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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2211.13327
|
Anna Feldman
|
Patrick Lee and Anna Feldman and Jing Peng
|
A Report on the Euphemisms Detection Shared Task
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents The Shared Task on Euphemism Detection for the Third
Workshop on Figurative Language Processing (FigLang 2022) held in conjunction
with EMNLP 2022. Participants were invited to investigate the euphemism
detection task: given input text, identify whether it contains a euphemism. The
input data is a corpus of sentences containing potentially euphemistic terms
(PETs) collected from the GloWbE corpus (Davies and Fuchs, 2015), and are
human-annotated as containing either a euphemistic or literal usage of a PET.
In this paper, we present the results and analyze the common themes, methods
and findings of the participating teams
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 22:06:35 GMT"
},
{
"version": "v2",
"created": "Sat, 3 Dec 2022 17:26:25 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Lee",
"Patrick",
""
],
[
"Feldman",
"Anna",
""
],
[
"Peng",
"Jing",
""
]
] |
new_dataset
| 0.953125 |
2211.14206
|
Belkacem Imine
|
Belkacem Imine, Naima Hadj-Said, Adda Ali-Pacha
|
McEliece cryptosystem based on Plotkin construction with QC-MDPC and
QC-LDPC codes
|
11 pages
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a new variant of the McEliece cryptosystem using
two families of quasi-cyclic codes: low density parity check codes (QC-LDPC)
and moderate density parity check codes (QC-MDPC). Due to the low weight
codewords in the dual of LDPC codes, this family of codes is vulnerable to dual
code attacks, making it unsuitable for use with the McEliece cryptosystem.
However, this is not the case in our proposal, and it is possible by using the
(U |U + V ) construction to concatenate LDPC codes with MDPC codes. We will
demonstrate that our proposed cryptosystem can withstand dual code and generic
decoding attacks, and that the public key can be reduced by leveraging the
quasi-cyclic property and the Plotkin construction.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 16:13:43 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Nov 2022 18:08:58 GMT"
},
{
"version": "v3",
"created": "Fri, 2 Dec 2022 19:08:32 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Imine",
"Belkacem",
""
],
[
"Hadj-Said",
"Naima",
""
],
[
"Ali-Pacha",
"Adda",
""
]
] |
new_dataset
| 0.999737 |
2211.16922
|
Jianwei Li
|
Jianwei Li, Zitong Yu, Jingang Shi
|
Learning Motion-Robust Remote Photoplethysmography through Arbitrary
Resolution Videos
|
Accepted by AAAI 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Remote photoplethysmography (rPPG) enables non-contact heart rate (HR)
estimation from facial videos which gives significant convenience compared with
traditional contact-based measurements. In the real-world long-term health
monitoring scenario, the distance of the participants and their head movements
usually vary by time, resulting in the inaccurate rPPG measurement due to the
varying face resolution and complex motion artifacts. Different from the
previous rPPG models designed for a constant distance between camera and
participants, in this paper, we propose two plug-and-play blocks (i.e.,
physiological signal feature extraction block (PFE) and temporal face alignment
block (TFA)) to alleviate the degradation of changing distance and head motion.
On one side, guided with representative-area information, PFE adaptively
encodes the arbitrary resolution facial frames to the fixed-resolution facial
structure features. On the other side, leveraging the estimated optical flow,
TFA is able to counteract the rPPG signal confusion caused by the head movement
thus benefit the motion-robust rPPG signal recovery. Besides, we also train the
model with a cross-resolution constraint using a two-stream dual-resolution
framework, which further helps PFE learn resolution-robust facial rPPG
features. Extensive experiments on three benchmark datasets (UBFC-rPPG, COHFACE
and PURE) demonstrate the superior performance of the proposed method. One
highlight is that with PFE and TFA, the off-the-shelf spatio-temporal rPPG
models can predict more robust rPPG signals under both varying face resolution
and severe head movement scenarios. The codes are available at
https://github.com/LJW-GIT/Arbitrary_Resolution_rPPG.
|
[
{
"version": "v1",
"created": "Wed, 30 Nov 2022 11:50:08 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Dec 2022 03:01:44 GMT"
},
{
"version": "v3",
"created": "Fri, 2 Dec 2022 19:40:26 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Li",
"Jianwei",
""
],
[
"Yu",
"Zitong",
""
],
[
"Shi",
"Jingang",
""
]
] |
new_dataset
| 0.996537 |
2212.01387
|
Maria Maistro
|
Mirko Biasini, Vittorio Carmignani, Nicola Ferro, Panagiotis Filianos,
Maria Maistro, Giorgio Maria di Nunzio
|
FullBrain: a Social E-learning Platform
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
We present FullBrain, a social e-learning platform where students share and
track their knowledge. FullBrain users can post notes, ask questions and share
learning resources in dedicated course and concept spaces. We detail two
components of FullBrain: a SIR system equipped with query autocomplete and
query autosuggestion, and a Leaderboard module to improve user experience. We
analyzed the day-to-day users' usage of the SIR system, measuring a
time-to-complete a request below 0.11s, matching or exceeding our UX targets.
Moreover, we performed stress tests which lead the way for more detailed
analysis. Through a preliminary user study and log data analysis, we observe
that 97% of the users' activity is directed to the top 4 positions in the
leaderboard.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 13:58:54 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Biasini",
"Mirko",
""
],
[
"Carmignani",
"Vittorio",
""
],
[
"Ferro",
"Nicola",
""
],
[
"Filianos",
"Panagiotis",
""
],
[
"Maistro",
"Maria",
""
],
[
"di Nunzio",
"Giorgio Maria",
""
]
] |
new_dataset
| 0.998332 |
2212.01424
|
Orr Zohar Mr
|
Orr Zohar, Kuan-Chieh Wang, Serena Yeung
|
PROB: Probabilistic Objectness for Open World Object Detection
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Open World Object Detection (OWOD) is a new and challenging computer vision
task that bridges the gap between classic object detection (OD) benchmarks and
object detection in the real world. In addition to detecting and classifying
seen/labeled objects, OWOD algorithms are expected to detect novel/unknown
objects - which can be classified and incrementally learned. In standard OD,
object proposals not overlapping with a labeled object are automatically
classified as background. Therefore, simply applying OD methods to OWOD fails
as unknown objects would be predicted as background. The challenge of detecting
unknown objects stems from the lack of supervision in distinguishing unknown
objects and background object proposals. Previous OWOD methods have attempted
to overcome this issue by generating supervision using pseudo-labeling -
however, unknown object detection has remained low. Probabilistic/generative
models may provide a solution for this challenge. Herein, we introduce a novel
probabilistic framework for objectness estimation, where we alternate between
probability distribution estimation and objectness likelihood maximization of
known objects in the embedded feature space - ultimately allowing us to
estimate the objectness probability of different proposals. The resulting
Probabilistic Objectness transformer-based open-world detector, PROB,
integrates our framework into traditional object detection models, adapting
them for the open-world setting. Comprehensive experiments on OWOD benchmarks
show that PROB outperforms all existing OWOD methods in both unknown object
detection ($\sim 2\times$ unknown recall) and known object detection ($\sim
10\%$ mAP). Our code will be made available upon publication at
https://github.com/orrzohar/PROB.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 20:04:24 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Zohar",
"Orr",
""
],
[
"Wang",
"Kuan-Chieh",
""
],
[
"Yeung",
"Serena",
""
]
] |
new_dataset
| 0.968535 |
2212.01444
|
Omur Arslan
|
\"Om\"ur Arslan
|
Time Governors for Safe Path-Following Control
|
11 pages, 6 figures, submitted to a journal publication
| null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Safe and smooth robot motion around obstacles is an essential skill for
autonomous robots, especially when operating around people and other robots.
Conventionally, due to real-time operation requirements and onboard computation
limitations, many robot motion planning and control methods follow a two-step
approach: first construct a (e.g., piecewise linear) collision-free reference
path for a simplified robot model, and then execute the reference plan via
path-following control for a more accurate and complex robot model. A challenge
of such a decoupled robot motion planning and control method for highly dynamic
robotic systems is ensuring the safety of path-following control as well as the
successful completion of the reference plan. In this paper, we introduce a
novel dynamical systems approach for online closed-loop time parametrization,
called $\textit{a time governor}$, of a reference path for provably correct and
safe path-following control based on feedback motion prediction, where the
safety of robot motion under path-following control is continuously monitored
using predicted robot motion. After introducing the general framework of time
governors for safe path following, we present an example application for the
fully actuated high-order robot dynamics using
proportional-and-higher-order-derivative (PhD) path-following control whose
feedback motion prediction is performed by Lyapunov ellipsoids and Vandemonde
simplexes. In numerical simulations, we investigate the role of reference
position and velocity feedback, and motion prediction on path-following
performance and robot motion.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 20:54:52 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Arslan",
"Ömür",
""
]
] |
new_dataset
| 0.957598 |
2212.01540
|
Hossein Rastgoftar
|
Aeris El Asslouj and Hossein Rastgoftar
|
Quadcopter Tracking Using Euler-Angle-Free Flatness-Based Control
|
8 pages
| null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quadcopter trajectory tracking control has been extensively investigated and
implemented in the past. Available controls mostly use the Euler angle
standards to describe the quadcopters rotational kinematics and dynamics. As a
result, the same rotation can be translated into different roll, pitch, and yaw
angles because there are multiple Euler angle standards for characterization of
rotation in a 3-dimensional motion space. Additionally, it is computationally
expensive to convert a quadcopters orientation to the associated roll, pitch,
and yaw angles, which may make it difficult to track quick and aggressive
trajectories. To address these issues, this paper will develop a flatness-based
trajectory tracking control without using Euler angles. We assess and test the
proposed controls performance in the Gazebo simulation environment and contrast
its functionality with the existing Mellinger controller, which has been widely
adopted by the robotics and unmanned aerial system (UAS) communities.
|
[
{
"version": "v1",
"created": "Sat, 3 Dec 2022 05:20:20 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Asslouj",
"Aeris El",
""
],
[
"Rastgoftar",
"Hossein",
""
]
] |
new_dataset
| 0.998147 |
2212.01638
|
Jintao Lin
|
Jintao Lin, Zhaoyang Liu, Wenhai Wang, Wayne Wu, Limin Wang
|
VLG: General Video Recognition with Web Textual Knowledge
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Video recognition in an open and dynamic world is quite challenging, as we
need to handle different settings such as close-set, long-tail, few-shot and
open-set. By leveraging semantic knowledge from noisy text descriptions crawled
from the Internet, we focus on the general video recognition (GVR) problem of
solving different recognition tasks within a unified framework. The core
contribution of this paper is twofold. First, we build a comprehensive video
recognition benchmark of Kinetics-GVR, including four sub-task datasets to
cover the mentioned settings. To facilitate the research of GVR, we propose to
utilize external textual knowledge from the Internet and provide multi-source
text descriptions for all action classes. Second, inspired by the flexibility
of language representation, we present a unified visual-linguistic framework
(VLG) to solve the problem of GVR by an effective two-stage training paradigm.
Our VLG is first pre-trained on video and language datasets to learn a shared
feature space, and then devises a flexible bi-modal attention head to
collaborate high-level semantic concepts under different settings. Extensive
results show that our VLG obtains the state-of-the-art performance under four
settings. The superior performance demonstrates the effectiveness and
generalization ability of our proposed framework. We hope our work makes a step
towards the general video recognition and could serve as a baseline for future
research. The code and models will be available at
https://github.com/MCG-NJU/VLG.
|
[
{
"version": "v1",
"created": "Sat, 3 Dec 2022 15:46:49 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Lin",
"Jintao",
""
],
[
"Liu",
"Zhaoyang",
""
],
[
"Wang",
"Wenhai",
""
],
[
"Wu",
"Wayne",
""
],
[
"Wang",
"Limin",
""
]
] |
new_dataset
| 0.999724 |
2212.01648
|
Christopher Tralie
|
Christopher J. Tralie, Zachary Schlamowitz, Jose Arbelo, Antonio I.
Delgado, Charley Kirk, Nicholas A. Scoville
|
The DOPE Distance is SIC: A Stable, Informative, and Computable Metric
on Time Series And Ordered Merge Trees
|
31 pages, 12 Figures
| null | null | null |
cs.IR math.AT
|
http://creativecommons.org/licenses/by/4.0/
|
Metrics for merge trees that are simultaneously stable, informative, and
efficiently computable have so far eluded researchers. We show in this work
that it is possible to devise such a metric when restricting merge trees to
ordered domains such as the interval and the circle. We present the ``dynamic
ordered persistence editing'' (DOPE) distance, which we prove is stable and
informative while satisfying metric properties. We then devise a simple
$O(N^2)$ dynamic programming algorithm to compute it on the interval and an
$O(N^3)$ algorithm to compute it on the circle. Surprisingly, we accomplish
this by ignoring all of the hierarchical information of the merge tree and
simply focusing on a sequence of ordered critical points, which can be
interpreted as a time series. Thus our algorithm is more similar to string edit
distance and dynamic time warping than it is to more conventional merge tree
comparison algorithms. In the context of time series with the interval as a
domain, we show empirically on the UCR time series classification dataset that
DOPE performs better than bottleneck/Wasserstein distances between persistence
diagrams.
|
[
{
"version": "v1",
"created": "Sat, 3 Dec 2022 16:34:19 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Tralie",
"Christopher J.",
""
],
[
"Schlamowitz",
"Zachary",
""
],
[
"Arbelo",
"Jose",
""
],
[
"Delgado",
"Antonio I.",
""
],
[
"Kirk",
"Charley",
""
],
[
"Scoville",
"Nicholas A.",
""
]
] |
new_dataset
| 0.995503 |
2212.01651
|
Slobodan Djukanovi\'c
|
Slobodan Djukanovi\'c, Nikola Bulatovi\'c, Ivana \v{C}avor
|
A dataset for audio-video based vehicle speed estimation
|
30th Telecommunications Forum TELFOR 2022, Belgrade, Serbia, November
15-16, 2022. 5 pages, 2 figures, 1 table
| null | null | null |
cs.LG cs.CV cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Accurate speed estimation of road vehicles is important for several reasons.
One is speed limit enforcement, which represents a crucial tool in decreasing
traffic accidents and fatalities. Compared with other research areas and
domains, the number of available datasets for vehicle speed estimation is still
very limited. We present a dataset of on-road audio-video recordings of single
vehicles passing by a camera at known speeds, maintained stable by the on-board
cruise control. The dataset contains thirteen vehicles, selected to be as
diverse as possible in terms of manufacturer, production year, engine type,
power and transmission, resulting in a total of $ 400 $ annotated audio-video
recordings. The dataset is fully available and intended as a public benchmark
to facilitate research in audio-video vehicle speed estimation. In addition to
the dataset, we propose a cross-validation strategy which can be used in a
machine learning model for vehicle speed estimation. Two approaches to
training-validation split of the dataset are proposed.
|
[
{
"version": "v1",
"created": "Sat, 3 Dec 2022 17:02:57 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Djukanović",
"Slobodan",
""
],
[
"Bulatović",
"Nikola",
""
],
[
"Čavor",
"Ivana",
""
]
] |
new_dataset
| 0.999822 |
2212.01672
|
Lorenzo Giusti
|
Lorenzo Giusti, Josue Garcia, Steven Cozine, Darrick Suen, Christina
Nguyen, Ryan Alimo
|
MaRF: Representing Mars as Neural Radiance Fields
|
ECCV 2022 (oral)
| null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
The aim of this work is to introduce MaRF, a novel framework able to
synthesize the Martian environment using several collections of images from
rover cameras. The idea is to generate a 3D scene of Mars' surface to address
key challenges in planetary surface exploration such as: planetary geology,
simulated navigation and shape analysis. Although there exist different methods
to enable a 3D reconstruction of Mars' surface, they rely on classical computer
graphics techniques that incur high amounts of computational resources during
the reconstruction process, and have limitations with generalizing
reconstructions to unseen scenes and adapting to new images coming from rover
cameras. The proposed framework solves the aforementioned limitations by
exploiting Neural Radiance Fields (NeRFs), a method that synthesize complex
scenes by optimizing a continuous volumetric scene function using a sparse set
of images. To speed up the learning process, we replaced the sparse set of
rover images with their neural graphics primitives (NGPs), a set of vectors of
fixed length that are learned to preserve the information of the original
images in a significantly smaller size. In the experimental section, we
demonstrate the environments created from actual Mars datasets captured by
Curiosity rover, Perseverance rover and Ingenuity helicopter, all of which are
available on the Planetary Data System (PDS).
|
[
{
"version": "v1",
"created": "Sat, 3 Dec 2022 18:58:00 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Giusti",
"Lorenzo",
""
],
[
"Garcia",
"Josue",
""
],
[
"Cozine",
"Steven",
""
],
[
"Suen",
"Darrick",
""
],
[
"Nguyen",
"Christina",
""
],
[
"Alimo",
"Ryan",
""
]
] |
new_dataset
| 0.999161 |
2212.01745
|
Vibhakar Mohta
|
Vibhakar Mohta, Adarsh Patnaik, Shivam Kumar Panda, Siva Vignesh
Krishnan, Abhinav Gupta, Abhay Shukla, Gauri Wadhwa, Shrey Verma, Aditya
Bandopadhyay
|
Design of an All-Purpose Terrace Farming Robot
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Automation in farming processes is a growing field of research in both
academia and industries. A considerable amount of work has been put into this
field to develop systems robust enough for farming. Terrace farming, in
particular, provides a varying set of challenges, including robust stair
climbing methods and stable navigation in unstructured terrains. We propose the
design of a novel autonomous terrace farming robot, Aarohi, that can
effectively climb steep terraces of considerable heights and execute several
farming operations. The design optimisation strategy for the overall mechanical
structure is elucidated. Further, the embedded and software architecture along
with fail-safe strategies are presented for a working prototype. Algorithms for
autonomous traversal over the terrace steps using the scissor lift mechanism
and performing various farming operations have also been discussed. The
adaptability of the design to specific operational requirements and modular
farm tools allow Aarohi to be customised for a wide variety of use cases.
|
[
{
"version": "v1",
"created": "Sun, 4 Dec 2022 05:45:25 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Mohta",
"Vibhakar",
""
],
[
"Patnaik",
"Adarsh",
""
],
[
"Panda",
"Shivam Kumar",
""
],
[
"Krishnan",
"Siva Vignesh",
""
],
[
"Gupta",
"Abhinav",
""
],
[
"Shukla",
"Abhay",
""
],
[
"Wadhwa",
"Gauri",
""
],
[
"Verma",
"Shrey",
""
],
[
"Bandopadhyay",
"Aditya",
""
]
] |
new_dataset
| 0.998391 |
2212.01769
|
Zicheng Zhang
|
Zicheng Zhang, Yi Zhu, Jianzhuang Liu, Xiaodan Liang, Wei Ke
|
CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for
Referring Image Segmentation
|
accept to NeurIPS 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Referring image segmentation aims at localizing all pixels of the visual
objects described by a natural language sentence. Previous works learn to
straightforwardly align the sentence embedding and pixel-level embedding for
highlighting the referred objects, but ignore the semantic consistency of
pixels within the same object, leading to incomplete masks and localization
errors in predictions. To tackle this problem, we propose CoupAlign, a simple
yet effective multi-level visual-semantic alignment method, to couple
sentence-mask alignment with word-pixel alignment to enforce object mask
constraint for achieving more accurate localization and segmentation.
