characterisation to give a positive answer
to Milner's question for this subset of expressions. In this paper, we analyse
Grabmayer and Fokkink's proof of completeness from the perspective of universal
coalgebra, and thereby give an abstract account of their proof method. We then
compare this proof method to another approach to completeness proofs from
coalgebraic language theory. This culminates in two abstract proof methods for
completeness, what we call the local and global approaches, and a description
of when one method can be used in place of the other.
|
[
{
"version": "v1",
"created": "Tue, 15 Jun 2021 12:04:32 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Dec 2021 09:10:14 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Mar 2022 15:06:52 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Schmid",
"Todd",
"",
"UCL, London, UK"
],
[
"Rot",
"Jurriaan",
"",
"Radboud University,\n Nijmegen, The Netherlands"
],
[
"Silva",
"Alexandra",
"",
"UCL, London, UK"
]
] |
new_dataset
| 0.976863 |
2108.11557
|
Zhifeng Huang
|
Yuhang Li, Yuhao Zhou, Junbin Huang, Zijun Wang, Shunjie Zhu, Kairong
Wu, Li Zheng, Jiajin Luo, Rui Cao, Yun Zhang, and Zhifeng Huang
|
Design of a Flying Humanoid Robot Based on Thrust Vector Control
|
The article has been submitted to IEEE Robotics and Automation
Letters (RA-L) with ICRA 2022 conference option. Supporting video:
https://youtu.be/Z5xm8um8Sv8&ab_channel=JetPowerandHumanoidRobotLab
|
IEEE Robotics and Automation Letters, vol. 7, no. 2, pp.
4590-4597, April 2022
|
10.1109/LRA.2022.3152231
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Achieving short-distance flight helps improve the efficiency of humanoid
robots moving in complex environments (e.g., crossing large obstacles or
reaching high places) for rapid emergency missions. This study proposes a
design of a flying humanoid robot named Jet-HR2. The robot has 10 joints driven
by brushless motors and harmonic drives for locomotion. To overcome the
challenge of the stable-attitude takeoff in small thrust-to-weight conditions,
the robot was designed based on the concept of thrust vectoring. The propulsion
system consists of four ducted fans, that is, two fixed on the waist of the
robot and the other two mounted on the feet, for thrust vector control. The
thrust vector is controlled by adjusting the attitude of the foot during the
flight. A simplified model and control strategies are proposed to solve the
problem of attitude instability caused by mass errors and joint position errors
during takeoff. The experimental results show that the robot's spin and dive
behaviors during takeoff were effectively suppressed by controlling the thrust
vector of the ducted fan on the foot. The robot successfully achieved takeoff
at a thrust-to-weight ratio of 1.17 (17 kg / 20 kg) and maintained a stable
attitude, reaching a takeoff height of over 1000 mm.
|
[
{
"version": "v1",
"created": "Thu, 26 Aug 2021 02:31:21 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Li",
"Yuhang",
""
],
[
"Zhou",
"Yuhao",
""
],
[
"Huang",
"Junbin",
""
],
[
"Wang",
"Zijun",
""
],
[
"Zhu",
"Shunjie",
""
],
[
"Wu",
"Kairong",
""
],
[
"Zheng",
"Li",
""
],
[
"Luo",
"Jiajin",
""
],
[
"Cao",
"Rui",
""
],
[
"Zhang",
"Yun",
""
],
[
"Huang",
"Zhifeng",
""
]
] |
new_dataset
| 0.983549 |
2110.05145
|
Antonella Barisic
|
Antonella Barisic and Frano Petric and Stjepan Bogdan
|
Sim2Air - Synthetic aerial dataset for UAV monitoring
| null |
IEEE Robotics and Automation Letters (vol. 7, no. 2, pp.
3757-3764, April 2022)
|
10.1109/LRA.2022.3147337
| null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this paper we propose a novel approach to generate a synthetic aerial
dataset for application in UAV monitoring. We propose to accentuate shape-based
object representation by applying texture randomization. A diverse dataset with
photorealism in all parameters such as shape, pose, lighting, scale, viewpoint,
etc. except for atypical textures is created in a 3D modelling software
Blender. Our approach specifically targets two conditions in aerial images
where texture of objects is difficult to detect, namely challenging
illumination and objects occupying only a small portion of the image.
Experimental evaluation of YOLO and Faster R-CNN detectors trained on synthetic
data with randomized textures confirmed our approach by increasing the mAP
value (17 and 3.7 percentage points for YOLO; 20 and 1.1 percentage points for
Faster R-CNN) on two test datasets of real images, both containing UAV-to-UAV
images with motion blur. Testing on different domains, we conclude that the
more the generalisation ability is put to the test, the more apparent are the
advantages of the shape-based representation.
|
[
{
"version": "v1",
"created": "Mon, 11 Oct 2021 10:36:33 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Mar 2022 06:20:18 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Barisic",
"Antonella",
""
],
[
"Petric",
"Frano",
""
],
[
"Bogdan",
"Stjepan",
""
]
] |
new_dataset
| 0.99972 |
2112.08162
|
Stefano Di Carlo
|
Franco Oberti, Alessandro Savino, Ernesto Sanchez, Filippo Parisi,
Stefano Di Carlo
|
EXT-TAURUM P2T: an Extended Secure CAN-FD Architecture for Road Vehicles
| null | null |
10.1109/TDMR.2022.3157000
| null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The automobile industry is no longer relying on pure mechanical systems;
instead, it benefits from advanced Electronic Control Units (ECUs) in order to
provide new and complex functionalities in the effort to move toward fully
connected cars. However, connected cars provide a dangerous playground for
hackers. Vehicles are becoming increasingly vulnerable to cyber attacks as they
come equipped with more connected features and control systems. This situation
may expose strategic assets in the automotive value chain. In this scenario,
the Controller Area Network (CAN) is the most widely used communication
protocol in the automotive domain. However, this protocol lacks encryption and
authentication. Consequently, any malicious/hijacked node can cause
catastrophic accidents and financial loss. Starting from the analysis of the
vulnerability connected to the CAN communication protocol in the automotive
domain, this paper proposes EXT-TAURUM P2T a new low-cost secure CAN-FD
architecture for the automotive domain implementing secure communication among
ECUs, a novel key provisioning strategy, intelligent throughput management, and
hardware signature mechanisms. The proposed architecture has been implemented,
resorting to a commercial Multi-Protocol Vehicle Interface module, and the
obtained results experimentally demonstrate the approach's feasibility.
