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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2206.01094
|
Qichao Ying
|
Yifei Wang, Qichao Ying, Zhenxing Qian, Sheng Li and Xinpeng Zhang
|
A DTCWT-SVD Based Video Watermarking resistant to frame rate conversion
| null | null | null | null |
cs.MM cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Videos can be easily tampered, copied and redistributed by attackers for
illegal and monetary usage. Such behaviors severely jeopardize the interest of
content owners. Despite huge efforts made in digital video watermarking for
copyright protection, typical distortions in video transmission including
signal attacks, geometric attacks and temporal synchronization attacks can
still easily erase the embedded signal. Among them, temporal synchronization
attacks which include frame dropping, frame insertion and frame rate conversion
is one of the most prevalent attacks. To address this issue, we present a new
video watermarking based on joint Dual-Tree Cosine Wavelet Transformation
(DTCWT) and Singular Value Decomposition (SVD), which is resistant to frame
rate conversion. We first extract a set of candidate coefficient by applying
SVD decomposition after DTCWT transform. Then, we simulate the watermark
embedding by adjusting the shape of candidate coefficient. Finally, we perform
group-level watermarking that includes moderate temporal redundancy to resist
temporal desynchronization attacks. Extensive experimental results show that
the proposed scheme is more resilient to temporal desynchronization attacks and
performs better than the existing blind video watermarking schemes.
|
[
{
"version": "v1",
"created": "Thu, 2 Jun 2022 15:20:52 GMT"
}
] | 2022-06-03T00:00:00 |
[
[
"Wang",
"Yifei",
""
],
[
"Ying",
"Qichao",
""
],
[
"Qian",
"Zhenxing",
""
],
[
"Li",
"Sheng",
""
],
[
"Zhang",
"Xinpeng",
""
]
] |
new_dataset
| 0.994924 |
2206.01146
|
R J Cintra
|
H. P. L. Arjuna Madanayake, R. J. Cintra, V. S. Dimitrov, L. Bruton
|
Block-Parallel Systolic-Array Architecture for 2-D NTT-based Fragile
Watermark Embedding
|
11 pages, 4 figures
|
Parallel Processing Letters, vol. 22, no. 03, 1250009, 2012
|
10.1142/S0129626412500090
| null |
cs.MM eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Number-theoretic transforms (NTTs) have been applied in the fragile
watermarking of digital images. A block-parallel systolic-array architecture is
proposed for watermarking based on the 2-D special Hartley NTT (HNTT). The
proposed core employs two 2-D special HNTT hardware cores, each using digital
arithmetic over $\mathrm{GF}(3)$, and processes $4\times4$ blocks of pixels in
parallel every clock cycle. Prototypes are operational on a Xilinx Sx35-10ff668
FPGA device. The maximum estimated throughput of the FPGA circuit is 100
million $4\times4$ HNTT fragile watermarked blocks per second, when clocked at
100 MHz. Potential applications exist in high-traffic back-end servers dealing
with large amounts of protected digital images requiring authentication, in
remote-sensing for high-security surveillance applications, in real-time video
processing of information of a sensitive nature or matters of national
security, in video/photographic content management of corporate clients, in
authenticating multimedia for the entertainment industry, in the authentication
of electronic evidence material, and in real-time news streaming.
|
[
{
"version": "v1",
"created": "Thu, 2 Jun 2022 16:52:54 GMT"
}
] | 2022-06-03T00:00:00 |
[
[
"Madanayake",
"H. P. L. Arjuna",
""
],
[
"Cintra",
"R. J.",
""
],
[
"Dimitrov",
"V. S.",
""
],
[
"Bruton",
"L.",
""
]
] |
new_dataset
| 0.998493 |
2206.01153
|
Ruoyi Du
|
Ruoyi Du, Wenqing Yu, Heqing Wang, Dongliang Chang, Ting-En Lin,
Yongbin Li, Zhanyu Ma
|
Multi-View Active Fine-Grained Recognition
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
As fine-grained visual classification (FGVC) being developed for decades,
great works related have exposed a key direction -- finding discriminative
local regions and revealing subtle differences. However, unlike identifying
visual contents within static images, for recognizing objects in the real
physical world, discriminative information is not only present within seen
local regions but also hides in other unseen perspectives. In other words, in
addition to focusing on the distinguishable part from the whole, for efficient
and accurate recognition, it is required to infer the key perspective with a
few glances, e.g., people may recognize a "Benz AMG GT" with a glance of its
front and then know that taking a look at its exhaust pipe can help to tell
which year's model it is. In this paper, back to reality, we put forward the
problem of active fine-grained recognition (AFGR) and complete this study in
three steps: (i) a hierarchical, multi-view, fine-grained vehicle dataset is
collected as the testbed, (ii) a simple experiment is designed to verify that
different perspectives contribute differently for FGVC and different categories
own different discriminative perspective, (iii) a policy-gradient-based
framework is adopted to achieve efficient recognition with active view
selection. Comprehensive experiments demonstrate that the proposed method
delivers a better performance-efficient trade-off than previous FGVC methods
and advanced neural networks.
|
[
{
"version": "v1",
"created": "Thu, 2 Jun 2022 17:12:14 GMT"
}
] | 2022-06-03T00:00:00 |
[
[
"Du",
"Ruoyi",
""
],
[
"Yu",
"Wenqing",
""
],
[
"Wang",
"Heqing",
""
],
[
"Chang",
"Dongliang",
""
],
[
"Lin",
"Ting-En",
""
],
[
"Li",
"Yongbin",
""
],
[
"Ma",
"Zhanyu",
""
]
] |
new_dataset
| 0.987328 |
2108.07467
|
Chiranjibi Sitaula
|
Chiranjibi Sitaula and Jinyuan He and Archana Priyadarshi and Mark
Tracy and Omid Kavehei and Murray Hinder and Anusha Withana and Alistair
McEwan and Faezeh Marzbanrad
|
Neonatal Bowel Sound Detection Using Convolutional Neural Network and
Laplace Hidden Semi-Markov Model
|
Published in IEEE/ACM Transactions on Audio Speech and Language
Processing journal
|
IEEE/ACM Transactions on Audio, Speech, and Language Processing,
2022
|
10.1109/TASLP.2022.3178225
| null |
cs.SD cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Abdominal auscultation is a convenient, safe and inexpensive method to assess
bowel conditions, which is essential in neonatal care. It helps early detection
of neonatal bowel dysfunctions and allows timely intervention. This paper
presents a neonatal bowel sound detection method to assist the auscultation.
Specifically, a Convolutional Neural Network (CNN) is proposed to classify
peristalsis and non-peristalsis sounds. The classification is then optimized
using a Laplace Hidden Semi-Markov Model (HSMM). The proposed method is
validated on abdominal sounds from 49 newborn infants admitted to our tertiary
Neonatal Intensive Care Unit (NICU). The results show that the method can
effectively detect bowel sounds with accuracy and area under curve (AUC) score
being 89.81% and 83.96% respectively, outperforming 13 baseline methods.
Furthermore, the proposed Laplace HSMM refinement strategy is proven capable to
enhance other bowel sound detection models. The outcomes of this work have the
potential to facilitate future telehealth applications for neonatal care. The
source code of our work can be found at:
https://bitbucket.org/chirudeakin/neonatal-bowel-sound-classification/
|
[
{
"version": "v1",
"created": "Tue, 17 Aug 2021 06:50:17 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Apr 2022 08:59:46 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Jun 2022 01:38:59 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Sitaula",
"Chiranjibi",
""
],
[
"He",
"Jinyuan",
""
],
[
"Priyadarshi",
"Archana",
""
],
[
"Tracy",
"Mark",
""
],
[
"Kavehei",
"Omid",
""
],
[
"Hinder",
"Murray",
""
],
[
"Withana",
"Anusha",
""
],
[
"McEwan",
"Alistair",
""
],
[
"Marzbanrad",
"Faezeh",
""
]
] |
new_dataset
| 0.953092 |
2108.07689
|
Raju Gottumukkala
|
Seyedmajid Hosseini, Satya Katragadda, Ravi Teja Bhupatiraju, Ziad
Ashkar, Christoph W. Borst, Kenneth Cochran, Raju Gottumukkala
|
A multimodal sensor dataset for continuous stress detection of nurses in
a hospital
|
14 pages, 9 images
| null | null | null |
cs.CY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Advances in wearable technologies provide the opportunity to monitor many
physiological variables continuously. Stress detection has gained increased
attention in recent years, mainly because early stress detection can help
individuals better manage health to minimize the negative impacts of long-term
stress exposure. This paper provides a unique stress detection dataset created
in a natural working environment in a hospital. This dataset is a collection of
biometric data of nurses during the COVID-19 outbreak. Studying stress in a
work environment is complex due to many social, cultural, and psychological
factors in dealing with stressful conditions. Therefore, we captured both the
physiological data and associated context pertaining to the stress events. We
monitored specifc physiological variables such as electrodermal activity, Heart
Rate, and skin temperature of the nurse subjects. A periodic
smartphone-administered survey also captured the contributing factors for the
detected stress events. A database containing the signals, stress events, and
survey responses is publicly available on Dryad.
|
[
{
"version": "v1",
"created": "Sun, 25 Jul 2021 22:24:25 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Jun 2022 11:50:32 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Hosseini",
"Seyedmajid",
""
],
[
"Katragadda",
"Satya",
""
],
[
"Bhupatiraju",
"Ravi Teja",
""
],
[
"Ashkar",
"Ziad",
""
],
[
"Borst",
"Christoph W.",
""
],
[
"Cochran",
"Kenneth",
""
],
[
"Gottumukkala",
"Raju",
""
]
] |
new_dataset
| 0.999743 |
2111.00282
|
\'Edouard Bonnet
|
\'Edouard Bonnet, Eun Jung Kim, Amadeus Reinald, St\'ephan Thomass\'e
|
Twin-width VI: the lens of contraction sequences
|
27 pages, 3 figures
| null | null | null |
cs.DS cs.DM cs.LO math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A contraction sequence of a graph consists of iteratively merging two of its
vertices until only one vertex remains. The recently introduced twin-width
graph invariant is based on contraction sequences. More precisely, if one puts
red edges between two vertices representing non-homogeneous subsets, the
twin-width is the minimum integer $d$ such that a contraction sequence keeps
red degree at most $d$. By changing the condition imposed on the trigraphs
(i.e., graphs with some edges being red) and possibly slightly tweaking the
notion of contractions, we show how to characterize the well-established
bounded rank-width, tree-width, linear rank-width, path-width, and proper
minor-closed classes by means of contraction sequences. As an application we
give a transparent alternative proof of the celebrated Courcelle's theorem
(actually of its generalization by Courcelle, Makowsky, and Rotics), that
MSO$_2$ (resp. MSO$_1$) model checking on graphs with bounded tree-width (resp.
bounded rank-width) is fixed-parameter tractable in the size of the input
sentence.
We then explore new avenues along the general theme of contraction sequences
both in order to refine the landscape between bounded tree-width and bounded
twin-width (via spanning twin-width) and to capture more general classes than
bounded twin-width. To this end, we define an oriented version of twin-width,
where appearing red edges are oriented away from the newly contracted vertex,
and the mere red out-degree should remain bounded. Surprisingly, classes of
bounded oriented twin-width coincide with those of bounded twin-width. Finally
we examine, from an algorithmic standpoint, the concept of partial contraction
sequences, where, instead of terminating on a single-vertex graph, the sequence
ends when reaching a particular target class.
|
[
{
"version": "v1",
"created": "Sat, 30 Oct 2021 16:28:03 GMT"
},
{
"version": "v2",
"created": "Tue, 31 May 2022 21:49:10 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Bonnet",
"Édouard",
""
],
[
"Kim",
"Eun Jung",
""
],
[
"Reinald",
"Amadeus",
""
],
[
"Thomassé",
"Stéphan",
""
]
] |
new_dataset
| 0.999804 |
2111.05463
|
Dimitrios Antoniadis
|
Dimitris Antoniadis, Andrea Mifsud, Peilong Feng, Timothy G.
Constandinou
|
An Open-Source RRAM Compiler
|
Final Version of NEWCAS 2022. 5 pages
| null | null | null |
cs.ET
|
http://creativecommons.org/licenses/by/4.0/
|
Memory compilers are necessary tools to boost the design procedure of digital
circuits. However, only a few are available to academia. Resistive Random
Access Memory (RRAM) is characterised by high density, high speed, non
volatility and is a potential candidate of future digital memories. To the best
of the authors' knowledge, this paper presents the first open source RRAM
compiler for automatic memory generation including its peripheral circuits,
verification and timing characterisation. The RRAM compiler is written with
Cadence SKILL programming language and is integrated in Cadence environment.
The layout verification procedure takes place in Siemens Mentor Calibre tool.
The technology used by the compiler is TSMC 180nm. This paper analyses the
novel results of a plethora of M x N RRAMs generated by the compiler, up to M =
128, N = 64 and word size B = 16 bits, for clock frequency equal to 12.5 MHz.
Finally, the compiler achieves density of up to 0.024 Mb/mm2.
|
[
{
"version": "v1",
"created": "Wed, 10 Nov 2021 00:10:42 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Feb 2022 20:14:14 GMT"
},
{
"version": "v3",
"created": "Thu, 17 Feb 2022 22:31:12 GMT"
},
{
"version": "v4",
"created": "Tue, 31 May 2022 21:09:14 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Antoniadis",
"Dimitris",
""
],
[
"Mifsud",
"Andrea",
""
],
[
"Feng",
"Peilong",
""
],
[
"Constandinou",
"Timothy G.",
""
]
] |
new_dataset
| 0.999197 |
2205.05883
|
Damla Senol Cali
|
Damla Senol Cali, Konstantinos Kanellopoulos, Joel Lindegger, Z\"ulal
Bing\"ol, Gurpreet S. Kalsi, Ziyi Zuo, Can Firtina, Meryem Banu Cavlak,
Jeremie Kim, Nika Mansouri Ghiasi, Gagandeep Singh, Juan G\'omez-Luna, Nour
Almadhoun Alserr, Mohammed Alser, Sreenivas Subramoney, Can Alkan, Saugata
Ghose, Onur Mutlu
|
SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph
and Sequence-to-Sequence Mapping
|
To appear in ISCA'22
| null |
10.1145/3470496.3527436
| null |
cs.AR q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A critical step of genome sequence analysis is the mapping of sequenced DNA
fragments (i.e., reads) collected from an individual to a known linear
reference genome sequence (i.e., sequence-to-sequence mapping). Recent works
replace the linear reference sequence with a graph-based representation of the
reference genome, which captures the genetic variations and diversity across
many individuals in a population. Mapping reads to the graph-based reference
genome (i.e., sequence-to-graph mapping) results in notable quality
improvements in genome analysis. Unfortunately, while sequence-to-sequence
mapping is well studied with many available tools and accelerators,
sequence-to-graph mapping is a more difficult computational problem, with a
much smaller number of practical software tools currently available.
We analyze two state-of-the-art sequence-to-graph mapping tools and reveal
four key issues. We find that there is a pressing need to have a specialized,
high-performance, scalable, and low-cost algorithm/hardware co-design that
alleviates bottlenecks in both the seeding and alignment steps of
sequence-to-graph mapping.
To this end, we propose SeGraM, a universal algorithm/hardware co-designed
genomic mapping accelerator that can effectively and efficiently support both
sequence-to-graph mapping and sequence-to-sequence mapping, for both short and
long reads. To our knowledge, SeGraM is the first algorithm/hardware co-design
for accelerating sequence-to-graph mapping. SeGraM consists of two main
components: (1) MinSeed, the first minimizer-based seeding accelerator; and (2)
BitAlign, the first bitvector-based sequence-to-graph alignment accelerator.
We demonstrate that SeGraM provides significant improvements for multiple
steps of the sequence-to-graph and sequence-to-sequence mapping pipelines.
|
[
{
"version": "v1",
"created": "Thu, 12 May 2022 05:27:26 GMT"
},
{
"version": "v2",
"created": "Tue, 31 May 2022 18:29:57 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Cali",
"Damla Senol",
""
],
[
"Kanellopoulos",
"Konstantinos",
""
],
[
"Lindegger",
"Joel",
""
],
[
"Bingöl",
"Zülal",
""
],
[
"Kalsi",
"Gurpreet S.",
""
],
[
"Zuo",
"Ziyi",
""
],
[
"Firtina",
"Can",
""
],
[
"Cavlak",
"Meryem Banu",
""
],
[
"Kim",
"Jeremie",
""
],
[
"Ghiasi",
"Nika Mansouri",
""
],
[
"Singh",
"Gagandeep",
""
],
[
"Gómez-Luna",
"Juan",
""
],
[
"Alserr",
"Nour Almadhoun",
""
],
[
"Alser",
"Mohammed",
""
],
[
"Subramoney",
"Sreenivas",
""
],
[
"Alkan",
"Can",
""
],
[
"Ghose",
"Saugata",
""
],
[
"Mutlu",
"Onur",
""
]
] |
new_dataset
| 0.999257 |
2205.06911
|
Wen Zhang
|
Wen Zhang, Eric Sheng, Michael Chang, Aurojit Panda, Mooly Sagiv,
Scott Shenker
|
Blockaid: Data Access Policy Enforcement for Web Applications
|
Extended technical report for OSDI 2022 paper
| null | null | null |
cs.DB
|
http://creativecommons.org/licenses/by/4.0/
|
Modern web applications serve large amounts of sensitive user data, access to
which is typically governed by data-access policies. Enforcing such policies is
crucial to preventing improper data access, and prior work has proposed many
enforcement mechanisms. However, these prior methods either alter application
semantics or require adopting a new programming model; the former can result in
unexpected application behavior, while the latter cannot be used with existing
web frameworks.
Blockaid is an access-policy enforcement system that preserves application
semantics and is compatible with existing web frameworks. It intercepts
database queries from the application, attempts to verify that each query is
policy-compliant, and blocks queries that are not. It verifies policy
compliance using SMT solvers and generalizes and caches previous compliance
decisions for better performance. We show that Blockaid supports existing web
applications while requiring minimal code changes and adding only modest
overheads.
|
[
{
"version": "v1",
"created": "Fri, 13 May 2022 22:13:15 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Jun 2022 01:59:02 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Zhang",
"Wen",
""
],
[
"Sheng",
"Eric",
""
],
[
"Chang",
"Michael",
""
],
[
"Panda",
"Aurojit",
""
],
[
"Sagiv",
"Mooly",
""
],
[
"Shenker",
"Scott",
""
]
] |
new_dataset
| 0.994182 |
2205.15951
|
Nihar Ranjan Sahoo
|
Sandhya Singh, Prapti Roy, Nihar Sahoo, Niteesh Mallela, Himanshu
Gupta, Pushpak Bhattacharyya, Milind Savagaonkar, Nidhi, Roshni Ramnani,
Anutosh Maitra, Shubhashis Sengupta
|
Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of
Movie Dialogues
| null | null | null | null |
cs.CL cs.CY cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Movies reflect society and also hold power to transform opinions. Social
biases and stereotypes present in movies can cause extensive damage due to
their reach. These biases are not always found to be the need of storyline but
can creep in as the author's bias. Movie production houses would prefer to
ascertain that the bias present in a script is the story's demand. Today, when
deep learning models can give human-level accuracy in multiple tasks, having an
AI solution to identify the biases present in the script at the writing stage
can help them avoid the inconvenience of stalled release, lawsuits, etc. Since
AI solutions are data intensive and there exists no domain specific data to
address the problem of biases in scripts, we introduce a new dataset of movie
scripts that are annotated for identity bias. The dataset contains dialogue
turns annotated for (i) bias labels for seven categories, viz., gender,
race/ethnicity, religion, age, occupation, LGBTQ, and other, which contains
biases like body shaming, personality bias, etc. (ii) labels for sensitivity,
stereotype, sentiment, emotion, emotion intensity, (iii) all labels annotated
with context awareness, (iv) target groups and reason for bias labels and (v)
expert-driven group-validation process for high quality annotations. We also
report various baseline performances for bias identification and category
detection on our dataset.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 16:49:51 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Jun 2022 05:43:53 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Singh",
"Sandhya",
""
],
[
"Roy",
"Prapti",
""
],
[
"Sahoo",
"Nihar",
""
],
[
"Mallela",
"Niteesh",
""
],
[
"Gupta",
"Himanshu",
""
],
[
"Bhattacharyya",
"Pushpak",
""
],
[
"Savagaonkar",
"Milind",
""
],
[
"Nidhi",
"",
""
],
[
"Ramnani",
"Roshni",
""
],
[
"Maitra",
"Anutosh",
""
],
[
"Sengupta",
"Shubhashis",
""
]
] |
new_dataset
| 0.998905 |
2205.15972
|
Hao Yang
|
Hao Yang, Yang Xu, Yong Li, Hyun-Deok Choi
|
K-Detector: Identifying Duplicate Crash Failures in Large-Scale Software
Delivery
|
6 pages, 7 figures, ISSRE 2020
| null |
10.1109/ISSREW51248.2020.00028
| null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
After a developer submits code, corresponding test cases arise to ensure the
quality of software delivery. Test failures would occur during this period,
such as crash, error, and timeout. Since it takes time for developers to
resolve them, many duplicate failures will happen during this period. In the
delivery practice of SAP HANA, crash triage is considered as the most
time-consuming task. If duplicate crash failures can be automatically
identified, the degree of automation will be significantly enhanced. To find
such duplicates, we propose a training-based mathematical model that utilizes
component information of SAP HANA to achieve better crash similarity
comparison. We implement our approach in a tool named Knowledge-based Detector
(K-Detector), which is verified by 11,208 samples and performs 0.986 in AUC.