Specifically, the Word-Pixel Alignment (WPA) module performs early fusion of
linguistic and pixel-level features in intermediate layers of the vision and
language encoders. Based on the word-pixel aligned embedding, a set of mask
proposals are generated to hypothesize possible objects. Then in the
Sentence-Mask Alignment (SMA) module, the masks are weighted by the sentence
embedding to localize the referred object, and finally projected back to
aggregate the pixels for the target. To further enhance the learning of the two
alignment modules, an auxiliary loss is designed to contrast the foreground and
background pixels. By hierarchically aligning pixels and masks with linguistic
features, our CoupAlign captures the pixel coherence at both visual and
semantic levels, thus generating more accurate predictions. Extensive
experiments on popular datasets (e.g., RefCOCO and G-Ref) show that our method
achieves consistent improvements over state-of-the-art methods, e.g., about 2%
oIoU increase on the validation and testing set of RefCOCO. Especially,
CoupAlign has remarkable ability in distinguishing the target from multiple
objects of the same class.
|
[
{
"version": "v1",
"created": "Sun, 4 Dec 2022 08:53:42 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Zhang",
"Zicheng",
""
],
[
"Zhu",
"Yi",
""
],
[
"Liu",
"Jianzhuang",
""
],
[
"Liang",
"Xiaodan",
""
],
[
"Ke",
"Wei",
""
]
] |
new_dataset
| 0.988851 |
2212.01791
|
Md Parvez Mollah
|
Md Parvez Mollah
|
An LSTM model for Twitter Sentiment Analysis
|
3 pages
| null | null | null |
cs.CL cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Sentiment analysis on social media such as Twitter provides organizations and
individuals an effective way to monitor public emotions towards them and their
competitors. As a result, sentiment analysis has become an important and
challenging task. In this work, we have collected seven publicly available and
manually annotated twitter sentiment datasets. We create a new training and
testing dataset from the collected datasets. We develop an LSTM model to
classify sentiment of a tweet and evaluate the model with the new dataset.
|
[
{
"version": "v1",
"created": "Sun, 4 Dec 2022 10:42:46 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Mollah",
"Md Parvez",
""
]
] |
new_dataset
| 0.992389 |
2212.01934
|
Benedikt Kolbe
|
Vincent Despr\'e, Benedikt Kolbe, Hugo Parlier, Monique Teillaud
|
Computing a Dirichlet domain for a hyperbolic surface
|
15 pages, 5 figures
| null | null | null |
cs.CG math.DG math.GT
|
http://creativecommons.org/licenses/by/4.0/
|
The goal of this paper is to exhibit and analyze an algorithm that takes a
given closed orientable hyperbolic surface and outputs an explicit Dirichlet
domain. The input is a fundamental polygon with side pairings. While grounded
in topological considerations, the algorithm makes key use of the geometry of
the surface. We introduce data structures that reflect this interplay between
geometry and topology and show that the algorithm finishes in polynomial time,
in terms of the initial perimeter and the genus of the surface.
|
[
{
"version": "v1",
"created": "Sun, 4 Dec 2022 21:58:41 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Despré",
"Vincent",
""
],
[
"Kolbe",
"Benedikt",
""
],
[
"Parlier",
"Hugo",
""
],
[
"Teillaud",
"Monique",
""
]
] |
new_dataset
| 0.973381 |
2212.01967
|
Zhaozhen Xu
|
Zhaozhen Xu, Nello Cristianini
|
QBERT: Generalist Model for Processing Questions
| null | null | null | null |
cs.CL cs.AI cs.IR cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Using a single model across various tasks is beneficial for training and
applying deep neural sequence models. We address the problem of developing
generalist representations of text that can be used to perform a range of
different tasks rather than being specialised to a single application. We focus
on processing short questions and developing an embedding for these questions
that is useful on a diverse set of problems, such as question topic
classification, equivalent question recognition, and question answering. This
paper introduces QBERT, a generalist model for processing questions. With
QBERT, we demonstrate how we can train a multi-task network that performs all
question-related tasks and has achieved similar performance compared to its
corresponding single-task models.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 00:56:28 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Xu",
"Zhaozhen",
""
],
[
"Cristianini",
"Nello",
""
]
] |
new_dataset
| 0.994098 |
2212.02007
|
Jianghong Dong
|
Jianghong Dong, Qing Xu, Jiawei Wang, Chunying Yang, Mengchi Cai,
Chaoyi Chen, Jianqiang Wang and Keqiang Li
|
Mixed Cloud Control Testbed: Validating Vehicle-Road-Cloud Integration
via Mixed Digital Twin
|
13 pages, 13 figures
| null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reliable and efficient validation technologies are critical for the recent
development of multi-vehicle cooperation and vehicle-road-cloud integration. In
this paper, we introduce our miniature experimental platform, Mixed Cloud
Control Testbed (MCCT), developed based on a new notion of Mixed Digital Twin
(mixedDT). Combining Mixed Reality with Digital Twin, mixedDT integrates the
virtual and physical spaces into a mixed one, where physical entities coexist
and interact with virtual entities via their digital counterparts. Under the
framework of mixedDT, MCCT contains three major experimental platforms in the
physical, virtual and mixed spaces respectively, and provides a unified access
for various human-machine interfaces and external devices such as driving
simulators. A cloud unit, where the mixed experimental platform is deployed, is
responsible for fusing multi-platform information and assigning control
instructions, contributing to synchronous operation and real-time
cross-platform interaction. Particularly, MCCT allows for multi-vehicle
coordination composed of different multi-source vehicles (\eg, physical
vehicles, virtual vehicles and human-driven vehicles). Validations on vehicle
platooning demonstrate the flexibility and scalability of MCCT.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 03:39:31 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Dong",
"Jianghong",
""
],
[
"Xu",
"Qing",
""
],
[
"Wang",
"Jiawei",
""
],
[
"Yang",
"Chunying",
""
],
[
"Cai",
"Mengchi",
""
],
[
"Chen",
"Chaoyi",
""
],
[
"Wang",
"Jianqiang",
""
],
[
"Li",
"Keqiang",
""
]
] |
new_dataset
| 0.995881 |
2212.02077
|
Zhongyang Zhu
|
Xuebo Tian, Zhongyang Zhu, Junqiao Zhao, Gengxuan Tian, and Chen Ye
|
DL-SLOT: Dynamic LiDAR SLAM and object tracking based on collaborative
graph optimization
|
10 pages, 10 figures, 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.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Ego-pose estimation and dynamic object tracking are two critical problems for
autonomous driving systems. The solutions to these problems are generally based
on their respective assumptions, \ie{the static world assumption for
simultaneous localization and mapping (SLAM) and the accurate ego-pose
assumption for object tracking}. However, these assumptions are challenging to
hold in dynamic road scenarios, where SLAM and object tracking become closely
correlated. Therefore, we propose DL-SLOT, a dynamic LiDAR SLAM and object
tracking method, to simultaneously address these two coupled problems. This
method integrates the state estimations of both the autonomous vehicle and the
stationary and dynamic objects in the environment into a unified optimization
framework. First, we used object detection to identify all points belonging to
potentially dynamic objects. Subsequently, a LiDAR odometry was conducted using
the filtered point cloud. Simultaneously, we proposed a sliding window-based
object association method that accurately associates objects according to the
historical trajectories of tracked objects. The ego-states and those of the
stationary and dynamic objects are integrated into the sliding window-based
collaborative graph optimization. The stationary objects are subsequently
restored from the potentially dynamic object set. Finally, a global pose-graph
is implemented to eliminate the accumulated error. Experiments on KITTI
datasets demonstrate that our method achieves better accuracy than SLAM and
object tracking baseline methods. This confirms that solving SLAM and object
tracking simultaneously is mutually advantageous, dramatically improving the
robustness and accuracy of SLAM and object tracking in dynamic road scenarios.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 07:46:14 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Tian",
"Xuebo",
""
],
[
"Zhu",
"Zhongyang",
""
],
[
"Zhao",
"Junqiao",
""
],
[
"Tian",
"Gengxuan",
""
],
[
"Ye",
"Chen",
""
]
] |
new_dataset
| 0.999345 |
2212.02127
|
\v{Z}iga Babnik
|
\v{Z}iga Babnik, Peter Peer, Vitomir \v{S}truc
|
FaceQAN: Face Image Quality Assessment Through Adversarial Noise
Exploration
|
The content of this paper was published in ICPR 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Recent state-of-the-art face recognition (FR) approaches have achieved
impressive performance, yet unconstrained face recognition still represents an
open problem. Face image quality assessment (FIQA) approaches aim to estimate
the quality of the input samples that can help provide information on the
confidence of the recognition decision and eventually lead to improved results
in challenging scenarios. While much progress has been made in face image
quality assessment in recent years, computing reliable quality scores for
diverse facial images and FR models remains challenging. In this paper, we
propose a novel approach to face image quality assessment, called FaceQAN, that
is based on adversarial examples and relies on the analysis of adversarial
noise which can be calculated with any FR model learned by using some form of
gradient descent. As such, the proposed approach is the first to link image
quality to adversarial attacks. Comprehensive (cross-model as well as
model-specific) experiments are conducted with four benchmark datasets, i.e.,
LFW, CFP-FP, XQLFW and IJB-C, four FR models, i.e., CosFace, ArcFace,
CurricularFace and ElasticFace, and in comparison to seven state-of-the-art
FIQA methods to demonstrate the performance of FaceQAN. Experimental results
show that FaceQAN achieves competitive results, while exhibiting several
desirable characteristics.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 09:37:32 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Babnik",
"Žiga",
""
],
[
"Peer",
"Peter",
""
],
[
"Štruc",
"Vitomir",
""
]
] |
new_dataset
| 0.981157 |
2212.02159
|
Jian Wang
|
Yourui Huangfu and Jian Wang and Shengchen Dai and Rong Li and Jun
Wang and Chongwen Huang and Zhaoyang Zhang
|
WAIR-D: Wireless AI Research Dataset
|
5 pages, 8 figures
| null | null | null |
cs.LG cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
It is a common sense that datasets with high-quality data samples play an
important role in artificial intelligence (AI), machine learning (ML) and
related studies. However, although AI/ML has been introduced in wireless
researches long time ago, few datasets are commonly used in the research
community. Without a common dataset, AI-based methods proposed for wireless
systems are hard to compare with both the traditional baselines and even each
other. The existing wireless AI researches usually rely on datasets generated
based on statistical models or ray-tracing simulations with limited
environments. The statistical data hinder the trained AI models from further
fine-tuning for a specific scenario, and ray-tracing data with limited
environments lower down the generalization capability of the trained AI models.
In this paper, we present the Wireless AI Research Dataset (WAIR-D)1, which
consists of two scenarios. Scenario 1 contains 10,000 environments with
sparsely dropped user equipments (UEs), and Scenario 2 contains 100
environments with densely dropped UEs. The environments are randomly picked up
from more than 40 cities in the real world map. The large volume of the data
guarantees that the trained AI models enjoy good generalization capability,
while fine-tuning can be easily carried out on a specific chosen environment.
Moreover, both the wireless channels and the corresponding environmental
information are provided in WAIR-D, so that extra-information-aided
communication mechanism can be designed and evaluated. WAIR-D provides the
researchers benchmarks to compare their different designs or reproduce results
of others. In this paper, we show the detailed construction of this dataset and
examples of using it.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 10:59:05 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Huangfu",
"Yourui",
""
],
[
"Wang",
"Jian",
""
],
[
"Dai",
"Shengchen",
""
],
[
"Li",
"Rong",
""
],
[
"Wang",
"Jun",
""
],
[
"Huang",
"Chongwen",
""
],
[
"Zhang",
"Zhaoyang",
""
]
] |
new_dataset
| 0.999844 |
2212.02168
|
Mika H\"am\"al\"ainen
|
Mika H\"am\"al\"ainen and Khalid Alnajjar and Thierry Poibeau
|
Video Games as a Corpus: Sentiment Analysis using Fallout New Vegas
Dialog
|
FDG 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present a method for extracting a multilingual sentiment annotated dialog
data set from Fallout New Vegas. The game developers have preannotated every
line of dialog in the game in one of the 8 different sentiments: \textit{anger,
disgust, fear, happy, neutral, pained, sad } and \textit{surprised}. The game
has been translated into English, Spanish, German, French and Italian. We
conduct experiments on multilingual, multilabel sentiment analysis on the
extracted data set using multilingual BERT, XLMRoBERTa and language specific
BERT models. In our experiments, multilingual BERT outperformed XLMRoBERTa for
most of the languages, also language specific models were slightly better than
multilingual BERT for most of the languages. The best overall accuracy was 54\%
and it was achieved by using multilingual BERT on Spanish data. The extracted
data set presents a challenging task for sentiment analysis. We have released
the data, including the testing and training splits, openly on Zenodo. The data
set has been shuffled for copyright reasons.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 11:09:05 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Hämäläinen",
"Mika",
""
],
[
"Alnajjar",
"Khalid",
""
],
[
"Poibeau",
"Thierry",
""
]
] |
new_dataset
| 0.995867 |
2212.02192
|
Arsi Ik\"aheimonen MSc
|
A. Ik\"aheimonen, A.M. Triana, N. Luong, A. Ziaei, J. Rantaharju, R.
Darst, and T. Aledavood
|
Niimpy: a toolbox for behavioral data analysis
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Behavioral studies using personal digital devices typically produce rich
longitudinal datasets of mixed data types. These data provide information about
the behavior of users of these devices in real-time and in the users' natural
environments. Analyzing the data requires multidisciplinary expertise and
dedicated software. Currently, no generalizable, device-agnostic, freely
available software exists within Python scientific computing ecosystem to
preprocess and analyze such data. This paper introduces a Python package,
Niimpy, for analyzing digital behavioral data. The Niimpy toolbox is a
user-friendly open-source package that can quickly be expanded and adapted to
specific research requirements. The toolbox facilitates the analysis phase by
offering tools for preprocessing, extracting features, and exploring the data.
It also aims to educate the user on behavioral data analysis and promotes open
science practices. Over time, Niimpy will expand with extra data analysis
features developed by the core group, new users, and developers. Niimpy can
help the fast-growing number of researchers with diverse backgrounds who
collect data from personal and consumer digital devices to systematically and
efficiently analyze the data and extract useful information. This novel
information is vital for answering research questions in various fields, from
medicine to psychology, sociology, and others.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 11:58:42 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Ikäheimonen",
"A.",
""
],
[
"Triana",
"A. M.",
""
],
[
"Luong",
"N.",
""
],
[
"Ziaei",
"A.",
""
],
[
"Rantaharju",
"J.",
""
],
[
"Darst",
"R.",
""
],
[
"Aledavood",
"T.",
""
]
] |
new_dataset
| 0.992864 |
2212.02228
|
Philippe Lacomme Dr
|
Lacomme Philippe, Prins Christian, Tanguy Alain
|
First Competitive Ant Colony Scheme for the CARP
| null | null | null |
Research Report LIMOS/RR-04-21
|
cs.NE cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper addresses the Capacitated Arc Routing Problem (CARP) using an Ant
Colony Optimization scheme. Ant Colony schemes can compute solutions for medium
scale instances of VRP. The proposed Ant Colony is dedicated to large-scale
instances of CARP with more than 140 nodes and 190 arcs to service. The Ant
Colony scheme is coupled with a local search procedure and provides high
quality solutions. The benchmarks we carried out prove possible to obtain
solutions as profitable as CARPET ones can be obtained using such scheme when a
sufficient number of iterations is devoted to the ants. It competes with the
Genetic Algorithm of Lacomme et al. regarding solution quality but it is more
time consuming on large scale instances. The method has been intensively
benchmarked on the well-known instances of Eglese, DeArmon and the last ones of
Belenguer and Benavent. This research report is a step forward CARP resolution
by Ant Colony proving ant schemes can compete with Taboo search methods and
Genetic Algorithms
|
[
{
"version": "v1",
"created": "Sat, 19 Nov 2022 10:31:27 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Philippe",
"Lacomme",
""
],
[
"Christian",
"Prins",
""
],
[
"Alain",
"Tanguy",
""
]
] |
new_dataset
| 0.999237 |
2212.02231
|
Junjie Lu
|
Junjie Lu, Bi Zeng, Jingtao Tang, and Tin Lun Lam
|
TMSTC*: A Turn-minimizing Algorithm For Multi-robot Coverage Path
Planning
|
8 pages, 9 figures, submitted to RA-L
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Coverage path planning is a major application for mobile robots, which
requires robots to move along a planned path to cover the entire map. For
large-scale tasks, coverage path planning benefits greatly from multiple
robots. In this paper, we describe Turn-minimizing Multirobot Spanning Tree
Coverage Star(TMSTC*), an improved multirobot coverage path planning (mCPP)
algorithm based on the MSTC*. Our algorithm partitions the map into minimum
bricks as tree's branches and thereby transforms the problem into finding the
maximum independent set of bipartite graph. We then connect bricks with greedy
strategy to form a tree, aiming to reduce the number of turns of corresponding
circumnavigating coverage path. Our experimental results show that our approach
enables multiple robots to make fewer turns and thus complete terrain coverage
tasks faster than other popular algorithms.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 13:00:25 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Lu",
"Junjie",
""
],
[
"Zeng",
"Bi",
""
],
[
"Tang",
"Jingtao",
""
],
[
"Lam",
"Tin Lun",
""
]
] |
new_dataset
| 0.993432 |
2212.02248
|
Juncheng Wang
|
Qi Wang, Juncheng Wang, Junyu Gao, Yuan Yuan, Xuelong Li
|
Counting Like Human: Anthropoid Crowd Counting on Modeling the
Similarity of Objects
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The mainstream crowd counting methods regress density map and integrate it to
obtain counting results. Since the density representation to one head accords
to its adjacent distribution, it embeds the same category objects with variant
values, while human beings counting models the invariant features namely
similarity to objects. Inspired by this, we propose a rational and anthropoid
crowd counting framework. To begin with, we leverage counting scalar as
supervision signal, which provides global and implicit guidance to similar
matters. Then, the large kernel CNN is utilized to imitate the paradigm of
human beings which models invariant knowledge firstly and slides to compare
similarity. Later, re-parameterization on pre-trained paralleled parameters is
presented to cater to the inner-class variance on similarity comparison.
Finally, the Random Scaling patches Yield (RSY) is proposed to facilitate
similarity modeling on long distance dependencies. Extensive experiments on
five challenging benchmarks in crowd counting show the proposed framework
achieves state-of-the-art.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 07:00:53 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Wang",
"Qi",
""
],
[
"Wang",
"Juncheng",
""
],
[
"Gao",
"Junyu",
""
],
[
"Yuan",
"Yuan",
""
],
[
"Li",
"Xuelong",
""
]
] |
new_dataset
| 0.989334 |
2212.02265
|
Burak Ekim
|
Burak Ekim, Timo T. Stomberg, Ribana Roscher, Michael Schmitt
|
MapInWild: A Remote Sensing Dataset to Address the Question What Makes
Nature Wild
|
9 pages, 9 figures. Accepted for inclusion in a future issue of the
IEEE Geoscience and Remote Sensing Magazine
| null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Antrophonegic pressure (i.e. human influence) on the environment is one of
the largest causes of the loss of biological diversity. Wilderness areas, in
contrast, are home to undisturbed ecological processes. However, there is no
biophysical definition of the term wilderness. Instead, wilderness is more of a
philosophical or cultural concept and thus cannot be easily delineated or
categorized in a technical manner. With this paper, (i) we introduce the task
of wilderness mapping by means of machine learning applied to satellite imagery
(ii) and publish MapInWild, a large-scale benchmark dataset curated for that
task. MapInWild is a multi-modal dataset and comprises various geodata acquired
and formed from a diverse set of Earth observation sensors. The dataset
consists of 8144 images with a shape of 1920 x 1920 pixels and is approximately
350 GB in size. The images are weakly annotated with three classes derived from
the World Database of Protected Areas - Strict Nature Reserves, Wilderness
Areas, and National Parks. With the dataset, which shall serve as a testbed for
developments in fields such as explainable machine learning and environmental
remote sensing, we hope to contribute to a deepening of our understanding of
the question "What makes nature wild?".