|
[
{
"version": "v1",
"created": "Wed, 15 Dec 2021 14:34:04 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Mar 2022 08:43:24 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Oberti",
"Franco",
""
],
[
"Savino",
"Alessandro",
""
],
[
"Sanchez",
"Ernesto",
""
],
[
"Parisi",
"Filippo",
""
],
[
"Di Carlo",
"Stefano",
""
]
] |
new_dataset
| 0.999361 |
2201.03331
|
Christian Ponte-Fern\'andez
|
Christian Ponte-Fern\'andez (1), Jorge Gonz\'alez-Dom\'inguez (1) and
Mar\'ia J. Mart\'in (1) ((1) Universidade da Coru\~na, CITIC, Computer
Architecture Group, A Coru\~na, Spain)
|
Fiuncho: a program for any-order epistasis detection in CPU clusters
|
Submitted to The Journal of Supercomputing. Source code available at
https://github.com/UDC-GAC/fiuncho
| null | null | null |
cs.DC cs.CE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Epistasis can be defined as the statistical interaction of genes during the
expression of a phenotype. It is believed that it plays a fundamental role in
gene expression, as individual genetic variants have reported a very small
increase in disease risk in previous Genome-Wide Association Studies. The most
successful approach to epistasis detection is the exhaustive method, although
its exponential time complexity requires a highly parallel implementation in
order to be used. This work presents Fiuncho, a program that exploits all
levels of parallelism present in \textit{x86\_64} CPU clusters in order to
mitigate the complexity of this approach. It supports epistasis interactions of
any order, and when compared with other exhaustive methods, it is on average
358, 7 and 3 times faster than MDR, MPI3SNP and BitEpi, respectively.
|
[
{
"version": "v1",
"created": "Mon, 10 Jan 2022 13:19:31 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Mar 2022 16:22:12 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Mar 2022 17:07:11 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Ponte-Fernández",
"Christian",
""
],
[
"González-Domínguez",
"Jorge",
""
],
[
"Martín",
"María J.",
""
]
] |
new_dataset
| 0.992294 |
2201.10865
|
Steffen Urban
|
Steffen Urban, Thomas Lindemeier, David Dobbelstein, Matthias Haenel
|
On the Issues of TrueDepth Sensor Data for Computer Vision Tasks Across
Different iPad Generations
|
17 pages
| null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In 2017 Apple introduced the TrueDepth sensor with the iPhone X release.
Although its primary use case is biometric face recognition, the exploitation
of accurate depth data for other computer vision tasks like segmentation,
portrait image generation and metric 3D reconstruction seems natural and lead
to the development of various applications. In this report, we investigate the
reliability of TrueDepth data - accessed through two different APIs - on
various devices including different iPhone and iPad generations and reveal two
different and significant issues on all tested iPads.
|
[
{
"version": "v1",
"created": "Wed, 26 Jan 2022 10:53:54 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Mar 2022 11:31:29 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Urban",
"Steffen",
""
],
[
"Lindemeier",
"Thomas",
""
],
[
"Dobbelstein",
"David",
""
],
[
"Haenel",
"Matthias",
""
]
] |
new_dataset
| 0.98645 |
2202.03755
|
Pietro Liguori
|
Pietro Liguori, Erfan Al-Hossami, Domenico Cotroneo, Roberto Natella,
Bojan Cukic, Samira Shaikh
|
Can We Generate Shellcodes via Natural Language? An Empirical Study
|
33 pages, 5 figures, 9 tables. To be published in Automated Software
Engineering journal
| null |
10.1007/s10515-022-00331-3
| null |
cs.SE cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Writing software exploits is an important practice for offensive security
analysts to investigate and prevent attacks. In particular, shellcodes are
especially time-consuming and a technical challenge, as they are written in
assembly language. In this work, we address the task of automatically
generating shellcodes, starting purely from descriptions in natural language,
by proposing an approach based on Neural Machine Translation (NMT). We then
present an empirical study using a novel dataset (Shellcode_IA32), which
consists of 3,200 assembly code snippets of real Linux/x86 shellcodes from
public databases, annotated using natural language. Moreover, we propose novel
metrics to evaluate the accuracy of NMT at generating shellcodes. The empirical
analysis shows that NMT can generate assembly code snippets from the natural
language with high accuracy and that in many cases can generate entire
shellcodes with no errors.
|
[
{
"version": "v1",
"created": "Tue, 8 Feb 2022 09:57:34 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Liguori",
"Pietro",
""
],
[
"Al-Hossami",
"Erfan",
""
],
[
"Cotroneo",
"Domenico",
""
],
[
"Natella",
"Roberto",
""
],
[
"Cukic",
"Bojan",
""
],
[
"Shaikh",
"Samira",
""
]
] |
new_dataset
| 0.998411 |
2202.11042
|
Michail Gkagkos
|
Michail Gkagkos, Krishna R. Narayanan, Jean-Francois Chamberland,
Costas N. Georghiades
|
FASURA: A Scheme for Quasi-Static Massive MIMO Unsourced Random Access
Channels
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This article considers the massive MIMO unsourced random access problem on a
quasi-static Rayleigh fading channel. Given a fixed message length and a
prescribed number of channel uses, the objective is to construct a coding
scheme that minimizes the energy-per-bit subject to a fixed probability of
error. The proposed scheme differs from other state-of-the-art schemes in that
it blends activity detection, single-user coding, pilot-aided and temporary
decisions-aided iterative channel estimation and decoding, minimum-mean squared
error (MMSE) estimation, and successive interference cancellation (SIC). We
show that an appropriate combination of these ideas can substantially
outperform state-of-the-art coding schemes when the number of active users is
more than 100, making this the best performing scheme known for this regime.