Furthermore, we have deployed K-Detector to the production environment, and it
can save 97% human efforts in crash triage as statistics.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 17:28:01 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Jun 2022 03:31:40 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Yang",
"Hao",
""
],
[
"Xu",
"Yang",
""
],
[
"Li",
"Yong",
""
],
[
"Choi",
"Hyun-Deok",
""
]
] |
new_dataset
| 0.997703 |
2206.00092
|
Fereshteh Shakeri
|
Fereshteh Shakeri, Malik Boudiaf, Sina Mohammadi, Ivaxi Sheth,
Mohammad Havaei, Ismail Ben Ayed, Samira Ebrahimi Kahou
|
FHIST: A Benchmark for Few-shot Classification of Histological Images
|
Code available at: https://github.com/mboudiaf/Few-shot-histology
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Few-shot learning has recently attracted wide interest in image
classification, but almost all the current public benchmarks are focused on
natural images. The few-shot paradigm is highly relevant in medical-imaging
applications due to the scarcity of labeled data, as annotations are expensive
and require specialized expertise. However, in medical imaging, few-shot
learning research is sparse, limited to private data sets and is at its early
stage. In particular, the few-shot setting is of high interest in histology due
to the diversity and fine granularity of cancer related tissue classification
tasks, and the variety of data-preparation techniques. This paper introduces a
highly diversified public benchmark, gathered from various public datasets, for
few-shot histology data classification. We build few-shot tasks and
base-training data with various tissue types, different levels of domain shifts
stemming from various cancer sites, and different class-granularity levels,
thereby reflecting realistic scenarios. We evaluate the performances of
state-of-the-art few-shot learning methods on our benchmark, and observe that
simple fine-tuning and regularization methods achieve better results than the
popular meta-learning and episodic-training paradigm. Furthermore, we introduce
three scenarios based on the domain shifts between the source and target
histology data: near-domain, middle-domain and out-domain. Our experiments
display the potential of few-shot learning in histology classification, with
state-of-art few shot learning methods approaching the supervised-learning
baselines in the near-domain setting. In our out-domain setting, for 5-way
5-shot, the best performing method reaches 60% accuracy. We believe that our
work could help in building realistic evaluations and fair comparisons of
few-shot learning methods and will further encourage research in the few-shot
paradigm.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 20:03:40 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Shakeri",
"Fereshteh",
""
],
[
"Boudiaf",
"Malik",
""
],
[
"Mohammadi",
"Sina",
""
],
[
"Sheth",
"Ivaxi",
""
],
[
"Havaei",
"Mohammad",
""
],
[
"Ayed",
"Ismail Ben",
""
],
[
"Kahou",
"Samira Ebrahimi",
""
]
] |
new_dataset
| 0.998056 |
2206.00101
|
Debopriya Roy Dipta
|
Debopriya Roy Dipta and Berk Gulmezoglu
|
MAD-EN: Microarchitectural Attack Detection through System-wide Energy
Consumption
| null | null | null | null |
cs.CR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Microarchitectural attacks have become more threatening the hardware security
than before with the increasing diversity of attacks such as Spectre and
Meltdown. Vendor patches cannot keep up with the pace of the new threats, which
makes the dynamic anomaly detection tools more evident than before.
Unfortunately, previous studies utilize hardware performance counters that lead
to high performance overhead and profile limited number of microarchitectural
attacks due to the small number of counters that can be profiled concurrently.
This yields those detection tools inefficient in real-world scenarios.
In this study, we introduce MAD-EN dynamic detection tool that leverages
system-wide energy consumption traces collected from a generic Intel RAPL tool
to detect ongoing anomalies in a system. In our experiments, we show that
CNN-based MAD-EN can detect 10 different microarchitectural attacks with a
total of 15 variants with the highest F1 score of 0.999, which makes our tool
the most generic attack detection tool so far. Moreover, individual attacks can
be distinguished with a 98% accuracy after an anomaly is detected in a system.
We demonstrate that MAD-EN introduces 69.3% less performance overhead compared
to performance counter-based detection mechanisms.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 20:25:21 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Dipta",
"Debopriya Roy",
""
],
[
"Gulmezoglu",
"Berk",
""
]
] |
new_dataset
| 0.998224 |
2206.00130
|
Jaein Lim
|
Jaein Lim and Panagiotis Tsiotras
|
CBS-Budget (CBSB): A Complete and Bounded Suboptimal Search for
Multi-Agent Path Finding
| null | null | null | null |
cs.MA cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multi-Agent Path Finding (MAPF) is the problem of finding a collection of
collision-free paths for a team of multiple agents while minimizing some global
cost, such as the sum of the time travelled by all agents, or the time
travelled by the last agent. Conflict Based Search (CBS) is a leading complete
and optimal MAPF solver which lazily explores the joint agent state space,
using an admissible heuristic joint plan. Such an admissible heuristic joint
plan is computed by combining individual shortest paths found without
considering inter-agent conflicts, and which becomes gradually more informed as
constraints are added to individual agents' path planning problems to avoid
discovered conflicts. In this paper, we seek to speedup CBS by finding a more
informed heuristic joint plan which is bounded from above. We first propose the
budgeted Class-Ordered A* (bCOA*), a novel algorithm that finds the shortest
path with minimal number of conflicts that is upper bounded in terms of length.
Then, we propose a novel bounded-cost variant of CBS, called CBS-Budget (CBSB)
by using a bCOA* search at the low-level search of the CBS and by using a
modified focal search at the high-level search of the CBS. We prove that CBSB
is complete and bounded-suboptimal. In our numerical experiments, CBSB finds a
near optimal solution for hundreds of agents within a fraction of a second.
CBSB shows state-of-the-art performance, comparable to Explicit Estimation CBS
(EECBS), an enhanced recent version of CBS. On the other hand, CBSB is easier
to implement than EECBS, since only two priority queues at the high-level
search are needed as in Enhanced CBS (ECBS).
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 22:22:33 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Lim",
"Jaein",
""
],
[
"Tsiotras",
"Panagiotis",
""
]
] |
new_dataset
| 0.972493 |
2206.00142
|
Artem Zholus
|
Artem Zholus, Alexey Skrynnik, Shrestha Mohanty, Zoya Volovikova,
Julia Kiseleva, Artur Szlam, Marc-Alexandre Cot\'e, Aleksandr I. Panov
|
IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents
| null | null | null | null |
cs.LG cs.AI cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present the IGLU Gridworld: a reinforcement learning environment for
building and evaluating language conditioned embodied agents in a scalable way.
The environment features visual agent embodiment, interactive learning through
collaboration, language conditioned RL, and combinatorically hard task (3d
blocks building) space.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 23:08:22 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Zholus",
"Artem",
""
],
[
"Skrynnik",
"Alexey",
""
],
[
"Mohanty",
"Shrestha",
""
],
[
"Volovikova",
"Zoya",
""
],
[
"Kiseleva",
"Julia",
""
],
[
"Szlam",
"Artur",
""
],
[
"Coté",
"Marc-Alexandre",
""
],
[
"Panov",
"Aleksandr I.",
""
]
] |
new_dataset
| 0.996969 |
2206.00251
|
Guillermo P\'erez
|
Swen Jacobs, Guillermo A. Perez, Remco Abraham, Veronique Bruyere,
Michael Cadilhac, Maximilien Colange, Charly Delfosse, Tom van Dijk,
Alexandre Duret-Lutz, Peter Faymonville, Bernd Finkbeiner, Ayrat Khalimov,
Felix Klein, Michael Luttenberger, Klara Meyer, Thibaud Michaud, Adrien
Pommellet, Florian Renkin, Philipp Schlehuber-Caissier, Mouhammad Sakr,
Salomon Sickert, Gaetan Staquet, Clement Tamines, Leander Tentrup, Adam
Walker
|
The Reactive Synthesis Competition (SYNTCOMP): 2018-2021
| null | null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
We report on the last four editions of the reactive synthesis competition
(SYNTCOMP 2018-2021). We briefly describe the evaluation scheme and the
experimental setup of SYNTCOMP. Then, we introduce new benchmark classes that
have been added to the SYNTCOMP library and give an overview of the
participants of SYNTCOMP. Finally, we present and analyze the results of our
experimental evaluations, including a ranking of tools with respect to quantity
and quality of solutions.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 06:28:01 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Jacobs",
"Swen",
""
],
[
"Perez",
"Guillermo A.",
""
],
[
"Abraham",
"Remco",
""
],
[
"Bruyere",
"Veronique",
""
],
[
"Cadilhac",
"Michael",
""
],
[
"Colange",
"Maximilien",
""
],
[
"Delfosse",
"Charly",
""
],
[
"van Dijk",
"Tom",
""
],
[
"Duret-Lutz",
"Alexandre",
""
],
[
"Faymonville",
"Peter",
""
],
[
"Finkbeiner",
"Bernd",
""
],
[
"Khalimov",
"Ayrat",
""
],
[
"Klein",
"Felix",
""
],
[
"Luttenberger",
"Michael",
""
],
[
"Meyer",
"Klara",
""
],
[
"Michaud",
"Thibaud",
""
],
[
"Pommellet",
"Adrien",
""
],
[
"Renkin",
"Florian",
""
],
[
"Schlehuber-Caissier",
"Philipp",
""
],
[
"Sakr",
"Mouhammad",
""
],
[
"Sickert",
"Salomon",
""
],
[
"Staquet",
"Gaetan",
""
],
[
"Tamines",
"Clement",
""
],
[
"Tentrup",
"Leander",
""
],
[
"Walker",
"Adam",
""
]
] |
new_dataset
| 0.993817 |
2206.00266
|
Hyungtae Lim
|
Dong-Uk Seo, Hyungtae Lim, Seungjae Lee, Hyun Myung
|
PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry
|
7 pages, 5 figures, conference
| null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Numerous researchers have conducted studies to achieve fast and robust
ground-optimized LiDAR odometry methods for terrestrial mobile platforms. In
particular, ground-optimized LiDAR odometry usually employs ground segmentation
as a preprocessing method. This is because most of the points in a 3D point
cloud captured by a 3D LiDAR sensor on a terrestrial platform are from the
ground. However, the effect of the performance of ground segmentation on LiDAR
odometry is still not closely examined. In this paper, a robust
ground-optimized LiDAR odometry framework is proposed to facilitate the study
to check the effect of ground segmentation on LiDAR SLAM based on the
state-of-the-art (SOTA) method. By using our proposed odometry framework, it is
easy and straightforward to test whether ground segmentation algorithms help
extract well-described features and thus improve SLAM performance. In addition,
by leveraging the SOTA ground segmentation method called Patchwork, which shows
robust ground segmentation even in complex and uneven urban environments with
little performance perturbation, a novel ground-optimized LiDAR odometry is
proposed, called PaGO-LOAM. The methods were tested using the KITTI odometry
dataset. \textit{PaGO-LOAM} shows robust and accurate performance compared with
the baseline method. Our code is available at
https://github.com/url-kaist/AlterGround-LeGO-LOAM.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 06:50:44 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Seo",
"Dong-Uk",
""
],
[
"Lim",
"Hyungtae",
""
],
[
"Lee",
"Seungjae",
""
],
[
"Myung",
"Hyun",
""
]
] |
new_dataset
| 0.968522 |
2206.00279
|
Depeng Liu
|
Depeng Liu, Lutan Zhao, Pengfei Yang, Bow-Yaw Wang, Rui Hou, Lijun
Zhang, Naijun Zhan
|
Defensive Design of Saturating Counters Based on Differential Privacy
| null | null | null | null |
cs.CR cs.FL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The saturating counter is the basic module of the dynamic branch predictor,
which involves the core technique to improve instruction level parallelism
performance in modern processors. However, most studies focus on the
performance improvement and hardware consumption of saturating counters, while
ignoring the security problems they may cause. In this paper, we creatively
propose to study and design saturating counters from the defense perspective of
differential privacy, so that attackers cannot distinguish the states that
saturating counters are in and further infer sensitive information. To obtain
theoretical guarantees, we use Markov chain to formalize the attack algorithm
applied to the saturating counter, investigate into the optimal attack strategy
and calculate the probability of successful attack. Furthermore, we find that
the attacker is able to accurately guess the branch execution of the victim's
process in the existing saturating counters. To avoid this, we design a new
probabilistic saturating counter, which generalizes the existing conventional
and probabilistic saturating counters. The guarantee of differential privacy is
applied to deduce parameters of the new saturating counters so that the
security requirement can be satisfied. We also theoretically calculate the
misprediction rate when the saturating counter reaches the steady state. The
experimental results on testing programs show that the calculated theoretical
results agree with the experimental performances. Compared with the existing
conventional and probabilistic saturating counters, when the parameters of our
designed models are selected appropriately, the new saturating counters can not
only ensure similar operational performance, but also establish strict security
guarantee.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 07:19:31 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Liu",
"Depeng",
""
],
[
"Zhao",
"Lutan",
""
],
[
"Yang",
"Pengfei",
""
],
[
"Wang",
"Bow-Yaw",
""
],
[
"Hou",
"Rui",
""
],
[
"Zhang",
"Lijun",
""
],
[
"Zhan",
"Naijun",
""
]
] |
new_dataset
| 0.996678 |
2206.00304
|
Alberto Sanfeliu
|
J. E. Dominguez-Vidal, Nicolas Rodriguez, Rene Alquezar and Alberto
Sanfeliu
|
Perception-Intention-Action Cycle in Human-Robot Collaborative Tasks
| null | null | null | null |
cs.RO cs.AI cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this work we argue that in Human-Robot Collaboration (HRC) tasks, the
Perception-Action cycle in HRC tasks can not fully explain the collaborative
behaviour of the human and robot and it has to be extended to
Perception-Intention-Action cycle, where Intention is a key topic. In some
cases, agent Intention can be perceived or inferred by the other agent, but in
others, it has to be explicitly informed to the other agent to succeed the goal
of the HRC task. The Perception-Intention-Action cycle includes three basic
functional procedures: Perception-Intention, Situation Awareness and Action.
The Perception and the Intention are the input of the Situation Awareness,
which evaluates the current situation and projects it, into the future
situation. The agents receive this information, plans and agree with the
actions to be executed and modify their action roles while perform the HRC
task. In this work, we validate the Perception-Intention-Action cycle in a
joint object transportation task, modeling the Perception-Intention-Action
cycle through a force model which uses real life and social forces. The
perceived world is projected into a force world and the human intention
(perceived or informed) is also modelled as a force that acts in the HRC task.
Finally, we show that the action roles (master-slave, collaborative, neutral or
adversary) are intrinsic to any HRC task and they appear in the different steps
of a collaborative sequence of actions performed during the task.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 08:13:39 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Dominguez-Vidal",
"J. E.",
""
],
[
"Rodriguez",
"Nicolas",
""
],
[
"Alquezar",
"Rene",
""
],
[
"Sanfeliu",
"Alberto",
""
]
] |
new_dataset
| 0.995783 |
2206.00325
|
Kun Wang
|
Yu Fu, Xueyuan Duan, Kun Wang, Bin Li
|
LDoS attack detection method based on traffic time-frequency
characteristics
| null | null | null | null |
cs.CR cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For the traditional denial-of-service attack detection methods have complex
algorithms and high computational overhead, which are difficult to meet the
demand of online detection; and the experimental environment is mostly a
simulation platform, which is difficult to deploy in real network environment,
we propose a real network environment-oriented LDoS attack detection method
based on the time-frequency characteristics of traffic data. All the traffic
data flowing through the Web server is obtained through the acquisition storage
system, and the detection data set is constructed using pre-processing; the
simple features of the flow fragments are used as input, and the deep neural
network is used to learn the time-frequency domain features of normal traffic
features and generate reconstructed sequences, and the LDoS attack is
discriminated based on the differences between the reconstructed sequences and
the input data in the time-frequency domain. The experimental results show that
the proposed method can accurately detect the attack features in the flow
fragments in a very short time and achieve high detection accuracy for complex
and diverse LDoS attacks; since only the statistical features of the packets
are used, there is no need to parse the packet data, which can be adapted to
different network environments.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 08:39:48 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Fu",
"Yu",
""
],
[
"Duan",
"Xueyuan",
""
],
[
"Wang",
"Kun",
""
],
[
"Li",
"Bin",
""
]
] |
new_dataset
| 0.997481 |
2206.00372
|
Nauros Romim
|
Nauros Romim, Mosahed Ahmed, Md. Saiful Islam, Arnab Sen Sharma,
Hriteshwar Talukder, Mohammad Ruhul Amin
|
BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate
Speech in Different Social Contexts
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Social media platforms and online streaming services have spawned a new breed
of Hate Speech (HS). Due to the massive amount of user-generated content on
these sites, modern machine learning techniques are found to be feasible and
cost-effective to tackle this problem. However, linguistically diverse datasets
covering different social contexts in which offensive language is typically
used are required to train generalizable models. In this paper, we identify the
shortcomings of existing Bangla HS datasets and introduce a large manually
labeled dataset BD-SHS that includes HS in different social contexts. The
labeling criteria were prepared following a hierarchical annotation process,
which is the first of its kind in Bangla HS to the best of our knowledge. The
dataset includes more than 50,200 offensive comments crawled from online social
networking sites and is at least 60% larger than any existing Bangla HS
datasets. We present the benchmark result of our dataset by training different
NLP models resulting in the best one achieving an F1-score of 91.0%. In our
experiments, we found that a word embedding trained exclusively using 1.47
million comments from social media and streaming sites consistently resulted in
better modeling of HS detection in comparison to other pre-trained embeddings.
