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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2209.13718
|
Sanjana Chintalapati
|
Sanjana Chintalapati, Jonathan Bragg, Lucy Lu Wang
|
A Dataset of Alt Texts from HCI Publications: Analyses and Uses Towards
Producing More Descriptive Alt Texts of Data Visualizations in Scientific
Papers
|
11 pages, 4 figures, 4 tables, published at ASSETS 2022
| null |
10.1145/3517428.3544796
| null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Figures in scientific publications contain important information and results,
and alt text is needed for blind and low vision readers to engage with their
content. We conduct a study to characterize the semantic content of alt text in
HCI publications based on a framework introduced by Lundgard and Satyanarayan.
Our study focuses on alt text for graphs, charts, and plots extracted from HCI
and accessibility publications; we focus on these communities due to the lack
of alt text in papers published outside of these disciplines. We find that the
capacity of author-written alt text to fulfill blind and low vision user needs
is mixed; for example, only 50% of alt texts in our sample contain information
about extrema or outliers, and only 31% contain information about major trends
or comparisons conveyed by the graph. We release our collected dataset of
author-written alt text, and outline possible ways that it can be used to
develop tools and models to assist future authors in writing better alt text.
Based on our findings, we also discuss recommendations that can be acted upon
by publishers and authors to encourage inclusion of more types of semantic
content in alt text.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 22:06:04 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Chintalapati",
"Sanjana",
""
],
[
"Bragg",
"Jonathan",
""
],
[
"Wang",
"Lucy Lu",
""
]
] |
new_dataset
| 0.999861 |
2209.13738
|
Vitor Jeronymo
|
Vitor Jeronymo, Mauricio Nascimento, Roberto Lotufo and Rodrigo
Nogueira
|
mRobust04: A Multilingual Version of the TREC Robust 2004 Benchmark
|
4 pages
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Robust 2004 is an information retrieval benchmark whose large number of
judgments per query make it a reliable evaluation dataset. In this paper, we
present mRobust04, a multilingual version of Robust04 that was translated to 8
languages using Google Translate. We also provide results of three different
multilingual retrievers on this dataset. The dataset is available at
https://huggingface.co/datasets/unicamp-dl/mrobust
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 23:14:37 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Jeronymo",
"Vitor",
""
],
[
"Nascimento",
"Mauricio",
""
],
[
"Lotufo",
"Roberto",
""
],
[
"Nogueira",
"Rodrigo",
""
]
] |
new_dataset
| 0.999841 |
2209.13750
|
Andrey Kutuzov
|
Anna Aksenova, Ekaterina Gavrishina, Elisey Rykov, Andrey Kutuzov
|
RuDSI: graph-based word sense induction dataset for Russian
|
TextGraphs-16 workshop at the CoLING-2022 conference
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present RuDSI, a new benchmark for word sense induction (WSI) in Russian.
The dataset was created using manual annotation and semi-automatic clustering
of Word Usage Graphs (WUGs). Unlike prior WSI datasets for Russian, RuDSI is
completely data-driven (based on texts from Russian National Corpus), with no
external word senses imposed on annotators. Depending on the parameters of
graph clustering, different derivative datasets can be produced from raw
annotation. We report the performance that several baseline WSI methods obtain
on RuDSI and discuss possibilities for improving these scores.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 00:08:24 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Aksenova",
"Anna",
""
],
[
"Gavrishina",
"Ekaterina",
""
],
[
"Rykov",
"Elisey",
""
],
[
"Kutuzov",
"Andrey",
""
]
] |
new_dataset
| 0.999668 |
2209.13773
|
Peilin Zhou
|
Peilin Zhou, Zeqiang Wang, Dading Chong, Zhijiang Guo, Yining Hua,
Zichang Su, Zhiyang Teng, Jiageng Wu, Jie Yang
|
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19
Related Tweets
|
10 pages, 6 figures, 6 tables, accepted by NeurIPS 2022 Datasets and
Benchmarks track
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The COVID-19 pandemic continues to bring up various topics discussed or
debated on social media. In order to explore the impact of pandemics on
people's lives, it is crucial to understand the public's concerns and attitudes
towards pandemic-related entities (e.g., drugs, vaccines) on social media.
However, models trained on existing named entity recognition (NER) or targeted
sentiment analysis (TSA) datasets have limited ability to understand
COVID-19-related social media texts because these datasets are not designed or
annotated from a medical perspective. This paper releases METS-CoV, a dataset
containing medical entities and targeted sentiments from COVID-19-related
tweets. METS-CoV contains 10,000 tweets with 7 types of entities, including 4
medical entity types (Disease, Drug, Symptom, and Vaccine) and 3 general entity
types (Person, Location, and Organization). To further investigate tweet users'
attitudes toward specific entities, 4 types of entities (Person, Organization,
Drug, and Vaccine) are selected and annotated with user sentiments, resulting
in a targeted sentiment dataset with 9,101 entities (in 5,278 tweets). To the
best of our knowledge, METS-CoV is the first dataset to collect medical
entities and corresponding sentiments of COVID-19-related tweets. We benchmark
the performance of classical machine learning models and state-of-the-art deep
learning models on NER and TSA tasks with extensive experiments. Results show
that the dataset has vast room for improvement for both NER and TSA tasks.
METS-CoV is an important resource for developing better medical social media
tools and facilitating computational social science research, especially in
epidemiology. Our data, annotation guidelines, benchmark models, and source
code are publicly available (https://github.com/YLab-Open/METS-CoV) to ensure
reproducibility.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 01:55:14 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Zhou",
"Peilin",
""
],
[
"Wang",
"Zeqiang",
""
],
[
"Chong",
"Dading",
""
],
[
"Guo",
"Zhijiang",
""
],
[
"Hua",
"Yining",
""
],
[
"Su",
"Zichang",
""
],
[
"Teng",
"Zhiyang",
""
],
[
"Wu",
"Jiageng",
""
],
[
"Yang",
"Jie",
""
]
] |
new_dataset
| 0.999728 |
2209.13801
|
Maoxun Yuan
|
Maoxun Yuan, Yinyan Wang, Xingxing Wei
|
Translation, Scale and Rotation: Cross-Modal Alignment Meets
RGB-Infrared Vehicle Detection
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Integrating multispectral data in object detection, especially visible and
infrared images, has received great attention in recent years. Since visible
(RGB) and infrared (IR) images can provide complementary information to handle
light variations, the paired images are used in many fields, such as
multispectral pedestrian detection, RGB-IR crowd counting and RGB-IR salient
object detection. Compared with natural RGB-IR images, we find detection in
aerial RGB-IR images suffers from cross-modal weakly misalignment problems,
which are manifested in the position, size and angle deviations of the same
object. In this paper, we mainly address the challenge of cross-modal weakly
misalignment in aerial RGB-IR images. Specifically, we firstly explain and
analyze the cause of the weakly misalignment problem. Then, we propose a
Translation-Scale-Rotation Alignment (TSRA) module to address the problem by
calibrating the feature maps from these two modalities. The module predicts the
deviation between two modality objects through an alignment process and
utilizes Modality-Selection (MS) strategy to improve the performance of
alignment. Finally, a two-stream feature alignment detector (TSFADet) based on
the TSRA module is constructed for RGB-IR object detection in aerial images.
With comprehensive experiments on the public DroneVehicle datasets, we verify
that our method reduces the effect of the cross-modal misalignment and achieve
robust detection results.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 03:06:18 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Yuan",
"Maoxun",
""
],
[
"Wang",
"Yinyan",
""
],
[
"Wei",
"Xingxing",
""
]
] |
new_dataset
| 0.998067 |
2209.13815
|
Yuntao Wang
|
Yuntao Wang, Zhou Su, Abderrahim Benslimane, Qichao Xu, Minghui Dai,
and Ruidong Li
|
A Learning-based Honeypot Game for Collaborative Defense in UAV Networks
|
Accepted by IEEE Globecom2022
| null | null | null |
cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The proliferation of unmanned aerial vehicles (UAVs) opens up new
opportunities for on-demand service provisioning anywhere and anytime, but it
also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is
regarded as a promising lightweight defense to actively protect mobile Internet
of things, especially UAV networks. Existing works primarily focused on
honeypot design and attack pattern recognition, the incentive issue for
motivating UAVs' participation (e.g., sharing trapped attack data in honeypots)
to collaboratively resist distributed and sophisticated attacks is still
under-explored. This paper proposes a novel game-based collaborative defense
approach to address optimal, fair, and feasible incentive mechanism design, in
the presence of network dynamics and UAVs' multi-dimensional private
information (e.g., valid defense data (VDD) volume, communication delay, and
UAV cost). Specifically, we first develop a honeypot game between UAVs under
both partial and complete information asymmetry scenarios. We then devise a
contract-theoretic method to solve the optimal VDD-reward contract design
problem with partial information asymmetry, while ensuring truthfulness,
fairness, and computational efficiency. Furthermore, under complete information
asymmetry, we devise a reinforcement learning based distributed method to
dynamically design optimal contracts for distinct types of UAVs in the
fast-changing network. Experimental simulations show that the proposed scheme
can motivate UAV's collaboration in VDD sharing and enhance defensive
effectiveness, compared with existing solutions.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 03:40:06 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Wang",
"Yuntao",
""
],
[
"Su",
"Zhou",
""
],
[
"Benslimane",
"Abderrahim",
""
],
[
"Xu",
"Qichao",
""
],
[
"Dai",
"Minghui",
""
],
[
"Li",
"Ruidong",
""
]
] |
new_dataset
| 0.9717 |
2209.13833
|
Yang Shen
|
Yang Shen, Xuhao Sun, Xiu-Shen Wei, Qing-Yuan Jiang, Jian Yang
|
SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image
Retrieval
|
ECCV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose Suppression-Enhancing Mask based attention and
Interactive Channel transformatiON (SEMICON) to learn binary hash codes for
dealing with large-scale fine-grained image retrieval tasks. In SEMICON, we
first develop a suppression-enhancing mask (SEM) based attention to dynamically
localize discriminative image regions. More importantly, different from
existing attention mechanism simply erasing previous discriminative regions,
our SEM is developed to restrain such regions and then discover other
complementary regions by considering the relation between activated regions in
a stage-by-stage fashion. In each stage, the interactive channel transformation
(ICON) module is afterwards designed to exploit correlations across channels of
attended activation tensors. Since channels could generally correspond to the
parts of fine-grained objects, the part correlation can be also modeled
accordingly, which further improves fine-grained retrieval accuracy. Moreover,
to be computational economy, ICON is realized by an efficient two-step process.
Finally, the hash learning of our SEMICON consists of both global- and
local-level branches for better representing fine-grained objects and then
generating binary hash codes explicitly corresponding to multiple levels.
Experiments on five benchmark fine-grained datasets show our superiority over
competing methods.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 04:38:04 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Shen",
"Yang",
""
],
[
"Sun",
"Xuhao",
""
],
[
"Wei",
"Xiu-Shen",
""
],
[
"Jiang",
"Qing-Yuan",
""
],
[
"Yang",
"Jian",
""
]
] |
new_dataset
| 0.997907 |
2209.13846
|
Haotian Xia
|
Haotian Xia, Rhys Tracy, Yun Zhao, Erwan Fraisse, Yuan-Fang Wang,
Linda Petzold
|
VREN: Volleyball Rally Dataset with Expression Notation Language
|
ICKG 2022
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This research is intended to accomplish two goals: The first goal is to
curate a large and information rich dataset that contains crucial and succinct
summaries on the players' actions and positions and the back-and-forth travel
patterns of the volleyball in professional and NCAA Div-I indoor volleyball
games. While several prior studies have aimed to create similar datasets for
other sports (e.g. badminton and soccer), creating such a dataset for indoor
volleyball is not yet realized. The second goal is to introduce a volleyball
descriptive language to fully describe the rally processes in the games and
apply the language to our dataset. Based on the curated dataset and our
descriptive sports language, we introduce three tasks for automated volleyball
action and tactic analysis using our dataset: (1) Volleyball Rally Prediction,
aimed at predicting the outcome of a rally and helping players and coaches
improve decision-making in practice, (2) Setting Type and Hitting Type
Prediction, to help coaches and players prepare more effectively for the game,
and (3) Volleyball Tactics and Attacking Zone Statistics, to provide advanced
volleyball statistics and help coaches understand the game and opponent's
tactics better. We conducted case studies to show how experimental results can
provide insights to the volleyball analysis community. Furthermore,
experimental evaluation based on real-world data establishes a baseline for
future studies and applications of our dataset and language. This study bridges
the gap between the indoor volleyball field and computer science.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 05:52:35 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Xia",
"Haotian",
""
],
[
"Tracy",
"Rhys",
""
],
[
"Zhao",
"Yun",
""
],
[
"Fraisse",
"Erwan",
""
],
[
"Wang",
"Yuan-Fang",
""
],
[
"Petzold",
"Linda",
""
]
] |
new_dataset
| 0.999898 |
2209.13850
|
Tuba Girgin
|
T. Baturhan Akbulut, G. Tuba C. Girgin, Arash Mehrabi, Minoru Asada,
Emre Ugur, Erhan Oztop
|
Bimanual rope manipulation skill synthesis through context dependent
correction policy learning from human demonstration
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Learning from demonstration (LfD) provides a convenient means to equip robots
with dexterous skills when demonstration can be obtained in robot intrinsic
coordinates. However, the problem of compounding errors in long and complex
skills reduces its wide deployment. Since most such complex skills are composed
of smaller movements that are combined, considering the target skill as a
sequence of compact motor primitives seems reasonable. Here the problem that
needs to be tackled is to ensure that a motor primitive ends in a state that
allows the successful execution of the subsequent primitive. In this study, we
focus on this problem by proposing to learn an explicit correction policy when
the expected transition state between primitives is not achieved. The
correction policy is itself learned via behavior cloning by the use of a
state-of-the-art movement primitive learning architecture, Conditional Neural
Motor Primitives (CNMPs). The learned correction policy is then able to produce
diverse movement trajectories in a context dependent way. The advantage of the
proposed system over learning the complete task as a single action is shown
with a table-top setup in simulation, where an object has to be pushed through
a corridor in two steps. Then, the applicability of the proposed method to
bi-manual knotting in the real world is shown by equipping an upper-body
humanoid robot with the skill of making knots over a bar in 3D space. The
experiments show that the robot can perform successful knotting even when the
faced correction cases are not part of the human demonstration set.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 06:07:40 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Akbulut",
"T. Baturhan",
""
],
[
"Girgin",
"G. Tuba C.",
""
],
[
"Mehrabi",
"Arash",
""
],
[
"Asada",
"Minoru",
""
],
[
"Ugur",
"Emre",
""
],
[
"Oztop",
"Erhan",
""
]
] |
new_dataset
| 0.950433 |
2209.13875
|
Thanh-Trung Ngo Mr
|
Thanh-Trung Ngo and Hajime Nagahara
|
A General Scattering Phase Function for Inverse Rendering
| null | null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
We tackle the problem of modeling light scattering in homogeneous translucent
material and estimating its scattering parameters. A scattering phase function
is one of such parameters which affects the distribution of scattered
radiation. It is the most complex and challenging parameter to be modeled in
practice, and empirical phase functions are usually used. Empirical phase
functions (such as Henyey-Greenstein (HG) phase function or its modified ones)
are usually presented and limited to a specific range of scattering materials.
This limitation raises concern for an inverse rendering problem where the
target material is generally unknown. In such a situation, a more general phase
function is preferred. Although there exists such a general phase function in
the polynomial form using a basis such as Legendre polynomials
\cite{Fowler1983}, inverse rendering with this phase function is not
straightforward. This is because the base polynomials may be negative
somewhere, while a phase function cannot. This research presents a novel
general phase function that can avoid this issue and an inverse rendering
application using this phase function. The proposed phase function was
positively evaluated with a wide range of materials modeled with Mie scattering
theory. The scattering parameters estimation with the proposed phase function
was evaluated with simulation and real-world experiments.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 07:19:05 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Ngo",
"Thanh-Trung",
""
],
[
"Nagahara",
"Hajime",
""
]
] |
new_dataset
| 0.993373 |
2209.13894
|
Fabian Huch
|
Fabian Huch, Vincent Bode
|
The Isabelle Community Benchmark
| null |
Proceedings of the Workshop on Practical Aspects of Automated
Reasoning Vol-3201 (2022)
| null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
Choosing hardware for theorem proving is no simple task: automated provers
are highly complex and optimized programs, often utilizing a parallel
computation model, and there is little prior research on the hardware impact on
prover performance. To alleviate the problem for Isabelle, we initiated a
community benchmark where the build time of HOL-Analysis is measured. On $54$
distinct CPUs, a total of $669$ runs with different Isabelle configurations
were reported by Isabelle users. Results range from $107$s to over $11$h. We
found that current consumer CPUs performed best, with an optimal number of $8$
to $16$ threads, largely independent of heap memory. As for hardware
parameters, CPU base clock affected multi-threaded execution most with a linear
correlation of $0.37$, whereas boost frequency was the most influential
parameter for single-threaded runs (correlation coefficient $0.55$); cache size
played no significant role. When comparing our benchmark scores with popular
high-performance computing benchmarks, we found a strong linear relationship
with Dolfyn ($R^2 = 0.79$) in the single-threaded scenario. Using data from the
3DMark CPU Profile consumer benchmark, we created a linear model for optimal
(multi-threaded) Isabelle performance. When validating, the model has an
average $R^2$-score of $0.87$; the mean absolute error in the final model
corresponds to a wall-clock time of $46.6$s. With a dataset of true median
values for the 3DMark, the error improves to $37.1$s.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 07:48:50 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Huch",
"Fabian",
""
],
[
"Bode",
"Vincent",
""
]
] |
new_dataset
| 0.99301 |
2209.13916
|
Changyi Lin
|
Changyi Lin, Ziqi Lin, Shaoxiong Wang, Huazhe Xu
|
DTact: A Vision-Based Tactile Sensor that Measures High-Resolution 3D
Geometry Directly from Darkness
|
Project website of DTact: https://sites.google.com/view/dtact-sensor
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision-based tactile sensors that can measure 3D geometry of the contacting
objects are crucial for robots to perform dexterous manipulation tasks.
