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value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2308.04162
|
Kailun Yang
|
Jiajun Chen, Jiacheng Lin, Zhiqiang Xiao, Haolong Fu, Ke Nai, Kailun
Yang, Zhiyong Li
|
EPCFormer: Expression Prompt Collaboration Transformer for Universal
Referring Video Object Segmentation
|
The source code will be made publicly available at
https://github.com/lab206/EPCFormer
| null | null | null |
cs.CV eess.AS eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Audio-guided Video Object Segmentation (A-VOS) and Referring Video Object
Segmentation (R-VOS) are two highly-related tasks, which both aim to segment
specific objects from video sequences according to user-provided expression
prompts. However, due to the challenges in modeling representations for
different modalities, contemporary methods struggle to strike a balance between
interaction flexibility and high-precision localization and segmentation. In
this paper, we address this problem from two perspectives: the alignment
representation of audio and text and the deep interaction among audio, text,
and visual features. First, we propose a universal architecture, the Expression
Prompt Collaboration Transformer, herein EPCFormer. Next, we propose an
Expression Alignment (EA) mechanism for audio and text expressions. By
introducing contrastive learning for audio and text expressions, the proposed
EPCFormer realizes comprehension of the semantic equivalence between audio and
text expressions denoting the same objects. Then, to facilitate deep
interactions among audio, text, and video features, we introduce an
Expression-Visual Attention (EVA) mechanism. The knowledge of video object
segmentation in terms of the expression prompts can seamlessly transfer between
the two tasks by deeply exploring complementary cues between text and audio.
Experiments on well-recognized benchmarks demonstrate that our universal
EPCFormer attains state-of-the-art results on both tasks. The source code of
EPCFormer will be made publicly available at
https://github.com/lab206/EPCFormer.
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 09:48:00 GMT"
}
] | 2023-08-09T00:00:00 |
[
[
"Chen",
"Jiajun",
""
],
[
"Lin",
"Jiacheng",
""
],
[
"Xiao",
"Zhiqiang",
""
],
[
"Fu",
"Haolong",
""
],
[
"Nai",
"Ke",
""
],
[
"Yang",
"Kailun",
""
],
[
"Li",
"Zhiyong",
""
]
] |
new_dataset
| 0.993309 |
2308.04189
|
Carsten Nielsen
|
Carsten Nielsen, Zhe Su, Giacomo Indiveri
|
Yak: An Asynchronous Bundled Data Pipeline Description Language
| null | null | null | null |
cs.AR
|
http://creativecommons.org/licenses/by/4.0/
|
The design of asynchronous circuits typically requires a judicious definition
of signals and modules, combined with a proper specification of their timing
constraints, which can be a complex and error-prone process, using standard
Hardware Description Languages (HDLs). In this paper we introduce Yak, a new
dataflow description language for asynchronous bundled data circuits. Yak
allows designers to generate Verilog and timing constraints automatically, from
a textual description of bundled data control flow structures and combinational
logic blocks. The timing constraints are generated using the Local Clock Set
methodology and can be consumed by standard industry tools. Yak includes
ergonomic language features such as structured bindings of channels undergoing
fork and join operations, named value scope propagation along channels, and
channel typing. Here we present Yak's language front-end and compare the
automated synthesis and layout results of an example circuit with a manual
constraint specification approach.
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 11:24:46 GMT"
}
] | 2023-08-09T00:00:00 |
[
[
"Nielsen",
"Carsten",
""
],
[
"Su",
"Zhe",
""
],
[
"Indiveri",
"Giacomo",
""
]
] |
new_dataset
| 0.999718 |
2308.04218
|
Muduo Xu
|
Muduo Xu, Jianhao Su, Yutao Liu
|
AquaSAM: Underwater Image Foreground Segmentation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Segment Anything Model (SAM) has revolutionized natural image
segmentation, nevertheless, its performance on underwater images is still
restricted. This work presents AquaSAM, the first attempt to extend the success
of SAM on underwater images with the purpose of creating a versatile method for
the segmentation of various underwater targets. To achieve this, we begin by
classifying and extracting various labels automatically in SUIM dataset.
Subsequently, we develop a straightforward fine-tuning method to adapt SAM to
general foreground underwater image segmentation. Through extensive experiments
involving eight segmentation tasks like human divers, we demonstrate that
AquaSAM outperforms the default SAM model especially at hard tasks like coral
reefs. AquaSAM achieves an average Dice Similarity Coefficient (DSC) of 7.13
(%) improvement and an average of 8.27 (%) on mIoU improvement in underwater
segmentation tasks.
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 12:30:36 GMT"
}
] | 2023-08-09T00:00:00 |
[
[
"Xu",
"Muduo",
""
],
[
"Su",
"Jianhao",
""
],
[
"Liu",
"Yutao",
""
]
] |
new_dataset
| 0.998866 |
2308.04249
|
Huiguang He
|
Yizhuo Lu, Changde Du, Qiongyi zhou, Dianpeng Wang, Huiguang He
|
MindDiffuser: Controlled Image Reconstruction from Human Brain Activity
with Semantic and Structural Diffusion
|
arXiv admin note: substantial text overlap with arXiv:2303.14139
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reconstructing visual stimuli from brain recordings has been a meaningful and
challenging task. Especially, the achievement of precise and controllable image
reconstruction bears great significance in propelling the progress and
utilization of brain-computer interfaces. Despite the advancements in complex
image reconstruction techniques, the challenge persists in achieving a cohesive
alignment of both semantic (concepts and objects) and structure (position,
orientation, and size) with the image stimuli. To address the aforementioned
issue, we propose a two-stage image reconstruction model called MindDiffuser.
In Stage 1, the VQ-VAE latent representations and the CLIP text embeddings
decoded from fMRI are put into Stable Diffusion, which yields a preliminary
image that contains semantic information. In Stage 2, we utilize the CLIP
visual feature decoded from fMRI as supervisory information, and continually
adjust the two feature vectors decoded in Stage 1 through backpropagation to
align the structural information. The results of both qualitative and
quantitative analyses demonstrate that our model has surpassed the current
state-of-the-art models on Natural Scenes Dataset (NSD). The subsequent
experimental findings corroborate the neurobiological plausibility of the
model, as evidenced by the interpretability of the multimodal feature employed,
which align with the corresponding brain responses.
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 13:28:34 GMT"
}
] | 2023-08-09T00:00:00 |
[
[
"Lu",
"Yizhuo",
""
],
[
"Du",
"Changde",
""
],
[
"zhou",
"Qiongyi",
""
],
[
"Wang",
"Dianpeng",
""
],
[
"He",
"Huiguang",
""
]
] |
new_dataset
| 0.9967 |
2308.04288
|
Daiheng Gao
|
Daiheng Gao, Xu Chen, Xindi Zhang, Qi Wang, Ke Sun, Bang Zhang,
Liefeng Bo, Qixing Huang
|
Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual
Try-On
|
15 pages, 15 figures
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Fabricating and designing 3D garments has become extremely demanding with the
increasing need for synthesizing realistic dressed persons for a variety of
applications, e.g. 3D virtual try-on, digitalization of 2D clothes into 3D
apparel, and cloth animation. It thus necessitates a simple and straightforward
pipeline to obtain high-quality texture from simple input, such as 2D reference
images. Since traditional warping-based texture generation methods require a
significant number of control points to be manually selected for each type of
garment, which can be a time-consuming and tedious process. We propose a novel
method, called Cloth2Tex, which eliminates the human burden in this process.
Cloth2Tex is a self-supervised method that generates texture maps with
reasonable layout and structural consistency. Another key feature of Cloth2Tex
is that it can be used to support high-fidelity texture inpainting. This is
done by combining Cloth2Tex with a prevailing latent diffusion model. We
evaluate our approach both qualitatively and quantitatively and demonstrate
that Cloth2Tex can generate high-quality texture maps and achieve the best
visual effects in comparison to other methods. Project page:
tomguluson92.github.io/projects/cloth2tex/
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 14:32:38 GMT"
}
] | 2023-08-09T00:00:00 |
[
[
"Gao",
"Daiheng",
""
],
[
"Chen",
"Xu",
""
],
[
"Zhang",
"Xindi",
""
],
[
"Wang",
"Qi",
""
],
[
"Sun",
"Ke",
""
],
[
"Zhang",
"Bang",
""
],
[
"Bo",
"Liefeng",
""
],
[
"Huang",
"Qixing",
""
]
] |
new_dataset
| 0.999841 |
2308.04323
|
Miguel Zamora
|
Zhaoting Li, Miguel Zamora, Hehui Zheng, Stelian Coros
|
Embracing Safe Contacts with Contact-aware Planning and Control
|
RSS 2023. Workshop: Experiment-oriented Locomotion and Manipulation
Research
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Unlike human beings that can employ the entire surface of their limbs as a
means to establish contact with their environment, robots are typically
programmed to interact with their environments via their end-effectors, in a
collision-free fashion, to avoid damaging their environment. In a departure
from such a traditional approach, this work presents a contact-aware controller
for reference tracking that maintains interaction forces on the surface of the
robot below a safety threshold in the presence of both rigid and soft contacts.
Furthermore, we leveraged the proposed controller to extend the BiTRRT
sample-based planning method to be contact-aware, using a simplified contact
model. The effectiveness of our framework is demonstrated in hardware
experiments using a Franka robot in a setup inspired by the Amazon stowing
task. A demo video of our results can be seen here:
https://youtu.be/2WeYytauhNg
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 15:16:51 GMT"
}
] | 2023-08-09T00:00:00 |
[
[
"Li",
"Zhaoting",
""
],
[
"Zamora",
"Miguel",
""
],
[
"Zheng",
"Hehui",
""
],
[
"Coros",
"Stelian",
""
]
] |
new_dataset
| 0.9965 |
2308.04328
|
Nadia Nahar
|
Nadia Nahar, Haoran Zhang, Grace Lewis, Shurui Zhou, Christian
K\"astner
|
A Dataset and Analysis of Open-Source Machine Learning Products
| null | null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Machine learning (ML) components are increasingly incorporated into software
products, yet developers face challenges in transitioning from ML prototypes to
products. Academic researchers struggle to propose solutions to these
challenges and evaluate interventions because they often do not have access to
close-sourced ML products from industry. In this study, we define and identify
open-source ML products, curating a dataset of 262 repositories from GitHub, to
facilitate further research and education. As a start, we explore six broad
research questions related to different development activities and report 21
findings from a sample of 30 ML products from the dataset. Our findings reveal
a variety of development practices and architectural decisions surrounding
different types and uses of ML models that offer ample opportunities for future
research innovations. We also find very little evidence of industry best
practices such as model testing and pipeline automation within the open-source
ML products, which leaves room for further investigation to understand its
potential impact on the development and eventual end-user experience for the
products.