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 13:45:06 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Ekim",
"Burak",
""
],
[
"Stomberg",
"Timo T.",
""
],
[
"Roscher",
"Ribana",
""
],
[
"Schmitt",
"Michael",
""
]
] |
new_dataset
| 0.999835 |
2212.02352
|
Berta Chulvi
|
Berta Chulvi, Alejandro Toselli, Paolo Rosso
|
Fake News and Hate Speech: Language in Common
|
2 pages
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper we raise the research question of whether fake news and hate
speech spreaders share common patterns in language. We compute a novel index,
the ingroup vs outgroup index, in three different datasets and we show that
both phenomena share an "us vs them" narrative.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 15:35:10 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Chulvi",
"Berta",
""
],
[
"Toselli",
"Alejandro",
""
],
[
"Rosso",
"Paolo",
""
]
] |
new_dataset
| 0.956972 |
2212.02425
|
Pedro Barroso
|
Pedro Barroso, M\'ario Pereira and Ant\'onio Ravara
|
Leroy and Blazy were right: their memory model soundness proof is
automatable (Extended Version)
|
To be published in VSTTE'22
| null | null | null |
cs.LO cs.PL
|
http://creativecommons.org/licenses/by/4.0/
|
Xavier Leroy and Sandrine Blazy in 2007 conducted a formal verification,
using the Coq proof assistant, of a memory model for low-level imperative
languages such as C. Considering their formalization was performed essentially
in first-order logic, one question left open by the authors was whether their
proofs could be automated using a verification framework for first-order logic.
We took the challenge and automated their formalization using Why3,
significantly reducing the proof effort. We systematically followed the Coq
proofs and realized that in many cases at around one third of the way Why3 was
able to discharge all VCs. Furthermore, the proofs still requiring interactions
(e.g. induction, witnesses for existential proofs, assertions) were factorized
isolating auxiliary results that we stated explicitly. In this way, we achieved
an almost-automatic soundness and safety proof of the memory model.
Nonetheless, our development allows an extraction of a correct-by-construction
concrete memory model, going thus further than the preliminary Why version of
Leroy and Blazy.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 17:08:18 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Barroso",
"Pedro",
""
],
[
"Pereira",
"Mário",
""
],
[
"Ravara",
"António",
""
]
] |
new_dataset
| 0.952702 |
2212.02439
|
Laurence Pelletier
|
Jason Lequyer, Wen-Hsin Hsu, Reuben Philip, Anna Christina Erpf,
Laurence Pelletier
|
Domino Denoise: An Accurate Blind Zero-Shot Denoiser using Domino
Tilings
| null | null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Because noise can interfere with downstream analysis, image denoising has
come to occupy an important place in the image processing toolbox. The most
accurate state-of-the-art denoisers typically train on a representative
dataset. But gathering a training set is not always feasible, so interest has
grown in blind zero-shot denoisers that train only on the image they are
denoising. The most accurate blind-zero shot methods are blind-spot networks,
which mask pixels and attempt to infer them from their surroundings. Other
methods exist where all neurons participate in forward inference, however they
are not as accurate and are susceptible to overfitting. Here we present a
hybrid approach. We first introduce a semi blind-spot network where the network
can see only a small percentage of inputs during gradient update. We then
resolve overfitting by introducing a validation scheme where we split pixels
into two groups and fill in pixel gaps using domino tilings. Our method
achieves an average PSNR increase of $0.28$ and a three fold increase in speed
over the current gold standard blind zero-shot denoiser Self2Self on synthetic
Gaussian noise. We demonstrate the broader applicability of Pixel Domino Tiling
by inserting it into a preciously published method.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 17:34:47 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Lequyer",
"Jason",
""
],
[
"Hsu",
"Wen-Hsin",
""
],
[
"Philip",
"Reuben",
""
],
[
"Erpf",
"Anna Christina",
""
],
[
"Pelletier",
"Laurence",
""
]
] |
new_dataset
| 0.99104 |
2212.02462
|
James P. Crutchfield
|
James P. Crutchfield and Alexandra M. Jurgens
|
Whale Casting: Remote mobile streaming humpback whale vocalizations to
the world
|
6 pages, 3 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/whalecasting.html
| null | null | null |
cs.HC q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Over several days in early August 2021, while at sea in Chatham Strait,
Southeast Alaska, aboard M/Y Blue Pearl, an online twitch.tv stream broadcast
in real-time humpback whale vocalizations monitored via hydrophone. Dozens on
mainland North American and around the planet listened in and chatted via the
stream. The webcasts demonstrated a proof-of-concept: only relatively
inexpensive commercial-off-the-shelf equipment is required for remote mobile
streaming at sea. These notes document what was required and make
recommendations for higher-quality and larger-scale deployments. One conclusion
is that real-time, automated audio documenting whale acoustic behavior is
readily accessible and, using the cloud, it can be directly integrated into
behavioral databases -- information sources that now often focus exclusively on
nonreal-time visual-sighting narrative reports and photography.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 18:08:40 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Crutchfield",
"James P.",
""
],
[
"Jurgens",
"Alexandra M.",
""
]
] |
new_dataset
| 0.994278 |
2004.02227
|
Mohammad Reza Zarrabi
|
Mohammad Reza Zarrabi, Nasrollah Moghaddam Charkari
|
A sufficient condition for visibility paths in simple polygons
| null | null | null | null |
cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The purpose of this note is to give a simple proof for a necessary and
sufficient condition for visibility paths in simple polygons. A visibility path
is a curve such that every point inside a simple polygon is visible from at
least one point on the path. This result is essential for finding the shortest
watchman route inside a simple polygon specially when the route is restricted
to curved paths.
|
[
{
"version": "v1",
"created": "Sun, 5 Apr 2020 15:08:31 GMT"
},
{
"version": "v2",
"created": "Tue, 5 May 2020 17:00:34 GMT"
},
{
"version": "v3",
"created": "Wed, 15 Jul 2020 11:19:29 GMT"
},
{
"version": "v4",
"created": "Mon, 7 Nov 2022 15:37:54 GMT"
},
{
"version": "v5",
"created": "Fri, 2 Dec 2022 16:02:31 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Zarrabi",
"Mohammad Reza",
""
],
[
"Charkari",
"Nasrollah Moghaddam",
""
]
] |
new_dataset
| 0.997389 |
2109.13098
|
Cencheng Shen
|
Cencheng Shen, Qizhe Wang, Carey E. Priebe
|
One-Hot Graph Encoder Embedding
|
7 pages main + 7 pages appendix
|
IEEE Transactions on Pattern Analysis and Machine Intelligence,
2023
|
10.1109/TPAMI.2022.3225073
| null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we propose a lightning fast graph embedding method called
one-hot graph encoder embedding. It has a linear computational complexity and
the capacity to process billions of edges within minutes on standard PC --
making it an ideal candidate for huge graph processing. It is applicable to
either adjacency matrix or graph Laplacian, and can be viewed as a
transformation of the spectral embedding. Under random graph models, the graph
encoder embedding is approximately normally distributed per vertex, and
asymptotically converges to its mean. We showcase three applications: vertex
classification, vertex clustering, and graph bootstrap. In every case, the
graph encoder embedding exhibits unrivalled computational advantages.
|
[
{
"version": "v1",
"created": "Mon, 27 Sep 2021 14:49:44 GMT"
},
{
"version": "v2",
"created": "Tue, 23 Aug 2022 13:33:52 GMT"
},
{
"version": "v3",
"created": "Fri, 2 Dec 2022 02:45:11 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Shen",
"Cencheng",
""
],
[
"Wang",
"Qizhe",
""
],
[
"Priebe",
"Carey E.",
""
]
] |
new_dataset
| 0.990165 |
2111.03823
|
Thanapong Chuangyanyong
|
Thanapong Chuangyanyong, Panusorn Chinsakuljaroen, Worachit Ketrungsri
and Thanacha Choopojcharoen
|
Flying Trapeze Act Motion Planning Algorithm for Two-Link Free-Flying
Acrobatic Robot
|
7 pages, 8 figures, 2 tables
| null |
10.1109/ICARM54641.2022.9959158
| null |
cs.RO
|
http://creativecommons.org/licenses/by-sa/4.0/
|
A flying trapeze act can be a challenging task for a robotics system since
some act requires the performer to catch another trapeze or catcher at the end
after being airborne. The objective of this paper is to design and validate a
motion planning algorithm for a two-link free-flying acrobatic robot that can
accurately land on another trapeze after free-flying in the air. First, the
proposed algorithm plan the robot trajectory with the non-linear constrained
optimization method. Then, a feedback controller is implemented to stabilize
the posture. However, since the spatial position of the center-of-mass of the
robot cannot be controlled, this paper proposes a trajectory correction scheme
that manipulates the robot's posture such that the robot is still able to land
on the target. Lastly, the whole algorithm is validated in the simulation that
mimics real-world circumstances.
|
[
{
"version": "v1",
"created": "Sat, 6 Nov 2021 07:32:49 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Feb 2022 13:02:46 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Chuangyanyong",
"Thanapong",
""
],
[
"Chinsakuljaroen",
"Panusorn",
""
],
[
"Ketrungsri",
"Worachit",
""
],
[
"Choopojcharoen",
"Thanacha",
""
]
] |
new_dataset
| 0.997756 |
2112.06596
|
Hang Zhou
|
Hang Zhou, Rui Ma, Ling-Xiao Zhang, Lin Gao, Ali Mahdavi-Amiri, Hao
Zhang
|
SAC-GAN: Structure-Aware Image Composition
|
Accepted to TVCG. Code: https://github.com/RyanHangZhou/SAC-GAN
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce an end-to-end learning framework for image-to-image composition,
aiming to plausibly compose an object represented as a cropped patch from an
object image into a background scene image. As our approach emphasizes more on
semantic and structural coherence of the composed images, rather than their
pixel-level RGB accuracies, we tailor the input and output of our network with
structure-aware features and design our network losses accordingly, with ground
truth established in a self-supervised setting through the object cropping.
Specifically, our network takes the semantic layout features from the input
scene image, features encoded from the edges and silhouette in the input object
patch, as well as a latent code as inputs, and generates a 2D spatial affine
transform defining the translation and scaling of the object patch. The learned
parameters are further fed into a differentiable spatial transformer network to
transform the object patch into the target image, where our model is trained
adversarially using an affine transform discriminator and a layout
discriminator. We evaluate our network, coined SAC-GAN, for various image
composition scenarios in terms of quality, composability, and generalizability
of the composite images. Comparisons are made to state-of-the-art alternatives,
including Instance Insertion, ST-GAN, CompGAN and PlaceNet, confirming
superiority of our method.
|
[
{
"version": "v1",
"created": "Mon, 13 Dec 2021 12:24:50 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Dec 2021 08:14:38 GMT"
},
{
"version": "v3",
"created": "Sat, 8 Jan 2022 04:10:44 GMT"
},
{
"version": "v4",
"created": "Tue, 5 Jul 2022 10:07:40 GMT"
},
{
"version": "v5",
"created": "Fri, 2 Dec 2022 09:27:41 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Zhou",
"Hang",
""
],
[
"Ma",
"Rui",
""
],
[
"Zhang",
"Ling-Xiao",
""
],
[
"Gao",
"Lin",
""
],
[
"Mahdavi-Amiri",
"Ali",
""
],
[
"Zhang",
"Hao",
""
]
] |
new_dataset
| 0.991545 |
2206.14077
|
Jos\'e \'Alamos
|
Jos\'e \'Alamos and Peter Kietzmann and Thomas Schmidt and Matthias
W\"ahlisch
|
DSME-LoRa: Seamless Long Range Communication Between Arbitrary Nodes in
the Constrained IoT
|
44 pages (incl. References), 27 figures,8 tables
|
ACM Transactions on Sensor Networks, Vol. 18, No. 4 (November
2022), 43 pages
|
10.1145/3552432
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Long range radio communication is preferred in many IoT deployments as it
avoids the complexity of multi-hop wireless networks. LoRa is a popular,
energy-efficient wireless modulation but its networking substrate LoRaWAN
introduces severe limitations to its users. In this paper, we present and
thoroughly analyze DSME-LoRa, a system design of LoRa with IEEE 802.15.4 DSME
as a MAC layer. DSME-LoRa offers the advantage of seamless client-to-client
communication beyond the pure gateway-centric transmission of LoRaWAN. We
evaluate its feasibility via a full-stack implementation on the popular RIOT
operating system, assess its steady-state packet flows in an analytical
stochastic Markov model, and quantify its scalability in massive communication
scenarios using large scale network simulations. Our findings indicate that
DSME-LoRa is indeed a powerful approach that opens LoRa to standard network
layers and outperforms LoRaWAN in many dimensions.
|
[
{
"version": "v1",
"created": "Tue, 28 Jun 2022 15:18:14 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Aug 2022 12:23:45 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Álamos",
"José",
""
],
[
"Kietzmann",
"Peter",
""
],
[
"Schmidt",
"Thomas",
""
],
[
"Wählisch",
"Matthias",
""
]
] |
new_dataset
| 0.998491 |
2208.09885
|
Bingchen Li
|
Bingchen Li, Xin Li, Yiting Lu, Sen Liu, Ruoyu Feng, Zhibo Chen
|
HST: Hierarchical Swin Transformer for Compressed Image Super-resolution
|
Accepted by ECCV2022 Workshop (AIM2022)
| null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Compressed Image Super-resolution has achieved great attention in recent
years, where images are degraded with compression artifacts and low-resolution
artifacts. Since the complex hybrid distortions, it is hard to restore the
distorted image with the simple cooperation of super-resolution and compression
artifacts removing. In this paper, we take a step forward to propose the
Hierarchical Swin Transformer (HST) network to restore the low-resolution
compressed image, which jointly captures the hierarchical feature
representations and enhances each-scale representation with Swin transformer,
respectively. Moreover, we find that the pretraining with Super-resolution (SR)
task is vital in compressed image super-resolution. To explore the effects of
different SR pretraining, we take the commonly-used SR tasks (e.g., bicubic and
different real super-resolution simulations) as our pretraining tasks, and
reveal that SR plays an irreplaceable role in the compressed image
super-resolution. With the cooperation of HST and pre-training, our HST
achieves the fifth place in AIM 2022 challenge on the low-quality compressed
image super-resolution track, with the PSNR of 23.51dB. Extensive experiments
and ablation studies have validated the effectiveness of our proposed methods.
The code and models are available at
https://github.com/USTC-IMCL/HST-for-Compressed-Image-SR.
|
[
{
"version": "v1",
"created": "Sun, 21 Aug 2022 13:41:51 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Dec 2022 02:54:40 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Li",
"Bingchen",
""
],
[
"Li",
"Xin",
""
],
[
"Lu",
"Yiting",
""
],
[
"Liu",
"Sen",
""
],
[
"Feng",
"Ruoyu",
""
],
[
"Chen",
"Zhibo",
""
]
] |
new_dataset
| 0.995022 |
2211.10973
|
Peng Qi
|
Peng Qi, Yuyan Bu, Juan Cao, Wei Ji, Ruihao Shui, Junbin Xiao, Danding
Wang, Tat-Seng Chua
|
FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News
Detection on Short Video Platforms
|
To appear in AAAI 2023 AISI track. This version contains appendix
with additional details
| null | null | null |
cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Short video platforms have become an important channel for news sharing, but
also a new breeding ground for fake news. To mitigate this problem, research of
fake news video detection has recently received a lot of attention. Existing
works face two roadblocks: the scarcity of comprehensive and largescale
datasets and insufficient utilization of multimodal information. Therefore, in
this paper, we construct the largest Chinese short video dataset about fake
news named FakeSV, which includes news content, user comments, and publisher
profiles simultaneously. To understand the characteristics of fake news videos,
we conduct exploratory analysis of FakeSV from different perspectives.
Moreover, we provide a new multimodal detection model named SV-FEND, which
exploits the cross-modal correlations to select the most informative features
and utilizes the social context information for detection. Extensive
experiments evaluate the superiority of the proposed method and provide
detailed comparisons of different methods and modalities for future works.
|
[
{
"version": "v1",
"created": "Sun, 20 Nov 2022 12:57:54 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Dec 2022 12:43:33 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Qi",
"Peng",
""
],
[
"Bu",
"Yuyan",
""
],
[
"Cao",
"Juan",
""
],
[
"Ji",
"Wei",
""
],
[
"Shui",
"Ruihao",
""
],
[
"Xiao",
"Junbin",
""
],
[
"Wang",
"Danding",
""
],
[
"Chua",
"Tat-Seng",
""
]
] |
new_dataset
| 0.999747 |
2211.15848
|
Chenyan Xiong
|
Arnold Overwijk, Chenyan Xiong, Xiao Liu, Cameron VandenBerg, Jamie
Callan
|
ClueWeb22: 10 Billion Web Documents with Visual and Semantic Information
| null | null | null | null |
cs.IR cs.AI cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
ClueWeb22, the newest iteration of the ClueWeb line of datasets, provides 10
billion web pages affiliated with rich information. Its design was influenced
by the need for a high quality, large scale web corpus to support a range of
academic and industry research, for example, in information systems,
retrieval-augmented AI systems, and model pretraining. Compared with earlier
ClueWeb corpora, the ClueWeb22 corpus is larger, more varied, of
higher-quality, and aligned with the document distributions in commercial web
search. Besides raw HTML, ClueWeb22 includes rich information about the web
pages provided by industry-standard document understanding systems, including
the visual representation of pages rendered by a web browser, parsed HTML
structure information from a neural network parser, and pre-processed cleaned
document text to lower the barrier to entry. Many of these signals have been
widely used in industry but are available to the research community for the
first time at this scale.
|
[
{
"version": "v1",
"created": "Tue, 29 Nov 2022 00:49:40 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Dec 2022 03:38:26 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Overwijk",
"Arnold",
""
],
[
"Xiong",
"Chenyan",
""
],
[
"Liu",
"Xiao",
""
],
[
"VandenBerg",
"Cameron",
""
],
[
"Callan",
"Jamie",
""
]
] |
new_dataset
| 0.999872 |
2212.00229
|
Shicheng Xu
|
Shicheng Xu, Liang Pang, Huawei Shen, Xueqi Cheng
|
NIR-Prompt: A Multi-task Generalized Neural Information Retrieval
Training Framework
|
This article is the extension of arXiv:2204.02725
| null | null | null |
cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Information retrieval aims to find information that meets users' needs from
the corpus. Different needs correspond to different IR tasks such as document
retrieval, open-domain question answering, retrieval-based dialogue, etc.,
while they share the same schema to estimate the relationship between texts. It
indicates that a good IR model can generalize to different tasks and domains.
However, previous studies indicate that state-of-the-art neural information
retrieval (NIR) models, e.g, pre-trained language models (PLMs) are hard to
generalize. Mainly because the end-to-end fine-tuning paradigm makes the model
overemphasize task-specific signals and domain biases but loses the ability to
capture generalized essential signals. To address this problem, we propose a
novel NIR training framework named NIR-Prompt for retrieval and reranking
stages based on the idea of decoupling signal capturing and combination.
NIR-Prompt exploits Essential Matching Module (EMM) to capture the essential
matching signals and gets the description of tasks by Matching Description
Module (MDM). The description is used as task-adaptation information to combine
the essential matching signals to adapt to different tasks. Experiments under
in-domain multi-task, out-of-domain multi-task, and new task adaptation
settings show that NIR-Prompt can improve the generalization of PLMs in NIR for
both retrieval and reranking stages compared with baselines.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 02:26:52 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Dec 2022 02:30:19 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Xu",
"Shicheng",
""
],
[
"Pang",
"Liang",
""
],
[
"Shen",
"Huawei",
""
],
[
"Cheng",
"Xueqi",
""
]
] |
new_dataset
| 0.992202 |
2212.00352
|
Kaibing Xie
|
Kaibing Xie (1), Jian Yang (1), Kang Qiu (1) ((1) Peng Cheng
Laboratory, Shenzhen, China)
|
A Dataset with Multibeam Forward-Looking Sonar for Underwater Object
Detection
| null | null |
10.1038/s41597-022-01854-w
| null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multibeam forward-looking sonar (MFLS) plays an important role in underwater
detection. There are several challenges to the research on underwater object
detection with MFLS. Firstly, the research is lack of available dataset.
