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1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2307.11341
|
Jiangli Shao
|
Boshen Shi, Yongqing Wang, Fangda Guo, Jiangli Shao, Huawei Shen and
Xueqi Cheng
|
OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning
|
Under Review
| null | null | null |
cs.AI cs.DL cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Graph domain adaptation models are widely adopted in cross-network learning
tasks, with the aim of transferring labeling or structural knowledge.
Currently, there mainly exist two limitations in evaluating graph domain
adaptation models. On one side, they are primarily tested for the specific
cross-network node classification task, leaving tasks at edge-level and
graph-level largely under-explored. Moreover, they are primarily tested in
limited scenarios, such as social networks or citation networks, lacking
validation of model's capability in richer scenarios. As comprehensively
assessing models could enhance model practicality in real-world applications,
we propose a benchmark, known as OpenGDA. It provides abundant pre-processed
and unified datasets for different types of tasks (node, edge, graph). They
originate from diverse scenarios, covering web information systems, urban
systems and natural systems. Furthermore, it integrates state-of-the-art models
with standardized and end-to-end pipelines. Overall, OpenGDA provides a
user-friendly, scalable and reproducible benchmark for evaluating graph domain
adaptation models. The benchmark experiments highlight the challenges of
applying GDA models to real-world applications with consistent good
performance, and potentially provide insights to future research. As an
emerging project, OpenGDA will be regularly updated with new datasets and
models. It could be accessed from https://github.com/Skyorca/OpenGDA.
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 04:11:43 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Shi",
"Boshen",
""
],
[
"Wang",
"Yongqing",
""
],
[
"Guo",
"Fangda",
""
],
[
"Shao",
"Jiangli",
""
],
[
"Shen",
"Huawei",
""
],
[
"Cheng",
"Xueqi",
""
]
] |
new_dataset
| 0.994201 |
2307.11344
|
Ipsita Mohanty
|
Ipsita Mohanty
|
DEFTri: A Few-Shot Label Fused Contextual Representation Learning For
Product Defect Triage in e-Commerce
|
In Proceedings of the Fifth Workshop on e-Commerce and NLP ECNLP 5
2022 Pages 1-7
|
mohanty-2022-deftri, Association for Computational Linguistics
| null | null |
cs.SE cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Defect Triage is a time-sensitive and critical process in a large-scale agile
software development lifecycle for e-commerce. Inefficiencies arising from
human and process dependencies in this domain have motivated research in
automated approaches using machine learning to accurately assign defects to
qualified teams. This work proposes a novel framework for automated defect
triage (DEFTri) using fine-tuned state-of-the-art pre-trained BERT on labels
fused text embeddings to improve contextual representations from
human-generated product defects. For our multi-label text classification defect
triage task, we also introduce a Walmart proprietary dataset of product defects
using weak supervision and adversarial learning, in a few-shot setting.
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 04:22:43 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Mohanty",
"Ipsita",
""
]
] |
new_dataset
| 0.994528 |
2307.11360
|
Daria Reshetova
|
Daria Reshetova, Guanhang Wu, Marcel Puyat, Chunhui Gu, Huizhong Chen
|
ParGANDA: Making Synthetic Pedestrians A Reality For Object Detection
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Object detection is the key technique to a number of Computer Vision
applications, but it often requires large amounts of annotated data to achieve
decent results. Moreover, for pedestrian detection specifically, the collected
data might contain some personally identifiable information (PII), which is
highly restricted in many countries. This label intensive and privacy
concerning task has recently led to an increasing interest in training the
detection models using synthetically generated pedestrian datasets collected
with a photo-realistic video game engine. The engine is able to generate
unlimited amounts of data with precise and consistent annotations, which gives
potential for significant gains in the real-world applications. However, the
use of synthetic data for training introduces a synthetic-to-real domain shift
aggravating the final performance. To close the gap between the real and
synthetic data, we propose to use a Generative Adversarial Network (GAN), which
performsparameterized unpaired image-to-image translation to generate more
realistic images. The key benefit of using the GAN is its intrinsic preference
of low-level changes to geometric ones, which means annotations of a given
synthetic image remain accurate even after domain translation is performed thus
eliminating the need for labeling real data. We extensively experimented with
the proposed method using MOTSynth dataset to train and MOT17 and MOT20
detection datasets to test, with experimental results demonstrating the
effectiveness of this method. Our approach not only produces visually plausible
samples but also does not require any labels of the real domain thus making it
applicable to the variety of downstream tasks.
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 05:26:32 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Reshetova",
"Daria",
""
],
[
"Wu",
"Guanhang",
""
],
[
"Puyat",
"Marcel",
""
],
[
"Gu",
"Chunhui",
""
],
[
"Chen",
"Huizhong",
""
]
] |
new_dataset
| 0.999505 |
2307.11371
|
Amit Kumar
|
Chiranjib Bhattacharyya and Ravindran Kannan and Amit Kumar
|
Random Separating Hyperplane Theorem and Learning Polytopes
| null | null | null | null |
cs.LG cs.CG
|
http://creativecommons.org/licenses/by/4.0/
|
The Separating Hyperplane theorem is a fundamental result in Convex Geometry
with myriad applications. Our first result, Random Separating Hyperplane
Theorem (RSH), is a strengthening of this for polytopes. $\rsh$ asserts that if
the distance between $a$ and a polytope $K$ with $k$ vertices and unit diameter
in $\Re^d$ is at least $\delta$, where $\delta$ is a fixed constant in $(0,1)$,
then a randomly chosen hyperplane separates $a$ and $K$ with probability at
least $1/poly(k)$ and margin at least $\Omega \left(\delta/\sqrt{d} \right)$.
An immediate consequence of our result is the first near optimal bound on the
error increase in the reduction from a Separation oracle to an Optimization
oracle over a polytope.
RSH has algorithmic applications in learning polytopes. We consider a
fundamental problem, denoted the ``Hausdorff problem'', of learning a unit
diameter polytope $K$ within Hausdorff distance $\delta$, given an optimization
oracle for $K$. Using RSH, we show that with polynomially many random queries
to the optimization oracle, $K$ can be approximated within error $O(\delta)$.
To our knowledge this is the first provable algorithm for the Hausdorff
Problem. Building on this result, we show that if the vertices of $K$ are
well-separated, then an optimization oracle can be used to generate a list of
points, each within Hausdorff distance $O(\delta)$ of $K$, with the property
that the list contains a point close to each vertex of $K$. Further, we show
how to prune this list to generate a (unique) approximation to each vertex of
the polytope. We prove that in many latent variable settings, e.g., topic
modeling, LDA, optimization oracles do exist provided we project to a suitable
SVD subspace. Thus, our work yields the first efficient algorithm for finding
approximations to the vertices of the latent polytope under the
well-separatedness assumption.
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 06:03:43 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Bhattacharyya",
"Chiranjib",
""
],
[
"Kannan",
"Ravindran",
""
],
[
"Kumar",
"Amit",
""
]
] |
new_dataset
| 0.99412 |
2307.11386
|
Yunhao Ge
|
Yunhao Ge, Yuecheng Li, Shuo Ni, Jiaping Zhao, Ming-Hsuan Yang,
Laurent Itti
|
CLR: Channel-wise Lightweight Reprogramming for Continual Learning
|
ICCV 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Continual learning aims to emulate the human ability to continually
accumulate knowledge over sequential tasks. The main challenge is to maintain
performance on previously learned tasks after learning new tasks, i.e., to
avoid catastrophic forgetting. We propose a Channel-wise Lightweight
Reprogramming (CLR) approach that helps convolutional neural networks (CNNs)
overcome catastrophic forgetting during continual learning. We show that a CNN
model trained on an old task (or self-supervised proxy task) could be
``reprogrammed" to solve a new task by using our proposed lightweight (very
cheap) reprogramming parameter. With the help of CLR, we have a better
stability-plasticity trade-off to solve continual learning problems: To
maintain stability and retain previous task ability, we use a common
task-agnostic immutable part as the shared ``anchor" parameter set. We then add
task-specific lightweight reprogramming parameters to reinterpret the outputs
of the immutable parts, to enable plasticity and integrate new knowledge. To
learn sequential tasks, we only train the lightweight reprogramming parameters
to learn each new task. Reprogramming parameters are task-specific and
exclusive to each task, which makes our method immune to catastrophic
forgetting. To minimize the parameter requirement of reprogramming to learn new
tasks, we make reprogramming lightweight by only adjusting essential kernels
and learning channel-wise linear mappings from anchor parameters to
task-specific domain knowledge. We show that, for general CNNs, the CLR
parameter increase is less than 0.6\% for any new task. Our method outperforms
13 state-of-the-art continual learning baselines on a new challenging sequence
of 53 image classification datasets. Code and data are available at
https://github.com/gyhandy/Channel-wise-Lightweight-Reprogramming
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 06:56:21 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Ge",
"Yunhao",
""
],
[
"Li",
"Yuecheng",
""
],
[
"Ni",
"Shuo",
""
],
[
"Zhao",
"Jiaping",
""
],
[
"Yang",
"Ming-Hsuan",
""
],
[
"Itti",
"Laurent",
""
]
] |
new_dataset
| 0.987894 |
2307.11454
|
Ravil Mussabayev
|
Ravil Mussabayev
|
Dissecting Code Vulnerabilities: Insights from C++ and Java
Vulnerability Analysis with ReVeal Model
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
This study presents an analysis conducted on a real-world dataset of Java
vulnerability-fixing commits. The dataset consists of commits with varying
numbers of modified methods, leading to a natural partitioning based on the
number of changed functions. The research aims to address several key
questions. Firstly, the study investigates the optimal parameter selection for
ReVeal, a state-of-the-art model, in order to achieve its best performance.
Secondly, it explores the contributions of different parts of the Java dataset
towards vulnerability detection. Lastly, the study evaluates the model's
performance in separating close-to-vulnerable methods (vulnerable methods and
their fixed versions) from randomly selected safe code, as well as the finer
separation of vulnerable methods from their fixed versions within the set of
close-to-vulnerable methods. The research employs a series of experiments to
answer these questions and derive meaningful insights.
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 09:35:29 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Mussabayev",
"Ravil",
""
]
] |
new_dataset
| 0.994577 |
2307.11519
|
Ponkoj Shill
|
Fariha Tahosin Boishakhi, Ponkoj Chandra Shill, Md. Golam Rabiul Alam
|
Multi-modal Hate Speech Detection using Machine Learning
|
5 pages, 2 figures, conference
| null |
10.1109/BigData52589.2021.9671955
| null |
cs.AI cs.CL cs.CV cs.LG cs.SD eess.AS
|
http://creativecommons.org/publicdomain/zero/1.0/
|
With the continuous growth of internet users and media content, it is very
hard to track down hateful speech in audio and video. Converting video or audio
into text does not detect hate speech accurately as human sometimes uses
hateful words as humorous or pleasant in sense and also uses different voice
tones or show different action in the video. The state-ofthe-art hate speech
detection models were mostly developed on a single modality. In this research,
a combined approach of multimodal system has been proposed to detect hate
speech from video contents by extracting feature images, feature values
extracted from the audio, text and used machine learning and Natural language
processing.
|
[
{
"version": "v1",
"created": "Thu, 15 Jun 2023 06:46:52 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Boishakhi",
"Fariha Tahosin",
""
],
[
"Shill",
"Ponkoj Chandra",
""
],
[
"Alam",
"Md. Golam Rabiul",
""
]
] |
new_dataset
| 0.998415 |
2307.11543
|
Alberto Pretto
|
Ivano Donadi and Alberto Pretto
|
KVN: Keypoints Voting Network with Differentiable RANSAC for Stereo Pose
Estimation
|
Submitted to IEEE Robotics and Automation Letters
| null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Object pose estimation is a fundamental computer vision task exploited in
several robotics and augmented reality applications. Many established
approaches rely on predicting 2D-3D keypoint correspondences using RANSAC
(Random sample consensus) and estimating the object pose using the PnP
(Perspective-n-Point) algorithm. Being RANSAC non-differentiable,
correspondences cannot be directly learned in an end-to-end fashion. In this
paper, we address the stereo image-based object pose estimation problem by (i)
introducing a differentiable RANSAC layer into a well-known monocular pose
estimation network; (ii) exploiting an uncertainty-driven multi-view PnP solver
which can fuse information from multiple views. We evaluate our approach on a
challenging public stereo object pose estimation dataset, yielding
state-of-the-art results against other recent approaches. Furthermore, in our
ablation study, we show that the differentiable RANSAC layer plays a
significant role in the accuracy of the proposed method. We release with this
paper the open-source implementation of our method.
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 12:43:07 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Donadi",
"Ivano",
""
],
[
"Pretto",
"Alberto",
""
]
] |
new_dataset
| 0.993038 |
2307.11554
|
Jan-Gerrit Habekost
|
Jan-Gerrit Habekost, Erik Strahl, Philipp Allgeuer, Matthias Kerzel,
Stefan Wermter
|
CycleIK: Neuro-inspired Inverse Kinematics
|
Accepted at ICANN 2023 (32nd International Conference on Artificial
Neural Networks)
| null | null | null |
cs.RO cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The paper introduces CycleIK, a neuro-robotic approach that wraps two novel
neuro-inspired methods for the inverse kinematics (IK) task, a Generative
Adversarial Network (GAN), and a Multi-Layer Perceptron architecture. These
methods can be used in a standalone fashion, but we also show how embedding
these into a hybrid neuro-genetic IK pipeline allows for further optimization
via sequential least-squares programming (SLSQP) or a genetic algorithm (GA).
The models are trained and tested on dense datasets that were collected from
random robot configurations of the new Neuro-Inspired COLlaborator (NICOL), a
semi-humanoid robot with two redundant 8-DoF manipulators. We utilize the
weighted multi-objective function from the state-of-the-art BioIK method to
support the training process and our hybrid neuro-genetic architecture. We show
that the neural models can compete with state-of-the-art IK approaches, which
allows for deployment directly to robotic hardware. Additionally, it is shown
that the incorporation of the genetic algorithm improves the precision while
simultaneously reducing the overall runtime.
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 13:03:27 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Habekost",
"Jan-Gerrit",
""
],
[
"Strahl",
"Erik",
""
],
[
"Allgeuer",
"Philipp",
""
],
[
"Kerzel",
"Matthias",
""
],
[
"Wermter",
"Stefan",
""
]
] |
new_dataset
| 0.975761 |
2307.11636
|
Shuyang Sun
|
Runjia Li, Shuyang Sun, Mohamed Elhoseiny, Philip Torr
|
OxfordTVG-HIC: Can Machine Make Humorous Captions from Images?
|
Accepted by ICCV 2023
| null | null | null |
cs.CV cs.CL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper presents OxfordTVG-HIC (Humorous Image Captions), a large-scale
dataset for humour generation and understanding. Humour is an abstract,
subjective, and context-dependent cognitive construct involving several
cognitive factors, making it a challenging task to generate and interpret.
Hence, humour generation and understanding can serve as a new task for
evaluating the ability of deep-learning methods to process abstract and
subjective information. Due to the scarcity of data, humour-related generation
tasks such as captioning remain under-explored. To address this gap,
OxfordTVG-HIC offers approximately 2.9M image-text pairs with humour scores to
train a generalizable humour captioning model. Contrary to existing captioning
datasets, OxfordTVG-HIC features a wide range of emotional and semantic
diversity resulting in out-of-context examples that are particularly conducive
to generating humour. Moreover, OxfordTVG-HIC is curated devoid of offensive
content. We also show how OxfordTVG-HIC can be leveraged for evaluating the
humour of a generated text. Through explainability analysis of the trained
models, we identify the visual and linguistic cues influential for evoking
humour prediction (and generation). We observe qualitatively that these cues
are aligned with the benign violation theory of humour in cognitive psychology.
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 14:58:44 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Li",
"Runjia",
""
],
[
"Sun",
"Shuyang",
""
],
[
"Elhoseiny",
"Mohamed",
""
],
[
"Torr",
"Philip",
""
]
] |
new_dataset
| 0.999702 |
2307.11662
|
Mariam Mahmoud
|
Mariam Ayman, Youssef El-harty, Ahmed Rashed, Ahmed Fathy, Ahmed
Abdullah, Omar Wassim, Walid Gomaa
|
BlockCampus: A Blockchain-Based DApp for enhancing Student Engagement
and Reward Mechanisms in an Academic Community for E-JUST University
| null | null | null | null |
cs.CY
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In today's digital age, online communities have become an integral part of
our lives, fostering collaboration, knowledge sharing, and community
engagement. Higher education institutions, in particular, can greatly benefit
from dedicated platforms that facilitate academic discussions and provide
incentives for active participation. This research paper presents a
comprehensive study and implementation of a decentralized application (DApp)
leveraging the blockchain technology to address these needs specifically for
E-JUST (Egypt-Japan University of Science and Technology) students and academic
staff.
|
[
{
"version": "v1",
"created": "Fri, 7 Jul 2023 19:12:19 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Ayman",
"Mariam",
""
],
[
"El-harty",
"Youssef",
""
],
[
"Rashed",
"Ahmed",
""
],
[
"Fathy",
"Ahmed",
""
],
[
"Abdullah",
"Ahmed",
""
],
[
"Wassim",
"Omar",
""
],
[
"Gomaa",
"Walid",
""
]
] |
new_dataset
| 0.999353 |
2307.11709
|
Aakash Bansal
|
Aakash Bansal, Siyuan Jiang, Sakib Haque, and Collin McMillan
|
Statement-based Memory for Neural Source Code Summarization
|
10 pages 2 figures
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Source code summarization is the task of writing natural language
descriptions of source code behavior. Code summarization underpins software
documentation for programmers. Short descriptions of code help programmers
understand the program quickly without having to read the code itself. Lately,
neural source code summarization has emerged as the frontier of research into
automated code summarization techniques. By far the most popular targets for
summarization are program subroutines. The idea, in a nutshell, is to train an
encoder-decoder neural architecture using large sets of examples of subroutines
extracted from code repositories. The encoder represents the code and the
decoder represents the summary. However, most current approaches attempt to
treat the subroutine as a single unit. For example, by taking the entire
subroutine as input to a Transformer or RNN-based encoder. But code behavior
tends to depend on the flow from statement to statement. Normally dynamic
analysis may shed light on this flow, but dynamic analysis on hundreds of
thousands of examples in large datasets is not practical. In this paper, we
present a statement-based memory encoder that learns the important elements of
flow during training, leading to a statement-based subroutine representation
without the need for dynamic analysis. We implement our encoder for code
summarization and demonstrate a significant improvement over the
state-of-the-art.
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 17:04:39 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Bansal",
"Aakash",
""
],
[
"Jiang",
"Siyuan",
""
],
[
"Haque",
"Sakib",
""
],
[
"McMillan",
"Collin",
""
]
] |
new_dataset
| 0.963948 |
2307.11717
|
Mahmoud Ali
|
Mahmoud Ali and Lantao Liu
|
GP-Frontier for Local Mapless Navigation
|
7 pages, 7 figures, accepted at the 2023 IEEE International
Conference on Robotics and Automation ICRA2023
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a new frontier concept called the Gaussian Process Frontier
(GP-Frontier) that can be used to locally navigate a robot towards a goal
without building a map. The GP-Frontier is built on the uncertainty assessment
of an efficient variant of sparse Gaussian Process. Based only on local ranging
sensing measurement, the GP-Frontier can be used for navigation in both known
and unknown environments. The proposed method is validated through intensive
evaluations, and the results show that the GP-Frontier can navigate the robot
in a safe and persistent way, i.e., the robot moves in the most open space
(thus reducing the risk of collision) without relying on a map or a path
planner.
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 17:21:30 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Ali",
"Mahmoud",
""
],
[
"Liu",
"Lantao",
""
]
] |
new_dataset
| 0.995353 |
2307.11719
|
Rita T. Sousa
|
Rita T. Sousa, Sara Silva, Catia Pesquita
|
Benchmark datasets for biomedical knowledge graphs with negative
statements
| null | null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Knowledge graphs represent facts about real-world entities. Most of these
facts are defined as positive statements. The negative statements are scarce
but highly relevant under the open-world assumption. Furthermore, they have
been demonstrated to improve the performance of several applications, namely in
the biomedical domain. However, no benchmark dataset supports the evaluation of
the methods that consider these negative statements.
We present a collection of datasets for three relation prediction tasks -
protein-protein interaction prediction, gene-disease association prediction and
disease prediction - that aim at circumventing the difficulties in building
benchmarks for knowledge graphs with negative statements. These datasets
include data from two successful biomedical ontologies, Gene Ontology and Human
Phenotype Ontology, enriched with negative statements.
We also generate knowledge graph embeddings for each dataset with two popular
path-based methods and evaluate the performance in each task. The results show
that the negative statements can improve the performance of knowledge graph
embeddings.