|
[
{
"version": "v1",
"created": "Tue, 22 Feb 2022 17:19:06 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Mar 2022 17:43:07 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Gkagkos",
"Michail",
""
],
[
"Narayanan",
"Krishna R.",
""
],
[
"Chamberland",
"Jean-Francois",
""
],
[
"Georghiades",
"Costas N.",
""
]
] |
new_dataset
| 0.999404 |
2202.11684
|
Dan Saattrup Nielsen
|
Dan Saattrup Nielsen and Ryan McConville
|
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation
Social Network Dataset
|
9+3 pages
| null | null | null |
cs.LG cs.CL cs.CY cs.IR cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Misinformation is becoming increasingly prevalent on social media and in news
articles. It has become so widespread that we require algorithmic assistance
utilising machine learning to detect such content. Training these machine
learning models require datasets of sufficient scale, diversity and quality.
However, datasets in the field of automatic misinformation detection are
predominantly monolingual, include a limited amount of modalities and are not
of sufficient scale and quality. Addressing this, we develop a data collection
and linking system (MuMiN-trawl), to build a public misinformation graph
dataset (MuMiN), containing rich social media data (tweets, replies, users,
images, articles, hashtags) spanning 21 million tweets belonging to 26 thousand
Twitter threads, each of which have been semantically linked to 13 thousand
fact-checked claims across dozens of topics, events and domains, in 41
different languages, spanning more than a decade. The dataset is made available
as a heterogeneous graph via a Python package (mumin). We provide baseline
results for two node classification tasks related to the veracity of a claim
involving social media, and demonstrate that these are challenging tasks, with
the highest macro-average F1-score being 62.55% and 61.45% for the two tasks,
respectively. The MuMiN ecosystem is available at
https://mumin-dataset.github.io/, including the data, documentation, tutorials
and leaderboards.
|
[
{
"version": "v1",
"created": "Wed, 23 Feb 2022 18:47:34 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Mar 2022 12:30:03 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Nielsen",
"Dan Saattrup",
""
],
[
"McConville",
"Ryan",
""
]
] |
new_dataset
| 0.999821 |
2203.01063
|
Konstantinos Kogkalidis
|
Konstantinos Kogkalidis and Gijs Wijnholds
|
Discontinuous Constituency and BERT: A Case Study of Dutch
|
8 pages plus references. To appear in Findings of the Association for
Computational Linguistics 2022
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we set out to quantify the syntactic capacity of BERT in the
evaluation regime of non-context free patterns, as occurring in Dutch. We
devise a test suite based on a mildly context-sensitive formalism, from which
we derive grammars that capture the linguistic phenomena of control verb
nesting and verb raising. The grammars, paired with a small lexicon, provide us
with a large collection of naturalistic utterances, annotated with verb-subject
pairings, that serve as the evaluation test bed for an attention-based span
selection probe. Our results, backed by extensive analysis, suggest that the
models investigated fail in the implicit acquisition of the dependencies
examined.
|
[
{
"version": "v1",
"created": "Wed, 2 Mar 2022 12:30:21 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Mar 2022 09:27:31 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Kogkalidis",
"Konstantinos",
""
],
[
"Wijnholds",
"Gijs",
""
]
] |
new_dataset
| 0.995863 |
2203.03704
|
Jeff Delaune
|
Jeff Delaune, Jacob Izraelevitz, Samuel Sirlin, David Sternberg, Louis
Giersch, L. Phillipe Tosi, Evgeniy Skliyanskiy, Larry Young, Michael Mischna,
Shannah Withrow-Maser, Juergen Mueller, Joshua Bowman, Mark S Wallace, Havard
F. Grip, Larry Matthies, Wayne Johnson, Matthew Keennon, Benjamin Pipenberg,
Harsh Patel, Christopher Lim, Aaron Schutte, Marcel Veismann, Haley Cummings,
Sarah Conley, Jonathan Bapst, Theodore Tzanetos, Roland Brockers, Abhinandan
Jain, David Bayard, Art Chmielewski, Olivier Toupet, Joel Burdick, Morteza
Gharib and J. (Bob) Balaram
|
Mid-Air Helicopter Delivery at Mars Using a Jetpack
|
Accepted in 2022 IEEE Aerospace Conference
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mid-Air Helicopter Delivery (MAHD) is a new Entry, Descent and Landing (EDL)
architecture to enable in situ mobility for Mars science at lower cost than
previous missions. It uses a jetpack to slow down a Mars Science Helicopter
(MSH) after separation from the backshell, and reach aerodynamic conditions
suitable for helicopter take-off in mid air. For given aeroshell dimensions,
only MAHD's lander-free approach leaves enough room in the aeroshell to
accommodate the largest rotor option for MSH. This drastically improves flight
performance, notably allowing +150\% increased science payload mass. Compared
to heritage EDL approaches, the simpler MAHD architecture is also likely to
reduce cost, and enables access to more hazardous and higher-elevation terrains
on Mars. This paper introduces a design for the MAHD system architecture and
operations. We present a mechanical configuration that fits both MSH and the
jetpack within the 2.65-m Mars heritage aeroshell, and a jetpack control
architecture which fully leverages the available helicopter avionics. We
discuss preliminary numerical models of the flow dynamics resulting from the
interaction between the jets, the rotors and the side winds. We define a
force-torque sensing architecture capable of handling the wind and trimming the
rotors to prepare for safe take-off. Finally, we analyze the dynamic
environment and closed-loop control simulation results to demonstrate the
preliminary feasibility of MAHD.