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 15:19:13 GMT"
}
] | 2023-08-09T00:00:00 |
[
[
"Nahar",
"Nadia",
""
],
[
"Zhang",
"Haoran",
""
],
[
"Lewis",
"Grace",
""
],
[
"Zhou",
"Shurui",
""
],
[
"Kästner",
"Christian",
""
]
] |
new_dataset
| 0.999812 |
2308.04337
|
Fadhil Muhammad
|
Fadhil Muhammad, Alif Bintang Elfandra, Iqbal Pahlevi Amin, Alfan
Farizki Wicaksono
|
Pengembangan Model untuk Mendeteksi Kerusakan pada Terumbu Karang dengan
Klasifikasi Citra
|
in Indonesian language
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The abundant biodiversity of coral reefs in Indonesian waters is a valuable
asset that needs to be preserved. Rapid climate change and uncontrolled human
activities have led to the degradation of coral reef ecosystems, including
coral bleaching, which is a critical indicator of coral health conditions.
Therefore, this research aims to develop an accurate classification model to
distinguish between healthy corals and corals experiencing bleaching. This
study utilizes a specialized dataset consisting of 923 images collected from
Flickr using the Flickr API. The dataset comprises two distinct classes:
healthy corals (438 images) and bleached corals (485 images). These images have
been resized to a maximum of 300 pixels in width or height, whichever is
larger, to maintain consistent sizes across the dataset.
The method employed in this research involves the use of machine learning
models, particularly convolutional neural networks (CNN), to recognize and
differentiate visual patterns associated with healthy and bleached corals. In
this context, the dataset can be used to train and test various classification
models to achieve optimal results. By leveraging the ResNet model, it was found
that a from-scratch ResNet model can outperform pretrained models in terms of
precision and accuracy. The success in developing accurate classification
models will greatly benefit researchers and marine biologists in gaining a
better understanding of coral reef health. These models can also be employed to
monitor changes in the coral reef environment, thereby making a significant
contribution to conservation and ecosystem restoration efforts that have
far-reaching impacts on life.
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 15:30:08 GMT"
}
] | 2023-08-09T00:00:00 |
[
[
"Muhammad",
"Fadhil",
""
],
[
"Elfandra",
"Alif Bintang",
""
],
[
"Amin",
"Iqbal Pahlevi",
""
],
[
"Wicaksono",
"Alfan Farizki",
""
]
] |
new_dataset
| 0.960006 |
2308.04352
|
Ziyu Zhu
|
Ziyu Zhu, Xiaojian Ma, Yixin Chen, Zhidong Deng, Siyuan Huang, Qing Li
|
3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
3D vision-language grounding (3D-VL) is an emerging field that aims to
connect the 3D physical world with natural language, which is crucial for
achieving embodied intelligence. Current 3D-VL models rely heavily on
sophisticated modules, auxiliary losses, and optimization tricks, which calls
for a simple and unified model. In this paper, we propose 3D-VisTA, a
pre-trained Transformer for 3D Vision and Text Alignment that can be easily
adapted to various downstream tasks. 3D-VisTA simply utilizes self-attention
layers for both single-modal modeling and multi-modal fusion without any
sophisticated task-specific design. To further enhance its performance on 3D-VL
tasks, we construct ScanScribe, the first large-scale 3D scene-text pairs
dataset for 3D-VL pre-training. ScanScribe contains 2,995 RGB-D scans for 1,185
unique indoor scenes originating from ScanNet and 3R-Scan datasets, along with
paired 278K scene descriptions generated from existing 3D-VL tasks, templates,
and GPT-3. 3D-VisTA is pre-trained on ScanScribe via masked language/object
modeling and scene-text matching. It achieves state-of-the-art results on
various 3D-VL tasks, ranging from visual grounding and dense captioning to
question answering and situated reasoning. Moreover, 3D-VisTA demonstrates
superior data efficiency, obtaining strong performance even with limited
annotations during downstream task fine-tuning.
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 15:59:17 GMT"
}
] | 2023-08-09T00:00:00 |
[
[
"Zhu",
"Ziyu",
""
],
[
"Ma",
"Xiaojian",
""
],
[
"Chen",
"Yixin",
""
],
[
"Deng",
"Zhidong",
""
],
[
"Huang",
"Siyuan",
""
],
[
"Li",
"Qing",
""
]
] |
new_dataset
| 0.999309 |
2308.04370
|
Juan Wen
|
Juan Wen, Shupeng Cheng, Peng Xu, Bowen Zhou, Radu Timofte, Weiyan
Hou, Luc Van Gool
|
When Super-Resolution Meets Camouflaged Object Detection: A Comparison
Study
|
23 pages with 8 figures
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Super Resolution (SR) and Camouflaged Object Detection (COD) are two hot
topics in computer vision with various joint applications. For instance,
low-resolution surveillance images can be successively processed by
super-resolution techniques and camouflaged object detection. However, in
previous work, these two areas are always studied in isolation. In this paper,
we, for the first time, conduct an integrated comparative evaluation for both.
Specifically, we benchmark different super-resolution methods on commonly used
COD datasets, and meanwhile, we evaluate the robustness of different COD models
by using COD data processed by SR methods. Our goal is to bridge these two
domains, discover novel experimental phenomena, summarize new experim.
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 16:17:46 GMT"
}
] | 2023-08-09T00:00:00 |
[
[
"Wen",
"Juan",
""
],
[
"Cheng",
"Shupeng",
""
],
[
"Xu",
"Peng",
""
],
[
"Zhou",
"Bowen",
""
],
[
"Timofte",
"Radu",
""
],
[
"Hou",
"Weiyan",
""
],
[
"Van Gool",
"Luc",
""
]
] |
new_dataset
| 0.996966 |
2308.04398
|
Josef Jon
|
Josef Jon and Ond\v{r}ej Bojar
|
Character-level NMT and language similarity
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We explore the effectiveness of character-level neural machine translation
using Transformer architecture for various levels of language similarity and
size of the training dataset on translation between Czech and Croatian, German,
Hungarian, Slovak, and Spanish. We evaluate the models using automatic MT
metrics and show that translation between similar languages benefits from
character-level input segmentation, while for less related languages,
character-level vanilla Transformer-base often lags behind subword-level
segmentation. We confirm previous findings that it is possible to close the gap
by finetuning the already trained subword-level models to character-level.
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 17:01:42 GMT"
}
] | 2023-08-09T00:00:00 |
[
[
"Jon",
"Josef",
""
],
[
"Bojar",
"Ondřej",
""
]
] |
new_dataset
| 0.984483 |
2308.04409
|
Yichao Shen
|
Yichao Shen, Zigang Geng, Yuhui Yuan, Yutong Lin, Ze Liu, Chunyu Wang,
Han Hu, Nanning Zheng, Baining Guo
|
V-DETR: DETR with Vertex Relative Position Encoding for 3D Object
Detection
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce a highly performant 3D object detector for point clouds using
the DETR framework. The prior attempts all end up with suboptimal results
because they fail to learn accurate inductive biases from the limited scale of
training data. In particular, the queries often attend to points that are far
away from the target objects, violating the locality principle in object
detection. To address the limitation, we introduce a novel 3D Vertex Relative
Position Encoding (3DV-RPE) method which computes position encoding for each
point based on its relative position to the 3D boxes predicted by the queries
in each decoder layer, thus providing clear information to guide the model to
focus on points near the objects, in accordance with the principle of locality.
In addition, we systematically improve the pipeline from various aspects such
as data normalization based on our understanding of the task. We show
exceptional results on the challenging ScanNetV2 benchmark, achieving
significant improvements over the previous 3DETR in
$\rm{AP}_{25}$/$\rm{AP}_{50}$ from 65.0\%/47.0\% to 77.8\%/66.0\%,
respectively. In addition, our method sets a new record on ScanNetV2 and SUN
RGB-D datasets.Code will be released at http://github.com/yichaoshen-MS/V-DETR.
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 17:14:14 GMT"
}
] | 2023-08-09T00:00:00 |
[
[
"Shen",
"Yichao",
""
],
[
"Geng",
"Zigang",
""
],
[
"Yuan",
"Yuhui",
""
],
[
"Lin",
"Yutong",
""
],
[
"Liu",
"Ze",
""
],
[
"Wang",
"Chunyu",
""
],
[
"Hu",
"Han",
""
],
[
"Zheng",
"Nanning",
""
],
[
"Guo",
"Baining",
""
]
] |
new_dataset
| 0.964982 |
2011.04400
|
Soumajyoti Sarkar Mr.
|
Soumajyoti Sarkar
|
Bandits in Matching Markets: Ideas and Proposals for Peer Lending
| null | null | null | null |
cs.GT cs.LG econ.GN q-fin.EC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Motivated by recent applications of sequential decision making in matching
markets, in this paper we attempt at formulating and abstracting market designs
for P2P lending. We describe a paradigm to set the stage for how peer to peer
investments can be conceived from a matching market perspective, especially
when both borrower and lender preferences are respected. We model these
specialized markets as an optimization problem and consider different utilities
for agents on both sides of the market while also understanding the impact of
equitable allocations to borrowers. We devise a technique based on sequential
decision making that allow the lenders to adjust their choices based on the
dynamics of uncertainty from competition over time and that also impacts the
rewards in return for their investments. Using simulated experiments we show
the dynamics of the regret based on the optimal borrower-lender matching and
find that the lender regret depends on the initial preferences set by the
lenders which could affect their learning over decision making steps.
|
[
{
"version": "v1",
"created": "Fri, 30 Oct 2020 20:12:26 GMT"
},
{
"version": "v2",
"created": "Wed, 20 Jan 2021 09:49:49 GMT"
},
{
"version": "v3",
"created": "Tue, 2 Mar 2021 08:14:30 GMT"
},
{
"version": "v4",
"created": "Fri, 16 Apr 2021 07:46:52 GMT"
},
{
"version": "v5",
"created": "Wed, 2 Aug 2023 16:09:47 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Sarkar",
"Soumajyoti",
""
]
] |
new_dataset
| 0.977797 |
2105.00689
|
Michael Kompatscher
|
Michael Kompatscher
|
CSAT and CEQV for nilpotent Maltsev algebras of Fitting length > 2
|
23 pages
| null | null | null |
cs.CC math.RA
|
http://creativecommons.org/licenses/by/4.0/
|
The circuit satisfaction problem CSAT(A) of an algebra A is the problem of
deciding whether an equation over A (encoded by two circuits) has a solution or
not. While solving systems of equations over finite algebras is either in P or
NP-complete, no such dichotomy result is known for CSAT(A). In fact, Idziak,
Kawalek and Krzaczkowski constructed examples of nilpotent Maltsev algebras A,
for which, under the assumption of ETH and an open conjecture in circuit
theory, CSAT(A) can be solved in quasipolynomial, but not polynomial time. The
same is true for the circuit equivalence problem CEQV(A).