Our dataset and all accompanying codes is publicly available at
github.com/naurosromim/hate-speech-dataset-for-Bengali-social-media
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 10:10:15 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Romim",
"Nauros",
""
],
[
"Ahmed",
"Mosahed",
""
],
[
"Islam",
"Md. Saiful",
""
],
[
"Sharma",
"Arnab Sen",
""
],
[
"Talukder",
"Hriteshwar",
""
],
[
"Amin",
"Mohammad Ruhul",
""
]
] |
new_dataset
| 0.999805 |
2206.00376
|
Giuseppe Romana
|
Antonio Restivo, Giuseppe Romana, Marinella Sciortino
|
String Attractors and Infinite Words
| null | null | null | null |
cs.FL cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
The notion of string attractor has been introduced in [Kempa and Prezza,
2018] in the context of Data Compression and it represents a set of positions
of a finite word in which all of its factors can be "attracted". The smallest
size $\gamma^*$ of a string attractor for a finite word is a lower bound for
several repetitiveness measures associated with the most common compression
schemes, including BWT-based and LZ-based compressors. The combinatorial
properties of the measure $\gamma^*$ have been studied in [Mantaci et al.,
2021]. Very recently, a complexity measure, called string attractor profile
function, has been introduced for infinite words, by evaluating $\gamma^*$ on
each prefix. Such a measure has been studied for automatic sequences and
linearly recurrent infinite words [Schaeffer and Shallit, 2021]. In this paper,
we study the relationship between such a complexity measure and other
well-known combinatorial notions related to repetitiveness in the context of
infinite words, such as the factor complexity and the recurrence. Furthermore,
we introduce new string attractor-based complexity measures, in which the
structure and the distribution of positions in a string attractor of the
prefixes of infinite words are considered. We show that such measures provide a
finer classification of some infinite families of words.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 10:22:59 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Restivo",
"Antonio",
""
],
[
"Romana",
"Giuseppe",
""
],
[
"Sciortino",
"Marinella",
""
]
] |
new_dataset
| 0.995543 |
2206.00437
|
Miryam de Lhoneux
|
Heather Lent, Kelechi Ogueji, Miryam de Lhoneux, Orevaoghene Ahia,
Anders S{\o}gaard
|
What a Creole Wants, What a Creole Needs
|
LREC 2022
| null | null | null |
cs.CL cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, the natural language processing (NLP) community has given
increased attention to the disparity of efforts directed towards high-resource
languages over low-resource ones. Efforts to remedy this delta often begin with
translations of existing English datasets into other languages. However, this
approach ignores that different language communities have different needs. We
consider a group of low-resource languages, Creole languages. Creoles are both
largely absent from the NLP literature, and also often ignored by society at
large due to stigma, despite these languages having sizable and vibrant
communities. We demonstrate, through conversations with Creole experts and
surveys of Creole-speaking communities, how the things needed from language
technology can change dramatically from one language to another, even when the
languages are considered to be very similar to each other, as with Creoles. We
discuss the prominent themes arising from these conversations, and ultimately
demonstrate that useful language technology cannot be built without involving
the relevant community.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 12:22:34 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Lent",
"Heather",
""
],
[
"Ogueji",
"Kelechi",
""
],
[
"de Lhoneux",
"Miryam",
""
],
[
"Ahia",
"Orevaoghene",
""
],
[
"Søgaard",
"Anders",
""
]
] |
new_dataset
| 0.984131 |
2206.00462
|
Xiuxin Tang
|
Xiuxin Tang and Rong Luo
|
MDS and AMDS symbol-pair codes constructed from repeated-root codes
|
22 pages. arXiv admin note: substantial text overlap with
arXiv:2204.02670
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Symbol-pair codes introduced by Cassuto and Blaum in 2010 are designed to
protect against the pair errors in symbol-pair read channels. One of the
central themes in symbol-error correction is the construction of maximal
distance separable (MDS) symbol-pair codes that possess the largest possible
pair-error correcting performance. Based on repeated-root cyclic codes, we
construct two classes of MDS symbol-pair codes for more general generator
polynomials and also give a new class of almost MDS (AMDS) symbol-pair codes
with the length $lp$. In addition, we derive all MDS and AMDS symbol-pair codes
with length $3p$, when the degree of the generator polynomials is no more than
10. The main results are obtained by determining the solutions of certain
equations over finite fields.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 12:51:47 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Tang",
"Xiuxin",
""
],
[
"Luo",
"Rong",
""
]
] |
new_dataset
| 0.998852 |
2206.00491
|
David Gillsj\"o
|
David Gillsj\"o, Gabrielle Flood, Kalle {\AA}str\"om
|
Semantic Room Wireframe Detection from a Single View
|
Accepted for ICPR2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reconstruction of indoor surfaces with limited texture information or with
repeated textures, a situation common in walls and ceilings, may be difficult
with a monocular Structure from Motion system. We propose a Semantic Room
Wireframe Detection task to predict a Semantic Wireframe from a single
perspective image. Such predictions may be used with shape priors to estimate
the Room Layout and aid reconstruction. To train and test the proposed
algorithm we create a new set of annotations from the simulated Structured3D
dataset. We show qualitatively that the SRW-Net handles complex room geometries
better than previous Room Layout Estimation algorithms while quantitatively
out-performing the baseline in non-semantic Wireframe Detection.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 13:44:50 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Gillsjö",
"David",
""
],
[
"Flood",
"Gabrielle",
""
],
[
"Åström",
"Kalle",
""
]
] |
new_dataset
| 0.999314 |
2206.00524
|
Tran Khanh Quoc
|
Khanh Q. Tran and An T. Nguyen and Phu Gia Hoang and Canh Duc Luu and
Trong-Hop Do and Kiet Van Nguyen
|
Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social
Media Streaming Data
| null | null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Society needs to develop a system to detect hate and offense to build a
healthy and safe environment. However, current research in this field still
faces four major shortcomings, including deficient pre-processing techniques,
indifference to data imbalance issues, modest performance models, and lacking
practical applications. This paper focused on developing an intelligent system
capable of addressing these shortcomings. Firstly, we proposed an efficient
pre-processing technique to clean comments collected from Vietnamese social
media. Secondly, a novel hate speech detection (HSD) model, which is the
combination of a pre-trained PhoBERT model and a Text-CNN model, was proposed
for solving tasks in Vietnamese. Thirdly, EDA techniques are applied to deal
with imbalanced data to improve the performance of classification models.
Besides, various experiments were conducted as baselines to compare and
investigate the proposed model's performance against state-of-the-art methods.
The experiment results show that the proposed PhoBERT-CNN model outperforms
SOTA methods and achieves an F1-score of 67,46% and 98,45% on two benchmark
datasets, ViHSD and HSD-VLSP, respectively. Finally, we also built a streaming
HSD application to demonstrate the practicality of our proposed system.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 14:33:25 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Tran",
"Khanh Q.",
""
],
[
"Nguyen",
"An T.",
""
],
[
"Hoang",
"Phu Gia",
""
],
[
"Luu",
"Canh Duc",
""
],
[
"Do",
"Trong-Hop",
""
],
[
"Van Nguyen",
"Kiet",
""
]
] |
new_dataset
| 0.960733 |
2206.00527
|
Jasmin Breitenstein
|
Jasmin Breitenstein and Tim Fingscheidt
|
Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic
Segmentation Challenge Baseline
|
This paper is accepted at IEEE Intelligent Vehicles Symposium 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Amodal perception terms the ability of humans to imagine the entire shapes of
occluded objects. This gives humans an advantage to keep track of everything
that is going on, especially in crowded situations. Typical perception
functions, however, lack amodal perception abilities and are therefore at a
disadvantage in situations with occlusions. Complex urban driving scenarios
often experience many different types of occlusions and, therefore, amodal
perception for automated vehicles is an important task to investigate. In this
paper, we consider the task of amodal semantic segmentation and propose a
generic way to generate datasets to train amodal semantic segmentation methods.
We use this approach to generate an amodal Cityscapes dataset. Moreover, we
propose and evaluate a method as baseline on Amodal Cityscapes, showing its
applicability for amodal semantic segmentation in automotive environment
perception. We provide the means to re-generate this dataset on github.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 14:38:33 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Breitenstein",
"Jasmin",
""
],
[
"Fingscheidt",
"Tim",
""
]
] |
new_dataset
| 0.99976 |
2206.00550
|
Jakob Moosbauer
|
Manuel Kauers, Jakob Moosbauer
|
A Normal Form for Matrix Multiplication Schemes
|
11 pages
| null | null | null |
cs.CC
|
http://creativecommons.org/licenses/by/4.0/
|
Schemes for exact multiplication of small matrices have a large symmetry
group. This group defines an equivalence relation on the set of multiplication
schemes. There are algorithms to decide whether two schemes are equivalent.
However, for a large number of schemes a pairwise equivalence check becomes
cumbersome. In this paper we propose an algorithm to compute a normal form of
matrix multiplication schemes. This allows us to decide pairwise equivalence of
a larger number of schemes efficiently.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 15:04:41 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Kauers",
"Manuel",
""
],
[
"Moosbauer",
"Jakob",
""
]
] |
new_dataset
| 0.998481 |
2206.00623
|
Matthias Jasny
|
Matthias Jasny, Lasse Thostrup, Tobias Ziegler, Carsten Binnig
|
P4DB -- The Case for In-Network OLTP (Extended Technical Report)
|
Extended Technical Report for: P4DB - The Case for In-Network OLTP
| null | null | null |
cs.DB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we present a new approach for distributed DBMSs called P4DB,
that uses a programmable switch to accelerate OLTP workloads. The main idea of
P4DB is that it implements a transaction processing engine on top of a
P4-programmable switch. The switch can thus act as an accelerator in the
network, especially when it is used to store and process hot (contended) tuples
on the switch. In our experiments, we show that P4DB hence provides significant
benefits compared to traditional DBMS architectures and can achieve a speedup
of up to 8x.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 16:48:47 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Jasny",
"Matthias",
""
],
[
"Thostrup",
"Lasse",
""
],
[
"Ziegler",
"Tobias",
""
],
[
"Binnig",
"Carsten",
""
]
] |
new_dataset
| 0.962792 |
2206.00666
|
Moses Openja
|
Moses Openja, Mohammad Mehdi Morovati, Le An, Foutse Khomh, Mouna
Abidi
|
Technical Debts and Faults in Open-source Quantum Software Systems: An
Empirical Study
| null | null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantum computing is a rapidly growing field attracting the interest of both
researchers and software developers. Supported by its numerous open-source
tools, developers can now build, test, or run their quantum algorithms.
Although the maintenance practices for traditional software systems have been
extensively studied, the maintenance of quantum software is still a new field
of study but a critical part to ensure the quality of a whole quantum computing
system. In this work, we set out to investigate the distribution and evolution
of technical debts in quantum software and their relationship with fault
occurrences. Understanding these problems could guide future quantum
development and provide maintenance recommendations for the key areas where
quantum software developers and researchers should pay more attention. In this
paper, we empirically studied 118 open-source quantum projects, which were
selected from GitHub. The projects are categorized into 10 categories. We found
that the studied quantum software suffers from the issues of code convention
violation, error-handling, and code design. We also observed a statistically
significant correlation between code design, redundant code or code convention,
and the occurrences of faults in quantum software.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 17:59:54 GMT"
}
] | 2022-06-02T00:00:00 |
[
[
"Openja",
"Moses",
""
],
[
"Morovati",
"Mohammad Mehdi",
""
],
[
"An",
"Le",
""
],
[
"Khomh",
"Foutse",
""
],
[
"Abidi",
"Mouna",
""
]
] |
new_dataset
| 0.995848 |
1812.11214
|
Eugene Belilovsky
|
Mathieu Andreux, Tom\'as Angles, Georgios Exarchakis, Roberto
Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, St\'ephane Mallat,
Joakim and\'en, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz
Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella,
Michael Eickenberg
|
Kymatio: Scattering Transforms in Python
| null | null | null | null |
cs.LG cs.CV cs.SD eess.AS stat.ML
|
http://creativecommons.org/publicdomain/zero/1.0/
|
The wavelet scattering transform is an invariant signal representation
suitable for many signal processing and machine learning applications. We
present the Kymatio software package, an easy-to-use, high-performance Python
implementation of the scattering transform in 1D, 2D, and 3D that is compatible
with modern deep learning frameworks. All transforms may be executed on a GPU
(in addition to CPU), offering a considerable speed up over CPU
implementations. The package also has a small memory footprint, resulting
inefficient memory usage. The source code, documentation, and examples are
available undera BSD license at https://www.kymat.io/
|
[
{
"version": "v1",
"created": "Fri, 28 Dec 2018 20:53:29 GMT"
},
{
"version": "v2",
"created": "Sat, 1 Jun 2019 06:00:28 GMT"
},
{
"version": "v3",
"created": "Tue, 31 May 2022 09:46:58 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Andreux",
"Mathieu",
""
],
[
"Angles",
"Tomás",
""
],
[
"Exarchakis",
"Georgios",
""
],
[
"Leonarduzzi",
"Roberto",
""
],
[
"Rochette",
"Gaspar",
""
],
[
"Thiry",
"Louis",
""
],
[
"Zarka",
"John",
""
],
[
"Mallat",
"Stéphane",
""
],
[
"andén",
"Joakim",
""
],
[
"Belilovsky",
"Eugene",
""
],
[
"Bruna",
"Joan",
""
],
[
"Lostanlen",
"Vincent",
""
],
[
"Chaudhary",
"Muawiz",
""
],
[
"Hirn",
"Matthew J.",
""
],
[
"Oyallon",
"Edouard",
""
],
[
"Zhang",
"Sixin",
""
],
[
"Cella",
"Carmine",
""
],
[
"Eickenberg",
"Michael",
""
]
] |
new_dataset
| 0.999647 |
1907.00829
|
Jesko Hecking-Harbusch
|
Raven Beutner, Bernd Finkbeiner, Jesko Hecking-Harbusch
|
Translating Asynchronous Games for Distributed Synthesis (Full Version)
| null | null |
10.4230/LIPIcs.CONCUR.2019.26
| null |
cs.LO cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In distributed synthesis, we generate a set of process implementations that,
together, accomplish an objective against all possible behaviors of the
environment. A lot of recent work has focussed on systems with causal memory,
i.e., sets of asynchronous processes that exchange their causal histories upon
synchronization. Decidability results for this problem have been stated either
in terms of control games, which extend Zielonka's asynchronous automata by
partitioning the actions into controllable and uncontrollable, or in terms of
Petri games, which extend Petri nets by partitioning the tokens into system and
environment players. The precise connection between these two models was so
far, however, an open question. In this paper, we provide the first formal
connection between control games and Petri games. We establish the equivalence
of the two game models based on weak bisimulations between their strategies.
For both directions, we show that a game of one type can be translated into an
equivalent game of the other type. We provide exponential upper and lower
bounds for the translations. Our translations make it possible to transfer and
combine decidability results between the two types of games. Exemplarily, we
translate decidability in acyclic communication architectures, originally
obtained for control games, to Petri games, and decidability in single-process
systems, originally obtained for Petri games, to control games.
|
[
{
"version": "v1",
"created": "Mon, 1 Jul 2019 14:42:47 GMT"
},
{
"version": "v2",
"created": "Mon, 2 Dec 2019 10:57:17 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Beutner",
"Raven",
""
],
[
"Finkbeiner",
"Bernd",
""
],
[
"Hecking-Harbusch",
"Jesko",
""
]
] |
new_dataset
| 0.997805 |
1911.10038
|
Matej Ul\v{c}ar
|
Matej Ul\v{c}ar, Kristiina Vaik, Jessica Lindstr\"om, Milda
Dailid\.enait\.e, Marko Robnik-\v{S}ikonja
|
Multilingual Culture-Independent Word Analogy Datasets
|
7 pages, LREC2020 conference
|
Proceedings of the 12th Conference on Language Resources and
Evaluation (LREC 2020), pages 4074-4080
| null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In text processing, deep neural networks mostly use word embeddings as an
input. Embeddings have to ensure that relations between words are reflected
through distances in a high-dimensional numeric space. To compare the quality
of different text embeddings, typically, we use benchmark datasets. We present
a collection of such datasets for the word analogy task in nine languages:
Croatian, English, Estonian, Finnish, Latvian, Lithuanian, Russian, Slovenian,
and Swedish. We redesigned the original monolingual analogy task to be much
more culturally independent and also constructed cross-lingual analogy datasets
for the involved languages. We present basic statistics of the created datasets
and their initial evaluation using fastText embeddings.
|
[
{
"version": "v1",
"created": "Fri, 22 Nov 2019 13:39:06 GMT"
},
{
"version": "v2",
"created": "Fri, 27 Mar 2020 15:32:16 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Ulčar",
"Matej",
""
],
[
"Vaik",
"Kristiina",
""
],
[
"Lindström",
"Jessica",
""
],
[
"Dailidėnaitė",
"Milda",
""
],
[
"Robnik-Šikonja",
"Marko",
""
]
] |
new_dataset
| 0.998513 |
2009.13619
|
Ruofei Chen
|
Ruofei Chen, Stephanie Balzer, Bernardo Toninho
|
Ferrite: A Judgmental Embedding of Session Types in Rust
| null | null | null | null |
cs.PL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
This paper introduces Ferrite, a shallow embedding of session types in Rust.
In contrast to existing session type libraries and embeddings for mainstream
languages, Ferrite not only supports linear session types but also shared
session types. Shared session types allow sharing (aliasing) of channels while
preserving session fidelity (preservation) using type modalities for acquiring
and releasing sessions. Ferrite adopts a propositions as types approach and
encodes typing derivations as Rust functions, with the proof of successful
type-checking manifesting as a Rust program. We provide an evaluation of
Ferrite using Servo as a practical example, and demonstrate how safe
communication can be achieved in the canvas component using Ferrite.
|
[
{
"version": "v1",
"created": "Mon, 28 Sep 2020 20:54:56 GMT"
},
{
"version": "v2",
"created": "Thu, 17 Dec 2020 21:09:58 GMT"
},
{
"version": "v3",
"created": "Thu, 25 Mar 2021 13:56:58 GMT"
},
{
"version": "v4",
"created": "Sun, 26 Sep 2021 17:40:57 GMT"
},
{
"version": "v5",
"created": "Fri, 27 May 2022 09:01:59 GMT"
},
{
"version": "v6",
"created": "Tue, 31 May 2022 07:48:37 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Chen",
"Ruofei",
""
],
[
"Balzer",
"Stephanie",
""
],
[
"Toninho",
"Bernardo",
""
]
] |
new_dataset
| 0.998669 |
2011.00096
|
Peter Lindner
|
Martin Grohe, Peter Lindner
|
Independence in Infinite Probabilistic Databases
| null | null | null | null |
cs.DB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Probabilistic databases (PDBs) model uncertainty in data. The current
standard is to view PDBs as finite probability spaces over relational database
instances. Since many attributes in typical databases have infinite domains,
such as integers, strings, or real numbers, it is often more natural to view
PDBs as infinite probability spaces over database instances. In this paper, we
lay the mathematical foundations of infinite probabilistic databases. Our focus
then is on independence assumptions. Tuple-independent PDBs play a central role
in theory and practice of PDBs. Here, we study infinite tuple-independent PDBs
as well as related models such as infinite block-independent disjoint PDBs.
While the standard model of PDBs focuses on a set-based semantics, we also
study tuple-independent PDBs with a bag semantics and independence in PDBs over
uncountable fact spaces.
We also propose a new approach to PDBs with an open-world assumption,
addressing issues raised by Ceylan et al. (Proc. KR 2016) and generalizing
their work, which is still rooted in finite tuple-independent PDBs.
Moreover, for countable PDBs we propose an approximate query answering
algorithm.
|
[
{
"version": "v1",
"created": "Fri, 30 Oct 2020 20:34:39 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Feb 2022 21:25:19 GMT"
},
{
"version": "v3",
"created": "Tue, 31 May 2022 09:52:01 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Grohe",
"Martin",
""
],
[
"Lindner",
"Peter",
""
]
] |
new_dataset
| 0.993641 |
2012.01813
|
Johanna Johansen Ms
|
Johanna Johansen, Tore Pedersen, Simone Fischer-H\"ubner, Christian
Johansen, Gerardo Schneider, Arnold Roosendaal, Harald Zwingelberg, Anders
Jakob Sivesind, Josef Noll
|
A Multidisciplinary Definition of Privacy Labels: The Story of Princess
Privacy and the Seven Helpers
|
29 pages, 6 figures
|
Information and Computer Security, Vol. 30, No. 3, (2022) pp.