However, the existing sensors are usually complicated to fabricate and delicate
to extend. In this work, we novelly take advantage of the reflection property
of semitransparent elastomer to design a robust, low-cost, and
easy-to-fabricate tactile sensor named DTact. DTact measures high-resolution 3D
geometry accurately from the darkness shown in the captured tactile images with
only a single image for calibration. In contrast to previous sensors, DTact is
robust under various illumination conditions. Then, we build prototypes of
DTact that have non-planar contact surfaces with minimal extra efforts and
costs. Finally, we perform two intelligent robotic tasks including pose
estimation and object recognition using DTact, in which DTact shows large
potential in applications.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 08:39:27 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Lin",
"Changyi",
""
],
[
"Lin",
"Ziqi",
""
],
[
"Wang",
"Shaoxiong",
""
],
[
"Xu",
"Huazhe",
""
]
] |
new_dataset
| 0.996138 |
2209.13925
|
Jiayin Cai
|
Jiayin Cai, Changlin Li, Xin Tao, Chun Yuan and Yu-Wing Tai
|
DeViT: Deformed Vision Transformers in Video Inpainting
| null |
ACMMM'22, October 10-14, 2022, Lisboa, Portugal
|
10.1145/3503161.3548395
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
This paper proposes a novel video inpainting method. We make three main
contributions: First, we extended previous Transformers with patch alignment by
introducing Deformed Patch-based Homography (DePtH), which improves patch-level
feature alignments without additional supervision and benefits challenging
scenes with various deformation. Second, we introduce Mask Pruning-based Patch
Attention (MPPA) to improve patch-wised feature matching by pruning out less
essential features and using saliency map. MPPA enhances matching accuracy
between warped tokens with invalid pixels. Third, we introduce a
Spatial-Temporal weighting Adaptor (STA) module to obtain accurate attention to
spatial-temporal tokens under the guidance of the Deformation Factor learned
from DePtH, especially for videos with agile motions. Experimental results
demonstrate that our method outperforms recent methods qualitatively and
quantitatively and achieves a new state-of-the-art.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 08:57:14 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Cai",
"Jiayin",
""
],
[
"Li",
"Changlin",
""
],
[
"Tao",
"Xin",
""
],
[
"Yuan",
"Chun",
""
],
[
"Tai",
"Yu-Wing",
""
]
] |
new_dataset
| 0.987192 |
2209.13948
|
Zhiyang Chen
|
Zhiyang Chen, Yousong Zhu, Zhaowen Li, Fan Yang, Wei Li, Haixin Wang,
Chaoyang Zhao, Liwei Wu, Rui Zhao, Jinqiao Wang, Ming Tang
|
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual
Tasks
|
Accepted by NeurIPS 2022. Code available at
https://github.com/CASIA-IVA-Lab/Obj2Seq
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Visual tasks vary a lot in their output formats and concerned contents,
therefore it is hard to process them with an identical structure. One main
obstacle lies in the high-dimensional outputs in object-level visual tasks. In
this paper, we propose an object-centric vision framework, Obj2Seq. Obj2Seq
takes objects as basic units, and regards most object-level visual tasks as
sequence generation problems of objects. Therefore, these visual tasks can be
decoupled into two steps. First recognize objects of given categories, and then
generate a sequence for each of these objects. The definition of the output
sequences varies for different tasks, and the model is supervised by matching
these sequences with ground-truth targets. Obj2Seq is able to flexibly
determine input categories to satisfy customized requirements, and be easily
extended to different visual tasks. When experimenting on MS COCO, Obj2Seq
achieves 45.7% AP on object detection, 89.0% AP on multi-label classification
and 65.0% AP on human pose estimation. These results demonstrate its potential
to be generally applied to different visual tasks. Code has been made available
at: https://github.com/CASIA-IVA-Lab/Obj2Seq.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 09:24:04 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Chen",
"Zhiyang",
""
],
[
"Zhu",
"Yousong",
""
],
[
"Li",
"Zhaowen",
""
],
[
"Yang",
"Fan",
""
],
[
"Li",
"Wei",
""
],
[
"Wang",
"Haixin",
""
],
[
"Zhao",
"Chaoyang",
""
],
[
"Wu",
"Liwei",
""
],
[
"Zhao",
"Rui",
""
],
[
"Wang",
"Jinqiao",
""
],
[
"Tang",
"Ming",
""
]
] |
new_dataset
| 0.99953 |
2209.13959
|
Fengyuan Shi
|
Fengyuan Shi, Ruopeng Gao, Weilin Huang, Limin Wang
|
Dynamic MDETR: A Dynamic Multimodal Transformer Decoder for Visual
Grounding
|
Technical report
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multimodal transformer exhibits high capacity and flexibility to align image
and text for visual grounding. However, the encoder-only grounding framework
(e.g., TransVG) suffers from heavy computation due to the self-attention
operation with quadratic time complexity. To address this issue, we present a
new multimodal transformer architecture, coined as Dynamic MDETR, by decoupling
the whole grounding process into encoding and decoding phases. The key
observation is that there exists high spatial redundancy in images. Thus, we
devise a new dynamic multimodal transformer decoder by exploiting this sparsity
prior to speed up the visual grounding process. Specifically, our dynamic
decoder is composed of a 2D adaptive sampling module and a text-guided decoding
module. The sampling module aims to select these informative patches by
predicting the offsets with respect to a reference point, while the decoding
module works for extracting the grounded object information by performing cross
attention between image features and text features. These two modules are
stacked alternatively to gradually bridge the modality gap and iteratively
refine the reference point of grounded object, eventually realizing the
objective of visual grounding. Extensive experiments on five benchmarks
demonstrate that our proposed Dynamic MDETR achieves competitive trade-offs
between computation and accuracy. Notably, using only 9% feature points in the
decoder, we can reduce ~44% GLOPs of the multimodal transformer, but still get
higher accuracy than the encoder-only counterpart. In addition, to verify its
generalization ability and scale up our Dynamic MDETR, we build the first
one-stage CLIP empowered visual grounding framework, and achieve the
state-of-the-art performance on these benchmarks.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 09:43:02 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Shi",
"Fengyuan",
""
],
[
"Gao",
"Ruopeng",
""
],
[
"Huang",
"Weilin",
""
],
[
"Wang",
"Limin",
""
]
] |
new_dataset
| 0.996848 |
2209.13999
|
Ahmab Baraani
|
Fereshteh Khoshnam, Ahmad Baraani-Dastjerdi, M.J. Liaghatdar
|
CEFER: A Four Facets Framework based on Context and Emotion embedded
features for Implicit and Explicit Emotion Recognition
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
People's conduct and reactions are driven by their emotions. Online social
media is becoming a great instrument for expressing emotions in written form.
Paying attention to the context and the entire sentence help us to detect
emotion from texts. However, this perspective inhibits us from noticing some
emotional words or phrases in the text, particularly when the words express an
emotion implicitly rather than explicitly. On the other hand, focusing only on
the words and ignoring the context results in a distorted understanding of the
sentence meaning and feeling. In this paper, we propose a framework that
analyses text at both the sentence and word levels. We name it CEFER (Context
and Emotion embedded Framework for Emotion Recognition). Our four approach
facets are to extracting data by considering the entire sentence and each
individual word simultaneously, as well as implicit and explicit emotions. The
knowledge gained from these data not only mitigates the impact of flaws in the
preceding approaches but also it strengthens the feature vector. We evaluate
several feature spaces using BERT family and design the CEFER based on them.
CEFER combines the emotional vector of each word, including explicit and
implicit emotions, with the feature vector of each word based on context. CEFER
performs better than the BERT family. The experimental results demonstrate that
identifying implicit emotions are more challenging than detecting explicit
emotions. CEFER, improves the accuracy of implicit emotion recognition.
According to the results, CEFER perform 5% better than the BERT family in
recognizing explicit emotions and 3% in implicit.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 11:16:32 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Khoshnam",
"Fereshteh",
""
],
[
"Baraani-Dastjerdi",
"Ahmad",
""
],
[
"Liaghatdar",
"M. J.",
""
]
] |
new_dataset
| 0.997572 |
2209.14003
|
Rajitha de Silva
|
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
|
Vision based Crop Row Navigation under Varying Field Conditions in
Arable Fields
|
This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible
| null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Accurate crop row detection is often challenged by the varying field
conditions present in real-world arable fields. Traditional colour based
segmentation is unable to cater for all such variations. The lack of
comprehensive datasets in agricultural environments limits the researchers from
developing robust segmentation models to detect crop rows. We present a dataset
for crop row detection with 11 field variations from Sugar Beet and Maize
crops. We also present a novel crop row detection algorithm for visual servoing
in crop row fields. Our algorithm can detect crop rows against varying field
conditions such as curved crop rows, weed presence, discontinuities, growth
stages, tramlines, shadows and light levels. Our method only uses RGB images
from a front-mounted camera on a Husky robot to predict crop rows. Our method
outperformed the classic colour based crop row detection baseline. Dense weed
presence within inter-row space and discontinuities in crop rows were the most
challenging field conditions for our crop row detection algorithm. Our method
can detect the end of the crop row and navigate the robot towards the headland
area when it reaches the end of the crop row.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 11:23:34 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"de Silva",
"Rajitha",
""
],
[
"Cielniak",
"Grzegorz",
""
],
[
"Gao",
"Junfeng",
""
]
] |
new_dataset
| 0.98626 |
2209.14024
|
Jiale Tao
|
Jiale Tao, Biao Wang, Tiezheng Ge, Yuning Jiang, Wen Li, and Lixin
Duan
|
Motion Transformer for Unsupervised Image Animation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Image animation aims to animate a source image by using motion learned from a
driving video. Current state-of-the-art methods typically use convolutional
neural networks (CNNs) to predict motion information, such as motion keypoints
and corresponding local transformations. However, these CNN based methods do
not explicitly model the interactions between motions; as a result, the
important underlying motion relationship may be neglected, which can
potentially lead to noticeable artifacts being produced in the generated
animation video. To this end, we propose a new method, the motion transformer,
which is the first attempt to build a motion estimator based on a vision
transformer. More specifically, we introduce two types of tokens in our
proposed method: i) image tokens formed from patch features and corresponding
position encoding; and ii) motion tokens encoded with motion information. Both
types of tokens are sent into vision transformers to promote underlying
interactions between them through multi-head self attention blocks. By adopting
this process, the motion information can be better learned to boost the model
performance. The final embedded motion tokens are then used to predict the
corresponding motion keypoints and local transformations. Extensive experiments
on benchmark datasets show that our proposed method achieves promising results
to the state-of-the-art baselines. Our source code will be public available.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 12:04:58 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Tao",
"Jiale",
""
],
[
"Wang",
"Biao",
""
],
[
"Ge",
"Tiezheng",
""
],
[
"Jiang",
"Yuning",
""
],
[
"Li",
"Wen",
""
],
[
"Duan",
"Lixin",
""
]
] |
new_dataset
| 0.977435 |
2209.14085
|
Pauline Puteaux
|
Moctar Abdoul Latif Sawadogo, Furkan Pala, Gurkirat Singh, Imen Selmi,
Pauline Puteaux and Alice Othmani
|
PTSD in the Wild: A Video Database for Studying Post-Traumatic Stress
Disorder Recognition in Unconstrained Environments
| null | null | null | null |
cs.HC cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
POST-traumatic stress disorder (PTSD) is a chronic and debilitating mental
condition that is developed in response to catastrophic life events, such as
military combat, sexual assault, and natural disasters. PTSD is characterized
by flashbacks of past traumatic events, intrusive thoughts, nightmares,
hypervigilance, and sleep disturbance, all of which affect a person's life and
lead to considerable social, occupational, and interpersonal dysfunction. The
diagnosis of PTSD is done by medical professionals using self-assessment
questionnaire of PTSD symptoms as defined in the Diagnostic and Statistical
Manual of Mental Disorders (DSM). In this paper, and for the first time, we
collected, annotated, and prepared for public distribution a new video database
for automatic PTSD diagnosis, called PTSD in the wild dataset. The database
exhibits "natural" and big variability in acquisition conditions with different
pose, facial expression, lighting, focus, resolution, age, gender, race,
occlusions and background. In addition to describing the details of the dataset
collection, we provide a benchmark for evaluating computer vision and machine
learning based approaches on PTSD in the wild dataset. In addition, we propose
and we evaluate a deep learning based approach for PTSD detection in respect to
the given benchmark. The proposed approach shows very promising results.
Interested researcher can download a copy of PTSD-in-the wild dataset from:
http://www.lissi.fr/PTSD-Dataset/
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 13:30:26 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Sawadogo",
"Moctar Abdoul Latif",
""
],
[
"Pala",
"Furkan",
""
],
[
"Singh",
"Gurkirat",
""
],
[
"Selmi",
"Imen",
""
],
[
"Puteaux",
"Pauline",
""
],
[
"Othmani",
"Alice",
""
]
] |
new_dataset
| 0.999438 |
2209.14130
|
Pino Caballero-Gil
|
J Su\'arez-Armas, P Caballero-Gil, C Caballero-Gil
|
Video surveillance robot powered by raspberry pi
| null |
Proceedings of the 1st International Conference on Internet of
Things and Machine Learning 1-4, 2017
| null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Video surveillance systems are increasingly used in different fields, from
the domestic to the commercial environment. Current systems are being improved
and complemented with new elements and functionalities. This paper proposes the
design of a video surveillance robot based on Raspberry Pi with the abilities
to perform tasks of motion detection, send video on real time, fire detection
and also, the possibility of control it remotely from the Internet. In order to
check the information received from the robot, as well as the video sent, a
client application has been developed to any device with an Internet
connection. In addition to this, in order to protect the information obtained
by the robot, a secure system is proposed, which uses different security
mechanisms to achieve this goal.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 14:27:54 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Suárez-Armas",
"J",
""
],
[
"Caballero-Gil",
"P",
""
],
[
"Caballero-Gil",
"C",
""
]
] |
new_dataset
| 0.998305 |
2209.14138
|
He Li
|
He Li, Tingnan Zhang, Wenhao Yu, Patrick M. Wensing
|
Versatile Real-Time Motion Synthesis via Kino-Dynamic MPC with
Hybrid-Systems DDP
|
7 pages, 7 figures, submitted to 2023 IEEE International Conference
on Robotics and Automation (ICRA)
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Specialized motions such as jumping are often achieved on quadruped robots by
solving a trajectory optimization problem once and executing the trajectory
using a tracking controller. This approach is in parallel with Model Predictive
Control (MPC) strategies that commonly control regular gaits via online
re-planning. In this work, we present a nonlinear MPC (NMPC) technique that
unlocks on-the-fly re-planning of specialized motion skills and regular
locomotion within a unified framework. The NMPC reasons about a hybrid
kinodynamic model, and is solved using a variant of a constrained Differential
Dynamic Programming (DDP) solver. The proposed NMPC enables the robot to
perform a variety of agile skills like jumping, bounding, and trotting, and the
rapid transition between these skills. We evaluated the proposed algorithm with
three challenging motion sequences that combine multiple agile skills, on two
quadruped platforms, Unitree A1, and MIT Mini Cheetah, showing its
effectiveness and generality.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 14:35:00 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Li",
"He",
""
],
[
"Zhang",
"Tingnan",
""
],
[
"Yu",
"Wenhao",
""
],
[
"Wensing",
"Patrick M.",
""
]
] |
new_dataset
| 0.962085 |
2209.14142
|
Toms Bergmanis
|
Toms Bergmanis and M\=arcis Pinnis
|
From Zero to Production: Baltic-Ukrainian Machine Translation Systems to
Aid Refugees
|
To be published in Baltic HLT 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
In this paper, we examine the development and usage of six low-resource
machine translation systems translating between the Ukrainian language and each
of the official languages of the Baltic states. We developed these systems in
reaction to the escalating Ukrainian refugee crisis caused by the Russian
military aggression in Ukraine in the hope that they might be helpful for
refugees and public administrations. Now, two months after MT systems were made
public, we analyze their usage patterns and statistics. Our findings show that
the Latvian-Ukrainian and Lithuanian-Ukrainian systems are integrated into the
public services of Baltic states, leading to more than 127 million translated
sentences for the Lithuanian-Ukrainian system. Motivated by these findings, we
further enhance our MT systems by better Ukrainian toponym translation and
publish an improved version of the Lithuanian-Ukrainian system.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 14:46:01 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Bergmanis",
"Toms",
""
],
[
"Pinnis",
"Mārcis",
""
]
] |
new_dataset
| 0.997754 |
2209.14147
|
Yitong Wang
|
Yitong Wang, Jun Zhao
|
Mobile Edge Computing, Metaverse, 6G Wireless Communications, Artificial
Intelligence, and Blockchain: Survey and Their Convergence
|
This paper appears in the Proceedings of 2022 IEEE 8th World Forum on
Internet of Things (WF-IoT). Please feel free to contact us for questions or
remarks
| null | null | null |
cs.DC cs.AI cs.LG cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
With the advances of the Internet of Things (IoT) and 5G/6G wireless
communications, the paradigms of mobile computing have developed dramatically
in recent years, from centralized mobile cloud computing to distributed fog
computing and mobile edge computing (MEC). MEC pushes compute-intensive
assignments to the edge of the network and brings resources as close to the
endpoints as possible, addressing the shortcomings of mobile devices with
regard to storage space, resource optimisation, computational performance and
efficiency. Compared to cloud computing, as the distributed and closer
infrastructure, the convergence of MEC with other emerging technologies,
including the Metaverse, 6G wireless communications, artificial intelligence
(AI), and blockchain, also solves the problems of network resource allocation,
more network load as well as latency requirements. Accordingly, this paper
investigates the computational paradigms used to meet the stringent
requirements of modern applications. The application scenarios of MEC in mobile
augmented reality (MAR) are provided. Furthermore, this survey presents the
motivation of MEC-based Metaverse and introduces the applications of MEC to the
Metaverse. Particular emphasis is given on a set of technical fusions mentioned
above, e.g., 6G with MEC paradigm, MEC strengthened by blockchain, etc.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 14:54:06 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Wang",
"Yitong",
""
],
[
"Zhao",
"Jun",
""
]
] |
new_dataset
| 0.96952 |
2209.14195
|
Pino Caballero-Gil
|
I Santos-Gonz\'alez, A Rivero-Garc\'ia, P Caballero-Gil
|
Secure Indoor Location for Airport Environments
| null |
018 4th International Conference on Big Data Innovations and
Applications (Innovate-Data) 2018
| null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This work presents a secure novel solution based on inertial measurement
units to provide indoor location and positioning in airports. The use of
different technologies allows to locate people with precision in this kind of
indoor places where the use of the GPS is not possible. The system has been
developed thinking in the low cost and in a possible future expansion of this
kind of systems to improve the Quality of Service of the users in airports. The
use of QR codes and low cost IMU devices through the use of people smartphones
ensure this premise. An Android application has been developed to show the
applicability and performance of the system. The security in this kind of
systems is essential. This kind of systems needs to avoid the traceability of
the IMU devices when users are using it. To solve this problem, the FourQ
elliptic curve has been used to generate a shared key using the elliptic curve
Diffie-Hellman protocol. The key generated with the FourQ is used then to
cipher all communications through the use of the SNOW 3G stream cipher. The
developed system offers promising results.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 16:07:41 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Santos-González",
"I",
""
],
[
"Rivero-García",
"A",
""
],
[
"Caballero-Gil",
"P",
""
]
] |
new_dataset
| 0.999185 |
2209.14200
|
Pino Caballero-Gil
|
N Garc\'ia-Moreno, P Caballero-Gil, C Caballero-Gil, J Molina-Gil
|
Building an Ethereum-Based Decentralized Vehicle Rental System
| null |
Computational Intelligence in Security for Information Systems
Conference, 45-53, 2019
| null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Blockchain technology, beyond cryptocurrencies, is called to be the new
information exchange ecosystem due to its unique properties, such as
immutability and transparency. The main objective of this work is to introduce
the design of a decentralized rental system, which leverages smart contracts
and the Ethereum public blockchain. The work started from an exhaustive
investigation on the Ethereum platform, emphasizing the aspect of cryptography
and all the technology behind this platform. In order to test the proposed
scheme in a realistic use, the implementation of a web application for the
rental of vehicles has been carried out. The application covers the entire
vehicle rental process offered in traditional web applications, adding more
autonomy and ease of use to users. Following Ethereum application development
guidelines, all business logic is located in the smart contracts implemented in
the Ethereum network, where these contracts control the entire vehicle rental
system of customers. While this is a work in progress, the results obtained in
the first proof of concept have been very promising.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 16:16:34 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"García-Moreno",
"N",
""
],
[
"Caballero-Gil",
"P",
""
],
[
"Caballero-Gil",
"C",
""
],
[
"Molina-Gil",
"J",
""
]
] |
new_dataset
| 0.998115 |
2209.14213
|
Angelo Marotta
|
Angelo Marotta
|
On abelian and cyclic group codes
|
14 pages
| null | null | null |
cs.IT math.GR math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We determine a condition on the minimum Hamming weight of some special
abelian group codes and, as a consequence of this result, we establish that any
such code is, up to permutational equivalence, a subspace of the direct sum of
$s$ copies of the repetition code of length $t$, for some suitable positive
integers $s$ and $t$. Moreover, we provide a complete characterisation of
permutation automorphisms of the linear code
$C=\bigoplus_{i=1}^{s}Rep_{t}(\mathbb{F}_{q})$ and we establish that such a
code is an abelian group code, for every pair of integers $s,t\geq1$. Finally,
in a similar fashion as for abelian group codes, we give an equivalent
characterisation of cyclic group codes.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 16:40:12 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Marotta",
"Angelo",
""
]
] |
new_dataset
| 0.992526 |
2209.14218
|
Alexander Mathis
|
Alberto Silvio Chiappa and Alessandro Marin Vargas and Alexander
Mathis
|
DMAP: a Distributed Morphological Attention Policy for Learning to
Locomote with a Changing Body
| null | null | null | null |
cs.RO cs.AI q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Biological and artificial agents need to deal with constant changes in the
real world. We study this problem in four classical continuous control
environments, augmented with morphological perturbations. Learning to locomote
when the length and the thickness of different body parts vary is challenging,
as the control policy is required to adapt to the morphology to successfully
balance and advance the agent. We show that a control policy based on the
proprioceptive state performs poorly with highly variable body configurations,
while an (oracle) agent with access to a learned encoding of the perturbation
performs significantly better. We introduce DMAP, a biologically-inspired,
attention-based policy network architecture. DMAP combines independent
proprioceptive processing, a distributed policy with individual controllers for
each joint, and an attention mechanism, to dynamically gate sensory information
from different body parts to different controllers. Despite not having access
to the (hidden) morphology information, DMAP can be trained end-to-end in all
the considered environments, overall matching or surpassing the performance of
an oracle agent. Thus DMAP, implementing principles from biological motor
control, provides a strong inductive bias for learning challenging sensorimotor
tasks. Overall, our work corroborates the power of these principles in
challenging locomotion tasks.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 16:45:35 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Chiappa",
"Alberto Silvio",
""
],
[
"Vargas",
"Alessandro Marin",
""
],
[
"Mathis",
"Alexander",
""
]
] |
new_dataset
| 0.999476 |
2209.14227
|
Ana de Almeida Borges
|
Ana de Almeida Borges, Mireia Gonz\'alez Bedmar, Juan Conejero
Rodr\'iguez, Eduardo Hermo Reyes, Joaquim Casals Bu\~nuel and Joost J.
Joosten
|
FV Time: a formally verified Coq library
| null | null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
FV Time is a small-scale verification project developed in the Coq proof
assistant using the Mathematical Components libraries. It is a library for
managing conversions between time formats (UTC and timestamps), as well as
commonly used functions for time arithmetic. As a library for time conversions,
its novelty is the implementation of leap seconds, which are part of the UTC
standard but usually not implemented in existing libraries. Since the verified
functions of FV Time are reasonably simple yet non-trivial, it nicely
illustrates our methodology for verifying software with Coq.