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 17:25:21 GMT"
}
] | 2023-07-24T00:00:00 |
[
[
"Sousa",
"Rita T.",
""
],
[
"Silva",
"Sara",
""
],
[
"Pesquita",
"Catia",
""
]
] |
new_dataset
| 0.997672 |
1912.08166
|
Matthew Walmer
|
Anneliese Braunegg, Amartya Chakraborty, Michael Krumdick, Nicole
Lape, Sara Leary, Keith Manville, Elizabeth Merkhofer, Laura Strickhart,
Matthew Walmer
|
APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection
|
23 pages, 14 figures, 3 tables. Updated version as accepted to ECCV
2020
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Physical adversarial attacks threaten to fool object detection systems, but
reproducible research on the real-world effectiveness of physical patches and
how to defend against them requires a publicly available benchmark dataset. We
present APRICOT, a collection of over 1,000 annotated photographs of printed
adversarial patches in public locations. The patches target several object
categories for three COCO-trained detection models, and the photos represent
natural variation in position, distance, lighting conditions, and viewing
angle. Our analysis suggests that maintaining adversarial robustness in
uncontrolled settings is highly challenging, but it is still possible to
produce targeted detections under white-box and sometimes black-box settings.
We establish baselines for defending against adversarial patches through
several methods, including a detector supervised with synthetic data and
unsupervised methods such as kernel density estimation, Bayesian uncertainty,
and reconstruction error. Our results suggest that adversarial patches can be
effectively flagged, both in a high-knowledge, attack-specific scenario, and in
an unsupervised setting where patches are detected as anomalies in natural
images. This dataset and the described experiments provide a benchmark for
future research on the effectiveness of and defenses against physical
adversarial objects in the wild.
|
[
{
"version": "v1",
"created": "Tue, 17 Dec 2019 18:08:01 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Aug 2020 21:37:23 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Braunegg",
"Anneliese",
""
],
[
"Chakraborty",
"Amartya",
""
],
[
"Krumdick",
"Michael",
""
],
[
"Lape",
"Nicole",
""
],
[
"Leary",
"Sara",
""
],
[
"Manville",
"Keith",
""
],
[
"Merkhofer",
"Elizabeth",
""
],
[
"Strickhart",
"Laura",
""
],
[
"Walmer",
"Matthew",
""
]
] |
new_dataset
| 0.999864 |
2105.06808
|
Sylwia Majchrowska Ms.
|
Sylwia Majchrowska, Agnieszka Miko{\l}ajczyk, Maria Ferlin, Zuzanna
Klawikowska, Marta A. Plantykow, Arkadiusz Kwasigroch, Karol Majek
|
Waste detection in Pomerania: non-profit project for detecting waste in
environment
|
Litter detection, Waste detection, Object detection
|
Waste Management, Volume 138, 1 February 2022, Pages 274-284
|
10.1016/j.wasman.2021.12.001
| null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Waste pollution is one of the most significant environmental issues in the
modern world. The importance of recycling is well known, either for economic or
ecological reasons, and the industry demands high efficiency. Our team
conducted comprehensive research on Artificial Intelligence usage in waste
detection and classification to fight the world's waste pollution problem. As a
result an open-source framework that enables the detection and classification
of litter was developed. The final pipeline consists of two neural networks:
one that detects litter and a second responsible for litter classification.
Waste is classified into seven categories: bio, glass, metal and plastic,
non-recyclable, other, paper and unknown. Our approach achieves up to 70% of
average precision in waste detection and around 75% of classification accuracy
on the test dataset. The code used in the studies is publicly available online.
|
[
{
"version": "v1",
"created": "Wed, 12 May 2021 09:33:22 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Majchrowska",
"Sylwia",
""
],
[
"Mikołajczyk",
"Agnieszka",
""
],
[
"Ferlin",
"Maria",
""
],
[
"Klawikowska",
"Zuzanna",
""
],
[
"Plantykow",
"Marta A.",
""
],
[
"Kwasigroch",
"Arkadiusz",
""
],
[
"Majek",
"Karol",
""
]
] |
new_dataset
| 0.995535 |
2204.13730
|
Ziyaur Rahman
|
Ziyaur Rahman, S. M. Zafaruddin, V. K. Chaubey
|
Direct Air-to-Underwater Optical Wireless Communication: Statistical
Characterization and Outage Performance
|
This work has been submitted to the IEEE for possible publication
|
IEEE Transactions on Vehicular Technology, Vol. 72, No. 2, Feb
2023
|
10.1109/TVT.2022.3211186
| null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In general, a buoy relay is used to connect the underwater communication to
the terrestrial network over a radio or optical wireless communication (OWC)
link. The use of relay deployment may pose security and deployment issues. This
paper investigates the feasibility of direct air-to-underwater (A2UW)
communication from an over-the-sea OWC system to an underwater submarine
without deploying a relaying node. We analyze the statistical performance of
the direct transmission over the combined channel fading effect of atmospheric
turbulence, random fog, air-to-water interface, oceanic turbulence, and
pointing errors. We develop novel analytical expressions for the probability
density function (PDF) and cumulative distribution function (CDF) of the
resultant signal-to-noise ratio (SNR) in terms of bivariate Meijer-G and Fox-H
functions. We use the derived statistical results to analyze the system
performance by providing exact and asymptotic results of the outage probability
in terms of system parameters. We use computer simulations to demonstrate the
performance of direct A2UW transmissions compared to the relay-assisted system.
|
[
{
"version": "v1",
"created": "Thu, 28 Apr 2022 18:21:37 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Rahman",
"Ziyaur",
""
],
[
"Zafaruddin",
"S. M.",
""
],
[
"Chaubey",
"V. K.",
""
]
] |
new_dataset
| 0.995451 |
2206.02248
|
Ahmet Kurt
|
Ahmet Kurt, Kemal Akkaya, Sabri Yilmaz, Suat Mercan, Omer Shlomovits,
Enes Erdin
|
LNGate$^2$: Secure Bidirectional IoT Micro-payments using Bitcoin's
Lightning Network and Threshold Cryptography
|
Revised again based on anonymous reviewers' comments. Journal
extension of https://doi.org/10.1145/3448300.3467833. arXiv admin note: text
overlap with arXiv:2105.08902
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Bitcoin has emerged as a revolutionary payment system with its decentralized
ledger concept; however it has significant problems such as high transaction
fees and low throughput. Lightning Network (LN), which was introduced much
later, solves most of these problems with an innovative concept called
off-chain payments. With this advancement, Bitcoin has become an attractive
venue to perform micro-payments which can also be adopted in many IoT
applications (e.g., toll payments). Nevertheless, it is not feasible to host LN
and Bitcoin on IoT devices due to the storage, memory, and processing
restrictions. Therefore, in this paper, we propose a secure and efficient
protocol that enables an IoT device to use LN's functions through an untrusted
gateway node. Through this gateway which hosts the LN and Bitcoin nodes, the
IoT device can open & close LN channels and send & receive LN payments. This
delegation approach is powered by a threshold cryptography based scheme that
requires the IoT device and the LN gateway to jointly perform all LN
operations. Specifically, we propose thresholdizing LN's Bitcoin public and
private keys as well as its public and private keys for the new channel states
(i.e., commitment points). We prove with a game theoretical security analysis
that the IoT device is secure against collusion attacks. We implemented the
proposed protocol by changing LN's source code and thoroughly evaluated its
performance using several Raspberry Pis. Our evaluation results show that the
protocol; is fast, does not bring extra cost overhead, can be run on low data
rate wireless networks, is scalable and has negligible energy consumption
overhead. To the best of our knowledge, this is the first work that implemented
threshold cryptography in LN.
|
[
{
"version": "v1",
"created": "Sun, 5 Jun 2022 19:50:11 GMT"
},
{
"version": "v2",
"created": "Tue, 25 Apr 2023 00:16:58 GMT"
},
{
"version": "v3",
"created": "Wed, 19 Jul 2023 18:30:53 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Kurt",
"Ahmet",
""
],
[
"Akkaya",
"Kemal",
""
],
[
"Yilmaz",
"Sabri",
""
],
[
"Mercan",
"Suat",
""
],
[
"Shlomovits",
"Omer",
""
],
[
"Erdin",
"Enes",
""
]
] |
new_dataset
| 0.964677 |
2206.08309
|
Cl\'ement Chadebec
|
Cl\'ement Chadebec and Louis J. Vincent and St\'ephanie
Allassonni\`ere
|
Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use
Case
|
Accepted to NeurIPS 2022
| null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, deep generative models have attracted increasing interest
due to their capacity to model complex distributions. Among those models,
variational autoencoders have gained popularity as they have proven both to be
computationally efficient and yield impressive results in multiple fields.
Following this breakthrough, extensive research has been done in order to
improve the original publication, resulting in a variety of different VAE
models in response to different tasks. In this paper we present Pythae, a
versatile open-source Python library providing both a unified implementation
and a dedicated framework allowing straightforward, reproducible and reliable
use of generative autoencoder models. We then propose to use this library to
perform a case study benchmark where we present and compare 19 generative
autoencoder models representative of some of the main improvements on
downstream tasks such as image reconstruction, generation, classification,
clustering and interpolation. The open-source library can be found at
https://github.com/clementchadebec/benchmark_VAE.
|
[
{
"version": "v1",
"created": "Thu, 16 Jun 2022 17:11:41 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Jul 2023 05:32:00 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Chadebec",
"Clément",
""
],
[
"Vincent",
"Louis J.",
""
],
[
"Allassonnière",
"Stéphanie",
""
]
] |
new_dataset
| 0.998977 |
2206.10552
|
Weixuan Sun
|
Weixuan Sun, Zhen Qin, Hui Deng, Jianyuan Wang, Yi Zhang, Kaihao
Zhang, Nick Barnes, Stan Birchfield, Lingpeng Kong, Yiran Zhong
|
Vicinity Vision Transformer
|
code: https://github.com/OpenNLPLab/Vicinity-Vision-Transformer
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Vision transformers have shown great success on numerous computer vision
tasks. However, its central component, softmax attention, prohibits vision
transformers from scaling up to high-resolution images, due to both the
computational complexity and memory footprint being quadratic. Although linear
attention was introduced in natural language processing (NLP) tasks to mitigate
a similar issue, directly applying existing linear attention to vision
transformers may not lead to satisfactory results. We investigate this problem
and find that computer vision tasks focus more on local information compared
with NLP tasks. Based on this observation, we present a Vicinity Attention that
introduces a locality bias to vision transformers with linear complexity.
Specifically, for each image patch, we adjust its attention weight based on its
2D Manhattan distance measured by its neighbouring patches. In this case, the
neighbouring patches will receive stronger attention than far-away patches.
Moreover, since our Vicinity Attention requires the token length to be much
larger than the feature dimension to show its efficiency advantages, we further
propose a new Vicinity Vision Transformer (VVT) structure to reduce the feature
dimension without degenerating the accuracy. We perform extensive experiments
on the CIFAR100, ImageNet1K, and ADE20K datasets to validate the effectiveness
of our method. Our method has a slower growth rate of GFlops than previous
transformer-based and convolution-based networks when the input resolution
increases. In particular, our approach achieves state-of-the-art image
classification accuracy with 50% fewer parameters than previous methods.
|
[
{
"version": "v1",
"created": "Tue, 21 Jun 2022 17:33:53 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Jul 2023 08:57:20 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Sun",
"Weixuan",
""
],
[
"Qin",
"Zhen",
""
],
[
"Deng",
"Hui",
""
],
[
"Wang",
"Jianyuan",
""
],
[
"Zhang",
"Yi",
""
],
[
"Zhang",
"Kaihao",
""
],
[
"Barnes",
"Nick",
""
],
[
"Birchfield",
"Stan",
""
],
[
"Kong",
"Lingpeng",
""
],
[
"Zhong",
"Yiran",
""
]
] |
new_dataset
| 0.9993 |
2208.06501
|
Zifeng Ding
|
Zifeng Ding, Zongyue Li, Ruoxia Qi, Jingpei Wu, Bailan He, Yunpu Ma,
Zhao Meng, Shuo Chen, Ruotong Liao, Zhen Han, Volker Tresp
|
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and
Forecasting over Temporal Knowledge Graphs
|
Accepted to ISWC 2023
| null | null | null |
cs.AI cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Question answering over temporal knowledge graphs (TKGQA) has recently found
increasing interest. TKGQA requires temporal reasoning techniques to extract
the relevant information from temporal knowledge bases. The only existing TKGQA
dataset, i.e., CronQuestions, consists of temporal questions based on the facts
from a fixed time period, where a temporal knowledge graph (TKG) spanning the
same period can be fully used for answer inference, allowing the TKGQA models
to use even the future knowledge to answer the questions based on the past
facts. In real-world scenarios, however, it is also common that given the
knowledge until now, we wish the TKGQA systems to answer the questions asking
about the future. As humans constantly seek plans for the future, building
TKGQA systems for answering such forecasting questions is important.
Nevertheless, this has still been unexplored in previous research. In this
paper, we propose a novel task: forecasting question answering over temporal
knowledge graphs. We also propose a large-scale TKGQA benchmark dataset, i.e.,
ForecastTKGQuestions, for this task. It includes three types of questions,
i.e., entity prediction, yes-no, and fact reasoning questions. For every
forecasting question in our dataset, QA models can only have access to the TKG
information before the timestamp annotated in the given question for answer
inference. We find that the state-of-the-art TKGQA methods perform poorly on
forecasting questions, and they are unable to answer yes-no questions and fact
reasoning questions. To this end, we propose ForecastTKGQA, a TKGQA model that
employs a TKG forecasting module for future inference, to answer all three
types of questions. Experimental results show that ForecastTKGQA outperforms
recent TKGQA methods on the entity prediction questions, and it also shows
great effectiveness in answering the other two types of questions.
|
[
{
"version": "v1",
"created": "Fri, 12 Aug 2022 21:02:35 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 15:05:49 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Ding",
"Zifeng",
""
],
[
"Li",
"Zongyue",
""
],
[
"Qi",
"Ruoxia",
""
],
[
"Wu",
"Jingpei",
""
],
[
"He",
"Bailan",
""
],
[
"Ma",
"Yunpu",
""
],
[
"Meng",
"Zhao",
""
],
[
"Chen",
"Shuo",
""
],
[
"Liao",
"Ruotong",
""
],
[
"Han",
"Zhen",
""
],
[
"Tresp",
"Volker",
""
]
] |
new_dataset
| 0.999728 |
2211.05939
|
Ayal Taitler
|
Ayal Taitler, Michael Gimelfarb, Jihwan Jeong, Sriram Gopalakrishnan,
Martin Mladenov, Xiaotian Liu, Scott Sanner
|
pyRDDLGym: From RDDL to Gym Environments
| null | null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym
environments from RDDL declerative description. The discrete time step
evolution of variables in RDDL is described by conditional probability
functions, which fits naturally into the Gym step scheme. Furthermore, since
RDDL is a lifted description, the modification and scaling up of environments
to support multiple entities and different configurations becomes trivial
rather than a tedious process prone to errors. We hope that pyRDDLGym will
serve as a new wind in the reinforcement learning community by enabling easy
and rapid development of benchmarks due to the unique expressive power of RDDL.
By providing explicit access to the model in the RDDL description, pyRDDLGym
can also facilitate research on hybrid approaches for learning from interaction
while leveraging model knowledge. We present the design and built-in examples
of pyRDDLGym, and the additions made to the RDDL language that were
incorporated into the framework.
|
[
{
"version": "v1",
"created": "Fri, 11 Nov 2022 00:58:16 GMT"
},
{
"version": "v2",
"created": "Mon, 14 Nov 2022 19:55:56 GMT"
},
{
"version": "v3",
"created": "Fri, 16 Dec 2022 23:43:52 GMT"
},
{
"version": "v4",
"created": "Wed, 19 Jul 2023 14:40:45 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Taitler",
"Ayal",
""
],
[
"Gimelfarb",
"Michael",
""
],
[
"Jeong",
"Jihwan",
""
],
[
"Gopalakrishnan",
"Sriram",
""
],
[
"Mladenov",
"Martin",
""
],
[
"Liu",
"Xiaotian",
""
],
[
"Sanner",
"Scott",
""
]
] |
new_dataset
| 0.999794 |
2212.04246
|
Yufei Xu
|
Yufei Xu, Jing Zhang, Qiming Zhang, Dacheng Tao
|
ViTPose++: Vision Transformer Foundation Model for Generic Body Pose
Estimation
|
Extension of ViTPose paper
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we show the surprisingly good properties of plain vision
transformers for body pose estimation from various aspects, namely simplicity
in model structure, scalability in model size, flexibility in training
paradigm, and transferability of knowledge between models, through a simple
baseline model dubbed ViTPose. Specifically, ViTPose employs the plain and
non-hierarchical vision transformer as an encoder to encode features and a
lightweight decoder to decode body keypoints in either a top-down or a
bottom-up manner. It can be scaled up from about 20M to 1B parameters by taking
advantage of the scalable model capacity and high parallelism of the vision
transformer, setting a new Pareto front for throughput and performance.
Besides, ViTPose is very flexible regarding the attention type, input
resolution, and pre-training and fine-tuning strategy. Based on the
flexibility, a novel ViTPose+ model is proposed to deal with heterogeneous body
keypoint categories in different types of body pose estimation tasks via
knowledge factorization, i.e., adopting task-agnostic and task-specific
feed-forward networks in the transformer. We also empirically demonstrate that
the knowledge of large ViTPose models can be easily transferred to small ones
via a simple knowledge token. Experimental results show that our ViTPose model
outperforms representative methods on the challenging MS COCO Human Keypoint
Detection benchmark at both top-down and bottom-up settings. Furthermore, our
ViTPose+ model achieves state-of-the-art performance simultaneously on a series
of body pose estimation tasks, including MS COCO, AI Challenger, OCHuman, MPII
for human keypoint detection, COCO-Wholebody for whole-body keypoint detection,
as well as AP-10K and APT-36K for animal keypoint detection, without
sacrificing inference speed.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 12:33:28 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Jul 2023 16:27:27 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Xu",
"Yufei",
""
],
[
"Zhang",
"Jing",
""
],
[
"Zhang",
"Qiming",
""
],
[
"Tao",
"Dacheng",
""
]
] |
new_dataset
| 0.991342 |
2212.10338
|
David Naumann
|
Anindya Banerjee, Ramana Nagasamudram, David A. Naumann
|
Making Relational Hoare Logic Alignment Complete
|
v2: streamline treatment of hypotheses in definition of command
equivalence; simplify normal form axioms. v3: add note referencing new paper
ArXiv 2307.10045 which incorporates the results in this paper and more
| null | null | null |
cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In relational verification, judicious alignment of computational steps
facilitates proof of relations between programs using simple relational
assertions. Relational Hoare logics (RHL) provide compositional rules that
embody various alignments. Seemingly more flexible alignments can be expressed
in terms of product automata based on program transition relations. A RHL can
be complete, in the ordinary sense, using a single degenerate alignment rule.
The notion of alignment completeness was previously proposed as a more
satisfactory measure, based on alignment automata, and some rules were shown to
be alignment complete with respect to a few ad hoc forms of alignment automata.
