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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2208.14738
|
Shang Xu
|
Jianlin Liu, Zhuofei Huang, Dihe Huang, Shang Xu, Ying Chen, and Yong
Liu
|
Scatter Points in Space: 3D Detection from Multi-view Monocular Images
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
3D object detection from monocular image(s) is a challenging and
long-standing problem of computer vision. To combine information from different
perspectives without troublesome 2D instance tracking, recent methods tend to
aggregate multiview feature by sampling regular 3D grid densely in space, which
is inefficient. In this paper, we attempt to improve multi-view feature
aggregation by proposing a learnable keypoints sampling method, which scatters
pseudo surface points in 3D space, in order to keep data sparsity. The
scattered points augmented by multi-view geometric constraints and visual
features are then employed to infer objects location and shape in the scene. To
make up the limitations of single frame and model multi-view geometry
explicitly, we further propose a surface filter module for noise suppression.
Experimental results show that our method achieves significantly better
performance than previous works in terms of 3D detection (more than 0.1 AP
improvement on some categories of ScanNet). The code will be publicly
available.
|
[
{
"version": "v1",
"created": "Wed, 31 Aug 2022 09:38:05 GMT"
}
] | 2022-09-01T00:00:00 |
[
[
"Liu",
"Jianlin",
""
],
[
"Huang",
"Zhuofei",
""
],
[
"Huang",
"Dihe",
""
],
[
"Xu",
"Shang",
""
],
[
"Chen",
"Ying",
""
],
[
"Liu",
"Yong",
""
]
] |
new_dataset
| 0.974683 |
2208.14743
|
Mohamed Sayed
|
Mohamed Sayed, John Gibson, Jamie Watson, Victor Prisacariu, Michael
Firman, Cl\'ement Godard
|
SimpleRecon: 3D Reconstruction Without 3D Convolutions
|
ECCV2022 version with improved timings. 14 pages + 5 pages of
references
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Traditionally, 3D indoor scene reconstruction from posed images happens in
two phases: per-image depth estimation, followed by depth merging and surface
reconstruction. Recently, a family of methods have emerged that perform
reconstruction directly in final 3D volumetric feature space. While these
methods have shown impressive reconstruction results, they rely on expensive 3D
convolutional layers, limiting their application in resource-constrained
environments. In this work, we instead go back to the traditional route, and
show how focusing on high quality multi-view depth prediction leads to highly
accurate 3D reconstructions using simple off-the-shelf depth fusion. We propose
a simple state-of-the-art multi-view depth estimator with two main
contributions: 1) a carefully-designed 2D CNN which utilizes strong image
priors alongside a plane-sweep feature volume and geometric losses, combined
with 2) the integration of keyframe and geometric metadata into the cost volume
which allows informed depth plane scoring. Our method achieves a significant
lead over the current state-of-the-art for depth estimation and close or better
for 3D reconstruction on ScanNet and 7-Scenes, yet still allows for online
real-time low-memory reconstruction. Code, models and results are available at
https://nianticlabs.github.io/simplerecon
|
[
{
"version": "v1",
"created": "Wed, 31 Aug 2022 09:46:34 GMT"
}
] | 2022-09-01T00:00:00 |
[
[
"Sayed",
"Mohamed",
""
],
[
"Gibson",
"John",
""
],
[
"Watson",
"Jamie",
""
],
[
"Prisacariu",
"Victor",
""
],
[
"Firman",
"Michael",
""
],
[
"Godard",
"Clément",
""
]
] |
new_dataset
| 0.998849 |
2208.14796
|
Baian Chen
|
Baian Chen, Liangliang Nan, Haoran Xie, Dening Lu, Fu Lee Wang and
Mingqiang Wei
|
3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature
Learning
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Capturing both local and global features of irregular point clouds is
essential to 3D object detection (3OD). However, mainstream 3D detectors, e.g.,
VoteNet and its variants, either abandon considerable local features during
pooling operations or ignore many global features in the whole scene context.
This paper explores new modules to simultaneously learn local-global features
of scene point clouds that serve 3OD positively. To this end, we propose an
effective 3OD network via simultaneous local-global feature learning (dubbed
3DLG-Detector). 3DLG-Detector has two key contributions. First, it develops a
Dynamic Points Interaction (DPI) module that preserves effective local features
during pooling. Besides, DPI is detachable and can be incorporated into
existing 3OD networks to boost their performance. Second, it develops a Global
Context Aggregation module to aggregate multi-scale features from different
layers of the encoder to achieve scene context-awareness. Our method shows
improvements over thirteen competitors in terms of detection accuracy and
robustness on both the SUN RGB-D and ScanNet datasets. Source code will be
available upon publication.
|
[
{
"version": "v1",
"created": "Wed, 31 Aug 2022 12:23:40 GMT"
}
] | 2022-09-01T00:00:00 |
[
[
"Chen",
"Baian",
""
],
[
"Nan",
"Liangliang",
""
],
[
"Xie",
"Haoran",
""
],
[
"Lu",
"Dening",
""
],
[
"Wang",
"Fu Lee",
""
],
[
"Wei",
"Mingqiang",
""
]
] |
new_dataset
| 0.999256 |
2208.14861
|
Andrew Kuznetsov
|
Andrew Kuznetsov, Joseph Chee Chang, Nathan Hahn, Napol Rachatasumrit,
Bradley Breneisen, Julina Coupland, Aniket Kittur
|
Fuse: In-Situ Sensemaking Support in the Browser
| null | null |
10.1145/3526113.3545693
| null |
cs.HC cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
People spend a significant amount of time trying to make sense of the
internet, collecting content from a variety of sources and organizing it to
make decisions and achieve their goals. While humans are able to fluidly
iterate on collecting and organizing information in their minds, existing tools
and approaches introduce significant friction into the process. We introduce
Fuse, a browser extension that externalizes users' working memory by combining
low-cost collection with lightweight organization of content in a compact
card-based sidebar that is always available. Fuse helps users simultaneously
extract key web content and structure it in a lightweight and visual way. We
discuss how these affordances help users externalize more of their mental model
into the system (e.g., saving, annotating, and structuring items) and support
fast reviewing and resumption of task contexts. Our 22-month public deployment
and follow-up interviews provide longitudinal insights into the structuring
behaviors of real-world users conducting information foraging tasks.
|
[
{
"version": "v1",
"created": "Wed, 31 Aug 2022 13:43:27 GMT"
}
] | 2022-09-01T00:00:00 |
[
[
"Kuznetsov",
"Andrew",
""
],
[
"Chang",
"Joseph Chee",
""
],
[
"Hahn",
"Nathan",
""
],
[
"Rachatasumrit",
"Napol",
""
],
[
"Breneisen",
"Bradley",
""
],
[
"Coupland",
"Julina",
""
],
[
"Kittur",
"Aniket",
""
]
] |
new_dataset
| 0.977679 |
2208.14877
|
Leonardo Bonati
|
Leonardo Bonati, Michele Polese, Salvatore D'Oro, Stefano Basagni,
Tommaso Melodia
|
Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym
|
6 pages, 4 figures
| null | null | null |
cs.NI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Softwarization, programmable network control and the use of all-encompassing
controllers acting at different timescales are heralded as the key drivers for
the evolution to next-generation cellular networks. These technologies have
fostered newly designed intelligent data-driven solutions for managing large
sets of diverse cellular functionalities, basically impossible to implement in
traditionally closed cellular architectures. Despite the evident interest of
industry on Artificial Intelligence (AI) and Machine Learning (ML) solutions
for closed-loop control of the Radio Access Network (RAN), and several research
works in the field, their design is far from mainstream, and it is still a
sophisticated and often overlooked operation. In this paper, we discuss how to
design AI/ML solutions for the intelligent closed-loop control of the Open RAN,
providing guidelines and insights based on exemplary solutions with
high-performance record. We then show how to embed these solutions into xApps
instantiated on the O-RAN near-real-time RAN Intelligent Controller (RIC)
through OpenRAN Gym, the first publicly available toolbox for data-driven O-RAN
experimentation at scale. We showcase a use case of an xApp developed with
OpenRAN Gym and tested on a cellular network with 7 base stations and 42 users
deployed on the Colosseum wireless network emulator. Our demonstration shows
the high degree of flexibility of the OpenRAN Gym-based xApp development
environment, which is independent of deployment scenarios and traffic demand.
|
[
{
"version": "v1",
"created": "Wed, 31 Aug 2022 14:09:12 GMT"
}
] | 2022-09-01T00:00:00 |
[
[
"Bonati",
"Leonardo",
""
],
[
"Polese",
"Michele",
""
],
[
"D'Oro",
"Salvatore",
""
],
[
"Basagni",
"Stefano",
""
],
[
"Melodia",
"Tommaso",
""
]
] |
new_dataset
| 0.993613 |
2208.14884
|
Federico Rossetto
|
Carlos Gemmell, Iain Mackie, Paul Owoicho, Federico Rossetto, Sophie
Fischer, Jeffrey Dalton
|
GRILLBot: An Assistant for Real-World Tasks with Neural Semantic Parsing
and Graph-Based Representations
| null | null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
GRILLBot is the winning system in the 2022 Alexa Prize TaskBot Challenge,
moving towards the next generation of multimodal task assistants. It is a voice
assistant to guide users through complex real-world tasks in the domains of
cooking and home improvement. These are long-running and complex tasks that
require flexible adjustment and adaptation. The demo highlights the core
aspects, including a novel Neural Decision Parser for contextualized semantic
parsing, a new "TaskGraph" state representation that supports conditional
execution, knowledge-grounded chit-chat, and automatic enrichment of tasks with
images and videos.
|
[
{
"version": "v1",
"created": "Wed, 31 Aug 2022 14:24:35 GMT"
}
] | 2022-09-01T00:00:00 |
[
[
"Gemmell",
"Carlos",
""
],
[
"Mackie",
"Iain",
""
],
[
"Owoicho",
"Paul",
""
],
[
"Rossetto",
"Federico",
""
],
[
"Fischer",
"Sophie",
""
],
[
"Dalton",
"Jeffrey",
""
]
] |
new_dataset
| 0.964436 |
2208.14885
|
Ray-Guang Cheng
|
Fransiscus Asisi Bimo, Ferlinda Feliana, Shu-Hua Liao, Chih-Wei Lin,
David F. Kinsey, James Li, Rittwik Jana, Richard Wright, Ray-Guang Cheng
|
OSC Community Lab: The Integration Test Bed for O-RAN Software Community
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
O-RAN Software Community (OSC) is an open-source project collaborated by
O-RAN Alliance and Linux Foundation, aiming to develop reference software
components based on 3GPP and O-RAN Alliance specifications. The OSC has twelve
projects. Among them, the Integration and Testing (INT) project is responsible
for testing the requirements documented in each release for end-to-end and use
case testing. Three OSC Community Laboratories were built to speed up the
integration and interoperability testing among different projects. This paper
summarizes the software components developed by OSC projects and the status of
the three OSC Community Laboratories. The activities of each laboratory, how
the community collaborates, and the challenges we encountered along the way
were elaborated.
|
[
{
"version": "v1",
"created": "Wed, 31 Aug 2022 14:25:06 GMT"
}
] | 2022-09-01T00:00:00 |
[
[
"Bimo",
"Fransiscus Asisi",
""
],
[
"Feliana",
"Ferlinda",
""
],
[
"Liao",
"Shu-Hua",
""
],
[
"Lin",
"Chih-Wei",
""
],
[
"Kinsey",
"David F.",
""
],
[
"Li",
"James",
""
],
[
"Jana",
"Rittwik",
""
],
[
"Wright",
"Richard",
""
],
[
"Cheng",
"Ray-Guang",
""
]
] |
new_dataset
| 0.964787 |
2208.14925
|
Tim Schreiter
|
Tim Schreiter, Tiago Rodrigues de Almeida, Yufei Zhu, Eduardo
Gutierrez Maestro, Lucas Morillo-Mendez, Andrey Rudenko, Tomasz P. Kucner,
Oscar Martinez Mozos, Martin Magnusson, Luigi Palmieri, Kai O. Arras, Achim
J. Lilienthal
|
The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural,
Semantically-Rich and Contextualized
|
in SIRRW Workshop held in conjunction with 31st IEEE International
Conference on Robot & Human Interactive Communication, 29/08 - 02/09 2022,
Naples (Italy)
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Rapid development of social robots stimulates active research in human motion
modeling, interpretation and prediction, proactive collision avoidance,
human-robot interaction and co-habitation in shared spaces. Modern approaches
to this end require high quality datasets for training and evaluation. However,
the majority of available datasets suffers from either inaccurate tracking data
or unnatural, scripted behavior of the tracked people. This paper attempts to
fill this gap by providing high quality tracking information from motion
capture, eye-gaze trackers and on-board robot sensors in a semantically-rich
environment. To induce natural behavior of the recorded participants, we
utilise loosely scripted task assignment, which induces the participants
navigate through the dynamic laboratory environment in a natural and purposeful
way. The motion dataset, presented in this paper, sets a high quality standard,
as the realistic and accurate data is enhanced with semantic information,
enabling development of new algorithms which rely not only on the tracking
information but also on contextual cues of the moving agents, static and
dynamic environment.
|
[
{
"version": "v1",
"created": "Wed, 31 Aug 2022 15:37:45 GMT"
}
] | 2022-09-01T00:00:00 |
[
[
"Schreiter",
"Tim",
""
],
[
"de Almeida",
"Tiago Rodrigues",
""
],
[
"Zhu",
"Yufei",
""
],
[
"Maestro",
"Eduardo Gutierrez",
""
],
[
"Morillo-Mendez",
"Lucas",
""
],
[
"Rudenko",
"Andrey",
""
],
[
"Kucner",
"Tomasz P.",
""
],
[
"Mozos",
"Oscar Martinez",
""
],
[
"Magnusson",
"Martin",
""
],
[
"Palmieri",
"Luigi",
""
],
[
"Arras",
"Kai O.",
""
],
[
"Lilienthal",
"Achim J.",
""
]
] |
new_dataset
| 0.999137 |
2208.14935
|
Qiange Wang
|
Qiange Wang, Xin Ai, Yanfeng Zhang, Jing Chen, Ge Yu
|
HyTGraph: GPU-Accelerated Graph Processing with Hybrid Transfer
Management
|
14 pages with 10 figures. Accepted by ICDE 2023
| null | null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Processing large graphs with memory-limited GPU needs to resolve issues of
host-GPU data transfer, which is a key performance bottleneck. Existing
GPU-accelerated graph processing frameworks reduce the data transfers by
managing the active subgraph transfer at runtime. Some frameworks adopt
explicit transfer management approaches based on explicit memory copy with
filter or compaction. In contrast, others adopt implicit transfer management
approaches based on on-demand access with zero-copy or unified-memory. Having
made intensive analysis, we find that as the active vertices evolve, the
performance of the two approaches varies in different workloads. Due to heavy
redundant data transfers, high CPU compaction overhead, or low bandwidth
utilization, adopting a single approach often results in suboptimal
performance.
In this work, we propose a hybrid transfer management approach to take the
merits of both the two approaches at runtime, with an objective to achieve the
shortest execution time in each iteration. Based on the hybrid approach, we
present HytGraph, a GPU-accelerated graph processing framework, which is
empowered by a set of effective task scheduling optimizations to improve the
performance. Our experimental results on real-world and synthesized graphs
demonstrate that HyTGraph achieves up to 10.27X speedup over existing
GPU-accelerated graph processing systems including Grus, Subway, and EMOGI.
|
[
{
"version": "v1",
"created": "Wed, 31 Aug 2022 16:05:19 GMT"
}
] | 2022-09-01T00:00:00 |
[
[
"Wang",
"Qiange",
""
],
[
"Ai",
"Xin",
""
],
[
"Zhang",
"Yanfeng",
""
],
[
"Chen",
"Jing",
""
],
[
"Yu",
"Ge",
""
]
] |
new_dataset
| 0.987815 |
2208.14971
|
Cameron Boeder
|
Cameron Boeder and Troy Januchowski
|
Zero-day DDoS Attack Detection
| null | null | null | null |
cs.CR cs.LG cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
The ability to detect zero-day (novel) attacks has become essential in the
network security industry. Due to ever evolving attack signatures, existing
network intrusion detection systems often fail to detect these threats. This
project aims to solve the task of detecting zero-day DDoS (distributed
denial-of-service) attacks by utilizing network traffic that is captured before
entering a private network. Modern feature extraction techniques are used in
conjunction with neural networks to determine if a network packet is either
benign or malicious.
|
[
{
"version": "v1",
"created": "Wed, 31 Aug 2022 17:14:43 GMT"
}
] | 2022-09-01T00:00:00 |
[
[
"Boeder",
"Cameron",
""
],
[
"Januchowski",
"Troy",
""
]
] |
new_dataset
| 0.997451 |
2208.15001
|
Mingyuan Zhang
|
Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo,
Lei Yang, Ziwei Liu
|
MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Human motion modeling is important for many modern graphics applications,
which typically require professional skills. In order to remove the skill
barriers for laymen, recent motion generation methods can directly generate
human motions conditioned on natural languages. However, it remains challenging
to achieve diverse and fine-grained motion generation with various text inputs.
To address this problem, we propose MotionDiffuse, the first diffusion
model-based text-driven motion generation framework, which demonstrates several
desired properties over existing methods. 1) Probabilistic Mapping. Instead of
a deterministic language-motion mapping, MotionDiffuse generates motions
through a series of denoising steps in which variations are injected. 2)
Realistic Synthesis. MotionDiffuse excels at modeling complicated data
distribution and generating vivid motion sequences. 3) Multi-Level
Manipulation. MotionDiffuse responds to fine-grained instructions on body
parts, and arbitrary-length motion synthesis with time-varied text prompts. Our
experiments show MotionDiffuse outperforms existing SoTA methods by convincing
margins on text-driven motion generation and action-conditioned motion
generation. A qualitative analysis further demonstrates MotionDiffuse's
controllability for comprehensive motion generation. Homepage:
https://mingyuan-zhang.github.io/projects/MotionDiffuse.html
|
[
{
"version": "v1",
"created": "Wed, 31 Aug 2022 17:58:54 GMT"
}
] | 2022-09-01T00:00:00 |
[
[
"Zhang",
"Mingyuan",
""
],
[
"Cai",
"Zhongang",
""
],
[
"Pan",
"Liang",
""
],
[
"Hong",
"Fangzhou",
""
],
[
"Guo",
"Xinying",
""
],
[
"Yang",
"Lei",
""
],
[
"Liu",
"Ziwei",
""
]
] |
new_dataset
| 0.957784 |
2009.01498
|
Kurt Mehlhorn
|
Vincenzo Bonifaci and Enrico Facca and Frederic Folz and Andreas
Karrenbauer and Pavel Kolev and Kurt Mehlhorn and Giovanna Morigi and
Golnoosh Shahkarami and Quentin Vermande
|
Physarum-Inspired Multi-Commodity Flow Dynamics
|
to appear in Theoretical Computer Science
|
Theoretical Computer Science 920, pp. 1-20 (2022)
| null | null |
cs.DS cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In wet-lab experiments, the slime mold Physarum polycephalum has demonstrated
its ability to solve shortest path problems and to design efficient networks.
For the shortest path problem, a mathematical model for the evolution of the
slime is available and it has been shown in computer experiments and through
mathematical analysis that the dynamics solves the shortest path problem. In
this paper, we introduce a dynamics for the network design problem. We
formulate network design as the problem of constructing a network that
efficiently supports a multi-commodity flow problem. We investigate the
dynamics in computer simulations and analytically. The simulations show that
the dynamics is able to construct efficient and elegant networks. In the
theoretical part we show that the dynamics minimizes an objective combining the
cost of the network and the cost of routing the demands through the network. We
also give alternative characterization of the optimum solution.
|
[
{
"version": "v1",
"created": "Thu, 3 Sep 2020 07:48:48 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Sep 2020 15:17:07 GMT"
},
{
"version": "v3",
"created": "Fri, 23 Oct 2020 11:36:33 GMT"
},
{
"version": "v4",
"created": "Wed, 10 Mar 2021 21:05:59 GMT"
},
{
"version": "v5",
"created": "Wed, 9 Feb 2022 07:22:56 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Bonifaci",
"Vincenzo",
""
],
[
"Facca",
"Enrico",
""
],
[
"Folz",
"Frederic",
""
],
[
"Karrenbauer",
"Andreas",
""
],
[
"Kolev",
"Pavel",
""
],
[
"Mehlhorn",
"Kurt",
""
],
[
"Morigi",
"Giovanna",
""
],
[
"Shahkarami",
"Golnoosh",
""
],
[
"Vermande",
"Quentin",
""
]
] |
new_dataset
| 0.994291 |
2102.01480
|
Muneeb Ul Hassan
|
Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen
|
VPT: Privacy Preserving Energy Trading and Block Mining Mechanism for
Blockchain based Virtual Power Plants
|
Article Submitted for Review
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The desire to overcome reliability issues of distributed energy resources
(DERs) lead researchers to development of a novel concept named as virtual
power plant (VPP). VPPs are supposed to carry out intelligent, secure, and
smart energy trading among prosumers, buyers, and generating stations along
with providing efficient energy management. Therefore, integrating blockchain
in a decentralized VPP network emerged as a new paradigm, and recent
experiments over this integration have shown fruitful results. However, this
decentralization also suffers with energy management, trust, reliability, and
efficiency issues due to the dynamic nature of DERs. In order to overcome this,
in this paper, we first work over providing an efficient energy management
strategy for VPP to enhance demand response, then we propose an energy oriented
trading and block mining protocol and name it as proof of energy market (PoEM).