In this paper we present a description of the project, emphasizing the main
problems faced while developing the library, as well as some general-purpose
solutions that were produced as by-products and may be used in other
verification projects. These include a refinement package between
proof-oriented MathComp numbers and computation-oriented primitive numbers from
the Coq standard library, as well as a set of tactics to automatically prove
certain arithmetical statements through brute-force computation.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 16:56:18 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Borges",
"Ana de Almeida",
""
],
[
"Bedmar",
"Mireia González",
""
],
[
"Rodríguez",
"Juan Conejero",
""
],
[
"Reyes",
"Eduardo Hermo",
""
],
[
"Buñuel",
"Joaquim Casals",
""
],
[
"Joosten",
"Joost J.",
""
]
] |
new_dataset
| 0.998945 |
2209.14250
|
Gautam Choudhary
|
Atanu R. Sinha, Gautam Choudhary, Mansi Agarwal, Shivansh Bindal,
Abhishek Pande, Camille Girabawe
|
B2B Advertising: Joint Dynamic Scoring of Account and Users
|
Published at KDD Workshop: AdKDD 2022
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When a business sells to another business (B2B), the buying business is
represented by a group of individuals, termed account, who collectively decide
whether to buy. The seller advertises to each individual and interacts with
them, mostly by digital means. The sales cycle is long, most often over a few
months. There is heterogeneity among individuals belonging to an account in
seeking information and hence the seller needs to score the interest of each
individual over a long horizon to decide which individuals must be reached and
when. Moreover, the buy decision rests with the account and must be scored to
project the likelihood of purchase, a decision that is subject to change all
the way up to the actual decision, emblematic of group decision making. We
score decision of the account and its individuals in a dynamic manner. Dynamic
scoring allows opportunity to influence different individual members at
different time points over the long horizon. The dataset contains behavior logs
of each individual's communication activities with the seller; but, there are
no data on consultations among individuals which result in the decision. Using
neural network architecture, we propose several ways to aggregate information
from individual members' activities, to predict the group's collective
decision. Multiple evaluations find strong model performance.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 17:10:03 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Sinha",
"Atanu R.",
""
],
[
"Choudhary",
"Gautam",
""
],
[
"Agarwal",
"Mansi",
""
],
[
"Bindal",
"Shivansh",
""
],
[
"Pande",
"Abhishek",
""
],
[
"Girabawe",
"Camille",
""
]
] |
new_dataset
| 0.996089 |
2209.14284
|
Qiuyu Chen
|
Zoey Qiuyu Chen, Karl Van Wyk, Yu-Wei Chao, Wei Yang, Arsalan
Mousavian, Abhishek Gupta, Dieter Fox
|
DexTransfer: Real World Multi-fingered Dexterous Grasping with Minimal
Human Demonstrations
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Teaching a multi-fingered dexterous robot to grasp objects in the real world
has been a challenging problem due to its high dimensional state and action
space. We propose a robot-learning system that can take a small number of human
demonstrations and learn to grasp unseen object poses given partially occluded
observations. Our system leverages a small motion capture dataset and generates
a large dataset with diverse and successful trajectories for a multi-fingered
robot gripper. By adding domain randomization, we show that our dataset
provides robust grasping trajectories that can be transferred to a policy
learner. We train a dexterous grasping policy that takes the point clouds of
the object as input and predicts continuous actions to grasp objects from
different initial robot states. We evaluate the effectiveness of our system on
a 22-DoF floating Allegro Hand in simulation and a 23-DoF Allegro robot hand
with a KUKA arm in real world. The policy learned from our dataset can
generalize well on unseen object poses in both simulation and the real world
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 17:51:49 GMT"
}
] | 2022-09-29T00:00:00 |
[
[
"Chen",
"Zoey Qiuyu",
""
],
[
"Van Wyk",
"Karl",
""
],
[
"Chao",
"Yu-Wei",
""
],
[
"Yang",
"Wei",
""
],
[
"Mousavian",
"Arsalan",
""
],
[
"Gupta",
"Abhishek",
""
],
[
"Fox",
"Dieter",
""
]
] |
new_dataset
| 0.999818 |
2104.01242
|
Peter Turney
|
Peter D. Turney
|
Evolution of Symbiosis in the Game of Life: Three Characteristics of
Successful Symbiotes
| null | null | null | null |
cs.NE nlin.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In past work, we developed a computational model of the evolution of
symbiotic entities (Model-S), based on Conway's Game of Life. In this article,
we examine three trends that biologists have observed in the evolution of
symbiotes. (1) Management: If one partner is able to control the symbiotic
relation, this control can reduce conflict; thus, evolutionary selection
favours symbiotes that have a manager. (2) Mutualism: Although partners in a
symbiote often have conflicting needs, evolutionary selection favours symbiotes
in which partners are better off together inside the symbiote than they would
be as individuals outside of the symbiote. (3) Interaction: Repeated
interaction among partners in symbiosis tends to promote increasing fitness due
to evolutionary selection. We have added new components to Model-S that allow
us to observe these three trends in runs of Model-S. The new components are
analogous to the practice of staining cells in biology research, to reveal
patterns that are not usually visible. When we measure the fitness of a
symbiote by making it compete with other symbiotes, we find that fitter
symbiotes have significantly more management, mutualism, and interaction than
less fit symbiotes. These results confirm the trends observed in nature by
biologists. Model-S allows biologists to study these evolutionary trends and
other characteristics of symbiosis in ways that are not tractable with living
organisms.
|
[
{
"version": "v1",
"created": "Fri, 2 Apr 2021 21:23:48 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Aug 2021 19:26:26 GMT"
},
{
"version": "v3",
"created": "Tue, 11 Jan 2022 21:54:23 GMT"
},
{
"version": "v4",
"created": "Thu, 30 Jun 2022 20:20:06 GMT"
},
{
"version": "v5",
"created": "Tue, 27 Sep 2022 00:20:57 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Turney",
"Peter D.",
""
]
] |
new_dataset
| 0.979217 |
2107.00613
|
Fengmin Zhu
|
Fengmin Zhu and Fei He
|
EqFix: Fixing LaTeX Equation Errors by Examples
| null | null | null | null |
cs.PL cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
LaTeX is a widely-used document preparation system. Its powerful ability in
mathematical equation editing is perhaps the main reason for its popularity in
academia. Sometimes, however, even an expert user may spend much time fixing an
erroneous equation. In this paper, we present EqFix, a synthesis-based
repairing system for LaTeX equations. It employs a set of fixing rules and can
suggest possible repairs for common errors in LaTeX equations. A
domain-specific language is proposed for formally expressing the fixing rules.
The fixing rules can be automatically synthesized from a set of input-output
examples. An extension of relaxers is also introduced to enhance the
practicality of EqFix. We evaluate EqFix on real-world examples and find that
it can synthesize rules with high generalization ability. Compared with a
state-of-the-art string transformation synthesizer, EqFix solved 37% more cases
and spent less than half of their synthesis time.
|
[
{
"version": "v1",
"created": "Thu, 1 Jul 2021 17:04:56 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Sep 2022 12:14:33 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Zhu",
"Fengmin",
""
],
[
"He",
"Fei",
""
]
] |
new_dataset
| 0.993275 |
2110.07954
|
Hans Wang
|
Gordon King, Hans Wang
|
HTTPA: HTTPS Attestable Protocol
|
10 pages, 8 figures
| null | null | null |
cs.CR cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Hypertext Transfer Protocol Secure (HTTPS) protocol has become an integral
part of modern Internet technology. Currently, it is the primary protocol for
commercialized web applications. It can provide a fast, secure connection with
a certain level of privacy and integrity, and it has become a basic assumption
on most web services on the Internet. However, HTTPS alone cannot provide
security assurances on request data in computing, so the computing environment
remains uncertain of risks and vulnerabilities. A hardware-based trusted
execution environment (TEE) such as Intel Software Guard Extension (Intel SGX)
or Intel Trust Domain Extensions (Intel TDX) provides in-memory encryption to
help protect runtime computation to reduce risks of illegal leaking or
modifying private information. (Note that we use SGX as an example for
illustration in the following texts.) The central concept of SGX enables
computation inside an enclave, a protected environment that encrypts the codes
and data pertaining to a security-sensitive computation. In addition, SGX
provides security assurances via remote attestation to the web client to
verify, including TCB identity, vendor identity, and verification identity.
Here, we propose an HTTP protocol extension, called HTTPS Attestable (HTTPA),
by including a remote attestation process onto the HTTPS protocol to address
the privacy and security concerns on the web and the access of trust over the
Internet. With HTTPA, we can provide security assurances for verification to
establish trustworthiness with web services and ensure the integrity of request
handling for web users. We expect that remote attestation will become a new
trend adopted to reduce the security risks of web services. We propose the
HTTPA protocol to unify the web attestation and accessing Internet services in
a standard and efficient way.
|
[
{
"version": "v1",
"created": "Fri, 15 Oct 2021 09:14:03 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Jan 2022 22:11:43 GMT"
},
{
"version": "v3",
"created": "Mon, 26 Sep 2022 23:14:13 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"King",
"Gordon",
""
],
[
"Wang",
"Hans",
""
]
] |
new_dataset
| 0.998925 |
2112.10869
|
Mohammadali Mohammadi
|
Mohammadali Mohammadi and Hien Quoc Ngo and Michail Matthaiou
|
Cell-Free Massive MIMO Meets OTFS Modulation
|
4 figures, Accepted in IEEE Transactions on Communications
| null | null | null |
cs.IT cs.PF math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
We provide the first-ever performance evaluation of orthogonal time frequency
space (OTFS) modulation in cell-free massive multiple-input multiple-output
(MIMO) systems. To investigate trade-off between performance and overhead, we
apply embedded pilot-aided and superimposed pilot-based channel estimation
methods. We then derive a closed-form expression for the individual user
downlink and uplink spectral efficiencies as a function of the numbers of APs,
users and delay-Doppler domain channel estimate parameters. Based on these
analytical results, we also present new scaling laws that the AP's and user's
transmit power should satisfy, to sustain a desirable quality of service. It is
found that when the number of APs, $M_a$, grows without bound, we can reduce
the transmit power of each user and AP proportionally to $1/M_a$ and $1/M_a^2$,
respectively, during the uplink and downlink phases. We compare the OTFS
performance with that of orthogonal frequency division multiplexing (OFDM) at
high-mobility conditions. Our findings reveal that with shadowing correlation,
OTFS modulation with embedded pilot-based channel estimation provides
$30$-folds gain over the OFDM counterpart in terms of $95\%$-likely per-user
downlink rate. Finally, with superimposed pilot-based channel estimation, the
increase in the per-user throughput is more pronounced at the median rates over
the correlated shadowing channels.
|
[
{
"version": "v1",
"created": "Mon, 20 Dec 2021 21:27:14 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Sep 2022 13:17:25 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Mohammadi",
"Mohammadali",
""
],
[
"Ngo",
"Hien Quoc",
""
],
[
"Matthaiou",
"Michail",
""
]
] |
new_dataset
| 0.996187 |
2201.02279
|
Felix Wimbauer
|
Felix Wimbauer, Shangzhe Wu, Christian Rupprecht
|
De-rendering 3D Objects in the Wild
| null |
Proceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition (CVPR), 2022, pp. 18490-18499
| null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With increasing focus on augmented and virtual reality applications (XR)
comes the demand for algorithms that can lift objects from images and videos
into representations that are suitable for a wide variety of related 3D tasks.
Large-scale deployment of XR devices and applications means that we cannot
solely rely on supervised learning, as collecting and annotating data for the
unlimited variety of objects in the real world is infeasible. We present a
weakly supervised method that is able to decompose a single image of an object
into shape (depth and normals), material (albedo, reflectivity and shininess)
and global lighting parameters. For training, the method only relies on a rough
initial shape estimate of the training objects to bootstrap the learning
process. This shape supervision can come for example from a pretrained depth
network or - more generically - from a traditional structure-from-motion
pipeline. In our experiments, we show that the method can successfully
de-render 2D images into a decomposed 3D representation and generalizes to
unseen object categories. Since in-the-wild evaluation is difficult due to the
lack of ground truth data, we also introduce a photo-realistic synthetic test
set that allows for quantitative evaluation.
|
[
{
"version": "v1",
"created": "Thu, 6 Jan 2022 23:50:09 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Sep 2022 14:36:16 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Wimbauer",
"Felix",
""
],
[
"Wu",
"Shangzhe",
""
],
[
"Rupprecht",
"Christian",
""
]
] |
new_dataset
| 0.996675 |
2202.00868
|
Youngsun Wi
|
Youngsun Wi, Pete Florence, Andy Zeng, Nima Fazeli
|
VIRDO: Visio-tactile Implicit Representations of Deformable Objects
|
This work has been accepted to ICRA 2022
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deformable object manipulation requires computationally efficient
representations that are compatible with robotic sensing modalities. In this
paper, we present VIRDO:an implicit, multi-modal, and continuous representation
for deformable-elastic objects. VIRDO operates directly on visual (point cloud)
and tactile (reaction forces) modalities and learns rich latent embeddings of
contact locations and forces to predict object deformations subject to external
contacts.Here, we demonstrate VIRDOs ability to: i) produce high-fidelity
cross-modal reconstructions with dense unsupervised correspondences, ii)
generalize to unseen contact formations,and iii) state-estimation with partial
visio-tactile feedback
|
[
{
"version": "v1",
"created": "Wed, 2 Feb 2022 04:10:23 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Sep 2022 21:17:54 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Wi",
"Youngsun",
""
],
[
"Florence",
"Pete",
""
],
[
"Zeng",
"Andy",
""
],
[
"Fazeli",
"Nima",
""
]
] |
new_dataset
| 0.999737 |
2203.04541
|
Zhuozhu Jian
|
Zhuozhu Jian, Zihong Lu, Xiao Zhou, Bin Lan, Anxing Xiao, Xueqian
Wang, Bin Liang
|
PUTN: A Plane-fitting based Uneven Terrain Navigation Framework
|
Accepted by IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS) 2022
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Autonomous navigation of ground robots has been widely used in indoor
structured 2D environments, but there are still many challenges in outdoor 3D
unstructured environments, especially in rough, uneven terrains. This paper
proposed a plane-fitting based uneven terrain navigation framework (PUTN) to
solve this problem. The implementation of PUTN is divided into three steps.
First, based on Rapidly-exploring Random Trees (RRT), an improved sample-based
algorithm called Plane Fitting RRT* (PF-RRT*) is proposed to obtain a sparse
trajectory. Each sampling point corresponds to a custom traversability index
and a fitted plane on the point cloud. These planes are connected in series to
form a traversable strip. Second, Gaussian Process Regression is used to
generate traversability of the dense trajectory interpolated from the sparse
trajectory, and the sampling tree is used as the training set. Finally, local
planning is performed using nonlinear model predictive control (NMPC). By
adding the traversability index and uncertainty to the cost function, and
adding obstacles generated by the real-time point cloud to the constraint
function, a safe motion planning algorithm with smooth speed and strong
robustness is available. Experiments in real scenarios are conducted to verify
the effectiveness of the method. The source code is released for the reference
of the community.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 06:22:14 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Sep 2022 08:13:23 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Jian",
"Zhuozhu",
""
],
[
"Lu",
"Zihong",
""
],
[
"Zhou",
"Xiao",
""
],
[
"Lan",
"Bin",
""
],
[
"Xiao",
"Anxing",
""
],
[
"Wang",
"Xueqian",
""
],
[
"Liang",
"Bin",
""
]
] |
new_dataset
| 0.975175 |
2204.03040
|
Georgia Maniati
|
Georgia Maniati, Alexandra Vioni, Nikolaos Ellinas, Karolos Nikitaras,
Konstantinos Klapsas, June Sig Sung, Gunu Jho, Aimilios Chalamandaris and
Pirros Tsiakoulis
|
SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural
Text-to-Speech Synthesis
|
Accepted to INTERSPEECH 2022
| null |
10.21437/Interspeech.2022-10922
| null |
cs.SD cs.CL cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we present the SOMOS dataset, the first large-scale mean
opinion scores (MOS) dataset consisting of solely neural text-to-speech (TTS)
samples. It can be employed to train automatic MOS prediction systems focused
on the assessment of modern synthesizers, and can stimulate advancements in
acoustic model evaluation. It consists of 20K synthetic utterances of the LJ
Speech voice, a public domain speech dataset which is a common benchmark for
building neural acoustic models and vocoders. Utterances are generated from 200
TTS systems including vanilla neural acoustic models as well as models which
allow prosodic variations. An LPCNet vocoder is used for all systems, so that
the samples' variation depends only on the acoustic models. The synthesized
utterances provide balanced and adequate domain and length coverage. We collect
MOS naturalness evaluations on 3 English Amazon Mechanical Turk locales and
share practices leading to reliable crowdsourced annotations for this task. We
provide baseline results of state-of-the-art MOS prediction models on the SOMOS
dataset and show the limitations that such models face when assigned to
evaluate TTS utterances.
|
[
{
"version": "v1",
"created": "Wed, 6 Apr 2022 18:45:20 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Aug 2022 14:24:57 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Maniati",
"Georgia",
""
],
[
"Vioni",
"Alexandra",
""
],
[
"Ellinas",
"Nikolaos",
""
],
[
"Nikitaras",
"Karolos",
""
],
[
"Klapsas",
"Konstantinos",
""
],
[
"Sung",
"June Sig",
""
],
[
"Jho",
"Gunu",
""
],
[
"Chalamandaris",
"Aimilios",
""
],
[
"Tsiakoulis",
"Pirros",
""
]
] |
new_dataset
| 0.99979 |
2205.04643
|
Debajyoti Mondal
|
J. Mark Keil, Debajyoti Mondal, Ehsan Moradi
|
Burning Number for the Points in the Plane
| null | null | null | null |
cs.CG cs.DM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The burning process on a graph $G$ starts with a single burnt vertex, and at
each subsequent step, burns the neighbors of the currently burnt vertices, as
well as one other unburnt vertex. The burning number of $G$ is the smallest
number of steps required to burn all the vertices of the graph. In this paper,
we examine the problem of computing the burning number in a geometric setting.
The input is a set of points $P$ in the Euclidean plane. The burning process
starts with a single burnt point, and at each subsequent step, burns all the
points that are within a distance of one unit from the currently burnt points
and one other unburnt point. The burning number of $P$ is the smallest number
of steps required to burn all the points of $P$. We call this variant
\emph{point burning}. We consider another variant called \emph{anywhere
burning}, where we are allowed to burn any point of the plane. We show that
point burning and anywhere burning problems are both NP-complete, but
$(2+\varepsilon)$ approximable for every $\varepsilon>0$. Moreover, if we put a
restriction on the number of burning sources that can be used, then the
anywhere burning problem becomes NP-hard to approximate within a factor of
$\frac{2}{\sqrt{3}}-\varepsilon$.
|
[
{
"version": "v1",
"created": "Tue, 10 May 2022 03:20:13 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Sep 2022 21:12:32 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Keil",
"J. Mark",
""
],
[
"Mondal",
"Debajyoti",
""
],
[
"Moradi",
"Ehsan",
""
]
] |
new_dataset
| 0.995924 |
2205.06093
|
Baudouin Denis de Senneville PhD
|
Vincent Estrade, Michel Daudon, Emmanuel Richard, Jean-Christophe
Bernhard, Franck Bladou, Gregoire Robert, Laurent Facq, Baudouin Denis de
Senneville
|
Deep morphological recognition of kidney stones using intra-operative
endoscopic digital videos
|
16 pages, 4 figures, 3 tables
|
Physics in Medicine & Biology 2022
|
10.1088/1361-6560/ac8592
| null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The collection and the analysis of kidney stone morphological criteria are
essential for an aetiological diagnosis of stone disease. However, in-situ
LASER-based fragmentation of urinary stones, which is now the most established
chirurgical intervention, may destroy the morphology of the targeted stone. In
the current study, we assess the performance and added value of processing
complete digital endoscopic video sequences for the automatic recognition of
stone morphological features during a standard-of-care intra-operative session.
To this end, a computer-aided video classifier was developed to predict in-situ
the morphology of stone using an intra-operative digital endoscopic video
acquired in a clinical setting.
The proposed technique was evaluated on pure (i.e. include one morphology)
and mixed (i.e. include at least two morphologies) stones involving "Ia/Calcium
Oxalate Monohydrate (COM)", "IIb/ Calcium Oxalate Dihydrate (COD)" and
"IIIb/Uric Acid (UA)" morphologies. 71 digital endoscopic videos (50 exhibited
only one morphological type and 21 displayed two) were analyzed using the
proposed video classifier (56840 frames processed in total). Using the proposed
approach, diagnostic performances (averaged over both pure and mixed stone
types) were as follows: balanced accuracy=88%, sensitivity=80%,
specificity=95%, precision=78% and F1-score=78%.