452-469
|
10.1108/ICS-06-2021-0080
| null |
cs.CR cs.CY
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Privacy is currently in distress and in need of rescue, much like princesses
in the all-familiar fairytales. We employ storytelling and metaphors from
fairytales to make reader-friendly and streamline our arguments about how a
complex concept of Privacy Labeling (the 'knight in shining armor') can be a
solution to the current state of Privacy (the 'princess in distress'). We give
a precise definition of Privacy Labeling (PL), painting a panoptic portrait
from seven different perspectives (the 'seven helpers'): Business, Legal,
Regulatory, Usability and Human Factors, Educative, Technological, and
Multidisciplinary. We describe a common vision, proposing several important
'traits of character' of PL as well as identifying 'undeveloped
potentialities', i.e., open problems on which the community can focus. More
specifically, this position paper identifies the stakeholders of the PL and
their needs with regard to privacy, describing how PL should be and look like
in order to address these needs. Throughout the paper, we highlight goals,
characteristics, open problems, and starting points for creating, what we
consider to be, the ideal PL. In the end we present three approaches to
establish and manage PL, through: self-evaluations, certifications, or
community endeavors. Based on these, we sketch a roadmap for future
developments.
|
[
{
"version": "v1",
"created": "Thu, 3 Dec 2020 10:42:30 GMT"
},
{
"version": "v2",
"created": "Tue, 9 Feb 2021 10:57:17 GMT"
},
{
"version": "v3",
"created": "Sun, 9 May 2021 16:54:58 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Johansen",
"Johanna",
""
],
[
"Pedersen",
"Tore",
""
],
[
"Fischer-Hübner",
"Simone",
""
],
[
"Johansen",
"Christian",
""
],
[
"Schneider",
"Gerardo",
""
],
[
"Roosendaal",
"Arnold",
""
],
[
"Zwingelberg",
"Harald",
""
],
[
"Sivesind",
"Anders Jakob",
""
],
[
"Noll",
"Josef",
""
]
] |
new_dataset
| 0.987549 |
2101.09563
|
Joseph Hejderup
|
Joseph Hejderup, Moritz Beller, Konstantinos Triantafyllou, Georgios
Gousios
|
Pr\"azi: From Package-based to Call-based Dependency Networks
|
42 pages, 14 figures, journal
| null |
10.1007/s10664-021-10071-9
| null |
cs.SE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Modern programming languages such as Java, JavaScript, and Rust encourage
software reuse by hosting diverse and fast-growing repositories of highly
interdependent packages (i.e., reusable libraries) for their users. The
standard way to study the interdependence between software packages is to infer
a package dependency network by parsing manifest data. Such networks help
answer questions such as "How many packages have dependencies to packages with
known security issues?" or "What are the most used packages?". However, an
overlooked aspect in existing studies is that manifest-inferred relationships
do not necessarily examine the actual usage of these dependencies in source
code. To better model dependencies between packages, we developed Pr\"azi, an
approach combining manifests and call graphs of packages. Pr\"azi constructs a
dependency network at the more fine-grained function-level, instead of at the
manifest level. This paper discusses a prototypical Pr\"azi implementation for
the popular system programming language Rust. We use Pr\"azi to characterize
Rust's package repository, Cratesio, at the function level and perform a
comparative study with metadata-based networks. Our results show that
metadata-based networks generalize how packages use their dependencies. Using
Pr\"azi, we find packages call only 40% of their resolved dependencies, and
that manual analysis of 34 cases reveals that not all packages use a dependency
the same way. We argue that researchers and practitioners interested in
understanding how developers or programs use dependencies should account for
its context -- not the sum of all resolved dependencies.
|
[
{
"version": "v1",
"created": "Sat, 23 Jan 2021 19:10:55 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Jan 2021 13:31:55 GMT"
},
{
"version": "v3",
"created": "Thu, 28 Jan 2021 09:07:31 GMT"
},
{
"version": "v4",
"created": "Wed, 30 Jun 2021 08:58:41 GMT"
},
{
"version": "v5",
"created": "Wed, 20 Oct 2021 11:22:04 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Hejderup",
"Joseph",
""
],
[
"Beller",
"Moritz",
""
],
[
"Triantafyllou",
"Konstantinos",
""
],
[
"Gousios",
"Georgios",
""
]
] |
new_dataset
| 0.997278 |
2110.05687
|
Qichao Ying
|
Qichao Ying, Xiaoxiao Hu, Xiangyu Zhang, Zhenxing Qian and Xinpeng
Zhang
|
RWN: Robust Watermarking Network for Image Cropping Localization
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Image cropping can be maliciously used to manipulate the layout of an image
and alter the underlying meaning. Previous image crop detection schemes only
predicts whether an image has been cropped, ignoring which part of the image is
cropped. This paper presents a novel robust watermarking network (RWN) for
image crop localization. We train an anti-crop processor (ACP) that embeds a
watermark into a target image. The visually indistinguishable protected image
is then posted on the social network instead of the original image. At the
recipient's side, ACP extracts the watermark from the attacked image, and we
conduct feature matching on the original and extracted watermark to locate the
position of the crop in the original image plane. We further extend our scheme
to detect tampering attack on the attacked image. Besides, we explore a simple
yet efficient method (JPEG-Mixup) to improve the generalization of JPEG
robustness. According to our comprehensive experiments, RWN is the first to
provide high-accuracy and robust image crop localization. Besides, the accuracy
of tamper detection is comparable with many state-of-the-art passive-based
methods.
|
[
{
"version": "v1",
"created": "Tue, 12 Oct 2021 02:19:42 GMT"
},
{
"version": "v2",
"created": "Tue, 31 May 2022 14:57:39 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Ying",
"Qichao",
""
],
[
"Hu",
"Xiaoxiao",
""
],
[
"Zhang",
"Xiangyu",
""
],
[
"Qian",
"Zhenxing",
""
],
[
"Zhang",
"Xinpeng",
""
]
] |
new_dataset
| 0.954058 |
2110.08733
|
Junjue Wang
|
Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu and Yanfei Zhong
|
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic
Segmentation
|
Accepted by NeurIPS 2021 Datasets and Benchmarks Track
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Deep learning approaches have shown promising results in remote sensing high
spatial resolution (HSR) land-cover mapping. However, urban and rural scenes
can show completely different geographical landscapes, and the inadequate
generalizability of these algorithms hinders city-level or national-level
mapping. Most of the existing HSR land-cover datasets mainly promote the
research of learning semantic representation, thereby ignoring the model
transferability. In this paper, we introduce the Land-cOVEr Domain Adaptive
semantic segmentation (LoveDA) dataset to advance semantic and transferable
learning. The LoveDA dataset contains 5987 HSR images with 166768 annotated
objects from three different cities. Compared to the existing datasets, the
LoveDA dataset encompasses two domains (urban and rural), which brings
considerable challenges due to the: 1) multi-scale objects; 2) complex
background samples; and 3) inconsistent class distributions. The LoveDA dataset
is suitable for both land-cover semantic segmentation and unsupervised domain
adaptation (UDA) tasks. Accordingly, we benchmarked the LoveDA dataset on
eleven semantic segmentation methods and eight UDA methods. Some exploratory
studies including multi-scale architectures and strategies, additional
background supervision, and pseudo-label analysis were also carried out to
address these challenges. The code and data are available at
https://github.com/Junjue-Wang/LoveDA.
|
[
{
"version": "v1",
"created": "Sun, 17 Oct 2021 06:12:48 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Oct 2021 05:35:35 GMT"
},
{
"version": "v3",
"created": "Thu, 21 Oct 2021 01:26:31 GMT"
},
{
"version": "v4",
"created": "Sun, 24 Oct 2021 10:58:21 GMT"
},
{
"version": "v5",
"created": "Wed, 29 Dec 2021 04:02:41 GMT"
},
{
"version": "v6",
"created": "Tue, 31 May 2022 11:03:05 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Wang",
"Junjue",
""
],
[
"Zheng",
"Zhuo",
""
],
[
"Ma",
"Ailong",
""
],
[
"Lu",
"Xiaoyan",
""
],
[
"Zhong",
"Yanfei",
""
]
] |
new_dataset
| 0.998961 |
2201.04288
|
Shen Yan
|
Shen Yan, Xuehan Xiong, Anurag Arnab, Zhichao Lu, Mi Zhang, Chen Sun,
Cordelia Schmid
|
Multiview Transformers for Video Recognition
|
CVPR 2022; arXiv v4: update results on Epic-Kitchens-100
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Video understanding requires reasoning at multiple spatiotemporal resolutions
-- from short fine-grained motions to events taking place over longer
durations. Although transformer architectures have recently advanced the
state-of-the-art, they have not explicitly modelled different spatiotemporal
resolutions. To this end, we present Multiview Transformers for Video
Recognition (MTV). Our model consists of separate encoders to represent
different views of the input video with lateral connections to fuse information
across views. We present thorough ablation studies of our model and show that
MTV consistently performs better than single-view counterparts in terms of
accuracy and computational cost across a range of model sizes. Furthermore, we
achieve state-of-the-art results on six standard datasets, and improve even
further with large-scale pretraining. Code and checkpoints are available at:
https://github.com/google-research/scenic/tree/main/scenic/projects/mtv.
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 03:33:57 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Jan 2022 05:38:20 GMT"
},
{
"version": "v3",
"created": "Sat, 23 Apr 2022 19:02:09 GMT"
},
{
"version": "v4",
"created": "Tue, 31 May 2022 06:19:59 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Yan",
"Shen",
""
],
[
"Xiong",
"Xuehan",
""
],
[
"Arnab",
"Anurag",
""
],
[
"Lu",
"Zhichao",
""
],
[
"Zhang",
"Mi",
""
],
[
"Sun",
"Chen",
""
],
[
"Schmid",
"Cordelia",
""
]
] |
new_dataset
| 0.977867 |
2201.11944
|
Yi Chen
|
Bruno Hexsel, Heethesh Vhavle and Yi Chen
|
DICP: Doppler Iterative Closest Point Algorithm
|
Accepted at Robotics: Science and Systems (RSS) 2022
| null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we present a novel algorithm for point cloud registration for
range sensors capable of measuring per-return instantaneous radial velocity:
Doppler ICP. Existing variants of ICP that solely rely on geometry or other
features generally fail to estimate the motion of the sensor correctly in
scenarios that have non-distinctive features and/or repetitive geometric
structures such as hallways, tunnels, highways, and bridges. We propose a new
Doppler velocity objective function that exploits the compatibility of each
point's Doppler measurement and the sensor's current motion estimate. We
jointly optimize the Doppler velocity objective function and the geometric
objective function which sufficiently constrains the point cloud alignment
problem even in feature-denied environments. Furthermore, the correspondence
matches used for the alignment are improved by pruning away the points from
dynamic targets which generally degrade the ICP solution. We evaluate our
method on data collected from real sensors and from simulation. Our results
show that with the added Doppler velocity residual terms, our method achieves a
significant improvement in registration accuracy along with faster convergence,
on average, when compared to classical point-to-plane ICP that solely relies on
geometric residuals.
|
[
{
"version": "v1",
"created": "Fri, 28 Jan 2022 05:51:07 GMT"
},
{
"version": "v2",
"created": "Tue, 31 May 2022 04:07:47 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Hexsel",
"Bruno",
""
],
[
"Vhavle",
"Heethesh",
""
],
[
"Chen",
"Yi",
""
]
] |
new_dataset
| 0.999378 |
2202.02179
|
Guanlan Zhang
|
Guanlan Zhang, Yipai Du, Hongyu Yu and Michael Yu Wang
|
DelTact: A Vision-based Tactile Sensor Using Dense Color Pattern
|
8 pages contents, 1 page references, 8 figures, 2 tables
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Tactile sensing is an essential perception for robots to complete dexterous
tasks. As a promising tactile sensing technique, vision-based tactile sensors
have been developed to improve robot performance in manipulation and grasping.
Here we propose a new design of a vision-based tactile sensor, DelTact. The
sensor uses a modular hardware architecture for compactness whilst maintaining
a contact measurement of full resolution (798*586) and large area (675mm2).
Moreover, it adopts an improved dense random color pattern based on the
previous version to achieve high accuracy of contact deformation tracking. In
particular, we optimize the color pattern generation process and select the
appropriate pattern for coordinating with a dense optical flow algorithm under
a real-world experimental sensory setting. The optical flow obtained from the
raw image is processed to determine shape and force distribution on the contact
surface. We also demonstrate the method to extract contact shape and force
distribution from the raw images. Experimental results demonstrate that the
sensor is capable of providing tactile measurements with low error and high
frequency (40Hz).
|
[
{
"version": "v1",
"created": "Fri, 4 Feb 2022 15:12:52 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Feb 2022 07:48:30 GMT"
},
{
"version": "v3",
"created": "Tue, 31 May 2022 07:03:57 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Zhang",
"Guanlan",
""
],
[
"Du",
"Yipai",
""
],
[
"Yu",
"Hongyu",
""
],
[
"Wang",
"Michael Yu",
""
]
] |
new_dataset
| 0.998931 |
2204.07649
|
Miriam Cha
|
Miriam Cha, Kuan Wei Huang, Morgan Schmidt, Gregory Angelides, Mark
Hamilton, Sam Goldberg, Armando Cabrera, Phillip Isola, Taylor Perron, Bill
Freeman, Yen-Chen Lin, Brandon Swenson, Jean Piou
|
MultiEarth 2022 -- Multimodal Learning for Earth and Environment
Workshop and Challenge
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The Multimodal Learning for Earth and Environment Challenge (MultiEarth 2022)
will be the first competition aimed at the monitoring and analysis of
deforestation in the Amazon rainforest at any time and in any weather
conditions. The goal of the Challenge is to provide a common benchmark for
multimodal information processing and to bring together the earth and
environmental science communities as well as multimodal representation learning
communities to compare the relative merits of the various multimodal learning
methods to deforestation estimation under well-defined and strictly comparable
conditions. MultiEarth 2022 will have three sub-challenges: 1) matrix
completion, 2) deforestation estimation, and 3) image-to-image translation.
This paper presents the challenge guidelines, datasets, and evaluation metrics
for the three sub-challenges. Our challenge website is available at
https://sites.google.com/view/rainforest-challenge.
|
[
{
"version": "v1",
"created": "Fri, 15 Apr 2022 20:59:02 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Apr 2022 02:49:45 GMT"
},
{
"version": "v3",
"created": "Tue, 31 May 2022 13:34:06 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Cha",
"Miriam",
""
],
[
"Huang",
"Kuan Wei",
""
],
[
"Schmidt",
"Morgan",
""
],
[
"Angelides",
"Gregory",
""
],
[
"Hamilton",
"Mark",
""
],
[
"Goldberg",
"Sam",
""
],
[
"Cabrera",
"Armando",
""
],
[
"Isola",
"Phillip",
""
],
[
"Perron",
"Taylor",
""
],
[
"Freeman",
"Bill",
""
],
[
"Lin",
"Yen-Chen",
""
],
[
"Swenson",
"Brandon",
""
],
[
"Piou",
"Jean",
""
]
] |
new_dataset
| 0.988822 |
2205.06921
|
Ruofei Chen
|
Ruo Fei Chen, Stephanie Balzer, and Bernardo Toninho
|
Ferrite: A Judgmental Embedding of Session Types in Rust
|
Accidental duplication of arXiv:2009.13619
| null | null | null |
cs.PL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
\emph{Session types} have proved viable in expressing and verifying the
protocols of message-passing systems. While message passing is a dominant
concurrency paradigm in practice, real world software is written without
session types. A limitation of existing session type libraries in mainstream
languages is their restriction to linear session types, precluding application
scenarios that demand sharing and thus aliasing of channel references.
This paper introduces Ferrite, a shallow embedding of session types in Rust
that supports both \emph{linear} and \emph{shared} sessions. The formal
foundation of Ferrite constitutes the shared session type calculus $\sills$,
which Ferrite encodes via a novel \emph{judgmental embedding} technique. The
fulcrum of the embedding is the notion of a typing judgment that allows
reasoning about shared and linear resources to type a session. Typing rules are
then encoded as functions over judgments, with a valid typing derivation
manifesting as a well-typed Rust program. This Rust program generated by
Ferrite serves as a \emph{certificate}, ensuring that the application will
proceed according to the protocol defined by the session type. The paper
details the features and implementation of Ferrite and includes a case study on
implementing Servo's canvas component in Ferrite.
|
[
{
"version": "v1",
"created": "Fri, 13 May 2022 23:05:32 GMT"
},
{
"version": "v2",
"created": "Tue, 31 May 2022 07:49:37 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Chen",
"Ruo Fei",
""
],
[
"Balzer",
"Stephanie",
""
],
[
"Toninho",
"Bernardo",
""
]
] |
new_dataset
| 0.99838 |
2205.07025
|
Gil Ben-Shachar
|
Gill Barequet and Gil Ben-Shachar
|
Minimal-Perimeter Lattice Animals and the Constant-Isomer Conjecture
| null | null | null | null |
cs.CG math.CO
|
http://creativecommons.org/licenses/by/4.0/
|
We consider minimal-perimeter lattice animals, providing a set of conditions
which are sufficient for a lattice to have the property that inflating all
minimal-perimeter animals of a certain size yields (without repetitions) all
minimal-perimeter animals of a new, larger size. We demonstrate this result on
the two-dimensional square and hexagonal lattices. In addition, we characterize
the sizes of minimal-perimeter animals on these lattices that are not created
by inflating members of another set of minimal-perimeter animals.
|
[
{
"version": "v1",
"created": "Sat, 14 May 2022 10:01:14 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Barequet",
"Gill",
""
],
[
"Ben-Shachar",
"Gil",
""
]
] |
new_dataset
| 0.999524 |
2205.09388
|
Raffaele De Rose Dr.
|
Raffaele De Rose, Tommaso Zanotti, Francesco Maria Puglisi, Felice
Crupi, Paolo Pavan, Marco Lanuzza
|
Smart Material Implication Using Spin-Transfer Torque Magnetic Tunnel
Junctions for Logic-in-Memory Computing
| null |
Solid-State Electronics 2022
|
10.1016/j.sse.2022.108390
| null |
cs.ET physics.app-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Smart material implication (SIMPLY) logic has been recently proposed for the
design of energy-efficient Logic-in-Memory (LIM) architectures based on
non-volatile resistive memory devices. The SIMPLY logic is enabled by adding a
comparator to the conventional IMPLY scheme. This allows performing a
preliminary READ operation and hence the SET operation only in the case it is
actually required. This work explores the SIMPLY logic scheme using nanoscale
spin-transfer torque magnetic tunnel junction (STT-MTJ) devices. The
performance of the STT-MTJ based SIMPLY architecture is analyzed by varying the
load resistor and applied voltages to implement both READ and SET operations,
while also investigating the effect of temperature on circuit operation.
Obtained results show an existing tradeoff between error rate and energy
consumption, which can be effectively managed by properly setting the values of
load resistor and applied voltages. In addition, our analysis proves that
tracking the temperature dependence of the MTJ properties through a
proportional to absolute temperature (PTAT) reference voltage at the input of
the comparator is beneficial to mitigate the reliability degradation under
temperature variations.
|
[
{
"version": "v1",
"created": "Thu, 19 May 2022 08:34:19 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"De Rose",
"Raffaele",
""
],
[
"Zanotti",
"Tommaso",
""
],
[
"Puglisi",
"Francesco Maria",
""
],
[
"Crupi",
"Felice",
""
],
[
"Pavan",
"Paolo",
""
],
[
"Lanuzza",
"Marco",
""
]
] |
new_dataset
| 0.991409 |
2205.14191
|
Lakmal Meegahapola
|
Wageesha Bangamuarachchi, Anju Chamantha, Lakmal Meegahapola, Salvador
Ruiz-Correa, Indika Perera, Daniel Gatica-Perez
|
Sensing Eating Events in Context: A Smartphone-Only Approach
|
Accepted for publication at IEEE Access
| null |
10.1109/ACCESS.2022.3179702
| null |
cs.HC cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While the task of automatically detecting eating events has been examined in
prior work using various wearable devices, the use of smartphones as standalone
devices to infer eating events remains an open issue. This paper proposes a
framework that infers eating vs. non-eating events from passive smartphone
sensing and evaluates it on a dataset of 58 college students. First, we show
that time of the day and features from modalities such as screen usage,
accelerometer, app usage, and location are indicative of eating and non-eating
events. Then, we show that eating events can be inferred with an AUROC (area
under the receiver operating characteristics curve) of 0.65 using
subject-independent machine learning models, which can be further improved up
to 0.81 for subject-dependent and 0.81 for hybrid models using personalization
techniques. Moreover, we show that users have different behavioral and
contextual routines around eating episodes requiring specific feature groups to
train fully personalized models. These findings are of potential value for
future mobile food diary apps that are context-aware by enabling scalable
sensing-based eating studies using only smartphones; detecting under-reported
eating events, thus increasing data quality in self report-based studies;
providing functionality to track food consumption and generate reminders for
on-time collection of food diaries; and supporting mobile interventions towards
healthy eating practices.
|
[
{
"version": "v1",
"created": "Fri, 27 May 2022 18:42:23 GMT"
},
{
"version": "v2",
"created": "Tue, 31 May 2022 09:49:33 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Bangamuarachchi",
"Wageesha",
""
],
[
"Chamantha",
"Anju",
""
],
[
"Meegahapola",
"Lakmal",
""
],
[
"Ruiz-Correa",
"Salvador",
""
],
[
"Perera",
"Indika",
""
],
[
"Gatica-Perez",
"Daniel",
""
]
] |
new_dataset
| 0.966383 |
2205.14247
|
Manuel Olgu\'in Mu\~noz
|
Manuel Olgu\'in Mu\~noz (1), Seyed Samie Mostafavi (1), Vishnu N.
Moothedath (1), James Gross (1) ((1) KTH Royal Institute of Technology)
|
Ainur: A Framework for Repeatable End-to-End Wireless Edge Computing
Testbed Research
|
6 pages, 6 figures, demo session paper
| null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Experimental research on wireless networking in combination with edge and
cloud computing has been the subject of explosive interest in the last decade.
This development has been driven by the increasing complexity of modern
wireless technologies and the extensive softwarization of these through
projects such as a Open Radio Access Network (O-RAN). In this context, a number
of small- to mid-scale testbeds have emerged, employing a variety of
technologies to target a wide array of use-cases and scenarios in the context
of novel mobile communication technologies such as 5G and beyond-5G. Little
work, however, has yet been devoted to developing a standard framework for
wireless testbed automation which is hardware-agnostic and compatible with
edge- and cloud-native technologies. Such a solution would simplify the
development of new testbeds by completely or partially removing the requirement
for custom management and orchestration software.
In this paper, we present the first such mostly hardware-agnostic wireless
testbed automation framework, Ainur. It is designed to configure, manage,
orchestrate, and deploy workloads from an end-to-end perspective. Ainur is
built on top of cloud-native technologies such as Docker, and is provided as
FOSS to the community through the KTH-EXPECA/Ainur repository on GitHub. We
demonstrate the utility of the platform with a series of scenarios, showcasing
in particular its flexibility with respect to physical link definition,
computation placement, and automation of arbitrarily complex experimental
scenarios.
|
[
{
"version": "v1",
"created": "Fri, 27 May 2022 21:48:25 GMT"
},
{
"version": "v2",
"created": "Tue, 31 May 2022 05:39:07 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Muñoz",
"Manuel Olguín",
"",
"KTH Royal Institute of Technology"
],
[
"Mostafavi",
"Seyed Samie",
"",
"KTH Royal Institute of Technology"
],
[
"Moothedath",
"Vishnu N.",
"",
"KTH Royal Institute of Technology"
],
[
"Gross",
"James",
"",
"KTH Royal Institute of Technology"
]
] |
new_dataset
| 0.998848 |
2205.14460
|
Chaofeng Wang
|
Chaofeng Wang, Sarah Elizabeth Antos, Jessica Grayson Gosling
Goldsmith, Luis Miguel Triveno
|
Visual Perception of Building and Household Vulnerability from Streets
| null | null | null | null |
cs.LG cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In developing countries, building codes often are outdated or not enforced.