To enhance it further, we integrate differential privacy in PoEM and propose a
Private PoEM (PPoEM) model. Collectively, we propose a private decentralized
VPP trading model and named it as Virtual Private Trading (VPT) model. We
further carry out extensive theoretical analysis and derive step-by-step
valuations for market race probability, market stability probability, energy
trading expectation, winning state probability, and prospective leading time
profit values. Afterwards, we carry out simulation-based experiments of our
proposed model. The performance evaluation and theoretical analysis of our VPT
model make it one of the most viable models for blockchain based VPP networks
as compared to other state-of-the-art works.
|
[
{
"version": "v1",
"created": "Tue, 2 Feb 2021 13:11:24 GMT"
},
{
"version": "v2",
"created": "Tue, 30 Aug 2022 00:49:26 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Hassan",
"Muneeb Ul",
""
],
[
"Rehmani",
"Mubashir Husain",
""
],
[
"Chen",
"Jinjun",
""
]
] |
new_dataset
| 0.992685 |
2107.06149
|
Haocheng Ren
|
Haocheng Ren and Hao Zhang and Jia Zheng and Jiaxiang Zheng and Rui
Tang and Yuchi Huo and Hujun Bao and Rui Wang
|
MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis
|
Accepted by Computer Graphics Forum, Pacific Graphics 2022. The two
first authors contribute equally. Project pape:
https://coohom.github.io/MINERVAS
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the rapid development of data-driven techniques, data has played an
essential role in various computer vision tasks. Many realistic and synthetic
datasets have been proposed to address different problems. However, there are
lots of unresolved challenges: (1) the creation of dataset is usually a tedious
process with manual annotations, (2) most datasets are only designed for a
single specific task, (3) the modification or randomization of the 3D scene is
difficult, and (4) the release of commercial 3D data may encounter copyright
issue. This paper presents MINERVAS, a Massive INterior EnviRonments VirtuAl
Synthesis system, to facilitate the 3D scene modification and the 2D image
synthesis for various vision tasks. In particular, we design a programmable
pipeline with Domain-Specific Language, allowing users to (1) select scenes
from the commercial indoor scene database, (2) synthesize scenes for different
tasks with customized rules, and (3) render various imagery data, such as
visual color, geometric structures, semantic label. Our system eases the
difficulty of customizing massive numbers of scenes for different tasks and
relieves users from manipulating fine-grained scene configurations by providing
user-controllable randomness using multi-level samplers. Most importantly, it
empowers users to access commercial scene databases with millions of indoor
scenes and protects the copyright of core data assets, e.g., 3D CAD models. We
demonstrate the validity and flexibility of our system by using our synthesized
data to improve the performance on different kinds of computer vision tasks.
|
[
{
"version": "v1",
"created": "Tue, 13 Jul 2021 14:53:01 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Jul 2021 14:21:45 GMT"
},
{
"version": "v3",
"created": "Sun, 12 Jun 2022 02:45:04 GMT"
},
{
"version": "v4",
"created": "Tue, 30 Aug 2022 09:21:25 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Ren",
"Haocheng",
""
],
[
"Zhang",
"Hao",
""
],
[
"Zheng",
"Jia",
""
],
[
"Zheng",
"Jiaxiang",
""
],
[
"Tang",
"Rui",
""
],
[
"Huo",
"Yuchi",
""
],
[
"Bao",
"Hujun",
""
],
[
"Wang",
"Rui",
""
]
] |
new_dataset
| 0.998516 |
2111.06102
|
Yoshinori Aono
|
Yoshinori Aono, Sitong Liu, Tomoki Tanaka, Shumpei Uno, Rodney Van
Meter, Naoyuki Shinohara, Ryo Nojima
|
The Present and Future of Discrete Logarithm Problems on Noisy Quantum
Computers
| null |
IEEE Transactions on Quantum Engineering, vol. 3, pp. 1-21, 2022
|
10.1109/TQE.2022.3183385
| null |
cs.CR quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The discrete logarithm problem (DLP) is the basis for several cryptographic
primitives. Since Shor's work, it has been known that the DLP can be solved by
combining a polynomial-size quantum circuit and a polynomial-time classical
post-processing algorithm. Evaluating and predicting the instance size that
quantum devices can solve is an emerging research topic. In this paper, we
propose a quantitative measure based on the success probability of the
post-processing algorithm to determine whether an experiment on a quantum
device (or a classical simulator) succeeded. We also propose a procedure to
modify bit strings observed from a Shor circuit to increase the success
probability of a lattice-based post-processing algorithm. We report preliminary
experiments conducted on IBM-Quantum quantum computers and near-future
predictions based on noisy-device simulations. We conducted our experiments
with the ibm_kawasaki device and discovered that the simplest circuit (7
qubits) from a 2-bit DLP instance achieves a sufficiently high success
probability to proclaim the experiment successful. Experiments on another
circuit from a slightly harder 2-bit DLP instance, on the other hand, did not
succeed, and we determined that reducing the noise level by half is required to
achieve a successful experiment. Finally, we give a near-term prediction based
on required noise levels to solve some selected small DLP and integer factoring
instances.
|
[
{
"version": "v1",
"created": "Thu, 11 Nov 2021 08:49:16 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Aono",
"Yoshinori",
""
],
[
"Liu",
"Sitong",
""
],
[
"Tanaka",
"Tomoki",
""
],
[
"Uno",
"Shumpei",
""
],
[
"Van Meter",
"Rodney",
""
],
[
"Shinohara",
"Naoyuki",
""
],
[
"Nojima",
"Ryo",
""
]
] |
new_dataset
| 0.995624 |
2112.14663
|
E Zhixuan Zeng
|
Yuhao Chen, E. Zhixuan Zeng, Maximilian Gilles, Alexander Wong
|
MetaGraspNet_v0: A Large-Scale Benchmark Dataset for Vision-driven
Robotic Grasping via Physics-based Metaverse Synthesis
| null | null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
There has been increasing interest in smart factories powered by robotics
systems to tackle repetitive, laborious tasks. One impactful yet challenging
task in robotics-powered smart factory applications is robotic grasping: using
robotic arms to grasp objects autonomously in different settings. Robotic
grasping requires a variety of computer vision tasks such as object detection,
segmentation, grasp prediction, pick planning, etc. While significant progress
has been made in leveraging of machine learning for robotic grasping,
particularly with deep learning, a big challenge remains in the need for
large-scale, high-quality RGBD datasets that cover a wide diversity of
scenarios and permutations. To tackle this big, diverse data problem, we are
inspired by the recent rise in the concept of metaverse, which has greatly
closed the gap between virtual worlds and the physical world. Metaverses allow
us to create digital twins of real-world manufacturing scenarios and to
virtually create different scenarios from which large volumes of data can be
generated for training models. In this paper, we present MetaGraspNet: a
large-scale benchmark dataset for vision-driven robotic grasping via
physics-based metaverse synthesis. The proposed dataset contains 100,000 images
and 25 different object types and is split into 5 difficulties to evaluate
object detection and segmentation model performance in different grasping
scenarios. We also propose a new layout-weighted performance metric alongside
the dataset for evaluating object detection and segmentation performance in a
manner that is more appropriate for robotic grasp applications compared to
existing general-purpose performance metrics. Our benchmark dataset is
available open-source on Kaggle, with the first phase consisting of detailed
object detection, segmentation, layout annotations, and a layout-weighted
performance metric script.
|
[
{
"version": "v1",
"created": "Wed, 29 Dec 2021 17:23:24 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Dec 2021 18:05:26 GMT"
},
{
"version": "v3",
"created": "Tue, 30 Aug 2022 17:53:40 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Chen",
"Yuhao",
""
],
[
"Zeng",
"E. Zhixuan",
""
],
[
"Gilles",
"Maximilian",
""
],
[
"Wong",
"Alexander",
""
]
] |
new_dataset
| 0.999863 |
2208.08900
|
Mohit Vaishnav
|
Mohit Vaishnav, Thomas Fel, Iva\'n Felipe Rodr\'iguez and Thomas Serre
|
Conviformers: Convolutionally guided Vision Transformer
|
12 pages; 4 Figures; 8 Tables
| null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Vision transformers are nowadays the de-facto choice for image classification
tasks. There are two broad categories of classification tasks, fine-grained and
coarse-grained. In fine-grained classification, the necessity is to discover
subtle differences due to the high level of similarity between sub-classes.
Such distinctions are often lost as we downscale the image to save the memory
and computational cost associated with vision transformers (ViT). In this work,
we present an in-depth analysis and describe the critical components for
developing a system for the fine-grained categorization of plants from
herbarium sheets. Our extensive experimental analysis indicated the need for a
better augmentation technique and the ability of modern-day neural networks to
handle higher dimensional images. We also introduce a convolutional transformer
architecture called Conviformer which, unlike the popular Vision Transformer
(ConViT), can handle higher resolution images without exploding memory and
computational cost. We also introduce a novel, improved pre-processing
technique called PreSizer to resize images better while preserving their
original aspect ratios, which proved essential for classifying natural plants.
With our simple yet effective approach, we achieved SoTA on Herbarium 202x and
iNaturalist 2019 dataset.
|
[
{
"version": "v1",
"created": "Wed, 17 Aug 2022 13:09:24 GMT"
},
{
"version": "v2",
"created": "Sun, 28 Aug 2022 11:46:25 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Vaishnav",
"Mohit",
""
],
[
"Fel",
"Thomas",
""
],
[
"Rodríguez",
"Ivań Felipe",
""
],
[
"Serre",
"Thomas",
""
]
] |
new_dataset
| 0.979163 |
2208.12037
|
Weixian Lei
|
Stan Weixian Lei, Difei Gao, Jay Zhangjie Wu, Yuxuan Wang, Wei Liu,
Mengmi Zhang, Mike Zheng Shou
|
Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA
Task
|
18 pages, 13 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
VQA is an ambitious task aiming to answer any image-related question.
However, in reality, it is hard to build such a system once for all since the
needs of users are continuously updated, and the system has to implement new
functions. Thus, Continual Learning (CL) ability is a must in developing
advanced VQA systems. Recently, a pioneer work split a VQA dataset into
disjoint answer sets to study this topic. However, CL on VQA involves not only
the expansion of label sets (new Answer sets). It is crucial to study how to
answer questions when deploying VQA systems to new environments (new Visual
scenes) and how to answer questions requiring new functions (new Question
types). Thus, we propose CLOVE, a benchmark for Continual Learning On Visual
quEstion answering, which contains scene- and function-incremental settings for
the two aforementioned CL scenarios. In terms of methodology, the main
difference between CL on VQA and classification is that the former additionally
involves expanding and preventing forgetting of reasoning mechanisms, while the
latter focusing on class representation. Thus, we propose a real-data-free
replay-based method tailored for CL on VQA, named Scene Graph as Prompt for
Symbolic Replay. Using a piece of scene graph as a prompt, it replays pseudo
scene graphs to represent the past images, along with correlated QA pairs. A
unified VQA model is also proposed to utilize the current and replayed data to
enhance its QA ability. Finally, experimental results reveal challenges in
CLOVE and demonstrate the effectiveness of our method. The dataset and code
will be available at https://github.com/showlab/CLVQA.
|
[
{
"version": "v1",
"created": "Wed, 24 Aug 2022 12:00:02 GMT"
},
{
"version": "v2",
"created": "Mon, 29 Aug 2022 10:22:20 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Lei",
"Stan Weixian",
""
],
[
"Gao",
"Difei",
""
],
[
"Wu",
"Jay Zhangjie",
""
],
[
"Wang",
"Yuxuan",
""
],
[
"Liu",
"Wei",
""
],
[
"Zhang",
"Mengmi",
""
],
[
"Shou",
"Mike Zheng",
""
]
] |
new_dataset
| 0.996628 |
2208.12886
|
Jean-Philippe Corbeil
|
Jean-Philippe Corbeil, Mia Taige Li, Hadi Abdi Ghavidel
|
Building the Intent Landscape of Real-World Conversational Corpora with
Extractive Question-Answering Transformers
| null | null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
For companies with customer service, mapping intents inside their
conversational data is crucial in building applications based on natural
language understanding (NLU). Nevertheless, there is no established automated
technique to gather the intents from noisy online chats or voice transcripts.
Simple clustering approaches are not suited to intent-sparse dialogues. To
solve this intent-landscape task, we propose an unsupervised pipeline that
extracts the intents and the taxonomy of intents from real-world dialogues. Our
pipeline mines intent-span candidates with an extractive Question-Answering
Electra model and leverages sentence embeddings to apply a low-level density
clustering followed by a top-level hierarchical clustering. Our results
demonstrate the generalization ability of an ELECTRA large model fine-tuned on
the SQuAD2 dataset to understand dialogues. With the right prompting question,
this model achieves a rate of linguistic validation on intent spans beyond 85%.
We furthermore reconstructed the intent schemes of five domains from the
MultiDoGo dataset with an average recall of 94.3%.
|
[
{
"version": "v1",
"created": "Fri, 26 Aug 2022 22:53:19 GMT"
},
{
"version": "v2",
"created": "Tue, 30 Aug 2022 16:03:38 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Corbeil",
"Jean-Philippe",
""
],
[
"Li",
"Mia Taige",
""
],
[
"Ghavidel",
"Hadi Abdi",
""
]
] |
new_dataset
| 0.994956 |
2208.13900
|
Erfan Pakdamanian
|
Erfan Pakdamanian, Erzhen Hu, Shili Sheng, Sarit Kraus, Seongkook Heo,
Lu Feng
|
Enjoy the Ride Consciously with CAWA: Context-Aware Advisory Warnings
for Automated Driving
|
Proceeding of the 14th International Conference on Automotive User
Interfaces and Interactive Vehicular Applications (AutomotiveUI '22)
| null |
10.1145/3543174.3546835
| null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In conditionally automated driving, drivers decoupled from driving while
immersed in non-driving-related tasks (NDRTs) could potentially either miss the
system-initiated takeover request (TOR) or a sudden TOR may startle them. To
better prepare drivers for a safer takeover in an emergency, we propose novel
context-aware advisory warnings (CAWA) for automated driving to gently inform
drivers. This will help them stay vigilant while engaging in NDRTs. The key
innovation is that CAWA adapts warning modalities according to the context of
NDRTs. We conducted a user study to investigate the effectiveness of CAWA. The
study results show that CAWA has statistically significant effects on safer
takeover behavior, improved driver situational awareness, less attention
demand, and more positive user feedback, compared with uniformly distributed
speech-based warnings across all NDRTs.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 21:44:49 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Pakdamanian",
"Erfan",
""
],
[
"Hu",
"Erzhen",
""
],
[
"Sheng",
"Shili",
""
],
[
"Kraus",
"Sarit",
""
],
[
"Heo",
"Seongkook",
""
],
[
"Feng",
"Lu",
""
]
] |
new_dataset
| 0.998667 |
2208.13935
|
Yingfu Xu
|
Yingfu Xu and Guido C. H. E. de Croon
|
CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for
Visual-Inertial Odometry
|
19 pages, 14 figures, 6 tables
| null | null | null |
cs.RO cs.CV cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Learning-based visual ego-motion estimation is promising yet not ready for
navigating agile mobile robots in the real world. In this article, we propose
CUAHN-VIO, a robust and efficient monocular visual-inertial odometry (VIO)
designed for micro aerial vehicles (MAVs) equipped with a downward-facing
camera. The vision frontend is a content-and-uncertainty-aware homography
network (CUAHN) that is robust to non-homography image content and failure
cases of network prediction. It not only predicts the homography transformation
but also estimates its uncertainty. The training is self-supervised, so that it
does not require ground truth that is often difficult to obtain. The network
has good generalization that enables "plug-and-play" deployment in new
environments without fine-tuning. A lightweight extended Kalman filter (EKF)
serves as the VIO backend and utilizes the mean prediction and variance
estimation from the network for visual measurement updates. CUAHN-VIO is
evaluated on a high-speed public dataset and shows rivaling accuracy to
state-of-the-art (SOTA) VIO approaches. Thanks to the robustness to motion
blur, low network inference time (~23ms), and stable processing latency
(~26ms), CUAHN-VIO successfully runs onboard an Nvidia Jetson TX2 embedded
processor to navigate a fast autonomous MAV.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 00:11:55 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Xu",
"Yingfu",
""
],
[
"de Croon",
"Guido C. H. E.",
""
]
] |
new_dataset
| 0.996235 |
2208.13947
|
Juan Manuel Perez
|
Tom\'as Alves Salgueiro, Emilio Recart Zapata, Dami\'an Furman, Juan
Manuel P\'erez, Pablo Nicol\'as Fern\'andez Larrosa
|
A Spanish dataset for Targeted Sentiment Analysis of political headlines
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Subjective texts have been studied by several works as they can induce
certain behaviours in their users. Most work focuses on user-generated texts in
social networks, but some other texts also comprise opinions on certain topics
and could influence judgement criteria during political decisions. In this
work, we address the task of Targeted Sentiment Analysis for the domain of news
headlines, published by the main outlets during the 2019 Argentinean
Presidential Elections. For this purpose, we present a polarity dataset of
1,976 headlines mentioning candidates in the 2019 elections at the target
level. Preliminary experiments with state-of-the-art classification algorithms
based on pre-trained linguistic models suggest that target information is
helpful for this task. We make our data and pre-trained models publicly
available.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 01:30:30 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Salgueiro",
"Tomás Alves",
""
],
[
"Zapata",
"Emilio Recart",
""
],
[
"Furman",
"Damián",
""
],
[
"Pérez",
"Juan Manuel",
""
],
[
"Larrosa",
"Pablo Nicolás Fernández",
""
]
] |
new_dataset
| 0.999858 |
2208.14023
|
Edward Vendrow
|
Edward Vendrow, Satyajit Kumar, Ehsan Adeli, Hamid Rezatofighi
|
SoMoFormer: Multi-Person Pose Forecasting with Transformers
|
10 pages, 6 figures. Submitted to WACV 2023. Our method was submitted
to the SoMoF benchmark leaderboard dated March 2022. See
https://somof.stanford.edu/result/217/
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Human pose forecasting is a challenging problem involving complex human body
motion and posture dynamics. In cases that there are multiple people in the
environment, one's motion may also be influenced by the motion and dynamic
movements of others. Although there are several previous works targeting the
problem of multi-person dynamic pose forecasting, they often model the entire
pose sequence as time series (ignoring the underlying relationship between
joints) or only output the future pose sequence of one person at a time. In
this paper, we present a new method, called Social Motion Transformer
(SoMoFormer), for multi-person 3D pose forecasting. Our transformer
architecture uniquely models human motion input as a joint sequence rather than
a time sequence, allowing us to perform attention over joints while predicting
an entire future motion sequence for each joint in parallel. We show that with
this problem reformulation, SoMoFormer naturally extends to multi-person scenes
by using the joints of all people in a scene as input queries. Using learned
embeddings to denote the type of joint, person identity, and global position,
our model learns the relationships between joints and between people, attending
more strongly to joints from the same or nearby people. SoMoFormer outperforms
state-of-the-art methods for long-term motion prediction on the SoMoF benchmark
as well as the CMU-Mocap and MuPoTS-3D datasets. Code will be made available
after publication.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 06:59:28 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Vendrow",
"Edward",
""
],
[
"Kumar",
"Satyajit",
""
],
[
"Adeli",
"Ehsan",
""
],
[
"Rezatofighi",
"Hamid",
""
]
] |
new_dataset
| 0.991266 |
2208.14039
|
Woon-Ha Yeo
|
Woon-Ha Yeo, Wang-Taek Oh, Kyung-Su Kang, Young-Il Kim, Han-Cheol Ryu
|
CAIR: Fast and Lightweight Multi-Scale Color Attention Network for
Instagram Filter Removal
|
Accepted to ECCV Workshop 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Image restoration is an important and challenging task in computer vision.