Using a rule of semantics-preserving rewrites based on Kleene algebra with
tests, an RHL is shown to be alignment complete with respect to a very general
class of alignment automata. Besides solving the open problem of general
alignment completeness, this result bridges between human-friendly syntax-based
reasoning and automata representations that facilitate automated verification.
|
[
{
"version": "v1",
"created": "Tue, 20 Dec 2022 15:24:57 GMT"
},
{
"version": "v2",
"created": "Sat, 18 Mar 2023 02:50:22 GMT"
},
{
"version": "v3",
"created": "Thu, 20 Jul 2023 02:29:44 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Banerjee",
"Anindya",
""
],
[
"Nagasamudram",
"Ramana",
""
],
[
"Naumann",
"David A.",
""
]
] |
new_dataset
| 0.968061 |
2212.13792
|
Fernando Alonso-Fernandez
|
Fernando Alonso-Fernandez, Josef Bigun, Julian Fierrez, Naser Damer,
Hugo Proen\c{c}a, Arun Ross
|
Periocular Biometrics: A Modality for Unconstrained Scenarios
|
Published at IEEE Computer journal
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Periocular refers to the externally visible region of the face that surrounds
the eye socket. This feature-rich area can provide accurate identification in
unconstrained or uncooperative scenarios, where the iris or face modalities may
not offer sufficient biometric cues due to factors such as partial occlusion or
high subject-to-camera distance. The COVID-19 pandemic has further highlighted
its importance, as the ocular region remained the only visible facial area even
in controlled settings due to the widespread use of masks. This paper discusses
the state of the art in periocular biometrics, presenting an overall framework
encompassing its most significant research aspects, which include: (a) ocular
definition, acquisition, and detection; (b) identity recognition, including
combination with other modalities and use of various spectra; and (c) ocular
soft-biometric analysis. Finally, we conclude by addressing current challenges
and proposing future directions.
|
[
{
"version": "v1",
"created": "Wed, 28 Dec 2022 12:08:27 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Jul 2023 12:37:06 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Alonso-Fernandez",
"Fernando",
""
],
[
"Bigun",
"Josef",
""
],
[
"Fierrez",
"Julian",
""
],
[
"Damer",
"Naser",
""
],
[
"Proença",
"Hugo",
""
],
[
"Ross",
"Arun",
""
]
] |
new_dataset
| 0.995466 |
2302.04450
|
Vishnuprasad Padinjaredath Suresh
|
Vishnuprasad Padinjaredath Suresh, Gianluca Nogara, Felipe Cardoso,
Stefano Cresci, Silvia Giordano, and Luca Luceri
|
Tracking Fringe and Coordinated Activity on Twitter Leading Up To the US
Capitol Attack
|
11 pages (including references), 8 figures, 1 table. Accepted at The
18th International AAAI Conference on Web and Social Media
|
Proceedings of the 18th International Conference on Web and Social
Media, 2024
| null | null |
cs.SI cs.HC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The aftermath of the 2020 US Presidential Election witnessed an unprecedented
attack on the democratic values of the country through the violent insurrection
at Capitol Hill on January 6th, 2021. The attack was fueled by the
proliferation of conspiracy theories and misleading claims about the integrity
of the election pushed by political elites and fringe communities on social
media. In this study, we explore the evolution of fringe content and conspiracy
theories on Twitter in the seven months leading up to the Capitol attack. We
examine the suspicious coordinated activity carried out by users sharing fringe
content, finding evidence of common adversarial manipulation techniques ranging
from targeted amplification to manufactured consensus. Further, we map out the
temporal evolution of, and the relationship between, fringe and conspiracy
theories, which eventually coalesced into the rhetoric of a stolen election,
with the hashtag #stopthesteal, alongside QAnon-related narratives. Our
findings further highlight how social media platforms offer fertile ground for
the widespread proliferation of conspiracies during major societal events,
which can potentially lead to offline coordinated actions and organized
violence.
|
[
{
"version": "v1",
"created": "Thu, 9 Feb 2023 05:54:16 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Jul 2023 09:31:22 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Suresh",
"Vishnuprasad Padinjaredath",
""
],
[
"Nogara",
"Gianluca",
""
],
[
"Cardoso",
"Felipe",
""
],
[
"Cresci",
"Stefano",
""
],
[
"Giordano",
"Silvia",
""
],
[
"Luceri",
"Luca",
""
]
] |
new_dataset
| 0.999592 |
2302.08292
|
Alexandre Almin
|
Alexandre Almin, L\'eo Lemari\'e, Anh Duong, B Ravi Kiran
|
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation
for autonomous vehicles
|
Accepted version to IEEE RA-L. Version with supplementary materials
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Autonomous driving (AD) perception today relies heavily on deep learning
based architectures requiring large scale annotated datasets with their
associated costs for curation and annotation. The 3D semantic data are useful
for core perception tasks such as obstacle detection and ego-vehicle
localization. We propose a new dataset, Navya 3D Segmentation (Navya3DSeg),
with a diverse label space corresponding to a large scale production grade
operational domain, including rural, urban, industrial sites and universities
from 13 countries. It contains 23 labeled sequences and 25 supplementary
sequences without labels, designed to explore self-supervised and
semi-supervised semantic segmentation benchmarks on point clouds. We also
propose a novel method for sequential dataset split generation based on
iterative multi-label stratification, and demonstrated to achieve a +1.2% mIoU
improvement over the original split proposed by SemanticKITTI dataset. A
complete benchmark for semantic segmentation task was performed, with state of
the art methods. Finally, we demonstrate an Active Learning (AL) based dataset
distillation framework. We introduce a novel heuristic-free sampling method
called ego-pose distance based sampling in the context of AL. A detailed
presentation on the dataset is available here
https://www.youtube.com/watch?v=5m6ALIs-s20.
|
[
{
"version": "v1",
"created": "Thu, 16 Feb 2023 13:41:19 GMT"
},
{
"version": "v2",
"created": "Mon, 22 May 2023 14:42:46 GMT"
},
{
"version": "v3",
"created": "Thu, 20 Jul 2023 08:35:26 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Almin",
"Alexandre",
""
],
[
"Lemarié",
"Léo",
""
],
[
"Duong",
"Anh",
""
],
[
"Kiran",
"B Ravi",
""
]
] |
new_dataset
| 0.999867 |
2303.00924
|
Lindsey Kuper
|
Gan Shen, Shun Kashiwa, Lindsey Kuper
|
HasChor: Functional Choreographic Programming for All (Functional Pearl)
| null | null |
10.1145/3607849
| null |
cs.PL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Choreographic programming is an emerging paradigm for programming distributed
systems. In choreographic programming, the programmer describes the behavior of
the entire system as a single, unified program -- a choreography -- which is
then compiled to individual programs that run on each node, via a compilation
step called endpoint projection. We present a new model for functional
choreographic programming where choreographies are expressed as computations in
a monad. Our model supports cutting-edge choreographic programming features
that enable modularity and code reuse: in particular, it supports higher-order
choreographies, in which a choreography may be passed as an argument to another
choreography, and location-polymorphic choreographies, in which a choreography
can abstract over nodes. Our model is implemented in a Haskell library,
HasChor, which lets programmers write choreographic programs while using the
rich Haskell ecosystem at no cost, bringing choreographic programming within
reach of everyday Haskellers. Moreover, thanks to Haskell's abstractions, the
implementation of the HasChor library itself is concise and understandable,
boiling down endpoint projection to its short and simple essence.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 02:54:05 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 19:33:30 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Shen",
"Gan",
""
],
[
"Kashiwa",
"Shun",
""
],
[
"Kuper",
"Lindsey",
""
]
] |
new_dataset
| 0.996568 |
2303.13501
|
Tolga Birdal
|
Nathan Mankovich and Tolga Birdal
|
Chordal Averaging on Flag Manifolds and Its Applications
|
Appears at ICCV 2023
| null | null | null |
cs.CV cs.LG math.DG math.OC stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a new, provably-convergent algorithm for computing the
flag-mean and flag-median of a set of points on a flag manifold under the
chordal metric. The flag manifold is a mathematical space consisting of flags,
which are sequences of nested subspaces of a vector space that increase in
dimension. The flag manifold is a superset of a wide range of known matrix
spaces, including Stiefel and Grassmanians, making it a general object that is
useful in a wide variety computer vision problems.
To tackle the challenge of computing first order flag statistics, we first
transform the problem into one that involves auxiliary variables constrained to
the Stiefel manifold. The Stiefel manifold is a space of orthogonal frames, and
leveraging the numerical stability and efficiency of Stiefel-manifold
optimization enables us to compute the flag-mean effectively. Through a series
of experiments, we show the competence of our method in Grassmann and rotation
averaging, as well as principal component analysis. We release our source code
under https://github.com/nmank/FlagAveraging.
|
[
{
"version": "v1",
"created": "Thu, 23 Mar 2023 17:57:28 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Jul 2023 18:27:49 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Mankovich",
"Nathan",
""
],
[
"Birdal",
"Tolga",
""
]
] |
new_dataset
| 0.999004 |
2305.01146
|
Dave Van Veen
|
Dave Van Veen, Cara Van Uden, Maayane Attias, Anuj Pareek, Christian
Bluethgen, Malgorzata Polacin, Wah Chiu, Jean-Benoit Delbrouck, Juan Manuel
Zambrano Chaves, Curtis P. Langlotz, Akshay S. Chaudhari, John Pauly
|
RadAdapt: Radiology Report Summarization via Lightweight Domain
Adaptation of Large Language Models
|
12 pages, 10 figures. Published in ACL BioNLP. Compared to v1, v2
includes minor edits and one additional figure in the appendix. Compared to
v2, v3 includes a link to the project's GitHub repository
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We systematically investigate lightweight strategies to adapt large language
models (LLMs) for the task of radiology report summarization (RRS).
Specifically, we focus on domain adaptation via pretraining (on natural
language, biomedical text, or clinical text) and via discrete prompting or
parameter-efficient fine-tuning. Our results consistently achieve best
performance by maximally adapting to the task via pretraining on clinical text
and fine-tuning on RRS examples. Importantly, this method fine-tunes a mere
0.32% of parameters throughout the model, in contrast to end-to-end fine-tuning
(100% of parameters). Additionally, we study the effect of in-context examples
and out-of-distribution (OOD) training before concluding with a radiologist
reader study and qualitative analysis. Our findings highlight the importance of
domain adaptation in RRS and provide valuable insights toward developing
effective natural language processing solutions for clinical tasks.
|
[
{
"version": "v1",
"created": "Tue, 2 May 2023 01:33:02 GMT"
},
{
"version": "v2",
"created": "Sat, 17 Jun 2023 13:17:07 GMT"
},
{
"version": "v3",
"created": "Thu, 20 Jul 2023 13:10:07 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Van Veen",
"Dave",
""
],
[
"Van Uden",
"Cara",
""
],
[
"Attias",
"Maayane",
""
],
[
"Pareek",
"Anuj",
""
],
[
"Bluethgen",
"Christian",
""
],
[
"Polacin",
"Malgorzata",
""
],
[
"Chiu",
"Wah",
""
],
[
"Delbrouck",
"Jean-Benoit",
""
],
[
"Chaves",
"Juan Manuel Zambrano",
""
],
[
"Langlotz",
"Curtis P.",
""
],
[
"Chaudhari",
"Akshay S.",
""
],
[
"Pauly",
"John",
""
]
] |
new_dataset
| 0.977808 |
2305.07290
|
Lei Jin
|
Jian Zhao, Jianan Li, Lei Jin, Jiaming Chu, Zhihao Zhang, Jun Wang,
Jiangqiang Xia, Kai Wang, Yang Liu, Sadaf Gulshad, Jiaojiao Zhao, Tianyang
Xu, Xuefeng Zhu, Shihan Liu, Zheng Zhu, Guibo Zhu, Zechao Li, Zheng Wang,
Baigui Sun, Yandong Guo, Shin ichi Satoh, Junliang Xing, Jane Shen Shengmei
|
The 3rd Anti-UAV Workshop & Challenge: Methods and Results
|
Technical report for 3rd Anti-UAV Workshop and Challenge. arXiv admin
note: text overlap with arXiv:2108.09909
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The 3rd Anti-UAV Workshop & Challenge aims to encourage research in
developing novel and accurate methods for multi-scale object tracking. The
Anti-UAV dataset used for the Anti-UAV Challenge has been publicly released.
There are two main differences between this year's competition and the previous
two. First, we have expanded the existing dataset, and for the first time,
released a training set so that participants can focus on improving their
models. Second, we set up two tracks for the first time, i.e., Anti-UAV
Tracking and Anti-UAV Detection & Tracking. Around 76 participating teams from
the globe competed in the 3rd Anti-UAV Challenge. In this paper, we provide a
brief summary of the 3rd Anti-UAV Workshop & Challenge including brief
introductions to the top three methods in each track. The submission
leaderboard will be reopened for researchers that are interested in the
Anti-UAV challenge. The benchmark dataset and other information can be found
at: https://anti-uav.github.io/.
|
[
{
"version": "v1",
"created": "Fri, 12 May 2023 07:37:04 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Jul 2023 05:32:55 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Zhao",
"Jian",
""
],
[
"Li",
"Jianan",
""
],
[
"Jin",
"Lei",
""
],
[
"Chu",
"Jiaming",
""
],
[
"Zhang",
"Zhihao",
""
],
[
"Wang",
"Jun",
""
],
[
"Xia",
"Jiangqiang",
""
],
[
"Wang",
"Kai",
""
],
[
"Liu",
"Yang",
""
],
[
"Gulshad",
"Sadaf",
""
],
[
"Zhao",
"Jiaojiao",
""
],
[
"Xu",
"Tianyang",
""
],
[
"Zhu",
"Xuefeng",
""
],
[
"Liu",
"Shihan",
""
],
[
"Zhu",
"Zheng",
""
],
[
"Zhu",
"Guibo",
""
],
[
"Li",
"Zechao",
""
],
[
"Wang",
"Zheng",
""
],
[
"Sun",
"Baigui",
""
],
[
"Guo",
"Yandong",
""
],
[
"Satoh",
"Shin ichi",
""
],
[
"Xing",
"Junliang",
""
],
[
"Shengmei",
"Jane Shen",
""
]
] |
new_dataset
| 0.958054 |
2305.11408
|
Sara Papi
|
Sara Papi, Marco Turchi, Matteo Negri
|
AlignAtt: Using Attention-based Audio-Translation Alignments as a Guide
for Simultaneous Speech Translation
|
Accepted at Interspeech 2023
| null | null | null |
cs.CL cs.LG cs.SD eess.AS
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Attention is the core mechanism of today's most used architectures for
natural language processing and has been analyzed from many perspectives,
including its effectiveness for machine translation-related tasks. Among these
studies, attention resulted to be a useful source of information to get
insights about word alignment also when the input text is substituted with
audio segments, as in the case of the speech translation (ST) task. In this
paper, we propose AlignAtt, a novel policy for simultaneous ST (SimulST) that
exploits the attention information to generate source-target alignments that
guide the model during inference. Through experiments on the 8 language pairs
of MuST-C v1.0, we show that AlignAtt outperforms previous state-of-the-art
SimulST policies applied to offline-trained models with gains in terms of BLEU
of 2 points and latency reductions ranging from 0.5s to 0.8s across the 8
languages.
|
[
{
"version": "v1",
"created": "Fri, 19 May 2023 03:31:42 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Jul 2023 00:58:30 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Papi",
"Sara",
""
],
[
"Turchi",
"Marco",
""
],
[
"Negri",
"Matteo",
""
]
] |
new_dataset
| 0.973917 |
2305.17079
|
Felix Stutz
|
Elaine Li, Felix Stutz, Thomas Wies, Damien Zufferey
|
Complete Multiparty Session Type Projection with Automata
|
24 pages, 44 pages including appendix; CAV 2023
| null | null | null |
cs.FL cs.DC cs.PL
|
http://creativecommons.org/licenses/by/4.0/
|
Multiparty session types (MSTs) are a type-based approach to verifying
communication protocols. Central to MSTs is a projection operator: a partial
function that maps protocols represented as global types to
correct-by-construction implementations for each participant, represented as a
communicating state machine. Existing projection operators are syntactic in
nature, and trade efficiency for completeness. We present the first projection
operator that is sound, complete, and efficient. Our projection separates
synthesis from checking implementability. For synthesis, we use a simple
automata-theoretic construction; for checking implementability, we present
succinct conditions that summarize insights into the property of
implementability. We use these conditions to show that MST implementability is
PSPACE-complete. This improves upon a previous decision procedure that is in
EXPSPACE and applies to a smaller class of MSTs. We demonstrate the
effectiveness of our approach using a prototype implementation, which handles
global types not supported by previous work without sacrificing performance.
|
[
{
"version": "v1",
"created": "Fri, 26 May 2023 16:38:37 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 22:23:37 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Li",
"Elaine",
""
],
[
"Stutz",
"Felix",
""
],
[
"Wies",
"Thomas",
""
],
[
"Zufferey",
"Damien",
""
]
] |
new_dataset
| 0.956833 |
2306.14030
|
Raviraj Joshi
|
Tanmay Chavan, Omkar Gokhale, Aditya Kane, Shantanu Patankar, Raviraj
Joshi
|
My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models
and Evaluation Benchmarks
| null | null | null | null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The research on code-mixed data is limited due to the unavailability of
dedicated code-mixed datasets and pre-trained language models. In this work, we
focus on the low-resource Indian language Marathi which lacks any prior work in
code-mixing. We present L3Cube-MeCorpus, a large code-mixed Marathi-English
(Mr-En) corpus with 10 million social media sentences for pretraining. We also
release L3Cube-MeBERT and MeRoBERTa, code-mixed BERT-based transformer models
pre-trained on MeCorpus. Furthermore, for benchmarking, we present three
supervised datasets MeHate, MeSent, and MeLID for downstream tasks like
code-mixed Mr-En hate speech detection, sentiment analysis, and language
identification respectively. These evaluation datasets individually consist of
manually annotated \url{~}12,000 Marathi-English code-mixed tweets. Ablations
show that the models trained on this novel corpus significantly outperform the
existing state-of-the-art BERT models. This is the first work that presents
artifacts for code-mixed Marathi research. All datasets and models are publicly
released at https://github.com/l3cube-pune/MarathiNLP .
|
[
{
"version": "v1",
"created": "Sat, 24 Jun 2023 18:17:38 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Jul 2023 13:54:05 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Chavan",
"Tanmay",
""
],
[
"Gokhale",
"Omkar",
""
],
[
"Kane",
"Aditya",
""
],
[
"Patankar",
"Shantanu",
""
],
[
"Joshi",
"Raviraj",
""
]
] |
new_dataset
| 0.999863 |
2306.14795
|
Xin Chen
|
Biao Jiang, Xin Chen, Wen Liu, Jingyi Yu, Gang Yu, Tao Chen
|
MotionGPT: Human Motion as a Foreign Language
|
Project Page: https://github.com/OpenMotionLab/MotionGPT
| null | null | null |
cs.CV cs.CL cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
Though the advancement of pre-trained large language models unfolds, the
exploration of building a unified model for language and other multi-modal
data, such as motion, remains challenging and untouched so far. Fortunately,
human motion displays a semantic coupling akin to human language, often
perceived as a form of body language. By fusing language data with large-scale
motion models, motion-language pre-training that can enhance the performance of
motion-related tasks becomes feasible. Driven by this insight, we propose
MotionGPT, a unified, versatile, and user-friendly motion-language model to
handle multiple motion-relevant tasks. Specifically, we employ the discrete
vector quantization for human motion and transfer 3D motion into motion tokens,
similar to the generation process of word tokens. Building upon this "motion
vocabulary", we perform language modeling on both motion and text in a unified
manner, treating human motion as a specific language. Moreover, inspired by
prompt learning, we pre-train MotionGPT with a mixture of motion-language data
and fine-tune it on prompt-based question-and-answer tasks. Extensive
experiments demonstrate that MotionGPT achieves state-of-the-art performances
on multiple motion tasks including text-driven motion generation, motion
captioning, motion prediction, and motion in-between.
|
[
{
"version": "v1",
"created": "Mon, 26 Jun 2023 15:53:02 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Jul 2023 03:39:19 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Jiang",
"Biao",
""
],
[
"Chen",
"Xin",
""
],
[
"Liu",
"Wen",
""
],
[
"Yu",
"Jingyi",
""
],
[
"Yu",
"Gang",
""
],
[
"Chen",
"Tao",
""
]
] |
new_dataset
| 0.999645 |
2307.01091
|
Rita Pucci
|
Rita Pucci, Niki Martinel
|
UW-ProCCaps: UnderWater Progressive Colourisation with Capsules
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Underwater images are fundamental for studying and understanding the status
of marine life. We focus on reducing the memory space required for image
storage while the memory space consumption in the collecting phase limits the
time lasting of this phase leading to the need for more image collection
campaigns. We present a novel machine-learning model that reconstructs the
colours of underwater images from their luminescence channel, thus saving 2/3
of the available storage space. Our model specialises in underwater colour
reconstruction and consists of an encoder-decoder architecture. The encoder is
composed of a convolutional encoder and a parallel specialised classifier
trained with webly-supervised data. The encoder and the decoder use layers of
capsules to capture the features of the entities in the image. The colour
reconstruction process recalls the progressive and the generative adversarial
training procedures. The progressive training gives the ground for a generative
adversarial routine focused on the refining of colours giving the image bright
and saturated colours which bring the image back to life. We validate the model
both qualitatively and quantitatively on four benchmark datasets. This is the
first attempt at colour reconstruction in greyscale underwater images.
Extensive results on four benchmark datasets demonstrate that our solution
outperforms state-of-the-art (SOTA) solutions. We also demonstrate that the
generated colourisation enhances the quality of images compared to enhancement
models at the SOTA.
|
[
{
"version": "v1",
"created": "Mon, 3 Jul 2023 15:09:32 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Jul 2023 09:40:13 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Pucci",
"Rita",
""
],
[
"Martinel",
"Niki",
""
]
] |
new_dataset
| 0.995826 |
2307.04005
|
EPTCS
|
Rineke Verbrugge (University of Groningen)
|
Proceedings Nineteenth conference on Theoretical Aspects of Rationality
and Knowledge
| null |
EPTCS 379, 2023
|
10.4204/EPTCS.379
| null |
cs.LO cs.AI cs.GT cs.MA
|
http://creativecommons.org/licenses/by/4.0/
|
The TARK conference (Theoretical Aspects of Rationality and Knowledge) is a
conference that aims to bring together researchers from a wide variety of
fields, including computer science, artificial intelligence, game theory,
decision theory, philosophy, logic, linguistics, and cognitive science. Its
goal is to further our understanding of interdisciplinary issues involving
reasoning about rationality and knowledge.
Previous conferences have been held biennially around the world since 1986,
on the initiative of Joe Halpern (Cornell University). Topics of interest
include, but are not limited to, semantic models for knowledge, belief,
awareness and uncertainty, bounded rationality and resource-bounded reasoning,
commonsense epistemic reasoning, epistemic logic, epistemic game theory,
knowledge and action, applications of reasoning about knowledge and other
mental states, belief revision, computational social choice, algorithmic game
theory, and foundations of multi-agent systems. Information about TARK,
including conference proceedings, is available at http://www.tark.org/
These proceedings contain the papers that have been accepted for presentation
at the Nineteenth Conference on Theoretical Aspects of Rationality and
Knowledge (TARK 2023), held between June 28 and June 30, 2023, at the
University of Oxford, United Kingdom. The conference website can be found at
https://sites.google.com/view/tark-2023
|
[
{
"version": "v1",
"created": "Sat, 8 Jul 2023 16:22:42 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 14:31:39 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Verbrugge",
"Rineke",
"",
"University of Groningen"
]
] |
new_dataset
| 0.983682 |
2307.08122
|
Tian Yu Liu
|
Tian Yu Liu, Aditya Golatkar and Stefano Soatto
|
Tangent Transformers for Composition, Privacy and Removal
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce Tangent Attention Fine-Tuning (TAFT), a method for fine-tuning
linearized transformers obtained by computing a First-order Taylor Expansion
around a pre-trained initialization. We show that the Jacobian-Vector Product
resulting from linearization can be computed efficiently in a single forward
pass, reducing training and inference cost to the same order of magnitude as
its original non-linear counterpart, while using the same number of parameters.