Secondly, the sonar image, generally processed at pixel level and transformed
to sector representation for the visual habits of human beings, is
disadvantageous to the research in artificial intelligence (AI) areas. Towards
these challenges, we present a novel dataset, the underwater acoustic target
detection (UATD) dataset, consisting of over 9000 MFLS images captured using
Tritech Gemini 1200ik sonar. Our dataset provides raw data of sonar images with
annotation of 10 categories of target objects (cube, cylinder, tyres, etc). The
data was collected from lake and shallow water. To verify the practicality of
UATD, we apply the dataset to the state-of-the-art detectors and provide
corresponding benchmarks for its accuracy and efficiency.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 08:26:03 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Dec 2022 01:38:51 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Xie",
"Kaibing",
""
],
[
"Yang",
"Jian",
""
],
[
"Qiu",
"Kang",
""
]
] |
new_dataset
| 0.98297 |
2212.00851
|
Tharindu Ranasinghe Dr
|
Tharindu Ranasinghe, Isuri Anuradha, Damith Premasiri, Kanishka Silva,
Hansi Hettiarachchi, Lasitha Uyangodage, Marcos Zampieri
|
SOLD: Sinhala Offensive Language Dataset
|
This is a preprint of an article submitted to Applied Intelligence,
Springer
| null | null | null |
cs.CL cs.AI cs.LG cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
The widespread of offensive content online, such as hate speech and
cyber-bullying, is a global phenomenon. This has sparked interest in the
artificial intelligence (AI) and natural language processing (NLP) communities,
motivating the development of various systems trained to detect potentially
harmful content automatically. These systems require annotated datasets to
train the machine learning (ML) models. However, with a few notable exceptions,
most datasets on this topic have dealt with English and a few other
high-resource languages. As a result, the research in offensive language
identification has been limited to these languages. This paper addresses this
gap by tackling offensive language identification in Sinhala, a low-resource
Indo-Aryan language spoken by over 17 million people in Sri Lanka. We introduce
the Sinhala Offensive Language Dataset (SOLD) and present multiple experiments
on this dataset. SOLD is a manually annotated dataset containing 10,000 posts
from Twitter annotated as offensive and not offensive at both sentence-level
and token-level, improving the explainability of the ML models. SOLD is the
first large publicly available offensive language dataset compiled for Sinhala.
We also introduce SemiSOLD, a larger dataset containing more than 145,000
Sinhala tweets, annotated following a semi-supervised approach.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 20:18:21 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Ranasinghe",
"Tharindu",
""
],
[
"Anuradha",
"Isuri",
""
],
[
"Premasiri",
"Damith",
""
],
[
"Silva",
"Kanishka",
""
],
[
"Hettiarachchi",
"Hansi",
""
],
[
"Uyangodage",
"Lasitha",
""
],
[
"Zampieri",
"Marcos",
""
]
] |
new_dataset
| 0.99987 |
2212.00891
|
Ian McQuillan
|
Oscar H. Ibarra, Jozef Jir\'asek, Ian McQuillan, and Luca Prigioniero
|
Space Complexity of Stack Automata Models
|
23 pages, 1 figure, 2 tables
|
International Journal of Foundations of Computer Science, 32 (6),
801--823 (2021)
|
10.1142/S0129054121420090
| null |
cs.FL
|
http://creativecommons.org/licenses/by/4.0/
|
This paper examines several measures of space complexity of variants of stack
automata: non-erasing stack automata and checking stack automata. These
measures capture the minimum stack size required to accept every word in the
language of the automaton (weak measure), the maximum stack size used in any
accepting computation on any accepted word (accept measure),and the maximum
stack size used in any computation (strong measure). We give a detailed
characterization of the accept and strong space complexity measures for
checking stack automata. Exactly one of three cases can occur: the complexity
is either bounded by a constant, behaves like a linear function, or it can not
be bounded by any function of the length of the input word (and it is decidable
which case occurs). However, this result does not hold for non-erasing stack
automata; we provide an example where the space complexity grows proportionally
to the square root of the length of the input. Furthermore, we study the
complexity bounds of machines which accept a given language, and decidability
of space complexity properties.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 22:16:42 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Ibarra",
"Oscar H.",
""
],
[
"Jirásek",
"Jozef",
""
],
[
"McQuillan",
"Ian",
""
],
[
"Prigioniero",
"Luca",
""
]
] |
new_dataset
| 0.984117 |
2212.00903
|
Xiaoran Wu
|
Xiaoran Wu, Zihan Yan, Xiang Anthony Chen
|
DeclutterCam: A Photographic Assistant System with Clutter Detection and
Removal
| null | null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Photographs convey the stories of photographers to the audience. However,
this story-telling aspect of photography is easily distracted by visual
clutter. Informed by a pilot study, we identified the kinds of clutter that
amateurs frequently include in their photos. We were thus inspired to develop
DeclutterCam, a photographic assistant system that incorporates novel user
interactions and AI algorithms for photographic decluttering. Clutter elements
are detected by an aesthetic quality evaluation algorithm and are highlighted
so that users can interactively identify distracting elements. A GAN-based
iterative clutter removal tool enables users to test their photographic ideas
in real-time. User studies with 32 photography beginners demonstrate that our
system provides flexible interfaces, accurate algorithms, and immediate
feedback that allow users to avoid clutter and explore more photographic ideas.
Evaluations by photography experts show that users can take higher-quality
photos that better convey the intended story using our system.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 23:02:37 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Wu",
"Xiaoran",
""
],
[
"Yan",
"Zihan",
""
],
[
"Chen",
"Xiang Anthony",
""
]
] |
new_dataset
| 0.999543 |
2212.00928
|
Manuel Ballester
|
Manuel Ballester, Heming Wang, Jiren Li, Oliver Cossairt, Florian
Willomitzer
|
Single-shot ToF sensing with sub-mm precision using conventional CMOS
sensors
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a novel single-shot interferometric ToF camera targeted for
precise 3D measurements of dynamic objects. The camera concept is based on
Synthetic Wavelength Interferometry, a technique that allows retrieval of depth
maps of objects with optically rough surfaces at submillimeter depth precision.
In contrast to conventional ToF cameras, our device uses only off-the-shelf
CCD/CMOS detectors and works at their native chip resolution (as of today,
theoretically up to 20 Mp and beyond). Moreover, we can obtain a full 3D model
of the object in single-shot, meaning that no temporal sequence of exposures or
temporal illumination modulation (such as amplitude or frequency modulation) is
necessary, which makes our camera robust against object motion.
In this paper, we introduce the novel camera concept and show first
measurements that demonstrate the capabilities of our system. We present 3D
measurements of small (cm-sized) objects with > 2 Mp point cloud resolution
(the resolution of our used detector) and up to sub-mm depth precision. We also
report a "single-shot 3D video" acquisition and a first single-shot
"Non-Line-of-Sight" measurement. Our technique has great potential for
high-precision applications with dynamic object movement, e.g., in AR/VR,
industrial inspection, medical imaging, and imaging through scattering media
like fog or human tissue.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 01:50:36 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Ballester",
"Manuel",
""
],
[
"Wang",
"Heming",
""
],
[
"Li",
"Jiren",
""
],
[
"Cossairt",
"Oliver",
""
],
[
"Willomitzer",
"Florian",
""
]
] |
new_dataset
| 0.980414 |
2212.00973
|
Zixun Guo
|
Z. Guo, J. Kang, D. Herremans
|
A Domain-Knowledge-Inspired Music Embedding Space and a Novel Attention
Mechanism for Symbolic Music Modeling
|
This paper is accepted at AAAI 2023
| null | null | null |
cs.SD cs.AI eess.AS eess.SP
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Following the success of the transformer architecture in the natural language
domain, transformer-like architectures have been widely applied to the domain
of symbolic music recently. Symbolic music and text, however, are two different
modalities. Symbolic music contains multiple attributes, both absolute
attributes (e.g., pitch) and relative attributes (e.g., pitch interval). These
relative attributes shape human perception of musical motifs. These important
relative attributes, however, are mostly ignored in existing symbolic music
modeling methods with the main reason being the lack of a musically-meaningful
embedding space where both the absolute and relative embeddings of the symbolic
music tokens can be efficiently represented. In this paper, we propose the
Fundamental Music Embedding (FME) for symbolic music based on a bias-adjusted
sinusoidal encoding within which both the absolute and the relative attributes
can be embedded and the fundamental musical properties (e.g., translational
invariance) are explicitly preserved. Taking advantage of the proposed FME, we
further propose a novel attention mechanism based on the relative index, pitch
and onset embeddings (RIPO attention) such that the musical domain knowledge
can be fully utilized for symbolic music modeling. Experiment results show that
our proposed model: RIPO transformer which utilizes FME and RIPO attention
outperforms the state-of-the-art transformers (i.e., music transformer, linear
transformer) in a melody completion task. Moreover, using the RIPO transformer
in a downstream music generation task, we notice that the notorious
degeneration phenomenon no longer exists and the music generated by the RIPO
transformer outperforms the music generated by state-of-the-art transformer
models in both subjective and objective evaluations.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 05:04:31 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Guo",
"Z.",
""
],
[
"Kang",
"J.",
""
],
[
"Herremans",
"D.",
""
]
] |
new_dataset
| 0.970299 |
2212.01022
|
Indranil Saha
|
Nikhil Kumar Singh and Indranil Saha
|
STL-Based Synthesis of Feedback Controllers Using Reinforcement Learning
|
Full version of the paper to be published in AAAI 2023
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep Reinforcement Learning (DRL) has the potential to be used for
synthesizing feedback controllers (agents) for various complex systems with
unknown dynamics. These systems are expected to satisfy diverse safety and
liveness properties best captured using temporal logic. In RL, the reward
function plays a crucial role in specifying the desired behaviour of these
agents. However, the problem of designing the reward function for an RL agent
to satisfy complex temporal logic specifications has received limited attention
in the literature. To address this, we provide a systematic way of generating
rewards in real-time by using the quantitative semantics of Signal Temporal
Logic (STL), a widely used temporal logic to specify the behaviour of
cyber-physical systems. We propose a new quantitative semantics for STL having
several desirable properties, making it suitable for reward generation. We
evaluate our STL-based reinforcement learning mechanism on several complex
continuous control benchmarks and compare our STL semantics with those
available in the literature in terms of their efficacy in synthesizing the
controller agent. Experimental results establish our new semantics to be the
most suitable for synthesizing feedback controllers for complex continuous
dynamical systems through reinforcement learning.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 08:31:46 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Singh",
"Nikhil Kumar",
""
],
[
"Saha",
"Indranil",
""
]
] |
new_dataset
| 0.974779 |
2212.01033
|
Jaidev Shriram
|
Jaidev Shriram and Makarand Tapaswi and Vinoo Alluri
|
Sonus Texere! Automated Dense Soundtrack Construction for Books using
Movie Adaptations
|
Accepted to ISMIR 2022. Project page:
https://auto-book-soundtrack.github.io/
| null | null | null |
cs.SD cs.AI cs.MM eess.AS
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Reading, much like music listening, is an immersive experience that
transports readers while taking them on an emotional journey. Listening to
complementary music has the potential to amplify the reading experience,
especially when the music is stylistically cohesive and emotionally relevant.
In this paper, we propose the first fully automatic method to build a dense
soundtrack for books, which can play high-quality instrumental music for the
entirety of the reading duration. Our work employs a unique text processing and
music weaving pipeline that determines the context and emotional composition of
scenes in a chapter. This allows our method to identify and play relevant
excerpts from the soundtrack of the book's movie adaptation. By relying on the
movie composer's craftsmanship, our book soundtracks include expert-made motifs
and other scene-specific musical characteristics. We validate the design
decisions of our approach through a perceptual study. Our readers note that the
book soundtrack greatly enhanced their reading experience, due to high
immersiveness granted via uninterrupted and style-consistent music, and a
heightened emotional state attained via high precision emotion and scene
context recognition.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 08:57:20 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Shriram",
"Jaidev",
""
],
[
"Tapaswi",
"Makarand",
""
],
[
"Alluri",
"Vinoo",
""
]
] |
new_dataset
| 0.998441 |
2212.01039
|
Yichong Leng
|
Yichong Leng, Xu Tan, Wenjie Liu, Kaitao Song, Rui Wang, Xiang-Yang
Li, Tao Qin, Edward Lin, Tie-Yan Liu
|
SoftCorrect: Error Correction with Soft Detection for Automatic Speech
Recognition
|
AAAI 2023
| null | null | null |
cs.CL cs.LG eess.AS
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Error correction in automatic speech recognition (ASR) aims to correct those
incorrect words in sentences generated by ASR models. Since recent ASR models
usually have low word error rate (WER), to avoid affecting originally correct
tokens, error correction models should only modify incorrect words, and
therefore detecting incorrect words is important for error correction. Previous
works on error correction either implicitly detect error words through
target-source attention or CTC (connectionist temporal classification) loss, or
explicitly locate specific deletion/substitution/insertion errors. However,
implicit error detection does not provide clear signal about which tokens are
incorrect and explicit error detection suffers from low detection accuracy. In
this paper, we propose SoftCorrect with a soft error detection mechanism to
avoid the limitations of both explicit and implicit error detection.
Specifically, we first detect whether a token is correct or not through a
probability produced by a dedicatedly designed language model, and then design
a constrained CTC loss that only duplicates the detected incorrect tokens to
let the decoder focus on the correction of error tokens. Compared with implicit
error detection with CTC loss, SoftCorrect provides explicit signal about which
words are incorrect and thus does not need to duplicate every token but only
incorrect tokens; compared with explicit error detection, SoftCorrect does not
detect specific deletion/substitution/insertion errors but just leaves it to
CTC loss. Experiments on AISHELL-1 and Aidatatang datasets show that
SoftCorrect achieves 26.1% and 9.4% CER reduction respectively, outperforming
previous works by a large margin, while still enjoying fast speed of parallel
generation.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 09:11:32 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Leng",
"Yichong",
""
],
[
"Tan",
"Xu",
""
],
[
"Liu",
"Wenjie",
""
],
[
"Song",
"Kaitao",
""
],
[
"Wang",
"Rui",
""
],
[
"Li",
"Xiang-Yang",
""
],
[
"Qin",
"Tao",
""
],
[
"Lin",
"Edward",
""
],
[
"Liu",
"Tie-Yan",
""
]
] |
new_dataset
| 0.986807 |
2212.01042
|
Hui Zhuang
|
Pengfei Hu, Hui Zhuang, Panneer Selvam Santhalingamy, Riccardo
Spolaor, Parth Pathaky, Guoming Zhang, Xiuzhen Cheng
|
AccEar: Accelerometer Acoustic Eavesdropping with Unconstrained
Vocabulary
|
2022 IEEE Symposium on Security and Privacy (SP)
|
2022 IEEE Symposium on Security and Privacy (SP)
|
10.1109/SP46214.2022.9833716
| null |
cs.SD cs.CR eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the increasing popularity of voice-based applications, acoustic
eavesdropping has become a serious threat to users' privacy. While on
smartphones the access to microphones needs an explicit user permission,
acoustic eavesdropping attacks can rely on motion sensors (such as
accelerometer and gyroscope), which access is unrestricted. However, previous
instances of such attacks can only recognize a limited set of pre-trained words
or phrases. In this paper, we present AccEar, an accelerometerbased acoustic
eavesdropping attack that can reconstruct any audio played on the smartphone's
loudspeaker with unconstrained vocabulary. We show that an attacker can employ
a conditional Generative Adversarial Network (cGAN) to reconstruct highfidelity
audio from low-frequency accelerometer signals. The presented cGAN model learns
to recreate high-frequency components of the user's voice from low-frequency
accelerometer signals through spectrogram enhancement. We assess the
feasibility and effectiveness of AccEar attack in a thorough set of experiments
using audio from 16 public personalities. As shown by the results in both
objective and subjective evaluations, AccEar successfully reconstructs user
speeches from accelerometer signals in different scenarios including varying
sampling rate, audio volume, device model, etc.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 09:13:28 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Hu",
"Pengfei",
""
],
[
"Zhuang",
"Hui",
""
],
[
"Santhalingamy",
"Panneer Selvam",
""
],
[
"Spolaor",
"Riccardo",
""
],
[
"Pathaky",
"Parth",
""
],
[
"Zhang",
"Guoming",
""
],
[
"Cheng",
"Xiuzhen",
""
]
] |
new_dataset
| 0.97891 |
2212.01210
|
Nedim Osmic
|
Nedim Osmic, Adnan Tahirovic and Bakir Lacevic
|
Octocopter Design: Modelling, Control and Motion Planning
|
100 pages, 57 Figures, 16 Tables
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
This book provides a solution to the control and motion planning design for
an octocopter system. It includes a particular choice of control and motion
planning algorithms which is based on the authors' previous research work, so
it can be used as a reference design guidance for students, researchers as well
as autonomous vehicles hobbyists. The control is constructed based on a fault
tolerant approach aiming to increase the chances of the system to detect and
isolate a potential failure in order to produce feasible control signals to the
remaining active motors. The used motion planning algorithm is risk-aware by
means that it takes into account the constraints related to the fault-dependant
and mission-related maneuverability analysis of the octocopter system during
the planning stage. Such a planner generates only those reference trajectories
along which the octocopter system would be safe and capable of good tracking in
case of a single motor fault and of majority of double motor fault scenarios.
The control and motion planning algorithms presented in the book aim to
increase the overall reliability of the system for completing the mission.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 14:43:25 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Osmic",
"Nedim",
""
],
[
"Tahirovic",
"Adnan",
""
],
[
"Lacevic",
"Bakir",
""
]
] |
new_dataset
| 0.999799 |
2212.01247
|
Tobias Fischer
|
Tobias Fischer, Yung-Hsu Yang, Suryansh Kumar, Min Sun, Fisher Yu
|
CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion
|
Project page: https://www.vis.xyz/pub/cc-3dt/
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
To track the 3D locations and trajectories of the other traffic participants
at any given time, modern autonomous vehicles are equipped with multiple
cameras that cover the vehicle's full surroundings. Yet, camera-based 3D object
tracking methods prioritize optimizing the single-camera setup and resort to
post-hoc fusion in a multi-camera setup. In this paper, we propose a method for
panoramic 3D object tracking, called CC-3DT, that associates and models object
trajectories both temporally and across views, and improves the overall
tracking consistency. In particular, our method fuses 3D detections from
multiple cameras before association, reducing identity switches significantly
and improving motion modeling. Our experiments on large-scale driving datasets
show that fusion before association leads to a large margin of improvement over
post-hoc fusion. We set a new state-of-the-art with 12.6% improvement in
average multi-object tracking accuracy (AMOTA) among all camera-based methods
on the competitive NuScenes 3D tracking benchmark, outperforming previously
published methods by 6.5% in AMOTA with the same 3D detector.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 15:43:55 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Fischer",
"Tobias",
""
],
[
"Yang",
"Yung-Hsu",
""
],
[
"Kumar",
"Suryansh",
""
],
[
"Sun",
"Min",
""
],
[
"Yu",
"Fisher",
""
]
] |
new_dataset
| 0.996995 |
2212.01260
|
Maxim Khomiakov
|
Maxim Khomiakov, Julius Holbech Radzikowski, Carl Anton Schmidt,
Mathias Bonde S{\o}rensen, Mads Andersen, Michael Riis Andersen and Jes
Frellsen
|
SolarDK: A high-resolution urban solar panel image classification and
localization dataset
|
7 pages, 2 figures, to access the dataset, see https://osf.io/aj539/
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The body of research on classification of solar panel arrays from aerial
imagery is increasing, yet there are still not many public benchmark datasets.
This paper introduces two novel benchmark datasets for classifying and
localizing solar panel arrays in Denmark: A human annotated dataset for
classification and segmentation, as well as a classification dataset acquired
using self-reported data from the Danish national building registry. We explore
the performance of prior works on the new benchmark dataset, and present
results after fine-tuning models using a similar approach as recent works.
Furthermore, we train models of newer architectures and provide benchmark
baselines to our datasets in several scenarios. We believe the release of these
datasets may improve future research in both local and global geospatial
domains for identifying and mapping of solar panel arrays from aerial imagery.