Reverting a filtered image to its original image is helpful in various computer
vision tasks. We employ a nonlinear activation function free network (NAFNet)
for a fast and lightweight model and add a color attention module that extracts
useful color information for better accuracy. We propose an accurate, fast,
lightweight network with multi-scale and color attention for Instagram filter
removal (CAIR). Experiment results show that the proposed CAIR outperforms
existing Instagram filter removal networks in fast and lightweight ways, about
11$\times$ faster and 2.4$\times$ lighter while exceeding 3.69 dB PSNR on IFFI
dataset. CAIR can successfully remove the Instagram filter with high quality
and restore color information in qualitative results. The source code and
pretrained weights are available at \url{https://github.com/HnV-Lab/CAIR}.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 07:42:45 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Yeo",
"Woon-Ha",
""
],
[
"Oh",
"Wang-Taek",
""
],
[
"Kang",
"Kyung-Su",
""
],
[
"Kim",
"Young-Il",
""
],
[
"Ryu",
"Han-Cheol",
""
]
] |
new_dataset
| 0.977017 |
2208.14045
|
Luca Frittoli
|
Andrea Bionda, Luca Frittoli, Giacomo Boracchi
|
Deep Autoencoders for Anomaly Detection in Textured Images using CW-SSIM
|
International Conference on Image Analysis and Processing (ICIAP
2021). NVIDIA Prize winner
| null |
10.1007/978-3-031-06430-2_56
| null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Detecting anomalous regions in images is a frequently encountered problem in
industrial monitoring. A relevant example is the analysis of tissues and other
products that in normal conditions conform to a specific texture, while defects
introduce changes in the normal pattern. We address the anomaly detection
problem by training a deep autoencoder, and we show that adopting a loss
function based on Complex Wavelet Structural Similarity (CW-SSIM) yields
superior detection performance on this type of images compared to traditional
autoencoder loss functions. Our experiments on well-known anomaly detection
benchmarks show that a simple model trained with this loss function can achieve
comparable or superior performance to state-of-the-art methods leveraging
deeper, larger and more computationally demanding neural networks.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 08:01:25 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Bionda",
"Andrea",
""
],
[
"Frittoli",
"Luca",
""
],
[
"Boracchi",
"Giacomo",
""
]
] |
new_dataset
| 0.980932 |
2208.14052
|
JingYang Chen
|
Songbin Chen
|
Intelligent Perception System for Vehicle-Road Cooperation
|
7 pages, 7 figures
| null | null | null |
cs.RO cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the development of autonomous driving, the improvement of autonomous
driving technology for individual vehicles has reached the bottleneck. The
advancement of vehicle-road cooperation autonomous driving technology can
expand the vehicle's perception range, supplement the perception blind area and
improve the perception accuracy, to promote the development of autonomous
driving technology and achieve vehicle-road integration. This project mainly
uses lidar to develop data fusion schemes to realize the sharing and
combination of vehicle and road equipment data and achieve the detection and
tracking of dynamic targets. At the same time, some test scenarios for the
vehicle-road cooperative system were designed and used to test our vehicle-road
cooperative awareness system, which proved the advantages of vehicle-road
cooperative autonomous driving over single-vehicle autonomous driving.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 08:10:34 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Chen",
"Songbin",
""
]
] |
new_dataset
| 0.99342 |
2208.14071
|
Luca Frittoli
|
Luca Frittoli, Diego Carrera, Beatrice Rossi, Pasqualina Fragneto,
Giacomo Boracchi
|
Deep Open-Set Recognition for Silicon Wafer Production Monitoring
| null |
Pattern Recognition Volume 124, April 2022, 108488
|
10.1016/j.patcog.2021.108488
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The chips contained in any electronic device are manufactured over circular
silicon wafers, which are monitored by inspection machines at different
production stages. Inspection machines detect and locate any defect within the
wafer and return a Wafer Defect Map (WDM), i.e., a list of the coordinates
where defects lie, which can be considered a huge, sparse, and binary image. In
normal conditions, wafers exhibit a small number of randomly distributed
defects, while defects grouped in specific patterns might indicate known or
novel categories of failures in the production line. Needless to say, a primary
concern of semiconductor industries is to identify these patterns and intervene
as soon as possible to restore normal production conditions.
Here we address WDM monitoring as an open-set recognition problem to
accurately classify WDM in known categories and promptly detect novel patterns.
In particular, we propose a comprehensive pipeline for wafer monitoring based
on a Submanifold Sparse Convolutional Network, a deep architecture designed to
process sparse data at an arbitrary resolution, which is trained on the known
classes. To detect novelties, we define an outlier detector based on a Gaussian
Mixture Model fitted on the latent representation of the classifier. Our
experiments on a real dataset of WDMs show that directly processing
full-resolution WDMs by Submanifold Sparse Convolutions yields superior
classification performance on known classes than traditional Convolutional
Neural Networks, which require a preliminary binning to reduce the size of the
binary images representing WDMs. Moreover, our solution outperforms
state-of-the-art open-set recognition solutions in detecting novelties.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 08:39:52 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Frittoli",
"Luca",
""
],
[
"Carrera",
"Diego",
""
],
[
"Rossi",
"Beatrice",
""
],
[
"Fragneto",
"Pasqualina",
""
],
[
"Boracchi",
"Giacomo",
""
]
] |
new_dataset
| 0.997742 |
2208.14093
|
Li Yi
|
Yi Li, Wenjie Pei, Zhenyu He
|
SSORN: Self-Supervised Outlier Removal Network for Robust Homography
Estimation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The traditional homography estimation pipeline consists of four main steps:
feature detection, feature matching, outlier removal and transformation
estimation. Recent deep learning models intend to address the homography
estimation problem using a single convolutional network. While these models are
trained in an end-to-end fashion to simplify the homography estimation problem,
they lack the feature matching step and/or the outlier removal step, which are
important steps in the traditional homography estimation pipeline. In this
paper, we attempt to build a deep learning model that mimics all four steps in
the traditional homography estimation pipeline. In particular, the feature
matching step is implemented using the cost volume technique. To remove
outliers in the cost volume, we treat this outlier removal problem as a
denoising problem and propose a novel self-supervised loss to solve the
problem. Extensive experiments on synthetic and real datasets demonstrate that
the proposed model outperforms existing deep learning models.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 09:12:18 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Li",
"Yi",
""
],
[
"Pei",
"Wenjie",
""
],
[
"He",
"Zhenyu",
""
]
] |
new_dataset
| 0.993693 |
2208.14139
|
Siyu Yuan
|
Siyu Yuan, Deqing Yang, Jiaqing Liang, Jilun Sun, Jingyue Huang,
Kaiyan Cao, Yanghua Xiao, Rui Xie
|
Large-scale Multi-granular Concept Extraction Based on Machine Reading
Comprehension
| null |
ISWC2021
|
10.1007/978-3-030-88361-4_6
| null |
cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
The concepts in knowledge graphs (KGs) enable machines to understand natural
language, and thus play an indispensable role in many applications. However,
existing KGs have the poor coverage of concepts, especially fine-grained
concepts. In order to supply existing KGs with more fine-grained and new
concepts, we propose a novel concept extraction framework, namely MRC-CE, to
extract large-scale multi-granular concepts from the descriptive texts of
entities. Specifically, MRC-CE is built with a machine reading comprehension
model based on BERT, which can extract more fine-grained concepts with a
pointer network. Furthermore, a random forest and rule-based pruning are also
adopted to enhance MRC-CE's precision and recall simultaneously. Our
experiments evaluated upon multilingual KGs, i.e., English Probase and Chinese
CN-DBpedia, justify MRC-CE's superiority over the state-of-the-art extraction
models in KG completion. Particularly, after running MRC-CE for each entity in
CN-DBpedia, more than 7,053,900 new concepts (instanceOf relations) are
supplied into the KG. The code and datasets have been released at
https://github.com/fcihraeipnusnacwh/MRC-CE
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 10:46:32 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Yuan",
"Siyu",
""
],
[
"Yang",
"Deqing",
""
],
[
"Liang",
"Jiaqing",
""
],
[
"Sun",
"Jilun",
""
],
[
"Huang",
"Jingyue",
""
],
[
"Cao",
"Kaiyan",
""
],
[
"Xiao",
"Yanghua",
""
],
[
"Xie",
"Rui",
""
]
] |
new_dataset
| 0.97282 |
2208.14149
|
Miguel Altamirano Cabrera
|
Miguel Altamirano Cabrera, Jonathan Tirado, Juan Heredia, and Dzmitry
Tsetserukou
|
LinkGlide-S: A Wearable Multi-Contact Tactile Display Aimed at Rendering
Object Softness at the Palm with Impedance Control in VR and Telemanipulation
|
Accepted paper in IEEE CASE (International Conference on Automation
Science and Engineering) 2022, IEEE copyrigh
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
LinkGlide-S is a novel wearable hand-worn tactile display to deliver
multi-contact and multi-modal stimuli at the user's palm.} The array of
inverted five-bar linkages generates three independent contact points to cover
the whole palm area. \textcolor{black} {The independent contact points generate
various tactile patterns at the user's hand, providing multi-contact tactile
feedback. An impedance control delivers the stiffness of objects according to
different parameters. Three experiments were performed to evaluate the
perception of patterns, investigate the realistic perception of object
interaction in Virtual Reality, and assess the users' softness perception by
the impedance control. The experimental results revealed a high recognition
rate for the generated patterns. These results confirm that the performance of
LinkGlide-S is adequate to detect and manipulate virtual objects with different
stiffness. This novel haptic device can potentially achieve a highly immersive
VR experience and more interactive applications during telemanipulation.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 11:09:00 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Cabrera",
"Miguel Altamirano",
""
],
[
"Tirado",
"Jonathan",
""
],
[
"Heredia",
"Juan",
""
],
[
"Tsetserukou",
"Dzmitry",
""
]
] |
new_dataset
| 0.999022 |
2208.14167
|
Fabian Herzog
|
Fabian Herzog, Junpeng Chen, Torben Teepe, Johannes Gilg, Stefan
H\"ormann, Gerhard Rigoll
|
Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Smart City applications such as intelligent traffic routing or accident
prevention rely on computer vision methods for exact vehicle localization and
tracking. Due to the scarcity of accurately labeled data, detecting and
tracking vehicles in 3D from multiple cameras proves challenging to explore. We
present a massive synthetic dataset for multiple vehicle tracking and
segmentation in multiple overlapping and non-overlapping camera views. Unlike
existing datasets, which only provide tracking ground truth for 2D bounding
boxes, our dataset additionally contains perfect labels for 3D bounding boxes
in camera- and world coordinates, depth estimation, and instance, semantic and
panoptic segmentation. The dataset consists of 17 hours of labeled video
material, recorded from 340 cameras in 64 diverse day, rain, dawn, and night
scenes, making it the most extensive dataset for multi-target multi-camera
tracking so far. We provide baselines for detection, vehicle re-identification,
and single- and multi-camera tracking. Code and data are publicly available.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 11:36:07 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Herzog",
"Fabian",
""
],
[
"Chen",
"Junpeng",
""
],
[
"Teepe",
"Torben",
""
],
[
"Gilg",
"Johannes",
""
],
[
"Hörmann",
"Stefan",
""
],
[
"Rigoll",
"Gerhard",
""
]
] |
new_dataset
| 0.999838 |
2208.14191
|
Lichen Jia
|
Lichen Jia, Bowen Tang, Chenggang Wu, Zhe Wang, Zihan Jiang, Yuanming
Lai, Yan Kang, Ning Liu, Jingfeng Zhang
|
FuncFooler: A Practical Black-box Attack Against Learning-based Binary
Code Similarity Detection Methods
|
9 pages, 4 figures
| null | null | null |
cs.CR cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The binary code similarity detection (BCSD) method measures the similarity of
two binary executable codes. Recently, the learning-based BCSD methods have
achieved great success, outperforming traditional BCSD in detection accuracy
and efficiency. However, the existing studies are rather sparse on the
adversarial vulnerability of the learning-based BCSD methods, which cause
hazards in security-related applications. To evaluate the adversarial
robustness, this paper designs an efficient and black-box adversarial code
generation algorithm, namely, FuncFooler. FuncFooler constrains the adversarial
codes 1) to keep unchanged the program's control flow graph (CFG), and 2) to
preserve the same semantic meaning. Specifically, FuncFooler consecutively 1)
determines vulnerable candidates in the malicious code, 2) chooses and inserts
the adversarial instructions from the benign code, and 3) corrects the semantic
side effect of the adversarial code to meet the constraints. Empirically, our
FuncFooler can successfully attack the three learning-based BCSD models,
including SAFE, Asm2Vec, and jTrans, which calls into question whether the
learning-based BCSD is desirable.
|
[
{
"version": "v1",
"created": "Fri, 26 Aug 2022 01:58:26 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Jia",
"Lichen",
""
],
[
"Tang",
"Bowen",
""
],
[
"Wu",
"Chenggang",
""
],
[
"Wang",
"Zhe",
""
],
[
"Jiang",
"Zihan",
""
],
[
"Lai",
"Yuanming",
""
],
[
"Kang",
"Yan",
""
],
[
"Liu",
"Ning",
""
],
[
"Zhang",
"Jingfeng",
""
]
] |
new_dataset
| 0.989189 |
2208.14209
|
Weixin Luo
|
Shuqiang Cao, Weixin Luo, Bairui Wang, Wei Zhang, Lin Ma
|
A Circular Window-based Cascade Transformer for Online Action Detection
|
Submitted to TPAMI
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Online action detection aims at the accurate action prediction of the current
frame based on long historical observations. Meanwhile, it demands real-time
inference on online streaming videos. In this paper, we advocate a novel and
efficient principle for online action detection. It merely updates the latest
and oldest historical representations in one window but reuses the intermediate
ones, which have been already computed. Based on this principle, we introduce a
window-based cascade Transformer with a circular historical queue, where it
conducts multi-stage attentions and cascade refinement on each window. We also
explore the association between online action detection and its counterpart
offline action segmentation as an auxiliary task. We find that such an extra
supervision helps discriminative history clustering and acts as feature
augmentation for better training the classifier and cascade refinement. Our
proposed method achieves the state-of-the-art performances on three challenging
datasets THUMOS'14, TVSeries, and HDD. Codes will be available after
acceptance.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 12:37:23 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Cao",
"Shuqiang",
""
],
[
"Luo",
"Weixin",
""
],
[
"Wang",
"Bairui",
""
],
[
"Zhang",
"Wei",
""
],
[
"Ma",
"Lin",
""
]
] |
new_dataset
| 0.960143 |
2208.14225
|
Tawfiq Aljohani
|
Tawfiq M. Aljohani
|
Cyberattacks on Energy Infrastructures: Modern War Weapons
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Recent high-profile cyberattacks on energy infrastructures, such as the
security breach of the Colonial Pipeline in 2021 and attacks that have
disrupted Ukraine's power grid from the mid-2010s till date, have pushed
cybersecurity as a top priority. As political tensions have escalated in Europe
this year, concerns about critical infrastructure security have increased.
Operators in the industrial sector face new cybersecurity threats that increase
the risk of disruptions in services, property damages, and environmental harm.
Amid rising geopolitical tensions, industrial companies, with their
network-connected systems, are now considered major targets for adversaries to
advance political, social, or military agendas. Moreover, the recent
Russian-Ukrainian conflict has set the alarm worldwide about the danger of
targeting energy grids via cyberattacks. Attack methodologies, techniques, and
procedures used successfully to hack energy grids in Ukraine can be used
elsewhere. This work aims to present a thorough analysis of the cybersecurity
of the energy infrastructure amid the increased rise of cyberwars. The article
navigates through the recent history of energy-related cyberattacks and their
reasoning, discusses the grid's vulnerability, and makes a precautionary
argument for securing the grids against them.
|
[
{
"version": "v1",
"created": "Sun, 28 Aug 2022 05:19:48 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Aljohani",
"Tawfiq M.",
""
]
] |
new_dataset
| 0.992989 |
2208.14303
|
Sima Mashafi
|
Farzad Vatandoust, Hoseyn A. Amiri, Sima Mas-hafi
|
DLDNN: Deterministic Lateral Displacement Design Automation by Neural
Networks
|
13 pages, 7 figures
| null | null | null |
cs.NE cs.AI math.OC physics.flu-dyn
|
http://creativecommons.org/licenses/by/4.0/
|
Size-based separation of bioparticles/cells is crucial to a variety of
biomedical processing steps for applications such as exosomes and DNA
isolation. Design and improvement of such microfluidic devices is a challenge
to best answer the demand for producing homogeneous end-result for study and
use. Deterministic lateral displacement (DLD) exploits a similar principle that
has drawn extensive attention over years. However, the lack of predictive
understanding of the particle trajectory and its induced mode makes designing a
DLD device an iterative procedure. Therefore, this paper investigates a fast
versatile design automation platform to address this issue. To do so,
convolutional and artificial neural networks were employed to learn velocity
fields and critical diameters of a wide range of DLD configurations. Later,
these networks were combined with a multi-objective evolutionary algorithm to
construct the automation tool. After ensuring the accuracy of the neural
networks, the developed tool was tested for 12 critical conditions. Reaching
the imposed conditions, the automation components performed reliably with
errors of less than 4%. Moreover, this tool is generalizable to other
field-based problems and since the neural network is an integral part of this
method, it enables transfer learning for similar physics. All the codes
generated and used in this study alongside the pre-trained neural network
models are available on https://github.com/HoseynAAmiri/DLDNN.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 14:38:17 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Vatandoust",
"Farzad",
""
],
[
"Amiri",
"Hoseyn A.",
""
],
[
"Mas-hafi",
"Sima",
""
]
] |
new_dataset
| 0.990098 |
2208.14345
|
Peiling Lu
|
Peiling Lu, Xu Tan, Botao Yu, Tao Qin, Sheng Zhao, Tie-Yan Liu
|
MeloForm: Generating Melody with Musical Form based on Expert Systems
and Neural Networks
| null | null | null | null |
cs.SD cs.CL cs.LG cs.MM eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Human usually composes music by organizing elements according to the musical
form to express music ideas. However, for neural network-based music
generation, it is difficult to do so due to the lack of labelled data on
musical form. In this paper, we develop MeloForm, a system that generates
melody with musical form using expert systems and neural networks.
Specifically, 1) we design an expert system to generate a melody by developing
musical elements from motifs to phrases then to sections with repetitions and
variations according to pre-given musical form; 2) considering the generated
melody is lack of musical richness, we design a Transformer based refinement
model to improve the melody without changing its musical form. MeloForm enjoys
the advantages of precise musical form control by expert systems and musical
richness learning via neural models. Both subjective and objective experimental
evaluations demonstrate that MeloForm generates melodies with precise musical
form control with 97.79% accuracy, and outperforms baseline systems in terms of
subjective evaluation score by 0.75, 0.50, 0.86 and 0.89 in structure,
thematic, richness and overall quality, without any labelled musical form data.
Besides, MeloForm can support various kinds of forms, such as verse and chorus
form, rondo form, variational form, sonata form, etc.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 15:44:15 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Lu",
"Peiling",
""
],
[
"Tan",
"Xu",
""
],
[
"Yu",
"Botao",
""
],
[
"Qin",
"Tao",
""
],
[
"Zhao",
"Sheng",
""
],
[
"Liu",
"Tie-Yan",
""
]
] |
new_dataset
| 0.999755 |
2208.14362
|
Nicholas Roberts
|
Nicholas Roberts, Xintong Li, Tzu-Heng Huang, Dyah Adila, Spencer
Schoenberg, Cheng-Yu Liu, Lauren Pick, Haotian Ma, Aws Albarghouthi, Frederic
Sala
|
AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100
Labels
| null | null | null | null |
cs.LG cs.AI cs.CV stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
Weak supervision (WS) is a powerful method to build labeled datasets for
training supervised models in the face of little-to-no labeled data. It
replaces hand-labeling data with aggregating multiple noisy-but-cheap label
estimates expressed by labeling functions (LFs). While it has been used
successfully in many domains, weak supervision's application scope is limited
by the difficulty of constructing labeling functions for domains with complex
or high-dimensional features. To address this, a handful of methods have
proposed automating the LF design process using a small set of ground truth
labels. In this work, we introduce AutoWS-Bench-101: a framework for evaluating
automated WS (AutoWS) techniques in challenging WS settings -- a set of diverse
application domains on which it has been previously difficult or impossible to
apply traditional WS techniques. While AutoWS is a promising direction toward
expanding the application-scope of WS, the emergence of powerful methods such
as zero-shot foundation models reveals the need to understand how AutoWS
techniques compare or cooperate with modern zero-shot or few-shot learners.