In this paper we generalize their result to all nilpotent Maltsev algebras of
Fitting length >2. This not only advances the project of classifying the
complexity of CSAT (and CEQV) for algebras from congruence modular varieties,
but we also believe that the tools we developed are of independent interest in
the study of nilpotent algebras.
|
[
{
"version": "v1",
"created": "Mon, 3 May 2021 08:51:57 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Aug 2023 16:41:14 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Kompatscher",
"Michael",
""
]
] |
new_dataset
| 0.999315 |
2106.02350
|
Giulio Ermanno Pibiri
|
Giulio Ermanno Pibiri and Roberto Trani
|
Parallel and External-Memory Construction of Minimal Perfect Hash
Functions with PTHash
|
Accepted by IEEE TKDE
| null | null | null |
cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
A function $f : U \to \{0,\ldots,n-1\}$ is a minimal perfect hash function
for a set $S \subseteq U$ of size $n$, if $f$ bijectively maps $S$ into the
first $n$ natural numbers. These functions are important for many practical
applications in computing, such as search engines, computer networks, and
databases. Several algorithms have been proposed to build minimal perfect hash
functions that: scale well to large sets, retain fast evaluation time, and take
very little space, e.g., 2 - 3 bits/key. PTHash is one such algorithm,
achieving very fast evaluation in compressed space, typically several times
faster than other techniques. In this work, we propose a new construction
algorithm for PTHash enabling: (1) multi-threading, to either build functions
more quickly or more space-efficiently, and (2) external-memory processing to
scale to inputs much larger than the available internal memory. Only few other
algorithms in the literature share these features, despite of their big
practical impact. We conduct an extensive experimental assessment on large
real-world string collections and show that, with respect to other techniques,
PTHash is competitive in construction time and space consumption, but retains 2
- 6$\times$ better lookup time.
|
[
{
"version": "v1",
"created": "Fri, 4 Jun 2021 09:02:36 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Aug 2023 10:14:25 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Pibiri",
"Giulio Ermanno",
""
],
[
"Trani",
"Roberto",
""
]
] |
new_dataset
| 0.961837 |
2106.08091
|
Catherine Ordun
|
Catherine Ordun, Edward Raff, Sanjay Purushotham
|
Generating Thermal Human Faces for Physiological Assessment Using
Thermal Sensor Auxiliary Labels
| null |
2021 IEEE International Conference on Image Processing (ICIP)
| null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Thermal images reveal medically important physiological information about
human stress, signs of inflammation, and emotional mood that cannot be seen on
visible images. Providing a method to generate thermal faces from visible
images would be highly valuable for the telemedicine community in order to show
this medical information. To the best of our knowledge, there are limited works
on visible-to-thermal (VT) face translation, and many current works go the
opposite direction to generate visible faces from thermal surveillance images
(TV) for law enforcement applications. As a result, we introduce favtGAN, a VT
GAN which uses the pix2pix image translation model with an auxiliary sensor
label prediction network for generating thermal faces from visible images.
Since most TV methods are trained on only one data source drawn from one
thermal sensor, we combine datasets from faces and cityscapes. These combined
data are captured from similar sensors in order to bootstrap the training and
transfer learning task, especially valuable because visible-thermal face
datasets are limited. Experiments on these combined datasets show that favtGAN
demonstrates an increase in SSIM and PSNR scores of generated thermal faces,
compared to training on a single face dataset alone.
|
[
{
"version": "v1",
"created": "Tue, 15 Jun 2021 12:32:52 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Ordun",
"Catherine",
""
],
[
"Raff",
"Edward",
""
],
[
"Purushotham",
"Sanjay",
""
]
] |
new_dataset
| 0.962641 |
2111.00221
|
Long Zhang
|
Long Zhang, Javier Ron, Benoit Baudry, and Martin Monperrus
|
Chaos Engineering of Ethereum Blockchain Clients
| null |
Distributed Ledger Technologies: Research and Practice, 2023
|
10.1145/3611649
| null |
cs.SE cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present ChaosETH, a chaos engineering approach for
resilience assessment of Ethereum blockchain clients. ChaosETH operates in the
following manner: First, it monitors Ethereum clients to determine their normal
behavior. Then, it injects system call invocation errors into one single
Ethereum client at a time, and observes the behavior resulting from
perturbation. Finally, ChaosETH compares the behavior recorded before, during,
and after perturbation to assess the impact of the injected system call
invocation errors. The experiments are performed on the two most popular
Ethereum client implementations: GoEthereum and Nethermind. We assess the
impact of 22 different system call errors on those Ethereum clients with
respect to 15 application-level metrics. Our results reveal a broad spectrum of
resilience characteristics of Ethereum clients w.r.t. system call invocation
errors, ranging from direct crashes to full resilience. The experiments clearly
demonstrate the feasibility of applying chaos engineering principles to
blockchain systems.
|
[
{
"version": "v1",
"created": "Sat, 30 Oct 2021 10:03:19 GMT"
},
{
"version": "v2",
"created": "Sun, 18 Jun 2023 00:43:29 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Zhang",
"Long",
""
],
[
"Ron",
"Javier",
""
],
[
"Baudry",
"Benoit",
""
],
[
"Monperrus",
"Martin",
""
]
] |
new_dataset
| 0.998722 |
2206.08083
|
Bonifaz Stuhr
|
Julian Gebele, Bonifaz Stuhr and Johann Haselberger
|
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation
from Simulation to multiple Real-World Domains
|
36th Conference on Neural Information Processing Systems (NeurIPS
2022) Track on Datasets and Benchmarks, 22 pages, 11 figures
| null |
10.34740/kaggle/dsv/3798459
| null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Unsupervised Domain Adaptation demonstrates great potential to mitigate
domain shifts by transferring models from labeled source domains to unlabeled
target domains. While Unsupervised Domain Adaptation has been applied to a wide
variety of complex vision tasks, only few works focus on lane detection for
autonomous driving. This can be attributed to the lack of publicly available
datasets. To facilitate research in these directions, we propose CARLANE, a
3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE
encompasses the single-target datasets MoLane and TuLane and the multi-target
dataset MuLane. These datasets are built from three different domains, which
cover diverse scenes and contain a total of 163K unique images, 118K of which
are annotated. In addition we evaluate and report systematic baselines,
including our own method, which builds upon Prototypical Cross-domain
Self-supervised Learning. We find that false positive and false negative rates
of the evaluated domain adaptation methods are high compared to those of fully
supervised baselines. This affirms the need for benchmarks such as CARLANE to
further strengthen research in Unsupervised Domain Adaptation for lane
detection. CARLANE, all evaluated models and the corresponding implementations
are publicly available at https://carlanebenchmark.github.io.
|
[
{
"version": "v1",
"created": "Thu, 16 Jun 2022 10:53:18 GMT"
},
{
"version": "v2",
"created": "Thu, 11 Aug 2022 14:51:41 GMT"
},
{
"version": "v3",
"created": "Tue, 20 Sep 2022 08:10:00 GMT"
},
{
"version": "v4",
"created": "Mon, 7 Aug 2023 13:24:06 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Gebele",
"Julian",
""
],
[
"Stuhr",
"Bonifaz",
""
],
[
"Haselberger",
"Johann",
""
]
] |
new_dataset
| 0.998959 |
2207.04438
|
Jiawen Zhu
|
Jiawen Zhu, Xin Chen, Pengyu Zhang, Xinying Wang, Dong Wang, Wenda
Zhao, Huchuan Lu
|
SRRT: Search Region Regulation Tracking
|
Under review
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The dominant trackers generate a fixed-size rectangular region based on the
previous prediction or initial bounding box as the model input, i.e., search
region. While this manner obtains promising tracking efficiency, a fixed-size
search region lacks flexibility and is likely to fail in some cases, e.g., fast
motion and distractor interference. Trackers tend to lose the target object due
to the limited search region or be interfered with by distractors due to the
excessive search region. Drawing inspiration from the pattern humans track an
object, we propose a novel tracking paradigm, called Search Region Regulation
Tracking (SRRT) that applies a small eyereach when the target is captured and
zooms out the search field when the target is about to be lost. SRRT applies a
proposed search region regulator to estimate an optimal search region
dynamically for each frame, by which the tracker can flexibly respond to
transient changes in the location of object occurrences. To adapt the object's
appearance variation during online tracking, we further propose a lockingstate
determined updating strategy for reference frame updating. The proposed SRRT is
concise without bells and whistles, yet achieves evident improvements and
competitive results with other state-of-the-art trackers on eight benchmarks.
On the large-scale LaSOT benchmark, SRRT improves SiamRPN++ and TransT with
absolute gains of 4.6% and 3.1% in terms of AUC. The code and models will be
released.
|
[
{
"version": "v1",
"created": "Sun, 10 Jul 2022 11:18:26 GMT"
},
{
"version": "v2",
"created": "Fri, 19 Aug 2022 06:55:56 GMT"
},
{
"version": "v3",
"created": "Sun, 6 Aug 2023 10:00:43 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Zhu",
"Jiawen",
""
],
[
"Chen",
"Xin",
""
],
[
"Zhang",
"Pengyu",
""
],
[
"Wang",
"Xinying",
""
],
[
"Wang",
"Dong",
""
],
[
"Zhao",
"Wenda",
""
],
[
"Lu",
"Huchuan",
""
]
] |
new_dataset
| 0.998333 |
2209.04265
|
Yubin Liu
|
Yubin Liu, Qiming Ye, Jose Escribano-Macias, Yuxiang Feng, Eduardo
Candela, and Panagiotis Angeloudis
|
Route Planning for Last-Mile Deliveries Using Mobile Parcel Lockers: A
Hybrid Q-Learning Network Approach
|
54 pages, 18 figures. This paper has been submitted to Transportation
Research Part E: Logistics and Transportation Review (Manuscript Number:
TRE-D-23-00202)
| null |
10.1016/j.tre.2023.103234
| null |
cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Mobile parcel lockers have been recently proposed by logistics operators as a
technology that could help reduce traffic congestion and operational costs in
urban freight distribution. Given their ability to relocate throughout their
area of deployment, they hold the potential to improve customer accessibility
and convenience. In this study, we formulate the Mobile Parcel Locker Problem
(MPLP) , a special case of the Location-Routing Problem (LRP) which determines
the optimal stopover location for MPLs throughout the day and plans
corresponding delivery routes. A Hybrid Q Learning Network based Method (HQM)
is developed to resolve the computational complexity of the resulting large
problem instances while escaping local optima. In addition, the HQM is
integrated with global and local search mechanisms to resolve the dilemma of
exploration and exploitation faced by classic reinforcement learning methods.