As a result, a large portion of the housing stock is substandard and vulnerable
to natural hazards and climate related events. Assessing housing quality is key
to inform public policies and private investments. Standard assessment methods
are typically carried out only on a sample / pilot basis due to its high costs
or, when complete, tend to be obsolete due to the lack of compliance with
recommended updating standards or not accessible to most users with the level
of detail needed to take key policy or business decisions. Thus, we propose an
evaluation framework that is cost-efficient for first capture and future
updates, and is reliable at the block level. The framework complements existing
work of using street view imagery combined with deep learning to automatically
extract building information to assist the identification of housing
characteristics. We then check its potential for scalability and higher level
reliability. For that purpose, we create an index, which synthesises the
highest possible level of granularity of data at the housing unit and at the
household level at the block level, and assess whether the predictions made by
our model could be used to approximate vulnerability conditions with a lower
budget and in selected areas. Our results indicated that the predictions from
the images are clearly correlated with the index.
|
[
{
"version": "v1",
"created": "Sat, 28 May 2022 15:35:47 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Wang",
"Chaofeng",
""
],
[
"Antos",
"Sarah Elizabeth",
""
],
[
"Goldsmith",
"Jessica Grayson Gosling",
""
],
[
"Triveno",
"Luis Miguel",
""
]
] |
new_dataset
| 0.99493 |
2205.14728
|
Raviraj Joshi
|
Raviraj Joshi
|
L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models,
and Library
| null | null | null | null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Despite being the third most popular language in India, the Marathi language
lacks useful NLP resources. Moreover, popular NLP libraries do not have support
for the Marathi language. With L3Cube-MahaNLP, we aim to build resources and a
library for Marathi natural language processing. We present datasets and
transformer models for supervised tasks like sentiment analysis, named entity
recognition, and hate speech detection. We have also published a monolingual
Marathi corpus for unsupervised language modeling tasks. Overall we present
MahaCorpus, MahaSent, MahaNER, and MahaHate datasets and their corresponding
MahaBERT models fine-tuned on these datasets. We aim to move ahead of benchmark
datasets and prepare useful resources for Marathi. The resources are available
at https://github.com/l3cube-pune/MarathiNLP.
|
[
{
"version": "v1",
"created": "Sun, 29 May 2022 17:51:00 GMT"
},
{
"version": "v2",
"created": "Tue, 31 May 2022 15:15:51 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Joshi",
"Raviraj",
""
]
] |
new_dataset
| 0.999765 |
2205.14942
|
Hao Wu
|
Siyuan Liang, Hao Wu
|
Edge YOLO: Real-Time Intelligent Object Detection System Based on
Edge-Cloud Cooperation in Autonomous Vehicles
| null | null |
10.1109/TITS.2022.3158253
| null |
cs.CV cs.LG eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
Driven by the ever-increasing requirements of autonomous vehicles, such as
traffic monitoring and driving assistant, deep learning-based object detection
(DL-OD) has been increasingly attractive in intelligent transportation systems.
However, it is difficult for the existing DL-OD schemes to realize the
responsible, cost-saving, and energy-efficient autonomous vehicle systems due
to low their inherent defects of low timeliness and high energy consumption. In
this paper, we propose an object detection (OD) system based on edge-cloud
cooperation and reconstructive convolutional neural networks, which is called
Edge YOLO. This system can effectively avoid the excessive dependence on
computing power and uneven distribution of cloud computing resources.
Specifically, it is a lightweight OD framework realized by combining pruning
feature extraction network and compression feature fusion network to enhance
the efficiency of multi-scale prediction to the largest extent. In addition, we
developed an autonomous driving platform equipped with NVIDIA Jetson for
system-level verification. We experimentally demonstrate the reliability and
efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively.
According to COCO2017 standard datasets with a speed of 26.6 frames per second
(FPS), the results show that the number of parameters in the entire network is
only 25.67 MB, while the accuracy (mAP) is up to 47.3%.
|
[
{
"version": "v1",
"created": "Mon, 30 May 2022 09:16:35 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Liang",
"Siyuan",
""
],
[
"Wu",
"Hao",
""
]
] |
new_dataset
| 0.999455 |
2205.15053
|
Thomas Germer
|
Thomas Germer, Tobias Uelwer and Stefan Harmeling
|
Deblurring Photographs of Characters Using Deep Neural Networks
|
15 pages, 13 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present our approach for the Helsinki Deblur Challenge
(HDC2021). The task of this challenge is to deblur images of characters without
knowing the point spread function (PSF). The organizers provided a dataset of
pairs of sharp and blurred images. Our method consists of three steps: First,
we estimate a warping transformation of the images to align the sharp images
with the blurred ones. Next, we estimate the PSF using a quasi-Newton method.
The estimated PSF allows to generate additional pairs of sharp and blurred
images. Finally, we train a deep convolutional neural network to reconstruct
the sharp images from the blurred images. Our method is able to successfully
reconstruct images from the first 10 stages of the HDC 2021 data. Our code is
available at https://github.com/hhu-machine-learning/hdc2021-psfnn.
|
[
{
"version": "v1",
"created": "Mon, 30 May 2022 12:32:26 GMT"
},
{
"version": "v2",
"created": "Tue, 31 May 2022 07:45:45 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Germer",
"Thomas",
""
],
[
"Uelwer",
"Tobias",
""
],
[
"Harmeling",
"Stefan",
""
]
] |
new_dataset
| 0.986426 |
2205.15359
|
Yoshimichi Nakatsuka
|
Yoshimichi Nakatsuka, Ercan Ozturk, Alex Shamis, Andrew Paverd, Peter
Pietzuch
|
CTR: Checkpoint, Transfer, and Restore for Secure Enclaves
| null | null | null | null |
cs.CR cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Hardware-based Trusted Execution Environments (TEEs) are becoming
increasingly prevalent in cloud computing, forming the basis for confidential
computing. However, the security goals of TEEs sometimes conflict with existing
cloud functionality, such as VM or process migration, because TEE memory cannot
be read by the hypervisor, OS, or other software on the platform. Whilst some
newer TEE architectures support migration of entire protected VMs, there is
currently no practical solution for migrating individual processes containing
in-process TEEs. The inability to migrate such processes leads to operational
inefficiencies or even data loss if the host platform must be urgently
restarted.
We present CTR, a software-only design to retrofit migration functionality
into existing TEE architectures, whilst maintaining their expected security
guarantees. Our design allows TEEs to be interrupted and migrated at arbitrary
points in their execution, thus maintaining compatibility with existing VM and
process migration techniques. By cooperatively involving the TEE in the
migration process, our design also allows application developers to specify
stateful migration-related policies, such as limiting the number of times a
particular TEE may be migrated. Our prototype implementation for Intel SGX
demonstrates that migration latency increases linearly with the size of the TEE
memory and is dominated by TEE system operations.
|
[
{
"version": "v1",
"created": "Mon, 30 May 2022 18:08:09 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Nakatsuka",
"Yoshimichi",
""
],
[
"Ozturk",
"Ercan",
""
],
[
"Shamis",
"Alex",
""
],
[
"Paverd",
"Andrew",
""
],
[
"Pietzuch",
"Peter",
""
]
] |
new_dataset
| 0.998732 |
2205.15452
|
Aitor Alvarez-Gila
|
Aitor Alvarez-Gila, Joost van de Weijer, Yaxing Wang, Estibaliz
Garrote
|
MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic
Segmentation
|
5 pages
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of
116,000 scenes containing randomly placed objects of 10 distinct classes and
captured from 25 camera locations in the upper hemisphere. MVMO comprises
photorealistic, path-traced image renders, together with semantic segmentation
ground truth for every view. Unlike existing multi-view datasets, MVMO features
wide baselines between cameras and high density of objects, which lead to large
disparities, heavy occlusions and view-dependent object appearance. Single view
semantic segmentation is hindered by self and inter-object occlusions that
could benefit from additional viewpoints. Therefore, we expect that MVMO will
propel research in multi-view semantic segmentation and cross-view semantic
transfer. We also provide baselines that show that new research is needed in
such fields to exploit the complementary information of multi-view setups.
|
[
{
"version": "v1",
"created": "Mon, 30 May 2022 22:37:43 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Alvarez-Gila",
"Aitor",
""
],
[
"van de Weijer",
"Joost",
""
],
[
"Wang",
"Yaxing",
""
],
[
"Garrote",
"Estibaliz",
""
]
] |
new_dataset
| 0.999837 |
2205.15473
|
Aaron Ray
|
Aaron Ray, Alyssa Pierson, Daniela Rus
|
Free-Space Ellipsoid Graphs for Multi-Agent Target Monitoring
|
IEEE Intl. Conf. on Robotics and Automation (ICRA) 2022
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We apply a novel framework for decomposing and reasoning about free space in
an environment to a multi-agent persistent monitoring problem. Our
decomposition method represents free space as a collection of ellipsoids
associated with a weighted connectivity graph. The same ellipsoids used for
reasoning about connectivity and distance during high level planning can be
used as state constraints in a Model Predictive Control algorithm to enforce
collision-free motion. This structure allows for streamlined implementation in
distributed multi-agent tasks in 2D and 3D environments. We illustrate its
effectiveness for a team of tracking agents tasked with monitoring a group of
target agents. Our algorithm uses the ellipsoid decomposition as a primitive
for the coordination, path planning, and control of the tracking agents.
Simulations with four tracking agents monitoring fifteen dynamic targets in
obstacle-rich environments demonstrate the performance of our algorithm.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 00:04:51 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Ray",
"Aaron",
""
],
[
"Pierson",
"Alyssa",
""
],
[
"Rus",
"Daniela",
""
]
] |
new_dataset
| 0.983272 |
2205.15501
|
Yiming Zeng
|
Yiming Zeng, Jiarui Zhang, Ji Liu, Zhenhua Liu, Yuanyuan Yang
|
Multi-Entanglement Routing Design over Quantum Networks
| null |
IEEE International Conference on Computer Communications 2022
| null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantum networks are considered as a promising future platform for quantum
information exchange and quantum applications, which have capabilities far
beyond the traditional communication networks. Remote quantum entanglement is
an essential component of a quantum network. How to efficiently design a
multi-routing entanglement protocol is a fundamental yet challenging problem.
In this paper, we study a quantum entanglement routing problem to
simultaneously maximize the number of quantum-user pairs and their expected
throughput. Our approach is to formulate the problem as two sequential integer
programming steps. We propose efficient entanglement routing algorithms for the
two integer programming steps and analyze their time complexity and performance
bounds. Results of evaluation highlight that our approach outperforms existing
solutions in both served quantum-user pairs numbers and the network expected
throughput.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 01:52:44 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Zeng",
"Yiming",
""
],
[
"Zhang",
"Jiarui",
""
],
[
"Liu",
"Ji",
""
],
[
"Liu",
"Zhenhua",
""
],
[
"Yang",
"Yuanyuan",
""
]
] |
new_dataset
| 0.995917 |
2205.15509
|
Bingqian Lin
|
Bingqian Lin, Yi Zhu, Zicong Chen, Xiwen Liang, Jianzhuang Liu,
Xiaodan Liang
|
ADAPT: Vision-Language Navigation with Modality-Aligned Action Prompts
|
Accepted to CVPR 2022
| null | null | null |
cs.CV cs.AI cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision-Language Navigation (VLN) is a challenging task that requires an
embodied agent to perform action-level modality alignment, i.e., make
instruction-asked actions sequentially in complex visual environments. Most
existing VLN agents learn the instruction-path data directly and cannot
sufficiently explore action-level alignment knowledge inside the multi-modal
inputs. In this paper, we propose modAlity-aligneD Action PrompTs (ADAPT),
which provides the VLN agent with action prompts to enable the explicit
learning of action-level modality alignment to pursue successful navigation.
Specifically, an action prompt is defined as a modality-aligned pair of an
image sub-prompt and a text sub-prompt, where the former is a single-view
observation and the latter is a phrase like ''walk past the chair''. When
starting navigation, the instruction-related action prompt set is retrieved
from a pre-built action prompt base and passed through a prompt encoder to
obtain the prompt feature. Then the prompt feature is concatenated with the
original instruction feature and fed to a multi-layer transformer for action
prediction. To collect high-quality action prompts into the prompt base, we use
the Contrastive Language-Image Pretraining (CLIP) model which has powerful
cross-modality alignment ability. A modality alignment loss and a sequential
consistency loss are further introduced to enhance the alignment of the action
prompt and enforce the agent to focus on the related prompt sequentially.
Experimental results on both R2R and RxR show the superiority of ADAPT over
state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 02:41:31 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Lin",
"Bingqian",
""
],
[
"Zhu",
"Yi",
""
],
[
"Chen",
"Zicong",
""
],
[
"Liang",
"Xiwen",
""
],
[
"Liu",
"Jianzhuang",
""
],
[
"Liang",
"Xiaodan",
""
]
] |
new_dataset
| 0.998794 |
2205.15535
|
Pei Liu
|
Pei Liu, Mattia Fazzini, John Grundy, and Li Li
|
Do Customized Android Frameworks Keep Pace with Android?
| null | null |
10.1145/3524842.3527963
|
MSR '22: Proceedings of the 19th International Conference on Mining
Software Repositories
|
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
To satisfy varying customer needs, device vendors and OS providers often rely
on the open-source nature of the Android OS and offer customized versions of
the Android OS. When a new version of the Android OS is released, device
vendors and OS providers need to merge the changes from the Android OS into
their customizations to account for its bug fixes, security patches, and new
features. Because developers of customized OSs might have made changes to code
locations that were also modified by the developers of the Android OS, the
merge task can be characterized by conflicts, which can be time-consuming and
error-prone to resolve.
To provide more insight into this critical aspect of the Android ecosystem,
we present an empirical study that investigates how eight open-source
customizations of the Android OS merge the changes from the Android OS into
their projects. The study analyzes how often the developers from the customized
OSs merge changes from the Android OS, how often the developers experience
textual merge conflicts, and the characteristics of these conflicts.
Furthermore, to analyze the effect of the conflicts, the study also analyzes
how the conflicts can affect a randomly selected sample of 1,000 apps. After
analyzing 1,148 merge operations, we identified that developers perform these
operations for 9.7\% of the released versions of the Android OS, developers
will encounter at least one conflict in 41.3\% of the merge operations, 58.1\%
of the conflicts required developers to change the customized OSs, and 64.4\%
of the apps considered use at least one method affected by a conflict. In
addition to detailing our results, the paper also discusses the implications of
our findings and provides insights for researchers and practitioners working
with Android and its customizations.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 04:45:59 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Liu",
"Pei",
""
],
[
"Fazzini",
"Mattia",
""
],
[
"Grundy",
"John",
""
],
[
"Li",
"Li",
""
]
] |
new_dataset
| 0.989309 |
2205.15572
|
Weikai Chen
|
Weikai Chen, Cheng Lin, Weiyang Li, Bo Yang
|
3PSDF: Three-Pole Signed Distance Function for Learning Surfaces with
Arbitrary Topologies
|
Accepted to CVPR 2022
| null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
Recent advances in learning 3D shapes using neural implicit functions have
achieved impressive results by breaking the previous barrier of resolution and
diversity for varying topologies. However, most of such approaches are limited
to closed surfaces as they require the space to be divided into inside and
outside. More recent works based on unsigned distance function have been
proposed to handle complex geometry containing both the open and closed
surfaces. Nonetheless, as their direct outputs are point clouds, robustly
obtaining high-quality meshing results from discrete points remains an open
question. We present a novel learnable implicit representation, called the
three-pole signed distance function (3PSDF), that can represent non-watertight
3D shapes with arbitrary topologies while supporting easy field-to-mesh
conversion using the classic Marching Cubes algorithm. The key to our method is
the introduction of a new sign, the NULL sign, in addition to the conventional
in and out labels. The existence of the null sign could stop the formation of a
closed isosurface derived from the bisector of the in/out regions. Further, we
propose a dedicated learning framework to effectively learn 3PSDF without
worrying about the vanishing gradient due to the null labels. Experimental
results show that our approach outperforms the previous state-of-the-art
methods in a wide range of benchmarks both quantitatively and qualitatively.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 07:24:04 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Chen",
"Weikai",
""
],
[
"Lin",
"Cheng",
""
],
[
"Li",
"Weiyang",
""
],
[
"Yang",
"Bo",
""
]
] |
new_dataset
| 0.998833 |
2205.15599
|
Alp \"Oktem
|
Alp \"Oktem, Rodolfo Zevallos, Yasmin Moslem, G\"une\c{s} \"Ozt\"urk,
Karen \c{S}arhon
|
Preparing an Endangered Language for the Digital Age: The Case of
Judeo-Spanish
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We develop machine translation and speech synthesis systems to complement the
efforts of revitalizing Judeo-Spanish, the exiled language of Sephardic Jews,
which survived for centuries, but now faces the threat of extinction in the
digital age. Building on resources created by the Sephardic community of Turkey
and elsewhere, we create corpora and tools that would help preserve this
language for future generations. For machine translation, we first develop a
Spanish to Judeo-Spanish rule-based machine translation system, in order to
generate large volumes of synthetic parallel data in the relevant language
pairs: Turkish, English and Spanish. Then, we train baseline neural machine
translation engines using this synthetic data and authentic parallel data
created from translations by the Sephardic community. For text-to-speech
synthesis, we present a 3.5 hour single speaker speech corpus for building a
neural speech synthesis engine. Resources, model weights and online inference
engines are shared publicly.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 08:26:33 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Öktem",
"Alp",
""
],
[
"Zevallos",
"Rodolfo",
""
],
[
"Moslem",
"Yasmin",
""
],
[
"Öztürk",
"Güneş",
""
],
[
"Şarhon",
"Karen",
""
]
] |
new_dataset
| 0.992374 |
2205.15627
|
Marco Antonio Stranisci
|
Marco Antonio Stranisci, Simona Frenda, Eleonora Ceccaldi, Valerio
Basile, Rossana Damiano, Viviana Patti
|
APPReddit: a Corpus of Reddit Posts Annotated for Appraisal
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Despite the large number of computational resources for emotion recognition,
there is a lack of data sets relying on appraisal models. According to
Appraisal theories, emotions are the outcome of a multi-dimensional evaluation
of events. In this paper, we present APPReddit, the first corpus of
non-experimental data annotated according to this theory. After describing its
development, we compare our resource with enISEAR, a corpus of events created
in an experimental setting and annotated for appraisal. Results show that the
two corpora can be mapped notwithstanding different typologies of data and
annotations schemes. A SVM model trained on APPReddit predicts four appraisal
dimensions without significant loss. Merging both corpora in a single training
set increases the prediction of 3 out of 4 dimensions. Such findings pave the
way to a better performing classification model for appraisal prediction.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 09:11:57 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Stranisci",
"Marco Antonio",
""
],
[
"Frenda",
"Simona",
""
],
[
"Ceccaldi",
"Eleonora",
""
],
[
"Basile",
"Valerio",
""
],
[
"Damiano",
"Rossana",
""
],
[
"Patti",
"Viviana",
""
]
] |
new_dataset
| 0.956352 |
2205.15648
|
Xing Wang
|
Xing Wang, Alvin Lim
|
Reliable and Efficient Broadcast Routing Using Multipoint Relays Over
VANET For Vehicle Platooning
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we design and implement a reliable broadcast algorithm over a
VANET for supporting multi-hop forwarding of vehicle sensor and control packets
that will enable vehicles to platoon with each other in order to form a road
train behind the lead truck. In particular, we use multipoint relays (MPRs) for
packet transmission, which leads to more efficient communication in a VANET. We
evaluate the performance based on simulation by running a platooning simulation
application program, and show that with MPRs, the communication in the VANET to
form a road train is more efficient and reliable.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 09:39:31 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Wang",
"Xing",
""
],
[
"Lim",
"Alvin",
""
]
] |
new_dataset
| 0.988813 |
2205.15661
|
Seyed Ali Bahrainian
|
Seyed Ali Bahrainian, Sheridan Feucht, Carsten Eickhoff
|
NEWTS: A Corpus for News Topic-Focused Summarization
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Text summarization models are approaching human levels of fidelity. Existing
benchmarking corpora provide concordant pairs of full and abridged versions of
Web, news or, professional content. To date, all summarization datasets operate
under a one-size-fits-all paradigm that may not reflect the full range of
organic summarization needs. Several recently proposed models (e.g., plug and
play language models) have the capacity to condition the generated summaries on
a desired range of themes. These capacities remain largely unused and
unevaluated as there is no dedicated dataset that would support the task of
topic-focused summarization.