The data is accessible at https://osf.io/aj539/.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 15:56:56 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Khomiakov",
"Maxim",
""
],
[
"Radzikowski",
"Julius Holbech",
""
],
[
"Schmidt",
"Carl Anton",
""
],
[
"Sørensen",
"Mathias Bonde",
""
],
[
"Andersen",
"Mads",
""
],
[
"Andersen",
"Michael Riis",
""
],
[
"Frellsen",
"Jes",
""
]
] |
new_dataset
| 0.999856 |
2212.01298
|
Yushan Siriwardhana
|
Sehan Samarakoon, Yushan Siriwardhana, Pawani Porambage, Madhusanka
Liyanage, Sang-Yoon Chang, Jinoh Kim, Jonghyun Kim, Mika Ylianttila
|
5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated
over 5G Wireless Network
|
Link to the Dataset http://ieee-dataport.org/10203
| null | null | null |
cs.CR cs.NI
|
http://creativecommons.org/publicdomain/zero/1.0/
|
With a plethora of new connections, features, and services introduced, the
5th generation (5G) wireless technology reflects the development of mobile
communication networks and is here to stay for the next decade. The multitude
of services and technologies that 5G incorporates have made modern
communication networks very complex and sophisticated in nature. This
complexity along with the incorporation of Machine Learning (ML) and Artificial
Intelligence (AI) provides the opportunity for the attackers to launch
intelligent attacks against the network and network devices. These attacks
often traverse undetected due to the lack of intelligent security mechanisms to
counter these threats. Therefore, the implementation of real-time, proactive,
and self-adaptive security mechanisms throughout the network would be an
integral part of 5G as well as future communication systems. Therefore, large
amounts of data collected from real networks will play an important role in the
training of AI/ML models to identify and detect malicious content in network
traffic. This work presents 5G-NIDD, a fully labeled dataset built on a
functional 5G test network that can be used by those who develop and test AI/ML
solutions. The work further analyses the collected data using common ML models
and shows the achieved accuracy levels.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 16:42:46 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Samarakoon",
"Sehan",
""
],
[
"Siriwardhana",
"Yushan",
""
],
[
"Porambage",
"Pawani",
""
],
[
"Liyanage",
"Madhusanka",
""
],
[
"Chang",
"Sang-Yoon",
""
],
[
"Kim",
"Jinoh",
""
],
[
"Kim",
"Jonghyun",
""
],
[
"Ylianttila",
"Mika",
""
]
] |
new_dataset
| 0.999786 |
2212.01301
|
Ian McQuillan
|
Oscar H. Ibarra and Ian McQuillan
|
Semilinearity of Families of Languages
|
20 pages
|
International Journal of Foundations of Computer Science, 31 (8),
1179-1198 (2020)
|
10.1142/S0129054120420095
| null |
cs.FL
|
http://creativecommons.org/licenses/by/4.0/
|
Techniques are developed for creating new and general language families of
only semilinear languages, and for showing families only contain semilinear
languages. It is shown that for language families L that are semilinear full
trios, the smallest full AFL containing L that is also closed under
intersection with languages in NCM (where NCM is the family of languages
accepted by NFAs augmented with reversal-bounded counters), is also semilinear.
If these closure properties are effective, this also immediately implies
decidability of membership, emptiness, and infiniteness for these general
families. From the general techniques, new grammar systems are given that are
extensions of well-known families of semilinear full trios, whereby it is
implied that these extensions must only describe semilinear languages. This
also implies positive decidability properties for the new systems. Some
characterizations of the new families are also given.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 16:49:56 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Ibarra",
"Oscar H.",
""
],
[
"McQuillan",
"Ian",
""
]
] |
new_dataset
| 0.998306 |
2212.01372
|
Mustafa Doger
|
Mustafa Doger and Sennur Ulukus
|
Bitcoin Security-Latency Under Network Delay
| null | null | null | null |
cs.CR cs.DC cs.DM cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We improve security-latency bounds of Nakamoto consensus by analyzing the
race between adversarial and honest chains in three different phases:
pre-mining, confirmation and post-confirmation. We find the probability
distribution of the length of the adversarial chain and the rigged adversarial
chain under jumper models during the confirmation interval. We analyze certain
properties of this race to model pre-mining and post-confirmation phases with
random walks that provide tighter bounds than existing results. Combining all
three phases provides novel upper and lower bounds for blockchains with small
$\lambda\Delta$.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 18:54:30 GMT"
}
] | 2022-12-05T00:00:00 |
[
[
"Doger",
"Mustafa",
""
],
[
"Ulukus",
"Sennur",
""
]
] |
new_dataset
| 0.984655 |
2006.02901
|
Gang Liu
|
Gang Liu, Yajing Pang, Shuai Yin, Xiaoke Niu, Jing Wang, Hong Wan
|
Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and
System Identification
|
Published in Mathematics
| null |
10.3390/math10234477
| null |
cs.LG cs.CV stat.ML
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Nonlinear mapping is an essential and common demand in online systems, such
as sensor systems and mobile phones. Accelerating nonlinear mapping will
directly speed up online systems. Previously the authors of this paper proposed
a Dendrite Net (DD) with enormously lower time complexity than the existing
nonlinear mapping algorithms; however, there still are redundant calculations
in DD. This paper presents a DD with an acceleration module (AC) to accelerate
nonlinear mapping further. We conduct three experiments to verify whether DD
with AC has lower time complexity while retaining DD's nonlinear mapping
properties and system identification properties: The first experiment is the
precision and identification of unary nonlinear mapping, reflecting the
calculation performance using DD with AC for basic functions in online systems.
The second experiment is the mapping precision and identification of the
multi-input nonlinear system, reflecting the performance for designing online
systems via DD with AC. Finally, this paper compares the time complexity of DD
and DD with AC and analyzes the theoretical reasons through repeated
experiments. Results: DD with AC retains DD's excellent mapping and
identification properties and has lower time complexity. Significance: DD with
AC can be used for most engineering systems, such as sensor systems, and will
speed up computation in these online systems. The code of DD with AC is
available on https://github.com/liugang1234567/Gang-neuron
|
[
{
"version": "v1",
"created": "Thu, 4 Jun 2020 17:56:24 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Dec 2022 17:51:04 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Liu",
"Gang",
""
],
[
"Pang",
"Yajing",
""
],
[
"Yin",
"Shuai",
""
],
[
"Niu",
"Xiaoke",
""
],
[
"Wang",
"Jing",
""
],
[
"Wan",
"Hong",
""
]
] |
new_dataset
| 0.994855 |
2106.04618
|
Laurens Bliek
|
Laurens Bliek, Arthur Guijt, Rickard Karlsson, Sicco Verwer, Mathijs
de Weerdt
|
EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on
Expensive Black-box Functions
|
33 pages
| null | null | null |
cs.LG cs.NE math.OC
|
http://creativecommons.org/licenses/by/4.0/
|
Surrogate algorithms such as Bayesian optimisation are especially designed
for black-box optimisation problems with expensive objectives, such as
hyperparameter tuning or simulation-based optimisation. In the literature,
these algorithms are usually evaluated with synthetic benchmarks which are well
established but have no expensive objective, and only on one or two real-life
applications which vary wildly between papers. There is a clear lack of
standardisation when it comes to benchmarking surrogate algorithms on
real-life, expensive, black-box objective functions. This makes it very
difficult to draw conclusions on the effect of algorithmic contributions and to
give substantial advice on which method to use when. A new benchmark library,
EXPObench, provides first steps towards such a standardisation. The library is
used to provide an extensive comparison of six different surrogate algorithms
on four expensive optimisation problems from different real-life applications.
This has led to new insights regarding the relative importance of exploration,
the evaluation time of the objective, and the used model. We also provide rules
of thumb for which surrogate algorithm to use in which situation. A further
contribution is that we make the algorithms and benchmark problem instances
publicly available, contributing to more uniform analysis of surrogate
algorithms. Most importantly, we include the performance of the six algorithms
on all evaluated problem instances. This results in a unique new dataset that
lowers the bar for researching new methods as the number of expensive
evaluations required for comparison is significantly reduced.
|
[
{
"version": "v1",
"created": "Tue, 8 Jun 2021 18:17:42 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Dec 2022 16:37:41 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Bliek",
"Laurens",
""
],
[
"Guijt",
"Arthur",
""
],
[
"Karlsson",
"Rickard",
""
],
[
"Verwer",
"Sicco",
""
],
[
"de Weerdt",
"Mathijs",
""
]
] |
new_dataset
| 0.992043 |
2110.06321
|
Jie Chen
|
Jie Chen, Prasanna Date, Nicholas Chancellor, Mohammed Atiquzzaman,
Cormac Sreenan
|
Controller-based Energy-Aware Wireless Sensor Network Routing using
Quantum Algorithms
| null |
IEEE Transactions on Quantum Engineering, vol. 3, pp. 1-12, 2022,
Art no. 3102912
|
10.1109/TQE.2022.3217297
| null |
cs.ET quant-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Energy efficient routing in wireless sensor networks has attracted attention
from researchers in both academia and industry, most recently motivated by the
opportunity to use SDN (software defined network)-inspired approaches. These
problems are NP-hard, with algorithms needing computation time which scales
faster than polynomial in the problem size. Consequently, heuristic algorithms
are used in practice, which are unable to guarantee optimally. In this short
paper, we show proof-of-principle for the use of a quantum annealing processor
instead of a classical processor, to find optimal or near-optimal solutions
very quickly. Our preliminary results for small networks show that this
approach using quantum computing has great promise and may open the door for
other significant improvements in the efficacy of network algorithms.
|
[
{
"version": "v1",
"created": "Tue, 12 Oct 2021 20:16:21 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Chen",
"Jie",
""
],
[
"Date",
"Prasanna",
""
],
[
"Chancellor",
"Nicholas",
""
],
[
"Atiquzzaman",
"Mohammed",
""
],
[
"Sreenan",
"Cormac",
""
]
] |
new_dataset
| 0.996689 |
2201.01051
|
Ashirbad Pradhan
|
Ashirbad Pradhan, Jiayuan He, Ning Jiang
|
Open Access Dataset for Electromyography based Multi-code Biometric
Authentication
|
manuscript for open access dataset (paper and appendix)
|
Sci Data 9, 733 (2022)
|
10.1038/s41597-022-01836-y
| null |
cs.CR eess.SP stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
Recently, surface electromyogram (EMG) has been proposed as a novel biometric
trait for addressing some key limitations of current biometrics, such as
spoofing and liveness. The EMG signals possess a unique characteristic: they
are inherently different for individuals (biometrics), and they can be
customized to realize multi-length codes or passwords (for example, by
performing different gestures). However, current EMG-based biometric research
has two critical limitations: 1) a small subject pool, compared to other more
established biometric traits, and 2) limited to single-session or single-day
data sets. In this study, forearm and wrist EMG data were collected from 43
participants over three different days with long separation while they
performed static hand and wrist gestures. The multi-day biometric
authentication resulted in a median EER of 0.017 for the forearm setup and
0.025 for the wrist setup, comparable to well-established biometric traits
suggesting consistent performance over multiple days. The presented
large-sample multi-day data set and findings could facilitate further research
on EMG-based biometrics and other gesture recognition-based applications.
|
[
{
"version": "v1",
"created": "Tue, 4 Jan 2022 09:20:34 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Jan 2022 07:15:08 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Pradhan",
"Ashirbad",
""
],
[
"He",
"Jiayuan",
""
],
[
"Jiang",
"Ning",
""
]
] |
new_dataset
| 0.999626 |
2201.08383
|
Chao-Yuan Wu
|
Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam, Haoqi Fan, Bo Xiong,
Jitendra Malik, Christoph Feichtenhofer
|
MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient
Long-Term Video Recognition
|
Technical report. arXiv v2: add link to code
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While today's video recognition systems parse snapshots or short clips
accurately, they cannot connect the dots and reason across a longer range of
time yet. Most existing video architectures can only process <5 seconds of a
video without hitting the computation or memory bottlenecks.
In this paper, we propose a new strategy to overcome this challenge. Instead
of trying to process more frames at once like most existing methods, we propose
to process videos in an online fashion and cache "memory" at each iteration.
Through the memory, the model can reference prior context for long-term
modeling, with only a marginal cost. Based on this idea, we build MeMViT, a
Memory-augmented Multiscale Vision Transformer, that has a temporal support 30x
longer than existing models with only 4.5% more compute; traditional methods
need >3,000% more compute to do the same. On a wide range of settings, the
increased temporal support enabled by MeMViT brings large gains in recognition
accuracy consistently. MeMViT obtains state-of-the-art results on the AVA,
EPIC-Kitchens-100 action classification, and action anticipation datasets. Code
and models are available at https://github.com/facebookresearch/memvit.
|
[
{
"version": "v1",
"created": "Thu, 20 Jan 2022 18:59:54 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Nov 2022 19:40:55 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Wu",
"Chao-Yuan",
""
],
[
"Li",
"Yanghao",
""
],
[
"Mangalam",
"Karttikeya",
""
],
[
"Fan",
"Haoqi",
""
],
[
"Xiong",
"Bo",
""
],
[
"Malik",
"Jitendra",
""
],
[
"Feichtenhofer",
"Christoph",
""
]
] |
new_dataset
| 0.998505 |
2204.06676
|
Khushal Sethi
|
Khushal Sethi
|
DRAGON (Differentiable Graph Execution) : A suite of Hardware Simulation
and Optimization tools for Modern AI/Non-AI Workloads
| null | null | null | null |
cs.AR cs.AI cs.ET cs.PF
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce DRAGON, an open-source, fast and explainable hardware simulation
and optimization toolchain that enables hardware architects to simulate
hardware designs, and to optimize hardware designs to efficiently execute
workloads.
The DRAGON toolchain provides the following tools: Hardware Model Generator
(DGen), Hardware Simulator (DSim) and Hardware Optimizer (DOpt).
DSim provides the simulation of running algorithms (represented as data-flow
graphs) on hardware described. DGen describes the hardware in detail, with user
input architectures/technology (represented in a custom description language).
A novel methodology of gradient descent from the simulation allows us optimize
the hardware model (giving the directions for improvements in technology
parameters and design parameters), provided by Dopt.
DRAGON framework (DSim) is much faster than previously avaible works for
simulation, which is possible through performance-first code writing practices,
mathematical formulas for common computing operations to avoid cycle-accurate
simulation steps, efficient algorithms for mapping, and data-structure
representations for hardware state. DRAGON framework (Dopt) generates
performance optimized architectures for both AI and Non-AI Workloads, and
provides technology improvement directions for 100x-1000x better future
computing systems.
|
[
{
"version": "v1",
"created": "Wed, 13 Apr 2022 23:57:12 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Apr 2022 04:50:22 GMT"
},
{
"version": "v3",
"created": "Wed, 4 May 2022 04:23:46 GMT"
},
{
"version": "v4",
"created": "Mon, 16 May 2022 02:08:48 GMT"
},
{
"version": "v5",
"created": "Mon, 30 May 2022 17:47:34 GMT"
},
{
"version": "v6",
"created": "Sat, 3 Sep 2022 21:28:41 GMT"
},
{
"version": "v7",
"created": "Wed, 30 Nov 2022 20:07:07 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Sethi",
"Khushal",
""
]
] |
new_dataset
| 0.97574 |
2204.09069
|
Roberto Bigazzi
|
Roberto Bigazzi, Federico Landi, Silvia Cascianelli, Marcella Cornia,
Lorenzo Baraldi and Rita Cucchiara
|
Embodied Navigation at the Art Gallery
|
Accepted by 21st International Conference on Image Analysis and
Processing (ICIAP 2021)
| null |
10.1007/978-3-031-06427-2_61
| null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Embodied agents, trained to explore and navigate indoor photorealistic
environments, have achieved impressive results on standard datasets and
benchmarks. So far, experiments and evaluations have involved domestic and
working scenes like offices, flats, and houses. In this paper, we build and
release a new 3D space with unique characteristics: the one of a complete art
museum. We name this environment ArtGallery3D (AG3D). Compared with existing 3D
scenes, the collected space is ampler, richer in visual features, and provides
very sparse occupancy information. This feature is challenging for
occupancy-based agents which are usually trained in crowded domestic
environments with plenty of occupancy information. Additionally, we annotate
the coordinates of the main points of interest inside the museum, such as
paintings, statues, and other items. Thanks to this manual process, we deliver
a new benchmark for PointGoal navigation inside this new space. Trajectories in
this dataset are far more complex and lengthy than existing ground-truth paths
for navigation in Gibson and Matterport3D. We carry on extensive experimental
evaluation using our new space for evaluation and prove that existing methods
hardly adapt to this scenario. As such, we believe that the availability of
this 3D model will foster future research and help improve existing solutions.
|
[
{
"version": "v1",
"created": "Tue, 19 Apr 2022 18:00:06 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Bigazzi",
"Roberto",
""
],
[
"Landi",
"Federico",
""
],
[
"Cascianelli",
"Silvia",
""
],
[
"Cornia",
"Marcella",
""
],
[
"Baraldi",
"Lorenzo",
""
],
[
"Cucchiara",
"Rita",
""
]
] |
new_dataset
| 0.963657 |
2205.00030
|
Syed Mohsin Abbas Dr.
|
Syed Mohsin Abbas, Marwan Jalaleddine and Warren J. Gross
|
GRAND for Rayleigh Fading Channels
|
To appear in IEEE Global Communications Conference (GLOBECOM) 2022
Workshops
|
GLOBECOM 2022 Workshops
| null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Guessing Random Additive Noise Decoding (GRAND) is a code-agnostic decoding
technique for short-length and high-rate channel codes. GRAND tries to guess
the channel noise by generating test error patterns (TEPs), and the sequence of
the TEPs is the main difference between different GRAND variants. In this work,
we extend the application of GRAND to multipath frequency non-selective
Rayleigh fading communication channels, and we refer to this GRAND variant as
Fading-GRAND. The proposed Fading-GRAND adapts its TEP generation to the fading
conditions of the underlying communication channel, outperforming traditional
channel code decoders in scenarios with $L$ spatial diversity branches as well
as scenarios with no diversity. Numerical simulation results show that the
Fading-GRAND outperforms the traditional Berlekamp-Massey (B-M) decoder for
decoding BCH code $(127,106)$ and BCH code $(127,113)$ by $\mathbf{0.5\sim6.5}$
dB at a target FER of $10^{-7}$. Similarly, Fading-GRAND outperforms GRANDAB,
the hard-input variation of GRAND, by $0.2\sim8$ dB at a target FER of
$10^{-7}$ with CRC $(128,104)$ code and RLC $(128,104)$. Furthermore the
average complexity of Fading-GRAND, at $\frac{E_b}{N_0}$ corresponding to
target FER of $10^{-7}$, is $\frac{1}{2}\times\sim \frac{1}{46}\times$ the
complexity of GRANDAB.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 18:22:06 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Dec 2022 03:50:37 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Abbas",
"Syed Mohsin",
""
],
[
"Jalaleddine",
"Marwan",
""
],
[
"Gross",
"Warren J.",
""
]
] |
new_dataset
| 0.967278 |
2208.13422
|
Hao Xu
|
Hao Xu, Bo Li and Fei Zhong
|
Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex
Fire Scenarios
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fire-detection technology is of great importance for successful
fire-prevention measures. Image-based fire detection is one effective method.