|
[
{
"version": "v1",
"created": "Mon, 7 Mar 2022 21:07:56 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Delaune",
"Jeff",
"",
"Bob"
],
[
"Izraelevitz",
"Jacob",
"",
"Bob"
],
[
"Sirlin",
"Samuel",
"",
"Bob"
],
[
"Sternberg",
"David",
"",
"Bob"
],
[
"Giersch",
"Louis",
"",
"Bob"
],
[
"Tosi",
"L. Phillipe",
"",
"Bob"
],
[
"Skliyanskiy",
"Evgeniy",
"",
"Bob"
],
[
"Young",
"Larry",
"",
"Bob"
],
[
"Mischna",
"Michael",
"",
"Bob"
],
[
"Withrow-Maser",
"Shannah",
"",
"Bob"
],
[
"Mueller",
"Juergen",
"",
"Bob"
],
[
"Bowman",
"Joshua",
"",
"Bob"
],
[
"Wallace",
"Mark S",
"",
"Bob"
],
[
"Grip",
"Havard F.",
"",
"Bob"
],
[
"Matthies",
"Larry",
"",
"Bob"
],
[
"Johnson",
"Wayne",
"",
"Bob"
],
[
"Keennon",
"Matthew",
"",
"Bob"
],
[
"Pipenberg",
"Benjamin",
"",
"Bob"
],
[
"Patel",
"Harsh",
"",
"Bob"
],
[
"Lim",
"Christopher",
"",
"Bob"
],
[
"Schutte",
"Aaron",
"",
"Bob"
],
[
"Veismann",
"Marcel",
"",
"Bob"
],
[
"Cummings",
"Haley",
"",
"Bob"
],
[
"Conley",
"Sarah",
"",
"Bob"
],
[
"Bapst",
"Jonathan",
"",
"Bob"
],
[
"Tzanetos",
"Theodore",
"",
"Bob"
],
[
"Brockers",
"Roland",
"",
"Bob"
],
[
"Jain",
"Abhinandan",
"",
"Bob"
],
[
"Bayard",
"David",
"",
"Bob"
],
[
"Chmielewski",
"Art",
"",
"Bob"
],
[
"Toupet",
"Olivier",
"",
"Bob"
],
[
"Burdick",
"Joel",
"",
"Bob"
],
[
"Gharib",
"Morteza",
"",
"Bob"
],
[
"J.",
"",
"",
"Bob"
],
[
"Balaram",
"",
""
]
] |
new_dataset
| 0.999676 |
2203.03759
|
Gabriele Sarti
|
Gabriele Sarti, Malvina Nissim
|
IT5: Large-scale Text-to-text Pretraining for Italian Language
Understanding and Generation
|
13 pages, 7 tables, 1 figure. Code and checkpoints available:
https://github.com/gsarti/it5
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
The T5 model and its unified text-to-text paradigm contributed in advancing
the state-of-the-art for many natural language processing tasks. While some
multilingual variants of the T5 model have recently been introduced, their
performances were found to provide suboptimal performances for languages other
than English if compared to monolingual variants. We are motivated by these
findings to introduce IT5, the first family of encoder-decoder transformer
models pretrained specifically on Italian. We perform a thorough cleaning of a
web-crawled Italian corpus including more than 40 billion words and use it to
pretrain three IT5 models of different sizes. The performance of IT5 models and
their multilingual counterparts is then evaluated on a broad range of natural
language understanding and generation benchmarks for Italian. We find the
monolingual IT5 models to provide the best scale-to-performance ratio across
tested models, consistently outperforming their multilingual counterparts and
setting a new state-of-the-art for most Italian conditional language generation
tasks.
|
[
{
"version": "v1",
"created": "Mon, 7 Mar 2022 22:39:01 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Sarti",
"Gabriele",
""
],
[
"Nissim",
"Malvina",
""
]
] |
new_dataset
| 0.98456 |
2203.03797
|
Junchi Liang
|
Junchi Liang, Bowen Wen, Kostas Bekris and Abdeslam Boularias
|
Learning Sensorimotor Primitives of Sequential Manipulation Tasks from
Visual Demonstrations
| null | null | null | null |
cs.RO cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
This work aims to learn how to perform complex robot manipulation tasks that
are composed of several, consecutively executed low-level sub-tasks, given as
input a few visual demonstrations of the tasks performed by a person. The
sub-tasks consist of moving the robot's end-effector until it reaches a
sub-goal region in the task space, performing an action, and triggering the
next sub-task when a pre-condition is met. Most prior work in this domain has
been concerned with learning only low-level tasks, such as hitting a ball or
reaching an object and grasping it. This paper describes a new neural
network-based framework for learning simultaneously low-level policies as well
as high-level policies, such as deciding which object to pick next or where to
place it relative to other objects in the scene. A key feature of the proposed
approach is that the policies are learned directly from raw videos of task
demonstrations, without any manual annotation or post-processing of the data.