This paper introduces the first topical summarization corpus NEWTS, based on
the well-known CNN/Dailymail dataset, and annotated via online crowd-sourcing.
Each source article is paired with two reference summaries, each focusing on a
different theme of the source document. We evaluate a representative range of
existing techniques and analyze the effectiveness of different prompting
methods.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 10:01:38 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Bahrainian",
"Seyed Ali",
""
],
[
"Feucht",
"Sheridan",
""
],
[
"Eickhoff",
"Carsten",
""
]
] |
new_dataset
| 0.998565 |
2205.15757
|
Alex Shamis
|
Alex Shamis, Peter Pietzuch, Antoine Delignat-Lavaud, Andrew Paverd,
and Manuel Costa
|
Dropbear: Machine Learning Marketplaces made Trustworthy with Byzantine
Model Agreement
| null | null | null | null |
cs.DC cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Marketplaces for machine learning (ML) models are emerging as a way for
organizations to monetize models. They allow model owners to retain control
over hosted models by using cloud resources to execute ML inference requests
for a fee, preserving model confidentiality. Clients that rely on hosted models
require trustworthy inference results, even when models are managed by third
parties. While the resilience and robustness of inference results can be
improved by combining multiple independent models, such support is unavailable
in today's marketplaces.
We describe Dropbear, the first ML model marketplace that provides clients
with strong integrity guarantees by combining results from multiple models in a
trustworthy fashion. Dropbear replicates inference computation across a model
group, which consists of multiple cloud-based GPU nodes belonging to different
model owners. Clients receive inference certificates that prove agreement using
a Byzantine consensus protocol, even under model heterogeneity and concurrent
model updates. To improve performance, Dropbear batches inference and consensus
operations separately: it first performs the inference computation across a
model group, before ordering requests and model updates. Despite its strong
integrity guarantees, Dropbear's performance matches that of state-of-the-art
ML inference systems: deployed across 3 cloud sites, it handles 800 requests/s
with ImageNet models.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 12:45:56 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Shamis",
"Alex",
""
],
[
"Pietzuch",
"Peter",
""
],
[
"Delignat-Lavaud",
"Antoine",
""
],
[
"Paverd",
"Andrew",
""
],
[
"Costa",
"Manuel",
""
]
] |
new_dataset
| 0.986735 |
2205.15768
|
Mark Boss
|
Mark Boss, Andreas Engelhardt, Abhishek Kar, Yuanzhen Li, Deqing Sun,
Jonathan T. Barron, Hendrik P. A. Lensch, Varun Jampani
|
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary
Image collections
| null | null | null | null |
cs.CV cs.GR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Inverse rendering of an object under entirely unknown capture conditions is a
fundamental challenge in computer vision and graphics. Neural approaches such
as NeRF have achieved photorealistic results on novel view synthesis, but they
require known camera poses. Solving this problem with unknown camera poses is
highly challenging as it requires joint optimization over shape, radiance, and
pose. This problem is exacerbated when the input images are captured in the
wild with varying backgrounds and illuminations. Standard pose estimation
techniques fail in such image collections in the wild due to very few estimated
correspondences across images. Furthermore, NeRF cannot relight a scene under
any illumination, as it operates on radiance (the product of reflectance and
illumination). We propose a joint optimization framework to estimate the shape,
BRDF, and per-image camera pose and illumination. Our method works on
in-the-wild online image collections of an object and produces relightable 3D
assets for several use-cases such as AR/VR. To our knowledge, our method is the
first to tackle this severely unconstrained task with minimal user interaction.
Project page: https://markboss.me/publication/2022-samurai/ Video:
https://youtu.be/LlYuGDjXp-8
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 13:16:48 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Boss",
"Mark",
""
],
[
"Engelhardt",
"Andreas",
""
],
[
"Kar",
"Abhishek",
""
],
[
"Li",
"Yuanzhen",
""
],
[
"Sun",
"Deqing",
""
],
[
"Barron",
"Jonathan T.",
""
],
[
"Lensch",
"Hendrik P. A.",
""
],
[
"Jampani",
"Varun",
""
]
] |
new_dataset
| 0.996581 |
2205.15848
|
Qiancheng Fu
|
Qiancheng Fu, Qingshan Xu, Yew-Soon Ong, Wenbing Tao
|
Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for
Multi-view Reconstruction
| null | null | null | null |
cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, neural implicit surfaces learning by volume rendering has become
popular for multi-view reconstruction. However, one key challenge remains:
existing approaches lack explicit multi-view geometry constraints, hence
usually fail to generate geometry consistent surface reconstruction. To address
this challenge, we propose geometry-consistent neural implicit surfaces
learning for multi-view reconstruction. We theoretically analyze that there
exists a gap between the volume rendering integral and point-based signed
distance function (SDF) modeling. To bridge this gap, we directly locate the
zero-level set of SDF networks and explicitly perform multi-view geometry
optimization by leveraging the sparse geometry from structure from motion (SFM)
and photometric consistency in multi-view stereo. This makes our SDF
optimization unbiased and allows the multi-view geometry constraints to focus
on the true surface optimization. Extensive experiments show that our proposed
method achieves high-quality surface reconstruction in both complex thin
structures and large smooth regions, thus outperforming the state-of-the-arts
by a large margin.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 14:52:07 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Fu",
"Qiancheng",
""
],
[
"Xu",
"Qingshan",
""
],
[
"Ong",
"Yew-Soon",
""
],
[
"Tao",
"Wenbing",
""
]
] |
new_dataset
| 0.985663 |
2205.15868
|
Ming Ding
|
Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, Jie Tang
|
CogVideo: Large-scale Pretraining for Text-to-Video Generation via
Transformers
| null | null | null | null |
cs.CV cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large-scale pretrained transformers have created milestones in text (GPT-3)
and text-to-image (DALL-E and CogView) generation. Its application to video
generation is still facing many challenges: The potential huge computation cost
makes the training from scratch unaffordable; The scarcity and weak relevance
of text-video datasets hinder the model understanding complex movement
semantics. In this work, we present 9B-parameter transformer CogVideo, trained
by inheriting a pretrained text-to-image model, CogView2. We also propose
multi-frame-rate hierarchical training strategy to better align text and video
clips. As (probably) the first open-source large-scale pretrained text-to-video
model, CogVideo outperforms all publicly available models at a large margin in
machine and human evaluations.
|
[
{
"version": "v1",
"created": "Sun, 29 May 2022 19:02:15 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Hong",
"Wenyi",
""
],
[
"Ding",
"Ming",
""
],
[
"Zheng",
"Wendi",
""
],
[
"Liu",
"Xinghan",
""
],
[
"Tang",
"Jie",
""
]
] |
new_dataset
| 0.982818 |
2205.15915
|
Lorenzo Ceragioli
|
Lorenzo Ceragioli, Letterio Galletta, Pierpaolo Degano and David Basin
|
IFCIL: An Information Flow Configuration Language for SELinux (Extended
Version)
|
Extended version of the paper "IFCIL: An Information Flow
Configuration Language for SELinux"
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Security Enhanced Linux (SELinux) is a security architecture for Linux
implementing mandatory access control. It has been used in numerous
security-critical contexts ranging from servers to mobile devices. But this is
challenging as SELinux security policies are difficult to write, understand,
and maintain. Recently, the intermediate language CIL was introduced to foster
the development of high-level policy languages and to write structured
configurations. However, CIL lacks mechanisms for ensuring that the resulting
configurations obey desired information flow policies. To remedy this, we
propose IFCIL, a backward compatible extension of CIL for specifying
fine-grained information flow requirements for CIL configurations. Using IFCIL,
administrators can express, e.g., confidentiality, integrity, and
non-interference properties. We also provide a tool to statically verify these
requirements.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 16:03:53 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Ceragioli",
"Lorenzo",
""
],
[
"Galletta",
"Letterio",
""
],
[
"Degano",
"Pierpaolo",
""
],
[
"Basin",
"David",
""
]
] |
new_dataset
| 0.997925 |
2205.15930
|
Elmurod Kuriyozov
|
Sanatbek Matlatipov, Hulkar Rahimboeva, Jaloliddin Rajabov, Elmurod
Kuriyozov
|
Uzbek Sentiment Analysis based on local Restaurant Reviews
|
The International Conference on Agglutinative Language Technologies
as a challenge of Natural Language Processing (ALTNLP) 2022, Koper, Slovenia
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Extracting useful information for sentiment analysis and classification
problems from a big amount of user-generated feedback, such as restaurant
reviews, is a crucial task of natural language processing, which is not only
for customer satisfaction where it can give personalized services, but can also
influence the further development of a company. In this paper, we present a
work done on collecting restaurant reviews data as a sentiment analysis dataset
for the Uzbek language, a member of the Turkic family which is heavily affected
by the low-resource constraint, and provide some further analysis of the novel
dataset by evaluation using different techniques, from logistic regression
based models, to support vector machines, and even deep learning models, such
as recurrent neural networks, as well as convolutional neural networks. The
paper includes detailed information on how the data was collected, how it was
pre-processed for better quality optimization, as well as experimental setups
for the evaluation process. The overall evaluation results indicate that by
performing pre-processing steps, such as stemming for agglutinative languages,
the system yields better results, eventually achieving 91% accuracy result in
the best performing model
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 16:21:00 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Matlatipov",
"Sanatbek",
""
],
[
"Rahimboeva",
"Hulkar",
""
],
[
"Rajabov",
"Jaloliddin",
""
],
[
"Kuriyozov",
"Elmurod",
""
]
] |
new_dataset
| 0.999744 |
2205.15943
|
Cameron Ballard
|
Cameron Ballard, Ian Goldstein, Pulak Mehta, Genesis Smothers, Kejsi
Take, Victoria Zhong, Rachel Greenstadt, Tobias Lauinger, Damon McCoy
|
Conspiracy Brokers: Understanding the Monetization of YouTube Conspiracy
Theories
| null |
WWW 2022 Proceedings of the ACM Web Conference, April 2022, Pages
2707-2718
|
10.1145/3485447.3512142
| null |
cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Conspiracy theories are increasingly a subject of research interest as
society grapples with their rapid growth in areas such as politics or public
health. Previous work has established YouTube as one of the most popular sites
for people to host and discuss different theories. In this paper, we present an
analysis of monetization methods of conspiracy theorist YouTube creators and
the types of advertisers potentially targeting this content. We collect 184,218
ad impressions from 6,347 unique advertisers found on conspiracy-focused
channels and mainstream YouTube content. We classify the ads into business
categories and compare their prevalence between conspiracy and mainstream
content. We also identify common offsite monetization methods. In comparison
with mainstream content, conspiracy videos had similar levels of ads from
well-known brands, but an almost eleven times higher prevalence of likely
predatory or deceptive ads. Additionally, we found that conspiracy channels
were more than twice as likely as mainstream channels to use offsite
monetization methods, and 53% of the demonetized channels we observed were
linking to third-party sites for alternative monetization opportunities. Our
results indicate that conspiracy theorists on YouTube had many potential
avenues to generate revenue, and that predatory ads were more frequently served
for conspiracy videos.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 16:42:52 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Ballard",
"Cameron",
""
],
[
"Goldstein",
"Ian",
""
],
[
"Mehta",
"Pulak",
""
],
[
"Smothers",
"Genesis",
""
],
[
"Take",
"Kejsi",
""
],
[
"Zhong",
"Victoria",
""
],
[
"Greenstadt",
"Rachel",
""
],
[
"Lauinger",
"Tobias",
""
],
[
"McCoy",
"Damon",
""
]
] |
new_dataset
| 0.972357 |
2205.15955
|
Junlin Han
|
Junlin Han, Lars Petersson, Hongdong Li, Ian Reid
|
CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping
|
Code: https://github.com/JunlinHan/CropMix
| null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a simple method, CropMix, for the purpose of producing a rich
input distribution from the original dataset distribution. Unlike single random
cropping, which may inadvertently capture only limited information, or
irrelevant information, like pure background, unrelated objects, etc, we crop
an image multiple times using distinct crop scales, thereby ensuring that
multi-scale information is captured. The new input distribution, serving as
training data, useful for a number of vision tasks, is then formed by simply
mixing multiple cropped views. We first demonstrate that CropMix can be
seamlessly applied to virtually any training recipe and neural network
architecture performing classification tasks. CropMix is shown to improve the
performance of image classifiers on several benchmark tasks across-the-board
without sacrificing computational simplicity and efficiency. Moreover, we show
that CropMix is of benefit to both contrastive learning and masked image
modeling towards more powerful representations, where preferable results are
achieved when learned representations are transferred to downstream tasks. Code
is available at GitHub.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 16:57:28 GMT"
}
] | 2022-06-01T00:00:00 |
[
[
"Han",
"Junlin",
""
],
[
"Petersson",
"Lars",
""
],
[
"Li",
"Hongdong",
""
],
[
"Reid",
"Ian",
""
]
] |
new_dataset
| 0.995474 |
1907.06357
|
Francisco Revson Fernandes Pereira
|
Francisco Revson F. Pereira, Ruud Pellikaan and Giuliano Gadioli La
Guardia and Francisco Marcos de Assis
|
Entanglement-assisted Quantum Codes from Algebraic Geometry Codes
|
Some results in this paper were presented at the 2019 IEEE
International Symposium on Information Theory
| null |
10.1109/TIT.2021.3113367
| null |
cs.IT math.IT quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantum error correcting codes play the role of suppressing noise and
decoherence in quantum systems by introducing redundancy. Some strategies can
be used to improve the parameters of these codes. For example, entanglement can
provide a way for quantum error correcting codes to achieve higher rates than
the one obtained via the traditional stabilizer formalism. Such codes are
called entanglement-assisted quantum (QUENTA) codes. In this paper, we use
algebraic geometry codes to construct several families of QUENTA codes via the
Euclidean and the Hermitian construction. Two of the families created have
maximal entanglement and have quantum Singleton defect equal to zero or one.
Comparing the other families with the codes with the respective quantum
Gilbert-Varshamov bound, we show that our codes have a rate that surpasses that
bound. At the end, asymptotically good towers of linear complementary dual
codes are used to obtain asymptotically good families of maximal entanglement
QUENTA codes. Furthermore, a simple comparison with the quantum
Gilbert-Varshamov bound demonstrates that using our construction it is possible
to create an asymptotically family of QUENTA codes that exceeds this bound.
|
[
{
"version": "v1",
"created": "Mon, 15 Jul 2019 08:08:21 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Aug 2019 09:57:20 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Pereira",
"Francisco Revson F.",
""
],
[
"Pellikaan",
"Ruud",
""
],
[
"La Guardia",
"Giuliano Gadioli",
""
],
[
"de Assis",
"Francisco Marcos",
""
]
] |
new_dataset
| 0.999798 |
2002.00911
|
Mathieu Gonzalez
|
Mathieu Gonzalez, Amine Kacete, Albert Murienne, Eric Marchand
|
L6DNet: Light 6 DoF Network for Robust and Precise Object Pose
Estimation with Small Datasets
|
This work has been accepted at IEEE Robotics and Automation Letters
| null |
10.1109/LRA.2021.3062605
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Estimating the 3D pose of an object is a challenging task that can be
considered within augmented reality or robotic applications. In this paper, we
propose a novel approach to perform 6 DoF object pose estimation from a single
RGB-D image. We adopt a hybrid pipeline in two stages: data-driven and
geometric respectively. The data-driven step consists of a classification CNN
to estimate the object 2D location in the image from local patches, followed by
a regression CNN trained to predict the 3D location of a set of keypoints in
the camera coordinate system. To extract the pose information, the geometric
step consists in aligning the 3D points in the camera coordinate system with
the corresponding 3D points in world coordinate system by minimizing a
registration error, thus computing the pose. Our experiments on the standard
dataset LineMod show that our approach is more robust and accurate than
state-of-the-art methods. The approach is also validated to achieve a 6 DoF
positioning task by visual servoing.
|
[
{
"version": "v1",
"created": "Mon, 3 Feb 2020 17:41:29 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Feb 2020 17:02:45 GMT"
},
{
"version": "v3",
"created": "Tue, 25 Feb 2020 07:47:38 GMT"
},
{
"version": "v4",
"created": "Thu, 15 Oct 2020 14:03:03 GMT"
},
{
"version": "v5",
"created": "Thu, 7 Jan 2021 08:18:10 GMT"
},
{
"version": "v6",
"created": "Sun, 29 May 2022 20:51:19 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Gonzalez",
"Mathieu",
""
],
[
"Kacete",
"Amine",
""
],
[
"Murienne",
"Albert",
""
],
[
"Marchand",
"Eric",
""
]
] |
new_dataset
| 0.997587 |
2006.04583
|
Leandro Montero
|
Leandro Montero
|
Vertex removal in biclique graphs
| null | null | null | null |
cs.DM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A \textit{biclique} is a maximal induced complete bipartite subgraph. The
\textit{biclique graph} of a graph $H$, denoted by $KB(H)$, is the intersection
graph of the family of all bicliques of $H$. In this work we address the
following question: Given a biclique graph $G=KB(H)$, is it possible to remove
a vertex $q$ of $G$, such that $G - \{q\}$ is a biclique graph? And if
possible, can we obtain a graph $H'$ such that $G - \{q\} = KB(H')$? We show
that the general question has a "no" for answer. However, we prove that if $G$
has a vertex $q$ such that $d(q) = 2$, then $G-\{q\}$ is a biclique graph and
we show how to obtain $H'$.
|
[
{
"version": "v1",
"created": "Mon, 8 Jun 2020 13:30:34 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Mar 2022 09:50:19 GMT"
},
{
"version": "v3",
"created": "Mon, 30 May 2022 13:58:25 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Montero",
"Leandro",
""
]
] |
new_dataset
| 0.987309 |
2008.05440
|
Jie Yang
|
Jie Yang, Kaichun Mo, Yu-Kun Lai, Leonidas J. Guibas, Lin Gao
|
DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape
Generation
|
Accept to ACM Transaction on Graphics 2022, 26 pages
| null | null | null |
cs.GR cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
D shape generation is a fundamental operation in computer graphics. While
significant progress has been made, especially with recent deep generative
models, it remains a challenge to synthesize high-quality shapes with rich
geometric details and complex structure, in a controllable manner. To tackle
this, we introduce DSG-Net, a deep neural network that learns a disentangled
structured and geometric mesh representation for 3D shapes, where two key
aspects of shapes, geometry, and structure, are encoded in a synergistic manner
to ensure plausibility of the generated shapes, while also being disentangled
as much as possible. This supports a range of novel shape generation
applications with disentangled control, such as interpolation of structure
(geometry) while keeping geometry (structure) unchanged. To achieve this, we
simultaneously learn structure and geometry through variational autoencoders
(VAEs) in a hierarchical manner for both, with bijective mappings at each
level. In this manner, we effectively encode geometry and structure in separate
latent spaces, while ensuring their compatibility: the structure is used to
guide the geometry and vice versa. At the leaf level, the part geometry is
represented using a conditional part VAE, to encode high-quality geometric
details, guided by the structure context as the condition. Our method not only
supports controllable generation applications but also produces high-quality
synthesized shapes, outperforming state-of-the-art methods. The code has been
released at https://github.com/IGLICT/DSG-Net.
|
[
{
"version": "v1",
"created": "Wed, 12 Aug 2020 17:06:51 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Aug 2020 02:38:45 GMT"
},
{
"version": "v3",
"created": "Mon, 24 May 2021 14:45:26 GMT"
},
{
"version": "v4",
"created": "Sat, 28 May 2022 17:40:15 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Yang",
"Jie",
""
],
[
"Mo",
"Kaichun",
""
],
[
"Lai",
"Yu-Kun",
""
],
[
"Guibas",
"Leonidas J.",
""
],
[
"Gao",
"Lin",
""
]
] |
new_dataset
| 0.998164 |
2103.04807
|
Peter Steiner
|
Peter Steiner (1), Azarakhsh Jalalvand (2), Simon Stone (1), Peter
Birkholz (2) ((1) Institute for Acoustics and Speech Communication,
Technische Universit\"at Dresden, Dresden, Germany, (2) IDLab, Ghent
University - imec, Ghent, Belgium)
|
PyRCN: A Toolbox for Exploration and Application of Reservoir Computing
Networks
|
Preprint accepted for publication in Engineering Applications of
Artificial Intelligence
|
Engineering Applications of Artificial Intelligence 113 (2022)
104964
|
10.1016/j.engappai.2022.104964
| null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Reservoir Computing Networks (RCNs) belong to a group of machine learning
techniques that project the input space non-linearly into a high-dimensional
feature space, where the underlying task can be solved linearly. Popular
variants of RCNs are capable of solving complex tasks equivalently to widely
used deep neural networks, but with a substantially simpler training paradigm
based on linear regression. In this paper, we show how to uniformly describe
RCNs with small and clearly defined building blocks, and we introduce the
Python toolbox PyRCN (Python Reservoir Computing Networks) for optimizing,
training and analyzing RCNs on arbitrarily large datasets. The tool is based on
widely-used scientific packages and complies with the scikit-learn interface
specification. It provides a platform for educational and exploratory analyses
of RCNs, as well as a framework to apply RCNs on complex tasks including
sequence processing. With a small number of building blocks, the framework
allows the implementation of numerous different RCN architectures. We provide
code examples on how to set up RCNs for time series prediction and for sequence
classification tasks. PyRCN is around ten times faster than reference toolboxes
on a benchmark task while requiring substantially less boilerplate code.
|
[
{
"version": "v1",
"created": "Mon, 8 Mar 2021 15:00:48 GMT"
},
{
"version": "v2",
"created": "Mon, 11 Oct 2021 14:27:14 GMT"
},
{
"version": "v3",
"created": "Tue, 10 May 2022 13:14:28 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Steiner",
"Peter",
""
],
[
"Jalalvand",
"Azarakhsh",
""
],
[
"Stone",
"Simon",
""
],
[
"Birkholz",
"Peter",
""
]
] |
new_dataset
| 0.954929 |
2104.04798
|
Kaleem Nawaz Khan Mr.
|
Kaleem Nawaz Khan, Najeeb Ullah, Sikandar Ali, Muhammad Salman Khan,
Mohammad Nauman and Anwar Ghani
|
Op2Vec: An Opcode Embedding Technique and Dataset Design for End-to-End
Detection of Android Malware
| null | null |
10.1155/2022/3710968
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Android is one of the leading operating systems for smart phones in terms of
market share and usage. Unfortunately, it is also an appealing target for
attackers to compromise its security through malicious applications. To tackle
this issue, domain experts and researchers are trying different techniques to
stop such attacks. All the attempts of securing Android platform are somewhat
successful. However, existing detection techniques have severe shortcomings,
including the cumbersome process of feature engineering. Designing
representative features require expert domain knowledge. There is a need for
minimizing human experts' intervention by circumventing handcrafted feature
engineering. Deep learning could be exploited by extracting deep features
automatically. Previous work has shown that operational codes (opcodes) of
executables provide key information to be used with deep learning models for
detection process of malicious applications. The only challenge is to feed
opcodes information to deep learning models. Existing techniques use one-hot
encoding to tackle the challenge. However, the one-hot encoding scheme has
severe limitations. In this paper, we introduce; (1) a novel technique for
opcodes embedding, which we name Op2Vec, (2) based on the learned Op2Vec we
have developed a dataset for end-to-end detection of android malware.