At present, object-detection algorithms are deficient in performing detection
speed and accuracy tasks when they are applied in complex fire scenarios. In
this study, a lightweight fire-detection algorithm, Light-YOLOv5 (You Only Look
Once version five), is presented. First, a separable vision transformer
(SepViT) block is used to replace several C3 modules in the final layer of a
backbone network to enhance both the contact of the backbone network to global
in-formation and the extraction of flame and smoke features; second, a light
bidirectional feature pyramid network (Light-BiFPN) is designed to lighten the
model while improving the feature extraction and balancing speed and accuracy
features during a fire-detection procedure; third, a global attention mechanism
(GAM) is fused into the network to cause the model to focus more on the global
dimensional features and further improve the detection accuracy of the model;
and finally, the Mish activation function and SIoU loss are utilized to
simultaneously increase the convergence speed and enhance the accuracy. The
experimental results show that compared to the original algorithm, the mean
average accuracy (mAP) of Light-YOLOv5 increases by 3.3%, the number of
parameters decreases by 27.1%, and the floating point operations (FLOPs)
decrease by 19.1%. The detection speed reaches 91.1 FPS, which can detect
targets in complex fire scenarios in real time.
|
[
{
"version": "v1",
"created": "Mon, 29 Aug 2022 08:36:04 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Oct 2022 08:38:40 GMT"
},
{
"version": "v3",
"created": "Thu, 1 Dec 2022 16:24:31 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Xu",
"Hao",
""
],
[
"Li",
"Bo",
""
],
[
"Zhong",
"Fei",
""
]
] |
new_dataset
| 0.997972 |
2210.12985
|
Christian Khairallah (Cayralat)
|
Shahd Dibas, Christian Khairallah, Nizar Habash, Omar Fayez Sadi,
Tariq Sairafy, Karmel Sarabta and Abrar Ardah
|
Maknuune: A Large Open Palestinian Arabic Lexicon
|
Fixed errors in the Total row of Table 4 on page 7
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present Maknuune, a large open lexicon for the Palestinian Arabic dialect.
Maknuune has over 36K entries from 17K lemmas, and 3.7K roots. All entries
include diacritized Arabic orthography, phonological transcription and English
glosses. Some entries are enriched with additional information such as broken
plurals and templatic feminine forms, associated phrases and collocations,
Standard Arabic glosses, and examples or notes on grammar, usage, or location
of collected entry.
|
[
{
"version": "v1",
"created": "Mon, 24 Oct 2022 07:19:03 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Dec 2022 14:27:38 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Dibas",
"Shahd",
""
],
[
"Khairallah",
"Christian",
""
],
[
"Habash",
"Nizar",
""
],
[
"Sadi",
"Omar Fayez",
""
],
[
"Sairafy",
"Tariq",
""
],
[
"Sarabta",
"Karmel",
""
],
[
"Ardah",
"Abrar",
""
]
] |
new_dataset
| 0.999821 |
2211.10716
|
Fanze Kong
|
Fanze Kong, Xiyuan Liu, Benxu Tang, Jiarong Lin, Yunfan Ren, Yixi Cai,
Fangcheng Zhu, Nan Chen, Fu Zhang
|
MARSIM: A light-weight point-realistic simulator for LiDAR-based UAVs
|
8 pages, 13 figures
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The emergence of low-cost, small form factor and light-weight solid-state
LiDAR sensors have brought new opportunities for autonomous unmanned aerial
vehicles (UAVs) by advancing navigation safety and computation efficiency. Yet
the successful developments of LiDAR-based UAVs must rely on extensive
simulations. Existing simulators can hardly perform simulations of real-world
environments due to the requirements of dense mesh maps that are difficult to
obtain. In this paper, we develop a point-realistic simulator of real-world
scenes for LiDAR-based UAVs. The key idea is the underlying point rendering
method, where we construct a depth image directly from the point cloud map and
interpolate it to obtain realistic LiDAR point measurements. Our developed
simulator is able to run on a light-weight computing platform and supports the
simulation of LiDARs with different resolution and scanning patterns, dynamic
obstacles, and multi-UAV systems. Developed in the ROS framework, the simulator
can easily communicate with other key modules of an autonomous robot, such as
perception, state estimation, planning, and control. Finally, the simulator
provides 10 high-resolution point cloud maps of various real-world
environments, including forests of different densities, historic building,
office, parking garage, and various complex indoor environments. These
realistic maps provide diverse testing scenarios for an autonomous UAV.
Evaluation results show that the developed simulator achieves superior
performance in terms of time and memory consumption against Gazebo and that the
simulated UAV flights highly match the actual one in real-world environments.
We believe such a point-realistic and light-weight simulator is crucial to
bridge the gap between UAV simulation and experiments and will significantly
facilitate the research of LiDAR-based autonomous UAVs in the future.
|
[
{
"version": "v1",
"created": "Sat, 19 Nov 2022 15:08:44 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Dec 2022 13:41:30 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Kong",
"Fanze",
""
],
[
"Liu",
"Xiyuan",
""
],
[
"Tang",
"Benxu",
""
],
[
"Lin",
"Jiarong",
""
],
[
"Ren",
"Yunfan",
""
],
[
"Cai",
"Yixi",
""
],
[
"Zhu",
"Fangcheng",
""
],
[
"Chen",
"Nan",
""
],
[
"Zhang",
"Fu",
""
]
] |
new_dataset
| 0.986733 |
2212.00003
|
Xinquan Wen
|
Xinquan Wen, Yiying Wu
|
Slowing Plants, Slowing Home
| null | null |
10.1145/3547522.3547691
| null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
The Anthropocene is causing a global crisis in recent decades. Facing this
challenge, increasing attempts are being made to explore the
more-than-human-centred perspective in HCI and design. Our research sets out to
explore the ways of experiencing and interacting with plants with a case study
on the slowness of plants. Utilising existing time-lapse technology, we
investigate the role of IoT technologies in associating biological slowness
with the networked technological environment of the home. In the experiment, we
chose the humidity level of the environment as the variable to synchronise the
movement of smart curtains and plants. We propose a relationship-centred
strategy that uses an inclusive feature of a microclimate, like humidity,
instead of the plant itself, for human-plant interaction. Furthermore, it
indicates a 'plant-decentred' perspective to spark critical reflection on the
taken-for-granted perception of isolating a person or a plant as an individual
entity.
|
[
{
"version": "v1",
"created": "Tue, 4 Oct 2022 07:16:36 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Wen",
"Xinquan",
""
],
[
"Wu",
"Yiying",
""
]
] |
new_dataset
| 0.995233 |
2212.00013
|
Vivien Van Veldhuizen
|
Vivien van Veldhuizen
|
Autotuning PID control using Actor-Critic Deep Reinforcement Learning
| null | null | null | null |
cs.LG cs.AI cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
This work is an exploratory research concerned with determining in what way
reinforcement learning can be used to predict optimal PID parameters for a
robot designed for apple harvest. To study this, an algorithm called Advantage
Actor Critic (A2C) is implemented on a simulated robot arm. The simulation
primarily relies on the ROS framework. Experiments for tuning one actuator at a
time and two actuators a a time are run, which both show that the model is able
to predict PID gains that perform better than the set baseline. In addition, it
is studied if the model is able to predict PID parameters based on where an
apple is located. Initial tests show that the model is indeed able to adapt its
predictions to apple locations, making it an adaptive controller.
|
[
{
"version": "v1",
"created": "Tue, 29 Nov 2022 11:15:50 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"van Veldhuizen",
"Vivien",
""
]
] |
new_dataset
| 0.979884 |
2212.00069
|
Tushar Agarwal
|
Tushar Agarwal, Nithin Sugavanam, and Emre Ertin
|
MrSARP: A Hierarchical Deep Generative Prior for SAR Image
Super-resolution
|
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.LG eess.SP
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Generative models learned from training using deep learning methods can be
used as priors in inverse under-determined inverse problems, including imaging
from sparse set of measurements. In this paper, we present a novel hierarchical
deep-generative model MrSARP for SAR imagery that can synthesize SAR images of
a target at different resolutions jointly. MrSARP is trained in conjunction
with a critic that scores multi resolution images jointly to decide if they are
realistic images of a target at different resolutions. We show how this deep
generative model can be used to retrieve the high spatial resolution image from
low resolution images of the same target. The cost function of the generator is
modified to improve its capability to retrieve the input parameters for a given
set of resolution images. We evaluate the model's performance using the three
standard error metrics used for evaluating super-resolution performance on
simulated data and compare it to upsampling and sparsity based image sharpening
approaches.
|
[
{
"version": "v1",
"created": "Wed, 30 Nov 2022 19:12:21 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Agarwal",
"Tushar",
""
],
[
"Sugavanam",
"Nithin",
""
],
[
"Ertin",
"Emre",
""
]
] |
new_dataset
| 0.957848 |
2212.00089
|
Yixin Xu
|
Yixin Xu, Zijian Zhao, Yi Xiao, Tongguang Yu, Halid Mulaosmanovic,
Dominik Kleimaier, Stefan Duenkel, Sven Beyer, Xiao Gong, Rajiv Joshi, X.
Sharon Hu, Shixian Wen, Amanda Sofie Rios, Kiran Lekkala, Laurent Itti, Eric
Homan, Sumitha George, Vijaykrishnan Narayanan, Kai Ni
|
Ferroelectric FET based Context-Switching FPGA Enabling Dynamic
Reconfiguration for Adaptive Deep Learning Machines
|
54 pages, 15 figures
| null | null | null |
cs.AR cs.ET
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Field Programmable Gate Array (FPGA) is widely used in acceleration of deep
learning applications because of its reconfigurability, flexibility, and fast
time-to-market. However, conventional FPGA suffers from the tradeoff between
chip area and reconfiguration latency, making efficient FPGA accelerations that
require switching between multiple configurations still elusive. In this paper,
we perform technology-circuit-architecture co-design to break this tradeoff
with no additional area cost and lower power consumption compared with
conventional designs while providing dynamic reconfiguration, which can hide
the reconfiguration time behind the execution time. Leveraging the intrinsic
transistor structure and non-volatility of ferroelectric FET (FeFET), compact
FPGA primitives are proposed and experimentally verified, including 1FeFET
look-up table (LUT) cell, 1FeFET routing cell for connection blocks (CBs) and
switch boxes (SBs). To support dynamic reconfiguration, two local copies of
primitives are placed in parallel, which enables loading of arbitrary
configuration without interrupting the active configuration execution. A
comprehensive evaluation shows that compared with the SRAM-based FPGA, our
dynamic reconfiguration design shows 63.0%/71.1% reduction in LUT/CB area and
82.7%/53.6% reduction in CB/SB power consumption with minimal penalty in the
critical path delay (9.6%). We further implement a Super-Sub network model to
show the benefit from the context-switching capability of our design. We also
evaluate the timing performance of our design over conventional FPGA in various
application scenarios. In one scenario that users switch between two preloaded
configurations, our design yields significant time saving by 78.7% on average.
In the other scenario of implementing multiple configurations with dynamic
reconfiguration, our design offers time saving of 20.3% on average.
|
[
{
"version": "v1",
"created": "Wed, 30 Nov 2022 20:00:20 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Xu",
"Yixin",
""
],
[
"Zhao",
"Zijian",
""
],
[
"Xiao",
"Yi",
""
],
[
"Yu",
"Tongguang",
""
],
[
"Mulaosmanovic",
"Halid",
""
],
[
"Kleimaier",
"Dominik",
""
],
[
"Duenkel",
"Stefan",
""
],
[
"Beyer",
"Sven",
""
],
[
"Gong",
"Xiao",
""
],
[
"Joshi",
"Rajiv",
""
],
[
"Hu",
"X. Sharon",
""
],
[
"Wen",
"Shixian",
""
],
[
"Rios",
"Amanda Sofie",
""
],
[
"Lekkala",
"Kiran",
""
],
[
"Itti",
"Laurent",
""
],
[
"Homan",
"Eric",
""
],
[
"George",
"Sumitha",
""
],
[
"Narayanan",
"Vijaykrishnan",
""
],
[
"Ni",
"Kai",
""
]
] |
new_dataset
| 0.998174 |
2212.00227
|
Maojun Zhang
|
Maojun Zhang, Yang Li, Zezhong Zhang, Guangxu Zhu, Caijun Zhong
|
Wireless Image Transmission with Semantic and Security Awareness
|
Submitted to IEEE WCL for possible publication
| null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Semantic communication is an increasingly popular framework for wireless
image transmission due to its high communication efficiency. With the aid of
the joint-source-and-channel (JSC) encoder implemented by neural network,
semantic communication directly maps original images into symbol sequences
containing semantic information. Compared with the traditional separate source
and channel coding design used in bitlevel communication systems, semantic
communication systems are known to be more efficient and accurate especially in
the low signal-to-the-noise ratio (SNR) regime. This thus prompts an critical
while yet to be tackled issue of security in semantic communication: it makes
the eavesdropper more easier to crack the semantic information as it can be
decoded even in a quite noisy channel. In this letter, we develop a semantic
communication framework that accounts for both semantic meaning decoding
efficiency and its risk of privacy leakage. To achieve this, targeting wireless
image transmission, we on the one hand propose an JSC autoencoder featuring
residual for efficient semantic meaning extraction and transmission, and on the
other hand, propose a data-driven scheme that balances the efficiency-privacy
tradeoff. Extensive experimental results are provided to show the effectiveness
and robustness of the proposed scheme.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 02:22:08 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Zhang",
"Maojun",
""
],
[
"Li",
"Yang",
""
],
[
"Zhang",
"Zezhong",
""
],
[
"Zhu",
"Guangxu",
""
],
[
"Zhong",
"Caijun",
""
]
] |
new_dataset
| 0.982062 |
2212.00228
|
N. Benjamin Erichson
|
N. Benjamin Erichson and Soon Hoe Lim and Michael W. Mahoney
|
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback
| null | null | null | null |
cs.LG cs.NE stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce a novel gated recurrent unit (GRU) with a weighted time-delay
feedback mechanism in order to improve the modeling of long-term dependencies
in sequential data. This model is a discretized version of a continuous-time
formulation of a recurrent unit, where the dynamics are governed by delay
differential equations (DDEs). By considering a suitable time-discretization
scheme, we propose $\tau$-GRU, a discrete-time gated recurrent unit with delay.
We prove the existence and uniqueness of solutions for the continuous-time
model, and we demonstrate that the proposed feedback mechanism can help improve
the modeling of long-term dependencies. Our empirical results show that
$\tau$-GRU can converge faster and generalize better than state-of-the-art
recurrent units and gated recurrent architectures on a range of tasks,
including time-series classification, human activity recognition, and speech
recognition.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 02:26:34 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Erichson",
"N. Benjamin",
""
],
[
"Lim",
"Soon Hoe",
""
],
[
"Mahoney",
"Michael W.",
""
]
] |
new_dataset
| 0.981518 |
2212.00244
|
Xidong Peng
|
Xidong Peng, Xinge Zhu, Yuexin Ma
|
CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection
|
Accepted by AAAI 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Domain adaptation for Cross-LiDAR 3D detection is challenging due to the
large gap on the raw data representation with disparate point densities and
point arrangements. By exploring domain-invariant 3D geometric characteristics
and motion patterns, we present an unsupervised domain adaptation method that
overcomes above difficulties. First, we propose the Spatial Geometry Alignment
module to extract similar 3D shape geometric features of the same object class
to align two domains, while eliminating the effect of distinct point
distributions. Second, we present Temporal Motion Alignment module to utilize
motion features in sequential frames of data to match two domains. Prototypes
generated from two modules are incorporated into the pseudo-label reweighting
procedure and contribute to our effective self-training framework for the
target domain. Extensive experiments show that our method achieves
state-of-the-art performance on cross-device datasets, especially for the
datasets with large gaps captured by mechanical scanning LiDARs and solid-state
LiDARs in various scenes. Project homepage is at
https://github.com/4DVLab/CL3D.git
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 03:22:55 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Peng",
"Xidong",
""
],
[
"Zhu",
"Xinge",
""
],
[
"Ma",
"Yuexin",
""
]
] |
new_dataset
| 0.982394 |
2212.00265
|
Nicolas Gu\'enon des Mesnards
|
Konstantine Arkoudas, Nicolas Guenon des Mesnards, Melanie Rubino,
Sandesh Swamy, Saarthak Khanna, Weiqi Sun, Khan Haidar
|
PIZZA: A new benchmark for complex end-to-end task-oriented parsing
|
Accepted for publication at AMLC 2022
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Much recent work in task-oriented parsing has focused on finding a middle
ground between flat slots and intents, which are inexpressive but easy to
annotate, and powerful representations such as the lambda calculus, which are
expressive but costly to annotate. This paper continues the exploration of
task-oriented parsing by introducing a new dataset for parsing pizza and drink
orders, whose semantics cannot be captured by flat slots and intents. We
perform an extensive evaluation of deep-learning techniques for task-oriented
parsing on this dataset, including different flavors of seq2seq systems and
RNNGs. The dataset comes in two main versions, one in a recently introduced
utterance-level hierarchical notation that we call TOP, and one whose targets
are executable representations (EXR). We demonstrate empirically that training
the parser to directly generate EXR notation not only solves the problem of
entity resolution in one fell swoop and overcomes a number of expressive
limitations of TOP notation, but also results in significantly greater parsing
accuracy.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 04:20:07 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Arkoudas",
"Konstantine",
""
],
[
"Mesnards",
"Nicolas Guenon des",
""
],
[
"Rubino",
"Melanie",
""
],
[
"Swamy",
"Sandesh",
""
],
[
"Khanna",
"Saarthak",
""
],
[
"Sun",
"Weiqi",
""
],
[
"Haidar",
"Khan",
""
]
] |
new_dataset
| 0.998292 |
2212.00280
|
Jialian Wu
|
Jialian Wu, Jianfeng Wang, Zhengyuan Yang, Zhe Gan, Zicheng Liu,
Junsong Yuan, Lijuan Wang
|
GRiT: A Generative Region-to-text Transformer for Object Understanding
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a Generative RegIon-to-Text transformer, GRiT, for object
understanding. The spirit of GRiT is to formulate object understanding as
<region, text> pairs, where region locates objects and text describes objects.
For example, the text in object detection denotes class names while that in
dense captioning refers to descriptive sentences. Specifically, GRiT consists
of a visual encoder to extract image features, a foreground object extractor to
localize objects, and a text decoder to generate open-set object descriptions.
With the same model architecture, GRiT can understand objects via not only
simple nouns, but also rich descriptive sentences including object attributes
or actions. Experimentally, we apply GRiT to object detection and dense
captioning tasks. GRiT achieves 60.4 AP on COCO 2017 test-dev for object
detection and 15.5 mAP on Visual Genome for dense captioning. Code is available
at https://github.com/JialianW/GRiT
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 04:59:44 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Wu",
"Jialian",
""
],
[
"Wang",
"Jianfeng",
""
],
[
"Yang",
"Zhengyuan",
""
],
[
"Gan",
"Zhe",
""
],
[
"Liu",
"Zicheng",
""
],
[
"Yuan",
"Junsong",
""
],
[
"Wang",
"Lijuan",
""
]
] |
new_dataset
| 0.99965 |
2212.00305
|
Trung Nghia Le
|
Tuan-Luc Huynh, Khoi-Nguyen Nguyen-Ngoc, Chi-Bien Chu, Minh-Triet
Tran, Trung-Nghia Le
|
Multilingual Communication System with Deaf Individuals Utilizing
Natural and Visual Languages
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
According to the World Federation of the Deaf, more than two hundred sign
languages exist. Therefore, it is challenging to understand deaf individuals,
even proficient sign language users, resulting in a barrier between the deaf
community and the rest of society. To bridge this language barrier, we propose
a novel multilingual communication system, namely MUGCAT, to improve the
communication efficiency of sign language users. By converting recognized
specific hand gestures into expressive pictures, which is universal usage and
language independence, our MUGCAT system significantly helps deaf people convey
their thoughts. To overcome the limitation of sign language usage, which is
mostly impossible to translate into complete sentences for ordinary people, we
propose to reconstruct meaningful sentences from the incomplete translation of
sign language. We also measure the semantic similarity of generated sentences
with fragmented recognized hand gestures to keep the original meaning.