Furthermore, we show that, when applied to various downstream visual
classification tasks, the resulting Tangent Transformer fine-tuned with TAFT
can perform comparably with fine-tuning the original non-linear network. Since
Tangent Transformers are linear with respect to the new set of weights, and the
resulting fine-tuning loss is convex, we show that TAFT enjoys several
advantages compared to non-linear fine-tuning when it comes to model
composition, parallel training, machine unlearning, and differential privacy.
|
[
{
"version": "v1",
"created": "Sun, 16 Jul 2023 18:31:25 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Jul 2023 03:07:28 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Liu",
"Tian Yu",
""
],
[
"Golatkar",
"Aditya",
""
],
[
"Soatto",
"Stefano",
""
]
] |
new_dataset
| 0.998846 |
2307.10165
|
Fernando Alonso-Fernandez
|
Moa Arvidsson, Sithichot Sawirot, Cristofer Englund, Fernando
Alonso-Fernandez, Martin Torstensson, Boris Duran
|
Drone navigation and license place detection for vehicle location in
indoor spaces
|
Published at VIII International Workshop on Artificial Intelligence
and Pattern Recognition, IWAIPR 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Millions of vehicles are transported every year, tightly parked in vessels or
boats. To reduce the risks of associated safety issues like fires, knowing the
location of vehicles is essential, since different vehicles may need different
mitigation measures, e.g. electric cars. This work is aimed at creating a
solution based on a nano-drone that navigates across rows of parked vehicles
and detects their license plates. We do so via a wall-following algorithm, and
a CNN trained to detect license plates. All computations are done in real-time
on the drone, which just sends position and detected images that allow the
creation of a 2D map with the position of the plates. Our solution is capable
of reading all plates across eight test cases (with several rows of plates,
different drone speeds, or low light) by aggregation of measurements across
several drone journeys.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 17:46:55 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Jul 2023 08:53:13 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Arvidsson",
"Moa",
""
],
[
"Sawirot",
"Sithichot",
""
],
[
"Englund",
"Cristofer",
""
],
[
"Alonso-Fernandez",
"Fernando",
""
],
[
"Torstensson",
"Martin",
""
],
[
"Duran",
"Boris",
""
]
] |
new_dataset
| 0.996888 |
2307.10214
|
Davide Sanvito
|
Giuseppe Siracusano, Davide Sanvito, Roberto Gonzalez, Manikantan
Srinivasan, Sivakaman Kamatchi, Wataru Takahashi, Masaru Kawakita, Takahiro
Kakumaru, Roberto Bifulco
|
Time for aCTIon: Automated Analysis of Cyber Threat Intelligence in the
Wild
| null | null | null | null |
cs.CR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cyber Threat Intelligence (CTI) plays a crucial role in assessing risks and
enhancing security for organizations. However, the process of extracting
relevant information from unstructured text sources can be expensive and
time-consuming. Our empirical experience shows that existing tools for
automated structured CTI extraction have performance limitations. Furthermore,
the community lacks a common benchmark to quantitatively assess their
performance. We fill these gaps providing a new large open benchmark dataset
and aCTIon, a structured CTI information extraction tool. The dataset includes
204 real-world publicly available reports and their corresponding structured
CTI information in STIX format. Our team curated the dataset involving three
independent groups of CTI analysts working over the course of several months.
To the best of our knowledge, this dataset is two orders of magnitude larger
than previously released open source datasets. We then design aCTIon,
leveraging recently introduced large language models (GPT3.5) in the context of
two custom information extraction pipelines. We compare our method with 10
solutions presented in previous work, for which we develop our own
implementations when open-source implementations were lacking. Our results show
that aCTIon outperforms previous work for structured CTI extraction with an
improvement of the F1-score from 10%points to 50%points across all tasks.
|
[
{
"version": "v1",
"created": "Fri, 14 Jul 2023 13:43:16 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Siracusano",
"Giuseppe",
""
],
[
"Sanvito",
"Davide",
""
],
[
"Gonzalez",
"Roberto",
""
],
[
"Srinivasan",
"Manikantan",
""
],
[
"Kamatchi",
"Sivakaman",
""
],
[
"Takahashi",
"Wataru",
""
],
[
"Kawakita",
"Masaru",
""
],
[
"Kakumaru",
"Takahiro",
""
],
[
"Bifulco",
"Roberto",
""
]
] |
new_dataset
| 0.998824 |
2307.10222
|
Amy Winecoff
|
Amy A. Winecoff and Johannes Lenhard
|
Techno-Utopians, Scammers, and Bullshitters: The Promise and Peril of
Web3 and Blockchain Technologies According to Operators and Venture Capital
Investors
| null | null | null | null |
cs.CY cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Proponents and developers of Web3 and blockchain argue that these
technologies can revolutionize how people live and work by empowering
individuals and distributing decision-making power. While technologists often
have expansive hopes for what their technologies will accomplish over the long
term, the practical challenges of developing, scaling, and maintaining systems
amidst present-day constraints can compromise progress toward this vision. How
technologists think about the technological future they hope to enable and how
they navigate day-to-day issues impacts the form technologies take, their
potential benefits, and their potential harms. In our current work, we aimed to
explore the visions of Web3 and blockchain technologists and identify the
immediate challenges that could threaten their visions. We conducted
semi-structured interviews with 29 operators and professional investors in the
Web3 and blockchain field. Our findings revealed that participants supported
several ideological goals for their projects, with decentralization being a
pivotal mechanism to enable user autonomy, distribute governance power, and
promote financial inclusion. However, participants acknowledged the practical
difficulties in fulfilling these promises, including the need for rapid
technology development, conflicts of interest among stakeholders due to
platform financing dynamics, and the challenge of expanding to mainstream users
who may not share the "Web3 ethos." If negotiated ineffectively, these
challenges could lead to negative outcomes, such as corrupt governance,
increased inequality, and increased prevalence of scams and dubious investment
schemes. While participants thought education, regulation, and a renewed
commitment to the original blockchain ideals could alleviate some problems,
they expressed skepticism about the potential of these solutions.
|
[
{
"version": "v1",
"created": "Fri, 14 Jul 2023 22:36:14 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Winecoff",
"Amy A.",
""
],
[
"Lenhard",
"Johannes",
""
]
] |
new_dataset
| 0.996311 |
2307.10226
|
Joohyung Lee
|
Joohyung Lee, Yunsong Meng
|
On Loop Formulas with Variables
|
10 pages. In Proc. Eleventh International Conference on Principles of
Knowledge Representation and Reasoning (KR 2008), pages 444-453. arXiv admin
note: text overlap with arXiv:1401.3898
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently Ferraris, Lee and Lifschitz proposed a new definition of stable
models that does not refer to grounding, which applies to the syntax of
arbitrary first-order sentences. We show its relation to the idea of loop
formulas with variables by Chen, Lin, Wang and Zhang, and generalize their loop
formulas to disjunctive programs and to arbitrary first-order sentences. We
also extend the syntax of logic programs to allow explicit quantifiers, and
define its semantics as a subclass of the new language of stable models by
Ferraris et al. Such programs inherit from the general language the ability to
handle nonmonotonic reasoning under the stable model semantics even in the
absence of the unique name and the domain closure assumptions, while yielding
more succinct loop formulas than the general language due to the restricted
syntax. We also show certain syntactic conditions under which query answering
for an extended program can be reduced to entailment checking in first-order
logic, providing a way to apply first-order theorem provers to reasoning about
non-Herbrand stable models.
|
[
{
"version": "v1",
"created": "Sat, 15 Jul 2023 06:20:43 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Lee",
"Joohyung",
""
],
[
"Meng",
"Yunsong",
""
]
] |
new_dataset
| 0.98941 |
2307.10267
|
Richard Wang
|
Raiyan Rahman, Christopher Indris, Tianxiao Zhang, Kaidong Li, Brian
McCornack, Daniel Flippo, Ajay Sharda, Guanghui Wang
|
On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Aphid infestations can cause extensive damage to wheat and sorghum fields and
spread plant viruses, resulting in significant yield losses in agriculture. To
address this issue, farmers often rely on chemical pesticides, which are
inefficiently applied over large areas of fields. As a result, a considerable
amount of pesticide is wasted on areas without pests, while inadequate amounts
are applied to areas with severe infestations. The paper focuses on the urgent
need for an intelligent autonomous system that can locate and spray
infestations within complex crop canopies, reducing pesticide use and
environmental impact. We have collected and labeled a large aphid image dataset
in the field, and propose the use of real-time semantic segmentation models to
segment clusters of aphids. A multiscale dataset is generated to allow for
learning the clusters at different scales. We compare the segmentation speeds
and accuracy of four state-of-the-art real-time semantic segmentation models on
the aphid cluster dataset, benchmarking them against nonreal-time models. The
study results show the effectiveness of a real-time solution, which can reduce
inefficient pesticide use and increase crop yields, paving the way towards an
autonomous pest detection system.
|
[
{
"version": "v1",
"created": "Mon, 17 Jul 2023 19:04:39 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Rahman",
"Raiyan",
""
],
[
"Indris",
"Christopher",
""
],
[
"Zhang",
"Tianxiao",
""
],
[
"Li",
"Kaidong",
""
],
[
"McCornack",
"Brian",
""
],
[
"Flippo",
"Daniel",
""
],
[
"Sharda",
"Ajay",
""
],
[
"Wang",
"Guanghui",
""
]
] |
new_dataset
| 0.984807 |
2307.10283
|
Anastasia Natsiou
|
Anastasia Natsiou, Luca Longo, Sean O'Leary
|
Interpretable Timbre Synthesis using Variational Autoencoders
Regularized on Timbre Descriptors
| null | null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Controllable timbre synthesis has been a subject of research for several
decades, and deep neural networks have been the most successful in this area.
Deep generative models such as Variational Autoencoders (VAEs) have the ability
to generate a high-level representation of audio while providing a structured
latent space. Despite their advantages, the interpretability of these latent
spaces in terms of human perception is often limited. To address this
limitation and enhance the control over timbre generation, we propose a
regularized VAE-based latent space that incorporates timbre descriptors.
Moreover, we suggest a more concise representation of sound by utilizing its
harmonic content, in order to minimize the dimensionality of the latent space.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 11:46:13 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Natsiou",
"Anastasia",
""
],
[
"Longo",
"Luca",
""
],
[
"O'Leary",
"Sean",
""
]
] |
new_dataset
| 0.997731 |
2307.10286
|
Mona Ghassemian
|
Dejan Vukobratovi\'c, Nikolaos Bartzoudis, Mona Ghassemian, Firooz
Saghezchi, Peizheng Li, Adnan Aijaz, Ricardo Martinez, Xueli An, Ranga Rao
Venkatesha Prasad, Helge L\"uders, and Shahid Mumtaz
|
Distributed Sensing, Computing, Communication, and Control Fabric: A
Unified Service-Level Architecture for 6G
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the advent of the multimodal immersive communication system, people can
interact with each other using multiple devices for sensing, communication
and/or control either onsite or remotely. As a breakthrough concept, a
distributed sensing, computing, communications, and control (DS3C) fabric is
introduced in this paper for provisioning 6G services in multi-tenant
environments in a unified manner. The DS3C fabric can be further enhanced by
natively incorporating intelligent algorithms for network automation and
managing networking, computing, and sensing resources efficiently to serve
vertical use cases with extreme and/or conflicting requirements. As such, the
paper proposes a novel end-to-end 6G system architecture with enhanced
intelligence spanning across different network, computing, and business
domains, identifies vertical use cases and presents an overview of the relevant
standardization and pre-standardization landscape.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 13:30:44 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Vukobratović",
"Dejan",
""
],
[
"Bartzoudis",
"Nikolaos",
""
],
[
"Ghassemian",
"Mona",
""
],
[
"Saghezchi",
"Firooz",
""
],
[
"Li",
"Peizheng",
""
],
[
"Aijaz",
"Adnan",
""
],
[
"Martinez",
"Ricardo",
""
],
[
"An",
"Xueli",
""
],
[
"Prasad",
"Ranga Rao Venkatesha",
""
],
[
"Lüders",
"Helge",
""
],
[
"Mumtaz",
"Shahid",
""
]
] |
new_dataset
| 0.994317 |
2307.10305
|
Vinayak Gupta
|
Vinayak Gupta and Srikanta Bedathur
|
Tapestry of Time and Actions: Modeling Human Activity Sequences using
Temporal Point Process Flows
|
Extended version of Gupta and Bedathur [arXiv:2206.05291] (SIGKDD
2022). Under review in a journal
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Human beings always engage in a vast range of activities and tasks that
demonstrate their ability to adapt to different scenarios. Any human activity
can be represented as a temporal sequence of actions performed to achieve a
certain goal. Unlike the time series datasets extracted from electronics or
machines, these action sequences are highly disparate in their nature -- the
time to finish a sequence of actions can vary between different persons.
Therefore, understanding the dynamics of these sequences is essential for many
downstream tasks such as activity length prediction, goal prediction, next
action recommendation, etc. Existing neural network-based approaches that learn
a continuous-time activity sequence (or CTAS) are limited to the presence of
only visual data or are designed specifically for a particular task, i.e.,
limited to next action or goal prediction. In this paper, we present ProActive,
a neural marked temporal point process (MTPP) framework for modeling the
continuous-time distribution of actions in an activity sequence while
simultaneously addressing three high-impact problems -- next action prediction,
sequence-goal prediction, and end-to-end sequence generation. Specifically, we
utilize a self-attention module with temporal normalizing flows to model the
influence and the inter-arrival times between actions in a sequence. In
addition, we propose a novel addition over the ProActive model that can handle
variations in the order of actions, i.e., different methods of achieving a
given goal. We demonstrate that this variant can learn the order in which the
person or actor prefers to do their actions. Extensive experiments on sequences
derived from three activity recognition datasets show the significant accuracy
boost of ProActive over the state-of-the-art in terms of action and goal
prediction, and the first-ever application of end-to-end action sequence
generation.
|
[
{
"version": "v1",
"created": "Thu, 13 Jul 2023 19:17:54 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Gupta",
"Vinayak",
""
],
[
"Bedathur",
"Srikanta",
""
]
] |
new_dataset
| 0.987677 |
2307.10314
|
Nafees Mansoor PhD
|
Maliha Mahajebin, Mohammad Rifat Ahmmad Rashid, Nafees Mansoor
|
Mood Classification of Bangla Songs Based on Lyrics
|
Presented at International Conference on. Inventive Communication and
Computational Technologies 2023
| null | null | null |
cs.IR cs.CL cs.LG cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Music can evoke various emotions, and with the advancement of technology, it
has become more accessible to people. Bangla music, which portrays different
human emotions, lacks sufficient research. The authors of this article aim to
analyze Bangla songs and classify their moods based on the lyrics. To achieve
this, this research has compiled a dataset of 4000 Bangla song lyrics, genres,
and used Natural Language Processing and the Bert Algorithm to analyze the
data. Among the 4000 songs, 1513 songs are represented for the sad mood, 1362
for the romantic mood, 886 for happiness, and the rest 239 are classified as
relaxation. By embedding the lyrics of the songs, the authors have classified
the songs into four moods: Happy, Sad, Romantic, and Relaxed. This research is
crucial as it enables a multi-class classification of songs' moods, making the
music more relatable to people's emotions. The article presents the automated
result of the four moods accurately derived from the song lyrics.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 03:31:41 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Mahajebin",
"Maliha",
""
],
[
"Rashid",
"Mohammad Rifat Ahmmad",
""
],
[
"Mansoor",
"Nafees",
""
]
] |
new_dataset
| 0.999231 |
2307.10346
|
Roberto Daza
|
\'Alvaro Becerra, Roberto Daza, Ruth Cobos, Aythami Morales, Julian
Fierrez
|
Estudio de la Experiencia de Usuario mediante un Sistema de Dashboards
de An\'alisis de Aprendizaje Multimodal
|
Accepted in "XXIII CONGRESO INTERNACIONAL DE INTERACCI\'ON
PERSONA-ORDENADOR 2023". Article in Spanish language. The abstract in English
and Spanish. There is an extended abstract of 2 pages in English
| null | null | null |
cs.HC cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the article, we present a Web-based System called M2LADS, which supports
the integration and visualization of multimodal data recorded in user
experiences (UX) in a Learning Analytics (LA) system in the form of Web-based
Dashboards. Based on the edBB platform, the multimodal data gathered contains
biometric and behavioral signals including electroencephalogram data to measure
learners' cognitive attention, heart rate for affective measures and visual
attention from the video recordings. Additionally, learners' static background
data and their learning performance measures are tracked using LOGGE tool.
M2LADS provides opportunities to capture learners' holistic experience during
their interactions with the learning analytic system in order to improve the
system and the user experience of the learners.
--
En este art\'iculo, presentamos M2LADS, un sistema que permite la
integraci\'on y visualizaci\'on de datos multimodales en forma de Dashboards
Web. Estos datos provienen de sesiones de experiencia de usuario en un sistema
de Learning Analytics (LA) llevadas a cabo por estudiantes de MOOCs. Los datos
multimodales incluyen se\~nales biom\'etricas y de comportamiento monitorizados
por la plataforma edBB, como electroencefalogramas (EEG) de 5 canales,
frecuencia card\'iaca, atenci\'on visual, videos en el espectro visible y NIR,
entre otros. Adem\'as, se incluyen datos de interacci\'on de los estudiantes
con el sistema de LA a trav\'es de la herramienta LOGGE. Toda esta
informaci\'on proporciona una comprensi\'on completa de la experiencia del
usuario al utilizar el sistema de LA, lo que ha permitido tanto mejorar el
sistema LA como la experiencia de aprendizaje de los estudiantes de MOOCs.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 17:10:56 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Becerra",
"Álvaro",
""
],
[
"Daza",
"Roberto",
""
],
[
"Cobos",
"Ruth",
""
],
[
"Morales",
"Aythami",
""
],
[
"Fierrez",
"Julian",
""
]
] |
new_dataset
| 0.962666 |
2307.10349
|
Hans Hanley
|
Hans W. A. Hanley, Zakir Durumeric
|
Twits, Toxic Tweets, and Tribal Tendencies: Trends in Politically
Polarized Posts on Twitter
| null | null | null | null |
cs.SI cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Social media platforms are often blamed for exacerbating political
polarization and worsening public dialogue. Many claim hyperpartisan users post
pernicious content, slanted to their political views, inciting contentious and
toxic conversations. However, what factors, actually contribute to increased
online toxicity and negative interactions? In this work, we explore the role
that political ideology plays in contributing to toxicity both on an individual
user level and a topic level on Twitter. To do this, we train and open-source a
DeBERTa-based toxicity detector with a contrastive objective that outperforms
the Google Jigsaw Persective Toxicity detector on the Civil Comments test
dataset. Then, after collecting 187 million tweets from 55,415 Twitter users,
we determine how several account-level characteristics, including political
ideology and account age, predict how often each user posts toxic content.
Running a linear regression, we find that the diversity of views and the
toxicity of the other accounts with which that user engages has a more marked
effect on their own toxicity. Namely, toxic comments are correlated with users
who engage with a wider array of political views. Performing topic analysis on
the toxic content posted by these accounts using the large language model MPNet
and a version of the DP-Means clustering algorithm, we find similar behavior
across 6,592 individual topics, with conversations on each topic becoming more
toxic as a wider diversity of users become involved.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 17:24:47 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Hanley",
"Hans W. A.",
""
],
[
"Durumeric",
"Zakir",
""
]
] |
new_dataset
| 0.999613 |
2307.10455
|
Zahra Gharaee
|
Zahra Gharaee, ZeMing Gong, Nicholas Pellegrino, Iuliia Zarubiieva,
Joakim Bruslund Haurum, Scott C. Lowe, Jaclyn T.A. McKeown, Chris C.Y. Ho,
Joschka McLeod, Yi-Yun C Wei, Jireh Agda, Sujeevan Ratnasingham, Dirk
Steinke, Angel X. Chang, Graham W. Taylor, Paul Fieguth
|
A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect
Dataset
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In an effort to catalog insect biodiversity, we propose a new large dataset
of hand-labelled insect images, the BIOSCAN-Insect Dataset. Each record is
taxonomically classified by an expert, and also has associated genetic
information including raw nucleotide barcode sequences and assigned barcode
index numbers, which are genetically-based proxies for species classification.