Empirical results on object manipulation tasks with a robotic arm show that the
proposed network can efficiently learn from real visual demonstrations to
perform the tasks, and outperforms popular imitation learning algorithms.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 01:36:48 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Liang",
"Junchi",
""
],
[
"Wen",
"Bowen",
""
],
[
"Bekris",
"Kostas",
""
],
[
"Boularias",
"Abdeslam",
""
]
] |
new_dataset
| 0.987427 |
2203.03856
|
Bowen Xing
|
Bowen Xing and Ivor W. Tsang
|
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for
Joint Dialog Sentiment Classification and Act Recognition
|
Long paper; ACL 2022 (Findings)
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The task of joint dialog sentiment classification (DSC) and act recognition
(DAR) aims to simultaneously predict the sentiment label and act label for each
utterance in a dialog. In this paper, we put forward a new framework which
models the explicit dependencies via integrating \textit{prediction-level
interactions} other than semantics-level interactions, more consistent with
human intuition. Besides, we propose a speaker-aware temporal graph (SATG) and
a dual-task relational temporal graph (DRTG) to introduce \textit{temporal
relations} into dialog understanding and dual-task reasoning. To implement our
framework, we propose a novel model dubbed DARER, which first generates the
context-, speaker- and temporal-sensitive utterance representations via
modeling SATG, then conducts recurrent dual-task relational reasoning on DRTG,
in which process the estimated label distributions act as key clues in
prediction-level interactions. Experiment results show that DARER outperforms
existing models by large margins while requiring much less computation resource
and costing less training time. Remarkably, on DSC task in Mastodon, DARER
gains a relative improvement of about 25% over previous best model in terms of
F1, with less than 50% parameters and about only 60% required GPU memory.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 05:19:18 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Xing",
"Bowen",
""
],
[
"Tsang",
"Ivor W.",
""
]
] |
new_dataset
| 0.998127 |
2203.03859
|
Arda Mavi
|
Arda Mavi and Zeynep Dikle
|
A New 27 Class Sign Language Dataset Collected from 173 Individuals
|
5 pages, 1 figure
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
After the interviews, it has been comprehended that speech-impaired
individuals who use sign languages have difficulty communicating with other
people who do not know sign language. Due to the communication problems, the
sense of independence of speech-impaired individuals could be damaged and lead
them to socialize less with society. To contribute to the development of
technologies, that can reduce the communication problems of speech-impaired
persons, a new dataset was presented with this paper. The dataset was created
by processing American Sign Language-based photographs collected from 173
volunteers, published as 27 Class Sign Language Dataset on the Kaggle Datasets
web page.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 05:30:03 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Mavi",
"Arda",
""
],
[
"Dikle",
"Zeynep",
""
]
] |
new_dataset
| 0.998524 |
2203.04021
|
Vittorio Lippi
|
Vittorio Lippi, Alessandro Filippeschi, Cristian Camardella, Francesco
Porcini, Christoph Maurer, Lucia Lencioni
|
EXOSMOOTH: Test of Innovative EXOskeleton Control for SMOOTH Assistance,
With and Without Ankle Actuation
| null | null |
10.5555/3523760.3523899
| null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
This work presents a description of the EXOSMOOTH project, oriented to the
benchmarking of lower limb exoskeletons performance. In the field of assisted
walking by powered lower limb exoskeletons, the EXOSMOOTH project proposes an
experiment that targets two scientific questions. The first question is related
to the effectiveness of a novel control strategy for smooth assistance. Current
assist strategies are based on controllers that switch the assistance level
based on the gait segmentation provided by a finite state machine. The proposed
strategy aims at managing phase transitions to provide a smoother assistance to
the user, thus increasing the device transparency and comfort for the user. The
second question is the role of the actuation at the ankle joint in assisted
walking. Many novel exoskeletons devised for industrial applications do not
feature an actuated ankle joint. In the EXOSMOOTH project, the ankle joint
actuation will be one experimental factor to have a direct assessment of the
role of an actuated joint in assisted walking. Preliminary results of 15
healthy subjects walking at different speeds while wearing a lower limb
exoskeleton supported the rationale behind this question: having an actuated
ankle joint could potentially reduce the torques applied by the user by a
maximum value of 85 Nm. The two aforementioned questions will be investigated
in a protocol that includes walking on a treadmill and on flat ground, with or
without slope, and with a load applied on the back. In addition, the
interaction forces measured at the exoskeleton harnesses will be used to assess
the comfort of the user and the effectiveness of the control strategy to
improve transparency.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 11:27:09 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Lippi",
"Vittorio",
""
],
[
"Filippeschi",
"Alessandro",
""
],
[
"Camardella",
"Cristian",
""
],
[
"Porcini",
"Francesco",
""
],
[
"Maurer",
"Christoph",
""
],
[
"Lencioni",
"Lucia",
""
]
] |
new_dataset
| 0.997076 |
2203.04024
|
Weiwei Wan
|
Ruishuang Liu, Weiwei Wan, Emiko Isomura, Kensuke Harada
|
Metal Wire Manipulation Planning for 3D Curving -- How a Low Payload
Robot Can Use a Bending Machine to Bend Stiff Metal Wire
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a combined task and motion planner for a robot arm to
carry out 3D metal wire curving tasks by collaborating with a bending machine.
We assume a collaborative robot that is safe to work in a human environment but
has a weak payload to bend objects with large stiffness, and developed a
combined planner for the robot to use a bending machine. Our method converts a
3D curve to a bending set and generates the feasible bending sequence, machine
usage, robotic grasp poses, and pick-and-place arm motion considering the
combined task and motion level constraints. Compared with previous deformable
linear object shaping work that relied on forces provided by robotic arms, the
proposed method is suitable for the material with high stiffness. We evaluate
the system using different tasks. The results show that the proposed system is
flexible and robust to generate robotic motion to corporate with the designed
bending machine.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 11:39:48 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Liu",
"Ruishuang",
""
],
[
"Wan",
"Weiwei",
""
],
[
"Isomura",
"Emiko",
""
],
[
"Harada",
"Kensuke",
""
]
] |
new_dataset
| 0.997304 |
2203.04079
|
Shaolin Yu
|
Shaolin Yu, Jihong Zhu, Jiali Yang, Wei Lu
|
A New Fault-Tolerant Synchronization Scheme with Anonymous Pulses
|
19 pages, 5 figures
| null | null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Robust pulse synchronization is fundamental in constructing reliable
synchronous applications in wired and wireless distributed systems. In wired
systems, self-stabilizing Byzantine pulse synchronization aims for
synchronizing fault-prone distributed components with arbitrary initial states
in bounded-delay message-passing systems. In wireless systems, fault-tolerant
synchronization of pulse-coupled oscillators is also built for a similar goal
but often works under specific system restrictions, such as low computation
power, low message complexity, and anonymous physical pulses whose senders
cannot be identified by the receivers. These restrictions often prevent us from
constructing high-reliable wireless synchronous applications. This paper tries
to break barriers between bounded-delay message-passing systems and classical
pulse-coupled oscillators by introducing a new fault-tolerant synchronization
scheme for the so-called anonymous bounded-delay pulsing systems in the
presence of indeterministic communication delays and inconsistent faults. For
low computation power and low message complexity, instead of involving in
consensus-based primitives, the proposed synchronization scheme integrates the
so-called discrete mean-fields, planar random walks, and some additional easy
operations in utilizing only sparsely generated anonymous pulses. For
fault-tolerance, we show that a square-root number of faulty oscillators can be
tolerated by utilizing planar random walks in anonymous pulse synchronization.