Experimental results show that the proposed system can work in a real-time
manner and synthesize exquisite stunning illustrations and meaningful sentences
from a few hand gestures of sign language. This proves that our MUGCAT has
promising potential in assisting deaf communication.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 06:43:44 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Huynh",
"Tuan-Luc",
""
],
[
"Nguyen-Ngoc",
"Khoi-Nguyen",
""
],
[
"Chu",
"Chi-Bien",
""
],
[
"Tran",
"Minh-Triet",
""
],
[
"Le",
"Trung-Nghia",
""
]
] |
new_dataset
| 0.95376 |
2212.00339
|
Zihao He
|
Kai Chen, Zihao He, Rong-Ching Chang, Jonathan May, Kristina Lerman
|
Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit
| null | null | null | null |
cs.CL cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Emotions play an important role in interpersonal interactions and social
conflict, yet their function in the development of controversy and disagreement
in online conversations has not been explored. To address this gap, we study
controversy on Reddit, a popular network of online discussion forums. We
collect discussions from a wide variety of topical forums and use emotion
detection to recognize a range of emotions from text, including anger, fear,
joy, admiration, etc. Our study has three main findings. First, controversial
comments express more anger and less admiration, joy and optimism than
non-controversial comments. Second, controversial comments affect emotions of
downstream comments in a discussion, usually resulting in long-term increase in
anger and a decrease in positive emotions, although the magnitude and direction
of emotional change depends on the forum. Finally, we show that emotions help
better predict which comments will become controversial. Understanding
emotional dynamics of online discussions can help communities to better manage
conversations.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 07:57:54 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Chen",
"Kai",
""
],
[
"He",
"Zihao",
""
],
[
"Chang",
"Rong-Ching",
""
],
[
"May",
"Jonathan",
""
],
[
"Lerman",
"Kristina",
""
]
] |
new_dataset
| 0.995699 |
2212.00342
|
Balaji Ganesan
|
Sukriti Jaitly, Deepa Mariam George, Balaji Ganesan, Muhammad Ameen,
Srinivas Pusapati
|
xEM: Explainable Entity Matching in Customer 360
|
4 pages, 5 figures. CODS-COMAD 2023 Demo
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Entity matching in Customer 360 is the task of determining if multiple
records represent the same real world entity. Entities are typically people,
organizations, locations, and events represented as attributed nodes in a
graph, though they can also be represented as records in relational data. While
probabilistic matching engines and artificial neural network models exist for
this task, explaining entity matching has received less attention. In this
demo, we present our Explainable Entity Matching (xEM) system and discuss the
different AI/ML considerations that went into its implementation.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 08:01:01 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Jaitly",
"Sukriti",
""
],
[
"George",
"Deepa Mariam",
""
],
[
"Ganesan",
"Balaji",
""
],
[
"Ameen",
"Muhammad",
""
],
[
"Pusapati",
"Srinivas",
""
]
] |
new_dataset
| 0.980059 |
2212.00486
|
Jind\v{r}ich Libovick\'y
|
Martin Popel, Jind\v{r}ich Libovick\'y, Jind\v{r}ich Helcl
|
CUNI Systems for the WMT22 Czech-Ukrainian Translation Task
|
6 pages; System description paper at WMT22
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We present Charles University submissions to the WMT22 General Translation
Shared Task on Czech-Ukrainian and Ukrainian-Czech machine translation. We
present two constrained submissions based on block back-translation and tagged
back-translation and experiment with rule-based romanization of Ukrainian. Our
results show that the romanization only has a minor effect on the translation
quality. Further, we describe Charles Translator, a system that was developed
in March 2022 as a response to the migration from Ukraine to the Czech
Republic. Compared to our constrained systems, it did not use the romanization
and used some proprietary data sources.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 13:25:10 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Popel",
"Martin",
""
],
[
"Libovický",
"Jindřich",
""
],
[
"Helcl",
"Jindřich",
""
]
] |
new_dataset
| 0.998908 |
2212.00500
|
Xiaohuan Zhou
|
Xiaohuan Zhou, Jiaming Wang, Zeyu Cui, Shiliang Zhang, Zhijie Yan,
Jingren Zhou, Chang Zhou
|
MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech
Recognition
|
Submitted to ICASSP 2023
| null | null | null |
cs.MM cs.CL cs.LG cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a novel multi-modal multi-task encoder-decoder
pre-training framework (MMSpeech) for Mandarin automatic speech recognition
(ASR), which employs both unlabeled speech and text data. The main difficulty
in speech-text joint pre-training comes from the significant difference between
speech and text modalities, especially for Mandarin speech and text. Unlike
English and other languages with an alphabetic writing system, Mandarin uses an
ideographic writing system where character and sound are not tightly mapped to
one another. Therefore, we propose to introduce the phoneme modality into
pre-training, which can help capture modality-invariant information between
Mandarin speech and text. Specifically, we employ a multi-task learning
framework including five self-supervised and supervised tasks with speech and
text data. For end-to-end pre-training, we introduce self-supervised
speech-to-pseudo-codes (S2C) and phoneme-to-text (P2T) tasks utilizing
unlabeled speech and text data, where speech-pseudo-codes pairs and
phoneme-text pairs are a supplement to the supervised speech-text pairs. To
train the encoder to learn better speech representation, we introduce
self-supervised masked speech prediction (MSP) and supervised phoneme
prediction (PP) tasks to learn to map speech into phonemes. Besides, we
directly add the downstream supervised speech-to-text (S2T) task into the
pre-training process, which can further improve the pre-training performance
and achieve better recognition results even without fine-tuning. Experiments on
AISHELL-1 show that our proposed method achieves state-of-the-art performance,
with a more than 40% relative improvement compared with other pre-training
methods.
|
[
{
"version": "v1",
"created": "Tue, 29 Nov 2022 13:16:09 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Zhou",
"Xiaohuan",
""
],
[
"Wang",
"Jiaming",
""
],
[
"Cui",
"Zeyu",
""
],
[
"Zhang",
"Shiliang",
""
],
[
"Yan",
"Zhijie",
""
],
[
"Zhou",
"Jingren",
""
],
[
"Zhou",
"Chang",
""
]
] |
new_dataset
| 0.976329 |
2212.00586
|
Jiaan Wang
|
Shaohui Zheng, Zhixu Li, Jiaan Wang, Jianfeng Qu, An Liu, Lei Zhao,
Zhigang Chen
|
Long-Document Cross-Lingual Summarization
|
Accepted by WSDM 2023
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cross-Lingual Summarization (CLS) aims at generating summaries in one
language for the given documents in another language. CLS has attracted wide
research attention due to its practical significance in the multi-lingual
world. Though great contributions have been made, existing CLS works typically
focus on short documents, such as news articles, short dialogues and guides.
Different from these short texts, long documents such as academic articles and
business reports usually discuss complicated subjects and consist of thousands
of words, making them non-trivial to process and summarize. To promote CLS
research on long documents, we construct Perseus, the first long-document CLS
dataset which collects about 94K Chinese scientific documents paired with
English summaries. The average length of documents in Perseus is more than two
thousand tokens. As a preliminary study on long-document CLS, we build and
evaluate various CLS baselines, including pipeline and end-to-end methods.
Experimental results on Perseus show the superiority of the end-to-end
baseline, outperforming the strong pipeline models equipped with sophisticated
machine translation systems. Furthermore, to provide a deeper understanding, we
manually analyze the model outputs and discuss specific challenges faced by
current approaches. We hope that our work could benchmark long-document CLS and
benefit future studies.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 15:24:16 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Zheng",
"Shaohui",
""
],
[
"Li",
"Zhixu",
""
],
[
"Wang",
"Jiaan",
""
],
[
"Qu",
"Jianfeng",
""
],
[
"Liu",
"An",
""
],
[
"Zhao",
"Lei",
""
],
[
"Chen",
"Zhigang",
""
]
] |
new_dataset
| 0.995074 |
2212.00638
|
Sachin Goyal
|
Sachin Goyal, Ananya Kumar, Sankalp Garg, Zico Kolter, and Aditi
Raghunathan
|
Finetune like you pretrain: Improved finetuning of zero-shot vision
models
|
20 Pages, 7 Tables, 5 Figures
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Finetuning image-text models such as CLIP achieves state-of-the-art
accuracies on a variety of benchmarks. However, recent works like WiseFT
(Wortsman et al., 2021) and LP-FT (Kumar et al., 2022) have shown that even
subtle differences in the finetuning process can lead to surprisingly large
differences in the final performance, both for in-distribution (ID) and
out-of-distribution (OOD) data. In this work, we show that a natural and simple
approach of mimicking contrastive pretraining consistently outperforms
alternative finetuning approaches. Specifically, we cast downstream class
labels as text prompts and continue optimizing the contrastive loss between
image embeddings and class-descriptive prompt embeddings (contrastive
finetuning).
Our method consistently outperforms baselines across 7 distribution shifts, 6
transfer learning, and 3 few-shot learning benchmarks. On WILDS-iWILDCam, our
proposed approach FLYP outperforms the top of the leaderboard by $2.3\%$ ID and
$2.7\%$ OOD, giving the highest reported accuracy. Averaged across 7 OOD
datasets (2 WILDS and 5 ImageNet associated shifts), FLYP gives gains of
$4.2\%$ OOD over standard finetuning and outperforms the current state of the
art (LP-FT) by more than $1\%$ both ID and OOD. Similarly, on 3 few-shot
learning benchmarks, our approach gives gains up to $4.6\%$ over standard
finetuning and $4.4\%$ over the state of the art. In total, these benchmarks
establish contrastive finetuning as a simple, intuitive, and state-of-the-art
approach for supervised finetuning of image-text models like CLIP. Code is
available at https://github.com/locuslab/FLYP.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 16:37:46 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Goyal",
"Sachin",
""
],
[
"Kumar",
"Ananya",
""
],
[
"Garg",
"Sankalp",
""
],
[
"Kolter",
"Zico",
""
],
[
"Raghunathan",
"Aditi",
""
]
] |
new_dataset
| 0.985268 |
2212.00689
|
Babak Jalalzadeh Fard
|
B. Jalalzadeh Fard, S. A. Hasan, J. E. Bell
|
CliMedBERT: A Pre-trained Language Model for Climate and Health-related
Text
|
5 pages, 1 figure. Presented at Tackling Climate Change with Machine
Learning: workshop at NeurIPS 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Climate change is threatening human health in unprecedented orders and many
ways. These threats are expected to grow unless effective and evidence-based
policies are developed and acted upon to minimize or eliminate them. Attaining
such a task requires the highest degree of the flow of knowledge from science
into policy. The multidisciplinary, location-specific, and vastness of
published science makes it challenging to keep track of novel work in this
area, as well as making the traditional knowledge synthesis methods inefficient
in infusing science into policy. To this end, we consider developing multiple
domain-specific language models (LMs) with different variations from Climate-
and Health-related information, which can serve as a foundational step toward
capturing available knowledge to enable solving different tasks, such as
detecting similarities between climate- and health-related concepts,
fact-checking, relation extraction, evidence of health effects to policy text
generation, and more. To our knowledge, this is the first work that proposes
developing multiple domain-specific language models for the considered domains.
We will make the developed models, resources, and codebase available for the
researchers.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 17:44:09 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Fard",
"B. Jalalzadeh",
""
],
[
"Hasan",
"S. A.",
""
],
[
"Bell",
"J. E.",
""
]
] |
new_dataset
| 0.998123 |
2212.00760
|
Kritika Garg
|
Kritika Garg, Himarsha R. Jayanetti, Sawood Alam, Michele C. Weigle,
Michael L. Nelson
|
Caching HTTP 404 Responses Eliminates Unnecessary Archival Replay
Requests
| null | null | null | null |
cs.NI cs.DL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Upon replay, JavaScript on archived web pages can generate recurring HTTP
requests that lead to unnecessary traffic to the web archive. In one example,
an archived page averaged more than 1000 requests per minute. These requests
are not visible to the user, so if a user leaves such an archived page open in
a browser tab, they would be unaware that their browser is continuing to
generate traffic to the web archive. We found that web pages that require
regular updates (e.g., radio playlists, updates for sports scores, image
carousels) are more likely to make such recurring requests. If the resources
requested by the web page are not archived, some web archives may attempt to
patch the archive by requesting the resources from the live web. If the
requested resources are unavailable on the live web, the resources cannot be
archived, and the responses remain HTTP 404. Some archived pages continue to
poll the server as frequently as they did on the live web, while some pages
poll the server even more frequently if their requests return HTTP 404
responses, creating a high amount of unnecessary traffic. On a large scale,
such web pages are effectively a denial of service attack on the web archive.
Significant computational, network and storage resources are required for web
archives to archive and then successfully replay pages as they were on the live
web, and these resources should not be spent on unnecessary HTTP traffic. Our
proposed solution is to optimize archival replay using Cache-Control HTTP
response headers. We implemented this approach in a test environment and cached
HTTP 404 responses that prevented the browser's requests from reaching the web
archive server.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 18:50:02 GMT"
}
] | 2022-12-02T00:00:00 |
[
[
"Garg",
"Kritika",
""
],
[
"Jayanetti",
"Himarsha R.",
""
],
[
"Alam",
"Sawood",
""
],
[
"Weigle",
"Michele C.",
""
],
[
"Nelson",
"Michael L.",
""
]
] |
new_dataset
| 0.997426 |
2106.08684
|
Niccol\`o Di Marco
|
Niccol\`o Di Marco, Matteo Cinelli, Walter Quattrociocchi
|
Reliability of Content and Echo Chambers on YouTube during the COVID-19
Debate
| null | null |
10.36190/2022.64
| null |
cs.CY physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The spread of inaccurate and misleading information may alter behaviours and
complicate crisis management, especially during an emergency like the COVID-19
pandemic. This paper aims to investigate information diffusion during the
COVID-19 pandemic by evaluating news consumption on YouTube. First, we analyse
more than 2 million users' engagement with 13,000 videos released by 68 YouTube
channels, labelled with a political bias and fact-checking index. Then, we
study the relationship between each user\~Os political preference and their
consumption of questionable (i.e., poorly fact-checked) and reliable
information. Our results, quantified using measures from information theory,
provide evidence for the existence of echo chambers across two dimensions
represented by political bias and the trustworthiness of information channels.
We observe that the echo chamber structure cannot be reproduced after properly
randomising the users' interaction patterns. Moreover, we observe a relation
between the political bias of users and their tendency to consume highly
questionable news.
|
[
{
"version": "v1",
"created": "Wed, 16 Jun 2021 10:44:29 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Nov 2022 21:10:12 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Di Marco",
"Niccolò",
""
],
[
"Cinelli",
"Matteo",
""
],
[
"Quattrociocchi",
"Walter",
""
]
] |
new_dataset
| 0.969242 |
2202.05728
|
Ahmad Hammoudeh
|
Ahmad Hammoudeh, Bastien Vanderplaetse, St\'ephane Dupont
|
Deep soccer captioning with transformer: dataset, semantics-related
losses, and multi-level evaluation
| null | null |
10.1016/j.procs.2022.10.125
| null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
This work aims at generating captions for soccer videos using deep learning.
In this context, this paper introduces a dataset, model, and triple-level
evaluation. The dataset consists of 22k caption-clip pairs and three visual
features (images, optical flow, inpainting) for ~500 hours of \emph{SoccerNet}
videos. The model is divided into three parts: a transformer learns language,
ConvNets learn vision, and a fusion of linguistic and visual features generates
captions. The paper suggests evaluating generated captions at three levels:
syntax (the commonly used evaluation metrics such as BLEU-score and CIDEr),
meaning (the quality of descriptions for a domain expert), and corpus (the
diversity of generated captions). The paper shows that the diversity of
generated captions has improved (from 0.07 reaching 0.18) with
semantics-related losses that prioritize selected words. Semantics-related
losses and the utilization of more visual features (optical flow, inpainting)
improved the normalized captioning score by 28\%. The web page of this work:
https://sites.google.com/view/soccercaptioning}{https://sites.google.com/view/soccercaptioning
|
[
{
"version": "v1",
"created": "Fri, 11 Feb 2022 16:04:03 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Nov 2022 12:26:31 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Hammoudeh",
"Ahmad",
""
],
[
"Vanderplaetse",
"Bastien",
""
],
[
"Dupont",
"Stéphane",
""
]
] |
new_dataset
| 0.993936 |
2202.11984
|
Jan Luxemburk
|
Jan Luxemburk, Tom\'a\v{s} \v{C}ejka
|
Fine-grained TLS services classification with reject option
| null |
Computer Networks, vol. 220, p. 109467, Jan. 2023
|
10.1016/j.comnet.2022.109467
| null |
cs.LG cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The recent success and proliferation of machine learning and deep learning
have provided powerful tools, which are also utilized for encrypted traffic
analysis, classification, and threat detection in computer networks. These
methods, neural networks in particular, are often complex and require a huge
corpus of training data. Therefore, this paper focuses on collecting a large
up-to-date dataset with almost 200 fine-grained service labels and 140 million
network flows extended with packet-level metadata. The number of flows is three
orders of magnitude higher than in other existing public labeled datasets of
encrypted traffic. The number of service labels, which is important to make the
problem hard and realistic, is four times higher than in the public dataset
with the most class labels. The published dataset is intended as a benchmark
for identifying services in encrypted traffic. Service identification can be
further extended with the task of "rejecting" unknown services, i.e., the
traffic not seen during the training phase. Neural networks offer superior
performance for tackling this more challenging problem. To showcase the
dataset's usefulness, we implemented a neural network with a multi-modal
architecture, which is the state-of-the-art approach, and achieved 97.04%
classification accuracy and detected 91.94% of unknown services with 5% false
positive rate.
|
[
{
"version": "v1",
"created": "Thu, 24 Feb 2022 09:44:12 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Nov 2022 19:05:29 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Luxemburk",
"Jan",
""
],
[
"Čejka",
"Tomáš",
""
]
] |
new_dataset
| 0.99885 |
2203.07473
|
Gunnar Kudrjavets
|
Gunnar Kudrjavets (University of Groningen), Nachiappan Nagappan
(Microsoft Research), Ayushi Rastogi (University of Groningen)
|
The Unexplored Treasure Trove of Phabricator Code Review
|
5 pages. To be published in Proceedings of MSR '22: Proceedings of
the 19th International Conference on Mining Software Repositories (MSR 2022).
ACM, New York, NY, USA
| null |
10.1145/3524842.3528005
| null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Phabricator is a modern code collaboration tool used by popular projects like
FreeBSD and Mozilla. However, unlike the other well-known code review
environments, such as Gerrit or GitHub, there is no readily accessible public
code review dataset for Phabricator. This paper describes our experience mining
code reviews from five different projects that use Phabricator (Blender,
FreeBSD, KDE, LLVM, and Mozilla). We discuss the challenges associated with the
data retrieval process and our solutions, resulting in a dataset with details
regarding 317,476 Phabricator code reviews. Our dataset is available in both
JSON and MySQL database dump formats. The dataset enables analyses of the
history of code reviews at a more granular level than other platforms. In
addition, given that the projects we mined are publicly accessible via the
Conduit API, our dataset can be used as a foundation to fetch additional
details and insights.
|
[
{
"version": "v1",
"created": "Mon, 14 Mar 2022 20:14:49 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Kudrjavets",
"Gunnar",
"",
"University of Groningen"
],
[
"Nagappan",
"Nachiappan",
"",
"Microsoft Research"
],
[
"Rastogi",
"Ayushi",
"",
"University of Groningen"
]
] |
new_dataset
| 0.996926 |
2203.11876
|
Yuhang Zang
|
Yuhang Zang, Wei Li, Kaiyang Zhou, Chen Huang, Chen Change Loy
|
Open-Vocabulary DETR with Conditional Matching
|
ECCV 2022 Oral
| null |
10.1007/978-3-031-20077-9_7
| null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Open-vocabulary object detection, which is concerned with the problem of
detecting novel objects guided by natural language, has gained increasing
attention from the community. Ideally, we would like to extend an
open-vocabulary detector such that it can produce bounding box predictions
based on user inputs in form of either natural language or exemplar image. This
offers great flexibility and user experience for human-computer interaction. To
this end, we propose a novel open-vocabulary detector based on DETR -- hence
the name OV-DETR -- which, once trained, can detect any object given its class
name or an exemplar image. The biggest challenge of turning DETR into an
open-vocabulary detector is that it is impossible to calculate the
classification cost matrix of novel classes without access to their labeled
images. To overcome this challenge, we formulate the learning objective as a
binary matching one between input queries (class name or exemplar image) and
the corresponding objects, which learns useful correspondence to generalize to
unseen queries during testing. For training, we choose to condition the
Transformer decoder on the input embeddings obtained from a pre-trained
vision-language model like CLIP, in order to enable matching for both text and
image queries. With extensive experiments on LVIS and COCO datasets, we
demonstrate that our OV-DETR -- the first end-to-end Transformer-based
open-vocabulary detector -- achieves non-trivial improvements over current
state of the arts.
|
[
{
"version": "v1",
"created": "Tue, 22 Mar 2022 16:54:52 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Nov 2022 02:42:54 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Zang",
"Yuhang",
""
],
[
"Li",
"Wei",
""
],
[
"Zhou",
"Kaiyang",
""
],
[
"Huang",
"Chen",
""
],
[
"Loy",
"Chen Change",
""
]
] |
new_dataset
| 0.994484 |
2204.06972
|
Geri Skenderi
|
Geri Skenderi, Christian Joppi, Matteo Denitto, Berniero Scarpa, Marco
Cristani
|
The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark
|
Accepted at the 5th Workshop on Computer Vision for Fashion, Art, and
Design @ CVPR22
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We present Visuelle 2.0, the first dataset useful for facing diverse
prediction problems that a fast-fashion company has to manage routinely.