The obtained results demonstrate that AI applied on digital endoscopic video
sequences is a promising tool for collecting morphological information during
the time-course of the stone fragmentation process without resorting to any
human intervention for stone delineation or selection of good quality steady
frames. To this end, irrelevant image information must be removed from the
prediction process at both frame and pixel levels, which is now feasible thanks
to the use of AI-dedicated networks.
|
[
{
"version": "v1",
"created": "Thu, 12 May 2022 13:58:57 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Estrade",
"Vincent",
""
],
[
"Daudon",
"Michel",
""
],
[
"Richard",
"Emmanuel",
""
],
[
"Bernhard",
"Jean-Christophe",
""
],
[
"Bladou",
"Franck",
""
],
[
"Robert",
"Gregoire",
""
],
[
"Facq",
"Laurent",
""
],
[
"de Senneville",
"Baudouin Denis",
""
]
] |
new_dataset
| 0.979118 |
2206.12628
|
Yongzhi Fan
|
Yongzhi Fan, Xin Du, Lun Luo, Jizhong Shen
|
FreSCo: Frequency-Domain Scan Context for LiDAR-based Place Recognition
with Translation and Rotation Invariance
|
8 pages, 10 figures. Accepted for ICARCV 2022
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Place recognition plays a crucial role in re-localization and loop closure
detection tasks for robots and vehicles. This paper seeks a well-defined global
descriptor for LiDAR-based place recognition. Compared to local descriptors,
global descriptors show remarkable performance in urban road scenes but are
usually viewpoint-dependent. To this end, we propose a simple yet robust global
descriptor dubbed FreSCo that decomposes the viewpoint difference during
revisit and achieves both translation and rotation invariance by leveraging
Fourier Transform and circular shift technique. Besides, a fast two-stage pose
estimation method is proposed to estimate the relative pose after place
retrieval by utilizing the compact 2D point clouds extracted from the original
data. Experiments show that FreSCo exhibited superior performance than
contemporaneous methods on sequences of different scenes from multiple
datasets. Code will be publicly available at https://github.com/soytony/FreSCo.
|
[
{
"version": "v1",
"created": "Sat, 25 Jun 2022 11:47:35 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Sep 2022 14:51:53 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Fan",
"Yongzhi",
""
],
[
"Du",
"Xin",
""
],
[
"Luo",
"Lun",
""
],
[
"Shen",
"Jizhong",
""
]
] |
new_dataset
| 0.998102 |
2207.02596
|
Shai Guendelman
|
Shaull Almagor, Shai Guendelman
|
Concurrent Games with Multiple Topologies
| null | null | null | null |
cs.GT cs.FL
|
http://creativecommons.org/licenses/by/4.0/
|
Concurrent multi-player games with $\omega$-regular objectives are a standard
model for systems that consist of several interacting components, each with its
own objective. The standard solution concept for such games is Nash
Equilibrium, which is a "stable" strategy profile for the players.
In many settings, the system is not fully observable by the interacting
components, e.g., due to internal variables. Then, the interaction is modelled
by a partial information game. Unfortunately, the problem of whether a partial
information game has an NE is not known to be decidable. A particular setting
of partial information arises naturally when processes are assigned IDs by the
system, but these IDs are not known to the processes. Then, the processes have
full information about the state of the system, but are uncertain of the effect
of their actions on the transitions.
We generalize the setting above and introduce Multi-Topology Games (MTGs) --
concurrent games with several possible topologies, where the players do not
know which topology is actually used. We show that extending the concept of NE
to these games can take several forms. To this end, we propose two notions of
NE: Conservative NE, in which a player deviates if she can strictly add
topologies to her winning set, and Greedy NE, where she deviates if she can win
in a previously-losing topology. We study the properties of these NE, and show
that the problem of whether a game admits them is decidable.
|
[
{
"version": "v1",
"created": "Wed, 6 Jul 2022 11:19:39 GMT"
},
{
"version": "v2",
"created": "Sat, 13 Aug 2022 15:12:21 GMT"
},
{
"version": "v3",
"created": "Tue, 27 Sep 2022 06:40:25 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Almagor",
"Shaull",
""
],
[
"Guendelman",
"Shai",
""
]
] |
new_dataset
| 0.951347 |
2207.09258
|
Sahidul Islam
|
Sahidul Islam, Shanglin Zhou, Ran Ran, Yufang Jin, Wujie Wen, Caiwen
Ding and Mimi Xie
|
EVE: Environmental Adaptive Neural Network Models for Low-power Energy
Harvesting System
| null | null | null | null |
cs.LG cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
IoT devices are increasingly being implemented with neural network models to
enable smart applications. Energy harvesting (EH) technology that harvests
energy from ambient environment is a promising alternative to batteries for
powering those devices due to the low maintenance cost and wide availability of
the energy sources. However, the power provided by the energy harvester is low
and has an intrinsic drawback of instability since it varies with the ambient
environment. This paper proposes EVE, an automated machine learning (autoML)
co-exploration framework to search for desired multi-models with shared weights
for energy harvesting IoT devices. Those shared models incur significantly
reduced memory footprint with different levels of model sparsity, latency, and
accuracy to adapt to the environmental changes. An efficient on-device
implementation architecture is further developed to efficiently execute each
model on device. A run-time model extraction algorithm is proposed that
retrieves individual model with negligible overhead when a specific model mode
is triggered.Experimental results show that the neural networks models
generated by EVE is on average 2.5X times faster than the baseline models
without pruning and shared weights.
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 20:53:46 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Sep 2022 18:44:22 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Islam",
"Sahidul",
""
],
[
"Zhou",
"Shanglin",
""
],
[
"Ran",
"Ran",
""
],
[
"Jin",
"Yufang",
""
],
[
"Wen",
"Wujie",
""
],
[
"Ding",
"Caiwen",
""
],
[
"Xie",
"Mimi",
""
]
] |
new_dataset
| 0.950974 |
2207.11919
|
Seungjae Lee
|
Seungjae Lee, Hyungtae Lim, and Hyun Myung
|
Patchwork++: Fast and Robust Ground Segmentation Solving Partial
Under-Segmentation Using 3D Point Cloud
|
This paper has been accepted for publication in the proceedings of
IROS 2022
| null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In the field of 3D perception using 3D LiDAR sensors, ground segmentation is
an essential task for various purposes, such as traversable area detection and
object recognition. Under these circumstances, several ground segmentation
methods have been proposed. However, some limitations are still encountered.
First, some ground segmentation methods require fine-tuning of parameters
depending on the surroundings, which is excessively laborious and
time-consuming. Moreover, even if the parameters are well adjusted, a partial
under-segmentation problem can still emerge, which implies ground segmentation
failures in some regions. Finally, ground segmentation methods typically fail
to estimate an appropriate ground plane when the ground is above another
structure, such as a retaining wall. To address these problems, we propose a
robust ground segmentation method called Patchwork++, an extension of
Patchwork. Patchwork++ exploits adaptive ground likelihood estimation (A-GLE)
to calculate appropriate parameters adaptively based on the previous ground
segmentation results. Moreover, temporal ground revert (TGR) alleviates a
partial under-segmentation problem by using the temporary ground property.
Also, region-wise vertical plane fitting (R-VPF) is introduced to segment the
ground plane properly even if the ground is elevated with different layers.
Finally, we present reflected noise removal (RNR) to eliminate virtual noise
points efficiently based on the 3D LiDAR reflection model. We demonstrate the
qualitative and quantitative evaluations using a SemanticKITTI dataset. Our
code is available at https://github.com/url-kaist/patchwork-plusplus
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 06:09:02 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Sep 2022 04:26:13 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Lee",
"Seungjae",
""
],
[
"Lim",
"Hyungtae",
""
],
[
"Myung",
"Hyun",
""
]
] |
new_dataset
| 0.99975 |
2207.12319
|
Piera Riccio
|
Piera Riccio and Bill Psomas and Francesco Galati and Francisco
Escolano and Thomas Hofmann and Nuria Oliver
|
OpenFilter: A Framework to Democratize Research Access to Social Media
AR Filters
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Augmented Reality or AR filters on selfies have become very popular on social
media platforms for a variety of applications, including marketing,
entertainment and aesthetics. Given the wide adoption of AR face filters and
the importance of faces in our social structures and relations, there is
increased interest by the scientific community to analyze the impact of such
filters from a psychological, artistic and sociological perspective. However,
there are few quantitative analyses in this area mainly due to a lack of
publicly available datasets of facial images with applied AR filters. The
proprietary, close nature of most social media platforms does not allow users,
scientists and practitioners to access the code and the details of the
available AR face filters. Scraping faces from these platforms to collect data
is ethically unacceptable and should, therefore, be avoided in research. In
this paper, we present OpenFilter, a flexible framework to apply AR filters
available in social media platforms on existing large collections of human
faces. Moreover, we share FairBeauty and B-LFW, two beautified versions of the
publicly available FairFace and LFW datasets and we outline insights derived
from the analysis of these beautified datasets.
|
[
{
"version": "v1",
"created": "Tue, 19 Jul 2022 17:05:25 GMT"
},
{
"version": "v2",
"created": "Mon, 1 Aug 2022 20:27:16 GMT"
},
{
"version": "v3",
"created": "Tue, 27 Sep 2022 09:25:40 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Riccio",
"Piera",
""
],
[
"Psomas",
"Bill",
""
],
[
"Galati",
"Francesco",
""
],
[
"Escolano",
"Francisco",
""
],
[
"Hofmann",
"Thomas",
""
],
[
"Oliver",
"Nuria",
""
]
] |
new_dataset
| 0.996593 |
2209.11035
|
Leandro Souza
|
Hugo Abonizio, Leandro Rodrigues de Souza, Roberto Lotufo, Rodrigo
Nogueira
|
MonoByte: A Pool of Monolingual Byte-level Language Models
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The zero-shot cross-lingual ability of models pretrained on multilingual and
even monolingual corpora has spurred many hypotheses to explain this intriguing
empirical result. However, due to the costs of pretraining, most research uses
public models whose pretraining methodology, such as the choice of
tokenization, corpus size, and computational budget, might differ drastically.
When researchers pretrain their own models, they often do so under a
constrained budget, and the resulting models might underperform significantly
compared to SOTA models. These experimental differences led to various
inconsistent conclusions about the nature of the cross-lingual ability of these
models. To help further research on the topic, we released 10 monolingual
byte-level models rigorously pretrained under the same configuration with a
large compute budget (equivalent to 420 days on a V100) and corpora that are 4
times larger than the original BERT's. Because they are tokenizer-free, the
problem of unseen token embeddings is eliminated, thus allowing researchers to
try a wider range of cross-lingual experiments in languages with different
scripts. Additionally, we release two models pretrained on non-natural language
texts that can be used in sanity-check experiments. Experiments on QA and NLI
tasks show that our monolingual models achieve competitive performance to the
multilingual one, and hence can be served to strengthen our understanding of
cross-lingual transferability in language models.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 14:32:48 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Sep 2022 11:55:33 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Abonizio",
"Hugo",
""
],
[
"de Souza",
"Leandro Rodrigues",
""
],
[
"Lotufo",
"Roberto",
""
],
[
"Nogueira",
"Rodrigo",
""
]
] |
new_dataset
| 0.995267 |
2209.11304
|
Erez Posner
|
Aniruddha Tamhane and Tse'ela Mida and Erez Posner and Moshe Bouhnik
|
Colonoscopy Landmark Detection using Vision Transformers
|
Accepted for publication at Imaging Systems for GI Endoscopy workshop
at the 25th International Conference on Medical Image Computing and Computer
Assisted Intervention- MICCAI 2022 ISGIE
| null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Colonoscopy is a routine outpatient procedure used to examine the colon and
rectum for any abnormalities including polyps, diverticula and narrowing of
colon structures. A significant amount of the clinician's time is spent in
post-processing snapshots taken during the colonoscopy procedure, for
maintaining medical records or further investigation. Automating this step can
save time and improve the efficiency of the process. In our work, we have
collected a dataset of 120 colonoscopy videos and 2416 snapshots taken during
the procedure, that have been annotated by experts. Further, we have developed
a novel, vision-transformer based landmark detection algorithm that identifies
key anatomical landmarks (the appendiceal orifice, ileocecal valve/cecum
landmark and rectum retroflexion) from snapshots taken during colonoscopy. Our
algorithm uses an adaptive gamma correction during preprocessing to maintain a
consistent brightness for all images. We then use a vision transformer as the
feature extraction backbone and a fully connected network based classifier head
to categorize a given frame into four classes: the three landmarks or a
non-landmark frame. We compare the vision transformer (ViT-B/16) backbone with
ResNet-101 and ConvNext-B backbones that have been trained similarly. We report
an accuracy of 82% with the vision transformer backbone on a test dataset of
snapshots.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 20:39:07 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Sep 2022 12:11:22 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Tamhane",
"Aniruddha",
""
],
[
"Mida",
"Tse'ela",
""
],
[
"Posner",
"Erez",
""
],
[
"Bouhnik",
"Moshe",
""
]
] |
new_dataset
| 0.999716 |
2209.12513
|
Ruihao Zhou
|
Ruihao Zhou, Li He, Hong Zhang, Xubin Lin, Yisheng Guan
|
NDD: A 3D Point Cloud Descriptor Based on Normal Distribution for Loop
Closure Detection
| null |
Proceedings of the IEEE/RSJ International Conference on
Intelligent Robots and Systems 2022
| null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Loop closure detection is a key technology for long-term robot navigation in
complex environments. In this paper, we present a global descriptor, named
Normal Distribution Descriptor (NDD), for 3D point cloud loop closure
detection. The descriptor encodes both the probability density score and
entropy of a point cloud as the descriptor. We also propose a fast rotation
alignment process and use correlation coefficient as the similarity between
descriptors. Experimental results show that our approach outperforms the
state-of-the-art point cloud descriptors in both accuracy and efficency. The
source code is available and can be integrated into existing LiDAR odometry and
mapping (LOAM) systems.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 08:39:54 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Zhou",
"Ruihao",
""
],
[
"He",
"Li",
""
],
[
"Zhang",
"Hong",
""
],
[
"Lin",
"Xubin",
""
],
[
"Guan",
"Yisheng",
""
]
] |
new_dataset
| 0.999711 |
2209.12962
|
Joel Brogan
|
Joel Brogan and Nell Barber and David Cornett and David Bolme
|
FaRO 2: an Open Source, Configurable Smart City Framework for Real-Time
Distributed Vision and Biometric Systems
| null | null | null | null |
cs.CV cs.AI cs.CR cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Recent global growth in the interest of smart cities has led to trillions of
dollars of investment toward research and development. These connected cities
have the potential to create a symbiosis of technology and society and
revolutionize the cost of living, safety, ecological sustainability, and
quality of life of societies on a world-wide scale. Some key components of the
smart city construct are connected smart grids, self-driving cars, federated
learning systems, smart utilities, large-scale public transit, and proactive
surveillance systems. While exciting in prospect, these technologies and their
subsequent integration cannot be attempted without addressing the potential
societal impacts of such a high degree of automation and data sharing.
Additionally, the feasibility of coordinating so many disparate tasks will
require a fast, extensible, unifying framework. To that end, we propose FaRO2,
a completely reimagined successor to FaRO1, built from the ground up. FaRO2
affords all of the same functionality as its predecessor, serving as a unified
biometric API harness that allows for seamless evaluation, deployment, and
simple pipeline creation for heterogeneous biometric software. FaRO2
additionally provides a fully declarative capability for defining and
coordinating custom machine learning and sensor pipelines, allowing the
distribution of processes across otherwise incompatible hardware and networks.
FaRO2 ultimately provides a way to quickly configure, hot-swap, and expand
large coordinated or federated systems online without interruptions for
maintenance. Because much of the data collected in a smart city contains
Personally Identifying Information (PII), FaRO2 also provides built-in tools
and layers to ensure secure and encrypted streaming, storage, and access of PII
data across distributed systems.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 18:52:53 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Brogan",
"Joel",
""
],
[
"Barber",
"Nell",
""
],
[
"Cornett",
"David",
""
],
[
"Bolme",
"David",
""
]
] |
new_dataset
| 0.998463 |
2209.13015
|
Ehsan Gholami
|
Ehsan Gholami, Mohammad Motamedi, Ashwin Aravindakshan
|
PARSRec: Explainable Personalized Attention-fused Recurrent Sequential
Recommendation Using Session Partial Actions
|
10 pages, 4 figures, this is the author's version of the work. The
definitive Version of Record was published in Proceedings of the 28th ACM
SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August
14-18, 2022, Washington, DC, USA
| null |
10.1145/3534678.3539432
| null |
cs.IR cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The emerging meta- and multi-verse landscape is yet another step towards the
more prevalent use of already ubiquitous online markets. In such markets,
recommender systems play critical roles by offering items of interest to the
users, thereby narrowing down a vast search space that comprises hundreds of
thousands of products. Recommender systems are usually designed to learn common
user behaviors and rely on them for inference. This approach, while effective,
is oblivious to subtle idiosyncrasies that differentiate humans from each
other. Focusing on this observation, we propose an architecture that relies on
common patterns as well as individual behaviors to tailor its recommendations
for each person. Simulations under a controlled environment show that our
proposed model learns interpretable personalized user behaviors. Our empirical
results on Nielsen Consumer Panel dataset indicate that the proposed approach
achieves up to 27.9% performance improvement compared to the state-of-the-art.
|
[
{
"version": "v1",
"created": "Fri, 16 Sep 2022 12:07:43 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Gholami",
"Ehsan",
""
],
[
"Motamedi",
"Mohammad",
""
],
[
"Aravindakshan",
"Ashwin",
""
]
] |
new_dataset
| 0.983372 |
2209.13023
|
Kai-Robin Lange
|
Kai-Robin Lange, Jonas Rieger, Carsten Jentsch
|
Lex2Sent: A bagging approach to unsupervised sentiment analysis
|
10 pages, 1 figure
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unsupervised sentiment analysis is traditionally performed by counting those
words in a text that are stored in a sentiment lexicon and then assigning a
label depending on the proportion of positive and negative words registered.
While these "counting" methods are considered to be beneficial as they rate a
text deterministically, their classification rates decrease when the analyzed
texts are short or the vocabulary differs from what the lexicon considers
default. The model proposed in this paper, called Lex2Sent, is an unsupervised
sentiment analysis method to improve the classification of sentiment lexicon
methods. For this purpose, a Doc2Vec-model is trained to determine the
distances between document embeddings and the embeddings of the positive and
negative part of a sentiment lexicon. These distances are then evaluated for
multiple executions of Doc2Vec on resampled documents and are averaged to
perform the classification task. For three benchmark datasets considered in
this paper, the proposed Lex2Sent outperforms every evaluated lexicon,
including state-of-the-art lexica like VADER or the Opinion Lexicon in terms of
classification rate.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 20:49:18 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Lange",
"Kai-Robin",
""
],
[
"Rieger",
"Jonas",
""
],
[
"Jentsch",
"Carsten",
""
]
] |
new_dataset
| 0.952567 |
2209.13064
|
Dima Damen
|
Ahmad Darkhalil, Dandan Shan, Bin Zhu, Jian Ma, Amlan Kar, Richard
Higgins, Sanja Fidler, David Fouhey, Dima Damen
|
EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations
|
10 pages main, 38 pages appendix. Accepted at NeurIPS 2022 Track on
Datasets and Benchmarks Data, code and leaderboards from:
http://epic-kitchens.github.io/VISOR
| null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce VISOR, a new dataset of pixel annotations and a benchmark suite
for segmenting hands and active objects in egocentric video. VISOR annotates
videos from EPIC-KITCHENS, which comes with a new set of challenges not
encountered in current video segmentation datasets. Specifically, we need to
ensure both short- and long-term consistency of pixel-level annotations as
objects undergo transformative interactions, e.g. an onion is peeled, diced and
cooked - where we aim to obtain accurate pixel-level annotations of the peel,
onion pieces, chopping board, knife, pan, as well as the acting hands. VISOR
introduces an annotation pipeline, AI-powered in parts, for scalability and
quality. In total, we publicly release 272K manual semantic masks of 257 object
classes, 9.9M interpolated dense masks, 67K hand-object relations, covering 36
hours of 179 untrimmed videos. Along with the annotations, we introduce three
challenges in video object segmentation, interaction understanding and
long-term reasoning.