This paper presents a curated million-image dataset, primarily to train
computer-vision models capable of providing image-based taxonomic assessment,
however, the dataset also presents compelling characteristics, the study of
which would be of interest to the broader machine learning community. Driven by
the biological nature inherent to the dataset, a characteristic long-tailed
class-imbalance distribution is exhibited. Furthermore, taxonomic labelling is
a hierarchical classification scheme, presenting a highly fine-grained
classification problem at lower levels. Beyond spurring interest in
biodiversity research within the machine learning community, progress on
creating an image-based taxonomic classifier will also further the ultimate
goal of all BIOSCAN research: to lay the foundation for a comprehensive survey
of global biodiversity. This paper introduces the dataset and explores the
classification task through the implementation and analysis of a baseline
classifier.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 20:54:08 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Gharaee",
"Zahra",
""
],
[
"Gong",
"ZeMing",
""
],
[
"Pellegrino",
"Nicholas",
""
],
[
"Zarubiieva",
"Iuliia",
""
],
[
"Haurum",
"Joakim Bruslund",
""
],
[
"Lowe",
"Scott C.",
""
],
[
"McKeown",
"Jaclyn T. A.",
""
],
[
"Ho",
"Chris C. Y.",
""
],
[
"McLeod",
"Joschka",
""
],
[
"Wei",
"Yi-Yun C",
""
],
[
"Agda",
"Jireh",
""
],
[
"Ratnasingham",
"Sujeevan",
""
],
[
"Steinke",
"Dirk",
""
],
[
"Chang",
"Angel X.",
""
],
[
"Taylor",
"Graham W.",
""
],
[
"Fieguth",
"Paul",
""
]
] |
new_dataset
| 0.999805 |
2307.10481
|
Min Chen
|
Yuanzhe Jin, Tim J. A. de Jong, Martijn Tennekes, and Min Chen
|
Radial Icicle Tree (RIT): Node Separation and Area Constancy
| null | null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Icicles and sunbursts are two commonly-used visual representations of trees.
While icicle trees can map data values faithfully to rectangles of different
sizes, often some rectangles are too narrow to be noticed easily. When an
icicle tree is transformed into a sunburst tree, the width of each rectangle
becomes the length of an annular sector that is usually longer than the
original width. While sunburst trees alleviate the problem of narrow rectangles
in icicle trees, it no longer maintains the consistency of size encoding. At
different tree depths, nodes of the same data values are displayed in annular
sections of different sizes in a sunburst tree, though they are represented by
rectangles of the same size in an icicle tree. Furthermore, two nodes from
different subtrees could sometimes appear as a single node in both icicle trees
and sunburst trees. In this paper, we propose a new visual representation,
referred to as \emph{radial icicle tree} (RIT), which transforms the
rectangular bounding box of an icicle tree into a circle, circular sector, or
annular sector while introducing gaps between nodes and maintaining area
constancy for nodes of the same size. We applied the new visual design to
several datasets. Both the analytical design process and user-centered
evaluation have confirmed that this new design has improved the design of
icicles and sunburst trees without introducing any relative demerit.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 22:25:14 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Jin",
"Yuanzhe",
""
],
[
"de Jong",
"Tim J. A.",
""
],
[
"Tennekes",
"Martijn",
""
],
[
"Chen",
"Min",
""
]
] |
new_dataset
| 0.969385 |
2307.10482
|
Heiko Kabutz
|
Heiko Kabutz, Kaushik Jayaram
|
Design of CLARI: A miniature modular origami passive shape-morphing
robot
| null | null | null | null |
cs.RO cs.SY eess.SY
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Miniature robots provide unprecedented access to confined environments and
show promising potential for novel applications such as search-and-rescue and
high-value asset inspection. The capability of body deformation further
enhances the reachability of these small robots in complex cluttered terrains
similar to those of insects and soft arthropods. Motivated by this concept, we
present CLARI, an insect-scale 2.59g quadrupedal robot capable of body
deformation with tethered electrical connections for power and control and
manufactured using laminate fabrication and assembled using origami pop-up
techniques. In order to enable locomotion in multiple shape configurations, we
designed a novel body architecture comprising of modular, actuated leg
mechanisms. Overall, CLARI has eight independently actuated degrees of freedom
(two per modular leg unit) driven by custom piezoelectric actuators, making it
mechanically dextrous. We characterize open-loop robot locomotion at multiple
stride frequencies (1-10Hz) using multiple gaits (trot, walk, etc.) in three
different fixed body shapes (long, symmetric, wide) and illustrate the robot's
capabilities. Finally, we demonstrate preliminary results of CLARI locomoting
with a compliant body in open terrain and through a laterally constrained gap,
a novel capability for legged robots. Our results represent the first step
towards achieving effective cluttered terrain navigation with adaptable
compliant robots in real-world environments.
|
[
{
"version": "v1",
"created": "Wed, 19 Jul 2023 22:26:31 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Kabutz",
"Heiko",
""
],
[
"Jayaram",
"Kaushik",
""
]
] |
new_dataset
| 0.999604 |
2307.10550
|
Yong-Hoon Choi
|
Daegyeom Kim, Seongho Hong, and Yong-Hoon Choi
|
SC VALL-E: Style-Controllable Zero-Shot Text to Speech Synthesizer
| null | null | null | null |
cs.SD cs.LG eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Expressive speech synthesis models are trained by adding corpora with diverse
speakers, various emotions, and different speaking styles to the dataset, in
order to control various characteristics of speech and generate the desired
voice. In this paper, we propose a style control (SC) VALL-E model based on the
neural codec language model (called VALL-E), which follows the structure of the
generative pretrained transformer 3 (GPT-3). The proposed SC VALL-E takes input
from text sentences and prompt audio and is designed to generate controllable
speech by not simply mimicking the characteristics of the prompt audio but by
controlling the attributes to produce diverse voices. We identify tokens in the
style embedding matrix of the newly designed style network that represent
attributes such as emotion, speaking rate, pitch, and voice intensity, and
design a model that can control these attributes. To evaluate the performance
of SC VALL-E, we conduct comparative experiments with three representative
expressive speech synthesis models: global style token (GST) Tacotron2,
variational autoencoder (VAE) Tacotron2, and original VALL-E. We measure word
error rate (WER), F0 voiced error (FVE), and F0 gross pitch error (F0GPE) as
evaluation metrics to assess the accuracy of generated sentences. For comparing
the quality of synthesized speech, we measure comparative mean option score
(CMOS) and similarity mean option score (SMOS). To evaluate the style control
ability of the generated speech, we observe the changes in F0 and
mel-spectrogram by modifying the trained tokens. When using prompt audio that
is not present in the training data, SC VALL-E generates a variety of
expressive sounds and demonstrates competitive performance compared to the
existing models. Our implementation, pretrained models, and audio samples are
located on GitHub.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 03:28:06 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Kim",
"Daegyeom",
""
],
[
"Hong",
"Seongho",
""
],
[
"Choi",
"Yong-Hoon",
""
]
] |
new_dataset
| 0.999623 |
2307.10551
|
Kaiwen Wei
|
Kaiwen Wei, Jie Yao, Jingyuan Zhang, Yangyang Kang, Fubang Zhao,
Yating Zhang, Changlong Sun, Xin Jin, Xin Zhang
|
PPN: Parallel Pointer-based Network for Key Information Extraction with
Complex Layouts
| null | null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Key Information Extraction (KIE) is a challenging multimodal task that aims
to extract structured value semantic entities from visually rich documents.
Although significant progress has been made, there are still two major
challenges that need to be addressed. Firstly, the layout of existing datasets
is relatively fixed and limited in the number of semantic entity categories,
creating a significant gap between these datasets and the complex real-world
scenarios. Secondly, existing methods follow a two-stage pipeline strategy,
which may lead to the error propagation problem. Additionally, they are
difficult to apply in situations where unseen semantic entity categories
emerge. To address the first challenge, we propose a new large-scale
human-annotated dataset named Complex Layout form for key information
EXtraction (CLEX), which consists of 5,860 images with 1,162 semantic entity
categories. To solve the second challenge, we introduce Parallel Pointer-based
Network (PPN), an end-to-end model that can be applied in zero-shot and
few-shot scenarios. PPN leverages the implicit clues between semantic entities
to assist extracting, and its parallel extraction mechanism allows it to
extract multiple results simultaneously and efficiently. Experiments on the
CLEX dataset demonstrate that PPN outperforms existing state-of-the-art methods
while also offering a much faster inference speed.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 03:29:09 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Wei",
"Kaiwen",
""
],
[
"Yao",
"Jie",
""
],
[
"Zhang",
"Jingyuan",
""
],
[
"Kang",
"Yangyang",
""
],
[
"Zhao",
"Fubang",
""
],
[
"Zhang",
"Yating",
""
],
[
"Sun",
"Changlong",
""
],
[
"Jin",
"Xin",
""
],
[
"Zhang",
"Xin",
""
]
] |
new_dataset
| 0.998575 |
2307.10567
|
Qi Zhang
|
Qi Zhang and Sipeng Zheng and Qin Jin
|
No-frills Temporal Video Grounding: Multi-Scale Neighboring Attention
and Zoom-in Boundary Detection
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Temporal video grounding (TVG) aims to retrieve the time interval of a
language query from an untrimmed video. A significant challenge in TVG is the
low "Semantic Noise Ratio (SNR)", which results in worse performance with lower
SNR. Prior works have addressed this challenge using sophisticated techniques.
In this paper, we propose a no-frills TVG model that consists of two core
modules, namely multi-scale neighboring attention and zoom-in boundary
detection. The multi-scale neighboring attention restricts each video token to
only aggregate visual contexts from its neighbor, enabling the extraction of
the most distinguishing information with multi-scale feature hierarchies from
high-ratio noises. The zoom-in boundary detection then focuses on local-wise
discrimination of the selected top candidates for fine-grained grounding
adjustment. With an end-to-end training strategy, our model achieves
competitive performance on different TVG benchmarks, while also having the
advantage of faster inference speed and lighter model parameters, thanks to its
lightweight architecture.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 04:12:10 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Zhang",
"Qi",
""
],
[
"Zheng",
"Sipeng",
""
],
[
"Jin",
"Qin",
""
]
] |
new_dataset
| 0.980045 |
2307.10593
|
Ziwei Wang
|
Ziwei Wang, Timothy Molloy, Pieter van Goor, Robert Mahony
|
Event Blob Tracking: An Asynchronous Real-Time Algorithm
|
17 pages, 8 figures, preprint version
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Event-based cameras have become increasingly popular for tracking fast-moving
objects due to their high temporal resolution, low latency, and high dynamic
range. In this paper, we propose a novel algorithm for tracking event blobs
using raw events asynchronously in real time. We introduce the concept of an
event blob as a spatio-temporal likelihood of event occurrence where the
conditional spatial likelihood is blob-like. Many real-world objects generate
event blob data, for example, flickering LEDs such as car headlights or any
small foreground object moving against a static or slowly varying background.
The proposed algorithm uses a nearest neighbour classifier with a dynamic
threshold criteria for data association coupled with a Kalman filter to track
the event blob state. Our algorithm achieves highly accurate tracking and event
blob shape estimation even under challenging lighting conditions and high-speed
motions. The microsecond time resolution achieved means that the filter output
can be used to derive secondary information such as time-to-contact or range
estimation, that will enable applications to real-world problems such as
collision avoidance in autonomous driving.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 05:15:03 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Wang",
"Ziwei",
""
],
[
"Molloy",
"Timothy",
""
],
[
"van Goor",
"Pieter",
""
],
[
"Mahony",
"Robert",
""
]
] |
new_dataset
| 0.99964 |
2307.10601
|
Dongyun Lin
|
Dongyun Lin, Yi Cheng, Aiyuan Guo, Shangbo Mao, Yiqun Li
|
SCA-PVNet: Self-and-Cross Attention Based Aggregation of Point Cloud and
Multi-View for 3D Object Retrieval
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To address 3D object retrieval, substantial efforts have been made to
generate highly discriminative descriptors of 3D objects represented by a
single modality, e.g., voxels, point clouds or multi-view images. It is
promising to leverage the complementary information from multi-modality
representations of 3D objects to further improve retrieval performance.
However, multi-modality 3D object retrieval is rarely developed and analyzed on
large-scale datasets. In this paper, we propose self-and-cross attention based
aggregation of point cloud and multi-view images (SCA-PVNet) for 3D object
retrieval. With deep features extracted from point clouds and multi-view
images, we design two types of feature aggregation modules, namely the
In-Modality Aggregation Module (IMAM) and the Cross-Modality Aggregation Module
(CMAM), for effective feature fusion. IMAM leverages a self-attention mechanism
to aggregate multi-view features while CMAM exploits a cross-attention
mechanism to interact point cloud features with multi-view features. The final
descriptor of a 3D object for object retrieval can be obtained via
concatenating the aggregated features from both modules. Extensive experiments
and analysis are conducted on three datasets, ranging from small to large
scale, to show the superiority of the proposed SCA-PVNet over the
state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 05:46:32 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Lin",
"Dongyun",
""
],
[
"Cheng",
"Yi",
""
],
[
"Guo",
"Aiyuan",
""
],
[
"Mao",
"Shangbo",
""
],
[
"Li",
"Yiqun",
""
]
] |
new_dataset
| 0.999583 |
2307.10615
|
Kshitiz Verma
|
Kshitiz Verma
|
Analyzing HC-NJDG Data to Understand the Pendency in High Courts in
India
|
25 pages, 31 figures, presented at Law Via Internet Conference, 2018
| null | null | null |
cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Indian Judiciary is suffering from burden of millions of cases that are lying
pending in its courts at all the levels. In this paper, we analyze the data
that we have collected on the pendency of 24 high courts in the Republic of
India as they were made available on High Court NJDG (HC-NJDG). We collected
data on 73 days beginning August 31, 2017 to December 26, 2018, including these
days. Thus, the data collected by us spans a period of almost sixteen months.
We have analyzed various statistics available on the NJDG portal for High
Courts, including but not limited to the number of judges in each high court,
the number of cases pending in each high court, cases that have been pending
for more than 10 years, cases filed, listed and disposed, cases filed by women
and senior citizens, etc. Our results show that: 1) statistics as important as
the number of judges in high courts have serious errors on NJDG (Fig. 1, 2, 10,
11, Table V). 2) pending cases in most of the high courts are increasing rather
than decreasing (Fig. 3, 13). 3) regular update of HC-NJDG is required for it
to be useful. Data related to some high courts is not being updated regularly
or is updated erroneously on the portal (Fig. 14). 4) there is a huge
difference in terms of average load of cases on judges of different high courts
(Fig. 6). 5) if all the high courts operate at their approved strength of
judges, then for most of the high courts pendency can be nullified within 20
years from now (Fig. 21, 22). 6) the pending cases filed by women and senior
citizens are disproportionately low, they together constitute less than 10% of
the total pending cases (Fig. 23 - 27) 7) a better scheduling process for
preparing causelists in courts can help reducing the number of pending cases in
the High Courts (Fig. 29). 8) some statistics are not well defined (Fig. 31).
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 06:25:53 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Verma",
"Kshitiz",
""
]
] |
new_dataset
| 0.997191 |
2307.10635
|
Yanqiao Zhu
|
Xiaoxuan Wang and Ziniu Hu and Pan Lu and Yanqiao Zhu and Jieyu Zhang
and Satyen Subramaniam and Arjun R. Loomba and Shichang Zhang and Yizhou Sun
and Wei Wang
|
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities
of Large Language Models
|
Work in progress, 18 pages
| null | null | null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent advances in large language models (LLMs) have demonstrated notable
progress on many mathematical benchmarks. However, most of these benchmarks
only feature problems grounded in junior and senior high school subjects,
contain only multiple-choice questions, and are confined to a limited scope of
elementary arithmetic operations. To address these issues, this paper
introduces an expansive benchmark suite SciBench that aims to systematically
examine the reasoning capabilities required for complex scientific problem
solving. SciBench contains two carefully curated datasets: an open set
featuring a range of collegiate-level scientific problems drawn from
mathematics, chemistry, and physics textbooks, and a closed set comprising
problems from undergraduate-level exams in computer science and mathematics.
Based on the two datasets, we conduct an in-depth benchmark study of two
representative LLMs with various prompting strategies. The results reveal that
current LLMs fall short of delivering satisfactory performance, with an overall
score of merely 35.80%. Furthermore, through a detailed user study, we
categorize the errors made by LLMs into ten problem-solving abilities. Our
analysis indicates that no single prompting strategy significantly outperforms
others and some strategies that demonstrate improvements in certain
problem-solving skills result in declines in other skills. We envision that
SciBench will catalyze further developments in the reasoning abilities of LLMs,
thereby ultimately contributing to scientific research and discovery.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 07:01:57 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Wang",
"Xiaoxuan",
""
],
[
"Hu",
"Ziniu",
""
],
[
"Lu",
"Pan",
""
],
[
"Zhu",
"Yanqiao",
""
],
[
"Zhang",
"Jieyu",
""
],
[
"Subramaniam",
"Satyen",
""
],
[
"Loomba",
"Arjun R.",
""
],
[
"Zhang",
"Shichang",
""
],
[
"Sun",
"Yizhou",
""
],
[
"Wang",
"Wei",
""
]
] |
new_dataset
| 0.99914 |
2307.10642
|
Qichao Ying
|
Qichao Ying, Jiaxin Liu, Sheng Li, Haisheng Xu, Zhenxing Qian, Xinpeng
Zhang
|
RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching
Detection
|
Under review
| null | null | null |
cs.CV cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
The widespread use of face retouching filters on short-video platforms has
raised concerns about the authenticity of digital appearances and the impact of
deceptive advertising. To address these issues, there is a pressing need to
develop advanced face retouching techniques. However, the lack of large-scale
and fine-grained face retouching datasets has been a major obstacle to progress
in this field. In this paper, we introduce RetouchingFFHQ, a large-scale and
fine-grained face retouching dataset that contains over half a million
conditionally-retouched images. RetouchingFFHQ stands out from previous
datasets due to its large scale, high quality, fine-grainedness, and
customization. By including four typical types of face retouching operations
and different retouching levels, we extend the binary face retouching detection
into a fine-grained, multi-retouching type, and multi-retouching level
estimation problem. Additionally, we propose a Multi-granularity Attention
Module (MAM) as a plugin for CNN backbones for enhanced cross-scale
representation learning. Extensive experiments using different baselines as
well as our proposed method on RetouchingFFHQ show decent performance on face
retouching detection. With the proposed new dataset, we believe there is great
potential for future work to tackle the challenging problem of real-world
fine-grained face retouching detection.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 07:12:56 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Ying",
"Qichao",
""
],
[
"Liu",
"Jiaxin",
""
],
[
"Li",
"Sheng",
""
],
[
"Xu",
"Haisheng",
""
],
[
"Qian",
"Zhenxing",
""
],
[
"Zhang",
"Xinpeng",
""
]
] |
new_dataset
| 0.999696 |
2307.10646
|
Mikko Majamaa
|
Mikko Majamaa, Henrik Martikainen, Jani Puttonen and Timo
H\"am\"alainen
|
On Enhancing Reliability in B5G NTNs with Packet Duplication via
Multi-Connectivity
|
Accepted for publication in 2023 IEEE International Conference on
Wireless for Space and Extreme Environments (WiSEE 2023)
| null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Non-Terrestrial Networks (NTNs) can be used to provide ubiquitous 5G and
beyond services to un(der)served areas. To ensure reliable communication in
such networks, packet duplication (PD) through multi-connectivity is a
promising solution. However, the existing PD schemes developed for terrestrial
environments may not be reactive enough for the NTN environment where
propagation delays are significantly longer. This paper proposes a dynamic PD
activation scheme for NTNs based on hybrid automatic repeat request feedback.
The scheme aims to reduce the number of duplicated packets while maintaining
high reliability. To evaluate the proposed scheme, simulations are conducted in
a scenario with two transparent payload lowearth orbit satellites. The results
show a significant reduction of 87.2% in the number of duplicated packets
compared to blind duplication, with only marginal compromise in reliability.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 07:16:05 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Majamaa",
"Mikko",
""
],
[
"Martikainen",
"Henrik",
""
],
[
"Puttonen",
"Jani",
""
],
[
"Hämälainen",
"Timo",
""
]
] |
new_dataset
| 0.996095 |
2307.10666
|
Jind\v{r}ich Libovick\'y
|
Hynek Kydl\'i\v{c}ek, Jind\v{r}ich Libovick\'y
|
A Dataset and Strong Baselines for Classification of Czech News Texts
|
12 pages, Accepted to Text, Speech and Dialogue (TSD) 2023
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Pre-trained models for Czech Natural Language Processing are often evaluated
on purely linguistic tasks (POS tagging, parsing, NER) and relatively simple
classification tasks such as sentiment classification or article classification
from a single news source. As an alternative, we present
CZEch~NEws~Classification~dataset (CZE-NEC), one of the largest Czech
classification datasets, composed of news articles from various sources
spanning over twenty years, which allows a more rigorous evaluation of such
models. We define four classification tasks: news source, news category,
inferred author's gender, and day of the week. To verify the task difficulty,
we conducted a human evaluation, which revealed that human performance lags
behind strong machine-learning baselines built upon pre-trained transformer
models. Furthermore, we show that language-specific pre-trained encoder
analysis outperforms selected commercially available large-scale generative
language models.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 07:47:08 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Kydlíček",
"Hynek",
""
],
[
"Libovický",
"Jindřich",
""
]
] |
new_dataset
| 0.999797 |
2307.10697
|
Fernando Alonso-Fernandez
|
Fernando Alonso-Fernandez, Kevin Hernandez-Diaz, Jose Maria Buades
Rubio, Josef Bigun
|
SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via
Filter Pruning
|
Published at VIII International Workshop on Artificial Intelligence
and Pattern Recognition, IWAIPR 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The widespread use of mobile devices for various digital services has created
a need for reliable and real-time person authentication. In this context,
facial recognition technologies have emerged as a dependable method for
verifying users due to the prevalence of cameras in mobile devices and their
integration into everyday applications. The rapid advancement of deep
Convolutional Neural Networks (CNNs) has led to numerous face verification
architectures. However, these models are often large and impractical for mobile
applications, reaching sizes of hundreds of megabytes with millions of
parameters. We address this issue by developing SqueezerFaceNet, a light face
recognition network which less than 1M parameters. This is achieved by applying
a network pruning method based on Taylor scores, where filters with small
importance scores are removed iteratively. Starting from an already small
network (of 1.24M) based on SqueezeNet, we show that it can be further reduced
(up to 40%) without an appreciable loss in performance. To the best of our
knowledge, we are the first to evaluate network pruning methods for the task of
face recognition.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 08:38:50 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Alonso-Fernandez",
"Fernando",
""
],
[
"Hernandez-Diaz",
"Kevin",
""
],
[
"Rubio",
"Jose Maria Buades",
""
],
[
"Bigun",
"Josef",
""
]
] |
new_dataset
| 0.995618 |
2307.10726
|
Ioanna Kantzavelou
|
Achilleas Spanos and Ioanna Kantzavelou
|
A Blockchain-based Electronic Voting System: EtherVote
|
2 pages, Poster presented in ACM 5th summit on Gender Equality in
Computing, GEC 2023, Athens University of Economics and Business, Athens,
Greece, 27 June 2023
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The development of an electronic voting system that would replace traditional
election procedures is a research topic of great interest for many years.