For self-stabilization, we show that the stabilization can be reached in an
expected constant number of observing windows in anonymous bounded-delay
pulsing systems with the pulsing-frequency restriction.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 13:48:34 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Yu",
"Shaolin",
""
],
[
"Zhu",
"Jihong",
""
],
[
"Yang",
"Jiali",
""
],
[
"Lu",
"Wei",
""
]
] |
new_dataset
| 0.999565 |
2203.04111
|
Shubham Kumar Nigam
|
Shubham Kumar Nigam and Mosab Shaheen
|
Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection
for English and Arabic Using Transformers and Data Augmentation
|
SemEval-2022 workshop paper, submitted in NAACL-2022 conference. 8
figures and 29 tables. 8 main pages, 4 appendix pages
| null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
This paper describes our submission to SemEval-2022 Task 6 on sarcasm
detection and its five subtasks for English and Arabic. Sarcasm conveys a
meaning which contradicts the literal meaning, and it is mainly found on social
networks. It has a significant role in understanding the intention of the user.
For detecting sarcasm, we used deep learning techniques based on transformers
due to its success in the field of Natural Language Processing (NLP) without
the need for feature engineering. The datasets were taken from tweets. We
created new datasets by augmenting with external data or by using word
embeddings and repetition of instances. Experiments were done on the datasets
with different types of preprocessing because it is crucial in this task. The
rank of our team was consistent across four subtasks (fourth rank in three
subtasks and sixth rank in one subtask); whereas other teams might be in the
top ranks for some subtasks but rank drastically less in other subtasks. This
implies the robustness and stability of the models and the techniques we used.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 14:33:45 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Nigam",
"Shubham Kumar",
""
],
[
"Shaheen",
"Mosab",
""
]
] |
new_dataset
| 0.99915 |
2203.04129
|
Yongxiang Gu
|
Yongxiang Gu, Xingbin Liao and Xiaolin Qin
|
YouTube-GDD: A challenging gun detection dataset with rich contextual
information
|
4 pages, 1 figure
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
An automatic gun detection system can detect potential gun-related violence
at an early stage that is of paramount importance for citizens security. In the
whole system, object detection algorithm is the key to perceive the environment
so that the system can detect dangerous objects such as pistols and rifles.
However, mainstream deep learning-based object detection algorithms depend
heavily on large-scale high-quality annotated samples, and the existing gun
datasets are characterized by low resolution, little contextual information and
little data volume. To promote the development of security, this work presents
a new challenging dataset called YouTube Gun Detection Dataset (YouTube-GDD).
Our dataset is collected from 343 high-definition YouTube videos and contains
5000 well-chosen images, in which 16064 instances of gun and 9046 instances of
person are annotated. Compared to other datasets, YouTube-GDD is "dynamic",
containing rich contextual information and recording shape changes of the gun
during shooting. To build a baseline for gun detection, we evaluate YOLOv5 on
YouTube-GDD and analyze the influence of additional related annotated
information on gun detection. YouTube-GDD and subsequent updates will be
released at https://github.com/UCAS-GYX/YouTube-GDD.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 14:55:10 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Gu",
"Yongxiang",
""
],
[
"Liao",
"Xingbin",
""
],
[
"Qin",
"Xiaolin",
""
]
] |
new_dataset
| 0.999806 |
2203.04130
|
Ziyu Wang
|
Ziyu Wang, Wei Yang, Junming Cao, Lan Xu, Junqing Yu, Jingyi Yu
|
NeReF: Neural Refractive Field for Fluid Surface Reconstruction and
Implicit Representation
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Existing neural reconstruction schemes such as Neural Radiance Field (NeRF)
are largely focused on modeling opaque objects. We present a novel neural
refractive field(NeReF) to recover wavefront of transparent fluids by
simultaneously estimating the surface position and normal of the fluid front.
Unlike prior arts that treat the reconstruction target as a single layer of the
surface, NeReF is specifically formulated to recover a volumetric normal field
with its corresponding density field. A query ray will be refracted by NeReF
according to its accumulated refractive point and normal, and we employ the
correspondences and uniqueness of refracted ray for NeReF optimization. We show
NeReF, as a global optimization scheme, can more robustly tackle refraction
distortions detrimental to traditional methods for correspondence matching.
Furthermore, the continuous NeReF representation of wavefront enables view
synthesis as well as normal integration. We validate our approach on both
synthetic and real data and show it is particularly suitable for sparse
multi-view acquisition. We hence build a small light field array and experiment
on various surface shapes to demonstrate high fidelity NeReF reconstruction.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 14:56:16 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Wang",
"Ziyu",
""
],
[
"Yang",
"Wei",
""
],
[
"Cao",
"Junming",
""
],
[
"Xu",
"Lan",
""
],
[
"Yu",
"Junqing",
""
],
[
"Yu",
"Jingyi",
""
]
] |
new_dataset
| 0.998572 |
2203.04225
|
Vahid Jamali
|
Vahid Jamali, Helene M. Loos, Andrea Buettner, Robert Schober, and H.