For data, code and leaderboards: http://epic-kitchens.github.io/VISOR
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 23:03:26 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Darkhalil",
"Ahmad",
""
],
[
"Shan",
"Dandan",
""
],
[
"Zhu",
"Bin",
""
],
[
"Ma",
"Jian",
""
],
[
"Kar",
"Amlan",
""
],
[
"Higgins",
"Richard",
""
],
[
"Fidler",
"Sanja",
""
],
[
"Fouhey",
"David",
""
],
[
"Damen",
"Dima",
""
]
] |
new_dataset
| 0.999821 |
2209.13101
|
Hoang Thang Ta Mr.
|
Hoang Thang Ta, Abu Bakar Siddiqur Rahman, Navonil Majumder, Amir
Hussain, Lotfollah Najjar, Newton Howard, Soujanya Poria and Alexander
Gelbukh
|
WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions
from Paragraphs
|
27 pages, 8 figures, 15 tables
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
As free online encyclopedias with massive volumes of content, Wikipedia and
Wikidata are key to many Natural Language Processing (NLP) tasks, such as
information retrieval, knowledge base building, machine translation, text
classification, and text summarization. In this paper, we introduce WikiDes, a
novel dataset to generate short descriptions of Wikipedia articles for the
problem of text summarization. The dataset consists of over 80k English samples
on 6987 topics. We set up a two-phase summarization method - description
generation (Phase I) and candidate ranking (Phase II) - as a strong approach
that relies on transfer and contrastive learning. For description generation,
T5 and BART show their superiority compared to other small-scale pre-trained
models. By applying contrastive learning with the diverse input from beam
search, the metric fusion-based ranking models outperform the direct
description generation models significantly up to 22 ROUGE in topic-exclusive
split and topic-independent split. Furthermore, the outcome descriptions in
Phase II are supported by human evaluation in over 45.33% chosen compared to
23.66% in Phase I against the gold descriptions. In the aspect of sentiment
analysis, the generated descriptions cannot effectively capture all sentiment
polarities from paragraphs while doing this task better from the gold
descriptions. The automatic generation of new descriptions reduces the human
efforts in creating them and enriches Wikidata-based knowledge graphs. Our
paper shows a practical impact on Wikipedia and Wikidata since there are
thousands of missing descriptions. Finally, we expect WikiDes to be a useful
dataset for related works in capturing salient information from short
paragraphs. The curated dataset is publicly available at:
https://github.com/declare-lab/WikiDes.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 01:28:02 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Ta",
"Hoang Thang",
""
],
[
"Rahman",
"Abu Bakar Siddiqur",
""
],
[
"Majumder",
"Navonil",
""
],
[
"Hussain",
"Amir",
""
],
[
"Najjar",
"Lotfollah",
""
],
[
"Howard",
"Newton",
""
],
[
"Poria",
"Soujanya",
""
],
[
"Gelbukh",
"Alexander",
""
]
] |
new_dataset
| 0.999808 |
2209.13202
|
Paraskevi Nousi
|
Paraskevi Nousi, Emmanouil Mpampis, Nikolaos Passalis, Ole Green,
Anastasios Tefas
|
A Novel Dataset for Evaluating and Alleviating Domain Shift for Human
Detection in Agricultural Fields
| null | null | null | null |
cs.CV cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper we evaluate the impact of domain shift on human detection
models trained on well known object detection datasets when deployed on data
outside the distribution of the training set, as well as propose methods to
alleviate such phenomena based on the available annotations from the target
domain. Specifically, we introduce the OpenDR Humans in Field dataset,
collected in the context of agricultural robotics applications, using the
Robotti platform, allowing for quantitatively measuring the impact of domain
shift in such applications. Furthermore, we examine the importance of manual
annotation by evaluating three distinct scenarios concerning the training data:
a) only negative samples, i.e., no depicted humans, b) only positive samples,
i.e., only images which contain humans, and c) both negative and positive
samples. Our results indicate that good performance can be achieved even when
using only negative samples, if additional consideration is given to the
training process. We also find that positive samples increase performance
especially in terms of better localization. The dataset is publicly available
for download at https://github.com/opendr-eu/datasets.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 07:04:28 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Nousi",
"Paraskevi",
""
],
[
"Mpampis",
"Emmanouil",
""
],
[
"Passalis",
"Nikolaos",
""
],
[
"Green",
"Ole",
""
],
[
"Tefas",
"Anastasios",
""
]
] |
new_dataset
| 0.983099 |
2209.13204
|
Xuefei Zhe
|
Weiqiang Wang, Xuefei Zhe, Huan Chen, Di Kang, Tingguang Li, Ruizhi
Chen, and Linchao Bao
|
NEURAL MARIONETTE: A Transformer-based Multi-action Human Motion
Synthesis System
| null | null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We present a neural network-based system for long-term, multi-action human
motion synthesis. The system, dubbed as NEURAL MARIONETTE, can produce
high-quality and meaningful motions with smooth transitions from simple user
input, including a sequence of action tags with expected action duration, and
optionally a hand-drawn moving trajectory if the user specifies. The core of
our system is a novel Transformer-based motion generation model, namely
MARIONET, which can generate diverse motions given action tags. Different from
existing motion generation models, MARIONET utilizes contextual information
from the past motion clip and future action tag, dedicated to generating
actions that can smoothly blend historical and future actions. Specifically,
MARIONET first encodes target action tag and contextual information into an
action-level latent code. The code is unfolded into frame-level control signals
via a time unrolling module, which could be then combined with other
frame-level control signals like the target trajectory. Motion frames are then
generated in an auto-regressive way. By sequentially applying MARIONET, the
system NEURAL MARIONETTE can robustly generate long-term, multi-action motions
with the help of two simple schemes, namely "Shadow Start" and "Action
Revision". Along with the novel system, we also present a new dataset dedicated
to the multi-action motion synthesis task, which contains both action tags and
their contextual information. Extensive experiments are conducted to study the
action accuracy, naturalism, and transition smoothness of the motions generated
by our system.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 07:10:20 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Wang",
"Weiqiang",
""
],
[
"Zhe",
"Xuefei",
""
],
[
"Chen",
"Huan",
""
],
[
"Kang",
"Di",
""
],
[
"Li",
"Tingguang",
""
],
[
"Chen",
"Ruizhi",
""
],
[
"Bao",
"Linchao",
""
]
] |
new_dataset
| 0.999549 |
2209.13219
|
Zhengyan Tong
|
Zhengyan Tong, Xiaohang Wang, Shengchao Yuan, Xuanhong Chen, Junjie
Wang, Xiangzhong Fang
|
Im2Oil: Stroke-Based Oil Painting Rendering with Linearly Controllable
Fineness Via Adaptive Sampling
|
ACM MM 2022 oral paper, accepted by the 30th ACM International
Conference on Multimedia
| null |
10.1145/3503161.3547759
| null |
cs.CV cs.LG cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper proposes a novel stroke-based rendering (SBR) method that
translates images into vivid oil paintings. Previous SBR techniques usually
formulate the oil painting problem as pixel-wise approximation. Different from
this technique route, we treat oil painting creation as an adaptive sampling
problem. Firstly, we compute a probability density map based on the texture
complexity of the input image. Then we use the Voronoi algorithm to sample a
set of pixels as the stroke anchors. Next, we search and generate an individual
oil stroke at each anchor. Finally, we place all the strokes on the canvas to
obtain the oil painting. By adjusting the hyper-parameter maximum sampling
probability, we can control the oil painting fineness in a linear manner.
Comparison with existing state-of-the-art oil painting techniques shows that
our results have higher fidelity and more realistic textures. A user opinion
test demonstrates that people behave more preference toward our oil paintings
than the results of other methods. More interesting results and the code are in
https://github.com/TZYSJTU/Im2Oil.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 07:41:04 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Tong",
"Zhengyan",
""
],
[
"Wang",
"Xiaohang",
""
],
[
"Yuan",
"Shengchao",
""
],
[
"Chen",
"Xuanhong",
""
],
[
"Wang",
"Junjie",
""
],
[
"Fang",
"Xiangzhong",
""
]
] |
new_dataset
| 0.996697 |
2209.13252
|
Hao Yu
|
Hao Yu, Ji Hou, Zheng Qin, Mahdi Saleh, Ivan Shugurov, Kai Wang,
Benjamin Busam, Slobodan Ilic
|
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud
Registration
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Successful point cloud registration relies on accurate correspondences
established upon powerful descriptors. However, existing neural descriptors
either leverage a rotation-variant backbone whose performance declines under
large rotations, or encode local geometry that is less distinctive. To address
this issue, we introduce RIGA to learn descriptors that are Rotation-Invariant
by design and Globally-Aware. From the Point Pair Features (PPFs) of sparse
local regions, rotation-invariant local geometry is encoded into geometric
descriptors. Global awareness of 3D structures and geometric context is
subsequently incorporated, both in a rotation-invariant fashion. More
specifically, 3D structures of the whole frame are first represented by our
global PPF signatures, from which structural descriptors are learned to help
geometric descriptors sense the 3D world beyond local regions. Geometric
context from the whole scene is then globally aggregated into descriptors.
Finally, the description of sparse regions is interpolated to dense point
descriptors, from which correspondences are extracted for registration. To
validate our approach, we conduct extensive experiments on both object- and
scene-level data. With large rotations, RIGA surpasses the state-of-the-art
methods by a margin of 8\degree in terms of the Relative Rotation Error on
ModelNet40 and improves the Feature Matching Recall by at least 5 percentage
points on 3DLoMatch.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 08:45:56 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Yu",
"Hao",
""
],
[
"Hou",
"Ji",
""
],
[
"Qin",
"Zheng",
""
],
[
"Saleh",
"Mahdi",
""
],
[
"Shugurov",
"Ivan",
""
],
[
"Wang",
"Kai",
""
],
[
"Busam",
"Benjamin",
""
],
[
"Ilic",
"Slobodan",
""
]
] |
new_dataset
| 0.999375 |
2209.13304
|
Manuel Vidigueira
|
Martina Camaioni, Rachid Guerraoui, Matteo Monti, Manuel Vidigueira
|
Oracular Byzantine Reliable Broadcast [Extended Version]
| null | null | null | null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Byzantine Reliable Broadcast (BRB) is a fundamental distributed computing
primitive, with applications ranging from notifications to asynchronous payment
systems. Motivated by practical consideration, we study Client-Server Byzantine
Reliable Broadcast (CSB), a multi-shot variant of BRB whose interface is split
between broadcasting clients and delivering servers. We present Draft, an
optimally resilient implementation of CSB. Like most implementations of BRB,
Draft guarantees both liveness and safety in an asynchronous environment. Under
good conditions, however, Draft achieves unparalleled efficiency. In a moment
of synchrony, free from Byzantine misbehaviour, and at the limit of infinitely
many broadcasting clients, a Draft server delivers a $b$-bits payload at an
asymptotic amortized cost of $0$ signature verifications, and $\log_2(c) + b$
bits exchanged, where $c$ is the number of clients in the system. This is the
information-theoretical minimum number of bits required to convey the payload
($b$ bits, assuming it is compressed), along with an identifier for its sender
($\log_2(c)$ bits, necessary to enumerate any set of $c$ elements, and optimal
if broadcasting frequencies are uniform or unknown). These two achievements
have profound practical implications. Real-world BRB implementations are often
bottlenecked either by expensive signature verifications, or by communication
overhead. For Draft, instead, the network is the limit: a server can deliver
payloads as quickly as it would receive them from an infallible oracle.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 11:09:54 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Camaioni",
"Martina",
""
],
[
"Guerraoui",
"Rachid",
""
],
[
"Monti",
"Matteo",
""
],
[
"Vidigueira",
"Manuel",
""
]
] |
new_dataset
| 0.999194 |
2209.13323
|
Shantanu Pal
|
Hejia Zhou, Shantanu Pal, Zahra Jadidi, Alireza Jolfaei
|
A Fog-Based Security Framework for Large-Scale Industrial Internet of
Things Environments
| null | null | null | null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
The Industrial Internet of Things (IIoT) is a developing research area with
potential global Internet connectivity, turning everyday objects into
intelligent devices with more autonomous activities. IIoT services and
applications are not only being used in smart homes and smart cities, but they
have also become an essential element of the Industry 4.0 concept. The
emergence of the IIoT helps traditional industries simplify production
processes, reduce production costs, and improve industrial efficiency. However,
the involvement of many heterogeneous devices, the use of third-party software,
and the resource-constrained nature of the IoT devices bring new security risks
to the production chain and expose vulnerabilities to the systems. The
Distributed Denial of Service (DDoS) attacks are significant, among others.
This article analyzes the threats and attacks in the IIoT and discusses how
DDoS attacks impact the production process and communication dysfunctions with
IIoT services and applications. This article also proposes a reference security
framework that enhances the advantages of fog computing to demonstrate
countermeasures against DDoS attacks and possible strategies to mitigate such
attacks at scale.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 11:58:13 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Zhou",
"Hejia",
""
],
[
"Pal",
"Shantanu",
""
],
[
"Jadidi",
"Zahra",
""
],
[
"Jolfaei",
"Alireza",
""
]
] |
new_dataset
| 0.967439 |
2209.13331
|
Jane Dwivedi-Yu
|
Jane Dwivedi-Yu, Timo Schick, Zhengbao Jiang, Maria Lomeli, Patrick
Lewis, Gautier Izacard, Edouard Grave, Sebastian Riedel, Fabio Petroni
|
EditEval: An Instruction-Based Benchmark for Text Improvements
| null | null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Evaluation of text generation to date has primarily focused on content
created sequentially, rather than improvements on a piece of text. Writing,
however, is naturally an iterative and incremental process that requires
expertise in different modular skills such as fixing outdated information or
making the style more consistent. Even so, comprehensive evaluation of a
model's capacity to perform these skills and the ability to edit remains
sparse. This work presents EditEval: An instruction-based, benchmark and
evaluation suite that leverages high-quality existing and new datasets for
automatic evaluation of editing capabilities such as making text more cohesive
and paraphrasing. We evaluate several pre-trained models, which shows that
InstructGPT and PEER perform the best, but that most baselines fall below the
supervised SOTA, particularly when neutralizing and updating information. Our
analysis also shows that commonly used metrics for editing tasks do not always
correlate well, and that optimization for prompts with the highest performance
does not necessarily entail the strongest robustness to different models.
Through the release of this benchmark and a publicly available leaderboard
challenge, we hope to unlock future research in developing models capable of
iterative and more controllable editing.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 12:26:05 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Dwivedi-Yu",
"Jane",
""
],
[
"Schick",
"Timo",
""
],
[
"Jiang",
"Zhengbao",
""
],
[
"Lomeli",
"Maria",
""
],
[
"Lewis",
"Patrick",
""
],
[
"Izacard",
"Gautier",
""
],
[
"Grave",
"Edouard",
""
],
[
"Riedel",
"Sebastian",
""
],
[
"Petroni",
"Fabio",
""
]
] |
new_dataset
| 0.998934 |
2209.13362
|
Yijin Li
|
Yijin Li, Xinyang Liu, Wenqi Dong, Han Zhou, Hujun Bao, Guofeng Zhang,
Yinda Zhang, Zhaopeng Cui
|
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image
|
Accepted to ECCV 2022. Project Page: https://zju3dv.github.io/deltar/
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Light-weight time-of-flight (ToF) depth sensors are small, cheap, low-energy
and have been massively deployed on mobile devices for the purposes like
autofocus, obstacle detection, etc. However, due to their specific measurements
(depth distribution in a region instead of the depth value at a certain pixel)
and extremely low resolution, they are insufficient for applications requiring
high-fidelity depth such as 3D reconstruction. In this paper, we propose
DELTAR, a novel method to empower light-weight ToF sensors with the capability
of measuring high resolution and accurate depth by cooperating with a color
image. As the core of DELTAR, a feature extractor customized for depth
distribution and an attention-based neural architecture is proposed to fuse the
information from the color and ToF domain efficiently. To evaluate our system
in real-world scenarios, we design a data collection device and propose a new
approach to calibrate the RGB camera and ToF sensor. Experiments show that our
method produces more accurate depth than existing frameworks designed for depth
completion and depth super-resolution and achieves on par performance with a
commodity-level RGB-D sensor. Code and data are available at
https://zju3dv.github.io/deltar/.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 13:11:37 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Li",
"Yijin",
""
],
[
"Liu",
"Xinyang",
""
],
[
"Dong",
"Wenqi",
""
],
[
"Zhou",
"Han",
""
],
[
"Bao",
"Hujun",
""
],
[
"Zhang",
"Guofeng",
""
],
[
"Zhang",
"Yinda",
""
],
[
"Cui",
"Zhaopeng",
""
]
] |
new_dataset
| 0.988847 |
2209.13373
|
Ville Salo
|
Ville Salo
|
On von Neumann regularity of cellular automata
|
16 pages, 3 figures; comments welcome! arXiv admin note: text overlap
with arXiv:1804.03913
| null | null | null |
cs.FL math.DS math.RA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We show that a cellular automaton on a one-dimensional two-sided mixing
subshift of finite type is a von Neumann regular element in the semigroup of
cellular automata if and only if it is split epic onto its image in the
category of sofic shifts and block maps. It follows from previous joint work of
the author and T\"orm\"a that von Neumann regularity is a decidable condition,
and we decide it for all elementary CA, obtaining the optimal radii for weak
generalized inverses. Two sufficient conditions for non-regularity are having a
proper sofic image or having a point in the image with no preimage of the same
period. We show that the non-regular ECA 9 and 28 cannot be proven non-regular
using these methods. We also show that a random cellular automaton is
non-regular with high probability.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 13:19:13 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Salo",
"Ville",
""
]
] |
new_dataset
| 0.950996 |
2209.13391
|
InnaSosunova
|
Inna Sosunova, Jari Porras, Ekaterina Makarova and Andrei Rybin
|
Waste Management Hackathon Providing New Ideas to Increase Citizen
Awareness, Motivation and Engagement
| null | null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper describes the International Disruptive Information Solutions
hackathon and one the winning solutions. The purpose of the hackathon was to
promote the use of disruptive ICT technologies (e.g. IoT, Big data, AI,
blockchain) in urban infrastructures to create innovative waste management
solutions in a smart city context. 29 students enrolled into this hackathon and
in the end 4 teams submitted their solutions to the challenges. The winning
proposal EcoQ, an approach for plogging collecting trashes while jogging,
answered more than well to the presented challenge on waste management and
engagement. The original idea was extended and partly refocused during an
internship. As the outcome of the internship a mobile application for
organizing and holding waste collection events was developed. This mobile
application was shortly tested in a real environment and it provides a working
citizen-centric platform, which enables anyone to arrange waste management
events, and motivates other residents to participate in these activities.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 13:55:05 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Sosunova",
"Inna",
""
],
[
"Porras",
"Jari",
""
],
[
"Makarova",
"Ekaterina",
""
],
[
"Rybin",
"Andrei",
""
]
] |
new_dataset
| 0.989331 |
2209.13418
|
Kushagra Srivastava
|
Kushagra Srivastava, Dhruv Patel, Aditya Kumar Jha, Mohhit Kumar Jha,
Jaskirat Singh, Ravi Kiran Sarvadevabhatla, Pradeep Kumar Ramancharla,
Harikumar Kandath and K. Madhava Krishna
|
UAV-based Visual Remote Sensing for Automated Building Inspection
|
Paper accepted at CVCIE Workshop at ECCV, 2022 and the project page
is https://uvrsabi.github.io/
| null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with
computer vision has demonstrated potential for assisting building construction
and in disaster management like damage assessment during earthquakes. The
vulnerability of a building to earthquake can be assessed through inspection
that takes into account the expected damage progression of the associated
component and the component's contribution to structural system performance.
Most of these inspections are done manually, leading to high utilization of
manpower, time, and cost. This paper proposes a methodology to automate these
inspections through UAV-based image data collection and a software library for
post-processing that helps in estimating the seismic structural parameters. The
key parameters considered here are the distances between adjacent buildings,
building plan-shape, building plan area, objects on the rooftop and rooftop
layout. The accuracy of the proposed methodology in estimating the
above-mentioned parameters is verified through field measurements taken using a
distance measuring sensor and also from the data obtained through Google Earth.
Additional details and code can be accessed from https://uvrsabi.github.io/ .
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 14:18:14 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Srivastava",
"Kushagra",
""
],
[
"Patel",
"Dhruv",
""
],
[
"Jha",
"Aditya Kumar",
""
],
[
"Jha",
"Mohhit Kumar",
""
],
[
"Singh",
"Jaskirat",
""
],
[
"Sarvadevabhatla",
"Ravi Kiran",
""
],
[
"Ramancharla",
"Pradeep Kumar",
""
],
[
"Kandath",
"Harikumar",
""
],
[
"Krishna",
"K. Madhava",
""
]
] |
new_dataset
| 0.970305 |
2209.13428
|
Qingyu Chen
|
Qingyu Chen, Alexis Allot, Robert Leaman, Chih-Hsuan Wei, Elaheh
Aghaarabi, John J. Guerrerio, Lilly Xu, Zhiyong Lu
|
LitCovid in 2022: an information resource for the COVID-19 literature
|
9 pages
| null | null | null |
cs.DL cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), first launched
in February 2020, is a first-of-its-kind literature hub for tracking up-to-date
published research on COVID-19. The number of articles in LitCovid has
increased from 55,000 to ~300,000 over the past two and half years, with a
consistent growth rate of ~10,000 articles per month. In addition to the rapid
literature growth, the COVID-19 pandemic has evolved dramatically. For
instance, the Omicron variant has now accounted for over 98% of new infections
in the U.S. In response to the continuing evolution of the COVID-19 pandemic,
this article describes significant updates to LitCovid over the last two years.