This informs the central question of AutoWS-Bench-101: given an initial set of
100 labels for each task, we ask whether a practitioner should use an AutoWS
method to generate additional labels or use some simpler baseline, such as
zero-shot predictions from a foundation model or supervised learning. We
observe that in many settings, it is necessary for AutoWS methods to
incorporate signal from foundation models if they are to outperform simple
few-shot baselines, and AutoWS-Bench-101 promotes future research in this
direction. We conclude with a thorough ablation study of AutoWS methods.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 16:09:42 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Roberts",
"Nicholas",
""
],
[
"Li",
"Xintong",
""
],
[
"Huang",
"Tzu-Heng",
""
],
[
"Adila",
"Dyah",
""
],
[
"Schoenberg",
"Spencer",
""
],
[
"Liu",
"Cheng-Yu",
""
],
[
"Pick",
"Lauren",
""
],
[
"Ma",
"Haotian",
""
],
[
"Albarghouthi",
"Aws",
""
],
[
"Sala",
"Frederic",
""
]
] |
new_dataset
| 0.997285 |
2208.14403
|
Ayoosh Bansal
|
Ayoosh Bansal, Hunmin Kim, Simon Yu, Bo Li, Naira Hovakimyan, Marco
Caccamo and Lui Sha
|
Verifiable Obstacle Detection
|
Accepted at ISSRE 2022
| null | null | null |
cs.RO cs.CV cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Perception of obstacles remains a critical safety concern for autonomous
vehicles. Real-world collisions have shown that the autonomy faults leading to
fatal collisions originate from obstacle existence detection. Open source
autonomous driving implementations show a perception pipeline with complex
interdependent Deep Neural Networks. These networks are not fully verifiable,
making them unsuitable for safety-critical tasks.
In this work, we present a safety verification of an existing LiDAR based
classical obstacle detection algorithm. We establish strict bounds on the
capabilities of this obstacle detection algorithm. Given safety standards, such
bounds allow for determining LiDAR sensor properties that would reliably
satisfy the standards. Such analysis has as yet been unattainable for neural
network based perception systems. We provide a rigorous analysis of the
obstacle detection system with empirical results based on real-world sensor
data.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 17:15:35 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Bansal",
"Ayoosh",
""
],
[
"Kim",
"Hunmin",
""
],
[
"Yu",
"Simon",
""
],
[
"Li",
"Bo",
""
],
[
"Hovakimyan",
"Naira",
""
],
[
"Caccamo",
"Marco",
""
],
[
"Sha",
"Lui",
""
]
] |
new_dataset
| 0.961766 |
2208.14433
|
Tianjia Zhang
|
Tianjia Zhang, Yuen-Fui Lau, and Qifeng Chen
|
A Portable Multiscopic Camera for Novel View and Time Synthesis in
Dynamic Scenes
|
To be presented at IROS2022
| null | null | null |
cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a portable multiscopic camera system with a dedicated model for
novel view and time synthesis in dynamic scenes. Our goal is to render
high-quality images for a dynamic scene from any viewpoint at any time using
our portable multiscopic camera. To achieve such novel view and time synthesis,
we develop a physical multiscopic camera equipped with five cameras to train a
neural radiance field (NeRF) in both time and spatial domains for dynamic
scenes. Our model maps a 6D coordinate (3D spatial position, 1D temporal
coordinate, and 2D viewing direction) to view-dependent and time-varying
emitted radiance and volume density. Volume rendering is applied to render a
photo-realistic image at a specified camera pose and time. To improve the
robustness of our physical camera, we propose a camera parameter optimization
module and a temporal frame interpolation module to promote information
propagation across time. We conduct experiments on both real-world and
synthetic datasets to evaluate our system, and the results show that our
approach outperforms alternative solutions qualitatively and quantitatively.
Our code and dataset are available at https://yuenfuilau.github.io.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 17:53:17 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Zhang",
"Tianjia",
""
],
[
"Lau",
"Yuen-Fui",
""
],
[
"Chen",
"Qifeng",
""
]
] |
new_dataset
| 0.999428 |
2208.14441
|
Krzysztof Sornat
|
Matthias K\"oppe, Martin Kouteck\'y, Krzysztof Sornat, Nimrod Talmon
|
Fine-Grained Liquid Democracy for Cumulative Ballots
|
15 pages, 1 table
| null | null | null |
cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate efficient ways for the incorporation of liquid democracy into
election settings in which voters submit cumulative ballots, i.e., when each
voter is assigned a virtual coin that she can then distribute as she wishes
among the available election options. In particular, we are interested in
fine-grained liquid democracy, meaning that voters are able to designate a
partial coin to a set of election options and delegate the decision on how to
further split this partial coin among those election options to another voter
of her choice. The fact that we wish such delegations to be transitive --
combined with our aim at fully respecting such delegations -- means that
inconsistencies and cycles can occur, thus we set to find
computationally-efficient ways of resolving voter delegations. To this aim we
develop a theory based fixed-point theorems and mathematical programming
techniques and we show that for various variants of definitions regarding how
to resolve such transitive delegations, there is always a feasible resolution;
and we identify under which conditions such solutions are efficiently
computable.
|
[
{
"version": "v1",
"created": "Tue, 30 Aug 2022 17:58:08 GMT"
}
] | 2022-08-31T00:00:00 |
[
[
"Köppe",
"Matthias",
""
],
[
"Koutecký",
"Martin",
""
],
[
"Sornat",
"Krzysztof",
""
],
[
"Talmon",
"Nimrod",
""
]
] |
new_dataset
| 0.994148 |
1601.05218
|
Yonatan Yehezkeally
|
Yonatan Yehezkeally and Moshe Schwartz
|
Limited-Magnitude Error-Correcting Gray Codes for Rank Modulation
|
Revised version for journal submission. Additional results include
more tight auxiliary constructions, a decoding shcema, ranking/unranking
procedures, and application to snake-in-the-box codes under the Kendall
tau-metric
| null |
10.1109/TIT.2017.2719710
| null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We construct Gray codes over permutations for the rank-modulation scheme,
which are also capable of correcting errors under the infinity-metric. These
errors model limited-magnitude or spike errors, for which only
single-error-detecting Gray codes are currently known. Surprisingly, the
error-correcting codes we construct achieve a better asymptotic rate than that
of presently known constructions not having the Gray property, and exceed the
Gilbert-Varshamov bound. Additionally, we present efficient ranking and
unranking procedures, as well as a decoding procedure that runs in linear time.
Finally, we also apply our methods to solve an outstanding issue with
error-detecting rank-modulation Gray codes (snake-in-the-box codes) under a
different metric, the Kendall $\tau$-metric, in the group of permutations over
an even number of elements $S_{2n}$, where we provide asymptotically optimal
codes.
|
[
{
"version": "v1",
"created": "Wed, 20 Jan 2016 09:46:02 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jan 2016 07:57:55 GMT"
},
{
"version": "v3",
"created": "Sun, 19 Jun 2016 17:56:06 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Yehezkeally",
"Yonatan",
""
],
[
"Schwartz",
"Moshe",
""
]
] |
new_dataset
| 0.992344 |
1911.04788
|
Carlo Tiseo
|
Keyhan Kouhkiloui Babarahmati, Carlo Tiseo, Joshua Smith, Hsiu Chin
Lin, Mustafa Suphi Erden and Michael Mistry
|
Fractal Impedance for Passive Controllers: A Framework for Interaction
Robotics
|
Nonlinear Dyn (2022). Video Available at https://youtu.be/Ny8zNyPS8AM
| null |
10.1007/s11071-022-07754-3
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
There is increasing interest in control frameworks capable of moving robots
from industrial cages to unstructured environments and coexisting with humans.
Despite significant improvement in some specific applications (e.g., medical
robotics), there is still the need for a general control framework that
improves interaction robustness and motion dynamics. Passive controllers show
promising results in this direction; however, they often rely on virtual energy
tanks that can guarantee passivity as long as they do not run out of energy. In
this paper, a Fractal Attractor is proposed to implement a variable impedance
controller that can retain passivity without relying on energy tanks. The
controller generates a Fractal Attractor around the desired state using an
asymptotic stable potential field, making the controller robust to
discretization and numerical integration errors. The results prove that it can
accurately track both trajectories and end-effector forces during interaction.
Therefore, these properties make the controller ideal for applications
requiring robust dynamic interaction at the end-effector.
|
[
{
"version": "v1",
"created": "Tue, 12 Nov 2019 10:54:20 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Dec 2020 11:42:59 GMT"
},
{
"version": "v3",
"created": "Fri, 28 May 2021 18:36:18 GMT"
},
{
"version": "v4",
"created": "Wed, 27 Jul 2022 17:09:41 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Babarahmati",
"Keyhan Kouhkiloui",
""
],
[
"Tiseo",
"Carlo",
""
],
[
"Smith",
"Joshua",
""
],
[
"Lin",
"Hsiu Chin",
""
],
[
"Erden",
"Mustafa Suphi",
""
],
[
"Mistry",
"Michael",
""
]
] |
new_dataset
| 0.989303 |
2107.01717
|
Canze Zhu
|
Canze Zhu and Qunying Liao
|
The $b$-weight distribution for MDS codes
| null | null | null | null |
cs.IT math.CO math.IT
|
http://creativecommons.org/licenses/by-sa/4.0/
|
For a positive integer $b\ge2$, the $b$-symbol code is a new coding framework
proposed to combat $b$-errors in $b$-symbol read channels. Especially, the
$2$-symbol code is called a symbol-pair code. Remarkably, a classical maximum
distance separable (MDS) code is also an MDS $b$-symbol code. Recently, for any
MDS code $\mathcal{C}$, Ma and Luo determined the symbol-pair weight
distribution of $\mathcal{C}$. In this paper, by calculating the number of
solutions for some equations and utilizing some shortened codes of
$\mathcal{C}$, we give the connection between the $b$-weight distribution and
the number of codewords in shortened codes of $\mathcal{C}$ with special shape.
Furthermore, note that shortened codes of $\mathcal{C}$ are also MDS codes, the
number of these codewords with special shape are also determined by the shorten
method. From the above calculation, the $b$-weight distribution of
$\mathcal{C}$ is determined. Our result generalies the corresonding result of
Ma and Luo.
|
[
{
"version": "v1",
"created": "Sun, 4 Jul 2021 19:47:32 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Dec 2021 22:42:33 GMT"
},
{
"version": "v3",
"created": "Fri, 13 May 2022 23:41:50 GMT"
},
{
"version": "v4",
"created": "Sat, 27 Aug 2022 03:07:48 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Zhu",
"Canze",
""
],
[
"Liao",
"Qunying",
""
]
] |
new_dataset
| 0.999353 |
2108.07920
|
Qian Zhang
|
Qian Zhang, Qing Guo, Ruijun Gao, Felix Juefei-Xu, Hongkai Yu, Wei
Feng
|
Adversarial Relighting Against Face Recognition
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep face recognition (FR) has achieved significantly high accuracy on
several challenging datasets and fosters successful real-world applications,
even showing high robustness to the illumination variation that is usually
regarded as a main threat to the FR system. However, in the real world,
illumination variation caused by diverse lighting conditions cannot be fully
covered by the limited face dataset. In this paper, we study the threat of
lighting against FR from a new angle, i.e., adversarial attack, and identify a
new task, i.e., adversarial relighting. Given a face image, adversarial
relighting aims to produce a naturally relighted counterpart while fooling the
state-of-the-art deep FR methods. To this end, we first propose the physical
modelbased adversarial relighting attack (ARA) denoted as albedoquotient-based
adversarial relighting attack (AQ-ARA). It generates natural adversarial light
under the physical lighting model and guidance of FR systems and synthesizes
adversarially relighted face images. Moreover, we propose the auto-predictive
adversarial relighting attack (AP-ARA) by training an adversarial relighting
network (ARNet) to automatically predict the adversarial light in a one-step
manner according to different input faces, allowing efficiency-sensitive
applications. More importantly, we propose to transfer the above digital
attacks to physical ARA (PhyARA) through a precise relighting device, making
the estimated adversarial lighting condition reproducible in the real world. We
validate our methods on three state-of-the-art deep FR methods, i.e., FaceNet,
ArcFace, and CosFace, on two public datasets. The extensive and insightful
results demonstrate our work can generate realistic adversarial relighted face
images fooling face recognition tasks easily, revealing the threat of specific
light directions and strengths.
|
[
{
"version": "v1",
"created": "Wed, 18 Aug 2021 01:05:53 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Sep 2021 04:09:51 GMT"
},
{
"version": "v3",
"created": "Tue, 16 Aug 2022 15:46:31 GMT"
},
{
"version": "v4",
"created": "Sat, 27 Aug 2022 02:39:18 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Zhang",
"Qian",
""
],
[
"Guo",
"Qing",
""
],
[
"Gao",
"Ruijun",
""
],
[
"Juefei-Xu",
"Felix",
""
],
[
"Yu",
"Hongkai",
""
],
[
"Feng",
"Wei",
""
]
] |
new_dataset
| 0.998071 |
2108.12790
|
Zhaoxin Fan
|
Zhaoxin Fan, Zhenbo Song, Wenping Zhang, Hongyan Liu, Jun He, and
Xiaoyong Du
|
RPR-Net: A Point Cloud-based Rotation-aware Large Scale Place
Recognition Network
|
Accept to ECCV 2022 AVVision Workshop
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Point cloud-based large scale place recognition is an important but
challenging task for many applications such as Simultaneous Localization and
Mapping (SLAM). Taking the task as a point cloud retrieval problem, previous
methods have made delightful achievements. However, how to deal with
catastrophic collapse caused by rotation problems is still under-explored. In
this paper, to tackle the issue, we propose a novel Point Cloud-based
Rotation-aware Large Scale Place Recognition Network (RPR-Net). In particular,
to solve the problem, we propose to learn rotation-invariant features in three
steps. First, we design three kinds of novel Rotation-Invariant Features
(RIFs), which are low-level features that can hold the rotation-invariant
property. Second, using these RIFs, we design an attentive module to learn
rotation-invariant kernels. Third, we apply these kernels to previous point
cloud features to generate new features, which is the well-known SO(3) mapping
process. By doing so, high-level scene-specific rotation-invariant features can
be learned. We call the above process an Attentive Rotation-Invariant
Convolution (ARIConv). To achieve the place recognition goal, we build RPR-Net,
which takes ARIConv as a basic unit to construct a dense network architecture.
Then, powerful global descriptors used for retrieval-based place recognition
can be sufficiently extracted from RPR-Net. Experimental results on prevalent
datasets show that our method achieves comparable results to existing
state-of-the-art place recognition models and significantly outperforms other
rotation-invariant baseline models when solving rotation problems.
|
[
{
"version": "v1",
"created": "Sun, 29 Aug 2021 09:10:56 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Mar 2022 14:23:55 GMT"
},
{
"version": "v3",
"created": "Sun, 28 Aug 2022 04:07:03 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Fan",
"Zhaoxin",
""
],
[
"Song",
"Zhenbo",
""
],
[
"Zhang",
"Wenping",
""
],
[
"Liu",
"Hongyan",
""
],
[
"He",
"Jun",
""
],
[
"Du",
"Xiaoyong",
""
]
] |
new_dataset
| 0.997775 |
2202.08471
|
Hongjie Fang
|
Hongjie Fang, Hao-Shu Fang, Sheng Xu and Cewu Lu
|
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth
Completion and a Grasping Baseline
|
project page: www.graspnet.net/transcg
|
IEEE Robotics and Automation Letters 7.3 (2022)
|
10.1109/LRA.2022.3183256
| null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Transparent objects are common in our daily life and frequently handled in
the automated production line. Robust vision-based robotic grasping and
manipulation for these objects would be beneficial for automation. However, the
majority of current grasping algorithms would fail in this case since they
heavily rely on the depth image, while ordinary depth sensors usually fail to
produce accurate depth information for transparent objects owing to the
reflection and refraction of light. In this work, we address this issue by
contributing a large-scale real-world dataset for transparent object depth
completion, which contains 57,715 RGB-D images from 130 different scenes. Our
dataset is the first large-scale, real-world dataset that provides ground truth
depth, surface normals, transparent masks in diverse and cluttered scenes.
Cross-domain experiments show that our dataset is more general and can enable
better generalization ability for models. Moreover, we propose an end-to-end
depth completion network, which takes the RGB image and the inaccurate depth
map as inputs and outputs a refined depth map. Experiments demonstrate superior
efficacy, efficiency and robustness of our method over previous works, and it
is able to process images of high resolutions under limited hardware resources.
Real robot experiments show that our method can also be applied to novel
transparent object grasping robustly. The full dataset and our method are
publicly available at www.graspnet.net/transcg
|
[
{
"version": "v1",
"created": "Thu, 17 Feb 2022 06:50:20 GMT"
},
{
"version": "v2",
"created": "Sun, 28 Aug 2022 03:38:12 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Fang",
"Hongjie",
""
],
[
"Fang",
"Hao-Shu",
""
],
[
"Xu",
"Sheng",
""
],
[
"Lu",
"Cewu",
""
]
] |
new_dataset
| 0.99861 |
2203.06243
|
Xinyi Zhang
|
Yalin Li, Xinyi Zhang, Victoria L. Morgan, Hannah A.C. Lohman, Lewis
S. Rowles, Smiti Mittal, Anna Kogler, Roland D. Cusick, William A. Tarpeh,
Jeremy S. Guest
|
QSDsan: An Integrated Platform for Quantitative Sustainable Design of
Sanitation and Resource Recovery Systems
| null | null |
10.1039/D2EW00455K
| null |
cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Sustainable sanitation and resource recovery technologies are needed to
address rapid environmental and socioeconomic changes. Research prioritization
is critical to expedite the development and deployment of such technologies
across their vast system space (e.g., technology choices, design and operating
decisions). In this study, we introduce QSDsan - an open-source tool written in
Python (under the object-oriented programming paradigm) and developed for the
quantitative sustainable design (QSD) of sanitation and resource recovery
systems. As an integrated platform for system design, process modeling and
simulation, techno-economic analysis (TEA), and life cycle assessment (LCA),
QSDsan can be used to enumerate and investigate the opportunity space for
emerging technologies under uncertainty, while considering contextual
parameters that are critical to technology deployment. We illustrate the core
capabilities of QSDsan through two distinct examples: (i) evaluation of a
complete sanitation value chain that compares three alternative systems; and
(ii) dynamic simulation of the wastewater treatment plant described in the
benchmark simulation model no. 1 (BSM1). Through these examples, we show the
utility of QSDsan to automate design, enable flexible process modeling, achieve
rapid and reproducible simulations, and to perform advanced statistical
analyses with integrated visualization. We strive to make QSDsan a
community-led platform with online documentation, tutorials (explanatory notes,
executable scripts, and video demonstrations), and a growing ecosystem of
supporting packages (e.g., DMsan for decision-making). This platform can be
freely accessed, used, and expanded by researchers, practitioners, and the
public alike, ultimately contributing to the advancement of safe and affordable
sanitation technologies around the globe.
|
[
{
"version": "v1",
"created": "Mon, 7 Mar 2022 18:42:15 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Li",
"Yalin",
""
],
[
"Zhang",
"Xinyi",
""
],
[
"Morgan",
"Victoria L.",
""
],
[
"Lohman",
"Hannah A. C.",
""
],
[
"Rowles",
"Lewis S.",
""
],
[
"Mittal",
"Smiti",
""
],
[
"Kogler",
"Anna",
""
],
[
"Cusick",
"Roland D.",
""
],
[
"Tarpeh",
"William A.",
""
],
[
"Guest",
"Jeremy S.",
""
]
] |
new_dataset
| 0.97903 |
2203.06357
|
Ling Ren
|
Dongning Guo and Ling Ren
|
Bitcoin's Latency--Security Analysis Made Simple
| null | null | null | null |
cs.CR cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Simple closed-form upper and lower bounds are developed for the security of
the Nakamoto consensus as a function of the confirmation depth, the honest and
adversarial block mining rates, and an upper bound on the block propagation
delay. The bounds are exponential in the confirmation depth and apply
regardless of the adversary's attack strategy. The gap between the upper and
lower bounds is small for Bitcoin's parameters. For example, assuming an
average block interval of 10 minutes, a network delay bound of ten seconds, and
10% adversarial mining power, the widely used 6-block confirmation rule yields
a safety violation between 0.11% and 0.35% probability.
|
[
{
"version": "v1",
"created": "Sat, 12 Mar 2022 06:36:56 GMT"
},
{
"version": "v2",
"created": "Fri, 13 May 2022 04:57:37 GMT"
},
{
"version": "v3",
"created": "Sat, 27 Aug 2022 03:31:44 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Guo",
"Dongning",
""
],
[
"Ren",
"Ling",
""
]
] |
new_dataset
| 0.999298 |
2203.07825
|
Shidi Li
|
Shidi Li, Christian Walder, Miaomiao Liu
|
SPA-VAE: Similar-Parts-Assignment for Unsupervised 3D Point Cloud
Generation
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
This paper addresses the problem of unsupervised parts-aware point cloud
generation with learned parts-based self-similarity. Our SPA-VAE infers a set
of latent canonical candidate shapes for any given object, along with a set of
rigid body transformations for each such candidate shape to one or more
locations within the assembled object. In this way, noisy samples on the
surface of, say, each leg of a table, are effectively combined to estimate a
single leg prototype. When parts-based self-similarity exists in the raw data,
sharing data among parts in this way confers numerous advantages: modeling
accuracy, appropriately self-similar generative outputs, precise in-filling of
occlusions, and model parsimony. SPA-VAE is trained end-to-end using a
variational Bayesian approach which uses the Gumbel-softmax trick for the
shared part assignments, along with various novel losses to provide appropriate
inductive biases. Quantitative and qualitative analyses on ShapeNet demonstrate
the advantage of SPA-VAE.
|
[
{
"version": "v1",
"created": "Tue, 15 Mar 2022 12:26:32 GMT"
},
{
"version": "v2",
"created": "Mon, 29 Aug 2022 01:04:23 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Li",
"Shidi",
""
],
[
"Walder",
"Christian",
""
],
[
"Liu",
"Miaomiao",
""
]
] |
new_dataset
| 0.994521 |
2204.06988
|
Sahraoui Dhelim Dr
|
Sahraoui Dhelim, Nyothiri Aung, Tahar Kechadi, Huansheng Ning, Liming
Chen and Abderrahmane Lakas
|
Trust2Vec: Large-Scale IoT Trust Management System based on Signed
Network Embeddings
|
\c{opyright} 20XX IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other works
|
IEEE Internet of Things Journal (2022).
https://ieeexplore.ieee.org/document/9866814
|
10.1109/JIOT.2022.3201772
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A trust management system (TMS) is an integral component of any IoT network.