Introducing the end-to-end Android malware detection technique avoids
expert-intensive handcrafted features extraction, and ensures automation. Some
of the recent deep learning-based techniques showed significantly improved
results when tested with the proposed approach and achieved an average
detection accuracy of 97.47%, precision of 0.976 and F1 score of 0.979.
|
[
{
"version": "v1",
"created": "Sat, 10 Apr 2021 15:56:37 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Mar 2022 16:30:43 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Khan",
"Kaleem Nawaz",
""
],
[
"Ullah",
"Najeeb",
""
],
[
"Ali",
"Sikandar",
""
],
[
"Khan",
"Muhammad Salman",
""
],
[
"Nauman",
"Mohammad",
""
],
[
"Ghani",
"Anwar",
""
]
] |
new_dataset
| 0.999461 |
2107.10938
|
Shi Zhou Dr.
|
Jie Li, Vasileios Giotsas, Yangyang Wang, Shi Zhou
|
BGP-Multipath Routing in the Internet
|
38 pages, 8 figures, 8 tables
|
Published in IEEE Transactions on Network and Service Management
(TNSM) in May 2022
|
10.1109/TNSM.2022.3177471
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
BGP-Multipath (BGP-M) is a multipath routing technique for load balancing.
Distinct from other techniques deployed at a router inside an Autonomous System
(AS), BGP-M is deployed at a border router that has installed multiple
inter-domain border links to a neighbour AS. It uses the equal-cost multi-path
(ECMP) function of a border router to share traffic to a destination prefix on
different border links. Despite recent research interests in multipath routing,
there is little study on BGP-M. Here we provide the first measurement and a
comprehensive analysis of BGP-M routing in the Internet. We extracted
information on BGP-M from query data collected from Looking Glass (LG) servers.
We revealed that BGP-M has already been extensively deployed and used in the
Internet. A particular example is Hurricane Electric (AS6939), a Tier-1 network
operator, which has implemented >1,000 cases of BGP-M at 69 of its border
routers to prefixes in 611 of its neighbour ASes, including many hyper-giant
ASes and large content providers, on both IPv4 and IPv6 Internet. We examined
the distribution and operation of BGP-M. We also ran traceroute using RIPE
Atlas to infer the routing paths, the schemes of traffic allocation, and the
delay on border links. This study provided the state-of-the-art knowledge on
BGP-M with novel insights into the unique features and the distinct advantages
of BGP-M as an effective and readily available technique for load balancing.
|
[
{
"version": "v1",
"created": "Thu, 22 Jul 2021 21:50:40 GMT"
},
{
"version": "v2",
"created": "Sun, 29 May 2022 18:45:19 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Li",
"Jie",
""
],
[
"Giotsas",
"Vasileios",
""
],
[
"Wang",
"Yangyang",
""
],
[
"Zhou",
"Shi",
""
]
] |
new_dataset
| 0.998935 |
2108.05539
|
Hongtao Wu
|
Hongtao Wu, Xin Meng, Sipu Ruan, Gregory Chirikjian
|
Put the Bear on the Chair! Intelligent Robot Interaction with Previously
Unseen Chairs via Robot Imagination
|
IEEE ICRA 2022. Video demos are available at
https://chirikjianlab.github.io/putbearonchair/
| null | null | null |
cs.RO cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we study the problem of autonomously seating a teddy bear on a
previously unseen chair. To achieve this goal, we present a novel method for
robots to imagine the sitting pose of the bear by physically simulating a
virtual humanoid agent sitting on the chair. We also develop a robotic system
which leverages motion planning to plan SE(2) motions for a humanoid robot to
walk to the chair and whole-body motions to put the bear on it. Furthermore, to
cope with cases where the chair is not in an accessible pose for placing the
bear, a human assistance module is introduced for a human to follow language
instructions given by the robot to rotate the chair and help make the chair
accessible. We implement our method with a robot arm and a humanoid robot. We
calibrate the proposed system with 3 chairs and test on 12 previously unseen
chairs in both accessible and inaccessible poses extensively. Results show that
our method enables the robot to autonomously seat the teddy bear on the 12
previously unseen chairs with a very high success rate. The human assistance
module is also shown to be very effective in changing the accessibility of the
chair. Video demos and more details are available at
https://chirikjianlab.github.io/putbearonchair/.
|
[
{
"version": "v1",
"created": "Thu, 12 Aug 2021 05:12:40 GMT"
},
{
"version": "v2",
"created": "Mon, 30 May 2022 07:19:15 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Wu",
"Hongtao",
""
],
[
"Meng",
"Xin",
""
],
[
"Ruan",
"Sipu",
""
],
[
"Chirikjian",
"Gregory",
""
]
] |
new_dataset
| 0.993233 |
2108.07425
|
Xutong Jin
|
Xutong Jin, Sheng Li, Guoping Wang, Dinesh Manocha
|
NeuralSound: Learning-based Modal Sound Synthesis With Acoustic Transfer
| null | null | null | null |
cs.SD cs.GR eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
We present a novel learning-based modal sound synthesis approach that
includes a mixed vibration solver for modal analysis and an end-to-end sound
radiation network for acoustic transfer. Our mixed vibration solver consists of
a 3D sparse convolution network and a Locally Optimal Block Preconditioned
Conjugate Gradient module (LOBPCG) for iterative optimization. Moreover, we
highlight the correlation between a standard modal vibration solver and our
network architecture. Our radiation network predicts the Far-Field Acoustic
Transfer maps (FFAT Maps) from the surface vibration of the object. The overall
running time of our learning method for any new object is less than one second
on a GTX 3080 Ti GPU while maintaining a high sound quality close to the ground
truth that is computed using standard numerical methods. We also evaluate the
numerical accuracy and perceptual accuracy of our sound synthesis approach on
different objects corresponding to various materials.
|
[
{
"version": "v1",
"created": "Tue, 17 Aug 2021 03:44:45 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Apr 2022 15:23:26 GMT"
},
{
"version": "v3",
"created": "Fri, 29 Apr 2022 10:16:35 GMT"
},
{
"version": "v4",
"created": "Sat, 28 May 2022 04:38:07 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Jin",
"Xutong",
""
],
[
"Li",
"Sheng",
""
],
[
"Wang",
"Guoping",
""
],
[
"Manocha",
"Dinesh",
""
]
] |
new_dataset
| 0.985837 |
2109.02818
|
Hao Chen
|
Hao Chen
|
List-decodable Codes and Covering Codes
|
54 pages, corrected version
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/publicdomain/zero/1.0/
|
The list-decodable code has been an active topic in theoretical computer
science.There are general results about the list-decodability to the Johnson
radius and the list-decoding capacity theorem. In this paper we show that
rates, list-decodable radius and list sizes are closely related to the
classical topic of covering codes. We prove new general simple but strong upper
bounds for list-decodable codes in general finite metric spaces based on
various covering codes. The general covering code upper bounds can be applied
to the case that the volumes of the balls depend on the centers, not only on
the radius. Then any good upper bound on the covering radius or the size of
covering code imply a good upper bound on the sizes of list-decodable codes.
Our results give exponential improvements on the recent generalized Singleton
upper bound in STOC 2020 for Hamming metric list-decodable codes, when the code
lengths are large. A generalized Singleton upper bound for average-radius
list-decodable codes is also given from our general covering code upper bound.
We also suggest to study the combinatorial covering list-decodable codes as a
natural generalization of combinatorial list-decodable codes. We apply our
general covering code upper bounds for list-decodable rank-metric codes,
list-decodable subspace codes, list-decodable insertion codes list-decodable
deletion codes,list-decodable sum-rank-metric codes and list decodable
permutation codes. Some new better results about non-list-decodability of
rank-metric codes, subspace codes, sum-rank-metric codes and permutation codes
with various metrics are obtained.
|
[
{
"version": "v1",
"created": "Tue, 7 Sep 2021 02:04:41 GMT"
},
{
"version": "v10",
"created": "Thu, 25 Nov 2021 23:02:44 GMT"
},
{
"version": "v11",
"created": "Wed, 5 Jan 2022 11:15:03 GMT"
},
{
"version": "v12",
"created": "Mon, 17 Jan 2022 23:32:54 GMT"
},
{
"version": "v13",
"created": "Fri, 27 May 2022 22:40:14 GMT"
},
{
"version": "v2",
"created": "Fri, 17 Sep 2021 13:30:00 GMT"
},
{
"version": "v3",
"created": "Sun, 3 Oct 2021 03:20:08 GMT"
},
{
"version": "v4",
"created": "Tue, 12 Oct 2021 04:03:10 GMT"
},
{
"version": "v5",
"created": "Tue, 19 Oct 2021 07:42:22 GMT"
},
{
"version": "v6",
"created": "Mon, 25 Oct 2021 16:05:38 GMT"
},
{
"version": "v7",
"created": "Fri, 29 Oct 2021 00:42:26 GMT"
},
{
"version": "v8",
"created": "Fri, 19 Nov 2021 10:16:02 GMT"
},
{
"version": "v9",
"created": "Mon, 22 Nov 2021 22:15:57 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Chen",
"Hao",
""
]
] |
new_dataset
| 0.997738 |
2112.09329
|
Mikaela Angelina Uy
|
Mikaela Angelina Uy, Yen-yu Chang, Minhyuk Sung, Purvi Goel, Joseph
Lambourne, Tolga Birdal, Leonidas Guibas
|
Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion
Cylinders
|
CVPR 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We propose Point2Cyl, a supervised network transforming a raw 3D point cloud
to a set of extrusion cylinders. Reverse engineering from a raw geometry to a
CAD model is an essential task to enable manipulation of the 3D data in shape
editing software and thus expand their usages in many downstream applications.
Particularly, the form of CAD models having a sequence of extrusion cylinders
-- a 2D sketch plus an extrusion axis and range -- and their boolean
combinations is not only widely used in the CAD community/software but also has
great expressivity of shapes, compared to having limited types of primitives
(e.g., planes, spheres, and cylinders). In this work, we introduce a neural
network that solves the extrusion cylinder decomposition problem in a
geometry-grounded way by first learning underlying geometric proxies.
Precisely, our approach first predicts per-point segmentation, base/barrel
labels and normals, then estimates for the underlying extrusion parameters in
differentiable and closed-form formulations. Our experiments show that our
approach demonstrates the best performance on two recent CAD datasets, Fusion
Gallery and DeepCAD, and we further showcase our approach on reverse
engineering and editing.
|
[
{
"version": "v1",
"created": "Fri, 17 Dec 2021 05:22:28 GMT"
},
{
"version": "v2",
"created": "Mon, 30 May 2022 00:55:47 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Uy",
"Mikaela Angelina",
""
],
[
"Chang",
"Yen-yu",
""
],
[
"Sung",
"Minhyuk",
""
],
[
"Goel",
"Purvi",
""
],
[
"Lambourne",
"Joseph",
""
],
[
"Birdal",
"Tolga",
""
],
[
"Guibas",
"Leonidas",
""
]
] |
new_dataset
| 0.999806 |
2201.01810
|
Kamil Erdayandi
|
Kamil Erdayandi, Amrit Paudel, Lucas Cordeiro, Mustafa A. Mustafa
|
Privacy-Friendly Peer-to-Peer Energy Trading: A Game Theoretical
Approach
|
To be published in IEEE Power & Energy Society General Meeting (GM),
2022
| null | null | null |
cs.GT cs.AI cs.CR cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a decentralized, privacy-friendly energy trading
platform (PFET) based on game theoretical approach - specifically Stackelberg
competition. Unlike existing trading schemes, PFET provides a competitive
market in which prices and demands are determined based on competition, and
computations are performed in a decentralized manner which does not rely on
trusted third parties. It uses homomorphic encryption cryptosystem to encrypt
sensitive information of buyers and sellers such as sellers$'$ prices and
buyers$'$ demands. Buyers calculate total demand on particular seller using an
encrypted data and sensitive buyer profile data is hidden from sellers. Hence,
privacy of both sellers and buyers is preserved. Through privacy analysis and
performance evaluation, we show that PFET preserves users$'$ privacy in an
efficient manner.
|
[
{
"version": "v1",
"created": "Wed, 5 Jan 2022 20:41:32 GMT"
},
{
"version": "v2",
"created": "Sun, 29 May 2022 00:27:56 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Erdayandi",
"Kamil",
""
],
[
"Paudel",
"Amrit",
""
],
[
"Cordeiro",
"Lucas",
""
],
[
"Mustafa",
"Mustafa A.",
""
]
] |
new_dataset
| 0.975774 |
2201.02374
|
Haisen Zhao
|
Fanchao Zhong and Yonglai Xu and Haisen Zhao and Lin Lu
|
As-Continuous-As-Possible Extrusion Fabrication of Surface Models
|
16 pages, 23 figures
| null | null | null |
cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a novel computational framework for optimizing the toolpath
continuity in fabricating surface models on an extrusion-based 3D printer.
Toolpath continuity has been a critical issue for extrusion-based fabrications
that affects both quality and efficiency. Transfer moves cause non-smoothor
bumpy surfaces and get worse for materials with large inertia like clay. For
surface models, the effects of continuity are even more severe, in terms of
surface quality and model stability. In this paper, we introduce an original
criterion "one-path-patch" (OPP), for representing a shell surface patch that
can be traversed in one path considering fabrication constraints. We study the
properties of an OPP and the merging operations for OPPs, and propose a
bottom-up OPP merging procedure for decomposing the given shell surface into a
minimal number of OPPs and generating the "as-continuous-as-possible" (ACAP)
toolpath. Furthermore, we customize the path planning algorithm with a curved
layer printing scheme, which reduces the staircase defect and improves the
toolpath continuity via possibly connecting multiple segments. We evaluate the
ACAP algorithm for both ceramic and thermoplastic materials, and results
demonstrate that it improves the fabrication of surface models in both surface
quality and efficiency.
|
[
{
"version": "v1",
"created": "Fri, 7 Jan 2022 09:18:59 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Jan 2022 20:22:50 GMT"
},
{
"version": "v3",
"created": "Sat, 28 May 2022 08:39:43 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Zhong",
"Fanchao",
""
],
[
"Xu",
"Yonglai",
""
],
[
"Zhao",
"Haisen",
""
],
[
"Lu",
"Lin",
""
]
] |
new_dataset
| 0.995073 |
2202.07133
|
Chuqing Hu
|
Chuqing Hu, Sinclair Hudson, Martin Ethier, Mohammad Al-Sharman, Derek
Rayside, William Melek
|
Sim-to-Real Domain Adaptation for Lane Detection and Classification in
Autonomous Driving
|
Accepted by IV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While supervised detection and classification frameworks in autonomous
driving require large labelled datasets to converge, Unsupervised Domain
Adaptation (UDA) approaches, facilitated by synthetic data generated from
photo-real simulated environments, are considered low-cost and less
time-consuming solutions. In this paper, we propose UDA schemes using
adversarial discriminative and generative methods for lane detection and
classification applications in autonomous driving. We also present Simulanes
dataset generator to create a synthetic dataset that is naturalistic utilizing
CARLA's vast traffic scenarios and weather conditions. The proposed UDA
frameworks take the synthesized dataset with labels as the source domain,
whereas the target domain is the unlabelled real-world data. Using adversarial
generative and feature discriminators, the learnt models are tuned to predict
the lane location and class in the target domain. The proposed techniques are
evaluated using both real-world and our synthetic datasets. The results
manifest that the proposed methods have shown superiority over other baseline
schemes in terms of detection and classification accuracy and consistency. The
ablation study reveals that the size of the simulation dataset plays important
roles in the classification performance of the proposed methods. Our UDA
frameworks are available at https://github.com/anita-hu/sim2real-lane-detection
and our dataset generator is released at https://github.com/anita-hu/simulanes
|
[
{
"version": "v1",
"created": "Tue, 15 Feb 2022 02:10:14 GMT"
},
{
"version": "v2",
"created": "Mon, 30 May 2022 12:12:24 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Hu",
"Chuqing",
""
],
[
"Hudson",
"Sinclair",
""
],
[
"Ethier",
"Martin",
""
],
[
"Al-Sharman",
"Mohammad",
""
],
[
"Rayside",
"Derek",
""
],
[
"Melek",
"William",
""
]
] |
new_dataset
| 0.962917 |
2203.05788
|
Xuyang Ma
|
Xuyang Ma, Han Wu, Du Xu, Katinka Wolter
|
CBlockSim: A Modular High-Performance Blockchain Simulator
| null | null | null | null |
cs.DC cs.PF
|
http://creativecommons.org/licenses/by/4.0/
|
Blockchain has attracted much attention from both academia and industry since
emerging in 2008. Due to the inconvenience of the deployment of large-scale
blockchains, blockchain simulators are used to facilitate blockchain design and
implementation. We evaluate state-of-the-art simulators applied to both Bitcoin
and Ethereum and find that they suffer from low performance and scalability
which are significant limitations. To build a more general and faster
blockchain simulator, we extend an existing blockchain simulator, i.e.