We examine the performance of HQM under different problem sizes (up to 200
nodes) and benchmarked it against the exact approach and Genetic Algorithm
(GA). Our results indicate that HQM achieves better optimisation performance
with shorter computation time than the exact approach solved by the Gurobi
solver in large problem instances. Additionally, the average reward obtained by
HQM is 1.96 times greater than GA, which demonstrates that HQM has a better
optimisation ability. Further, we identify critical factors that contribute to
fleet size requirements, travel distances, and service delays. Our findings
outline that the efficiency of MPLs is mainly contingent on the length of time
windows and the deployment of MPL stopovers. Finally, we highlight managerial
implications based on parametric analysis to provide guidance for logistics
operators in the context of efficient last-mile distribution operations.
|
[
{
"version": "v1",
"created": "Fri, 9 Sep 2022 11:59:42 GMT"
},
{
"version": "v2",
"created": "Sat, 19 Nov 2022 08:05:17 GMT"
},
{
"version": "v3",
"created": "Fri, 10 Feb 2023 02:39:29 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Liu",
"Yubin",
""
],
[
"Ye",
"Qiming",
""
],
[
"Escribano-Macias",
"Jose",
""
],
[
"Feng",
"Yuxiang",
""
],
[
"Candela",
"Eduardo",
""
],
[
"Angeloudis",
"Panagiotis",
""
]
] |
new_dataset
| 0.997081 |
2210.12364
|
Lvxiaowei Xu
|
Lvxiaowei Xu, Jianwang Wu, Jiawei Peng, Jiayu Fu, Ming Cai
|
FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction
|
Long paper, accepted at the Findings of EMNLP 2022
| null |
10.18653/v1/2022.findings-emnlp.137
| null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Grammatical Error Correction (GEC) has been broadly applied in automatic
correction and proofreading system recently. However, it is still immature in
Chinese GEC due to limited high-quality data from native speakers in terms of
category and scale. In this paper, we present FCGEC, a fine-grained corpus to
detect, identify and correct the grammatical errors. FCGEC is a human-annotated
corpus with multiple references, consisting of 41,340 sentences collected
mainly from multi-choice questions in public school Chinese examinations.
Furthermore, we propose a Switch-Tagger-Generator (STG) baseline model to
correct the grammatical errors in low-resource settings. Compared to other GEC
benchmark models, experimental results illustrate that STG outperforms them on
our FCGEC. However, there exists a significant gap between benchmark models and
humans that encourages future models to bridge it.
|
[
{
"version": "v1",
"created": "Sat, 22 Oct 2022 06:29:05 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Xu",
"Lvxiaowei",
""
],
[
"Wu",
"Jianwang",
""
],
[
"Peng",
"Jiawei",
""
],
[
"Fu",
"Jiayu",
""
],
[
"Cai",
"Ming",
""
]
] |
new_dataset
| 0.999734 |
2211.08264
|
Priyanka Agrawal
|
Priyanka Agrawal, Chris Alberti, Fantine Huot, Joshua Maynez, Ji Ma,
Sebastian Ruder, Kuzman Ganchev, Dipanjan Das, Mirella Lapata
|
QAmeleon: Multilingual QA with Only 5 Examples
|
To Appear at Transactions of Association for Computational
Linguistics (TACL)
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The availability of large, high-quality datasets has been one of the main
drivers of recent progress in question answering (QA). Such annotated datasets
however are difficult and costly to collect, and rarely exist in languages
other than English, rendering QA technology inaccessible to underrepresented
languages. An alternative to building large monolingual training datasets is to
leverage pre-trained language models (PLMs) under a few-shot learning setting.
Our approach, QAmeleon, uses a PLM to automatically generate multilingual data
upon which QA models are trained, thus avoiding costly annotation. Prompt
tuning the PLM for data synthesis with only five examples per language delivers
accuracy superior to translation-based baselines, bridges nearly 60% of the gap
between an English-only baseline and a fully supervised upper bound trained on
almost 50,000 hand labeled examples, and always leads to substantial
improvements compared to fine-tuning a QA model directly on labeled examples in
low resource settings. Experiments on the TyDiQA-GoldP and MLQA benchmarks show
that few-shot prompt tuning for data synthesis scales across languages and is a
viable alternative to large-scale annotation.
|
[
{
"version": "v1",
"created": "Tue, 15 Nov 2022 16:14:39 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Aug 2023 11:22:16 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Agrawal",
"Priyanka",
""
],
[
"Alberti",
"Chris",
""
],
[
"Huot",
"Fantine",
""
],
[
"Maynez",
"Joshua",
""
],
[
"Ma",
"Ji",
""
],
[
"Ruder",
"Sebastian",
""
],
[
"Ganchev",
"Kuzman",
""
],
[
"Das",
"Dipanjan",
""
],
[
"Lapata",
"Mirella",
""
]
] |
new_dataset
| 0.998766 |
2211.15300
|
Fabian Ruffy
|
Fabian Ruffy, Jed Liu, Prathima Kotikalapudi, Vojt\v{e}ch Havel,
Hanneli Tavante, Rob Sherwood, Vladyslav Dubina, Volodymyr Peschanenko,
Anirudh Sivaraman, and Nate Foster
|
P4Testgen: An Extensible Test Oracle For P4
| null |
ACM SIGCOMM 2023 Conference (ACM SIGCOMM '23)
|
10.1145/3603269.3604834
| null |
cs.NI cs.SC cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
We present P4Testgen, a test oracle for the P4$_{16}$ language. P4Testgen
supports automatic test generation for any P4 target and is designed to be
extensible to many P4 targets. It models the complete semantics of the target's
packet-processing pipeline including the P4 language, architectures and
externs, and target-specific extensions. To handle non-deterministic behaviors
and complex externs (e.g., checksums and hash functions), P4Testgen uses taint
tracking and concolic execution. It also provides path selection strategies
that reduce the number of tests required to achieve full coverage.
We have instantiated P4Testgen for the V1model, eBPF, PNA, and Tofino P4
architectures. Each extension required effort commensurate with the complexity
of the target. We validated the tests generated by P4Testgen by running them
across the entire P4C test suite as well as the programs supplied with the
Tofino P4 Studio. Using the tool, we have also confirmed 25 bugs in mature,
production toolchains for BMv2 and Tofino.
|
[
{
"version": "v1",
"created": "Mon, 28 Nov 2022 13:31:42 GMT"
},
{
"version": "v2",
"created": "Thu, 2 Mar 2023 21:35:00 GMT"
},
{
"version": "v3",
"created": "Sun, 6 Aug 2023 11:15:37 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Ruffy",
"Fabian",
""
],
[
"Liu",
"Jed",
""
],
[
"Kotikalapudi",
"Prathima",
""
],
[
"Havel",
"Vojtěch",
""
],
[
"Tavante",
"Hanneli",
""
],
[
"Sherwood",
"Rob",
""
],
[
"Dubina",
"Vladyslav",
""
],
[
"Peschanenko",
"Volodymyr",
""
],
[
"Sivaraman",
"Anirudh",
""
],
[
"Foster",
"Nate",
""
]
] |
new_dataset
| 0.998835 |
2212.05098
|
Daniel Lemire
|
Robert Clausecker and Daniel Lemire
|
Transcoding Unicode Characters with AVX-512 Instructions
| null | null | null | null |
cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
Intel includes in its recent processors a powerful set of instructions
capable of processing 512-bit registers with a single instruction (AVX-512).
Some of these instructions have no equivalent in earlier instruction sets. We
leverage these instructions to efficiently transcode strings between the most
common formats: UTF-8 and UTF-16. With our novel algorithms, we are often twice
as fast as the previous best solutions. For example, we transcode Chinese text
from UTF-8 to UTF-16 at more than 5 GiB/s using fewer than 2 CPU instructions
per character. To ensure reproducibility, we make our software freely available
as an open source library. Our library is part of the popular Node.js
JavaScript runtime.
|
[
{
"version": "v1",
"created": "Fri, 9 Dec 2022 19:55:19 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Dec 2022 20:35:53 GMT"
},
{
"version": "v3",
"created": "Thu, 13 Jul 2023 18:12:09 GMT"
},
{
"version": "v4",
"created": "Sat, 5 Aug 2023 17:40:07 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Clausecker",
"Robert",
""
],
[
"Lemire",
"Daniel",
""
]
] |
new_dataset
| 0.999604 |
2212.08254
|
Zhikai Li
|
Zhikai Li, Junrui Xiao, Lianwei Yang, and Qingyi Gu
|
RepQ-ViT: Scale Reparameterization for Post-Training Quantization of
Vision Transformers
|
ICCV 2023
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Post-training quantization (PTQ), which only requires a tiny dataset for
calibration without end-to-end retraining, is a light and practical model
compression technique. Recently, several PTQ schemes for vision transformers
(ViTs) have been presented; unfortunately, they typically suffer from
non-trivial accuracy degradation, especially in low-bit cases. In this paper,
we propose RepQ-ViT, a novel PTQ framework for ViTs based on quantization scale
reparameterization, to address the above issues. RepQ-ViT decouples the
quantization and inference processes, where the former employs complex
quantizers and the latter employs scale-reparameterized simplified quantizers.