A reliable trust management system must guarantee the network security, data
integrity, and act as a referee that promotes legitimate devices, and punishes
any malicious activities. Trust scores assigned by TMSs reflect devices'
reputations, which can help predict the future behaviours of network entities
and subsequently judge the reliability of different network entities in IoT
networks. Many TMSs have been proposed in the literature, these systems are
designed for small-scale trust attacks, and can deal with attacks where a
malicious device tries to undermine TMS by spreading fake trust reports.
However, these systems are prone to large-scale trust attacks. To address this
problem, in this paper, we propose a TMS for large-scale IoT systems called
Trust2Vec, which can manage trust relationships in large-scale IoT systems and
can mitigate large-scale trust attacks that are performed by hundreds of
malicious devices. Trust2Vec leverages a random-walk network exploration
algorithm that navigates the trust relationship among devices and computes
trust network embeddings, which enables it to analyze the latent network
structure of trust relationships, even if there is no direct trust rating
between two malicious devices. To detect large-scale attacks, suck as
self-promoting and bad-mouthing, we propose a network embeddings community
detection algorithm that detects and blocks communities of malicious nodes. The
effectiveness of Trust2Vec is validated through large-scale IoT network
simulation. The results show that Trust2Vec can achieve up to 94\% mitigation
rate in various network scenarios.
|
[
{
"version": "v1",
"created": "Thu, 14 Apr 2022 14:25:46 GMT"
},
{
"version": "v2",
"created": "Sat, 27 Aug 2022 08:37:41 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Dhelim",
"Sahraoui",
""
],
[
"Aung",
"Nyothiri",
""
],
[
"Kechadi",
"Tahar",
""
],
[
"Ning",
"Huansheng",
""
],
[
"Chen",
"Liming",
""
],
[
"Lakas",
"Abderrahmane",
""
]
] |
new_dataset
| 0.994921 |
2205.02895
|
Philipp Wiesner
|
Philipp Wiesner, Dominik Scheinert, Thorsten Wittkopp, Lauritz
Thamsen, Odej Kao
|
Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and
Edge Workloads
|
Accepted at Euro-Par 2022. GitHub repository:
https://github.com/dos-group/cucumber
| null |
10.1007/978-3-031-12597-3_14
| null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The growing electricity demand of cloud and edge computing increases
operational costs and will soon have a considerable impact on the environment.
A possible countermeasure is equipping IT infrastructure directly with on-site
renewable energy sources. Yet, particularly smaller data centers may not be
able to use all generated power directly at all times, while feeding it into
the public grid or energy storage is often not an option. To maximize the usage
of renewable excess energy, we propose Cucumber, an admission control policy
that accepts delay-tolerant workloads only if they can be computed within their
deadlines without the use of grid energy. Using probabilistic forecasting of
computational load, energy consumption, and energy production, Cucumber can be
configured towards more optimistic or conservative admission. We evaluate our
approach on two scenarios using real solar production forecasts for Berlin,
Mexico City, and Cape Town in a simulation environment. For scenarios where
excess energy was actually available, our results show that Cucumber's default
configuration achieves acceptance rates close to the optimal case and causes
97.0% of accepted workloads to be powered using excess energy, while more
conservative admission results in 18.5% reduced acceptance at almost zero grid
power usage.
|
[
{
"version": "v1",
"created": "Thu, 5 May 2022 19:21:16 GMT"
},
{
"version": "v2",
"created": "Tue, 23 Aug 2022 09:14:48 GMT"
},
{
"version": "v3",
"created": "Sat, 27 Aug 2022 17:53:21 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Wiesner",
"Philipp",
""
],
[
"Scheinert",
"Dominik",
""
],
[
"Wittkopp",
"Thorsten",
""
],
[
"Thamsen",
"Lauritz",
""
],
[
"Kao",
"Odej",
""
]
] |
new_dataset
| 0.999621 |
2206.05053
|
Debarpan Bhattacharya
|
Debarpan Bhattacharya, Debottam Dutta, Neeraj Kumar Sharma, Srikanth
Raj Chetupalli, Pravin Mote, Sriram Ganapathy, Chandrakiran C, Sahiti Nori,
Suhail K K, Sadhana Gonuguntla and Murali Alagesan
|
Coswara: A website application enabling COVID-19 screening by analysing
respiratory sound samples and health symptoms
| null |
Interspeech, 2022
| null | null |
cs.HC cs.LG cs.SD eess.AS eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
The COVID-19 pandemic has accelerated research on design of alternative,
quick and effective COVID-19 diagnosis approaches. In this paper, we describe
the Coswara tool, a website application designed to enable COVID-19 detection
by analysing respiratory sound samples and health symptoms. A user using this
service can log into a website using any device connected to the internet,
provide there current health symptom information and record few sound sampled
corresponding to breathing, cough, and speech. Within a minute of analysis of
this information on a cloud server the website tool will output a COVID-19
probability score to the user. As the COVID-19 pandemic continues to demand
massive and scalable population level testing, we hypothesize that the proposed
tool provides a potential solution towards this.
|
[
{
"version": "v1",
"created": "Thu, 9 Jun 2022 05:50:18 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Bhattacharya",
"Debarpan",
""
],
[
"Dutta",
"Debottam",
""
],
[
"Sharma",
"Neeraj Kumar",
""
],
[
"Chetupalli",
"Srikanth Raj",
""
],
[
"Mote",
"Pravin",
""
],
[
"Ganapathy",
"Sriram",
""
],
[
"C",
"Chandrakiran",
""
],
[
"Nori",
"Sahiti",
""
],
[
"K",
"Suhail K",
""
],
[
"Gonuguntla",
"Sadhana",
""
],
[
"Alagesan",
"Murali",
""
]
] |
new_dataset
| 0.996118 |
2206.10779
|
Howard Zhang
|
Yunhao Ba, Howard Zhang, Ethan Yang, Akira Suzuki, Arnold Pfahnl,
Chethan Chinder Chandrappa, Celso de Melo, Suya You, Stefano Soatto, Alex
Wong, Achuta Kadambi
|
Not Just Streaks: Towards Ground Truth for Single Image Deraining
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a large-scale dataset of real-world rainy and clean image pairs
and a method to remove degradations, induced by rain streaks and rain
accumulation, from the image. As there exists no real-world dataset for
deraining, current state-of-the-art methods rely on synthetic data and thus are
limited by the sim2real domain gap; moreover, rigorous evaluation remains a
challenge due to the absence of a real paired dataset. We fill this gap by
collecting a real paired deraining dataset through meticulous control of
non-rain variations. Our dataset enables paired training and quantitative
evaluation for diverse real-world rain phenomena (e.g. rain streaks and rain
accumulation). To learn a representation robust to rain phenomena, we propose a
deep neural network that reconstructs the underlying scene by minimizing a
rain-robust loss between rainy and clean images. Extensive experiments
demonstrate that our model outperforms the state-of-the-art deraining methods
on real rainy images under various conditions. Project website:
https://visual.ee.ucla.edu/gt_rain.htm/.
|
[
{
"version": "v1",
"created": "Wed, 22 Jun 2022 00:10:06 GMT"
},
{
"version": "v2",
"created": "Sun, 28 Aug 2022 18:27:27 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Ba",
"Yunhao",
""
],
[
"Zhang",
"Howard",
""
],
[
"Yang",
"Ethan",
""
],
[
"Suzuki",
"Akira",
""
],
[
"Pfahnl",
"Arnold",
""
],
[
"Chandrappa",
"Chethan Chinder",
""
],
[
"de Melo",
"Celso",
""
],
[
"You",
"Suya",
""
],
[
"Soatto",
"Stefano",
""
],
[
"Wong",
"Alex",
""
],
[
"Kadambi",
"Achuta",
""
]
] |
new_dataset
| 0.987065 |
2207.04232
|
Ruhao Wan
|
Ruhao Wan, Shixin Zhu, Jin Li
|
Construction of MDS self-dual codes from generalized Reed-Solomon codes
|
24 pages,2 table
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
MDS codes and self-dual codes are important families of classical codes in
coding theory. It is of interest to investigate MDS self-dual codes. The
existence of MDS self-dual codes over finite field $F_q$ is completely solved
for $q$ is even. In this paper, for finite field with odd characteristic, we
construct some new classes of MDS self-dual codes by (extended) generalized
Reed-Solomon codes.
|
[
{
"version": "v1",
"created": "Sat, 9 Jul 2022 09:26:42 GMT"
},
{
"version": "v2",
"created": "Sat, 23 Jul 2022 14:15:24 GMT"
},
{
"version": "v3",
"created": "Sat, 27 Aug 2022 07:32:46 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Wan",
"Ruhao",
""
],
[
"Zhu",
"Shixin",
""
],
[
"Li",
"Jin",
""
]
] |
new_dataset
| 0.994482 |
2208.08425
|
Zhuqing Liu
|
Zhuqing Liu, Xin Zhang, Jia Liu
|
SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient
Method for Distributed Learning in Computing Clusters
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To increase the training speed of distributed learning, recent years have
witnessed a significant amount of interest in developing both synchronous and
asynchronous distributed stochastic variance-reduced optimization methods.
However, all existing synchronous and asynchronous distributed training
algorithms suffer from various limitations in either convergence speed or
implementation complexity. This motivates us to propose an algorithm called
STNTHESIS (semi-asynchronous path-integrated stochastic gradient search), which
leverages the special structure of the variance-reduction framework to overcome
the limitations of both synchronous and asynchronous distributed learning
algorithms while retaining their salient features. We consider two
implementations of STNTHESIS under distributed and shared memory architectures.
We show that our STNTHESIS algorithms have
$O(\sqrt{N}\epsilon^{-2}(\Delta+1)+N)$ and $O(\sqrt{N}\epsilon^{-2}(\Delta+1)
d+N)$ computational complexities for achieving an $\epsilon$-stationary point
in non-convex learning under distributed and shared memory architectures,
respectively, where N denotes the total number of training samples and $\Delta$
represents the maximum delay of the workers. Moreover, we investigate the
generalization performance of \algname by establishing algorithmic stability
bounds for quadratic strongly convex and non-convex optimization. We further
conduct extensive numerical experiments to verify our theoretical findings
|
[
{
"version": "v1",
"created": "Wed, 17 Aug 2022 17:42:33 GMT"
},
{
"version": "v2",
"created": "Sat, 27 Aug 2022 15:46:48 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Liu",
"Zhuqing",
""
],
[
"Zhang",
"Xin",
""
],
[
"Liu",
"Jia",
""
]
] |
new_dataset
| 0.986508 |
2208.08482
|
Huaishu Peng
|
Jiasheng Li, Zeyu Yan, Ebrima Jarjue, Ashrith Shetty, Huaishu Peng
|
TangibleGrid: Tangible Web Layout Design for Blind Users
| null |
UIST '22, October 29-November 2, 2022, Bend, OR, USA
|
10.1145/3526113.3545627
| null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present TangibleGrid, a novel device that allows blind users to understand
and design the layout of a web page with real-time tangible feedback. We
conducted semi-structured interviews and a series of co-design sessions with
blind users to elicit insights that guided the design of TangibleGrid. Our
final prototype contains shape-changing brackets representing the web elements
and a baseboard representing the web page canvas. Blind users can design a web
page layout through creating and editing web elements by snapping or adjusting
tangible brackets on top of the baseboard. The baseboard senses the brackets'
type, size, and location, verbalizes the information, and renders the web page
on the client browser. Through a formative user study, we found that blind
users could understand a web page layout through TangibleGrid. They were also
able to design a new web layout from scratch without the help of sighted
people.
|
[
{
"version": "v1",
"created": "Wed, 17 Aug 2022 18:51:18 GMT"
},
{
"version": "v2",
"created": "Sat, 27 Aug 2022 21:14:18 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Li",
"Jiasheng",
""
],
[
"Yan",
"Zeyu",
""
],
[
"Jarjue",
"Ebrima",
""
],
[
"Shetty",
"Ashrith",
""
],
[
"Peng",
"Huaishu",
""
]
] |
new_dataset
| 0.999699 |
2208.08502
|
Huaishu Peng
|
Zeyu Yan, Anup Sathya, Sahra Yusuf, Jyh-Ming Lien, Huaishu Peng
|
Fibercuit: Prototyping High-Resolution Flexible and Kirigami Circuits
with a Fiber Laser Engraver
| null |
UIST '22, October 29-November 2, 2022, Bend, OR, USA
|
10.1145/3526113.3545652
| null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Prototyping compact devices with unique form factors often requires the PCB
manufacturing process to be outsourced, which can be expensive and
time-consuming. In this paper, we present Fibercuit, a set of rapid prototyping
techniques to fabricate high-resolution, flexible circuits on-demand using a
fiber laser engraver. We showcase techniques that can laser cut copper-based
composites to form fine-pitch conductive traces, laser fold copper substrates
that can form kirigami structures, and laser solder surface-mount electrical
components using off-the-shelf soldering pastes. Combined with our software
pipeline, an end user can design and fabricate flexible circuits which are
dual-layer and three-dimensional, thereby exhibiting a wide range of form
factors. We demonstrate Fibercuit by showcasing a set of examples, including a
custom dice, flex cables, custom end-stop switches, electromagnetic coils, LED
earrings and a circuit in the form of kirigami crane.
|
[
{
"version": "v1",
"created": "Wed, 17 Aug 2022 19:42:04 GMT"
},
{
"version": "v2",
"created": "Sat, 27 Aug 2022 21:20:40 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Yan",
"Zeyu",
""
],
[
"Sathya",
"Anup",
""
],
[
"Yusuf",
"Sahra",
""
],
[
"Lien",
"Jyh-Ming",
""
],
[
"Peng",
"Huaishu",
""
]
] |
new_dataset
| 0.999507 |
2208.09815
|
Pengqian Yu
|
Xinhan Di, Pengqian Yu
|
LWA-HAND: Lightweight Attention Hand for Interacting Hand Reconstruction
|
Accepted by ECCV 2022 Computer Vision for Metaverse Workshop (16
pages, 6 figures, 1 table)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent years have witnessed great success for hand reconstruction in
real-time applications such as visual reality and augmented reality while
interacting with two-hand reconstruction through efficient transformers is left
unexplored. In this paper, we propose a method called lightweight attention
hand (LWA-HAND) to reconstruct hands in low flops from a single RGB image. To
solve the occlusion and interaction problem in efficient attention
architectures, we propose three mobile attention modules in this paper. The
first module is a lightweight feature attention module that extracts both local
occlusion representation and global image patch representation in a
coarse-to-fine manner. The second module is a cross image and graph bridge
module which fuses image context and hand vertex. The third module is a
lightweight cross-attention mechanism that uses element-wise operation for the
cross-attention of two hands in linear complexity. The resulting model achieves
comparable performance on the InterHand2.6M benchmark in comparison with the
state-of-the-art models. Simultaneously, it reduces the flops to $0.47GFlops$
while the state-of-the-art models have heavy computations between $10GFlops$
and $20GFlops$.
|
[
{
"version": "v1",
"created": "Sun, 21 Aug 2022 06:25:56 GMT"
},
{
"version": "v2",
"created": "Tue, 23 Aug 2022 03:54:47 GMT"
},
{
"version": "v3",
"created": "Sat, 27 Aug 2022 13:06:34 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Di",
"Xinhan",
""
],
[
"Yu",
"Pengqian",
""
]
] |
new_dataset
| 0.989354 |
2208.11090
|
Alessandra Rossi Dr
|
Alessandra Rossi, Patrick Holthaus, S\`ilvia Moros and Gabriella
Lakatos
|
IEEE Trust, Acceptance and Social Cues in Human-Robot Interaction --
SCRITA 2022 Workshop
|
SCRITA 2022 workshop proceedings including 8 articles
|
31st IEEE International Conference on Robot & Human Interactive
Communication, 29 August - 3 September 2022
| null |
SCRITA/2022
|
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
The Trust, Acceptance and Social Cues in Human-Robot Interaction - SCRITA is
the 5th edition of a series of workshops held in conjunction with the IEEE
RO-MAN conference. This workshop focuses on addressing the challenges and
development of the dynamics between people and robots in order to foster short
interactions and long-lasting relationships in different fields, from
educational, service, collaborative, companion, care-home and medical robotics.
In particular, we aimed in investigating how robots can manipulate (i.e.
creating, improving, and recovering) people's ability of accepting and trusting
them for a fruitful and successful coexistence between humans and people. While
advanced progresses are reached in studying and evaluating the factors
affecting acceptance and trust of people in robots in controlled or short-term
(repeated interactions) setting, developing service and personal robots, that
are accepted and trusted by people where the supervision of operators is not
possible, still presents an open challenge for scientists in robotics, AI and
HRI fields. In such unstructured static and dynamic human-centred environments
scenarios, robots should be able to learn and adapt their behaviours to the
situational context, but also to people's prior experiences and learned
associations, their expectations, and their and the robot's ability to predict
and understand each other's behaviours. Although the previous editions valued
the participation of leading researchers in the field and several exceptional
invited speakers who tackled down some fundamental points in this research
domains, we wish to continue to further explore the role of trust in robotics
to present groundbreaking research to effectively design and develop socially
acceptable and trustable robots to be deployed "in the wild".
Website: https://scrita.herts.ac.uk
|
[
{
"version": "v1",
"created": "Mon, 22 Aug 2022 14:17:01 GMT"
},
{
"version": "v2",
"created": "Sun, 28 Aug 2022 23:03:34 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Rossi",
"Alessandra",
""
],
[
"Holthaus",
"Patrick",
""
],
[
"Moros",
"Sìlvia",
""
],
[
"Lakatos",
"Gabriella",
""
]
] |
new_dataset
| 0.994903 |
2208.11235
|
Colin Gordon
|
Sergey Matskevich, Colin S. Gordon
|
Preprocessing Source Code Comments for Linguistic Models
|
Correcting author name
| null | null | null |
cs.SE cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Comments are an important part of the source code and are a primary source of
documentation. This has driven interest in using large bodies of comments to
train or evaluate tools that consume or produce them -- such as generating
oracles or even code from comments, or automatically generating code summaries.
Most of this work makes strong assumptions about the structure and quality of
comments, such as assuming they consist mostly of proper English sentences.