BlockSim. We add a network module integrated with a network topology generation
algorithm and a block propagation algorithm to generate a realistic blockchain
network and simulate the block propagation efficiently. We design a binary
transaction pool structure and migrate BlockSim from Python to C++ so that
bitwise operations can be used to accelerate the simulation and reduce memory
usage. Moreover, we modularize the simulator based on five primary blockchain
processes. Significant blockchain elements including consensus protocols (PoW
and PoS), information propagation algorithms (Gossip) and finalization rules
(Longest rule and GHOST rule) are implemented in individual modules and can be
combined flexibly to simulate different types of blockchains. Experiments
demonstrate that the new simulator reduces the simulation time by an order of
magnitude and improves scalability, enabling us to simulate more than ten
thousand nodes, roughly the size of the Bitcoin and Ethereum networks. Two
typical use cases are proposed to investigate network-related issues which are
not covered by most other simulators.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 08:03:19 GMT"
},
{
"version": "v2",
"created": "Mon, 30 May 2022 14:24:46 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Ma",
"Xuyang",
""
],
[
"Wu",
"Han",
""
],
[
"Xu",
"Du",
""
],
[
"Wolter",
"Katinka",
""
]
] |
new_dataset
| 0.998794 |
2203.13655
|
Ahmad Khajenezhad
|
Ahmad Khajenezhad and Seyed Ali Osia and Mahmood Karimian and Hamid
Beigy
|
Gransformer: Transformer-based Graph Generation
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Transformers have become widely used in modern models for various tasks such
as natural language processing and machine vision. This paper proposes
Gransformer, an algorithm for generating graphs based on the Transformer. We
extend a simple autoregressive Transformer encoder to exploit the structural
information of the given graph through efficient modifications. The attention
mechanism is modified to consider the presence or absence of edges between each
pair of nodes. We also introduce a graph-based familiarity measure between node
pairs that applies to both the attention and the positional encoding. This
measure of familiarity is based on message passing algorithms and contains
structural information about the graph. Furthermore, the proposed measure is
autoregressive, which allows our mode to acquire the necessary conditional
probabilities in a single forward pass. In the output layer, we also use a
masked autoencoder for density estimation to efficiently model the sequential
generation of dependent edges. Moreover, since we use BFS node orderings, we
propose a technique to prevent the model from generating isolated nodes without
connection to preceding nodes. We evaluate this method on two real-world
datasets and compare it with other state-of-the-art autoregressive graph
generation methods. Experimental results have shown that the proposed method
performs comparatively to these methods, including recurrent models and graph
convolutional networks.
|
[
{
"version": "v1",
"created": "Fri, 25 Mar 2022 14:05:12 GMT"
},
{
"version": "v2",
"created": "Mon, 30 May 2022 04:29:58 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Khajenezhad",
"Ahmad",
""
],
[
"Osia",
"Seyed Ali",
""
],
[
"Karimian",
"Mahmood",
""
],
[
"Beigy",
"Hamid",
""
]
] |
new_dataset
| 0.958502 |
2204.09903
|
Chunbo Lang
|
Chunbo Lang, Binfei Tu, Gong Cheng, Junwei Han
|
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot
Segmentation
|
accepted to IJCAI 2022 Long Oral
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Few-shot segmentation, which aims to segment unseen-class objects given only
a handful of densely labeled samples, has received widespread attention from
the community. Existing approaches typically follow the prototype learning
paradigm to perform meta-inference, which fails to fully exploit the underlying
information from support image-mask pairs, resulting in various segmentation
failures, e.g., incomplete objects, ambiguous boundaries, and distractor
activation. To this end, we propose a simple yet versatile framework in the
spirit of divide-and-conquer. Specifically, a novel self-reasoning scheme is
first implemented on the annotated support image, and then the coarse
segmentation mask is divided into multiple regions with different properties.
Leveraging effective masked average pooling operations, a series of
support-induced proxies are thus derived, each playing a specific role in
conquering the above challenges. Moreover, we devise a unique parallel decoder
structure that integrates proxies with similar attributes to boost the
discrimination power. Our proposed approach, named divide-and-conquer proxies
(DCP), allows for the development of appropriate and reliable information as a
guide at the "episode" level, not just about the object cues themselves.
Extensive experiments on PASCAL-5i and COCO-20i demonstrate the superiority of
DCP over conventional prototype-based approaches (up to 5~10% on average),
which also establishes a new state-of-the-art. Code is available at
github.com/chunbolang/DCP.
|
[
{
"version": "v1",
"created": "Thu, 21 Apr 2022 06:21:14 GMT"
},
{
"version": "v2",
"created": "Mon, 30 May 2022 12:28:14 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Lang",
"Chunbo",
""
],
[
"Tu",
"Binfei",
""
],
[
"Cheng",
"Gong",
""
],
[
"Han",
"Junwei",
""
]
] |
new_dataset
| 0.984872 |
2204.10050
|
Harish Tayyar Madabushi PhD
|
Harish Tayyar Madabushi, Edward Gow-Smith, Marcos Garcia, Carolina
Scarton, Marco Idiart, Aline Villavicencio
|
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence
Embedding
|
Data available at
https://github.com/H-TayyarMadabushi/SemEval_2022_Task2-idiomaticity and
competition website at
https://sites.google.com/view/semeval2022task2-idiomaticity
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
This paper presents the shared task on Multilingual Idiomaticity Detection
and Sentence Embedding, which consists of two subtasks: (a) a binary
classification task aimed at identifying whether a sentence contains an
idiomatic expression, and (b) a task based on semantic text similarity which
requires the model to adequately represent potentially idiomatic expressions in
context. Each subtask includes different settings regarding the amount of
training data. Besides the task description, this paper introduces the datasets
in English, Portuguese, and Galician and their annotation procedure, the
evaluation metrics, and a summary of the participant systems and their results.
The task had close to 100 registered participants organised into twenty five
teams making over 650 and 150 submissions in the practice and evaluation phases
respectively.
|
[
{
"version": "v1",
"created": "Thu, 21 Apr 2022 12:20:52 GMT"
},
{
"version": "v2",
"created": "Mon, 30 May 2022 14:35:24 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Madabushi",
"Harish Tayyar",
""
],
[
"Gow-Smith",
"Edward",
""
],
[
"Garcia",
"Marcos",
""
],
[
"Scarton",
"Carolina",
""
],
[
"Idiart",
"Marco",
""
],
[
"Villavicencio",
"Aline",
""
]
] |
new_dataset
| 0.999762 |
2204.14095
|
Yuting Gao
|
Yuting Gao, Jinfeng Liu, Zihan Xu, Jun Zhang, Ke Li, Rongrong Ji,
Chunhua Shen
|
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model
Pretraining
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Large-scale vision-language pre-training has achieved promising results on
downstream tasks. Existing methods highly rely on the assumption that the
image-text pairs crawled from the Internet are in perfect one-to-one
correspondence. However, in real scenarios, this assumption can be difficult to
hold: the text description, obtained by crawling the affiliated metadata of the
image, often suffers from the semantic mismatch and the mutual compatibility.
To address these issues, we introduce PyramidCLIP, which constructs an input
pyramid with different semantic levels for each modality, and aligns visual
elements and linguistic elements in the form of hierarchy via peer-level
semantics alignment and cross-level relation alignment. Furthermore, we soften
the loss of negative samples (unpaired samples) so as to weaken the strict
constraint during the pre-training stage, thus mitigating the risk of forcing
the model to distinguish compatible negative pairs. Experiments on five
downstream tasks demonstrate the effectiveness of the proposed PyramidCLIP. In
particular, with the same amount of 15 million pre-training image-text pairs,
PyramidCLIP exceeds CLIP on ImageNet zero-shot classification top-1 accuracy by
10.6%/13.2%/10.0% with ResNet50/ViT-B32/ViT-B16 based image encoder
respectively. When scaling to larger datasets, PyramidCLIP achieves the
state-of-the-art results on several downstream tasks. In particular, the
results of PyramidCLIP-ResNet50 trained on 143M image-text pairs surpass that
of CLIP using 400M data on ImageNet zero-shot classification task,
significantly improving the data efficiency of CLIP.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 13:38:42 GMT"
},
{
"version": "v2",
"created": "Sat, 28 May 2022 08:52:58 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Gao",
"Yuting",
""
],
[
"Liu",
"Jinfeng",
""
],
[
"Xu",
"Zihan",
""
],
[
"Zhang",
"Jun",
""
],
[
"Li",
"Ke",
""
],
[
"Ji",
"Rongrong",
""
],
[
"Shen",
"Chunhua",
""
]
] |
new_dataset
| 0.995449 |
2205.06985
|
Jingya Zang
|
Jingya Zang, Cuiyun Gao, Yupan Chen, Ruifeng Xu, Lanjun Zhou, Xuan
Wang
|
Generating Tips from Song Reviews: A New Dataset and Framework
| null | null | null | null |
cs.IR cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reviews of songs play an important role in online music service platforms.
Prior research shows that users can make quicker and more informed decisions
when presented with meaningful song reviews. However, reviews of music songs
are generally long in length and most of them are non-informative for users. It
is difficult for users to efficiently grasp meaningful messages for making
decisions. To solve this problem, one practical strategy is to provide tips,
i.e., short, concise, empathetic, and self-contained descriptions about songs.
Tips are produced from song reviews and should express non-trivial insights
about the songs. To the best of our knowledge, no prior studies have explored
the tip generation task in music domain. In this paper, we create a dataset
named MTips for the task and propose a framework named GENTMS for automatically
generating tips from song reviews. The dataset involves 8,003 Chinese
tips/non-tips from 128 songs which are distributed in five different song
genres. Experimental results show that GENTMS achieves top-10 precision at
85.56%, outperforming the baseline models by at least 3.34%. Besides, to
simulate the practical usage of our proposed framework, we also experiment with
previously-unseen songs, during which GENTMS also achieves the best performance
with top-10 precision at 78.89% on average. The results demonstrate the
effectiveness of the proposed framework in tip generation of the music domain.
|
[
{
"version": "v1",
"created": "Sat, 14 May 2022 06:40:49 GMT"
},
{
"version": "v2",
"created": "Mon, 30 May 2022 07:13:52 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Zang",
"Jingya",
""
],
[
"Gao",
"Cuiyun",
""
],
[
"Chen",
"Yupan",
""
],
[
"Xu",
"Ruifeng",
""
],
[
"Zhou",
"Lanjun",
""
],
[
"Wang",
"Xuan",
""
]
] |
new_dataset
| 0.998972 |
2205.10929
|
Alain Tchana
|
Alain Tchana, Raphael Colin, Adrien Le Berre, Vincent Berger, Benoit
Combemale, Natacha Crooks, Ludovic Pailler
|
rgpdOS: GDPR Enforcement By The Operating System
| null | null | null | null |
cs.OS cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The General Data Protection Regulation (GDPR) forces IT companies to comply
with a number of principles when dealing with European citizens' personal data.
Non-compliant companies are exposed to penalties which may represent up to 4%
of their turnover. Currently, it is very hard for companies driven by personal
data to make their applications GDPR-compliant, especially if those
applications were developed before the GDPR was established. We present rgpdOS,
a GDPR-aware operating system that aims to bring GDPR-compliance to every
application, while requiring minimal changes to application code.
|
[
{
"version": "v1",
"created": "Sun, 22 May 2022 20:50:20 GMT"
},
{
"version": "v2",
"created": "Mon, 30 May 2022 11:36:09 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Tchana",
"Alain",
""
],
[
"Colin",
"Raphael",
""
],
[
"Berre",
"Adrien Le",
""
],
[
"Berger",
"Vincent",
""
],
[
"Combemale",
"Benoit",
""
],
[
"Crooks",
"Natacha",
""
],
[
"Pailler",
"Ludovic",
""
]
] |
new_dataset
| 0.988467 |
2205.14156
|
Andrea Passarella
|
Marco Conti, Andrea Passarella, Sajal K. Das
|
The Internet of People (IoP): A New Wave in Pervasive Mobile Computing
|
arXiv admin note: text overlap with arXiv:2205.13970
|
Pervasive and Mobile Computing, Volume 41, 2017, Pages 1-27, ISSN
1574-1192
|
10.1016/j.pmcj.2017.07.009
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cyber-Physical convergence, the fast expansion of the Internet at its edge,
and tighter interactions between human users and their personal mobile devices
push towards an Internet where the human user becomes more central than ever,
and where their personal devices become their proxies in the cyber world, in
addition to acting as a fundamental tool to sense the physical world. The
current Internet paradigm, which is infrastructure-centric, is not the right
one to cope with such emerging scenario with a wider range of applications.
This calls for a radically new Internet paradigm, that we name the Internet of
People (IoP), where the humans and their personal devices are not seen merely
as end users of applications, but become active elements of the Internet. Note
that IoP is not a replacement of the current Internet infrastructure, but it
exploits legacy Internet services as (reliable) primitives to achieve
end-to-end connectivity on a global-scale. In this visionary paper, we first
discuss the key features of the IoP paradigm along with the underlying research
issues and challenges. Then we present emerging networking and computing
paradigms that are anticipating IoP
|
[
{
"version": "v1",
"created": "Fri, 27 May 2022 17:07:28 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Conti",
"Marco",
""
],
[
"Passarella",
"Andrea",
""
],
[
"Das",
"Sajal K.",
""
]
] |
new_dataset
| 0.998729 |
2205.14212
|
Viresh Ranjan
|
Viresh Ranjan and Minh Hoai
|
Exemplar Free Class Agnostic Counting
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We tackle the task of Class Agnostic Counting, which aims to count objects in
a novel object category at test time without any access to labeled training
data for that category. All previous class agnostic counting methods cannot
work in a fully automated setting, and require computationally expensive test
time adaptation. To address these challenges, we propose a visual counter which
operates in a fully automated setting and does not require any test time
adaptation. Our proposed approach first identifies exemplars from repeating
objects in an image, and then counts the repeating objects. We propose a novel
region proposal network for identifying the exemplars. After identifying the
exemplars, we obtain the corresponding count by using a density estimation
based Visual Counter. We evaluate our proposed approach on FSC-147 dataset, and
show that it achieves superior performance compared to the existing approaches.
|
[
{
"version": "v1",
"created": "Fri, 27 May 2022 19:44:39 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Ranjan",
"Viresh",
""
],
[
"Hoai",
"Minh",
""
]
] |
new_dataset
| 0.968045 |
2205.14269
|
Amir Pouya Aghasadeghi
|
Amir Pouya Aghasadeghi, Jan Van den Bussche, Julia Stoyanovich
|
Temporal graph patterns by timed automata
| null | null | null | null |
cs.DB
|
http://creativecommons.org/licenses/by/4.0/
|
Temporal graphs represent graph evolution over time, and have been receiving
considerable research attention. Work on expressing temporal graph patterns or
discovering temporal motifs typically assumes relatively simple temporal
constraints, such as journeys or, more generally, existential constraints,
possibly with finite delays. In this paper we propose to use timed automata to
express temporal constraints, leading to a general and powerful notion of
temporal basic graph pattern (BGP). The new difficulty is the evaluation of the
temporal constraint on a large set of matchings. An important benefit of timed
automata is that they support an iterative state assignment, which can be
useful for early detection of matches and pruning of non-matches. We introduce
algorithms to retrieve all instances of a temporal BGP match in a graph, and
present results of an extensive experimental evaluation, demonstrating
interesting performance trade-offs. We show that an on-demand algorithm that
processes total matchings incrementally over time is preferable when dealing
with cyclic patterns on sparse graphs. On acyclic patterns or dense graphs, and
when connectivity of partial matchings can be guaranteed, the best performance
is achieved by maintaining partial matchings over time and allowing automaton
evaluation to be fully incremental.
|
[
{
"version": "v1",
"created": "Fri, 27 May 2022 23:09:09 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Aghasadeghi",
"Amir Pouya",
""
],
[
"Bussche",
"Jan Van den",
""
],
[
"Stoyanovich",
"Julia",
""
]
] |
new_dataset
| 0.990286 |
2205.14290
|
Joshua Tan
|
Joshua Z. Tan and Luke V. Miller
|
Building net-native agreement systems
| null | null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Agreements and contracts are everywhere, but they are built on layers and
layers of legal and social institutions. Software is slowly entering into this
stack. In this article, we introduce agreement paths, a general model for
understanding and decomposing digital agreement systems, and Agreement Engine,
an open-source software service for building net-native agreement systems. We
demonstrate Agreement Engine by building two example agreement systems: Scarce
Knowledge, an app for crowdfunding essays, and Twitter Social Capital, a bot
that allows users to form and enforce Twitter agreements.
|
[
{
"version": "v1",
"created": "Sat, 28 May 2022 01:12:05 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Tan",
"Joshua Z.",
""
],
[
"Miller",
"Luke V.",
""
]
] |
new_dataset
| 0.998283 |
2205.14319
|
Jinli Liao
|
Jinli Liao, Yikang Ding, Yoli Shavit, Dihe Huang, Shihao Ren, Jia Guo,
Wensen Feng, Kai Zhang
|
WT-MVSNet: Window-based Transformers for Multi-view Stereo
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, Transformers were shown to enhance the performance of multi-view
stereo by enabling long-range feature interaction. In this work, we propose
Window-based Transformers (WT) for local feature matching and global feature
aggregation in multi-view stereo. We introduce a Window-based Epipolar
Transformer (WET) which reduces matching redundancy by using epipolar
constraints. Since point-to-line matching is sensitive to erroneous camera pose
and calibration, we match windows near the epipolar lines. A second Shifted WT
is employed for aggregating global information within cost volume. We present a
novel Cost Transformer (CT) to replace 3D convolutions for cost volume
regularization. In order to better constrain the estimated depth maps from
multiple views, we further design a novel geometric consistency loss (Geo Loss)
which punishes unreliable areas where multi-view consistency is not satisfied.
Our WT multi-view stereo method (WT-MVSNet) achieves state-of-the-art
performance across multiple datasets and ranks $1^{st}$ on Tanks and Temples
benchmark.
|
[
{
"version": "v1",
"created": "Sat, 28 May 2022 03:32:09 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Liao",
"Jinli",
""
],
[
"Ding",
"Yikang",
""
],
[
"Shavit",
"Yoli",
""
],
[
"Huang",
"Dihe",
""
],
[
"Ren",
"Shihao",
""
],
[
"Guo",
"Jia",
""
],
[
"Feng",
"Wensen",
""
],
[
"Zhang",
"Kai",
""
]
] |
new_dataset
| 0.993372 |
2205.14376
|
Ashkan Nikseresht
|
Marziyeh Beygi Khormaei, Ashkan Nikseresht and Shohreh Namazi
|
One-Sided Repeated-Root Two-Dimensional Cyclic and Constacyclic Codes
| null | null | null | null |
cs.IT math.AC math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we study some repeated-root two-dimensional cyclic and
constacyclic codes over a finite field $F=\mathbb{F}_q$. We obtain the
generator matrices and generator polynomials of these codes and their duals. We
also investigate when such codes are self-dual. Moreover, we prove that if
there exists an asymptotically good family of one-sided repeated-root
two-dimensional cyclic or constacyclic codes, then there exists an
asymptotically good family of simple root two-dimensional cyclic or
constacyclic codes with parameters at least as good as the first family.
Furthermore, we show that several of the main results of the papers Rajabi and
Khashyarmanesh (2018) and Sepasdar and Khashyarmanesh (2016) are not accurate
and find other conditions needed for them to hold.
|
[
{
"version": "v1",
"created": "Sat, 28 May 2022 09:21:01 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Khormaei",
"Marziyeh Beygi",
""
],
[
"Nikseresht",
"Ashkan",
""
],
[
"Namazi",
"Shohreh",
""
]
] |
new_dataset
| 0.955204 |
2205.14409
|
Haoran Xie
|
Qi Zhou, Jiahao Weng, Haoran Xie
|
Find Your ASMR: A Perceptual Retrieval Interface for Autonomous Sensory
Meridian Response Videos
|
12 pages, 8 figures, in proceedings of HCII2022
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Autonomous sensory meridian response (ASMR) is a type of video contents
designed to help people relax and feel comfortable. Users usually retrieve ASMR
contents from various video websites using only keywords. However, it is
challenging to examine satisfactory contents to reflect users' needs for ASMR
videos using keywords or content-based retrieval. To solve this issue, we
propose a perceptual video retrieval system for ASMR videos and provide a novel
retrieval user interface that allows users to retrieve content according to
watching purpose and anticipated expectations, such as excitement, calmness,
stress and sadness. An ASMR video perception dataset is constructed with
annotations on affective responses after watching the videos. To verify the
proposed video retrieval system, a user study is conducted showing that users
can retrieve satisfactory ASMR contents easily and efficiently compared to
conventional keywords-based retrieval systems.
|
[
{
"version": "v1",
"created": "Sat, 28 May 2022 12:03:21 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Zhou",
"Qi",
""
],
[
"Weng",
"Jiahao",
""
],
[
"Xie",
"Haoran",
""
]
] |
new_dataset
| 0.99927 |
2205.14434
|
Raveena Raveena
|
Raveena and Krishnendra Shekhawat
|
A Theory of L-shaped Floor-plans
|
35 pages, 61 figures
| null | null | null |
cs.DM cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Existing graph theoretic approaches are mainly restricted to floor-plans with
rectangular boundary. In this paper, we introduce floor-plans with $L$-shaped
boundary (boundary with only one concave corner). To ensure the L-shaped
boundary, we introduce the concept of non-triviality of a floor-plan. A
floor-plan with a rectilinear boundary with at least one concave corner is
non-trivial if the number of concave corners can not be reduced, without
affecting the modules adjacencies within it. Further, we present necessary and
sufficient conditions for the existence of a non-trivial L-shaped floor-plan
corresponding to a properly triangulated planar graph (PTPG) $G$. Also, we
develop an $O(n^2)$ algorithm for its construction, if it exists.
|
[
{
"version": "v1",
"created": "Sat, 28 May 2022 13:46:48 GMT"
}
] | 2022-05-31T00:00:00 |
[
[
"Raveena",
"",
""
],
[
"Shekhawat",
"Krishnendra",
""
]
] |
new_dataset
| 0.996598 |
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