This ensures both accurate quantization and efficient inference, which
distinguishes it from existing approaches that sacrifice quantization
performance to meet the target hardware. More specifically, we focus on two
components with extreme distributions: post-LayerNorm activations with severe
inter-channel variation and post-Softmax activations with power-law features,
and initially apply channel-wise quantization and log$\sqrt{2}$ quantization,
respectively. Then, we reparameterize the scales to hardware-friendly
layer-wise quantization and log2 quantization for inference, with only slight
accuracy or computational costs. Extensive experiments are conducted on
multiple vision tasks with different model variants, proving that RepQ-ViT,
without hyperparameters and expensive reconstruction procedures, can outperform
existing strong baselines and encouragingly improve the accuracy of 4-bit PTQ
of ViTs to a usable level. Code is available at
https://github.com/zkkli/RepQ-ViT.
|
[
{
"version": "v1",
"created": "Fri, 16 Dec 2022 02:52:37 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Aug 2023 03:00:41 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Li",
"Zhikai",
""
],
[
"Xiao",
"Junrui",
""
],
[
"Yang",
"Lianwei",
""
],
[
"Gu",
"Qingyi",
""
]
] |
new_dataset
| 0.99938 |
2212.08283
|
Feiqi Cao
|
Feiqi Cao, Siwen Luo, Felipe Nunez, Zean Wen, Josiah Poon, Caren Han
|
SceneGATE: Scene-Graph based co-Attention networks for TExt visual
question answering
|
Published in Robotics (Q1, SCI indexed Journal):
https://www.mdpi.com/2218-6581/12/4/114
| null |
10.3390/robotics12040114
| null |
cs.CV cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most TextVQA approaches focus on the integration of objects, scene texts and
question words by a simple transformer encoder. But this fails to capture the
semantic relations between different modalities. The paper proposes a Scene
Graph based co-Attention Network (SceneGATE) for TextVQA, which reveals the
semantic relations among the objects, Optical Character Recognition (OCR)
tokens and the question words. It is achieved by a TextVQA-based scene graph
that discovers the underlying semantics of an image. We created a
guided-attention module to capture the intra-modal interplay between the
language and the vision as a guidance for inter-modal interactions. To make
explicit teaching of the relations between the two modalities, we proposed and
integrated two attention modules, namely a scene graph-based semantic
relation-aware attention and a positional relation-aware attention. We
conducted extensive experiments on two benchmark datasets, Text-VQA and ST-VQA.
It is shown that our SceneGATE method outperformed existing ones because of the
scene graph and its attention modules.
|
[
{
"version": "v1",
"created": "Fri, 16 Dec 2022 05:10:09 GMT"
},
{
"version": "v2",
"created": "Mon, 1 May 2023 05:22:40 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Aug 2023 08:32:54 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Cao",
"Feiqi",
""
],
[
"Luo",
"Siwen",
""
],
[
"Nunez",
"Felipe",
""
],
[
"Wen",
"Zean",
""
],
[
"Poon",
"Josiah",
""
],
[
"Han",
"Caren",
""
]
] |
new_dataset
| 0.997935 |
2212.12294
|
Joo Chan Lee
|
Joo Chan Lee, Daniel Rho, Jong Hwan Ko, Eunbyung Park
|
FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos
|
Our project page including code is available at
https://maincold2.github.io/ffnerv/
| null |
10.1145/3581783.3612444
| null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Neural fields, also known as coordinate-based or implicit neural
representations, have shown a remarkable capability of representing,
generating, and manipulating various forms of signals. For video
representations, however, mapping pixel-wise coordinates to RGB colors has
shown relatively low compression performance and slow convergence and inference
speed. Frame-wise video representation, which maps a temporal coordinate to its
entire frame, has recently emerged as an alternative method to represent
videos, improving compression rates and encoding speed. While promising, it has
still failed to reach the performance of state-of-the-art video compression
algorithms. In this work, we propose FFNeRV, a novel method for incorporating
flow information into frame-wise representations to exploit the temporal
redundancy across the frames in videos inspired by the standard video codecs.
Furthermore, we introduce a fully convolutional architecture, enabled by
one-dimensional temporal grids, improving the continuity of spatial features.
Experimental results show that FFNeRV yields the best performance for video
compression and frame interpolation among the methods using frame-wise
representations or neural fields. To reduce the model size even further, we
devise a more compact convolutional architecture using the group and pointwise
convolutions. With model compression techniques, including quantization-aware
training and entropy coding, FFNeRV outperforms widely-used standard video
codecs (H.264 and HEVC) and performs on par with state-of-the-art video
compression algorithms.
|
[
{
"version": "v1",
"created": "Fri, 23 Dec 2022 12:51:42 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Aug 2023 01:21:19 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Lee",
"Joo Chan",
""
],
[
"Rho",
"Daniel",
""
],
[
"Ko",
"Jong Hwan",
""
],
[
"Park",
"Eunbyung",
""
]
] |
new_dataset
| 0.990813 |
2301.04643
|
Hugo Sousa
|
Hugo Sousa, Al\'ipio Jorge, Ricardo Campos
|
tieval: An Evaluation Framework for Temporal Information Extraction
Systems
|
10 pages
| null |
10.1145/3539618.3591892
| null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Temporal information extraction (TIE) has attracted a great deal of interest
over the last two decades, leading to the development of a significant number
of datasets. Despite its benefits, having access to a large volume of corpora
makes it difficult when it comes to benchmark TIE systems. On the one hand,
different datasets have different annotation schemes, thus hindering the
comparison between competitors across different corpora. On the other hand, the
fact that each corpus is commonly disseminated in a different format requires a
considerable engineering effort for a researcher/practitioner to develop
parsers for all of them. This constraint forces researchers to select a limited
amount of datasets to evaluate their systems which consequently limits the
comparability of the systems. Yet another obstacle that hinders the
comparability of the TIE systems is the evaluation metric employed. While most
research works adopt traditional metrics such as precision, recall, and $F_1$,
a few others prefer temporal awareness -- a metric tailored to be more
comprehensive on the evaluation of temporal systems. Although the reason for
the absence of temporal awareness in the evaluation of most systems is not
clear, one of the factors that certainly weights this decision is the necessity
to implement the temporal closure algorithm in order to compute temporal
awareness, which is not straightforward to implement neither is currently
easily available. All in all, these problems have limited the fair comparison
between approaches and consequently, the development of temporal extraction
systems. To mitigate these problems, we have developed tieval, a Python library
that provides a concise interface for importing different corpora and
facilitates system evaluation. In this paper, we present the first public
release of tieval and highlight its most relevant features.
|
[
{
"version": "v1",
"created": "Wed, 11 Jan 2023 18:55:22 GMT"
},
{
"version": "v2",
"created": "Fri, 21 Apr 2023 15:24:09 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Sousa",
"Hugo",
""
],
[
"Jorge",
"Alípio",
""
],
[
"Campos",
"Ricardo",
""
]
] |
new_dataset
| 0.981671 |
2301.06648
|
Zhongyang Zhang
|
Zhongyang Zhang, Kaidong Chai, Haowen Yu, Ramzi Majaj, Francesca
Walsh, Edward Wang, Upal Mahbub, Hava Siegelmann, Donghyun Kim, Tauhidur
Rahman
|
Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic
Environment
| null |
Neurocomputing, Volume 547, 2023, 126388
|
10.1016/j.neucom.2023.126388
|
ISSN 0925-2312
|
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As a beloved sport worldwide, dancing is getting integrated into traditional
and virtual reality-based gaming platforms nowadays. It opens up new
opportunities in the technology-mediated dancing space. These platforms
primarily rely on passive and continuous human pose estimation as an input
capture mechanism. Existing solutions are mainly based on RGB or RGB-Depth
cameras for dance games. The former suffers in low-lighting conditions due to
the motion blur and low sensitivity, while the latter is too power-hungry, has
a low frame rate, and has limited working distance. With ultra-low latency,
energy efficiency, and wide dynamic range characteristics, the event camera is
a promising solution to overcome these shortcomings. We propose YeLan, an event
camera-based 3-dimensional high-frequency human pose estimation(HPE) system
that survives low-lighting conditions and dynamic backgrounds. We collected the
world's first event camera dance dataset and developed a fully customizable
motion-to-event physics-aware simulator. YeLan outperforms the baseline models
in these challenging conditions and demonstrated robustness against different
types of clothing, background motion, viewing angle, occlusion, and lighting
fluctuations.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 00:55:12 GMT"
},
{
"version": "v2",
"created": "Fri, 27 Jan 2023 05:02:29 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Zhang",
"Zhongyang",
""
],
[
"Chai",
"Kaidong",
""
],
[
"Yu",
"Haowen",
""
],
[
"Majaj",
"Ramzi",
""
],
[
"Walsh",
"Francesca",
""
],
[
"Wang",
"Edward",
""
],
[
"Mahbub",
"Upal",
""
],
[
"Siegelmann",
"Hava",
""
],
[
"Kim",
"Donghyun",
""
],
[
"Rahman",
"Tauhidur",
""
]
] |
new_dataset
| 0.995891 |
2301.10880
|
Hans Hanley
|
Hans W. A. Hanley, Deepak Kumar, Zakir Durumeric
|
A Golden Age: Conspiracy Theories' Relationship with Misinformation
Outlets, News Media, and the Wider Internet
|
Accepted to CSCW 2023
| null | null | null |
cs.CY cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Do we live in a "Golden Age of Conspiracy Theories?" In the last few decades,
conspiracy theories have proliferated on the Internet with some having
dangerous real-world consequences. A large contingent of those who participated
in the January 6th attack on the US Capitol fervently believed in the QAnon
conspiracy theory. In this work, we study the relationships amongst five
prominent conspiracy theories (QAnon, COVID, UFO/Aliens, 9/11, and Flat-Earth)
and each of their respective relationships to the news media, both authentic
news and misinformation. Identifying and publishing a set of 755 different
conspiracy theory websites dedicated to our five conspiracy theories, we find
that each set often hyperlinks to the same external domains, with COVID and
QAnon conspiracy theory websites having the largest amount of shared
connections. Examining the role of news media, we further find that not only do
outlets known for spreading misinformation hyperlink to our set of conspiracy
theory websites more often than authentic news websites but also that this
hyperlinking increased dramatically between 2018 and 2021, with the advent of
QAnon and the start of COVID-19 pandemic. Using partial Granger-causality, we
uncover several positive correlative relationships between the hyperlinks from
misinformation websites and the popularity of conspiracy theory websites,
suggesting the prominent role that misinformation news outlets play in
popularizing many conspiracy theories.
|
[
{
"version": "v1",
"created": "Thu, 26 Jan 2023 00:20:02 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Apr 2023 20:50:21 GMT"
},
{
"version": "v3",
"created": "Sun, 6 Aug 2023 00:56:21 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Hanley",
"Hans W. A.",
""
],
[
"Kumar",
"Deepak",
""
],
[
"Durumeric",
"Zakir",
""
]
] |
new_dataset
| 0.993512 |
2302.13026
|
JinYuan Liu
|
Jinyuan Liu, Minglei Fu, Andong Liu, Wenan Zhang, and Bo Chen
|
A Homotopy Invariant Based on Convex Dissection Topology and a Distance
Optimal Path Planning Algorithm
|
Please note that the letter version of this paper is currently under
review by IEEE Robotics and Automation Letters (RA-L). In comparison to the
letter version, this full version provides more rigorous proofs and reasoning
for the CDT encoder, along with numerous practical theorems and corollaries.