However, we know little about the actual quality of existing comments for these
use cases. Comments often contain unique structures and elements that are not
seen in other types of text, and filtering or extracting information from them
requires some extra care. This paper explores the contents and quality of
Python comments drawn from 840 most popular open source projects from GitHub
and 8422 projects from SriLab dataset, and the impact of na\"ive vs. in-depth
filtering can have on the use of existing comments for training and evaluation
of systems that generate comments.
|
[
{
"version": "v1",
"created": "Tue, 23 Aug 2022 23:44:09 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Aug 2022 23:46:49 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Matskevich",
"Sergey",
""
],
[
"Gordon",
"Colin S.",
""
]
] |
new_dataset
| 0.967201 |
2208.11484
|
Aly Mostafa
|
Aly Mostafa, Omar Mohamed, Ali Ashraf, Ahmed Elbehery, Salma Jamal,
Anas Salah, Amr S. Ghoneim
|
An End-to-End OCR Framework for Robust Arabic-Handwriting Recognition
using a Novel Transformers-based Model and an Innovative 270 Million-Words
Multi-Font Corpus of Classical Arabic with Diacritics
| null | null | null | null |
cs.CV cs.CL cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This research is the second phase in a series of investigations on developing
an Optical Character Recognition (OCR) of Arabic historical documents and
examining how different modeling procedures interact with the problem. The
first research studied the effect of Transformers on our custom-built Arabic
dataset. One of the downsides of the first research was the size of the
training data, a mere 15000 images from our 30 million images, due to lack of
resources. Also, we add an image enhancement layer, time and space
optimization, and Post-Correction layer to aid the model in predicting the
correct word for the correct context. Notably, we propose an end-to-end text
recognition approach using Vision Transformers as an encoder, namely BEIT, and
vanilla Transformer as a decoder, eliminating CNNs for feature extraction and
reducing the model's complexity. The experiments show that our end-to-end model
outperforms Convolutions Backbones. The model attained a CER of 4.46%.
|
[
{
"version": "v1",
"created": "Sat, 20 Aug 2022 22:21:19 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Aug 2022 21:02:07 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Mostafa",
"Aly",
""
],
[
"Mohamed",
"Omar",
""
],
[
"Ashraf",
"Ali",
""
],
[
"Elbehery",
"Ahmed",
""
],
[
"Jamal",
"Salma",
""
],
[
"Salah",
"Anas",
""
],
[
"Ghoneim",
"Amr S.",
""
]
] |
new_dataset
| 0.999511 |
2208.12349
|
Tiago Guerreiro
|
Tiago Guerreiro, Ana Pires, Lu\'is Carri\c{c}o
|
Snooping on Snoopers: Logging as a Security Response to Physical Attacks
on Mobile Devices
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
When users leave their mobile devices unattended, or let others use them
momentarily, they are susceptible to privacy breaches. Existing technological
defenses, such as unlock authentication or account switching, have proven to be
unpopular. We conducted interviews to uncover practices users currently engage
in to cope with the threat, and found that it is common for users to try to
keep their devices under close supervision at all times. One obstacle to this
strategy is that displaying such protective behavior can be detrimental to
social relationships. To address these concerns, we built a software tool that
gathers activity logs in the background. Logs can later be reviewed as a
timeline of opened apps and the actions performed within each, with events
decorated with pictures captured inconspicuously with the front-facing camera.
We evaluated this approach in a user study, and found participants to be
generally eager to adopt the technology, although in different ways. Most users
foresaw using it as a deterrent, or to check if they were snooped on, if that
suspicion were ever to arise. Yet, some voiced the intention of creating "honey
traps". The results highlight both the opportunities and the potential dangers
of the logging approach.
|
[
{
"version": "v1",
"created": "Thu, 25 Aug 2022 21:26:04 GMT"
},
{
"version": "v2",
"created": "Mon, 29 Aug 2022 11:04:27 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Guerreiro",
"Tiago",
""
],
[
"Pires",
"Ana",
""
],
[
"Carriço",
"Luís",
""
]
] |
new_dataset
| 0.972413 |
2208.12804
|
Maximilian Weininger
|
Florian J\"ungermann, Jan K\v{r}et\'insk\'y, and Maximilian Weininger
|
Algebraically Explainable Controllers: Decision Trees and Support Vector
Machines Join Forces
| null | null | null | null |
cs.LG cs.AI cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Recently, decision trees (DT) have been used as an explainable representation
of controllers (a.k.a. strategies, policies, schedulers). Although they are
often very efficient and produce small and understandable controllers for
discrete systems, complex continuous dynamics still pose a challenge. In
particular, when the relationships between variables take more complex forms,
such as polynomials, they cannot be obtained using the available DT learning
procedures. In contrast, support vector machines provide a more powerful
representation, capable of discovering many such relationships, but not in an
explainable form. Therefore, we suggest to combine the two frameworks in order
to obtain an understandable representation over richer, domain-relevant
algebraic predicates. We demonstrate and evaluate the proposed method
experimentally on established benchmarks.
|
[
{
"version": "v1",
"created": "Fri, 26 Aug 2022 17:57:37 GMT"
},
{
"version": "v2",
"created": "Mon, 29 Aug 2022 11:28:10 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Jüngermann",
"Florian",
""
],
[
"Křetínský",
"Jan",
""
],
[
"Weininger",
"Maximilian",
""
]
] |
new_dataset
| 0.996082 |
2208.12833
|
Francesca Favaro
|
Francesca Favaro, Keith Hutchings, Philip Nemec, Leticia Cavalcante,
Trent Victor
|
Waymo's Fatigue Risk Management Framework: Prevention, Monitoring, and
Mitigation of Fatigue-Induced Risks while Testing Automated Driving Systems
| null | null | null | null |
cs.RO cs.CY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This report presents Waymo's proposal for a systematic fatigue risk
management framework that addresses prevention, monitoring, and mitigation of
fatigue-induced risks during on-road testing of ADS technology. The proposed
framework remains flexible to incorporate continuous improvements, and was
informed by state of the art practices, research, learnings, and experience
(both internal and external to Waymo). Fatigue is a recognized contributory
factor in a substantial fraction of on-road crashes involving human drivers,
and mitigation of fatigue-induced risks is still an open concern researched
world-wide. While the proposed framework was specifically designed in relation
to on-road testing of SAE Level 4 ADS technology, it has implications and
applicability to lower levels of automation as well.
|
[
{
"version": "v1",
"created": "Fri, 26 Aug 2022 18:22:50 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Favaro",
"Francesca",
""
],
[
"Hutchings",
"Keith",
""
],
[
"Nemec",
"Philip",
""
],
[
"Cavalcante",
"Leticia",
""
],
[
"Victor",
"Trent",
""
]
] |
new_dataset
| 0.991209 |
2208.12850
|
Michael Baddeley Dr
|
Michael Baddeley, Yevgen Gyl, Markus Schuss, Xiaoyuan Ma, and Carlo
Alberto Boano
|
OSF: An Open-Source Framework for Synchronous Flooding over Multiple
Physical Layers
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Flooding protocols based on concurrent transmissions are regarded as the most
reliable way to collect or disseminate data across a multi-hop low-power
wireless mesh network. Recent works have shown that such protocols are
effective for narrowband communication not only over IEEE 802.15.4, but also
over the BLE 5 physical layers (PHYs). However, to date, existing literature
has only built synchronous flooding solutions on top of a single PHY, and there
has been no attempt to leverage different PHYs at runtime to increase
performance. This paper fills this gap and presents OSF, an open-source
framework that enables the design of multi-PHY synchronous flooding solutions
thanks to a novel radio driver and middle-ware architecture capable of
dynamically switching the underlying physical layer. This allows exploitation
of the specific benefits of each PHY (e.g., higher data-rate, increased
robustness) on-demand during each flood, increasing performance. We tailor OSF
to the off-the-shelf nRF52840 platform, and showcase its benefits by comparing
single-PHY and multi-PHY synchronous flooding solutions on a real-world
testbed.
|
[
{
"version": "v1",
"created": "Fri, 26 Aug 2022 19:40:29 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Baddeley",
"Michael",
""
],
[
"Gyl",
"Yevgen",
""
],
[
"Schuss",
"Markus",
""
],
[
"Ma",
"Xiaoyuan",
""
],
[
"Boano",
"Carlo Alberto",
""
]
] |
new_dataset
| 0.984803 |
2208.12864
|
Nestaly Mar\'in
|
J.M. D\'iaz-B\'a\~nez (1), P. Horn (2), M.A. Lopez (3), N. Mar\'in
(4), A. Ram\'irez-Vigueras (5), O. Sol\'e-Pi (6), A. Stevens (3), J. Urrutia
(5) ((1) Departamento de Matem\'atica Aplicada II, Universidad de Sevilla,
Spain. (2) Department of Mathematics, University of Denver, USA. (3)
Department of Computer Science, University of Denver, USA. (4) Posgrado en
Ciencia e Ingenier\'ia de la Computaci\'on, Universidad Nacional Aut\'onoma
de M\'exico, Mexico., (5) Instituto de Matem\'aticas, Universidad Nacional
Aut\'onoma de M\'exico, Mexico. (6) Facultad de Ciencias, Universidad
Nacional Aut\'onoma de M\'exico, Mexico)
|
Ortho-unit polygons can be guarded with at most $\lfloor \frac{n-4}{8}
\rfloor$ guards
|
9 pages, 8 figures
| null | null | null |
cs.CG math.CO
|
http://creativecommons.org/licenses/by/4.0/
|
An orthogonal polygon is called an ortho-unit polygon if its vertices have
integer coordinates, and all of its edges have length one. In this paper we
prove that any ortho-unit polygon with $n \geq 12$ vertices can be guarded with
at most $\lfloor \frac{n-4}{8} \rfloor$ guards.
|
[
{
"version": "v1",
"created": "Fri, 26 Aug 2022 20:43:36 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Díaz-Báñez",
"J. M.",
""
],
[
"Horn",
"P.",
""
],
[
"Lopez",
"M. A.",
""
],
[
"Marín",
"N.",
""
],
[
"Ramírez-Vigueras",
"A.",
""
],
[
"Solé-Pi",
"O.",
""
],
[
"Stevens",
"A.",
""
],
[
"Urrutia",
"J.",
""
]
] |
new_dataset
| 0.991548 |
2208.12898
|
Myroslav Kryven
|
Reyan Ahmed, Stephen Kobourov, Myroslav Kryven
|
An FPT Algorithm for Bipartite Vertex Splitting
|
Appears in the Proceedings of the 30th International Symposium on
Graph Drawing and Network Visualization (GD 2022)
| null | null | null |
cs.CG cs.DM
|
http://creativecommons.org/licenses/by/4.0/
|
Bipartite graphs model the relationship between two disjoint sets of objects.
They have a wide range of applications and are often visualized as a 2-layered
drawing, where each set of objects is visualized as a set of vertices (points)
on one of the two parallel horizontal lines and the relationships are
represented by edges (simple curves) between the two lines connecting the
corresponding vertices. One of the common objectives in such drawings is to
minimize the number of crossings this, however, is computationally expensive
and may still result in drawings with so many crossings that they affect the
readability of the drawing. We consider a recent approach to remove crossings
in such visualizations by splitting vertices, where the goal is to find the
minimum number of vertices to be split to obtain a planar drawing. We show that
determining whether a planar drawing exists after splitting at most $k$
vertices is fixed parameter tractable in $k$.
|
[
{
"version": "v1",
"created": "Sat, 27 Aug 2022 00:19:31 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Ahmed",
"Reyan",
""
],
[
"Kobourov",
"Stephen",
""
],
[
"Kryven",
"Myroslav",
""
]
] |
new_dataset
| 0.995793 |
2208.12934
|
Astitva Srivastava
|
Astitva Srivastava, Chandradeep Pokhariya, Sai Sagar Jinka and Avinash
Sharma
|
xCloth: Extracting Template-free Textured 3D Clothes from a Monocular
Image
|
Accepted at ACM Multimedia-2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Existing approaches for 3D garment reconstruction either assume a predefined
template for the garment geometry (restricting them to fixed clothing styles)
or yield vertex colored meshes (lacking high-frequency textural details). Our
novel framework co-learns geometric and semantic information of garment surface
from the input monocular image for template-free textured 3D garment
digitization. More specifically, we propose to extend PeeledHuman
representation to predict the pixel-aligned, layered depth and semantic maps to
extract 3D garments. The layered representation is further exploited to UV
parametrize the arbitrary surface of the extracted garment without any human
intervention to form a UV atlas. The texture is then imparted on the UV atlas
in a hybrid fashion by first projecting pixels from the input image to UV space
for the visible region, followed by inpainting the occluded regions. Thus, we
are able to digitize arbitrarily loose clothing styles while retaining
high-frequency textural details from a monocular image. We achieve
high-fidelity 3D garment reconstruction results on three publicly available
datasets and generalization on internet images.
|
[
{
"version": "v1",
"created": "Sat, 27 Aug 2022 05:57:00 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Srivastava",
"Astitva",
""
],
[
"Pokhariya",
"Chandradeep",
""
],
[
"Jinka",
"Sai Sagar",
""
],
[
"Sharma",
"Avinash",
""
]
] |
new_dataset
| 0.999525 |
2208.12961
|
Takahito Murakami
|
Takahito Murakami, Maya Grace Torii, Xanat Vargas Meza, Yoichi Ochiai
|
Kuchibashi: 3D-Printed Tweezers Bioinspired by the New Caledonian Crow's
Beak
|
2 pages, 2figures,ACM SIGGRAPH2022
|
ACM SIGGRAPH 2022. Posters Article 18. 1-2
|
10.1145/3532719.3543254
| null |
cs.HC cs.GR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this study we implemented Kuchibashi, the New Caledonian Crow beak-like
tweezers, and conducted a user study to evaluate the prototype's usability. We
proved that Kuchibashi is superior in interacting with large spherical objects
than hands and tweezers. Also, impressions of security and safeness were
perceived positively by the participants.
|
[
{
"version": "v1",
"created": "Sat, 27 Aug 2022 08:51:22 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Murakami",
"Takahito",
""
],
[
"Torii",
"Maya Grace",
""
],
[
"Meza",
"Xanat Vargas",
""
],
[
"Ochiai",
"Yoichi",
""
]
] |
new_dataset
| 0.99609 |
2208.12970
|
Wang Chen
|
Yi Fang, Wang Chen, Pingping Chen, Yiwei Tao, Mohsen Guizani
|
SR-DCSK Cooperative Communication System with Code Index Modulation: A
New Design for 6G New Radios
| null | null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper proposes a high-throughput short reference differential chaos
shift keying cooperative communication system with the aid of code index
modulation, referred to as CIM-SR-DCSK-CC system. In the proposed
CIM-SR-DCSK-CC system, the source transmits information bits to both the relay
and destination in the first time slot, while the relay not only forwards the
source information bits but also sends new information bits to the destination
in the second time slot. To be specific, the relay employs an $N$-order Walsh
code to carry additional ${{\log }_{2}}N$ information bits, which are
superimposed onto the SR-DCSK signal carrying the decoded source information
bits. Subsequently, the superimposed signal carrying both the source and relay
information bits is transmitted to the destination. Moreover, the theoretical
bit error rate (BER) expressions of the proposed CIM-SR-DCSK-CC system are
derived over additive white Gaussian noise (AWGN) and multipath Rayleigh fading
channels. Compared with the conventional DCSK-CC system and SR-DCSK-CC system,
the proposed CIM-SR-DCSK-CC system can significantly improve the throughput
without deteriorating any BER performance. As a consequence, the proposed
system is very promising for the applications of the 6G-enabled low-power and
high-rate communication.
|
[
{
"version": "v1",
"created": "Sat, 27 Aug 2022 09:39:38 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Fang",
"Yi",
""
],
[
"Chen",
"Wang",
""
],
[
"Chen",
"Pingping",
""
],
[
"Tao",
"Yiwei",
""
],
[
"Guizani",
"Mohsen",
""
]
] |
new_dataset
| 0.999542 |
2208.12981
|
Sangho Suh
|
Sangho Suh, Jian Zhao, and Edith Law
|
CodeToon: Story Ideation, Auto Comic Generation, and Structure Mapping
for Code-Driven Storytelling
| null | null |
10.1145/3526113.3545617
| null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent work demonstrated how we can design and use coding strips, a form of
comic strips with corresponding code, to enhance teaching and learning in
programming. However, creating coding strips is a creative, time-consuming
process. Creators have to generate stories from code (code->story) and design
comics from stories (story->comic). We contribute CodeToon, a comic authoring
tool that facilitates this code-driven storytelling process with two
mechanisms: (1) story ideation from code using metaphor and (2) automatic comic
generation from the story. We conducted a two-part user study that evaluates
the tool and the comics generated by participants to test whether CodeToon
facilitates the authoring process and helps generate quality comics. Our
results show that CodeToon helps users create accurate, informative, and useful
coding strips in a significantly shorter time. Overall, this work contributes
methods and design guidelines for code-driven storytelling and opens up
opportunities for using art to support computer science education.
|
[
{
"version": "v1",
"created": "Sat, 27 Aug 2022 10:34:54 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Suh",
"Sangho",
""
],
[
"Zhao",
"Jian",
""
],
[
"Law",
"Edith",
""
]
] |
new_dataset
| 0.9997 |
2208.12983
|
Michael Baddeley Dr
|
Chloe Bae, Shiwen Yang, Michael Baddeley, Atis Elsts, and Israat Haque
|
BlueTiSCH: A Multi-PHY Simulation of Low-Power 6TiSCH IoT Networks
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Low-power wireless IoT networks have traditionally operated over a single
physical layer (PHY) -- many based on the IEEE 802.15.4 standard. However,
recent low-power wireless chipsets offer both the IEEE 802.15.4 and all four
PHYs of the Bluetooth 5 (BT 5) standard. This introduces the intriguing
possibility that IoT solutions might not necessarily be bound by the limits of
a single PHY, and could actively or proactively adapt their PHY depending on RF
or networking conditions (e.g., to offer a higher throughput or a longer radio
range). Several recent studies have explored such use-cases. However, these
studies lack comprehensive evaluation over various metrics (such as
reliability, latency, and energy) with regards to scalability and the Radio
Frequency (RF) environment. In this work we evaluate the performance of IEEE
802.15.4 and the four BT 5 2.4GHz PHY options for the recently completed IETF
6TiSCH low-power wireless standard. To the best of our knowledge, this is the
first work to directly compare these PHYs in identical settings. Specifically,
we use a recently released 6TiSCH simulator, TSCH-Sim, to compare these PHY
options in networks of up to 250 nodes over different RF environments (home,
industrial, and outdoor), and highlight from these results how different PHY
options might be better suited to particular application use-cases.
|
[
{
"version": "v1",
"created": "Sat, 27 Aug 2022 10:52:20 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Bae",
"Chloe",
""
],
[
"Yang",
"Shiwen",
""
],
[
"Baddeley",
"Michael",
""
],
[
"Elsts",
"Atis",
""
],
[
"Haque",
"Israat",
""
]
] |
new_dataset
| 0.998961 |
2208.12986
|
Bowen Fu
|
Bowen Fu, Sek Kun Leong, Xiaocong Lian and Xiangyang Ji
|
6D Robotic Assembly Based on RGB-only Object Pose Estimation
|
Accepted by IROS 2022
| null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision-based robotic assembly is a crucial yet challenging task as the
interaction with multiple objects requires high levels of precision. In this
paper, we propose an integrated 6D robotic system to perceive, grasp,
manipulate and assemble blocks with tight tolerances. Aiming to provide an
off-the-shelf RGB-only solution, our system is built upon a monocular 6D object
pose estimation network trained solely with synthetic images leveraging
physically-based rendering. Subsequently, pose-guided 6D transformation along
with collision-free assembly is proposed to construct any designed structure
with arbitrary initial poses. Our novel 3-axis calibration operation further
enhances the precision and robustness by disentangling 6D pose estimation and
robotic assembly. Both quantitative and qualitative results demonstrate the
effectiveness of our proposed 6D robotic assembly system.
|
[
{
"version": "v1",
"created": "Sat, 27 Aug 2022 11:26:24 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Fu",
"Bowen",
""
],
[
"Leong",
"Sek Kun",
""
],
[
"Lian",
"Xiaocong",
""
],
[
"Ji",
"Xiangyang",
""
]
] |
new_dataset
| 0.999403 |
2208.13054
|
Shreyas Kulkarni
|
Shreyas Kulkarni, Shreyas Singh, Dhananjay Balakrishnan, Siddharth
Sharma, Saipraneeth Devunuri, Sai Chowdeswara Rao Korlapati
|
CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets
and Frameworks
| null | null | null | null |
cs.CV
|
http://creativecommons.org/publicdomain/zero/1.0/
|
The detection of cracks is a crucial task in monitoring structural health and
ensuring structural safety. The manual process of crack detection is
time-consuming and subjective to the inspectors. Several researchers have tried
tackling this problem using traditional Image Processing or learning-based
techniques. However, their scope of work is limited to detecting cracks on a
single type of surface (walls, pavements, glass, etc.). The metrics used to
evaluate these methods are also varied across the literature, making it
challenging to compare techniques. This paper addresses these problems by
combining previously available datasets and unifying the annotations by
tackling the inherent problems within each dataset, such as noise and
distortions. We also present a pipeline that combines Image Processing and Deep
Learning models. Finally, we benchmark the results of proposed models on these
metrics on our new dataset and compare them with state-of-the-art models in the
literature.
|
[
{
"version": "v1",
"created": "Sat, 27 Aug 2022 16:47:04 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Kulkarni",
"Shreyas",
""
],
[
"Singh",
"Shreyas",
""
],
[
"Balakrishnan",
"Dhananjay",
""
],
[
"Sharma",
"Siddharth",
""
],
[
"Devunuri",
"Saipraneeth",
""
],
[
"Korlapati",
"Sai Chowdeswara Rao",
""
]
] |
new_dataset
| 0.99871 |
2208.13078
|
Xiaoyu Shen
|
Qingyu Zhang, Xiaoyu Shen, Ernie Chang, Jidong Ge and Pengke Chen
|
MDIA: A Benchmark for Multilingual Dialogue Generation in 46 Languages
|
The dataset and processing scripts are available in
https://github.com/DoctorDream/mDIA
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Owing to the lack of corpora for low-resource languages, current works on
dialogue generation have mainly focused on English. In this paper, we present
mDIA, the first large-scale multilingual benchmark for dialogue generation
across low- to high-resource languages. It covers real-life conversations in 46
languages across 19 language families. We present baseline results obtained by
fine-tuning the multilingual, non-dialogue-focused pre-trained model mT5 as
well as English-centric, dialogue-focused pre-trained chatbot DialoGPT. The
results show that mT5-based models perform better on sacreBLEU and BertScore
but worse on diversity. Even though promising results are found in few-shot and
zero-shot scenarios, there is a large gap between the generation quality in
English and other languages. We hope that the release of mDIA could encourage
more works on multilingual dialogue generation to promote language diversity.
|
[
{
"version": "v1",
"created": "Sat, 27 Aug 2022 19:35:20 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Zhang",
"Qingyu",
""
],
[
"Shen",
"Xiaoyu",
""
],
[
"Chang",
"Ernie",
""
],
[
"Ge",
"Jidong",
""
],
[
"Chen",
"Pengke",
""
]
] |
new_dataset
| 0.998404 |
2208.13169
|
Martin Molan
|
Martin Molan, Andrea Borghesi, Daniele Cesarini, Luca Benini, Andrea
Bartolini
|
RUAD: unsupervised anomaly detection in HPC systems
| null | null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The increasing complexity of modern high-performance computing (HPC) systems
necessitates the introduction of automated and data-driven methodologies to
support system administrators' effort toward increasing the system's
availability. Anomaly detection is an integral part of improving the
availability as it eases the system administrator's burden and reduces the time
between an anomaly and its resolution. However, current state-of-the-art (SoA)
approaches to anomaly detection are supervised and semi-supervised, so they
require a human-labelled dataset with anomalies - this is often impractical to
collect in production HPC systems. Unsupervised anomaly detection approaches
based on clustering, aimed at alleviating the need for accurate anomaly data,
have so far shown poor performance.