Blockchain technology could provide some guarantees and fulfill strong
requirements for electronic voting platforms, such as transparency,
immutability, and confidentiality. From time to time research is conducted to
address problems in voting systems. Many research works attempt to implement
secure and reliable voting systems, which address known security, anonymity,
and fraud issues that might threaten such systems.
This paper presents a proposal of a secure electronic voting system, the
EtherVote, using the Ethereum Blockchain network that focuses deeply on the
field of identification of eligible citizens. The proposed system will be
entirely based on Blockchain without any central authority servers or
databases, thus improving security, privacy, and election cost. Limitations,
problems, and solutions are discussed, in order to make the proposed electronic
voting system ideal and ready to use for national elections.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 09:39:29 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Spanos",
"Achilleas",
""
],
[
"Kantzavelou",
"Ioanna",
""
]
] |
new_dataset
| 0.98417 |
2307.10757
|
Weidong Chen
|
Weidong Chen, Xiaofen Xing, Peihao Chen, Xiangmin Xu
|
Vesper: A Compact and Effective Pretrained Model for Speech Emotion
Recognition
|
13 pages, 5 figures, 8 tables
| null | null | null |
cs.SD cs.CL eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a paradigm that adapts general large-scale pretrained
models (PTMs) to speech emotion recognition task. Although PTMs shed new light
on artificial general intelligence, they are constructed with general tasks in
mind, and thus, their efficacy for specific tasks can be further improved.
Additionally, employing PTMs in practical applications can be challenging due
to their considerable size. Above limitations spawn another research direction,
namely, optimizing large-scale PTMs for specific tasks to generate
task-specific PTMs that are both compact and effective. In this paper, we focus
on the speech emotion recognition task and propose an improved emotion-specific
pretrained encoder called Vesper. Vesper is pretrained on a speech dataset
based on WavLM and takes into account emotional characteristics. To enhance
sensitivity to emotional information, Vesper employs an emotion-guided masking
strategy to identify the regions that need masking. Subsequently, Vesper
employs hierarchical and cross-layer self-supervision to improve its ability to
capture acoustic and semantic representations, both of which are crucial for
emotion recognition. Experimental results on the IEMOCAP, MELD, and CREMA-D
datasets demonstrate that Vesper with 4 layers outperforms WavLM Base with 12
layers, and the performance of Vesper with 12 layers surpasses that of WavLM
Large with 24 layers.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 10:42:16 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Chen",
"Weidong",
""
],
[
"Xing",
"Xiaofen",
""
],
[
"Chen",
"Peihao",
""
],
[
"Xu",
"Xiangmin",
""
]
] |
new_dataset
| 0.999711 |
2307.10781
|
Ahmad Rostami
|
Ahmad Rostami, Dhruvin Patel, Madhusudan Giyyarpuram, Finn Pedersen
|
5G Non-Public Network for Industrial IoT: Operation Models
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
5G non-public networks (NPNs) play a key role in enabling critical Industrial
Internet of Things (IoT) applications in various vertical industries. Among
other features, 5G NPNs enable novel operation models, where the roles and
responsibilities for setting up and operating the network can be distributed
among several stakeholders, i.e., among the public mobile network operators
(MNOs), the industrial party who uses the 5G NPN services and 3rd parties. This
results in many theoretically feasible operation models for 5G NPN, each with
its own advantages and disadvantages. We investigate the resulting operation
models and identify a set of nine prime models taking into account today's
practical considerations. Additionally, we define a framework to qualitatively
analyze the operation models and use it to evaluate and compare the identified
operation models.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 11:30:32 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Rostami",
"Ahmad",
""
],
[
"Patel",
"Dhruvin",
""
],
[
"Giyyarpuram",
"Madhusudan",
""
],
[
"Pedersen",
"Finn",
""
]
] |
new_dataset
| 0.992602 |
2307.10814
|
Richard Sutcliffe
|
Ephrem Afele Retta, Richard Sutcliffe, Jabar Mahmood, Michael Abebe
Berwo, Eiad Almekhlafi, Sajjad Ahmed Khan, Shehzad Ashraf Chaudhry, Mustafa
Mhamed, Jun Feng
|
Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other
Languages
|
16 pages, 9 tables, 5 figures
| null | null | null |
cs.CL cs.NE cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In a conventional Speech emotion recognition (SER) task, a classifier for a
given language is trained on a pre-existing dataset for that same language.
However, where training data for a language does not exist, data from other
languages can be used instead. We experiment with cross-lingual and
multilingual SER, working with Amharic, English, German and URDU. For Amharic,
we use our own publicly-available Amharic Speech Emotion Dataset (ASED). For
English, German and Urdu we use the existing RAVDESS, EMO-DB and URDU datasets.
We followed previous research in mapping labels for all datasets to just two
classes, positive and negative. Thus we can compare performance on different
languages directly, and combine languages for training and testing. In
Experiment 1, monolingual SER trials were carried out using three classifiers,
AlexNet, VGGE (a proposed variant of VGG), and ResNet50. Results averaged for
the three models were very similar for ASED and RAVDESS, suggesting that
Amharic and English SER are equally difficult. Similarly, German SER is more
difficult, and Urdu SER is easier. In Experiment 2, we trained on one language
and tested on another, in both directions for each pair: Amharic<->German,
Amharic<->English, and Amharic<->Urdu. Results with Amharic as target suggested
that using English or German as source will give the best result. In Experiment
3, we trained on several non-Amharic languages and then tested on Amharic. The
best accuracy obtained was several percent greater than the best accuracy in
Experiment 2, suggesting that a better result can be obtained when using two or
three non-Amharic languages for training than when using just one non-Amharic
language. Overall, the results suggest that cross-lingual and multilingual
training can be an effective strategy for training a SER classifier when
resources for a language are scarce.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 12:24:23 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Retta",
"Ephrem Afele",
""
],
[
"Sutcliffe",
"Richard",
""
],
[
"Mahmood",
"Jabar",
""
],
[
"Berwo",
"Michael Abebe",
""
],
[
"Almekhlafi",
"Eiad",
""
],
[
"Khan",
"Sajjad Ahmed",
""
],
[
"Chaudhry",
"Shehzad Ashraf",
""
],
[
"Mhamed",
"Mustafa",
""
],
[
"Feng",
"Jun",
""
]
] |
new_dataset
| 0.999738 |
2307.10837
|
Zhen Gao
|
Li Qiao, Anwen Liao, Zhuoran Li, Hua Wang, Zhen Gao, Xiang Gao, Yu Su,
Pei Xiao, Li You, and Derrick Wing Kwan Ng
|
Sensing User's Activity, Channel, and Location with Near-Field
Extra-Large-Scale MIMO
|
Submitted to IEEE Transactions on Communications, Major revision.
Codes will be open to all on https://gaozhen16.github.io/ soon
| null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper proposes a grant-free massive access scheme based on the
millimeter wave (mmWave) extra-large-scale multiple-input multiple-output
(XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency,
high data rate, and high localization accuracy in the upcoming sixth-generation
(6G) networks. The XL-MIMO consists of multiple antenna subarrays that are
widely spaced over the service area to ensure line-of-sight (LoS)
transmissions. First, we establish the XL-MIMO-based massive access model
considering the near-field spatial non-stationary (SNS) property. Then, by
exploiting the block sparsity of subarrays and the SNS property, we propose a
structured block orthogonal matching pursuit algorithm for efficient active
user detection (AUD) and channel estimation (CE). Furthermore, different
sensing matrices are applied in different pilot subcarriers for exploiting the
diversity gains. Additionally, a multi-subarray collaborative localization
algorithm is designed for localization. In particular, the angle of arrival
(AoA) and time difference of arrival (TDoA) of the LoS links between active
users and related subarrays are extracted from the estimated XL-MIMO channels,
and then the coordinates of active users are acquired by jointly utilizing the
AoAs and TDoAs. Simulation results show that the proposed algorithms outperform
existing algorithms in terms of AUD and CE performance and can achieve
centimeter-level localization accuracy.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 12:57:15 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Qiao",
"Li",
""
],
[
"Liao",
"Anwen",
""
],
[
"Li",
"Zhuoran",
""
],
[
"Wang",
"Hua",
""
],
[
"Gao",
"Zhen",
""
],
[
"Gao",
"Xiang",
""
],
[
"Su",
"Yu",
""
],
[
"Xiao",
"Pei",
""
],
[
"You",
"Li",
""
],
[
"Ng",
"Derrick Wing Kwan",
""
]
] |
new_dataset
| 0.993141 |
2307.10847
|
Jan Maty\'a\v{s} K\v{r}i\v{s}\v{t}an
|
Jan Maty\'a\v{s} K\v{r}i\v{s}\v{t}an, Jakub Svoboda
|
Shortest Dominating Set Reconfiguration under Token Sliding
|
To appear at FCT 2023 (Fundamentals of Computation Theory)
| null | null | null |
cs.DS cs.DM math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present novel algorithms that efficiently compute a
shortest reconfiguration sequence between two given dominating sets in trees
and interval graphs under the Token Sliding model. In this problem, a graph is
provided along with its two dominating sets, which can be imagined as tokens
placed on vertices. The objective is to find a shortest sequence of dominating
sets that transforms one set into the other, with each set in the sequence
resulting from sliding a single token in the previous set. While identifying
any sequence has been well studied, our work presents the first polynomial
algorithms for this optimization variant in the context of dominating sets.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 13:11:01 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Křišťan",
"Jan Matyáš",
""
],
[
"Svoboda",
"Jakub",
""
]
] |
new_dataset
| 0.981621 |
2307.10934
|
Harshith Mohan Kumar
|
Aditya Nalgunda Ganesh and Dhruval Pobbathi Badrinath and Harshith
Mohan Kumar and Priya SS and Surabhi Narayan
|
OCTraN: 3D Occupancy Convolutional Transformer Network in Unstructured
Traffic Scenarios
|
This work was accepted as a spotlight presentation at the
Transformers for Vision Workshop @CVPR 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modern approaches for vision-centric environment perception for autonomous
navigation make extensive use of self-supervised monocular depth estimation
algorithms that output disparity maps. However, when this disparity map is
projected onto 3D space, the errors in disparity are magnified, resulting in a
depth estimation error that increases quadratically as the distance from the
camera increases. Though Light Detection and Ranging (LiDAR) can solve this
issue, it is expensive and not feasible for many applications. To address the
challenge of accurate ranging with low-cost sensors, we propose, OCTraN, a
transformer architecture that uses iterative-attention to convert 2D image
features into 3D occupancy features and makes use of convolution and transpose
convolution to efficiently operate on spatial information. We also develop a
self-supervised training pipeline to generalize the model to any scene by
eliminating the need for LiDAR ground truth by substituting it with
pseudo-ground truth labels obtained from boosted monocular depth estimation.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 15:06:44 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Ganesh",
"Aditya Nalgunda",
""
],
[
"Badrinath",
"Dhruval Pobbathi",
""
],
[
"Kumar",
"Harshith Mohan",
""
],
[
"SS",
"Priya",
""
],
[
"Narayan",
"Surabhi",
""
]
] |
new_dataset
| 0.976223 |
2307.10953
|
Xiangchen Yin
|
Xiangchen Yin, Zhenda Yu, Zetao Fei, Wenjun Lv, Xin Gao
|
PE-YOLO: Pyramid Enhancement Network for Dark Object Detection
|
Accepted at ICANN 2023
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Current object detection models have achieved good results on many benchmark
datasets, detecting objects in dark conditions remains a large challenge. To
address this issue, we propose a pyramid enhanced network (PENet) and joint it
with YOLOv3 to build a dark object detection framework named PE-YOLO. Firstly,
PENet decomposes the image into four components of different resolutions using
the Laplacian pyramid. Specifically we propose a detail processing module (DPM)
to enhance the detail of images, which consists of context branch and edge
branch. In addition, we propose a low-frequency enhancement filter (LEF) to
capture low-frequency semantics and prevent high-frequency noise. PE-YOLO
adopts an end-to-end joint training approach and only uses normal detection
loss to simplify the training process. We conduct experiments on the low-light
object detection dataset ExDark to demonstrate the effectiveness of ours. The
results indicate that compared with other dark detectors and low-light
enhancement models, PE-YOLO achieves the advanced results, achieving 78.0% in
mAP and 53.6 in FPS, respectively, which can adapt to object detection under
different low-light conditions. The code is available at
https://github.com/XiangchenYin/PE-YOLO.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 15:25:55 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Yin",
"Xiangchen",
""
],
[
"Yu",
"Zhenda",
""
],
[
"Fei",
"Zetao",
""
],
[
"Lv",
"Wenjun",
""
],
[
"Gao",
"Xin",
""
]
] |
new_dataset
| 0.992343 |
2307.10954
|
Xi Fang
|
Xi Fang, Daeseung Kim, Xuanang Xu, Tianshu Kuang, Nathan Lampen,
Jungwook Lee, Hannah H. Deng, Jaime Gateno, Michael A.K. Liebschner, James J.
Xia, Pingkun Yan
|
Soft-tissue Driven Craniomaxillofacial Surgical Planning
|
Early accepted by MICCAI 2023
| null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In CMF surgery, the planning of bony movement to achieve a desired facial
outcome is a challenging task. Current bone driven approaches focus on
normalizing the bone with the expectation that the facial appearance will be
corrected accordingly. However, due to the complex non-linear relationship
between bony structure and facial soft-tissue, such bone-driven methods are
insufficient to correct facial deformities. Despite efforts to simulate facial
changes resulting from bony movement, surgical planning still relies on
iterative revisions and educated guesses. To address these issues, we propose a
soft-tissue driven framework that can automatically create and verify surgical
plans. Our framework consists of a bony planner network that estimates the bony
movements required to achieve the desired facial outcome and a facial simulator
network that can simulate the possible facial changes resulting from the
estimated bony movement plans. By combining these two models, we can verify and
determine the final bony movement required for planning. The proposed framework
was evaluated using a clinical dataset, and our experimental results
demonstrate that the soft-tissue driven approach greatly improves the accuracy
and efficacy of surgical planning when compared to the conventional bone-driven
approach.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 15:26:01 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Fang",
"Xi",
""
],
[
"Kim",
"Daeseung",
""
],
[
"Xu",
"Xuanang",
""
],
[
"Kuang",
"Tianshu",
""
],
[
"Lampen",
"Nathan",
""
],
[
"Lee",
"Jungwook",
""
],
[
"Deng",
"Hannah H.",
""
],
[
"Gateno",
"Jaime",
""
],
[
"Liebschner",
"Michael A. K.",
""
],
[
"Xia",
"James J.",
""
],
[
"Yan",
"Pingkun",
""
]
] |
new_dataset
| 0.992343 |
2307.10955
|
Shaowu Peng
|
Shaowu Peng, Pengcheng Zhao, Yongyu Ye, Junying Chen, Yunbing Chang,
Xiaoqing Zheng
|
Spinal nerve segmentation method and dataset construction in endoscopic
surgical scenarios
|
Accepted by MICCAI 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Endoscopic surgery is currently an important treatment method in the field of
spinal surgery and avoiding damage to the spinal nerves through video guidance
is a key challenge. This paper presents the first real-time segmentation method
for spinal nerves in endoscopic surgery, which provides crucial navigational
information for surgeons. A finely annotated segmentation dataset of
approximately 10,000 consec-utive frames recorded during surgery is constructed
for the first time for this field, addressing the problem of semantic
segmentation. Based on this dataset, we propose FUnet (Frame-Unet), which
achieves state-of-the-art performance by utilizing inter-frame information and
self-attention mechanisms. We also conduct extended exper-iments on a similar
polyp endoscopy video dataset and show that the model has good generalization
ability with advantageous performance. The dataset and code of this work are
presented at: https://github.com/zzzzzzpc/FUnet .
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 15:26:57 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Peng",
"Shaowu",
""
],
[
"Zhao",
"Pengcheng",
""
],
[
"Ye",
"Yongyu",
""
],
[
"Chen",
"Junying",
""
],
[
"Chang",
"Yunbing",
""
],
[
"Zheng",
"Xiaoqing",
""
]
] |
new_dataset
| 0.999701 |
2307.11023
|
Saleh Kalantari
|
Tong Bill Xu and Saleh Kalantari
|
Visual Flow-based Programming Plugin for Brain Computer Interface in
Computer-Aided Design
| null | null | null | null |
cs.HC cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Over the last half century, the main application of Brain Computer
Interfaces, BCIs has been controlling wheelchairs and neural prostheses or
generating text or commands for people with restricted mobility. There has been
very limited attention in the field to applications for computer aided design,
despite the potential of BCIs to provide a new form of environmental
interaction. In this paper we introduce the development and application of
Neuron, a novel BCI tool that enables designers with little experience in
neuroscience or computer programming to gain access to neurological data, along
with established metrics relevant to design, create BCI interaction prototypes,
both with digital onscreen objects and physical devices, and evaluate designs
based on neurological information and record measurements for further analysis.
After discussing the BCI tool development, the article presents its
capabilities through two case studies, along with a brief evaluation of the
tool performance and a discussion of implications, limitations, and future
improvement.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 16:50:39 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Xu",
"Tong Bill",
""
],
[
"Kalantari",
"Saleh",
""
]
] |
new_dataset
| 0.995928 |
2307.11057
|
L\^e Th\`anh D\~ung (Tito) Nguy\^en
|
L\^e Th\`anh D\~ung Nguy\^en, Camille No\^us, C\'ecilia Pradic
|
Two-way automata and transducers with planar behaviours are aperiodic
|
18 pages, DMTCS submission
| null | null | null |
cs.FL
|
http://creativecommons.org/licenses/by/4.0/
|
We consider a notion of planarity for two-way finite automata and
transducers, inspired by Temperley-Lieb monoids of planar diagrams. We show
that this restriction captures star-free languages and first-order
transductions.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 17:37:48 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Nguyên",
"Lê Thành Dũng",
""
],
[
"Noûs",
"Camille",
""
],
[
"Pradic",
"Cécilia",
""
]
] |
new_dataset
| 0.986736 |
2307.11073
|
Oscar Michel
|
Oscar Michel, Anand Bhattad, Eli VanderBilt, Ranjay Krishna, Aniruddha
Kembhavi, Tanmay Gupta
|
OBJECT 3DIT: Language-guided 3D-aware Image Editing
| null | null | null | null |
cs.CV cs.AI cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
Existing image editing tools, while powerful, typically disregard the
underlying 3D geometry from which the image is projected. As a result, edits
made using these tools may become detached from the geometry and lighting
conditions that are at the foundation of the image formation process. In this
work, we formulate the newt ask of language-guided 3D-aware editing, where
objects in an image should be edited according to a language instruction in
context of the underlying 3D scene. To promote progress towards this goal, we
release OBJECT: a dataset consisting of 400K editing examples created from
procedurally generated 3D scenes. Each example consists of an input image,
editing instruction in language, and the edited image. We also introduce 3DIT :
single and multi-task models for four editing tasks. Our models show impressive
abilities to understand the 3D composition of entire scenes, factoring in
surrounding objects, surfaces, lighting conditions, shadows, and
physically-plausible object configurations. Surprisingly, training on only
synthetic scenes from OBJECT, editing capabilities of 3DIT generalize to
real-world images.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 17:53:46 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Michel",
"Oscar",
""
],
[
"Bhattad",
"Anand",
""
],
[
"VanderBilt",
"Eli",
""
],
[
"Krishna",
"Ranjay",
""
],
[
"Kembhavi",
"Aniruddha",
""
],
[
"Gupta",
"Tanmay",
""
]
] |
new_dataset
| 0.999832 |
2307.11086
|
Shichong Peng
|
Yanshu Zhang, Shichong Peng, Alireza Moazeni, Ke Li
|
PAPR: Proximity Attention Point Rendering
| null | null | null | null |
cs.CV cs.AI cs.GR cs.LG cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Learning accurate and parsimonious point cloud representations of scene
surfaces from scratch remains a challenge in 3D representation learning.