The complete paper consists of 17 pages, 14 figures, and 7 tables
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The concept of path homotopy has received widely attention in the field of
path planning in recent years. In this article, a homotopy invariant based on
convex dissection for a two-dimensional bounded Euclidean space is developed,
which can efficiently encode all homotopy path classes between any two points.
Thereafter, the optimal path planning task consists of two steps: (i) search
for the homotopy path class that may contain the optimal path, and (ii) obtain
the shortest homotopy path in this class. Furthermore, an optimal path planning
algorithm called CDT-RRT* (Rapidly-exploring Random Tree Star based on Convex
Division Topology) is proposed. We designed an efficient sampling formula for
CDT-RRT*, which gives it a tendency to actively explore unknown homotopy
classes, and incorporated the principles of the Elastic Band algorithm to
obtain the shortest path in each class. Through a series of experiments, it was
determined that the performance of the proposed algorithm is comparable with
state-of-the-art path planning algorithms. Hence, the application significance
of the developed homotopy invariant in the field of path planning was verified.
|
[
{
"version": "v1",
"created": "Sat, 25 Feb 2023 08:52:48 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Aug 2023 12:47:51 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Liu",
"Jinyuan",
""
],
[
"Fu",
"Minglei",
""
],
[
"Liu",
"Andong",
""
],
[
"Zhang",
"Wenan",
""
],
[
"Chen",
"Bo",
""
]
] |
new_dataset
| 0.987273 |
2303.01711
|
Chathura Gamage
|
Chathura Gamage, Vimukthini Pinto, Cheng Xue, Peng Zhang, Ekaterina
Nikonova, Matthew Stephenson, Jochen Renz
|
NovPhy: A Testbed for Physical Reasoning in Open-world Environments
|
Testbed website: https://github.com/phy-q/novphy
| null | null | null |
cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Due to the emergence of AI systems that interact with the physical
environment, there is an increased interest in incorporating physical reasoning
capabilities into those AI systems. But is it enough to only have physical
reasoning capabilities to operate in a real physical environment? In the real
world, we constantly face novel situations we have not encountered before. As
humans, we are competent at successfully adapting to those situations.
Similarly, an agent needs to have the ability to function under the impact of
novelties in order to properly operate in an open-world physical environment.
To facilitate the development of such AI systems, we propose a new testbed,
NovPhy, that requires an agent to reason about physical scenarios in the
presence of novelties and take actions accordingly. The testbed consists of
tasks that require agents to detect and adapt to novelties in physical
scenarios. To create tasks in the testbed, we develop eight novelties
representing a diverse novelty space and apply them to five commonly
encountered scenarios in a physical environment. According to our testbed
design, we evaluate two capabilities of an agent: the performance on a novelty
when it is applied to different physical scenarios and the performance on a
physical scenario when different novelties are applied to it. We conduct a
thorough evaluation with human players, learning agents, and heuristic agents.
Our evaluation shows that humans' performance is far beyond the agents'
performance. Some agents, even with good normal task performance, perform
significantly worse when there is a novelty, and the agents that can adapt to
novelties typically adapt slower than humans. We promote the development of
intelligent agents capable of performing at the human level or above when
operating in open-world physical environments. Testbed website:
https://github.com/phy-q/novphy
|
[
{
"version": "v1",
"created": "Fri, 3 Mar 2023 04:59:03 GMT"
},
{
"version": "v2",
"created": "Sat, 5 Aug 2023 12:47:07 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Gamage",
"Chathura",
""
],
[
"Pinto",
"Vimukthini",
""
],
[
"Xue",
"Cheng",
""
],
[
"Zhang",
"Peng",
""
],
[
"Nikonova",
"Ekaterina",
""
],
[
"Stephenson",
"Matthew",
""
],
[
"Renz",
"Jochen",
""
]
] |
new_dataset
| 0.999442 |
2303.04320
|
Aniket Bera
|
Rashmi Bhaskara and Maurice Chiu and Aniket Bera
|
SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowds
|
To appear in 2023 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2023)
| null | null | null |
cs.RO cs.AI cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the increasing availability and affordability of personal robots, they
will no longer be confined to large corporate warehouses or factories but will
instead be expected to operate in less controlled environments alongside larger
groups of people. In addition to ensuring safety and efficiency, it is crucial
to minimize any negative psychological impact robots may have on humans and
follow unwritten social norms in these situations. Our research aims to develop
a model that can predict the movements of pedestrians and perceptually-social
groups in crowded environments. We introduce a new Social Group Long Short-term
Memory (SG-LSTM) model that models human groups and interactions in dense
environments using a socially-aware LSTM to produce more accurate trajectory
predictions. Our approach enables navigation algorithms to calculate
collision-free paths faster and more accurately in crowded environments.
Additionally, we also release a large video dataset with labeled pedestrian
groups for the broader social navigation community. We show comparisons with
different metrics on different datasets (ETH, Hotel, MOT15) and different
prediction approaches (LIN, LSTM, O-LSTM, S-LSTM) as well as runtime
performance.
|
[
{
"version": "v1",
"created": "Wed, 8 Mar 2023 01:38:20 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Aug 2023 17:17:05 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Bhaskara",
"Rashmi",
""
],
[
"Chiu",
"Maurice",
""
],
[
"Bera",
"Aniket",
""
]
] |
new_dataset
| 0.999444 |
2303.04322
|
Aniket Bera
|
Dipam Patel and Phu Pham and Aniket Bera
|
DroNeRF: Real-time Multi-agent Drone Pose Optimization for Computing
Neural Radiance Fields
|
To appear in 2023 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2023)
| null | null | null |
cs.RO cs.AI cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a novel optimization algorithm called DroNeRF for the autonomous
positioning of monocular camera drones around an object for real-time 3D
reconstruction using only a few images. Neural Radiance Fields or NeRF, is a
novel view synthesis technique used to generate new views of an object or scene
from a set of input images. Using drones in conjunction with NeRF provides a
unique and dynamic way to generate novel views of a scene, especially with
limited scene capabilities of restricted movements. Our approach focuses on
calculating optimized pose for individual drones while solely depending on the
object geometry without using any external localization system. The unique
camera positioning during the data-capturing phase significantly impacts the
quality of the 3D model. To evaluate the quality of our generated novel views,
we compute different perceptual metrics like the Peak Signal-to-Noise Ratio
(PSNR) and Structural Similarity Index Measure(SSIM). Our work demonstrates the
benefit of using an optimal placement of various drones with limited mobility
to generate perceptually better results.
|
[
{
"version": "v1",
"created": "Wed, 8 Mar 2023 01:46:19 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Aug 2023 17:20:41 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Patel",
"Dipam",
""
],
[
"Pham",
"Phu",
""
],
[
"Bera",
"Aniket",
""
]
] |
new_dataset
| 0.996254 |
2303.07792
|
George Alexandropoulos
|
Ioannis Gavras, Md Atiqul Islam, Besma Smida, and George C.
Alexandropoulos
|
Full Duplex Holographic MIMO for Near-Field Integrated Sensing and
Communications
|
5 pages, 3 figures, EUSIPCO 2023
| null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper presents an in-band Full Duplex (FD) integrated sensing and
communications system comprising a holographic Multiple-Input Multiple-Output
(MIMO) base station, which is capable to simultaneously communicate with
multiple users in the downlink direction, while sensing targets being randomly
distributed within its coverage area. Considering near-field wireless operation
at THz frequencies, the FD node adopts dynamic metasurface antenna panels for
both transmission and reception, which consist of massive numbers of
sub-wavelength-spaced metamaterials, enabling reduced cost and power
consumption analog precoding and combining. We devise an optimization framework
for the FD node's reconfigurable parameters with the dual objective of
maximizing the targets' parameters estimation accuracy and the downlink
communication performance. Our simulation results verify the integrated sensing
and communications capability of the proposed FD holographic MIMO system,
showcasing the interplays among its various design parameters.
|
[
{
"version": "v1",
"created": "Tue, 14 Mar 2023 11:06:49 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Aug 2023 09:27:56 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Gavras",
"Ioannis",
""
],
[
"Islam",
"Md Atiqul",
""
],
[
"Smida",
"Besma",
""
],
[
"Alexandropoulos",
"George C.",
""
]
] |
new_dataset
| 0.99952 |
2304.00989
|
Yaojie Hu
|
Yaojie Hu, Jin Tian
|
Neuro-Symbolic Execution of Generic Source Code
| null | null | null | null |
cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Can a Python program be executed statement-by-statement by neural networks
composed according to the source code? We formulate the Neuro-Symbolic
Execution Problem and introduce Neural Interpretation (NI), the first neural
model for the execution of generic source code that allows missing definitions.