In this work, we overcome these limitations by proposing RUAD, a novel
Recurrent Unsupervised Anomaly Detection model. RUAD achieves better results
than the current semi-supervised and unsupervised SoA approaches. This is
achieved by considering temporal dependencies in the data and including
long-short term memory cells in the model architecture. The proposed approach
is assessed on a complete ten-month history of a Tier-0 system (Marconi100 from
CINECA with 980 nodes). RUAD achieves an area under the curve (AUC) of 0.763 in
semi-supervised training and an AUC of 0.767 in unsupervised training, which
improves upon the SoA approach that achieves an AUC of 0.747 in semi-supervised
training and an AUC of 0.734 in unsupervised training. It also vastly
outperforms the current SoA unsupervised anomaly detection approach based on
clustering, achieving the AUC of 0.548.
|
[
{
"version": "v1",
"created": "Sun, 28 Aug 2022 08:30:52 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Molan",
"Martin",
""
],
[
"Borghesi",
"Andrea",
""
],
[
"Cesarini",
"Daniele",
""
],
[
"Benini",
"Luca",
""
],
[
"Bartolini",
"Andrea",
""
]
] |
new_dataset
| 0.980246 |
2208.13170
|
Raoul Blin
|
Raoul Blin and Fabien Cromi\`eres
|
CJaFr-v3 : A Freely Available Filtered Japanese-French Aligned Corpus
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present a free Japanese-French parallel corpus. It includes 15M aligned
segments and is obtained by compiling and filtering several existing resources.
In this paper, we describe the existing resources, their quantity and quality,
the filtering we applied to improve the quality of the corpus, and the content
of the ready-to-use corpus. We also evaluate the usefulness of this corpus and
the quality of our filtering by training and evaluating some standard MT
systems with it.
|
[
{
"version": "v1",
"created": "Sun, 28 Aug 2022 08:33:18 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Blin",
"Raoul",
""
],
[
"Cromières",
"Fabien",
""
]
] |
new_dataset
| 0.998601 |
2208.13249
|
Jian Du
|
Jian Du and Tianxi Ji and Jamie Cui and Lei Zhang and Yufei Lu and Pu
Duan
|
DP-PSI: Private and Secure Set Intersection
| null | null | null | null |
cs.CR cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
One way to classify private set intersection (PSI) for secure 2-party
computation is whether the intersection is (a) revealed to both parties or (b)
hidden from both parties while only the computing function of the matched
payload is exposed. Both aim to provide cryptographic security while avoiding
exposing the unmatched elements of the other. They may, however, be
insufficient to achieve security and privacy in one practical scenario: when
the intersection is required and the information leaked through the function's
output must be considered for legal, ethical, and competitive reasons. Two
parties, such as the advertiser and the ads supplier, hold sets of users for
PSI computation, for example, to reveal common users to the ads supplier in
joint marketing applications. In addition to the security guarantees required
by standard PSIs to secure unmatched elements, neither party is allowed to
"single out" whether an element/user belongs to the other party or not, even
though common users are required for joint advertising. This is a fascinating
problem for which none of the PSI techniques have provided a solution. In light
of this shortcoming, we compose differential privacy (DP) and S2PC to provide
the best of both worlds and propose differentially-private PSI (DP-PSI), a new
privacy model that shares PSI's strong security protection while adhering to
the GDPR's recent formalization of the notion of excluding "signaling out"
attacks by each party except with very low probability.
|
[
{
"version": "v1",
"created": "Sun, 28 Aug 2022 16:50:22 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Du",
"Jian",
""
],
[
"Ji",
"Tianxi",
""
],
[
"Cui",
"Jamie",
""
],
[
"Zhang",
"Lei",
""
],
[
"Lu",
"Yufei",
""
],
[
"Duan",
"Pu",
""
]
] |
new_dataset
| 0.99735 |
2208.13319
|
Krishna Vardhan
|
Daniel Minati, Ludwik Sams, Karen Li, Bo Ji and Krishna Vardhan
|
Minute ventilation measurement using Plethysmographic Imaging and
lighting parameters
|
6 pages, 4 figures
| null | null | null |
cs.LG eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Breathing disorders such as sleep apnea is a critical disorder that affects a
large number of individuals due to the insufficient capacity of the lungs to
contain/exchange oxygen and carbon dioxide to ensure that the body is in the
stable state of homeostasis. Respiratory Measurements such as minute
ventilation can be used in correlation with other physiological measurements
such as heart rate and heart rate variability for remote monitoring of health
and detecting symptoms of such breathing related disorders. In this work, we
formulate a deep learning based approach to measure remote ventilation on a
private dataset. The dataset will be made public upon acceptance of this work.
We use two versions of a deep neural network to estimate the minute ventilation
from data streams obtained through wearable heart rate and respiratory devices.
We demonstrate that the simple design of our pipeline - which includes
lightweight deep neural networks - can be easily incorporate into real time
health monitoring systems.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 00:42:48 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Minati",
"Daniel",
""
],
[
"Sams",
"Ludwik",
""
],
[
"Li",
"Karen",
""
],
[
"Ji",
"Bo",
""
],
[
"Vardhan",
"Krishna",
""
]
] |
new_dataset
| 0.999736 |
2208.13333
|
Chen Cheng
|
Chen Cheng
|
Real-Time Mask Detection Based on SSD-MobileNetV2
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
After the outbreak of COVID-19, mask detection, as the most convenient and
effective means of prevention, plays a crucial role in epidemic prevention and
control. An excellent automatic real-time mask detection system can reduce a
lot of work pressure for relevant staff. However, by analyzing the existing
mask detection approaches, we find that they are mostly resource-intensive and
do not achieve a good balance between speed and accuracy. And there is no
perfect face mask dataset at present. In this paper, we propose a new
architecture for mask detection. Our system uses SSD as the mask locator and
classifier, and further replaces VGG-16 with MobileNetV2 to extract the
features of the image and reduce a lot of parameters. Therefore, our system can
be deployed on embedded devices. Transfer learning methods are used to transfer
pre-trained models from other domains to our model. Data enhancement methods in
our system such as MixUp effectively prevent overfitting. It also effectively
reduces the dependence on large-scale datasets. By doing experiments in
practical scenarios, the results demonstrate that our system performed well in
real-time mask detection.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 01:59:22 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Cheng",
"Chen",
""
]
] |
new_dataset
| 0.999517 |
2208.13361
|
Yingjie Lao
|
Faysal Hossain Shezan, Yingjie Lao, Minlong Peng, Xin Wang, Mingming
Sun, Ping Li
|
NL2GDPR: Automatically Develop GDPR Compliant Android Application
Features from Natural Language
|
37 pages
| null | null | null |
cs.CR cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The recent privacy leakage incidences and the more strict policy regulations
demand a much higher standard of compliance for companies and mobile apps.
However, such obligations also impose significant challenges on app developers
for complying with these regulations that contain various perspectives,
activities, and roles, especially for small companies and developers who are
less experienced in this matter or with limited resources. To address these
hurdles, we develop an automatic tool, NL2GDPR, which can generate policies
from natural language descriptions from the developer while also ensuring the
app's functionalities are compliant with General Data Protection Regulation
(GDPR). NL2GDPR is developed by leveraging an information extraction tool, OIA
(Open Information Annotation), developed by Baidu Cognitive Computing Lab.
At the core, NL2GDPR is a privacy-centric information extraction model,
appended with a GDPR policy finder and a policy generator. We perform a
comprehensive study to grasp the challenges in extracting privacy-centric
information and generating privacy policies, while exploiting optimizations for
this specific task. With NL2GDPR, we can achieve 92.9%, 95.2%, and 98.4%
accuracy in correctly identifying GDPR policies related to personal data
storage, process, and share types, respectively. To the best of our knowledge,
NL2GDPR is the first tool that allows a developer to automatically generate
GDPR compliant policies, with only the need of entering the natural language
for describing the app features. Note that other non-GDPR-related features
might be integrated with the generated features to build a complex app.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 04:16:50 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Shezan",
"Faysal Hossain",
""
],
[
"Lao",
"Yingjie",
""
],
[
"Peng",
"Minlong",
""
],
[
"Wang",
"Xin",
""
],
[
"Sun",
"Mingming",
""
],
[
"Li",
"Ping",
""
]
] |
new_dataset
| 0.999463 |
2208.13388
|
Fabrizio Montecchiani
|
Michael A. Bekos, Martin Gronemann, Fabrizio Montecchiani, Antonios
Symvonis
|
Strictly-Convex Drawings of $3$-Connected Planar Graphs
|
Appears in the Proceedings of the 30th International Symposium on
Graph Drawing and Network Visualization (GD 2022)
| null | null | null |
cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Strictly-convex straight-line drawings of $3$-connected planar graphs in
small area form a classical research topic in Graph Drawing. Currently, the
best-known area bound for such drawings is $O(n^2) \times O(n^2)$, as shown by
B\'{a}r\'{a}ny and Rote by means of a sophisticated technique based on
perturbing (non-strictly) convex drawings. Unfortunately, the hidden constants
in such area bound are in the $10^4$ order.
We present a new and easy-to-implement technique that yields strictly-convex
straight-line planar drawings of $3$-connected planar graphs on an integer grid
of size $2(n-1) \times (5n^3-4n^2)$.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 06:41:38 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Bekos",
"Michael A.",
""
],
[
"Gronemann",
"Martin",
""
],
[
"Montecchiani",
"Fabrizio",
""
],
[
"Symvonis",
"Antonios",
""
]
] |
new_dataset
| 0.995266 |
2208.13424
|
Stefano Maria Nicoletti
|
Stefano M. Nicoletti and E. Moritz Hahn and Marielle Stoelinga
|
BFL: a Logic to Reason about Fault Trees
| null | null |
10.1109/DSN53405.2022.00051
| null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Safety-critical infrastructures must operate safely and reliably. Fault tree
analysis is a widespread method used to assess risks in these systems: fault
trees (FTs) are required - among others - by the Federal Aviation Authority,
the Nuclear Regulatory Commission, in the ISO26262 standard for autonomous
driving and for software development in aerospace systems. Although popular
both in industry and academia, FTs lack a systematic way to formulate powerful
and understandable analysis queries. In this paper, we aim to fill this gap and
introduce Boolean Fault tree Logic (BFL), a logic to reason about FTs. BFL is a
simple, yet expressive logic that supports easier formulation of complex
scenarios and specification of FT properties. Alongside BFL, we present model
checking algorithms based on binary decision diagrams (BDDs) to analyse
specified properties in BFL, patterns and an algorithm to construct
counterexamples. Finally, we propose a case-study application of BFL by
analysing a COVID19-related FT.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 08:48:23 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Nicoletti",
"Stefano M.",
""
],
[
"Hahn",
"E. Moritz",
""
],
[
"Stoelinga",
"Marielle",
""
]
] |
new_dataset
| 0.999352 |
2208.13427
|
Sun Woo Park
|
Sun Woo Park, Yun Young Choi, Dosang Joe, U Jin Choi, Youngho Woo
|
The PWLR Graph Representation: A Persistent Weisfeiler-Lehman scheme
with Random Walks for Graph Classification
|
Accepted to the ICML 2022 Workshop on Topology, Algebra, and Geometry
in Machine Learning
| null | null | null |
cs.LG math.AT
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents the Persistent Weisfeiler-Lehman Random walk scheme
(abbreviated as PWLR) for graph representations, a novel mathematical framework
which produces a collection of explainable low-dimensional representations of
graphs with discrete and continuous node features. The proposed scheme
effectively incorporates normalized Weisfeiler-Lehman procedure, random walks
on graphs, and persistent homology. We thereby integrate three distinct
properties of graphs, which are local topological features, node degrees, and
global topological invariants, while preserving stability from graph
perturbations. This generalizes many variants of Weisfeiler-Lehman procedures,
which are primarily used to embed graphs with discrete node labels. Empirical
results suggest that these representations can be efficiently utilized to
produce comparable results to state-of-the-art techniques in classifying graphs
with discrete node labels, and enhanced performances in classifying those with
continuous node features.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 08:50:37 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Park",
"Sun Woo",
""
],
[
"Choi",
"Yun Young",
""
],
[
"Joe",
"Dosang",
""
],
[
"Choi",
"U Jin",
""
],
[
"Woo",
"Youngho",
""
]
] |
new_dataset
| 0.991885 |
2208.13486
|
Sadra Sabouri
|
Sadra Sabouri, Elnaz Rahmati, Soroush Gooran, Hossein Sameti
|
naab: A ready-to-use plug-and-play corpus for Farsi
|
6 pages, 2 figures
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Huge corpora of textual data are always known to be a crucial need for
training deep models such as transformer-based ones. This issue is emerging
more in lower resource languages - like Farsi. We propose naab, the biggest
cleaned and ready-to-use open-source textual corpus in Farsi. It contains about
130GB of data, 250 million paragraphs, and 15 billion words. The project name
is derived from the Farsi word NAAB K which means pure and high grade. We also
provide the raw version of the corpus called naab-raw and an easy-to-use
preprocessor that can be employed by those who wanted to make a customized
corpus.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 10:40:58 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Sabouri",
"Sadra",
""
],
[
"Rahmati",
"Elnaz",
""
],
[
"Gooran",
"Soroush",
""
],
[
"Sameti",
"Hossein",
""
]
] |
new_dataset
| 0.994451 |
2208.13523
|
Zainab Zaidi
|
Zainab Zaidi, Mengbin Ye, Fergus John Samon, Abdisalam Jama, Binduja
Gopalakrishnan, Chenhao Gu, Shanika Karunasekera, Jamie Evans, and Yoshihisa
Kashima
|
Demystifying the COVID-19 vaccine discourse on Twitter
| null | null | null | null |
cs.SI cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Developing an understanding of the public discourse on COVID-19 vaccination
on social media is important not only for addressing the current COVID-19
pandemic, but also for future pathogen outbreaks. We examine a Twitter dataset
containing 75 million English tweets discussing COVID-19 vaccination from March
2020 to March 2021. We train a stance detection algorithm using natural
language processing (NLP) techniques to classify tweets as `anti-vax' or
`pro-vax', and examine the main topics of discourse using topic modelling
techniques. While pro-vax tweets (37 million) far outnumbered anti-vax tweets
(10 million), a majority of tweets from both stances (63% anti-vax and 53%
pro-vax tweets) came from dual-stance users who posted both pro- and anti-vax
tweets during the observation period. Pro-vax tweets focused mostly on vaccine
development, while anti-vax tweets covered a wide range of topics, some of
which included genuine concerns, though there was a large dose of falsehoods. A
number of topics were common to both stances, though pro- and anti-vax tweets
discussed them from opposite viewpoints. Memes and jokes were amongst the most
retweeted messages. Whereas concerns about polarisation and online prevalence
of anti-vax discourse are unfounded, targeted countering of falsehoods is
important.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 11:56:21 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Zaidi",
"Zainab",
""
],
[
"Ye",
"Mengbin",
""
],
[
"Samon",
"Fergus John",
""
],
[
"Jama",
"Abdisalam",
""
],
[
"Gopalakrishnan",
"Binduja",
""
],
[
"Gu",
"Chenhao",
""
],
[
"Karunasekera",
"Shanika",
""
],
[
"Evans",
"Jamie",
""
],
[
"Kashima",
"Yoshihisa",
""
]
] |
new_dataset
| 0.998297 |
2208.13550
|
Snehasis Banerjee
|
Vivek Chandel, Snehasis Banerjee, Avik Ghose
|
ProxiTrak: Intelligent Enablement of Social Distancing & Contact Tracing
for a Safer Workplace in the New Normal
|
CSI YITPA Region II Winning Paper
| null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper describes an innovative solution that enables the enterprises to
bring their associates (or employees) back to physical workspaces for critical
operations in a safe manner in the wake of current COVID-19 pandemic.
|
[
{
"version": "v1",
"created": "Thu, 25 Aug 2022 12:50:12 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Chandel",
"Vivek",
""
],
[
"Banerjee",
"Snehasis",
""
],
[
"Ghose",
"Avik",
""
]
] |
new_dataset
| 0.984275 |
2208.13560
|
Marco Vassena
|
Marco Vassena, Alejandro Russo, Deepak Garg, Vineet Rajani, Deian
Stefan
|
From Fine- to Coarse-Grained Dynamic Information Flow Control and Back,
a Tutorial on Dynamic Information Flow
| null | null | null | null |
cs.PL cs.CR
|
http://creativecommons.org/licenses/by-sa/4.0/
|
This tutorial provides a complete and homogeneous account of the latest
advances in fine- and coarse-grained dynamic information-flow control (IFC)
security. Since the 70s, the programming language and the operating system
communities have proposed different IFC approaches. IFC operating systems track
information flows in a coarse-grained fashion, at the granularity of a process.
In contrast, traditional language-based approaches to IFC are fine-grained:
they track information flows at the granularity of program variables. For
decades, researchers believed coarse-grained IFC to be strictly less permissive
than fine-grained IFC -- coarse-grained IFC systems seem inherently less
precise because they track less information -- and so granularity appeared to
be a fundamental feature of IFC systems. We show that the granularity of the
tracking system does not fundamentally restrict how precise or permissive
dynamic IFC systems can be. To this end, we mechanize two mostly standard
languages, one with a fine-grained dynamic IFC system and the other with a
coarse-grained dynamic IFC system, and prove a semantics-preserving translation
from each language to the other. In addition, we derive the standard security
property of non-interference of each language from that of the other via our
verified translation. These translations stand to have important implications
on the usability of IFC approaches. The coarse- to fine-grained direction can
be used to remove the label annotation burden that fine-grained systems impose
on developers, while the fine- to coarse-grained translation shows that
coarse-grained systems -- which are easier to design and implement -- can track
information as precisely as fine-grained systems and provides an algorithm for
automatically retrofitting legacy applications to run on existing
coarse-grained systems.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 12:48:20 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Vassena",
"Marco",
""
],
[
"Russo",
"Alejandro",
""
],
[
"Garg",
"Deepak",
""
],
[
"Rajani",
"Vineet",
""
],
[
"Stefan",
"Deian",
""
]
] |
new_dataset
| 0.995478 |
2208.13626
|
Vasu Sharma
|
Vasu Sharma, Prasoon Goyal, Kaixiang Lin, Govind Thattai, Qiaozi Gao,
Gaurav S. Sukhatme
|
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous
Multi-Agent Reinforcement Learning
| null | null | null | null |
cs.AI cs.CV cs.LG cs.MA cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a multimodal (vision-and-language) benchmark for cooperative and
heterogeneous multi-agent learning. We introduce a benchmark multimodal dataset
with tasks involving collaboration between multiple simulated heterogeneous
robots in a rich multi-room home environment. We provide an integrated learning
framework, multimodal implementations of state-of-the-art multi-agent
reinforcement learning techniques, and a consistent evaluation protocol. Our
experiments investigate the impact of different modalities on multi-agent
learning performance. We also introduce a simple message passing method between
agents. The results suggest that multimodality introduces unique challenges for
cooperative multi-agent learning and there is significant room for advancing
multi-agent reinforcement learning methods in such settings.
|
[
{
"version": "v1",
"created": "Fri, 26 Aug 2022 02:21:31 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Sharma",
"Vasu",
""
],
[
"Goyal",
"Prasoon",
""
],
[
"Lin",
"Kaixiang",
""
],
[
"Thattai",
"Govind",
""
],
[
"Gao",
"Qiaozi",
""
],
[
"Sukhatme",
"Gaurav S.",
""
]
] |
new_dataset
| 0.999316 |
2208.13679
|
Abtin Molavi
|
Abtin Molavi, Amanda Xu, Martin Diges, Lauren Pick, Swamit Tannu, Aws
Albarghouthi
|
Qubit Mapping and Routing via MaxSAT
| null | null | null | null |
cs.AR quant-ph
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Near-term quantum computers will operate in a noisy environment, without
error correction. A critical problem for near-term quantum computing is laying
out a logical circuit onto a physical device with limited connectivity between
qubits. This is known as the qubit mapping and routing (QMR) problem, an
intractable combinatorial problem. It is important to solve QMR as optimally as
possible to reduce the amount of added noise, which may render a quantum
computation useless. In this paper, we present a novel approach for optimally
solving the QMR problem via a reduction to maximum satisfiability (MAXSAT).