Furthermore, we demonstrate how the use of computer vision is substantial in
this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing
products of Nuna Lie, a famous Italian company with hundreds of shops located
in different areas within the country. In particular, we focus on a specific
prediction problem, namely short-observation new product sale forecasting
(SO-fore). SO-fore assumes that the season has started and a set of new
products is on the shelves of the different stores. The goal is to forecast the
sales for a particular horizon, given a short, available past (few weeks),
since no earlier statistics are available. To be successful, SO-fore approaches
should capture this short past and exploit other modalities or exogenous data.
To these aims, Visuelle 2.0 is equipped with disaggregated data at the
item-shop level and multi-modal information for each clothing item, allowing
computer vision approaches to come into play. The main message that we deliver
is that the use of image data with deep networks boosts performances obtained
when using the time series in long-term forecasting scenarios, ameliorating the
WAPE and MAE by up to 5.48% and 7% respectively compared to competitive
baseline methods. The dataset is available at
https://humaticslab.github.io/forecasting/visuelle
|
[
{
"version": "v1",
"created": "Thu, 14 Apr 2022 13:53:46 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Nov 2022 15:06:22 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Skenderi",
"Geri",
""
],
[
"Joppi",
"Christian",
""
],
[
"Denitto",
"Matteo",
""
],
[
"Scarpa",
"Berniero",
""
],
[
"Cristani",
"Marco",
""
]
] |
new_dataset
| 0.999766 |
2205.09045
|
Renjie Li
|
Xinyu Chen, Renjie Li, Yueyao Yu, Yuanwen Shen, Wenye Li, Zhaoyu
Zhang, Yin Zhang
|
POViT: Vision Transformer for Multi-objective Design and
Characterization of Nanophotonic Devices
|
The loss function should have been RMSE, not MSE, in the model
evaluation section. As a result, the training results are all wrong. We need
to withdraw this paper until we have come up with a solution to this issue
| null | null | null |
cs.LG physics.app-ph physics.optics
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We solve a fundamental challenge in semiconductor IC design: the fast and
accurate characterization of nanoscale photonic devices. Much like the fusion
between AI and EDA, many efforts have been made to apply DNNs such as
convolutional neural networks (CNN) to prototype and characterize next-gen
optoelectronic devices commonly found in photonic integrated circuits (PIC) and
LiDAR. These prior works generally strive to predict the quality factor (Q) and
modal volume (V) of for instance, photonic crystals, with ultra-high accuracy
and speed. However, state-of-the-art models are still far from being directly
applicable in the real-world: e.g. the correlation coefficient of V
($V_{coeff}$ ) is only about 80%, which is much lower than what it takes to
generate reliable and reproducible nanophotonic designs. Recently,
attention-based transformer models have attracted extensive interests and been
widely used in CV and NLP. In this work, we propose the first-ever Transformer
model (POViT) to efficiently design and simulate semiconductor photonic devices
with multiple objectives. Unlike the standard Vision Transformer (ViT), we
supplied photonic crystals as data input and changed the activation layer from
GELU to an absolute-value function (ABS). Our experiments show that POViT
exceeds results reported by previous models significantly. The correlation
coefficient $V_{coeff}$ increases by over 12% (i.e., to 92.0%) and the
prediction errors of Q is reduced by an order of magnitude, among several other
key metric improvements. Our work has the potential to drive the expansion of
EDA to fully automated photonic design. The complete dataset and code will be
released to aid researchers endeavoring in the interdisciplinary field of
physics and computer science.
|
[
{
"version": "v1",
"created": "Tue, 17 May 2022 01:58:34 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Nov 2022 00:42:12 GMT"
},
{
"version": "v3",
"created": "Wed, 30 Nov 2022 01:10:56 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Chen",
"Xinyu",
""
],
[
"Li",
"Renjie",
""
],
[
"Yu",
"Yueyao",
""
],
[
"Shen",
"Yuanwen",
""
],
[
"Li",
"Wenye",
""
],
[
"Zhang",
"Zhaoyu",
""
],
[
"Zhang",
"Yin",
""
]
] |
new_dataset
| 0.99935 |
2210.12152
|
Matthew Ho
|
Matthew Ho, Aditya Sharma, Justin Chang, Michael Saxon, Sharon Levy,
Yujie Lu, William Yang Wang
|
WikiWhy: Answering and Explaining Cause-and-Effect Questions
| null | null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As large language models (LLMs) grow larger and more sophisticated, assessing
their "reasoning" capabilities in natural language grows more challenging.
Recent question answering (QA) benchmarks that attempt to assess reasoning are
often limited by a narrow scope of covered situations and subject matters. We
introduce WikiWhy, a QA dataset built around a novel auxiliary task: explaining
why an answer is true in natural language. WikiWhy contains over 9,000 "why"
question-answer-rationale triples, grounded on Wikipedia facts across a diverse
set of topics. Each rationale is a set of supporting statements connecting the
question to the answer. WikiWhy serves as a benchmark for the reasoning
capabilities of LLMs because it demands rigorous explicit rationales for each
answer to demonstrate the acquisition of implicit commonsense knowledge, which
is unlikely to be easily memorized. GPT-3 baselines achieve only 38.7%
human-evaluated correctness in the end-to-end answer & explain condition,
leaving significant room for future improvements.
|
[
{
"version": "v1",
"created": "Fri, 21 Oct 2022 17:59:03 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Nov 2022 07:49:19 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Ho",
"Matthew",
""
],
[
"Sharma",
"Aditya",
""
],
[
"Chang",
"Justin",
""
],
[
"Saxon",
"Michael",
""
],
[
"Levy",
"Sharon",
""
],
[
"Lu",
"Yujie",
""
],
[
"Wang",
"William Yang",
""
]
] |
new_dataset
| 0.999595 |
2211.07610
|
Ashwin Rao
|
Pulak Malhotra and Ashwin Rao
|
Pied Piper: Meta Search for Music
|
9 pages, 6 figures. To be published in conference proceedings of
International Conference on Innovations in Computational Intelligence and
Computer Vision (ICICV) 2022
|
International Conference on Innovations in Computational
Intelligence and Computer Vision (ICICV) 2022
| null | null |
cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
Internet search engines have become an integral part of life, but for pop
music, people still rely on textual search engines like Google. We propose Pied
piper, a meta search engine for music. It can search for music lyrics, song
metadata and song audio or a combination of any of these as the input query and
efficiently return the relevant results.
|
[
{
"version": "v1",
"created": "Mon, 14 Nov 2022 18:31:41 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Malhotra",
"Pulak",
""
],
[
"Rao",
"Ashwin",
""
]
] |
new_dataset
| 0.998509 |
2211.11982
|
Guangsen Wang
|
Guangsen Wang, Samson Tan, Shafiq Joty, Gang Wu, Jimmy Au, Steven Hoi
|
BotSIM: An End-to-End Bot Simulation Framework for Commercial
Task-Oriented Dialog Systems
|
Paper accepted by the EMNLP 2022 System Demo Track; We have
open-sourced the toolkit at https://github.com/salesforce/botsim
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We present BotSIM, a data-efficient end-to-end Bot SIMulation toolkit for
commercial text-based task-oriented dialog (TOD) systems. BotSIM consists of
three major components: 1) a Generator that can infer semantic-level dialog
acts and entities from bot definitions and generate user queries via
model-based paraphrasing; 2) an agenda-based dialog user Simulator (ABUS) to
simulate conversations with the dialog agents; 3) a Remediator to analyze the
simulated conversations, visualize the bot health reports and provide
actionable remediation suggestions for bot troubleshooting and improvement. We
demonstrate BotSIM's effectiveness in end-to-end evaluation, remediation and
multi-intent dialog generation via case studies on two commercial bot
platforms. BotSIM's "generation-simulation-remediation" paradigm accelerates
the end-to-end bot evaluation and iteration process by: 1) reducing manual test
cases creation efforts; 2) enabling a holistic gauge of the bot in terms of NLU
and end-to-end performance via extensive dialog simulation; 3) improving the
bot troubleshooting process with actionable suggestions. A demo of our system
can be found at https://tinyurl.com/mryu74cd and a demo video at
https://youtu.be/qLi5iSoly30. We have open-sourced the toolkit at
https://github.com/salesforce/botsim
|
[
{
"version": "v1",
"created": "Tue, 22 Nov 2022 03:34:36 GMT"
},
{
"version": "v2",
"created": "Fri, 25 Nov 2022 02:11:49 GMT"
},
{
"version": "v3",
"created": "Wed, 30 Nov 2022 12:37:08 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Wang",
"Guangsen",
""
],
[
"Tan",
"Samson",
""
],
[
"Joty",
"Shafiq",
""
],
[
"Wu",
"Gang",
""
],
[
"Au",
"Jimmy",
""
],
[
"Hoi",
"Steven",
""
]
] |
new_dataset
| 0.987828 |
2211.13523
|
Jacob Solawetz
|
Floriana Ciaglia, Francesco Saverio Zuppichini, Paul Guerrie, Mark
McQuade, and Jacob Solawetz
|
Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The evaluation of object detection models is usually performed by optimizing
a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and
Pascal VOC. Due to image retrieval and annotation costs, these datasets consist
largely of images found on the web and do not represent many real-life domains
that are being modelled in practice, e.g. satellite, microscopic and gaming,
making it difficult to assert the degree of generalization learned by the
model. We introduce the Roboflow-100 (RF100) consisting of 100 datasets, 7
imagery domains, 224,714 images, and 805 class labels with over 11,170
labelling hours. We derived RF100 from over 90,000 public datasets, 60 million
public images that are actively being assembled and labelled by computer vision
practitioners in the open on the web application Roboflow Universe. By
releasing RF100, we aim to provide a semantically diverse, multi-domain
benchmark of datasets to help researchers test their model's generalizability
with real-life data. RF100 download and benchmark replication are available on
GitHub.
|
[
{
"version": "v1",
"created": "Thu, 24 Nov 2022 10:44:06 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Nov 2022 22:04:16 GMT"
},
{
"version": "v3",
"created": "Wed, 30 Nov 2022 14:53:33 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Ciaglia",
"Floriana",
""
],
[
"Zuppichini",
"Francesco Saverio",
""
],
[
"Guerrie",
"Paul",
""
],
[
"McQuade",
"Mark",
""
],
[
"Solawetz",
"Jacob",
""
]
] |
new_dataset
| 0.990378 |
2211.15516
|
Shilong Liu
|
Shilong Liu, Yaoyuan Liang, Feng Li, Shijia Huang, Hao Zhang, Hang Su,
Jun Zhu, Lei Zhang
|
DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and
Grounding
|
Accepted to AAAI 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we study the problem of visual grounding by considering both
phrase extraction and grounding (PEG). In contrast to the previous
phrase-known-at-test setting, PEG requires a model to extract phrases from text
and locate objects from images simultaneously, which is a more practical
setting in real applications. As phrase extraction can be regarded as a $1$D
text segmentation problem, we formulate PEG as a dual detection problem and
propose a novel DQ-DETR model, which introduces dual queries to probe different
features from image and text for object prediction and phrase mask prediction.
Each pair of dual queries is designed to have shared positional parts but
different content parts. Such a design effectively alleviates the difficulty of
modality alignment between image and text (in contrast to a single query
design) and empowers Transformer decoder to leverage phrase mask-guided
attention to improve performance. To evaluate the performance of PEG, we also
propose a new metric CMAP (cross-modal average precision), analogous to the AP
metric in object detection. The new metric overcomes the ambiguity of Recall@1
in many-box-to-one-phrase cases in phrase grounding. As a result, our PEG
pre-trained DQ-DETR establishes new state-of-the-art results on all visual
grounding benchmarks with a ResNet-101 backbone. For example, it achieves
$91.04\%$ and $83.51\%$ in terms of recall rate on RefCOCO testA and testB with
a ResNet-101 backbone. Code will be availabl at
\url{https://github.com/IDEA-Research/DQ-DETR}.
|
[
{
"version": "v1",
"created": "Mon, 28 Nov 2022 16:30:46 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Nov 2022 17:49:14 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Liu",
"Shilong",
""
],
[
"Liang",
"Yaoyuan",
""
],
[
"Li",
"Feng",
""
],
[
"Huang",
"Shijia",
""
],
[
"Zhang",
"Hao",
""
],
[
"Su",
"Hang",
""
],
[
"Zhu",
"Jun",
""
],
[
"Zhang",
"Lei",
""
]
] |
new_dataset
| 0.958286 |
2211.15916
|
Guangsen Wang
|
Guangsen Wang and Shafiq Joty and Junnan Li and Steven Hoi
|
BotSIM: An End-to-End Bot Simulation Toolkit for Commercial
Task-Oriented Dialog Systems
|
Accompanying code documentation at
https://opensource.salesforce.com/botsim/latest/index.html. arXiv admin note:
text overlap with arXiv:2211.11982
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We introduce BotSIM, a modular, open-source Bot SIMulation environment with
dialog generation, user simulation and conversation analytics capabilities.
BotSIM aims to serve as a one-stop solution for large-scale data-efficient
end-to-end evaluation, diagnosis and remediation of commercial task-oriented
dialog (TOD) systems to significantly accelerate commercial bot development and
evaluation, reduce cost and time-to-market. BotSIM adopts a layered design
comprising the infrastructure layer, the adaptor layer and the application
layer. The infrastructure layer hosts key models and components to support
BotSIM's major functionalities via a streamlined
"generation-simulation-remediation" pipeline. The adaptor layer is used to
extend BotSIM to accommodate new bot platforms. The application layer provides
a suite of command line tools and a Web App to significantly lower the entry
barrier for BotSIM users such as bot admins or practitioners. In this report,
we focus on the technical designs of various system components. A detailed case
study using Einstein BotBuilder is also presented to show how to apply BotSIM
pipeline for bot evaluation and remediation. The detailed system descriptions
can be found in our system demo paper. The toolkit is available at:
https://github.com/salesforce/BotSIM .
|
[
{
"version": "v1",
"created": "Tue, 29 Nov 2022 04:13:25 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Nov 2022 12:42:43 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Wang",
"Guangsen",
""
],
[
"Joty",
"Shafiq",
""
],
[
"Li",
"Junnan",
""
],
[
"Hoi",
"Steven",
""
]
] |
new_dataset
| 0.998676 |
2211.16135
|
Xiaochen Li
|
Sicong Liu, Xiaochen Li, Zimu Zhou, Bin Guo, Meng Zhang, Haochen Shen
and Zhiwen Yu
|
AdaEnlight: Energy-aware Low-light Video Stream Enhancement on Mobile
Devices
| null | null |
10.1145/3569464
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The ubiquity of camera-embedded devices and the advances in deep learning
have stimulated various intelligent mobile video applications. These
applications often demand on-device processing of video streams to deliver
real-time, high-quality services for privacy and robustness concerns. However,
the performance of these applications is constrained by the raw video streams,
which tend to be taken with small-aperture cameras of ubiquitous mobile
platforms in dim light. Despite extensive low-light video enhancement
solutions, they are unfit for deployment to mobile devices due to their complex
models and and ignorance of system dynamics like energy budgets. In this paper,
we propose AdaEnlight, an energy-aware low-light video stream enhancement
system on mobile devices. It achieves real-time video enhancement with
competitive visual quality while allowing runtime behavior adaptation to the
platform-imposed dynamic energy budgets. We report extensive experiments on
diverse datasets, scenarios, and platforms and demonstrate the superiority of
AdaEnlight compared with state-of-the-art low-light image and video enhancement
solutions.
|
[
{
"version": "v1",
"created": "Tue, 29 Nov 2022 12:12:34 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Nov 2022 03:27:27 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Liu",
"Sicong",
""
],
[
"Li",
"Xiaochen",
""
],
[
"Zhou",
"Zimu",
""
],
[
"Guo",
"Bin",
""
],
[
"Zhang",
"Meng",
""
],
[
"Shen",
"Haochen",
""
],
[
"Yu",
"Zhiwen",
""
]
] |
new_dataset
| 0.999133 |
2211.16611
|
Zhijie Qiao
|
Zhijie Qiao, Gedaliah Knizhnik, and Mark Yim
|
Holonomic Control of Arbitrary Configurations of Docked Modboats
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
The Modboat is a low-cost, underactuated, modular robot capable of surface
swimming, docking to other modules, and undocking from them using only a single
motor and two passive flippers. Undocking is achieved by causing intentional
self-collision between the tails of neighboring modules in certain
configurations; this becomes a challenge, however, when collective swimming as
one connected component is desirable. Prior work has developed controllers that
turn arbitrary configurations of docked Modboats into steerable vehicles, but
they cannot counteract lateral forces and disturbances. In this work we present
a centralized control strategy to create holonomic vehicles out of arbitrary
configurations of docked Modboats using an iterative potential-field based
search. We experimentally demonstrate that our controller performs well and can
control surge and sway velocities and yaw angle simultaneously.
|
[
{
"version": "v1",
"created": "Tue, 29 Nov 2022 22:14:46 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Qiao",
"Zhijie",
""
],
[
"Knizhnik",
"Gedaliah",
""
],
[
"Yim",
"Mark",
""
]
] |
new_dataset
| 0.999571 |
2211.16649
|
Vishnu Sashank Dorbala
|
Vishnu Sashank Dorbala, Gunnar Sigurdsson, Robinson Piramuthu, Jesse
Thomason, Gaurav S. Sukhatme
|
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation
|
8 pages, Accepted at LangRob Workshop at Conference on Robot Learning
(CoRL), 2022
| null | null | null |
cs.CV cs.AI cs.CL cs.RO
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Household environments are visually diverse. Embodied agents performing
Vision-and-Language Navigation (VLN) in the wild must be able to handle this
diversity, while also following arbitrary language instructions. Recently,
Vision-Language models like CLIP have shown great performance on the task of
zero-shot object recognition. In this work, we ask if these models are also
capable of zero-shot language grounding. In particular, we utilize CLIP to
tackle the novel problem of zero-shot VLN using natural language referring
expressions that describe target objects, in contrast to past work that used
simple language templates describing object classes. We examine CLIP's
capability in making sequential navigational decisions without any
dataset-specific finetuning, and study how it influences the path that an agent
takes. Our results on the coarse-grained instruction following task of REVERIE
demonstrate the navigational capability of CLIP, surpassing the supervised
baseline in terms of both success rate (SR) and success weighted by path length
(SPL). More importantly, we quantitatively show that our CLIP-based zero-shot
approach generalizes better to show consistent performance across environments
when compared to SOTA, fully supervised learning approaches when evaluated via
Relative Change in Success (RCS).
|
[
{
"version": "v1",
"created": "Wed, 30 Nov 2022 00:38:54 GMT"
}
] | 2022-12-01T00:00:00 |
[
[
"Dorbala",
"Vishnu Sashank",
""
],
[
"Sigurdsson",
"Gunnar",
""
],
[
"Piramuthu",
"Robinson",
""
],
[
"Thomason",
"Jesse",
""
],
[
"Sukhatme",
"Gaurav S.",
""
]
] |
new_dataset
| 0.994289 |
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