NI preserves source code structure, where every variable has a vector encoding,
and every function executes a neural network. NI is a novel neural model of
computers with a compiler architecture that can assemble neural layers
"programmed" by source code. NI is the first neural model capable of executing
Py150 dataset programs, including library functions without concrete inputs,
and it can be trained with flexible code understanding objectives. We
demonstrate white-box execution without concrete inputs for variable misuse
localization and repair.
|
[
{
"version": "v1",
"created": "Thu, 23 Mar 2023 17:56:45 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Aug 2023 18:15:05 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Hu",
"Yaojie",
""
],
[
"Tian",
"Jin",
""
]
] |
new_dataset
| 0.998379 |
2304.13458
|
Rodothea Myrsini Tsoupidi
|
Rodothea Myrsini Tsoupidi, Elena Troubitsyna, Panagiotis
Papadimitratos
|
Thwarting Code-Reuse and Side-Channel Attacks in Embedded Systems
| null | null | null | null |
cs.CR cs.PF
|
http://creativecommons.org/licenses/by/4.0/
|
Embedded devices are increasingly present in our everyday life. They often
process critical information, and hence, rely on cryptographic protocols to
achieve security. However, embedded devices remain vulnerable to attackers
seeking to hijack their operation and extract sensitive information by
exploiting side channels and code reuse. Code-Reuse Attacks (CRAs) can steer
the execution of a program to malicious outcomes, altering existing on-board
code without direct access to the device memory. Moreover, Side-Channel Attacks
(SCAs) may reveal secret information to the attacker based on mere observation
of the device. Thwarting CRAs and SCAs against embedded devices is challenging
because embedded devices are often resource constrained. Fine-grained code
diversification hinders CRAs by introducing uncertainty to the binary code;
while software mechanisms can thwart timing or power SCAs. The resilience to
either attack may come at the price of the overall efficiency. Moreover, a
unified approach that preserves these mitigations against both CRAs and SCAs is
not available. In this paper, we propose a novel Secure Diversity by
Construction (SecDivCon) approach that tackles this challenge. SecDivCon is a
combinatorial compiler-based approach that combines software diversification
against CRAs with software mitigations against SCAs. SecDivCon restricts the
performance overhead introduced by the generated code that thwarts the attacks
and hence, offers a secure-by-design approach enabling control over the
performance-security trade-off. Our experiments, using 16 benchmark programs,
show that SCA-aware diversification is effective against CRAs, while preserving
SCA mitigation properties at a low, controllable overhead. Given the
combinatorial nature of our approach, SecDivCon is suitable for small,
performance-critical functions that are sensitive to SCAs.
|
[
{
"version": "v1",
"created": "Wed, 26 Apr 2023 11:31:45 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Apr 2023 07:03:49 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Aug 2023 08:08:09 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Tsoupidi",
"Rodothea Myrsini",
""
],
[
"Troubitsyna",
"Elena",
""
],
[
"Papadimitratos",
"Panagiotis",
""
]
] |
new_dataset
| 0.999643 |
2305.05880
|
Aozhu Chen
|
Aozhu Chen, Ziyuan Wang, Chengbo Dong, Kaibin Tian, Ruixiang Zhao, Xun
Liang, Zhanhui Kang, Xirong Li
|
ChinaOpen: A Dataset for Open-world Multimodal Learning
|
Accepted by ACMMM 2023
| null |
10.1145/3581783.3612156
| null |
cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper introduces ChinaOpen, a dataset sourced from Bilibili, a popular
Chinese video-sharing website, for open-world multimodal learning. While the
state-of-the-art multimodal learning networks have shown impressive performance
in automated video annotation and cross-modal video retrieval, their training
and evaluation are primarily conducted on YouTube videos with English text.
Their effectiveness on Chinese data remains to be verified. In order to support
multimodal learning in the new context, we construct ChinaOpen-50k, a webly
annotated training set of 50k Bilibili videos associated with user-generated
titles and tags. Both text-based and content-based data cleaning are performed
to remove low-quality videos in advance. For a multi-faceted evaluation, we
build ChinaOpen-1k, a manually labeled test set of 1k videos. Each test video
is accompanied with a manually checked user title and a manually written
caption. Besides, each video is manually tagged to describe objects / actions /
scenes shown in the visual content. The original user tags are also manually
checked. Moreover, with all the Chinese text translated into English,
ChinaOpen-1k is also suited for evaluating models trained on English data. In
addition to ChinaOpen, we propose Generative Video-to-text Transformer (GVT)
for Chinese video captioning. We conduct an extensive evaluation of the
state-of-the-art single-task / multi-task models on the new dataset, resulting
in a number of novel findings and insights.
|
[
{
"version": "v1",
"created": "Wed, 10 May 2023 04:00:54 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Aug 2023 10:43:25 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Chen",
"Aozhu",
""
],
[
"Wang",
"Ziyuan",
""
],
[
"Dong",
"Chengbo",
""
],
[
"Tian",
"Kaibin",
""
],
[
"Zhao",
"Ruixiang",
""
],
[
"Liang",
"Xun",
""
],
[
"Kang",
"Zhanhui",
""
],
[
"Li",
"Xirong",
""
]
] |
new_dataset
| 0.999835 |
2306.06505
|
Catherine Ordun
|
Catherine Ordun, Edward Raff, Sanjay Purushotham
|
Vista-Morph: Unsupervised Image Registration of Visible-Thermal Facial
Pairs
| null |
2023, 7th IEEE International Joint Conference on Biometrics (IJCB)
| null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For a variety of biometric cross-spectral tasks, Visible-Thermal (VT) facial
pairs are used. However, due to a lack of calibration in the lab, photographic
capture between two different sensors leads to severely misaligned pairs that
can lead to poor results for person re-identification and generative AI. To
solve this problem, we introduce our approach for VT image registration called
Vista Morph. Unlike existing VT facial registration that requires manual,
hand-crafted features for pixel matching and/or a supervised thermal reference,
Vista Morph is completely unsupervised without the need for a reference. By
learning the affine matrix through a Vision Transformer (ViT)-based Spatial
Transformer Network (STN) and Generative Adversarial Networks (GAN), Vista
Morph successfully aligns facial and non-facial VT images. Our approach learns
warps in Hard, No, and Low-light visual settings and is robust to geometric
perturbations and erasure at test time. We conduct a downstream generative AI
task to show that registering training data with Vista Morph improves subject
identity of generated thermal faces when performing V2T image translation.
|
[
{
"version": "v1",
"created": "Sat, 10 Jun 2023 18:42:36 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Ordun",
"Catherine",
""
],
[
"Raff",
"Edward",
""
],
[
"Purushotham",
"Sanjay",
""
]
] |
new_dataset
| 0.999115 |
2307.11315
|
Kathleen M Lewis
|
Kathleen M. Lewis and Emily Mu and Adrian V. Dalca and John Guttag
|
GIST: Generating Image-Specific Text for Fine-grained Object
Classification
|
The first two authors contributed equally to this work and are listed
in alphabetical order
| null | null | null |
cs.CV cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent vision-language models outperform vision-only models on many image
classification tasks. However, because of the absence of paired text/image
descriptions, it remains difficult to fine-tune these models for fine-grained
image classification. In this work, we propose a method, GIST, for generating
image-specific fine-grained text descriptions from image-only datasets, and
show that these text descriptions can be used to improve classification. Key
parts of our method include 1. prompting a pretrained large language model with
domain-specific prompts to generate diverse fine-grained text descriptions for
each class and 2. using a pretrained vision-language model to match each image
to label-preserving text descriptions that capture relevant visual features in
the image. We demonstrate the utility of GIST by fine-tuning vision-language
models on the image-and-generated-text pairs to learn an aligned
vision-language representation space for improved classification. We evaluate
our learned representation space in full-shot and few-shot scenarios across
four diverse fine-grained classification datasets, each from a different
domain. Our method achieves an average improvement of $4.1\%$ in accuracy over
CLIP linear probes and an average of $1.1\%$ improvement in accuracy over the
previous state-of-the-art image-text classification method on the full-shot
datasets. Our method achieves similar improvements across few-shot regimes.
Code is available at https://github.com/emu1729/GIST.
|
[
{
"version": "v1",
"created": "Fri, 21 Jul 2023 02:47:18 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Aug 2023 19:36:31 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Lewis",
"Kathleen M.",
""
],
[
"Mu",
"Emily",
""
],
[
"Dalca",
"Adrian V.",
""
],
[
"Guttag",
"John",
""
]
] |
new_dataset
| 0.999741 |
2307.13294
|
You Jiang
|
Junbin Fang, Canjian Jiang, You Jiang, Puxi Lin, Zhaojie Chen, Yujing
Sun, Siu-Ming Yiu, Zoe L. Jiang
|
Imperceptible Physical Attack against Face Recognition Systems via LED
Illumination Modulation
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Although face recognition starts to play an important role in our daily life,
we need to pay attention that data-driven face recognition vision systems are
vulnerable to adversarial attacks. However, the current two categories of
adversarial attacks, namely digital attacks and physical attacks both have
drawbacks, with the former ones impractical and the latter one conspicuous,
high-computational and inexecutable. To address the issues, we propose a
practical, executable, inconspicuous and low computational adversarial attack
based on LED illumination modulation. To fool the systems, the proposed attack
generates imperceptible luminance changes to human eyes through fast intensity
modulation of scene LED illumination and uses the rolling shutter effect of
CMOS image sensors in face recognition systems to implant luminance information
perturbation to the captured face images. In summary,we present a
denial-of-service (DoS) attack for face detection and a dodging attack for face
verification. We also evaluate their effectiveness against well-known face
detection models, Dlib, MTCNN and RetinaFace , and face verification models,
Dlib, FaceNet,and ArcFace.The extensive experiments show that the success rates
of DoS attacks against face detection models reach 97.67%, 100%, and 100%,
respectively, and the success rates of dodging attacks against all face
verification models reach 100%.
|
[
{
"version": "v1",
"created": "Tue, 25 Jul 2023 07:20:21 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Aug 2023 08:12:57 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Fang",
"Junbin",
""
],
[
"Jiang",
"Canjian",
""
],
[
"Jiang",
"You",
""
],
[
"Lin",
"Puxi",
""
],
[
"Chen",
"Zhaojie",
""
],
[
"Sun",
"Yujing",
""
],
[
"Yiu",
"Siu-Ming",
""
],
[
"Jiang",
"Zoe L.",
""
]
] |
new_dataset
| 0.989555 |
2308.00628
|
Wenzhao Zheng
|
Bohao Fan, Siqi Wang, Wenxuan Guo, Wenzhao Zheng, Jianjiang Feng, Jie
Zhou
|
Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation
in Outdoor Scenes
|
Code and data will be released on
https://github.com/soullessrobot/Human-M3-Dataset
| null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
3D human pose estimation in outdoor environments has garnered increasing
attention recently. However, prevalent 3D human pose datasets pertaining to
outdoor scenes lack diversity, as they predominantly utilize only one type of
modality (RGB image or pointcloud), and often feature only one individual
within each scene. This limited scope of dataset infrastructure considerably
hinders the variability of available data. In this article, we propose
Human-M3, an outdoor multi-modal multi-view multi-person human pose database
which includes not only multi-view RGB videos of outdoor scenes but also
corresponding pointclouds. In order to obtain accurate human poses, we propose
an algorithm based on multi-modal data input to generate ground truth
annotation. This benefits from robust pointcloud detection and tracking, which
solves the problem of inaccurate human localization and matching ambiguity that
may exist in previous multi-view RGB videos in outdoor multi-person scenes, and
generates reliable ground truth annotations. Evaluation of multiple different
modalities algorithms has shown that this database is challenging and suitable
for future research. Furthermore, we propose a 3D human pose estimation
algorithm based on multi-modal data input, which demonstrates the advantages of
multi-modal data input for 3D human pose estimation. Code and data will be
released on https://github.com/soullessrobot/Human-M3-Dataset.