Vincent Poor
|
Olfaction-inspired MCs: Molecule Mixture Shift Keying and Cross-Reactive
Receptor Arrays
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we propose a novel concept for engineered molecular
communication (MC) systems inspired by animal olfaction. We focus on a
multi-user scenario where several transmitters wish to communicate with a
central receiver. We assume that each transmitter employs a unique mixture of
different types of signaling molecules to represent its message and the
receiver is equipped with an array comprising $R$ different types of receptors
in order to detect the emitted molecule mixtures. The design of an MC system
based on \textit{orthogonal} molecule-receptor pairs implies that the hardware
complexity of the receiver linearly scales with the number of signaling
molecule types $Q$ (i.e., $R=Q$). Natural olfaction systems avoid such high
complexity by employing arrays of \textit{cross-reactive} receptors, where each
type of molecule activates multiple types of receptors and each type of
receptor is predominantly activated by multiple types of molecules albeit with
different activation strengths. For instance, the human olfactory system is
believed to discriminate several thousands of chemicals using only a few
hundred receptor types, i.e., $Q\gg R$. Motivated by this observation, we first
develop an end-to-end MC channel model that accounts for the key properties of
olfaction. Subsequently, we present the proposed transmitter and receiver
designs. In particular, given a set of signaling molecules, we develop
algorithms that allocate molecules to different transmitters and optimize the
mixture alphabet for communication. Moreover, we formulate the molecule mixture
recovery as a convex compressive sensing problem which can be efficiently
solved via available numerical solvers.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 17:45:24 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"Jamali",
"Vahid",
""
],
[
"Loos",
"Helene M.",
""
],
[
"Buettner",
"Andrea",
""
],
[
"Schober",
"Robert",
""
],
[
"Poor",
"H. Vincent",
""
]
] |
new_dataset
| 0.971828 |
2203.04250
|
Vitor de Luca
|
Vitor T. F. de Luca, Mar\'ia P\'ia Mazzoleni, Fabiano S. Oliveira,
Tanilson D. Santos, Jayme L. Szwarcfiter
|
Edge Intersection Graphs of Paths on a Triangular Grid
|
19 pages, 12 figures
| null | null | null |
cs.DM cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce a new class of intersection graphs, the edge intersection graphs
of paths on a triangular grid, called EPGt graphs. We show similarities and
differences from this new class to the well-known class of EPG graphs. A turn
of a path at a grid point is called a bend. An EPGt representation in which
every path has at most $k$ bends is called a B$_k$-EPGt representation and the
corresponding graphs are called B$_k$-EPGt graphs. We provide examples of
B$_{2}$-EPG graphs that are B$_{1}$-EPGt. We characterize the representation of
cliques with three vertices and chordless 4-cycles in B$_{1}$-EPGt
representations. We also prove that B$_{1}$-EPGt graphs have Strong Helly
number $3$. Furthermore, we prove that B$_{1}$-EPGt graphs are $7$-clique
colorable.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 18:09:15 GMT"
}
] | 2022-03-09T00:00:00 |
[
[
"de Luca",
"Vitor T. F.",
""
],
[
"Mazzoleni",
"María Pía",
""
],
[
"Oliveira",
"Fabiano S.",
""
],
[
"Santos",
"Tanilson D.",
""
],
[
"Szwarcfiter",
"Jayme L.",
""
]
] |
new_dataset
| 0.98838 |
1603.02080
|
Mikkel Abrahamsen
|
Mikkel Abrahamsen and Mikkel Thorup
|
How to Cut Corners and Get Bounded Convex Curvature
|
This version has been accepted for publication in Discrete &
Computational Geometry. A preliminary version was presented at SoCG 2016
| null | null | null |
cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We describe an algorithm for solving an important geometric problem arising
in computer-aided manufacturing. When cutting away a region from a solid piece
of material -- such as steel, wood, ceramics, or plastic -- using a rough tool
in a milling machine, sharp convex corners of the region cannot be done
properly, but have to be left for finer tools that are more expensive to use.
We want to determine a toolpath that maximizes the use of the rough tool. In
order to formulate the problem in mathematical terms, we introduce the notion
of bounded convex curvature. A region of points in the plane $Q$ has
\emph{bounded convex curvature} if for any point $x\in\partial Q$, there is a
unit disk $U$ and $\varepsilon>0$ such that $x\in \partial U$ and all points in
$U$ within distance $\varepsilon$ from $x$ are in $Q$. This translates to
saying that as we traverse the boundary $\partial Q$ with the interior of $Q$
on the left side, then $\partial Q$ turns to the left with curvature at most
$1$. There is no bound on the curvature where $\partial Q$ turns to the right.
Given a region of points $P$ in the plane, we are now interested in computing
the maximum subset $Q\subseteq P$ of bounded convex curvature. The difference
in the requirement to left- and right-curvature is a natural consequence of
different conditions when machining convex and concave areas of $Q$. We devise
an algorithm to compute the unique maximum such set $Q$, when the boundary of
$P$ consists of $n$ line segments and circular arcs of arbitrary radii. In the
general case where $P$ may have holes, the algorithm runs in time $O(n^2)$ and
uses $O(n)$ space. If $P$ is simply-connected, we describe a faster $O(n\log
n)$ time algorithm.
|
[
{
"version": "v1",
"created": "Mon, 7 Mar 2016 14:18:53 GMT"
},
{
"version": "v2",
"created": "Mon, 8 Mar 2021 16:11:04 GMT"
},
{
"version": "v3",
"created": "Sat, 5 Mar 2022 07:20:53 GMT"
}
] | 2022-03-08T00:00:00 |
[
[
"Abrahamsen",
"Mikkel",
""
],
[
"Thorup",
"Mikkel",
""
]
] |
new_dataset
| 0.980761 |
1903.09711
|
Mouhyemen Khan
|
Mouhyemen Khan, Munzir Zafar, Abhijit Chatterjee
|
Barrier Functions in Cascaded Controller: Safe Quadrotor Control
|
Submitted to ACC 2020, 8 pages, 7 figures
| null |
10.23919/ACC45564.2020.9147864
| null |
cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Safe control for inherently unstable systems such as quadrotors is crucial.
Imposing multiple dynamic constraints simultaneously on the states for safety
regulation can be a challenging problem. In this paper, we propose a quadratic
programming (QP) based approach on a cascaded control architecture for
quadrotors to enforce safety. Safety regions are constructed using control
barrier functions (CBF) while explicitly considering the nonlinear
underactuated dynamics of the quadrotor. The safety regions constructed using
CBFs establish a non-conservative forward invariant safe region for quadrotor
navigation. Barriers imposed across the cascaded architecture allows
independent safety regulation in quadrotor's altitude and lateral domains.