Additionally, we present two novel relaxation ideas that shrink the size of the
MAXSAT constraints by exploiting the structure of a quantum circuit. Our
thorough empirical evaluation demonstrates (1) the scalability of our approach
compared to state-of-the-art optimal QMR techniques (solves more than 3x
benchmarks with 40x speedup), (2) the significant cost reduction compared to
state-of-the-art heuristic approaches (an average of ~5x swap reduction), and
(3) the power of our proposed constraint relaxations.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 15:39:04 GMT"
}
] | 2022-08-30T00:00:00 |
[
[
"Molavi",
"Abtin",
""
],
[
"Xu",
"Amanda",
""
],
[
"Diges",
"Martin",
""
],
[
"Pick",
"Lauren",
""
],
[
"Tannu",
"Swamit",
""
],
[
"Albarghouthi",
"Aws",
""
]
] |
new_dataset
| 0.99708 |
1712.05474
|
Roozbeh Mottaghi
|
Eric Kolve, Roozbeh Mottaghi, Winson Han, Eli VanderBilt, Luca Weihs,
Alvaro Herrasti, Matt Deitke, Kiana Ehsani, Daniel Gordon, Yuke Zhu,
Aniruddha Kembhavi, Abhinav Gupta, Ali Farhadi
|
AI2-THOR: An Interactive 3D Environment for Visual AI
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce The House Of inteRactions (THOR), a framework for visual AI
research, available at http://ai2thor.allenai.org. AI2-THOR consists of near
photo-realistic 3D indoor scenes, where AI agents can navigate in the scenes
and interact with objects to perform tasks. AI2-THOR enables research in many
different domains including but not limited to deep reinforcement learning,
imitation learning, learning by interaction, planning, visual question
answering, unsupervised representation learning, object detection and
segmentation, and learning models of cognition. The goal of AI2-THOR is to
facilitate building visually intelligent models and push the research forward
in this domain.
|
[
{
"version": "v1",
"created": "Thu, 14 Dec 2017 23:17:24 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Mar 2019 23:45:48 GMT"
},
{
"version": "v3",
"created": "Fri, 15 Mar 2019 18:29:15 GMT"
},
{
"version": "v4",
"created": "Fri, 26 Aug 2022 17:12:17 GMT"
}
] | 2022-08-29T00:00:00 |
[
[
"Kolve",
"Eric",
""
],
[
"Mottaghi",
"Roozbeh",
""
],
[
"Han",
"Winson",
""
],
[
"VanderBilt",
"Eli",
""
],
[
"Weihs",
"Luca",
""
],
[
"Herrasti",
"Alvaro",
""
],
[
"Deitke",
"Matt",
""
],
[
"Ehsani",
"Kiana",
""
],
[
"Gordon",
"Daniel",
""
],
[
"Zhu",
"Yuke",
""
],
[
"Kembhavi",
"Aniruddha",
""
],
[
"Gupta",
"Abhinav",
""
],
[
"Farhadi",
"Ali",
""
]
] |
new_dataset
| 0.997938 |
1802.07944
|
Anthony Labarre
|
Laurent Bulteau and Danny Hermelin and Anthony Labarre and St\'ephane
Vialette
|
The Clever Shopper Problem
|
15 pages, 3 figures, to appear at the 13th International Computer
Science Symposium in Russia (CSR 2018)
| null |
10.1007/978-3-319-90530-3_6
| null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate a variant of the so-called "Internet Shopping Problem"
introduced by Blazewicz et al. (2010), where a customer wants to buy a list of
products at the lowest possible total cost from shops which offer discounts
when purchases exceed a certain threshold. Although the problem is NP-hard, we
provide exact algorithms for several cases, e.g. when each shop sells only two
items, and an FPT algorithm for the number of items, or for the number of shops
when all prices are equal. We complement each result with hardness proofs in
order to draw a tight boundary between tractable and intractable cases.
Finally, we give an approximation algorithm and hardness results for the
problem of maximising the sum of discounts.
|
[
{
"version": "v1",
"created": "Thu, 22 Feb 2018 08:58:30 GMT"
}
] | 2022-08-29T00:00:00 |
[
[
"Bulteau",
"Laurent",
""
],
[
"Hermelin",
"Danny",
""
],
[
"Labarre",
"Anthony",
""
],
[
"Vialette",
"Stéphane",
""
]
] |
new_dataset
| 0.992276 |
2110.02035
|
Adri\`a Salvador Palau
|
David Amat Ol\'ondriz and Pon\c{c} Palau Puigdevall and Adri\`a
Salvador Palau
|
FooDI-ML: a large multi-language dataset of food, drinks and groceries
images and descriptions
| null | null | null | null |
cs.CV cs.CL cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this paper we introduce the FooDI-ML dataset. This dataset contains over
1.5M unique images and over 9.5M store names, product names descriptions, and
collection sections gathered from the Glovo application. The data made
available corresponds to food, drinks and groceries products from 37 countries
in Europe, the Middle East, Africa and Latin America. The dataset comprehends
33 languages, including 870K samples of languages of countries from Eastern
Europe and Western Asia such as Ukrainian and Kazakh, which have been so far
underrepresented in publicly available visio-linguistic datasets. The dataset
also includes widely spoken languages such as Spanish and English. To assist
further research, we include benchmarks over two tasks: text-image retrieval
and conditional image generation.
|
[
{
"version": "v1",
"created": "Tue, 5 Oct 2021 13:33:08 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Aug 2022 11:23:29 GMT"
}
] | 2022-08-29T00:00:00 |
[
[
"Olóndriz",
"David Amat",
""
],
[
"Puigdevall",
"Ponç Palau",
""
],
[
"Palau",
"Adrià Salvador",
""
]
] |
new_dataset
| 0.999893 |
2111.10970
|
Scott Davidoff
|
Rebecca Castano, Tiago Vaquero, Federico Rossi, Vandi Verma, Ellen Van
Wyk, Dan Allard, Bennett Huffmann, Erin M. Murphy, Nihal Dhamani, Robert A.
Hewitt, Scott Davidoff, Rashied Amini, Anthony Barrett, Julie Castillo-Rogez,
Steve A. Chien, Mathieu Choukroun, Alain Dadaian, Raymond Francis, Benjamin
Gorr, Mark Hofstadter, Mitch Ingham, Cristina Sorice and Iain Tierney
|
Operations for Autonomous Spacecraft
|
16 pages, 18 Figures, 1 Table, to be published in IEEE Aerospace 2022
(AeroConf 2022)
|
Proceedings of the 2022 IEEE Aerospace Conference (IEEE AERO
2022), 1-20
|
10.1109/AERO53065.2022.9843352
| null |
cs.RO cs.AI cs.HC cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Onboard autonomy technologies such as planning and scheduling, identification
of scientific targets, and content-based data summarization, will lead to
exciting new space science missions. However, the challenge of operating
missions with such onboard autonomous capabilities has not been studied to a
level of detail sufficient for consideration in mission concepts. These
autonomy capabilities will require changes to current operations processes,
practices, and tools. We have developed a case study to assess the changes
needed to enable operators and scientists to operate an autonomous spacecraft
by facilitating a common model between the ground personnel and the onboard
algorithms. We assess the new operations tools and workflows necessary to
enable operators and scientists to convey their desired intent to the
spacecraft, and to be able to reconstruct and explain the decisions made
onboard and the state of the spacecraft. Mock-ups of these tools were used in a
user study to understand the effectiveness of the processes and tools in
enabling a shared framework of understanding, and in the ability of the
operators and scientists to effectively achieve mission science objectives.
|
[
{
"version": "v1",
"created": "Mon, 22 Nov 2021 03:26:22 GMT"
}
] | 2022-08-29T00:00:00 |
[
[
"Castano",
"Rebecca",
""
],
[
"Vaquero",
"Tiago",
""
],
[
"Rossi",
"Federico",
""
],
[
"Verma",
"Vandi",
""
],
[
"Van Wyk",
"Ellen",
""
],
[
"Allard",
"Dan",
""
],
[
"Huffmann",
"Bennett",
""
],
[
"Murphy",
"Erin M.",
""
],
[
"Dhamani",
"Nihal",
""
],
[
"Hewitt",
"Robert A.",
""
],
[
"Davidoff",
"Scott",
""
],
[
"Amini",
"Rashied",
""
],
[
"Barrett",
"Anthony",
""
],
[
"Castillo-Rogez",
"Julie",
""
],
[
"Chien",
"Steve A.",
""
],
[
"Choukroun",
"Mathieu",
""
],
[
"Dadaian",
"Alain",
""
],
[
"Francis",
"Raymond",
""
],
[
"Gorr",
"Benjamin",
""
],
[
"Hofstadter",
"Mark",
""
],
[
"Ingham",
"Mitch",
""
],
[
"Sorice",
"Cristina",
""
],
[
"Tierney",
"Iain",
""
]
] |
new_dataset
| 0.955969 |
2201.00589
|
Timo H\"ackel
|
Timo H\"ackel, Philipp Meyer, Franz Korf, Thomas C. Schmidt
|
Secure Time-Sensitive Software-Defined Networking in Vehicles
| null | null |
10.1109/TVT.2022.3202368
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current designs of future In-Vehicle Networks (IVN) prepare for switched
Ethernet backbones, which can host advanced LAN technologies such as IEEE
Time-Sensitive Networking (TSN) and Software-Defined Networking (SDN). In this
paper, we present an integrated Time-Sensitive Software-Defined Networking
(TSSDN) architecture that simultaneously enables control of synchronous and
asynchronous real-time and best-effort communication for all IVN traffic
classes. Despite the central SDN controller, we can validate that control can
operate without a delay penalty for TSN traffic, provided protocols are
properly mapped. We demonstrate how TSSDN adaptably and reliably enhances
network security for in-vehicle communication. A systematic investigation of
the possible control flow integrations with switched Ether-networks reveals
that these strategies allow for shaping the attack surface of a
software-defined IVN. We discuss embeddings of control flow identifiers on
different layers, covering the range from a fully exposed mapping to deep
encapsulation. We experimentally evaluate these strategies in a production
vehicle, which we map to a modern Ethernet topology. Our findings indicate that
visibility of automotive control flows on lower network layers enables
isolation and access control throughout the network infrastructure. Such a
TSSDN backbone can establish and survey trust zones within the IVN and reduce
the attack surface of connected cars in various attack scenarios.
|
[
{
"version": "v1",
"created": "Mon, 3 Jan 2022 11:27:28 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Aug 2022 10:05:55 GMT"
}
] | 2022-08-29T00:00:00 |
[
[
"Häckel",
"Timo",
""
],
[
"Meyer",
"Philipp",
""
],
[
"Korf",
"Franz",
""
],
[
"Schmidt",
"Thomas C.",
""
]
] |
new_dataset
| 0.98911 |
2203.13296
|
Kevis-Kokitsi Maninis
|
Micha{\l} J. Tyszkiewicz, Kevis-Kokitsi Maninis, Stefan Popov,
Vittorio Ferrari
|
RayTran: 3D pose estimation and shape reconstruction of multiple objects
from videos with ray-traced transformers
|
ECCV 2022 camera ready
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a transformer-based neural network architecture for multi-object
3D reconstruction from RGB videos. It relies on two alternative ways to
represent its knowledge: as a global 3D grid of features and an array of
view-specific 2D grids. We progressively exchange information between the two
with a dedicated bidirectional attention mechanism. We exploit knowledge about
the image formation process to significantly sparsify the attention weight
matrix, making our architecture feasible on current hardware, both in terms of
memory and computation. We attach a DETR-style head on top of the 3D feature
grid in order to detect the objects in the scene and to predict their 3D pose
and 3D shape. Compared to previous methods, our architecture is single stage,
end-to-end trainable, and it can reason holistically about a scene from
multiple video frames without needing a brittle tracking step. We evaluate our
method on the challenging Scan2CAD dataset, where we outperform (1) recent
state-of-the-art methods for 3D object pose estimation from RGB videos; and (2)
a strong alternative method combining Multi-view Stereo with RGB-D CAD
alignment. We plan to release our source code.
|
[
{
"version": "v1",
"created": "Thu, 24 Mar 2022 18:49:12 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Aug 2022 08:18:52 GMT"
}
] | 2022-08-29T00:00:00 |
[
[
"Tyszkiewicz",
"Michał J.",
""
],
[
"Maninis",
"Kevis-Kokitsi",
""
],
[
"Popov",
"Stefan",
""
],
[
"Ferrari",
"Vittorio",
""
]
] |
new_dataset
| 0.999758 |
2203.15448
|
H\"armel Nestra
|
Dan Bogdanov (1), Joosep J\"a\"ager (1), Peeter Laud (1), H\"armel
Nestra (1), Martin Pettai (1), Jaak Randmets (1), Ville Sokk (1), Kert Tali
(1), Sandhra-Mirella Valdma (1) ((1) Cybernetica AS)
|
ZK-SecreC: a Domain-Specific Language for Zero Knowledge Proofs
|
75 pp
| null | null | null |
cs.PL cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present ZK-SecreC, a domain-specific language for zero-knowledge proofs.
We present the rationale for its design, its syntax and semantics, and
demonstrate its usefulness on the basis of a number of non-trivial examples.
The design features a type system, where each piece of data is assigned both a
confidentiality and an integrity type, which are not orthogonal to each other.
We perform an empiric evaluation of the statements produced by its compiler in
terms of their size. We also show the integration of the compiler with the
implementation of a zero-knowledge proof technique, and evaluate the running
time of both Prover and Verifier.
|
[
{
"version": "v1",
"created": "Tue, 29 Mar 2022 11:35:11 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Aug 2022 13:43:41 GMT"
}
] | 2022-08-29T00:00:00 |
[
[
"Bogdanov",
"Dan",
"",
"Cybernetica AS"
],
[
"Jääger",
"Joosep",
"",
"Cybernetica AS"
],
[
"Laud",
"Peeter",
"",
"Cybernetica AS"
],
[
"Nestra",
"Härmel",
"",
"Cybernetica AS"
],
[
"Pettai",
"Martin",
"",
"Cybernetica AS"
],
[
"Randmets",
"Jaak",
"",
"Cybernetica AS"
],
[
"Sokk",
"Ville",
"",
"Cybernetica AS"
],
[
"Tali",
"Kert",
"",
"Cybernetica AS"
],
[
"Valdma",
"Sandhra-Mirella",
"",
"Cybernetica AS"
]
] |
new_dataset
| 0.999801 |
2204.00907
|
Antoine Lavault
|
Antoine Lavault and Axel Roebel and Matthieu Voiry
|
StyleWaveGAN: Style-based synthesis of drum sounds with extensive
controls using generative adversarial networks
|
Accepted for publication in Sound and Music Computing 2022
| null |
10.5281/zenodo.6573360
| null |
cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper we introduce StyleWaveGAN, a style-based drum sound generator
that is a variation of StyleGAN, a state-of-the-art image generator. By
conditioning StyleWaveGAN on both the type of drum and several audio
descriptors, we are able to synthesize waveforms faster than real-time on a GPU
directly in CD quality up to a duration of 1.5s while retaining a considerable
amount of control over the generation. We also introduce an alternative to the
progressive growing of GANs and experimented on the effect of dataset balancing
for generative tasks. The experiments are carried out on an augmented subset of
a publicly available dataset comprised of different drums and cymbals. We
evaluate against two recent drum generators, WaveGAN and NeuroDrum,
demonstrating significantly improved generation quality (measured with the
Frechet Audio Distance) and interesting results with perceptual features.
|
[
{
"version": "v1",
"created": "Sat, 2 Apr 2022 17:27:17 GMT"
}
] | 2022-08-29T00:00:00 |
[
[
"Lavault",
"Antoine",
""
],
[
"Roebel",
"Axel",
""
],
[
"Voiry",
"Matthieu",
""
]
] |
new_dataset
| 0.999106 |
2205.03911
|
Orian Leitersdorf
|
Adir Kobovich, Orian Leitersdorf, Daniella Bar-Lev, Eitan Yaakobi
|
Codes for Constrained Periodicity
|
Accepted to The International Symposium on Information Theory and Its
Applications (ISITA) 2022
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Reliability is an inherent challenge for the emerging nonvolatile technology
of racetrack memories, and there exists a fundamental relationship between
codes designed for racetrack memories and codes with constrained periodicity.
Previous works have sought to construct codes that avoid periodicity in
windows, yet have either only provided existence proofs or required high
redundancy. This paper provides the first constructions for avoiding
periodicity that are both efficient (average-linear time) and with low
redundancy (near the lower bound). The proposed algorithms are based on
iteratively repairing windows which contain periodicity until all the windows
are valid. Intuitively, such algorithms should not converge as there is no
monotonic progression; yet, we prove convergence with average-linear time
complexity by exploiting subtle properties of the encoder. Overall, we both
provide constructions that avoid periodicity in all windows, and we also study
the cardinality of such constraints.
|
[
{
"version": "v1",
"created": "Sun, 8 May 2022 16:32:17 GMT"
},
{
"version": "v2",
"created": "Thu, 25 Aug 2022 22:31:20 GMT"
}
] | 2022-08-29T00:00:00 |
[
[
"Kobovich",
"Adir",
""
],
[
"Leitersdorf",
"Orian",
""
],
[
"Bar-Lev",
"Daniella",
""
],
[
"Yaakobi",
"Eitan",
""
]
] |
new_dataset
| 0.97066 |
2205.07403
|
Guangsheng Shi
|
Guangsheng Shi, Ruifeng Li and Chao Ma
|
PillarNet: Real-Time and High-Performance Pillar-based 3D Object
Detection
|
ECCV 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Real-time and high-performance 3D object detection is of critical importance
for autonomous driving. Recent top-performing 3D object detectors mainly rely
on point-based or 3D voxel-based convolutions, which are both computationally
inefficient for onboard deployment. In contrast, pillar-based methods use
solely 2D convolutions, which consume less computation resources, but they lag
far behind their voxel-based counterparts in detection accuracy. In this paper,
by examining the primary performance gap between pillar- and voxel-based
detectors, we develop a real-time and high-performance pillar-based detector,
dubbed PillarNet.The proposed PillarNet consists of a powerful encoder network
for effective pillar feature learning, a neck network for spatial-semantic
feature fusion and the commonly used detect head. Using only 2D convolutions,
PillarNet is flexible to an optional pillar size and compatible with classical
2D CNN backbones, such as VGGNet and ResNet. Additionally, PillarNet benefits
from our designed orientation-decoupled IoU regression loss along with the
IoU-aware prediction branch. Extensive experimental results on the large-scale
nuScenes Dataset and Waymo Open Dataset demonstrate that the proposed PillarNet
performs well over state-of-the-art 3D detectors in terms of effectiveness and
efficiency. Code is available at \url{https://github.com/agent-sgs/PillarNet}.
|
[
{
"version": "v1",
"created": "Mon, 16 May 2022 00:14:50 GMT"
},
{
"version": "v2",
"created": "Thu, 19 May 2022 07:37:11 GMT"
},
{
"version": "v3",
"created": "Tue, 31 May 2022 07:52:07 GMT"
},
{
"version": "v4",
"created": "Tue, 14 Jun 2022 14:02:33 GMT"
},
{
"version": "v5",
"created": "Fri, 26 Aug 2022 03:21:15 GMT"
}
] | 2022-08-29T00:00:00 |
[
[
"Shi",
"Guangsheng",
""
],
[
"Li",
"Ruifeng",
""
],
[
"Ma",
"Chao",
""
]
] |
new_dataset
| 0.98363 |
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