Existing point-based methods often suffer from the vanishing gradient problem
or require a large number of points to accurately model scene geometry and
texture. To address these limitations, we propose Proximity Attention Point
Rendering (PAPR), a novel method that consists of a point-based scene
representation and a differentiable renderer. Our scene representation uses a
point cloud where each point is characterized by its spatial position,
foreground score, and view-independent feature vector. The renderer selects the
relevant points for each ray and produces accurate colours using their
associated features. PAPR effectively learns point cloud positions to represent
the correct scene geometry, even when the initialization drastically differs
from the target geometry. Notably, our method captures fine texture details
while using only a parsimonious set of points. We also demonstrate four
practical applications of our method: geometry editing, object manipulation,
texture transfer, and exposure control. More results and code are available on
our project website at https://zvict.github.io/papr/.
|
[
{
"version": "v1",
"created": "Thu, 20 Jul 2023 17:59:33 GMT"
}
] | 2023-07-21T00:00:00 |
[
[
"Zhang",
"Yanshu",
""
],
[
"Peng",
"Shichong",
""
],
[
"Moazeni",
"Alireza",
""
],
[
"Li",
"Ke",
""
]
] |
new_dataset
| 0.9967 |
2201.03601
|
Arion Pons
|
Arion Pons and Fehmi Cirak
|
Multiaxis nose-pointing-and-shooting in a biomimetic morphing-wing
aircraft
| null |
Journal of Guidance, Control, and Dynamics, 46(3), 2023
|
10.2514/1.G006381
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modern high-performance combat aircraft exceed conventional flight-envelope
limits on maneuverability through the use of thrust vectoring, and so achieve
supermaneuverability. With ongoing development of biomimetic unmanned aerial
vehicles (UAVs), the potential for supermaneuverability through biomimetic
mechanisms becomes apparent. So far, this potential has not been well studied:
biomimetic UAVs have not yet been shown to be capable of any of the forms of
classical supermaneuverability available to thrust-vectored aircraft. Here we
show this capability, by demonstrating how biomimetic morphing-wing UAVs can
perform sophisticated multiaxis nose-pointing-and-shooting (NPAS) maneuvers at
low morphing complexity. Nonlinear flight-dynamic analysis is used to
characterize the extent and stability of the multidimensional space of aircraft
trim states that arises from biomimetic morphing. Navigating this trim space
provides an effective model-based guidance strategy for generating open-loop
NPAS maneuvers in simulation. Our results demonstrate the capability of
biomimetic aircraft for air combat-relevant supermaneuverability, and provide
strategies for the exploration, characterization, and guidance of further forms
of classical and non-classical supermaneuverability in such aircraft.
|
[
{
"version": "v1",
"created": "Mon, 10 Jan 2022 19:11:07 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Pons",
"Arion",
""
],
[
"Cirak",
"Fehmi",
""
]
] |
new_dataset
| 0.963196 |
2209.13780
|
Jingchao Peng
|
Jingchao Peng, Haitao Zhao, Kaijie Zhao, Zhongze Wang, Lujian Yao
|
CourtNet for Infrared Small-Target Detection
| null | null |
10.1016/j.eswa.2023.120996
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Infrared small-target detection (ISTD) is an important computer vision task.
ISTD aims at separating small targets from complex background clutter. The
infrared radiation decays over distances, making the targets highly dim and
prone to confusion with the background clutter, which makes the detector
challenging to balance the precision and recall rate. To deal with this
difficulty, this paper proposes a neural-network-based ISTD method called
CourtNet, which has three sub-networks: the prosecution network is designed for
improving the recall rate; the defendant network is devoted to increasing the
precision rate; the jury network weights their results to adaptively balance
the precision and recall rate. Furthermore, the prosecution network utilizes a
densely connected transformer structure, which can prevent small targets from
disappearing in the network forward propagation. In addition, a fine-grained
attention module is adopted to accurately locate the small targets.
Experimental results show that CourtNet achieves the best F1-score on the two
ISTD datasets, MFIRST (0.62) and SIRST (0.73).
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 02:16:24 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Apr 2023 07:16:17 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Peng",
"Jingchao",
""
],
[
"Zhao",
"Haitao",
""
],
[
"Zhao",
"Kaijie",
""
],
[
"Wang",
"Zhongze",
""
],
[
"Yao",
"Lujian",
""
]
] |
new_dataset
| 0.993257 |
2211.12955
|
Weijie Yuan
|
Weijie Yuan, Shuangyang Li, Zhiqiang Wei, Yuanhao Cui, Jiamo Jiang,
Haijun Zhang, Pingzhi Fan
|
New Delay Doppler Communication Paradigm in 6G era: A Survey of
Orthogonal Time Frequency Space (OTFS)
|
Survey paper on OTFS, accepted by China Communications; Cover paper
of the 6th issue
|
China Communications. 2023, 20(6): 1-25
|
10.23919/JCC.fa.2022-0578.202306
| null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
In the 6G era, space-air-Ground integrated networks (SAGIN) are anticipated
to deliver global coverage, necessitating support for a diverse array of
emerging applications in high-mobility, hostile environments. Under such
conditions, conventional orthogonal frequency division multiplexing (OFDM)
modulation, widely employed in cellular and Wi-Fi communication systems,
experiences performance degradation due to significant Doppler shifts. To
overcome this obstacle, a novel two-dimensional (2D) modulation approach,
namely orthogonal time frequency space (OTFS), has emerged as a key enabler for
future high-mobility use cases. Distinctively, OTFS modulates information
within the delay-Doppler (DD) domain, as opposed to the time-frequency (TF)
domain utilized by OFDM. This offers advantages such as Doppler and delay
resilience, reduced signaling latency, a lower peak-to-average ratio (PAPR),
and a reduced-complexity implementation. Recent studies further indicate that
the direct interplay between information and the physical world in the DD
domain positions OTFS as a promising waveform for achieving integrated sensing
and communications (ISAC). In this article, we present an in-depth review of
OTFS technology in the context of the 6G era, encompassing fundamentals, recent
advancements, and future directions. Our objective is to provide a valuable
resource for researchers engaged in the field of OTFS.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 13:55:47 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 02:27:20 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Yuan",
"Weijie",
""
],
[
"Li",
"Shuangyang",
""
],
[
"Wei",
"Zhiqiang",
""
],
[
"Cui",
"Yuanhao",
""
],
[
"Jiang",
"Jiamo",
""
],
[
"Zhang",
"Haijun",
""
],
[
"Fan",
"Pingzhi",
""
]
] |
new_dataset
| 0.993422 |
2212.07253
|
Sean Moran
|
Sae Young Moon, Gregor Kerr, Fran Silavong, Sean Moran
|
API-Miner: an API-to-API Specification Recommendation Engine
| null | null | null | null |
cs.SE cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
When designing a new API for a large project, developers need to make smart
design choices so that their code base can grow sustainably. To ensure that new
API components are well designed, developers can learn from existing API
components. However, the lack of standardized methods for comparing API designs
makes this learning process time-consuming and difficult. To address this gap
we developed API-Miner, to the best of our knowledge, one of the first
API-to-API specification recommendation engines. API-Miner retrieves relevant
specification components written in OpenAPI (a widely adopted language used to
describe web APIs). API-miner presents several significant contributions,
including: (1) novel methods of processing and extracting key information from
OpenAPI specifications, (2) innovative feature extraction techniques that are
optimized for the highly technical API specification domain, and (3) a novel
log-linear probabilistic model that combines multiple signals to retrieve
relevant and high quality OpenAPI specification components given a query
specification. We evaluate API-Miner in both quantitative and qualitative tasks
and achieve an overall of 91.7% recall@1 and 56.2% F1, which surpasses baseline
performance by 15.4% in recall@1 and 3.2% in F1. Overall, API-Miner will allow
developers to retrieve relevant OpenAPI specification components from a public
or internal database in the early stages of the API development cycle, so that
they can learn from existing established examples and potentially identify
redundancies in their work. It provides the guidance developers need to
accelerate development process and contribute thoughtfully designed APIs that
promote code maintainability and quality. Code is available on GitHub at
https://github.com/jpmorganchase/api-miner.
|
[
{
"version": "v1",
"created": "Wed, 14 Dec 2022 14:43:51 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 17:30:33 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Moon",
"Sae Young",
""
],
[
"Kerr",
"Gregor",
""
],
[
"Silavong",
"Fran",
""
],
[
"Moran",
"Sean",
""
]
] |
new_dataset
| 0.980999 |
2212.10551
|
Fei Yuan
|
Fei Yuan, Yinquan Lu, WenHao Zhu, Lingpeng Kong, Lei Li, Yu Qiao,
Jingjing Xu
|
Lego-MT: Learning Detachable Models for Massively Multilingual Machine
Translation
|
ACL 2023 Findings
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Multilingual neural machine translation (MNMT) aims to build a unified model
for many language directions. Existing monolithic models for MNMT encounter two
challenges: parameter interference among languages and inefficient inference
for large models. In this paper, we revisit the classic multi-way structures
and develop a detachable model by assigning each language (or group of
languages) to an individual branch that supports plug-and-play training and
inference. To address the needs of learning representations for all languages
in a unified space, we propose a novel efficient training recipe, upon which we
build an effective detachable model, Lego-MT. For a fair comparison, we collect
data from OPUS and build a translation benchmark covering 433 languages and
1.3B parallel data. Experiments show that Lego-MT with 1.2B parameters brings
an average gain of 3.2 spBLEU. It even outperforms M2M-100 with 12B parameters.
The proposed training recipe brings a 28.2$\times$ speedup over the
conventional multi-way training method.\footnote{
\url{https://github.com/CONE-MT/Lego-MT}.}
|
[
{
"version": "v1",
"created": "Tue, 20 Dec 2022 18:54:08 GMT"
},
{
"version": "v2",
"created": "Mon, 29 May 2023 03:39:44 GMT"
},
{
"version": "v3",
"created": "Wed, 19 Jul 2023 05:52:32 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Yuan",
"Fei",
""
],
[
"Lu",
"Yinquan",
""
],
[
"Zhu",
"WenHao",
""
],
[
"Kong",
"Lingpeng",
""
],
[
"Li",
"Lei",
""
],
[
"Qiao",
"Yu",
""
],
[
"Xu",
"Jingjing",
""
]
] |
new_dataset
| 0.997522 |
2301.02307
|
Kumar Ashutosh
|
Kumar Ashutosh, Rohit Girdhar, Lorenzo Torresani, Kristen Grauman
|
What You Say Is What You Show: Visual Narration Detection in
Instructional Videos
|
Technical Report
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Narrated ''how-to'' videos have emerged as a promising data source for a wide
range of learning problems, from learning visual representations to training
robot policies. However, this data is extremely noisy, as the narrations do not
always describe the actions demonstrated in the video. To address this problem
we introduce the novel task of visual narration detection, which entails
determining whether a narration is visually depicted by the actions in the
video. We propose What You Say is What You Show (WYS^2), a method that
leverages multi-modal cues and pseudo-labeling to learn to detect visual
narrations with only weakly labeled data. Our model successfully detects visual
narrations in in-the-wild videos, outperforming strong baselines, and we
demonstrate its impact for state-of-the-art summarization and temporal
alignment of instructional videos.
|
[
{
"version": "v1",
"created": "Thu, 5 Jan 2023 21:43:19 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 17:29:16 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Ashutosh",
"Kumar",
""
],
[
"Girdhar",
"Rohit",
""
],
[
"Torresani",
"Lorenzo",
""
],
[
"Grauman",
"Kristen",
""
]
] |
new_dataset
| 0.969373 |
2303.01589
|
Xijun Wang
|
Xijun Wang, Ruiqi Xian, Tianrui Guan, Celso M. de Melo, Stephen M.
Nogar, Aniket Bera, Dinesh Manocha
|
AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal
Reasoning
|
Accepted for publication at ICRA 2023
| null |
10.1109/ICRA48891.2023.10160564
| null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a novel approach for aerial video action recognition. Our method
is designed for videos captured using UAVs and can run on edge or mobile
devices. We present a learning-based approach that uses customized auto zoom to
automatically identify the human target and scale it appropriately. This makes
it easier to extract the key features and reduces the computational overhead.
We also present an efficient temporal reasoning algorithm to capture the action
information along the spatial and temporal domains within a controllable
computational cost. Our approach has been implemented and evaluated both on the
desktop with high-end GPUs and on the low power Robotics RB5 Platform for
robots and drones. In practice, we achieve 6.1-7.4% improvement over SOTA in
Top-1 accuracy on the RoCoG-v2 dataset, 8.3-10.4% improvement on the UAV-Human
dataset and 3.2% improvement on the Drone Action dataset.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 21:24:19 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Wang",
"Xijun",
""
],
[
"Xian",
"Ruiqi",
""
],
[
"Guan",
"Tianrui",
""
],
[
"de Melo",
"Celso M.",
""
],
[
"Nogar",
"Stephen M.",
""
],
[
"Bera",
"Aniket",
""
],
[
"Manocha",
"Dinesh",
""
]
] |
new_dataset
| 0.999561 |
2303.02775
|
Yuxiang Peng
|
Yuxiang Peng, Jacob Young, Pengyu Liu, Xiaodi Wu
|
SimuQ: A Domain-Specific Language For Quantum Simulation With Analog
Compilation
|
26 pages, 12 figures, 6 tables. Code is available at
https://github.com/PicksPeng/SimuQ. A website is available at
https://pickspeng.github.io/SimuQ/
| null | null | null |
cs.PL quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantum Hamiltonian simulation, which simulates the evolution of quantum
systems and probes quantum phenomena, is one of the most promising applications
of quantum computing. Recent experimental results suggest that
Hamiltonian-oriented analog quantum simulation would be advantageous over
circuit-oriented digital quantum simulation in the Noisy Intermediate-Scale
Quantum (NISQ) machine era. However, programming analog quantum simulators is
much more challenging due to the lack of a unified interface between hardware
and software. In this paper, we design and implement SimuQ, the first
domain-specific language for quantum Hamiltonian simulation that supports
pulse-level compilation to heterogeneous analog quantum simulators.
Specifically, in SimuQ, front-end users specify the target quantum system with
Hamiltonian Modeling Language, and the Hamiltonian-level programmability of
analog quantum simulators is specified through a new abstraction called the
abstract analog instruction set (AAIS) and programmed in AAIS Specification
Language by hardware providers. Through a solver-based compilation, SimuQ
generates executable pulse schedules for real devices to simulate the evolution
of desired quantum systems, which is demonstrated on superconducting (IBM),
neutral-atom (QuEra), and trapped-ion (IonQ) quantum devices. Moreover, we
demonstrate the advantages of exposing the Hamiltonian-level programmability of
devices with native operations or interaction-based gates and establish a small
benchmark of quantum simulation to evaluate SimuQ's compiler with the above
analog quantum simulators.
|
[
{
"version": "v1",
"created": "Sun, 5 Mar 2023 21:28:05 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 06:00:41 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Peng",
"Yuxiang",
""
],
[
"Young",
"Jacob",
""
],
[
"Liu",
"Pengyu",
""
],
[
"Wu",
"Xiaodi",
""
]
] |
new_dataset
| 0.997552 |
2303.08096
|
Axel Levy
|
Axel Levy, Mark Matthews, Matan Sela, Gordon Wetzstein, Dmitry Lagun
|
MELON: NeRF with Unposed Images in SO(3)
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Neural radiance fields enable novel-view synthesis and scene reconstruction
with photorealistic quality from a few images, but require known and accurate
camera poses. Conventional pose estimation algorithms fail on smooth or
self-similar scenes, while methods performing inverse rendering from unposed
views require a rough initialization of the camera orientations. The main
difficulty of pose estimation lies in real-life objects being almost invariant
under certain transformations, making the photometric distance between rendered
views non-convex with respect to the camera parameters. Using an equivalence
relation that matches the distribution of local minima in camera space, we
reduce this space to its quotient set, in which pose estimation becomes a more
convex problem. Using a neural-network to regularize pose estimation, we
demonstrate that our method - MELON - can reconstruct a neural radiance field
from unposed images with state-of-the-art accuracy while requiring ten times
fewer views than adversarial approaches.
|
[
{
"version": "v1",
"created": "Tue, 14 Mar 2023 17:33:39 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 08:19:58 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Levy",
"Axel",
""
],
[
"Matthews",
"Mark",
""
],
[
"Sela",
"Matan",
""
],
[
"Wetzstein",
"Gordon",
""
],
[
"Lagun",
"Dmitry",
""
]
] |
new_dataset
| 0.998173 |
2303.11103
|
Jakob Hoydis
|
Jakob Hoydis, Fay\c{c}al A\"it Aoudia, Sebastian Cammerer, Merlin
Nimier-David, Nikolaus Binder, Guillermo Marcus, Alexander Keller
|
Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling
|
5 pages, 5 figures, update reflects new features of Sionna RT
introduced in release v0.15
| null | null | null |
cs.IT cs.AI cs.LG cs.NI math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sionna is a GPU-accelerated open-source library for link-level simulations
based on TensorFlow. Since release v0.14 it integrates a differentiable ray
tracer (RT) for the simulation of radio wave propagation. This unique feature
allows for the computation of gradients of the channel impulse response and
other related quantities with respect to many system and environment
parameters, such as material properties, antenna patterns, array geometries, as
well as transmitter and receiver orientations and positions. In this paper, we
outline the key components of Sionna RT and showcase example applications such
as learning radio materials and optimizing transmitter orientations by gradient
descent. While classic ray tracing is a crucial tool for 6G research topics
like reconfigurable intelligent surfaces, integrated sensing and
communications, as well as user localization, differentiable ray tracing is a
key enabler for many novel and exciting research directions, for example,
digital twins.
|
[
{
"version": "v1",
"created": "Mon, 20 Mar 2023 13:40:11 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 14:42:10 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Hoydis",
"Jakob",
""
],
[
"Aoudia",
"Fayçal Aït",
""
],
[
"Cammerer",
"Sebastian",
""
],
[
"Nimier-David",
"Merlin",
""
],
[
"Binder",
"Nikolaus",
""
],
[
"Marcus",
"Guillermo",
""
],
[
"Keller",
"Alexander",
""
]
] |
new_dataset
| 0.99933 |
2304.04578
|
Juan Ignacio Iba\~nez
|
Juan Ignacio Iba\~nez, Alexander Freier
|
Bitcoin's Carbon Footprint Revisited: Proof of Work Mining for Renewable
Energy Expansion
|
A previous version of this paper was titled "Can Bitcoin Stop Climate
Change? Proof of Work, Energy Consumption and Carbon Footprint (SoK)"
|
Challenges, EISSN 2078-1547, Published by MDPI
| null | null |
cs.DC cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Despite their potential in many respects, blockchain and distributed ledger
technology (DLT) technology have been the target of criticism for the energy
intensity of the proof-of-work (PoW) consensus algorithm in general and of
Bitcoin mining in particular. However, mining is also believed to have the
potential to drive net decarbonization and renewable penetration in the energy
grid by providing ancillary and other services. In this paper, we systematize
the state of the art in this regard. Although not completely absent from the
literature, the extent to which flexible load response (FLR) through PoW mining
may support grid decarbonization remains insufficiently studied and hence
contested. We approach this research gap by systematizing both the strengths
and the limitations of mining to provide FLR services for energy grids. We find
that a net-decarbonizing effect led by renewable-based mining is indeed
plausible.
|
[
{
"version": "v1",
"created": "Fri, 3 Feb 2023 19:53:55 GMT"
},
{
"version": "v2",
"created": "Wed, 10 May 2023 20:44:11 GMT"
},
{
"version": "v3",
"created": "Wed, 19 Jul 2023 13:19:09 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Ibañez",
"Juan Ignacio",
""
],
[
"Freier",
"Alexander",
""
]
] |
new_dataset
| 0.975777 |
2304.05417
|
Fabio Poiesi
|
Luigi Riz, Andrea Caraffa, Matteo Bortolon, Mohamed Lamine Mekhalfi,
Davide Boscaini, Andr\'e Moura, Jos\'e Antunes, Andr\'e Dias, Hugo Silva,
Andreas Leonidou, Christos Constantinides, Christos Keleshis, Dante Abate,
Fabio Poiesi
|
The MONET dataset: Multimodal drone thermal dataset recorded in rural
scenarios
|
Published in Computer Vision and Pattern Recognition (CVPR) Workshops
2023 - 6th Multimodal Learning and Applications Workshop
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We present MONET, a new multimodal dataset captured using a thermal camera
mounted on a drone that flew over rural areas, and recorded human and vehicle
activities. We captured MONET to study the problem of object localisation and
behaviour understanding of targets undergoing large-scale variations and being
recorded from different and moving viewpoints. Target activities occur in two
different land sites, each with unique scene structures and cluttered
backgrounds. MONET consists of approximately 53K images featuring 162K manually
annotated bounding boxes. Each image is timestamp-aligned with drone metadata
that includes information about attitudes, speed, altitude, and GPS
coordinates. MONET is different from previous thermal drone datasets because it
features multimodal data, including rural scenes captured with thermal cameras
containing both person and vehicle targets, along with trajectory information
and metadata. We assessed the difficulty of the dataset in terms of transfer
learning between the two sites and evaluated nine object detection algorithms
to identify the open challenges associated with this type of data. Project
page: https://github.com/fabiopoiesi/monet_dataset.