First, we introduced the Long Covid collection consisting of the articles on
COVID-19 survivors experiencing ongoing multisystemic symptoms, including
respiratory issues, cardiovascular disease, cognitive impairment, and profound
fatigue. Second, we provided new annotations on the latest COVID-19 strains and
vaccines mentioned in the literature. Third, we improved several existing
features with more accurate machine learning algorithms for annotating topics
and classifying articles relevant to COVID-19. LitCovid has been widely used
with millions of accesses by users worldwide on various information needs and
continues to play a critical role in collecting, curating, and standardizing
the latest knowledge on the COVID-19 literature.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 14:32:20 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Chen",
"Qingyu",
""
],
[
"Allot",
"Alexis",
""
],
[
"Leaman",
"Robert",
""
],
[
"Wei",
"Chih-Hsuan",
""
],
[
"Aghaarabi",
"Elaheh",
""
],
[
"Guerrerio",
"John J.",
""
],
[
"Xu",
"Lilly",
""
],
[
"Lu",
"Zhiyong",
""
]
] |
new_dataset
| 0.997827 |
2209.13431
|
Gracy Christopher
|
M. Gracy, B. Rebecca Jeyavadhanam
|
MTTBA- A Key Contributor for Sustainable Energy Consumption Time and
Space Utility for Highly Secured Crypto Transactions in Blockchain Technology
|
15 pages, 13 figures
| null | null | null |
cs.CR
|
http://creativecommons.org/publicdomain/zero/1.0/
|
A Merkle tree is an information construction that is used in Blockchain to
verify data or transactions in a large content pool in a safe manner. The role
of the Merkle tree is crucial in Bitcoin and other cryptocurrencies in a
Blockchain network. In this paper, we propose a bright and enhanced
verification method, Merkle Trim Tree-based Blockchain Authentication (MTTBA)
for the hash node traversal to reach the Merkle Root in a minimum time. MTTBA
is a unique mechanism for verifying the Merkle Tree's accumulated transactions
specifically for an odd number of transactions. The future impact of
cryptocurrency is going to be massive and MTTBA proves its efficacy in
transaction speed and eliminating node duplication. Our method enables any
block to validate transactions' full availability without duplicating hash
nodes. Performance has been evaluated in different parameters and the results
show marked improvement in throughput(1680ms), processing time(29700kbps),
memory usage(140MB), and security(99.30%). The energy consumption factor is
crucial in the scenario, and MTTBA has achieved the lowest of 240 joules.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 14:38:30 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Gracy",
"M.",
""
],
[
"Jeyavadhanam",
"B. Rebecca",
""
]
] |
new_dataset
| 0.992452 |
2209.13458
|
Kayol Mayer
|
Jonathan Aguiar Soares, Kayol Soares Mayer, Pedro Benevenuto
Valadares, Dalton Soares Arantes
|
PCA-based Channel Estimation for MIMO Communications
|
5 pages, 7 figures, XL SIMP\'OSIO BRASILEIRO DE
TELECOMUNICA\c{C}\~OES E PROCESSAMENTO DE SINAIS (SBrT 2022)
| null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
In multiple-input multiple-output communications, channel estimation is
paramount to keep base stations and users on track. This paper proposes a novel
PCA-based-principal component analysis-channel estimation approach for MIMO
orthogonal frequency division multiplexing systems. The channel frequency
response is firstly estimated with the least squares method, and then PCA is
used to filter only the higher singular components of the channel impulse
response, which is then converted back to the frequency domain. The proposed
approach is compared with the MMSE, the minimum mean square error estimation,
in terms of bit error rate versus Eb/N0.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 15:25:49 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Soares",
"Jonathan Aguiar",
""
],
[
"Mayer",
"Kayol Soares",
""
],
[
"Valadares",
"Pedro Benevenuto",
""
],
[
"Arantes",
"Dalton Soares",
""
]
] |
new_dataset
| 0.996732 |
2209.13461
|
Tasnim Sakib Apon
|
Tasnim Sakib Apon, Ramisa Anan, Elizabeth Antora Modhu, Arjun Suter,
Ifrit Jamal Sneha, MD. Golam Rabiul Alam
|
BanglaSarc: A Dataset for Sarcasm Detection
| null | null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Being one of the most widely spoken language in the world, the use of Bangla
has been increasing in the world of social media as well. Sarcasm is a positive
statement or remark with an underlying negative motivation that is extensively
employed in today's social media platforms. There has been a significant
improvement in sarcasm detection in English over the previous many years,
however the situation regarding Bangla sarcasm detection remains unchanged. As
a result, it is still difficult to identify sarcasm in bangla, and a lack of
high-quality data is a major contributing factor. This article proposes
BanglaSarc, a dataset constructed specifically for bangla textual data sarcasm
detection. This dataset contains of 5112 comments/status and contents collected
from various online social platforms such as Facebook, YouTube, along with a
few online blogs. Due to the limited amount of data collection of categorized
comments in Bengali, this dataset will aid in the of study identifying sarcasm,
recognizing people's emotion, detecting various types of Bengali expressions,
and other domains. The dataset is publicly available at
https://www.kaggle.com/datasets/sakibapon/banglasarc.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 15:28:21 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Apon",
"Tasnim Sakib",
""
],
[
"Anan",
"Ramisa",
""
],
[
"Modhu",
"Elizabeth Antora",
""
],
[
"Suter",
"Arjun",
""
],
[
"Sneha",
"Ifrit Jamal",
""
],
[
"Alam",
"MD. Golam Rabiul",
""
]
] |
new_dataset
| 0.999882 |
2209.13479
|
Yee Yang Tee
|
Yee-Yang Tee, Deruo Cheng, Chye-Soon Chee, Tong Lin, Yiqiong Shi,
Bah-Hwee Gwee
|
Unsupervised Domain Adaptation with Histogram-gated Image Translation
for Delayered IC Image Analysis
|
7 pages, 4 figures, To be presented at IEEE PAINE 2022 (oral)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep learning has achieved great success in the challenging circuit
annotation task by employing Convolutional Neural Networks (CNN) for the
segmentation of circuit structures. The deep learning approaches require a
large amount of manually annotated training data to achieve a good performance,
which could cause a degradation in performance if a deep learning model trained
on a given dataset is applied to a different dataset. This is commonly known as
the domain shift problem for circuit annotation, which stems from the possibly
large variations in distribution across different image datasets. The different
image datasets could be obtained from different devices or different layers
within a single device. To address the domain shift problem, we propose
Histogram-gated Image Translation (HGIT), an unsupervised domain adaptation
framework which transforms images from a given source dataset to the domain of
a target dataset, and utilize the transformed images for training a
segmentation network. Specifically, our HGIT performs generative adversarial
network (GAN)-based image translation and utilizes histogram statistics for
data curation. Experiments were conducted on a single labeled source dataset
adapted to three different target datasets (without labels for training) and
the segmentation performance was evaluated for each target dataset. We have
demonstrated that our method achieves the best performance compared to the
reported domain adaptation techniques, and is also reasonably close to the
fully supervised benchmark.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 15:53:22 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Tee",
"Yee-Yang",
""
],
[
"Cheng",
"Deruo",
""
],
[
"Chee",
"Chye-Soon",
""
],
[
"Lin",
"Tong",
""
],
[
"Shi",
"Yiqiong",
""
],
[
"Gwee",
"Bah-Hwee",
""
]
] |
new_dataset
| 0.954861 |
2209.13509
|
Jair Augusto Bottega
|
Jair A. Bottega, Victor A. Kich, Alisson H. Kolling, Jardel D. S.
Dyonisio, Pedro L. Cor\c{c}aque, Rodrigo da S. Guerra, Daniel F. T. Gamarra
|
Jubileo: An Open-Source Robot and Framework for Research in Human-Robot
Social Interaction
|
IEEE Humanoids 2022 (Accepted)
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Human-robot interaction (HRI) is essential to the widespread use of robots in
daily life. Robots will eventually be able to carry out a variety of duties in
human civilization through effective social interaction. Creating
straightforward and understandable interfaces to engage with robots as they
start to proliferate in the personal workspace is essential. Typically,
interactions with simulated robots are displayed on screens. A more appealing
alternative is virtual reality (VR), which gives visual cues more like those
seen in the real world. In this study, we introduce Jubileo, a robotic
animatronic face with various tools for research and application development in
human-robot social interaction field. Jubileo project offers more than just a
fully functional open-source physical robot; it also gives a comprehensive
framework to operate with a VR interface, enabling an immersive environment for
HRI application tests and noticeably better deployment speed.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 16:24:39 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Bottega",
"Jair A.",
""
],
[
"Kich",
"Victor A.",
""
],
[
"Kolling",
"Alisson H.",
""
],
[
"Dyonisio",
"Jardel D. S.",
""
],
[
"Corçaque",
"Pedro L.",
""
],
[
"Guerra",
"Rodrigo da S.",
""
],
[
"Gamarra",
"Daniel F. T.",
""
]
] |
new_dataset
| 0.999563 |
2209.13538
|
J. Miguel Diaz-Ba\~nez
|
Jos\'e-Miguel D\'iaz-B\'a\~nez
|
Mathematics and Flamenco: An Unexpected Partnership
| null | null | null | null |
cs.CG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this paper, we present a series of mathematical problems which throw
interesting lights on flamenco music. More specifically, these are problems in
discrete and computational mathematics suggested by an analytical (not
compositional) examination of flamenco ``cante'' (singing). As a consequence,
since the problems are taken from a culturally specific context, the examples
can make more effective mathematics education.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 16:50:33 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Díaz-Báñez",
"José-Miguel",
""
]
] |
new_dataset
| 0.998957 |
2209.13542
|
Raju Gottumukkala
|
Majid Hosseini, Fahad Sohrab, Raju Gottumukkala, Ravi Teja
Bhupatiraju, Satya Katragadda, Jenni Raitoharju, Alexandros Iosifidis, Moncef
Gabbouj
|
EmpathicSchool: A multimodal dataset for real-time facial expressions
and physiological data analysis under different stress conditions
| null | null | null | null |
cs.MM eess.SP
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Affective computing has garnered researchers' attention and interest in
recent years as there is a need for AI systems to better understand and react
to human emotions. However, analyzing human emotions, such as mood or stress,
is quite complex. While various stress studies use facial expressions and
wearables, most existing datasets rely on processing data from a single
modality. This paper presents EmpathicSchool, a novel dataset that captures
facial expressions and the associated physiological signals, such as heart
rate, electrodermal activity, and skin temperature, under different stress
levels. The data was collected from 20 participants at different sessions for
26 hours. The data includes nine different signal types, including both
computer vision and physiological features that can be used to detect stress.
In addition, various experiments were conducted to validate the signal quality.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 22:19:18 GMT"
}
] | 2022-09-28T00:00:00 |
[
[
"Hosseini",
"Majid",
""
],
[
"Sohrab",
"Fahad",
""
],
[
"Gottumukkala",
"Raju",
""
],
[
"Bhupatiraju",
"Ravi Teja",
""
],
[
"Katragadda",
"Satya",
""
],
[
"Raitoharju",
"Jenni",
""
],
[
"Iosifidis",
"Alexandros",
""
],
[
"Gabbouj",
"Moncef",
""
]
] |
new_dataset
| 0.999815 |
1901.09089
|
Adithya Murali
|
Adithya Murali, Lucas Pe\~na, Christof L\"oding, P. Madhusudan
|
A First-Order Logic with Frames
|
This manuscript is an extended and revised version of the publication
with the same title that appeared at ESOP 2022
(https://doi.org/10.1007/978-3-030-44914-8_19). It is currently under review
| null | null | null |
cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a novel logic, called Frame Logic (FL), that extends first-order
logic (with recursive definitions) using a construct Sp(.) that captures the
implicit supports of formulas -- the precise subset of the universe upon which
their meaning depends. Using such supports, we formulate proof rules that
facilitate frame reasoning elegantly when the underlying model undergoes
change. We show that the logic is expressive by capturing several
data-structures and also exhibit a translation from a precise fragment of
separation logic to frame logic. Finally, we design a program logic based on
frame logic for reasoning with programs that dynamically update heaps that
facilitates local specifications and frame reasoning. This program logic
consists of both localized proof rules as well as rules that derive the weakest
tightest preconditions in FL.
|
[
{
"version": "v1",
"created": "Fri, 25 Jan 2019 21:33:21 GMT"
},
{
"version": "v2",
"created": "Mon, 22 Jul 2019 16:15:42 GMT"
},
{
"version": "v3",
"created": "Tue, 25 Feb 2020 01:50:51 GMT"
},
{
"version": "v4",
"created": "Mon, 26 Sep 2022 16:54:26 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Murali",
"Adithya",
""
],
[
"Peña",
"Lucas",
""
],
[
"Löding",
"Christof",
""
],
[
"Madhusudan",
"P.",
""
]
] |
new_dataset
| 0.999182 |
2010.04108
|
Sebastian Wild
|
Konstantinos Tsakalidis, Sebastian Wild, Viktor Zamaraev
|
Succinct Permutation Graphs
|
updated to match final Algorithmica version
| null |
10.1007/s00453-022-01039-2
| null |
cs.DS cs.DM
|
http://creativecommons.org/licenses/by/4.0/
|
We present a succinct data structure for permutation graphs, and their
superclass of circular permutation graphs, i.e., data structures using optimal
space up to lower order terms. Unlike concurrent work on circle graphs (Acan et
al. 2022), our data structure also supports distance and shortest-path queries,
as well as adjacency and neighborhood queries, all in optimal time. We present
in particular the first succinct exact distance oracle for (circular)
permutation graphs. A second succinct data structure also supports degree
queries in time independent of the neighborhood's size at the expense of an
$O(\log n/\log \log n)$-factor overhead in all running times. Furthermore, we
develop a succinct data structure for the class of bipartite permutation
graphs. We demonstrate how to run algorithms directly over our succinct
representations for several problems on permutation graphs: Clique, Coloring,
Independent Set, Hamiltonian Cycle, All-Pair Shortest Paths, and others.
Finally, we initiate the study of semi-distributed graph representations; a
concept that smoothly interpolates between distributed (labeling schemes) and
centralized (standard data structures). We show how to turn some of our data
structures into semi-distributed representations by storing only $O(n)$ bits of
additional global information, circumventing the lower bound on distance
labeling schemes for permutation graphs.
|
[
{
"version": "v1",
"created": "Thu, 8 Oct 2020 16:47:10 GMT"
},
{
"version": "v2",
"created": "Sat, 24 Sep 2022 17:02:26 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Tsakalidis",
"Konstantinos",
""
],
[
"Wild",
"Sebastian",
""
],
[
"Zamaraev",
"Viktor",
""
]
] |
new_dataset
| 0.985854 |
2011.14497
|
Kavisha Vidanapathirana
|
Kavisha Vidanapathirana, Peyman Moghadam, Ben Harwood, Muming Zhao,
Sridha Sridharan, Clinton Fookes
|
Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order
Pooling
|
ICRA 2021. Implementation available at:
https://github.com/csiro-robotics/locus
| null |
10.1109/ICRA48506.2021.9560915
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Place Recognition enables the estimation of a globally consistent map and
trajectory by providing non-local constraints in Simultaneous Localisation and
Mapping (SLAM). This paper presents Locus, a novel place recognition method
using 3D LiDAR point clouds in large-scale environments. We propose a method
for extracting and encoding topological and temporal information related to
components in a scene and demonstrate how the inclusion of this auxiliary
information in place description leads to more robust and discriminative scene
representations. Second-order pooling along with a non-linear transform is used
to aggregate these multi-level features to generate a fixed-length global
descriptor, which is invariant to the permutation of input features. The
proposed method outperforms state-of-the-art methods on the KITTI dataset.
Furthermore, Locus is demonstrated to be robust across several challenging
situations such as occlusions and viewpoint changes in 3D LiDAR point clouds.
The open-source implementation is available at:
https://github.com/csiro-robotics/locus .
|
[
{
"version": "v1",
"created": "Mon, 30 Nov 2020 01:45:55 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Mar 2021 06:49:52 GMT"
},
{
"version": "v3",
"created": "Thu, 8 Apr 2021 00:09:27 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Vidanapathirana",
"Kavisha",
""
],
[
"Moghadam",
"Peyman",
""
],
[
"Harwood",
"Ben",
""
],
[
"Zhao",
"Muming",
""
],
[
"Sridharan",
"Sridha",
""
],
[
"Fookes",
"Clinton",
""
]
] |
new_dataset
| 0.998605 |
2109.00648
|
Natalia Tomashenko
|
Natalia Tomashenko, Xin Wang, Emmanuel Vincent, Jose Patino, Brij
Mohan Lal Srivastava, Paul-Gauthier No\'e, Andreas Nautsch, Nicholas Evans,
Junichi Yamagishi, Benjamin O'Brien, Ana\"is Chanclu, Jean-Fran\c{c}ois
Bonastre, Massimiliano Todisco, Mohamed Maouche
|
The VoicePrivacy 2020 Challenge: Results and findings
|
Submitted to the Special Issue on Voice Privacy (Computer Speech and
Language Journal - Elsevier); under review
| null |
10.1016/j.csl.2022.101362
| null |
cs.CL cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents the results and analyses stemming from the first
VoicePrivacy 2020 Challenge which focuses on developing anonymization solutions
for speech technology. We provide a systematic overview of the challenge design
with an analysis of submitted systems and evaluation results. In particular, we
describe the voice anonymization task and datasets used for system development
and evaluation. Also, we present different attack models and the associated
objective and subjective evaluation metrics. We introduce two anonymization
baselines and provide a summary description of the anonymization systems
developed by the challenge participants. We report objective and subjective
evaluation results for baseline and submitted systems. In addition, we present
experimental results for alternative privacy metrics and attack models
developed as a part of the post-evaluation analysis. Finally, we summarize our
insights and observations that will influence the design of the next
VoicePrivacy challenge edition and some directions for future voice
anonymization research.
|
[
{
"version": "v1",
"created": "Wed, 1 Sep 2021 23:40:38 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Oct 2021 21:05:51 GMT"
},
{
"version": "v3",
"created": "Thu, 18 Nov 2021 07:47:29 GMT"
},
{
"version": "v4",
"created": "Mon, 26 Sep 2022 05:52:52 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Tomashenko",
"Natalia",
""
],
[
"Wang",
"Xin",
""
],
[
"Vincent",
"Emmanuel",
""
],
[
"Patino",
"Jose",
""
],
[
"Srivastava",
"Brij Mohan Lal",
""
],
[
"Noé",
"Paul-Gauthier",
""
],
[
"Nautsch",
"Andreas",
""
],
[
"Evans",
"Nicholas",
""
],
[
"Yamagishi",
"Junichi",
""
],
[
"O'Brien",
"Benjamin",
""
],
[
"Chanclu",
"Anaïs",
""
],
[
"Bonastre",
"Jean-François",
""
],
[
"Todisco",
"Massimiliano",
""
],
[
"Maouche",
"Mohamed",
""
]
] |
new_dataset
| 0.987837 |
2109.06550
|
Xiyuan Liu
|
Xiyuan Liu, Chongjian Yuan, Fu Zhang
|
Targetless Extrinsic Calibration of Multiple Small FoV LiDARs and
Cameras using Adaptive Voxelization
|
12 pages, 15 figures
| null |
10.1109/TIM.2022.3176889
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Determining the extrinsic parameter between multiple LiDARs and cameras is
essential for autonomous robots, especially for solid-state LiDARs, where each
LiDAR unit has a very small Field-of-View (FoV), and multiple units are often
used collectively. The majority of extrinsic calibration methods are proposed
for 360$^\circ$ mechanical spinning LiDARs where the FoV overlap with other
LiDAR or camera sensors is assumed. Few research works have been focused on the
calibration of small FoV LiDARs and cameras nor on the improvement of the
calibration speed. In this work, we consider the problem of extrinsic
calibration among small FoV LiDARs and cameras, with the aim to shorten the
total calibration time and further improve the calibration precision. We first
implement an adaptive voxelization technique in the extraction and matching of
LiDAR feature points. Such a process could avoid the redundant creation of
$k$-d trees in LiDAR extrinsic calibration and extract LiDAR feature points in
a more reliable and fast manner than existing methods. We then formulate the
multiple LiDAR extrinsic calibration into a LiDAR Bundle Adjustment (BA)
problem. By deriving the cost function up to second-order, the solving time and
precision of the non-linear least square problem are further boosted. Our
proposed method has been verified on data collected in four targetless scenes
and under two types of solid-state LiDARs with a completely different scanning
pattern, density, and FoV. The robustness of our work has also been validated
under eight initial setups, with each setup containing 100 independent trials.