Despite barriers appearing in a cascaded fashion, we show preservation of
safety for quadrotor motion in SE(3). We demonstrate the feasibility of our
method on a quadrotor in simulation with static and dynamic constraints
enforced on position and velocity spaces simultaneously.
|
[
{
"version": "v1",
"created": "Fri, 22 Mar 2019 21:13:22 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Feb 2020 15:59:35 GMT"
}
] | 2022-03-08T00:00:00 |
[
[
"Khan",
"Mouhyemen",
""
],
[
"Zafar",
"Munzir",
""
],
[
"Chatterjee",
"Abhijit",
""
]
] |
new_dataset
| 0.986658 |
2007.04584
|
Dazhen Deng
|
Dazhen Deng, Yihong Wu, Xinhuan Shu, Jiang Wu, Siwei Fu, Weiwei Cui,
Yingcai Wu
|
VisImages: A Fine-Grained Expert-Annotated Visualization Dataset
| null | null |
10.1109/TVCG.2022.3155440
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Images in visualization publications contain rich information, e.g., novel
visualization designs and implicit design patterns of visualizations. A
systematic collection of these images can contribute to the community in many
aspects, such as literature analysis and automated tasks for visualization. In
this paper, we build and make public a dataset, VisImages, which collects
12,267 images with captions from 1,397 papers in IEEE InfoVis and VAST. Built
upon a comprehensive visualization taxonomy, the dataset includes 35,096
visualizations and their bounding boxes in the images.We demonstrate the
usefulness of VisImages through three use cases: 1) investigating the use of
visualizations in the publications with VisImages Explorer, 2) training and
benchmarking models for visualization classification, and 3) localizing
visualizations in the visual analytics systems automatically.
|
[
{
"version": "v1",
"created": "Thu, 9 Jul 2020 06:47:49 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Jul 2020 02:22:28 GMT"
},
{
"version": "v3",
"created": "Wed, 20 Jan 2021 03:44:16 GMT"
},
{
"version": "v4",
"created": "Thu, 10 Jun 2021 11:29:09 GMT"
},
{
"version": "v5",
"created": "Sun, 6 Mar 2022 13:02:40 GMT"
}
] | 2022-03-08T00:00:00 |
[
[
"Deng",
"Dazhen",
""
],
[
"Wu",
"Yihong",
""
],
[
"Shu",
"Xinhuan",
""
],
[
"Wu",
"Jiang",
""
],
[
"Fu",
"Siwei",
""
],
[
"Cui",
"Weiwei",
""
],
[
"Wu",
"Yingcai",
""
]
] |
new_dataset
| 0.998707 |
2011.06996
|
Markus Grassl
|
Markus Grassl
|
Algebraic Quantum Codes: Linking Quantum Mechanics and Discrete
Mathematics
|
19 pages; written for both mathematicians and physicists
|
International Journal of Computer Mathematics: Computer Systems
Theory, vol. 6, no. 4, pp. 243-250, 2021
|
10.1080/23799927.2020.1850530
| null |
cs.IT math.CO math.IT quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a general framework of quantum error-correcting codes (QECCs) as a
subspace of a complex Hilbert space and the corresponding error models. Then we
illustrate how QECCs can be constructed using techniques from algebraic coding
theory. Additionally, we discuss secondary constructions for QECCs, leading to
propagation rules for the parameters of QECCs.
|
[
{
"version": "v1",
"created": "Fri, 13 Nov 2020 16:25:31 GMT"
}
] | 2022-03-08T00:00:00 |
[
[
"Grassl",
"Markus",
""
]
] |
new_dataset
| 0.954062 |
2012.09221
|
Yucel Aydin
|
Yucel Aydin, Gunes Karabulut Kurt, Enver Ozdemir, Halim Yanikomeroglu
|
Group Handover for Drone Base Stations
|
Published in IEEE Internet of Things Journal 2021
|
IEEE Internet of Things Journal 2021
|
10.1109/JIOT.2021.3068297
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The widespread use of new technologies such as the Internet of things (IoT)
and machine type communication(MTC) forces an increase on the number of user
equipments(UEs) and MTC devices that are connecting to mobile networks.
Inherently, as the number of UEs inside a base station's (BS) coverage area
surges, the quality of service (QoS) tends to decline. The use of drone-mounted
BS (UxNB) is a solution in places where UEs are densely populated, such as
stadiums. UxNB emerges as a promising technology that can be used for capacity
injection purposes in the future due to its fast deployment. However, this
emerging technology introduces a new security issue. Mutual authentication,
creating a communication channel between terrestrial BS and UxNB, and fast
handover operations may cause security issues in the use of UxNB for capacity
injection. This new protocol also suggests performing UE handover from
terrestrial to UxNB as a group. To the best of the authors' knowledge, there is
no authentication solution between BSs according to LTE and 5G standards. The
proposed scheme provides a solution for the authentication of UxNB by the
terrestrial BS. Additionally, a credential sharing phase for each UE in
handover is not required in the proposed method. The absence of a credential
sharing step saves resources by reducing the number of communications between
BSs. Moreover, many UE handover operations are completed in concise time within
the proposed group handover method.
|
[
{
"version": "v1",
"created": "Wed, 16 Dec 2020 19:28:48 GMT"
},
{
"version": "v2",
"created": "Thu, 4 Mar 2021 18:07:13 GMT"
},
{
"version": "v3",
"created": "Sun, 21 Mar 2021 09:42:13 GMT"
},
{
"version": "v4",
"created": "Fri, 4 Mar 2022 23:26:38 GMT"
}
] | 2022-03-08T00:00:00 |
[
[
"Aydin",
"Yucel",
""
],
[
"Kurt",
"Gunes Karabulut",
""
],
[
"Ozdemir",
"Enver",
""
],
[
"Yanikomeroglu",
"Halim",
""
]
] |
new_dataset
| 0.995095 |
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