|
[
{
"version": "v1",
"created": "Tue, 11 Apr 2023 18:00:02 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 10:01:29 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Riz",
"Luigi",
""
],
[
"Caraffa",
"Andrea",
""
],
[
"Bortolon",
"Matteo",
""
],
[
"Mekhalfi",
"Mohamed Lamine",
""
],
[
"Boscaini",
"Davide",
""
],
[
"Moura",
"André",
""
],
[
"Antunes",
"José",
""
],
[
"Dias",
"André",
""
],
[
"Silva",
"Hugo",
""
],
[
"Leonidou",
"Andreas",
""
],
[
"Constantinides",
"Christos",
""
],
[
"Keleshis",
"Christos",
""
],
[
"Abate",
"Dante",
""
],
[
"Poiesi",
"Fabio",
""
]
] |
new_dataset
| 0.999853 |
2304.10727
|
Seulki Park
|
Seulki Park, Daeho Um, Hajung Yoon, Sanghyuk Chun, Sangdoo Yun and Jin
Young Choi
|
RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text
Matching Models
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a robustness benchmark for image-text matching
models to assess their vulnerabilities. To this end, we insert adversarial
texts and images into the search pool (i.e., gallery set) and evaluate models
with the adversarial data. Specifically, we replace a word in the text to
change the meaning of the text and mix images with different images to create
perceptible changes in pixels. We assume that such explicit alterations would
not deceive a robust model, as they should understand the holistic meaning of
texts and images simultaneously. However, in our evaluations on the proposed
benchmark, many state-of-the-art models show significant performance
degradation, e.g., Recall@1: 81.9% $\rightarrow$ 64.5% in BLIP, 66.1%
$\rightarrow$ 37.5% in VSE$\infty$, where the models favor adversarial
texts/images over the original ones. This reveals the current vision-language
models may not account for subtle changes or understand the overall context of
texts and images. Our findings can provide insights for improving the
robustness of the vision-language models and devising more diverse stress-test
methods in cross-modal retrieval task. Source code and dataset will be
available at https://github.com/pseulki/rococo.
|
[
{
"version": "v1",
"created": "Fri, 21 Apr 2023 03:45:59 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Jul 2023 04:34:57 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Park",
"Seulki",
""
],
[
"Um",
"Daeho",
""
],
[
"Yoon",
"Hajung",
""
],
[
"Chun",
"Sanghyuk",
""
],
[
"Yun",
"Sangdoo",
""
],
[
"Choi",
"Jin Young",
""
]
] |
new_dataset
| 0.989909 |
2306.03308
|
Manuel Delgado
|
Manuel Delgado and Jaume Us\'o i Cubertorer
|
Kunz languages for numerical semigroups are context sensitive
|
11 pages
| null | null | null |
cs.FL math.AC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
There is a one-to-one and onto correspondence between the class of numerical
semigroups of depth $n$, where $n$ is an integer, and a certain language over
the alphabet $\{1,\ldots,n\}$ which we call a Kunz language of depth $n$. The
Kunz language associated with the numerical semigroups of depth $2$ is the
regular language $\{1,2\}^*2\{1,2\}^*$. We prove that Kunz languages associated
with numerical semigroups of larger depth are context-sensitive but not
regular.
|
[
{
"version": "v1",
"created": "Mon, 5 Jun 2023 23:30:30 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 17:36:31 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Delgado",
"Manuel",
""
],
[
"Cubertorer",
"Jaume Usó i",
""
]
] |
new_dataset
| 0.994914 |
2306.07591
|
Raz Lapid
|
Raz Lapid, Moshe Sipper
|
I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models
| null |
Proceedings of the European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2023)
| null | null |
cs.CV cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modern image-to-text systems typically adopt the encoder-decoder framework,
which comprises two main components: an image encoder, responsible for
extracting image features, and a transformer-based decoder, used for generating
captions. Taking inspiration from the analysis of neural networks' robustness
against adversarial perturbations, we propose a novel gray-box algorithm for
creating adversarial examples in image-to-text models. Unlike image
classification tasks that have a finite set of class labels, finding visually
similar adversarial examples in an image-to-text task poses greater challenges
because the captioning system allows for a virtually infinite space of possible
captions. In this paper, we present a gray-box adversarial attack on
image-to-text, both untargeted and targeted. We formulate the process of
discovering adversarial perturbations as an optimization problem that uses only
the image-encoder component, meaning the proposed attack is language-model
agnostic. Through experiments conducted on the ViT-GPT2 model, which is the
most-used image-to-text model in Hugging Face, and the Flickr30k dataset, we
demonstrate that our proposed attack successfully generates visually similar
adversarial examples, both with untargeted and targeted captions. Notably, our
attack operates in a gray-box manner, requiring no knowledge about the decoder
module. We also show that our attacks fool the popular open-source platform
Hugging Face.
|
[
{
"version": "v1",
"created": "Tue, 13 Jun 2023 07:35:28 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Jul 2023 09:45:54 GMT"
},
{
"version": "v3",
"created": "Wed, 19 Jul 2023 12:04:59 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Lapid",
"Raz",
""
],
[
"Sipper",
"Moshe",
""
]
] |
new_dataset
| 0.987685 |
2307.05588
|
Mich\`ele Duguay
|
Mich\`ele Duguay, Kate Mancey, Johanna Devaney
|
Collaborative Song Dataset (CoSoD): An annotated dataset of multi-artist
collaborations in popular music
|
To be published in the Proceedings of the 24th International Society
for Music Information Retrieval Conference (ISMIR)
| null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Collaborative Song Dataset (CoSoD) is a corpus of 331 multi-artist
collaborations from the 2010-2019 Billboard "Hot 100" year-end charts. The
corpus is annotated with formal sections, aspects of vocal production
(including reverberation, layering, panning, and gender of the performers), and
relevant metadata. CoSoD complements other popular music datasets by focusing
exclusively on musical collaborations between independent acts. In addition to
facilitating the study of song form and vocal production, CoSoD allows for the
in-depth study of gender as it relates to various timbral, pitch, and formal
parameters in musical collaborations. In this paper, we detail the contents of
the dataset and outline the annotation process. We also present an experiment
using CoSoD that examines how the use of reverberation, layering, and panning
are related to the gender of the artist. In this experiment, we find that men's
voices are on average treated with less reverberation and occupy a more narrow
position in the stereo mix than women's voices.
|
[
{
"version": "v1",
"created": "Mon, 10 Jul 2023 15:57:42 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Jul 2023 18:59:22 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Duguay",
"Michèle",
""
],
[
"Mancey",
"Kate",
""
],
[
"Devaney",
"Johanna",
""
]
] |
new_dataset
| 0.999597 |
2307.05944
|
Fengshi Tian
|
Xiaomeng Wang, Fengshi Tian, Xizi Chen, Jiakun Zheng, Xuejiao Liu,
Fengbin Tu, Jie Yang, Mohamad Sawan, Kwang-Ting Cheng, Chi-Ying Tsui
|
A 137.5 TOPS/W SRAM Compute-in-Memory Macro with 9-b Memory
Cell-Embedded ADCs and Signal Margin Enhancement Techniques for AI Edge
Applications
|
Submitted to IEEE ASSCC 2023
| null | null | null |
cs.AR cs.NE eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a high-precision SRAM-based CIM macro that can
perform 4x4-bit MAC operations and yield 9-bit signed output. The inherent
discharge branches of SRAM cells are utilized to apply time-modulated MAC and
9-bit ADC readout operations on two bit-line capacitors. The same principle is
used for both MAC and A-to-D conversion ensuring high linearity and thus
supporting large number of analog MAC accumulations. The memory cell-embedded
ADC eliminates the use of separate ADCs and enhances energy and area
efficiency. Additionally, two signal margin enhancement techniques, namely the
MAC-folding and boosted-clipping schemes, are proposed to further improve the
CIM computation accuracy.
|
[
{
"version": "v1",
"created": "Wed, 12 Jul 2023 06:20:19 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Jul 2023 03:08:13 GMT"
},
{
"version": "v3",
"created": "Wed, 19 Jul 2023 08:58:58 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Wang",
"Xiaomeng",
""
],
[
"Tian",
"Fengshi",
""
],
[
"Chen",
"Xizi",
""
],
[
"Zheng",
"Jiakun",
""
],
[
"Liu",
"Xuejiao",
""
],
[
"Tu",
"Fengbin",
""
],
[
"Yang",
"Jie",
""
],
[
"Sawan",
"Mohamad",
""
],
[
"Cheng",
"Kwang-Ting",
""
],
[
"Tsui",
"Chi-Ying",
""
]
] |
new_dataset
| 0.998686 |
2307.07813
|
Pietro Bonazzi
|
Pietro Bonazzi, Thomas Ruegg, Sizhen Bian, Yawei Li, Michele Magno
|
TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for
Gaze Estimation
| null | null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Intelligent edge vision tasks encounter the critical challenge of ensuring
power and latency efficiency due to the typically heavy computational load they
impose on edge platforms.This work leverages one of the first "AI in sensor"
vision platforms, IMX500 by Sony, to achieve ultra-fast and ultra-low-power
end-to-end edge vision applications. We evaluate the IMX500 and compare it to
other edge platforms, such as the Google Coral Dev Micro and Sony Spresense, by
exploring gaze estimation as a case study. We propose TinyTracker, a highly
efficient, fully quantized model for 2D gaze estimation designed to maximize
the performance of the edge vision systems considered in this study.
TinyTracker achieves a 41x size reduction (600Kb) compared to iTracker [1]
without significant loss in gaze estimation accuracy (maximum of 0.16 cm when
fully quantized). TinyTracker's deployment on the Sony IMX500 vision sensor
results in end-to-end latency of around 19ms. The camera takes around 17.9ms to
read, process and transmit the pixels to the accelerator. The inference time of
the network is 0.86ms with an additional 0.24 ms for retrieving the results
from the sensor. The overall energy consumption of the end-to-end system is 4.9
mJ, including 0.06 mJ for inference. The end-to-end study shows that IMX500 is
1.7x faster than CoralMicro (19ms vs 34.4ms) and 7x more power efficient (4.9mJ
VS 34.2mJ)
|
[
{
"version": "v1",
"created": "Sat, 15 Jul 2023 14:34:25 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 16:35:36 GMT"
},
{
"version": "v3",
"created": "Wed, 19 Jul 2023 08:06:34 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Bonazzi",
"Pietro",
""
],
[
"Ruegg",
"Thomas",
""
],
[
"Bian",
"Sizhen",
""
],
[
"Li",
"Yawei",
""
],
[
"Magno",
"Michele",
""
]
] |
new_dataset
| 0.977342 |
2307.07859
|
Yao Huang
|
Xingxing Wei, Yao Huang, Yitong Sun, Jie Yu
|
Unified Adversarial Patch for Cross-modal Attacks in the Physical World
|
10 pages, 8 figures, accepted by ICCV2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, physical adversarial attacks have been presented to evade
DNNs-based object detectors. To ensure the security, many scenarios are
simultaneously deployed with visible sensors and infrared sensors, leading to
the failures of these single-modal physical attacks. To show the potential
risks under such scenes, we propose a unified adversarial patch to perform
cross-modal physical attacks, i.e., fooling visible and infrared object
detectors at the same time via a single patch. Considering different imaging
mechanisms of visible and infrared sensors, our work focuses on modeling the
shapes of adversarial patches, which can be captured in different modalities
when they change. To this end, we design a novel boundary-limited shape
optimization to achieve the compact and smooth shapes, and thus they can be
easily implemented in the physical world. In addition, to balance the fooling
degree between visible detector and infrared detector during the optimization
process, we propose a score-aware iterative evaluation, which can guide the
adversarial patch to iteratively reduce the predicted scores of the multi-modal
sensors. We finally test our method against the one-stage detector: YOLOv3 and
the two-stage detector: Faster RCNN. Results show that our unified patch
achieves an Attack Success Rate (ASR) of 73.33% and 69.17%, respectively. More
importantly, we verify the effective attacks in the physical world when visible
and infrared sensors shoot the objects under various settings like different
angles, distances, postures, and scenes.
|
[
{
"version": "v1",
"created": "Sat, 15 Jul 2023 17:45:17 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 03:04:50 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Wei",
"Xingxing",
""
],
[
"Huang",
"Yao",
""
],
[
"Sun",
"Yitong",
""
],
[
"Yu",
"Jie",
""
]
] |
new_dataset
| 0.999749 |
2307.08222
|
Yunlong Wang
|
Guang Jiang, Jiahui Zhu, Yunsong Li, Pengcheng An, Yunlong Wang
|
NaMemo2: Facilitating Teacher-Student Interaction with Theory-Based
Design and Student Autonomy Consideration
|
This paper has been accepted in July 2023 for publication in
Education and Information Technologies
| null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Teacher-student interaction (TSI) is essential for learning efficiency and
harmonious teacher-student interpersonal relationships. However, studies on TSI
support tools often focus on teacher needs while neglecting student needs and
autonomy. To enhance both lecturer competence in delivering interpersonal
interaction and student autonomy in TSI, we developed NaMemo2, a novel
augmented-reality system that allows students to express their willingness to
TSI and displays student information to teachers during lectures. The design
and evaluation process follows a new framework, STUDIER, which can facilitate
the development of theory-based ethnics-aware TSI support tools in general. The
quantitative results of our four-week field study with four classes in a
university suggested that NaMemo2 can improve 1) TSI in the classroom from both
teacher and student perspectives, 2) student attitudes and willingness to TSI,
and 3) student attitudes to the deployment of NaMemo2. The qualitative feedback
from students and teachers indicated that improving TSI may be responsible for
improved attention in students and a better classroom atmosphere during
lectures.
|
[
{
"version": "v1",
"created": "Mon, 17 Jul 2023 03:52:28 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 03:33:29 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Jiang",
"Guang",
""
],
[
"Zhu",
"Jiahui",
""
],
[
"Li",
"Yunsong",
""
],
[
"An",
"Pengcheng",
""
],
[
"Wang",
"Yunlong",
""
]
] |
new_dataset
| 0.997507 |
2307.09191
|
Federico Matteucci
|
Federico Matteucci, Vadim Arzamasov, Klemens Boehm
|
A benchmark of categorical encoders for binary classification
|
Submitted to the 37th Conference on Neural Information Processing
Systems (NeurIPS 2023) Track on Datasets and Benchmarks
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Categorical encoders transform categorical features into numerical
representations that are indispensable for a wide range of machine learning
models. Existing encoder benchmark studies lack generalizability because of
their limited choice of (1) encoders, (2) experimental factors, and (3)
datasets. Additionally, inconsistencies arise from the adoption of varying
aggregation strategies. This paper is the most comprehensive benchmark of
categorical encoders to date, including an extensive evaluation of 32
configurations of encoders from diverse families, with 36 combinations of
experimental factors, and on 50 datasets. The study shows the profound
influence of dataset selection, experimental factors, and aggregation
strategies on the benchmark's conclusions -- aspects disregarded in previous
encoder benchmarks.
|
[
{
"version": "v1",
"created": "Mon, 17 Jul 2023 13:17:26 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 16:24:31 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Matteucci",
"Federico",
""
],
[
"Arzamasov",
"Vadim",
""
],
[
"Boehm",
"Klemens",
""
]
] |
new_dataset
| 0.991821 |
2307.09288
|
Thomas Scialom
|
Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and
Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and
Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and
Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu
and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia
Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar
Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and
Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and
Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and
Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar
Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy
Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan
Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan
and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and
Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and
Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic
and Sergey Edunov and Thomas Scialom
|
Llama 2: Open Foundation and Fine-Tuned Chat Models
| null | null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we develop and release Llama 2, a collection of pretrained and
fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70
billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for
dialogue use cases. Our models outperform open-source chat models on most
benchmarks we tested, and based on our human evaluations for helpfulness and
safety, may be a suitable substitute for closed-source models. We provide a
detailed description of our approach to fine-tuning and safety improvements of
Llama 2-Chat in order to enable the community to build on our work and
contribute to the responsible development of LLMs.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 14:31:57 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 17:08:59 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Touvron",
"Hugo",
""
],
[
"Martin",
"Louis",
""
],
[
"Stone",
"Kevin",
""
],
[
"Albert",
"Peter",
""
],
[
"Almahairi",
"Amjad",
""
],
[
"Babaei",
"Yasmine",
""
],
[
"Bashlykov",
"Nikolay",
""
],
[
"Batra",
"Soumya",
""
],
[
"Bhargava",
"Prajjwal",
""
],
[
"Bhosale",
"Shruti",
""
],
[
"Bikel",
"Dan",
""
],
[
"Blecher",
"Lukas",
""
],
[
"Ferrer",
"Cristian Canton",
""
],
[
"Chen",
"Moya",
""
],
[
"Cucurull",
"Guillem",
""
],
[
"Esiobu",
"David",
""
],
[
"Fernandes",
"Jude",
""
],
[
"Fu",
"Jeremy",
""
],
[
"Fu",
"Wenyin",
""
],
[
"Fuller",
"Brian",
""
],
[
"Gao",
"Cynthia",
""
],
[
"Goswami",
"Vedanuj",
""
],
[
"Goyal",
"Naman",
""
],
[
"Hartshorn",
"Anthony",
""
],
[
"Hosseini",
"Saghar",
""
],
[
"Hou",
"Rui",
""
],
[
"Inan",
"Hakan",
""
],
[
"Kardas",
"Marcin",
""
],
[
"Kerkez",
"Viktor",
""
],
[
"Khabsa",
"Madian",
""
],
[
"Kloumann",
"Isabel",
""
],
[
"Korenev",
"Artem",
""
],
[
"Koura",
"Punit Singh",
""
],
[
"Lachaux",
"Marie-Anne",
""
],
[
"Lavril",
"Thibaut",
""
],
[
"Lee",
"Jenya",
""
],
[
"Liskovich",
"Diana",
""
],
[
"Lu",
"Yinghai",
""
],
[
"Mao",
"Yuning",
""
],
[
"Martinet",
"Xavier",
""
],
[
"Mihaylov",
"Todor",
""
],
[
"Mishra",
"Pushkar",
""
],
[
"Molybog",
"Igor",
""
],
[
"Nie",
"Yixin",
""
],
[
"Poulton",
"Andrew",
""
],
[
"Reizenstein",
"Jeremy",
""
],
[
"Rungta",
"Rashi",
""
],
[
"Saladi",
"Kalyan",
""
],
[
"Schelten",
"Alan",
""
],
[
"Silva",
"Ruan",
""
],
[
"Smith",
"Eric Michael",
""
],
[
"Subramanian",
"Ranjan",
""
],
[
"Tan",
"Xiaoqing Ellen",
""
],
[
"Tang",
"Binh",
""
],
[
"Taylor",
"Ross",
""
],
[
"Williams",
"Adina",
""
],
[
"Kuan",
"Jian Xiang",
""
],
[
"Xu",
"Puxin",
""
],
[
"Yan",
"Zheng",
""
],
[
"Zarov",
"Iliyan",
""
],
[
"Zhang",
"Yuchen",
""
],
[
"Fan",
"Angela",
""
],
[
"Kambadur",
"Melanie",
""
],
[
"Narang",
"Sharan",
""
],
[
"Rodriguez",
"Aurelien",
""
],
[
"Stojnic",
"Robert",
""
],
[
"Edunov",
"Sergey",
""
],
[
"Scialom",
"Thomas",
""
]
] |
new_dataset
| 0.997362 |
2307.09362
|
Zhixiang Wei
|
Zhixiang Wei, Lin Chen, Tao Tu, Huaian Chen, Pengyang Ling, Yi Jin
|
Disentangle then Parse:Night-time Semantic Segmentation with
Illumination Disentanglement
|
Accepted by ICCV2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most prior semantic segmentation methods have been developed for day-time
scenes, while typically underperforming in night-time scenes due to
insufficient and complicated lighting conditions. In this work, we tackle this
challenge by proposing a novel night-time semantic segmentation paradigm, i.e.,
disentangle then parse (DTP). DTP explicitly disentangles night-time images
into light-invariant reflectance and light-specific illumination components and
then recognizes semantics based on their adaptive fusion. Concretely, the
proposed DTP comprises two key components: 1) Instead of processing
lighting-entangled features as in prior works, our Semantic-Oriented
Disentanglement (SOD) framework enables the extraction of reflectance component
without being impeded by lighting, allowing the network to consistently
recognize the semantics under cover of varying and complicated lighting
conditions. 2) Based on the observation that the illumination component can
serve as a cue for some semantically confused regions, we further introduce an
Illumination-Aware Parser (IAParser) to explicitly learn the correlation
between semantics and lighting, and aggregate the illumination features to
yield more precise predictions. Extensive experiments on the night-time
segmentation task with various settings demonstrate that DTP significantly
outperforms state-of-the-art methods. Furthermore, with negligible additional
parameters, DTP can be directly used to benefit existing day-time methods for
night-time segmentation.
|
[
{
"version": "v1",
"created": "Tue, 18 Jul 2023 15:46:21 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jul 2023 13:21:30 GMT"
}
] | 2023-07-20T00:00:00 |
[
[
"Wei",
"Zhixiang",
""
],
[
"Chen",
"Lin",
""
],
[
"Tu",
"Tao",
""
],
[
"Chen",
"Huaian",
""
],
[
"Ling",
"Pengyang",
""
],
[
"Jin",
"Yi",
""
]
] |
new_dataset
| 0.987216 |
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