Compared with the state-of-the-art methods, our work has increased the
calibration speed 15 times for LiDAR-LiDAR extrinsic calibration and 1.5 times
for LiDAR-Camera extrinsic calibration (averaged result from 50 independent
trials) while remaining accurate.
|
[
{
"version": "v1",
"created": "Tue, 14 Sep 2021 09:45:56 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Feb 2022 13:28:37 GMT"
},
{
"version": "v3",
"created": "Sat, 24 Sep 2022 07:07:51 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Liu",
"Xiyuan",
""
],
[
"Yuan",
"Chongjian",
""
],
[
"Zhang",
"Fu",
""
]
] |
new_dataset
| 0.954143 |
2109.08336
|
Kavisha Vidanapathirana
|
Kavisha Vidanapathirana, Milad Ramezani, Peyman Moghadam, Sridha
Sridharan, Clinton Fookes
|
LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place
Recognition
|
Accepted - ICRA 2022
| null |
10.1109/ICRA46639.2022.9811753
| null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Retrieval-based place recognition is an efficient and effective solution for
re-localization within a pre-built map, or global data association for
Simultaneous Localization and Mapping (SLAM). The accuracy of such an approach
is heavily dependent on the quality of the extracted scene-level
representation. While end-to-end solutions - which learn a global descriptor
from input point clouds - have demonstrated promising results, such approaches
are limited in their ability to enforce desirable properties at the local
feature level. In this paper, we introduce a local consistency loss to guide
the network towards learning local features which are consistent across
revisits, hence leading to more repeatable global descriptors resulting in an
overall improvement in 3D place recognition performance. We formulate our
approach in an end-to-end trainable architecture called LoGG3D-Net. Experiments
on two large-scale public benchmarks (KITTI and MulRan) show that our method
achieves mean $F1_{max}$ scores of $0.939$ and $0.968$ on KITTI and MulRan
respectively, achieving state-of-the-art performance while operating in near
real-time. The open-source implementation is available at:
https://github.com/csiro-robotics/LoGG3D-Net.
|
[
{
"version": "v1",
"created": "Fri, 17 Sep 2021 03:32:43 GMT"
},
{
"version": "v2",
"created": "Wed, 22 Sep 2021 06:49:35 GMT"
},
{
"version": "v3",
"created": "Thu, 17 Feb 2022 04:33:16 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Vidanapathirana",
"Kavisha",
""
],
[
"Ramezani",
"Milad",
""
],
[
"Moghadam",
"Peyman",
""
],
[
"Sridharan",
"Sridha",
""
],
[
"Fookes",
"Clinton",
""
]
] |
new_dataset
| 0.971546 |
2110.05472
|
Shubham Goel
|
Shubham Goel, Georgia Gkioxari, Jitendra Malik
|
Differentiable Stereopsis: Meshes from multiple views using
differentiable rendering
|
In CVPR2022. Project webpage: https://shubham-goel.github.io/ds/
|
In CVPR 2022 (pp. 8635-8644)
| null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We propose Differentiable Stereopsis, a multi-view stereo approach that
reconstructs shape and texture from few input views and noisy cameras. We pair
traditional stereopsis and modern differentiable rendering to build an
end-to-end model which predicts textured 3D meshes of objects with varying
topologies and shape. We frame stereopsis as an optimization problem and
simultaneously update shape and cameras via simple gradient descent. We run an
extensive quantitative analysis and compare to traditional multi-view stereo
techniques and state-of-the-art learning based methods. We show compelling
reconstructions on challenging real-world scenes and for an abundance of object
types with complex shape, topology and texture. Project webpage:
https://shubham-goel.github.io/ds/
|
[
{
"version": "v1",
"created": "Mon, 11 Oct 2021 17:59:40 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Sep 2022 18:46:52 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Goel",
"Shubham",
""
],
[
"Gkioxari",
"Georgia",
""
],
[
"Malik",
"Jitendra",
""
]
] |
new_dataset
| 0.988789 |
2110.12329
|
Doina Bucur
|
Doina Bucur
|
The network signature of constellation line figures
|
Data repository: https://github.com/doinab/constellation-lines (in
progress)
|
PLOS ONE 17(7): e0272270 (2022)
|
10.1371/journal.pone.0272270
| null |
cs.SI cs.LG physics.hist-ph
|
http://creativecommons.org/licenses/by/4.0/
|
In traditional astronomies across the world, groups of stars in the night sky
were linked into constellations -- symbolic representations rich in meaning and
with practical roles. In some sky cultures, constellations are represented as
line (or connect-the-dot) figures, which are spatial networks drawn over the
fixed background of stars. We analyse 1802 line figures from 56 sky cultures
spanning all continents, in terms of their network, spatial, and brightness
features, and ask what associations exist between these visual features and
culture type or sky region. First, an embedded map of constellations is learnt,
to show clusters of line figures. We then form the network of constellations
(as linked by their similarity), to study how similar cultures are by computing
their assortativity (or homophily) over the network. Finally, we measure the
diversity (or entropy) index for the set of constellations drawn per sky
region. Our results show distinct types of line figures, and that many folk
astronomies with oral traditions have widespread similarities in constellation
design, which do not align with cultural ancestry. In a minority of sky
regions, certain line designs appear universal, but this is not the norm: in
the majority of sky regions, the line geometries are diverse.
|
[
{
"version": "v1",
"created": "Tue, 19 Oct 2021 16:11:53 GMT"
},
{
"version": "v2",
"created": "Sat, 13 Nov 2021 20:41:57 GMT"
},
{
"version": "v3",
"created": "Thu, 28 Jul 2022 20:13:53 GMT"
},
{
"version": "v4",
"created": "Mon, 26 Sep 2022 10:15:47 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Bucur",
"Doina",
""
]
] |
new_dataset
| 0.987849 |
2112.02866
|
Tommaso Cesari
|
Nicol\`o Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Claudio
Gentile, Yishay Mansour
|
Nonstochastic Bandits with Composite Anonymous Feedback
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate a nonstochastic bandit setting in which the loss of an action
is not immediately charged to the player, but rather spread over the subsequent
rounds in an adversarial way. The instantaneous loss observed by the player at
the end of each round is then a sum of many loss components of previously
played actions. This setting encompasses as a special case the easier task of
bandits with delayed feedback, a well-studied framework where the player
observes the delayed losses individually.
Our first contribution is a general reduction transforming a standard bandit
algorithm into one that can operate in the harder setting: We bound the regret
of the transformed algorithm in terms of the stability and regret of the
original algorithm. Then, we show that the transformation of a suitably tuned
FTRL with Tsallis entropy has a regret of order $\sqrt{(d+1)KT}$, where $d$ is
the maximum delay, $K$ is the number of arms, and $T$ is the time horizon.
Finally, we show that our results cannot be improved in general by exhibiting a
matching (up to a log factor) lower bound on the regret of any algorithm
operating in this setting.
|
[
{
"version": "v1",
"created": "Mon, 6 Dec 2021 08:44:04 GMT"
},
{
"version": "v2",
"created": "Sat, 24 Sep 2022 11:55:22 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Cesa-Bianchi",
"Nicolò",
""
],
[
"Cesari",
"Tommaso",
""
],
[
"Colomboni",
"Roberto",
""
],
[
"Gentile",
"Claudio",
""
],
[
"Mansour",
"Yishay",
""
]
] |
new_dataset
| 0.975654 |
2202.07503
|
Guanchu Wang
|
Guanchu Wang and Zaid Pervaiz Bhat and Zhimeng Jiang and Yi-Wei Chen
and Daochen Zha and Alfredo Costilla Reyes and Afshin Niktash and Gorkem
Ulkar and Erman Okman and Xuanting Cai and Xia Hu
|
BED: A Real-Time Object Detection System for Edge Devices
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deploying deep neural networks~(DNNs) on edge devices provides efficient and
effective solutions for the real-world tasks. Edge devices have been used for
collecting a large volume of data efficiently in different domains. DNNs have
been an effective tool for data processing and analysis. However, designing
DNNs on edge devices is challenging due to the limited computational resources
and memory. To tackle this challenge, we demonstrate Object Detection System
for Edge Devices~(BED) on the MAX78000 DNN accelerator. It integrates on-device
DNN inference with a camera and an LCD display for image acquisition and
detection exhibition, respectively. BED is a concise, effective and detailed
solution, including model training, quantization, synthesis and deployment. The
entire repository is open-sourced on Github, including a Graphical User
Interface~(GUI) for on-chip debugging. Experiment results indicate that BED can
produce accurate detection with a 300-KB tiny DNN model, which takes only 91.9
ms of inference time and 1.845 mJ of energy. The real-time detection is
available at YouTube.
|
[
{
"version": "v1",
"created": "Mon, 14 Feb 2022 18:24:20 GMT"
},
{
"version": "v2",
"created": "Fri, 17 Jun 2022 03:32:04 GMT"
},
{
"version": "v3",
"created": "Sun, 14 Aug 2022 16:00:25 GMT"
},
{
"version": "v4",
"created": "Sun, 25 Sep 2022 20:21:48 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Wang",
"Guanchu",
""
],
[
"Bhat",
"Zaid Pervaiz",
""
],
[
"Jiang",
"Zhimeng",
""
],
[
"Chen",
"Yi-Wei",
""
],
[
"Zha",
"Daochen",
""
],
[
"Reyes",
"Alfredo Costilla",
""
],
[
"Niktash",
"Afshin",
""
],
[
"Ulkar",
"Gorkem",
""
],
[
"Okman",
"Erman",
""
],
[
"Cai",
"Xuanting",
""
],
[
"Hu",
"Xia",
""
]
] |
new_dataset
| 0.992447 |
2203.01414
|
Chaolin Rao
|
Chaolin Rao, Huangjie Yu, Haochuan Wan, Jindong Zhou, Yueyang Zheng,
Yu Ma, Anpei Chen, Minye Wu, Binzhe Yuan, Pingqiang Zhou, Xin Lou and Jingyi
Yu
|
ICARUS: A Specialized Architecture for Neural Radiance Fields Rendering
| null | null | null | null |
cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The practical deployment of Neural Radiance Fields (NeRF) in rendering
applications faces several challenges, with the most critical one being low
rendering speed on even high-end graphic processing units (GPUs). In this
paper, we present ICARUS, a specialized accelerator architecture tailored for
NeRF rendering. Unlike GPUs using general purpose computing and memory
architectures for NeRF, ICARUS executes the complete NeRF pipeline using
dedicated plenoptic cores (PLCore) consisting of a positional encoding unit
(PEU), a multi-layer perceptron (MLP) engine, and a volume rendering unit
(VRU). A PLCore takes in positions \& directions and renders the corresponding
pixel colors without any intermediate data going off-chip for temporary storage
and exchange, which can be time and power consuming. To implement the most
expensive component of NeRF, i.e., the MLP, we transform the fully connected
operations to approximated reconfigurable multiple constant multiplications
(MCMs), where common subexpressions are shared across different multiplications
to improve the computation efficiency. We build a prototype ICARUS using
Synopsys HAPS-80 S104, a field programmable gate array (FPGA)-based prototyping
system for large-scale integrated circuits and systems design. We evaluate the
power-performance-area (PPA) of a PLCore using 40nm LP CMOS technology. Working
at 400 MHz, a single PLCore occupies 16.5 $mm^2$ and consumes 282.8 mW,
translating to 0.105 uJ/sample. The results are compared with those of GPU and
tensor processing unit (TPU) implementations.
|
[
{
"version": "v1",
"created": "Tue, 1 Mar 2022 03:24:28 GMT"
},
{
"version": "v2",
"created": "Fri, 27 May 2022 10:36:14 GMT"
},
{
"version": "v3",
"created": "Mon, 26 Sep 2022 08:35:22 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Rao",
"Chaolin",
""
],
[
"Yu",
"Huangjie",
""
],
[
"Wan",
"Haochuan",
""
],
[
"Zhou",
"Jindong",
""
],
[
"Zheng",
"Yueyang",
""
],
[
"Ma",
"Yu",
""
],
[
"Chen",
"Anpei",
""
],
[
"Wu",
"Minye",
""
],
[
"Yuan",
"Binzhe",
""
],
[
"Zhou",
"Pingqiang",
""
],
[
"Lou",
"Xin",
""
],
[
"Yu",
"Jingyi",
""
]
] |
new_dataset
| 0.991027 |
2203.03183
|
Zehao Wang
|
Zehao Wang, Mingxiao Li, Minye Wu, Marie-Francine Moens, Tinne
Tuytelaars
|
Find a Way Forward: a Language-Guided Semantic Map Navigator
|
content revised
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this paper, we introduce the map-language navigation task where an agent
executes natural language instructions and moves to the target position based
only on a given 3D semantic map. To tackle the task, we design the
instruction-aware Path Proposal and Discrimination model (iPPD). Our approach
leverages map information to provide instruction-aware path proposals, i.e., it
selects all potential instruction-aligned candidate paths to reduce the
solution space. Next, to represent the map observations along a path for a
better modality alignment, a novel Path Feature Encoding scheme tailored for
semantic maps is proposed. An attention-based Language Driven Discriminator is
designed to evaluate path candidates and determine the best path as the final
result. Our method can naturally avoid error accumulation compared with
single-step greedy decision methods. Comparing to a single-step imitation
learning approach, iPPD has performance gains above 17% on navigation success
and 0.18 on path matching measurement nDTW in challenging unseen environments.
|
[
{
"version": "v1",
"created": "Mon, 7 Mar 2022 07:40:33 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Sep 2022 06:31:47 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Wang",
"Zehao",
""
],
[
"Li",
"Mingxiao",
""
],
[
"Wu",
"Minye",
""
],
[
"Moens",
"Marie-Francine",
""
],
[
"Tuytelaars",
"Tinne",
""
]
] |
new_dataset
| 0.991284 |
2204.10455
|
Marisa Kirisame
|
Marisa Kirisame, Pranav Shenoy, Pavel Panchekha
|
Optimal Heap Limits for Reducing Browser Memory Use
| null | null | null | null |
cs.PL cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Garbage-collected language runtimes carefully tune heap limits to reduce
garbage collection time and memory usage. However, there's a trade-off: a lower
heap limit reduces memory use but increases garbage collection time. Classic
methods for setting heap limits include manually tuned heap limits and
multiple-of-live-size rules of thumb, but it is not clear when one rule is
better than another or how to compare them.
We address this problem with a new framework where heap limits are set for
multiple heaps at once. Our key insight is that every heap limit rule induces a
particular allocation of memory across multiple processes, and this allocation
can be sub-optimal. We use our framework to derive an optimal "square-root"
heap limit rule, which minimizes total memory usage for any amount of total
garbage collection time. Paradoxically, the square-root heap limit rule
achieves this coordination without communication: it allocates memory optimally
across multiple heaps without requiring any communication between those heaps.
To demonstrate that this heap limit rule is effective, we prototype it for
V8, the JavaScript runtime used in Google Chrome, Microsoft Edge, and other
browsers, as well as in server-side frameworks like node.js and Deno. On
real-world web pages, our prototype achieves reductions of approximately 16.0%
of memory usage while keeping garbage collection time constant. On
memory-intensive benchmarks, reductions of up to 30.0% of garbage collection
time are possible with no change in total memory usage.
|
[
{
"version": "v1",
"created": "Fri, 22 Apr 2022 01:26:48 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Apr 2022 09:27:34 GMT"
},
{
"version": "v3",
"created": "Fri, 16 Sep 2022 16:59:32 GMT"
},
{
"version": "v4",
"created": "Sun, 25 Sep 2022 06:22:19 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Kirisame",
"Marisa",
""
],
[
"Shenoy",
"Pranav",
""
],
[
"Panchekha",
"Pavel",
""
]
] |
new_dataset
| 0.997016 |
2205.01052
|
Hans Wang
|
Gordon King and Hans Wang
|
HTTPA/2: a Trusted End-to-End Protocol for Web Services
|
24 pages, 6 figures
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the advent of cloud computing and the Internet, the commercialized
website becomes capable of providing more web services, such as software as a
service (SaaS) or function as a service (FaaS), for great user experiences.
Undoubtedly, web services have been thriving in popularity that will continue
growing to serve modern human life. As expected, there came the ineluctable
need for preserving privacy, enhancing security, and building trust. However,
HTTPS alone cannot provide a remote attestation for building trust with web
services, which remains lacking in trust. At the same time, cloud computing is
actively adopting the use of TEEs and will demand a web-based protocol for
remote attestation with ease of use. Here, we propose HTTPA/2 as an upgraded
version of HTTP-Attestable (HTTPA) by augmenting existing HTTP to enable
end-to-end trusted communication between endpoints at layer 7 (L7). HTTPA/2
allows for L7 message protection without relying on TLS. In practice, HTTPA/2
is designed to be compatible with the in-network processing of the modern cloud
infrastructure, including L7 gateway, L7 load balancer, caching, etc. We
envision that \acs{httpa}/2 will further enable trustworthy web services and
trustworthy AI applications in the future, accelerating the transformation of
the web-based digital world to be more trustworthy.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 17:37:54 GMT"
},
{
"version": "v2",
"created": "Fri, 20 May 2022 18:51:44 GMT"
},
{
"version": "v3",
"created": "Wed, 15 Jun 2022 15:44:21 GMT"
},
{
"version": "v4",
"created": "Fri, 17 Jun 2022 21:37:12 GMT"
},
{
"version": "v5",
"created": "Sun, 25 Sep 2022 20:27:35 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"King",
"Gordon",
""
],
[
"Wang",
"Hans",
""
]
] |
new_dataset
| 0.997803 |
2206.13995
|
Hao Chen
|
Hao Chen
|
New MDS Entanglement-Assisted Quantum Codes from MDS Hermitian
Self-Orthogonal Codes
|
18 pages, MDS quantum codes can be transformed to MDS
Entanglement-assisted quantum codes with nonzero c parameters directly
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/publicdomain/zero/1.0/
|
The intersection ${\bf C}\bigcap {\bf C}^{\perp_H}$ of a linear code ${\bf C}
\subset {\bf F}_{q^2}$ and its Hermitian dual ${\bf C}^{\perp_H}$ is called the
Hermitian hull of this code. A linear code ${\bf C} \subset {\bf F}_{q^2}$
satisfying ${\bf C} \subset {\bf C}^{\perp_H}$ is called Hermitian
self-orthogonal. Many Hermitian self-orthogonal codes were given for the
construction of MDS quantum error correction codes (QECCs). In this paper we
prove that for a nonnegative integer $h$ satisfying $0 \leq h \leq k$, a linear
Hermitian self-orthogonal $[n, k]_{q^2}$ code is equivalent to a linear
$h$-dimension Hermitian hull code. Therefore a lot of new MDS
entanglement-assisted quantum error correction (EAQEC) codes can be constructed
from previous known Hermitian self-orthogonal codes. Actually our method shows
that previous constructed quantum MDS codes from Hermitian self-orthogonal
codes can be transformed to MDS entanglement-assisted quantum codes with
nonzero consumption parameter $c$ directly. We prove that MDS EAQEC $[[n, k, d,
c]]_q$ codes with nonzero $c$ parameters and $d\leq \frac{n+2}{2}$ exist for
arbitrary length $n \leq q^2+1$. Moreover any QECC constructed from
$k$-dimensional Hermitian self-orthogonal codes can be transformed to $k$
different EAQEC codes.
|
[
{
"version": "v1",
"created": "Tue, 28 Jun 2022 13:31:16 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Jun 2022 09:23:17 GMT"
},
{
"version": "v3",
"created": "Sun, 3 Jul 2022 14:18:02 GMT"
},
{
"version": "v4",
"created": "Fri, 29 Jul 2022 07:34:20 GMT"
},
{
"version": "v5",
"created": "Mon, 26 Sep 2022 00:49:47 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Chen",
"Hao",
""
]
] |
new_dataset
| 0.995643 |
2206.14764
|
Peng Liang
|
Liming Fu, Peng Liang, Zeeshan Rasheed, Zengyang Li, Amjed Tahir,
Xiaofeng Han
|
Potential Technical Debt and Its Resolution in Code Reviews: An
Exploratory Study of the OpenStack and Qt Communities
|
The 16th ACM/IEEE International Symposium on Empirical Software
Engineering and Measurement (ESEM)
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Technical Debt (TD) refers to the situation where developers make trade-offs
to achieve short-term goals at the expense of long-term code quality, which can
have a negative impact on the quality of software systems. In the context of
code review, such sub-optimal implementations have chances to be timely
resolved during the review process before the code is merged. Therefore, we
could consider them as Potential Technical Debt (PTD) since PTD will evolve
into TD when it is injected into software systems without being resolved. To
date, little is known about the extent to which PTD is identified in code
reviews. To this end, we conducted an exploratory study in an attempt to
understand the nature of PTD in code reviews and track down the resolution of
PTD after being identified. We randomly collected 2,030 review comments from
the Nova project of OpenStack and the Qt Base project of Qt. We then manually
checked these review comments, and obtained 163 PTD-related review comments for
further analysis. Our results show that: (1) PTD can be identified in code
reviews but is not prevalent. (2) Design, defect, documentation, requirement,
test, and code PTD are identified in code reviews, in which code and
documentation PTD are the dominant. (3) 81.0% of the PTD identified in code
reviews has been resolved by developers, and 78.0% of the resolved TD was
resolved by developers within a week. (4) Code refactoring is the main practice
used by developers to resolve the PTD identified in code reviews. Our findings
indicate that: (1) review-based detection of PTD is seen as one of the
trustworthy mechanisms in development, and (2) there is still a significant
proportion of PTD (19.0%) remaining unresolved when injected into the software
systems. Practitioners and researchers should establish effective strategies to
manage and resolve PTD in development.
|
[
{
"version": "v1",
"created": "Wed, 29 Jun 2022 16:53:46 GMT"
},
{
"version": "v2",
"created": "Sat, 24 Sep 2022 05:44:52 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Fu",
"Liming",
""
],
[
"Liang",
"Peng",
""
],
[
"Rasheed",
"Zeeshan",
""
],
[
"Li",
"Zengyang",
""
],
[
"Tahir",
"Amjed",
""
],
[
"Han",
"Xiaofeng",
""
]
] |
new_dataset
| 0.997521 |
2207.02202
|
Zhengzhong Tu
|
Runsheng Xu, Zhengzhong Tu, Hao Xiang, Wei Shao, Bolei Zhou, Jiaqi Ma
|
CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse
Transformers
|
CoRL 2022; code: https://github.com/DerrickXuNu/CoBEVT
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Bird's eye view (BEV) semantic segmentation plays a crucial role in spatial
sensing for autonomous driving. Although recent literature has made significant
progress on BEV map understanding, they are all based on single-agent
camera-based systems. These solutions sometimes have difficulty handling
occlusions or detecting distant objects in complex traffic scenes.
Vehicle-to-Vehicle (V2V) communication technologies have enabled autonomous
vehicles to share sensing information, dramatically improving the perception
performance and range compared to single-agent systems. In this paper, we
propose CoBEVT, the first generic multi-agent multi-camera perception framework
that can cooperatively generate BEV map predictions. To efficiently fuse camera
features from multi-view and multi-agent data in an underlying Transformer
architecture, we design a fused axial attention module (FAX), which captures
sparsely local and global spatial interactions across views and agents. The
extensive experiments on the V2V perception dataset, OPV2V, demonstrate that
CoBEVT achieves state-of-the-art performance for cooperative BEV semantic
segmentation. Moreover, CoBEVT is shown to be generalizable to other tasks,
including 1) BEV segmentation with single-agent multi-camera and 2) 3D object
detection with multi-agent LiDAR systems, achieving state-of-the-art
performance with real-time inference speed. The code is available at
https://github.com/DerrickXuNu/CoBEVT.
|
[
{
"version": "v1",
"created": "Tue, 5 Jul 2022 17:59:28 GMT"
},
{
"version": "v2",
"created": "Sun, 25 Sep 2022 07:19:32 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Xu",
"Runsheng",
""
],
[
"Tu",
"Zhengzhong",
""
],
[
"Xiang",
"Hao",
""
],
[
"Shao",
"Wei",
""
],
[
"Zhou",
"Bolei",
""
],
[
"Ma",
"Jiaqi",
""
]
] |
new_dataset
| 0.990747 |
2207.07540
|
Emmanuel Senft
|
Emmanuel Senft, David Porfirio, Katie Winkle
|
PD/EUP Workshop Proceedings
|
HTML file with clickable links to papers - All papers have been
reviewed by two reviewers in a single blind fashion - Symposium website:
https://sites.google.com/wisc.edu/hri22pdeupworkshop/
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
People who need robots are often not the same as people who can program them.
This key observation in human-robot interaction (HRI) has lead to a number of
challenges when developing robotic applications, since developers must
understand the exact needs of end-users.
Participatory Design (PD), the process of including stakeholders such as end
users early in the robot design process, has been used with noteworthy success
in HRI, but typically remains limited to the early phases of development.
Resulting robot behaviors are often then hardcoded by engineers or utilized in
Wizard-of-Oz (WoZ) systems that rarely achieve autonomy. End-User Programming
(EUP), i.e., the research of tools allowing end users with limited computer
knowledge to program systems, has been widely applied to the design of robot
behaviors for interaction with humans, but these tools risk being used solely
as research demonstrations only existing for the amount of time required for
them to be evaluated and published.
In the PD/EUP Workshop, we aim to facilitate mutual learning between these
communities and to create communication opportunities that could help the
larger HRI community work towards end-user personalized and adaptable
interactions. Both PD and EUP will be key requirements if we want robots to be
useful for wider society. From this workshop, we expect new collaboration
opportunities to emerge and we aim to formalize new methodologies that
integrate PD and EUP approaches.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 15:32:55 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Sep 2022 11:43:37 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Senft",
"Emmanuel",
""
],
[
"Porfirio",
"David",
""
],
[
"Winkle",
"Katie",
""
]
] |
new_dataset
| 0.993149 |
2208.10431
|
Mengqi Xue
|
Mengqi Xue, Qihan Huang, Haofei Zhang, Lechao Cheng, Jie Song, Minghui
Wu, Mingli Song
|
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers
for Interpretable Image Recognition
|
Arxiv preprint; 18 pages, 12 figures, 7 tables
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Prototypical part network (ProtoPNet) has drawn wide attention and boosted
many follow-up studies due to its self-explanatory property for explainable
artificial intelligence (XAI). However, when directly applying ProtoPNet on
vision transformer (ViT) backbones, learned prototypes have a "distraction"
problem: they have a relatively high probability of being activated by the
background and pay less attention to the foreground. The powerful capability of
modeling long-term dependency makes the transformer-based ProtoPNet hard to
focus on prototypical parts, thus severely impairing its inherent
interpretability. This paper proposes prototypical part transformer
(ProtoPFormer) for appropriately and effectively applying the prototype-based
method with ViTs for interpretable image recognition. The proposed method
introduces global and local prototypes for capturing and highlighting the
representative holistic and partial features of targets according to the
architectural characteristics of ViTs. The global prototypes are adopted to
provide the global view of objects to guide local prototypes to concentrate on
the foreground while eliminating the influence of the background. Afterwards,
local prototypes are explicitly supervised to concentrate on their respective
prototypical visual parts, increasing the overall interpretability. Extensive
experiments demonstrate that our proposed global and local prototypes can
mutually correct each other and jointly make final decisions, which faithfully
and transparently reason the decision-making processes associatively from the
whole and local perspectives, respectively. Moreover, ProtoPFormer consistently
achieves superior performance and visualization results over the
state-of-the-art (SOTA) prototype-based baselines. Our code has been released
at https://github.com/zju-vipa/ProtoPFormer.
|
[
{
"version": "v1",
"created": "Mon, 22 Aug 2022 16:36:32 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Sep 2022 16:18:27 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Xue",
"Mengqi",
""
],
[
"Huang",
"Qihan",
""
],
[
"Zhang",
"Haofei",
""
],
[
"Cheng",
"Lechao",
""
],
[
"Song",
"Jie",
""
],
[
"Wu",
"Minghui",
""
],
[
"Song",
"Mingli",
""
]
] |
new_dataset
| 0.986706 |
2208.13583
|
Anitha Gollamudi
|
Alexandra E. Michael, Anitha Gollamudi, Jay Bosamiya, Craig
Disselkoen, Aidan Denlinger, Conrad Watt, Bryan Parno, Marco Patrignani,
Marco Vassena, Deian Stefan
|
MSWasm: Soundly Enforcing Memory-Safe Execution of Unsafe Code
| null | null | null | null |
cs.CR cs.PL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Most programs compiled to WebAssembly (Wasm) today are written in unsafe
languages like C and C++. Unfortunately, memory-unsafe C code remains unsafe
when compiled to Wasm -- and attackers can exploit buffer overflows and
use-after-frees in Wasm almost as easily as they can on native platforms.
Memory-Safe WebAssembly (MSWasm) proposes to extend Wasm with language-level
memory-safety abstractions to precisely address this problem. In this paper, we
build on the original MSWasm position paper to realize this vision. We give a
precise and formal semantics of MSWasm, and prove that well-typed MSWasm
programs are, by construction, robustly memory safe. To this end, we develop a
novel, language-independent memory-safety property based on colored memory
locations and pointers. This property also lets us reason about the security
guarantees of a formal C-to-MSWasm compiler -- and prove that it always
produces memory-safe programs (and preserves the semantics of safe programs).
We use these formal results to then guide several implementations: Two
compilers of MSWasm to native code, and a C-to-MSWasm compiler (that extends
Clang). Our MSWasm compilers support different enforcement mechanisms, allowing
developers to make security-performance trade-offs according to their needs.
Our evaluation shows that the overhead of enforcing memory safety in software
ranges from 22% (enforcing spatial safety alone) to 198% (enforcing full memory
safety) on the PolyBenchC suite. More importantly, MSWasm's design makes it
easy to swap between enforcement mechanisms; as fast (especially
hardware-based) enforcement techniques become available, MSWasm will be able to
take advantage of these advances almost for free.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 13:22:28 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Sep 2022 16:50:30 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Michael",
"Alexandra E.",
""
],
[
"Gollamudi",
"Anitha",
""
],
[
"Bosamiya",
"Jay",
""
],
[
"Disselkoen",
"Craig",
""
],
[
"Denlinger",
"Aidan",
""
],
[
"Watt",
"Conrad",
""
],
[
"Parno",
"Bryan",
""
],
[
"Patrignani",
"Marco",
""
],
[
"Vassena",
"Marco",
""
],
[
"Stefan",
"Deian",
""
]
] |
new_dataset
| 0.999389 |
2209.09313
|
Terence Smith Dr
|
Terence R. Smith
|
Natural Wave Numbers, Natural Wave Co-numbers, and the Computation of
the Primes
|
16 pages
| null | null | null |
cs.DS math.NT
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The paper exploits an isomorphism between the natural numbers N and a space U
of periodic sequences of the roots of unity in constructing a recursive
procedure for representing and computing the prime numbers. The nth wave number
${\bf u}_n$ is the countable sequence of the nth roots of unity having
frequencies k/n for all integer phases k. The space U is closed under a
commutative and associative binary operation ${\bf u}_m \odot{\bf u}_n={\bf
u}_{mn}$, termed the circular product, and is isomorphic with N under their
respective product operators. Functions are defined on U that partition wave
numbers into two complementary sequences, of which the co-number $ {\overset
{\bf \ast }{ \bf u}}_n$ is a function of a wave number in which zeros replace
its positive roots of unity. The recursive procedure $ {\overset {\bf \ast }{
\bf U}}_{N+1}= {\overset {\bf \ast }{ \bf U}}_{N}\odot{\overset {\bf \ast }{\bf
u}}_{{N+1}}$ represents prime numbers explicitly in terms of preceding prime
numbers, starting with $p_1=2$, and is shown never to terminate. If ${p}_1, ...
, { p}_{N+1}$ are the first $N+1$ prime phases, then the phases in the range
$p_{N+1} \leq k < p^2_{N+1}$ that are associated with the non-zero terms of $
{\overset {\bf \ast }{\bf U}}_{N}$ are, together with $ p_1, ...,p_N$, all of
the prime phases less than $p^2_{N+1}$. When applied with all of the primes
identified at the previous step, the recursive procedure identifies
approximately $7^{2(N-1)}/(2(N-1)ln7)$ primes at each iteration for $ N>1$.
When the phases of wave numbers are represented in modular arithmetic, the
prime phases are representable in terms of sums of reciprocals of the initial
set of prime phases and have a relation with the zeta-function.
|
[
{
"version": "v1",
"created": "Mon, 19 Sep 2022 19:18:40 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Smith",
"Terence R.",
""
]
] |
new_dataset
| 0.999551 |
2209.11554
|
Kun Woo Cho
|
Kun Woo Cho, Mohammad H. Mazaheri, Jeremy Gummeson, Omid Abari, Kyle
Jamieson
|
mmWall: A Transflective Metamaterial Surface for mmWave Networks
|
18 pages, 18 figures
| null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mobile operators are poised to leverage millimeter wave technology as 5G
evolves, but despite efforts to bolster their reliability indoors and outdoors,
mmWave links remain vulnerable to blockage by walls, people, and obstacles.
Further, there is significant interest in bringing outdoor mmWave coverage
indoors, which for similar reasons remains challenging today. This paper
presents the design, hardware implementation, and experimental evaluation of
mmWall, the first electronically almost-360 degree steerable metamaterial
surface that operates above 24 GHz and both refracts or reflects incoming
mmWave transmissions. Our metamaterial design consists of arrays of
varactor-split ring resonator unit cells, miniaturized for mmWave. Custom
control circuitry drives each resonator, overcoming coupling challenges that
arise at scale. Leveraging beam steering algorithms, we integrate mmWall into
the link layer discovery protocols of common mmWave networks. We have
fabricated a 10 cm by 20 cm mmWall prototype consisting of a 28 by 76 unit cell
array, and evaluate in indoor, outdoor-to-indoor, and multi-beam scenarios.
Indoors, mmWall guarantees 91% of locations outage-free under 128-QAM mmWave
data rates and boosts SNR by up to 15 dB. Outdoors, mmWall reduces the
probability of complete link failure by a ratio of up to 40% under 0-80% path
blockage and boosts SNR by up to 30 dB.
|
[
{
"version": "v1",
"created": "Fri, 23 Sep 2022 12:25:33 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Sep 2022 00:42:20 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Cho",
"Kun Woo",
""
],
[
"Mazaheri",
"Mohammad H.",
""
],
[
"Gummeson",
"Jeremy",
""
],
[
"Abari",
"Omid",
""
],
[
"Jamieson",
"Kyle",
""
]
] |
new_dataset
| 0.999663 |
2209.11772
|
Istvan Gyongy
|
Istvan Gyongy, Ahmet T. Erdogan, Neale A.W. Dutton, Germ\'an Mora
Mart\'in, Alistair Gorman, Hanning Mai, Francesco Mattioli Della Rocca,
Robert K. Henderson
|
A direct time-of-flight image sensor with in-pixel surface detection and
dynamic vision
|
24 pages, 16 figures. The visualisations may be viewed by clicking on
the hyperlinks in the text
| null | null | null |
cs.CV eess.IV physics.ins-det
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
3D flash LIDAR is an alternative to the traditional scanning LIDAR systems,
promising precise depth imaging in a compact form factor, and free of moving
parts, for applications such as self-driving cars, robotics and augmented
reality (AR). Typically implemented using single-photon, direct time-of-flight
(dToF) receivers in image sensor format, the operation of the devices can be
hindered by the large number of photon events needing to be processed and
compressed in outdoor scenarios, limiting frame rates and scalability to larger
arrays. We here present a 64x32 pixel (256x128 SPAD) dToF imager that overcomes
these limitations by using pixels with embedded histogramming, which lock onto
and track the return signal. This reduces the size of output data frames
considerably, enabling maximum frame rates in the 10 kFPS range or 100 kFPS for
direct depth readings. The sensor offers selective readout of pixels detecting
surfaces, or those sensing motion, leading to reduced power consumption and
off-chip processing requirements. We demonstrate the application of the sensor
in mid-range LIDAR.
|
[
{
"version": "v1",
"created": "Fri, 23 Sep 2022 14:38:00 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Gyongy",
"Istvan",
""
],
[
"Erdogan",
"Ahmet T.",
""
],
[
"Dutton",
"Neale A. W.",
""
],
[
"Martín",
"Germán Mora",
""
],
[
"Gorman",
"Alistair",
""
],
[
"Mai",
"Hanning",
""
],
[
"Della Rocca",
"Francesco Mattioli",
""
],
[
"Henderson",
"Robert K.",
""
]
] |
new_dataset
| 0.999702 |
2209.11867
|
Luis Garcia Pueyo
|
Llu\'is Garcia-Pueyo, Panayiotis Tsaparas, Anand Bhaskar, Prathyusha
Senthil Kumar, Roelof van Zwol, Timos Sellis, Anthony McCosker, Paolo Papotti
|
Integrity 2022: Integrity in Social Networks and Media
| null | null | null | null |
cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This is the proposal for the third edition of the Workshop on Integrity in
Social Networks and Media, Integrity 2022, following the success of the first
two Workshops held in conjunction with the 13th & 14th ACM Conference on Web
Search and Data Mining (WSDM) in 2020 and 2021. The goal of the workshop is to
bring together researchers and practitioners to discuss content and interaction
integrity challenges in social networks and social media platforms. The event
consists of (1) a series of invited talks by reputed members of the Integrity
community from both academia and industry, (2) a call-for-papers for
contributed talks and posters, and (3) a panel with the speakers.
|
[
{
"version": "v1",
"created": "Fri, 23 Sep 2022 21:29:42 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Garcia-Pueyo",
"Lluís",
""
],
[
"Tsaparas",
"Panayiotis",
""
],
[
"Bhaskar",
"Anand",
""
],
[
"Kumar",
"Prathyusha Senthil",
""
],
[
"van Zwol",
"Roelof",
""
],
[
"Sellis",
"Timos",
""
],
[
"McCosker",
"Anthony",
""
],
[
"Papotti",
"Paolo",
""
]
] |
new_dataset
| 0.98922 |
2209.11871
|
Ann Clifton
|
Edgar Tanaka, Ann Clifton, Joana Correia, Sharmistha Jat, Rosie Jones,
Jussi Karlgren, Winstead Zhu
|
Cem Mil Podcasts: A Spoken Portuguese Document Corpus
|
6 pages, 1 figure
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This document describes the Portuguese language podcast dataset released by
Spotify for academic research purposes. We give an overview of how the data was
sampled, some basic statistics over the collection, as well as brief
information of distribution over Brazilian and Portuguese dialects.
|
[
{
"version": "v1",
"created": "Fri, 23 Sep 2022 21:41:10 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Tanaka",
"Edgar",
""
],
[
"Clifton",
"Ann",
""
],
[
"Correia",
"Joana",
""
],
[
"Jat",
"Sharmistha",
""
],
[
"Jones",
"Rosie",
""
],
[
"Karlgren",
"Jussi",
""
],
[
"Zhu",
"Winstead",
""
]
] |
new_dataset
| 0.999784 |
2209.11946
|
Niranjan Hasabnis
|
Niranjan Hasabnis
|
Are Machine Programming Systems using Right Source-Code Measures to
Select Code Repositories?
|
6 pages, 1 figure, to be presented at MaLTeSQuE 2022 workshop to be
held with ACM Joint European Software Engineering Conference and Symposium on
the Foundations of Software Engineering (ESEC-FSE) 2022, November 18,
Singapore,
| null | null | null |
cs.SE cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Machine programming (MP) is an emerging field at the intersection of
deterministic and probabilistic computing, and it aims to assist software and
hardware engineers, among other applications. Along with powerful compute
resources, MP systems often rely on vast amount of open-source code to learn
interesting properties about code and programming and solve problems in the
areas of debugging, code recommendation, auto-completion, etc. Unfortunately,
several of the existing MP systems either do not consider quality of code
repositories or use atypical quality measures than those typically used in
software engineering community to select them. As such, impact of quality of
code repositories on the performance of these systems needs to be studied.
In this preliminary paper, we evaluate impact of different quality
repositories on the performance of a candidate MP system. Towards that
objective, we develop a framework, named GitRank, to rank open-source
repositories on quality, maintainability, and popularity by leveraging existing
research on this topic. We then apply GitRank to evaluate correlation between
the quality measures used by the candidate MP system and the quality measures
used by our framework. Our preliminary results reveal some correlation between
the quality measures used in GitRank and ControlFlag's performance, suggesting
that some of the measures used in GitRank are applicable to ControlFlag. But it
also raises questions around right quality measures for code repositories used
in MP systems. We believe that our findings also generate interesting insights
towards code quality measures that affect performance of MP systems.
|
[
{
"version": "v1",
"created": "Sat, 24 Sep 2022 07:34:18 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Hasabnis",
"Niranjan",
""
]
] |
new_dataset
| 0.971956 |
2209.11971
|
Xunzhao Yin
|
Xunzhao Yin, Qingrong Huang, Franz M\"uller, Shan Deng, Alptekin
Vardar, Sourav De, Zhouhang Jiang, Mohsen Imani, Cheng Zhuo, Thomas K\"ampfe,
Kai Ni
|
A Homogeneous Processing Fabric for Matrix-Vector Multiplication and
Associative Search Using Ferroelectric Time-Domain Compute-in-Memory
|
8 pages, 8 figures
| null | null | null |
cs.ET eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we propose a ferroelectric FET(FeFET) time-domain
compute-in-memory (TD-CiM) array as a homogeneous processing fabric for binary
multiplication-accumulation (MAC) and content addressable memory (CAM). We
demonstrate that: i) the XOR(XNOR)/AND logic function can be realized using a
single cell composed of 2FeFETs connected in series; ii) a two-phase
computation in an inverter chain with each stage featuring the XOR/AND cell to
control the associated capacitor loading and the computation results of binary
MAC and CAM are reflected in the chain output signal delay, illustrating full
digital compatibility; iii) comprehensive theoretical and experimental
validation of the proposed 2FeFET cell and inverter delay chains and their
robustness against FeFET variation; iv) the homogeneous processing fabric is
applied in hyperdimensional computing to show dynamic and fine-grain resource
allocation to accommodate different tasks requiring varying demands over the
binary MAC and CAM resources.
|
[
{
"version": "v1",
"created": "Sat, 24 Sep 2022 09:40:41 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Yin",
"Xunzhao",
""
],
[
"Huang",
"Qingrong",
""
],
[
"Müller",
"Franz",
""
],
[
"Deng",
"Shan",
""
],
[
"Vardar",
"Alptekin",
""
],
[
"De",
"Sourav",
""
],
[
"Jiang",
"Zhouhang",
""
],
[
"Imani",
"Mohsen",
""
],
[
"Zhuo",
"Cheng",
""
],
[
"Kämpfe",
"Thomas",
""
],
[
"Ni",
"Kai",
""
]
] |
new_dataset
| 0.979043 |
2209.12023
|
\'Edouard Bonnet
|
\'Edouard Bonnet, Ugo Giocanti, Patrice Ossona de Mendez, St\'ephan
Thomass\'e
|
Twin-width V: linear minors, modular counting, and matrix multiplication
|
45 pages, 9 figures
| null | null | null |
cs.DS cs.DM cs.LO math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We continue developing the theory around the twin-width of totally ordered
binary structures, initiated in the previous paper of the series. We first
introduce the notion of parity and linear minors of a matrix, which consists of
iteratively replacing consecutive rows or consecutive columns with a linear
combination of them. We show that a matrix class has bounded twin-width if and
only if its linear-minor closure does not contain all matrices. We observe that
the fixed-parameter tractable algorithm for first-order model checking on
structures given with an $O(1)$-sequence (certificate of bounded twin-width)
and the fact that first-order transductions of bounded twin-width classes have
bounded twin-width, both established in Twin-width I, extend to first-order
logic with modular counting quantifiers. We make explicit a win-win argument
obtained as a by-product of Twin-width IV, and somewhat similar to
bidimensionality, that we call rank-bidimensionality. Armed with the
above-mentioned extension to modular counting, we show that the twin-width of
the product of two conformal matrices $A, B$ over a finite field is bounded by
a function of the twin-width of $A$, of $B$, and of the size of the field.
Furthermore, if $A$ and $B$ are $n \times n$ matrices of twin-width $d$ over
$\mathbb F_q$, we show that $AB$ can be computed in time $O_{d,q}(n^2 \log n)$.
We finally present an ad hoc algorithm to efficiently multiply two matrices of
bounded twin-width, with a single-exponential dependence in the twin-width
bound: If the inputs are given in a compact tree-like form, called
twin-decomposition (of width $d$), then two $n \times n$ matrices $A, B$ over
$\mathbb F_2$, a twin-decomposition of $AB$ with width $2^{d+o(d)}$ can be
computed in time $4^{d+o(d)}n$ (resp. $4^{d+o(d)}n^{1+\varepsilon}$), and
entries queried in doubly-logarithmic (resp. constant) time.
|
[
{
"version": "v1",
"created": "Sat, 24 Sep 2022 14:41:54 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Bonnet",
"Édouard",
""
],
[
"Giocanti",
"Ugo",
""
],
[
"de Mendez",
"Patrice Ossona",
""
],
[
"Thomassé",
"Stéphan",
""
]
] |
new_dataset
| 0.992791 |
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