|
[
{
"version": "v1",
"created": "Tue, 1 Aug 2023 15:55:41 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Aug 2023 14:47:00 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Fan",
"Bohao",
""
],
[
"Wang",
"Siqi",
""
],
[
"Guo",
"Wenxuan",
""
],
[
"Zheng",
"Wenzhao",
""
],
[
"Feng",
"Jianjiang",
""
],
[
"Zhou",
"Jie",
""
]
] |
new_dataset
| 0.999886 |
2308.01390
|
Anas Awadalla
|
Anas Awadalla and Irena Gao and Josh Gardner and Jack Hessel and Yusuf
Hanafy and Wanrong Zhu and Kalyani Marathe and Yonatan Bitton and Samir Gadre
and Shiori Sagawa and Jenia Jitsev and Simon Kornblith and Pang Wei Koh and
Gabriel Ilharco and Mitchell Wortsman and Ludwig Schmidt
|
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive
Vision-Language Models
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce OpenFlamingo, a family of autoregressive vision-language models
ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce
an open-source replication of DeepMind's Flamingo models. On seven
vision-language datasets, OpenFlamingo models average between 80 - 89% of
corresponding Flamingo performance. This technical report describes our models,
training data, hyperparameters, and evaluation suite. We share our models and
code at https://github.com/mlfoundations/open_flamingo.
|
[
{
"version": "v1",
"created": "Wed, 2 Aug 2023 19:10:23 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Aug 2023 17:53:09 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Awadalla",
"Anas",
""
],
[
"Gao",
"Irena",
""
],
[
"Gardner",
"Josh",
""
],
[
"Hessel",
"Jack",
""
],
[
"Hanafy",
"Yusuf",
""
],
[
"Zhu",
"Wanrong",
""
],
[
"Marathe",
"Kalyani",
""
],
[
"Bitton",
"Yonatan",
""
],
[
"Gadre",
"Samir",
""
],
[
"Sagawa",
"Shiori",
""
],
[
"Jitsev",
"Jenia",
""
],
[
"Kornblith",
"Simon",
""
],
[
"Koh",
"Pang Wei",
""
],
[
"Ilharco",
"Gabriel",
""
],
[
"Wortsman",
"Mitchell",
""
],
[
"Schmidt",
"Ludwig",
""
]
] |
new_dataset
| 0.977283 |
2308.02524
|
AICHA SEKHARI
|
Paweena Suebsombut (DISP, CMU), Pradorn Sureephong (CMU), Aicha
Sekhari (DISP), Suepphong Chernbumroong (CMU), Abdelaziz Bouras
|
Chatbot Application to Support Smart Agriculture in Thailand
| null |
2022 Joint International Conference on Digital Arts, Media and
Technology with ECTI Northern Section Conference on Electrical, Electronics,
Computer and Telecommunications Engineering (ECTI DAMT and NCON), Chiang Rai
university, Jan 2022, Chiang Rai, Thailand. pp.364-367
|
10.1109/ectidamtncon53731.2022.9720318
| null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A chatbot is a software developed to help reply to text or voice
conversations automatically and quickly in real time. In the agriculture
sector, the existing smart agriculture systems just use data from sensing and
internet of things (IoT) technologies that exclude crop cultivation knowledge
to support decision-making by farmers. To enhance this, the chatbot application
can be an assistant to farmers to provide crop cultivation knowledge.
Consequently, we propose the LINE chatbot application as an information and
knowledge representation providing crop cultivation recommendations to farmers.
It works with smart agriculture and recommendation systems. Our proposed LINE
chatbot application consists of five main functions (start/stop menu, main
page, drip irri gation page, mist irrigation page, and monitor page). Farmers
will receive information for data monitoring to support their decision-making.
Moreover, they can control the irrigation system via the LINE chatbot.
Furthermore, farmers can ask questions relevant to the crop environment via a
chat box. After implementing our proposed chatbot, farmers are very satisfied
with the application, scoring a 96% satisfaction score. However, in terms of
asking questions via chat box, this LINE chatbot application is a rule-based
bot or script bot. Farmers have to type in the correct keywords as prescribed,
otherwise they won't get a response from the chatbots. In the future, we will
enhance the asking function of our LINE chatbot to be an intelligent bot.
|
[
{
"version": "v1",
"created": "Mon, 31 Jul 2023 11:42:44 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Suebsombut",
"Paweena",
"",
"DISP, CMU"
],
[
"Sureephong",
"Pradorn",
"",
"CMU"
],
[
"Sekhari",
"Aicha",
"",
"DISP"
],
[
"Chernbumroong",
"Suepphong",
"",
"CMU"
],
[
"Bouras",
"Abdelaziz",
""
]
] |
new_dataset
| 0.991291 |
2308.02594
|
Amirhossein Zolfagharian
|
Amirhossein Zolfagharian, Manel Abdellatif, Lionel C. Briand, and
Ramesh S
|
SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning
Agents
| null | null | null | null |
cs.LG cs.AI cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep reinforcement learning algorithms (DRL) are increasingly being used in
safety-critical systems. Ensuring the safety of DRL agents is a critical
concern in such contexts. However, relying solely on testing is not sufficient
to ensure safety as it does not offer guarantees. Building safety monitors is
one solution to alleviate this challenge. This paper proposes SMARLA, a machine
learning-based safety monitoring approach designed for DRL agents. For
practical reasons, SMARLA is designed to be black-box (as it does not require
access to the internals of the agent) and leverages state abstraction to reduce
the state space and thus facilitate the learning of safety violation prediction
models from agent's states. We validated SMARLA on two well-known RL case
studies. Empirical analysis reveals that SMARLA achieves accurate violation
prediction with a low false positive rate, and can predict safety violations at
an early stage, approximately halfway through the agent's execution before
violations occur.
|
[
{
"version": "v1",
"created": "Thu, 3 Aug 2023 21:08:51 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Zolfagharian",
"Amirhossein",
""
],
[
"Abdellatif",
"Manel",
""
],
[
"Briand",
"Lionel C.",
""
],
[
"S",
"Ramesh",
""
]
] |
new_dataset
| 0.980006 |
2308.02618
|
Saipraneeth Devunuri
|
Saipraneeth Devunuri, Shirin Qiam, Lewis Lehe
|
ChatGPT for GTFS: From Words to Information
|
18 pages, 7 figures, 1 table, Transportation Research Board
| null | null | null |
cs.IR cs.AI cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The General Transit Feed Specification (GTFS) standard for publishing transit
data is ubiquitous. GTFS being tabular data, with information spread across
different files, necessitates specialized tools or packages to retrieve
information. Concurrently, the use of Large Language Models for text and
information retrieval is growing. The idea of this research is to see if the
current widely adopted LLMs (ChatGPT) are able to retrieve information from
GTFS using natural language instructions. We first test whether ChatGPT
(GPT-3.5) understands the GTFS specification. GPT-3.5 answers 77% of our
multiple-choice questions (MCQ) correctly. Next, we task the LLM with
information extractions from a filtered GTFS feed with 4 routes. For
information retrieval, we compare zero-shot and program synthesis. Program
synthesis works better, achieving ~90% accuracy on simple questions and ~40%
accuracy on complex questions.
|
[
{
"version": "v1",
"created": "Fri, 4 Aug 2023 14:50:37 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Devunuri",
"Saipraneeth",
""
],
[
"Qiam",
"Shirin",
""
],
[
"Lehe",
"Lewis",
""
]
] |
new_dataset
| 0.998184 |
2308.02640
|
Ahmed Sabbah
|
Ahmed Sabbah, Mohammed Kharma, Mustafa Jarrar
|
Creating Android Malware Knowledge Graph Based on a Malware Ontology
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
As mobile and smart connectivity continue to grow, malware presents a
permanently evolving threat to different types of critical domains such as
health, logistics, banking, and community segments. Different types of malware
have dynamic behaviors and complicated characteristics that are shared among
members of the same malware family. Malware threat intelligence reports play a
crucial role in describing and documenting the detected malware, providing a
wealth of information regarding its attributes, patterns, and behaviors. There
is a large amount of intelligent threat information regarding malware. The
ontology allows the systematic organization and categorization of this
information to ensure consistency in representing concepts and entities across
various sources. In this study, we reviewed and extended an existing malware
ontology to cover Android malware. Our extended ontology is called AndMalOnt.
It consisted of 13 new classes, 16 object properties, and 31 data properties.
Second, we created an Android malware knowledge graph by extracting reports
from the MalwareBazaar repository and representing them in AndMalOnt. This
involved generating a knowledge graph that encompasses over 2600 malware
samples. Our ontology, knowledge graph, and source code are all open-source and
accessible via GitHub
|
[
{
"version": "v1",
"created": "Fri, 4 Aug 2023 18:00:44 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Sabbah",
"Ahmed",
""
],
[
"Kharma",
"Mohammed",
""
],
[
"Jarrar",
"Mustafa",
""
]
] |
new_dataset
| 0.998873 |
2308.02666
|
Justin Stevens
|
Justin Stevens, Vadim Bulitko, David Thue
|
Solving Witness-type Triangle Puzzles Faster with an Automatically
Learned Human-Explainable Predicate
|
10 pages
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Automatically solving puzzle instances in the game The Witness can guide
players toward solutions and help puzzle designers generate better puzzles. In
the latter case such an Artificial Intelligence puzzle solver can inform a
human puzzle designer and procedural puzzle generator to produce better
instances. The puzzles, however, are combinatorially difficult and search-based
solvers can require large amounts of time and memory. We accelerate such search
by automatically learning a human-explainable predicate that predicts whether a
partial path to a Witness-type puzzle is not completable to a solution path. We
prove a key property of the learned predicate which allows us to use it for
pruning successor states in search thereby accelerating search by an average of
six times while maintaining completeness of the underlying search. Conversely
given a fixed search time budget per puzzle our predicate-accelerated search
can solve more puzzle instances of larger sizes than the baseline search.
|
[
{
"version": "v1",
"created": "Fri, 4 Aug 2023 18:52:18 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Stevens",
"Justin",
""
],
[
"Bulitko",
"Vadim",
""
],
[
"Thue",
"David",
""
]
] |
new_dataset
| 0.9977 |
2308.02670
|
Weihan Wang
|
Weihan Wang, Jiani Li, Yuhang Ming, Philippos Mordohai
|
EDI: ESKF-based Disjoint Initialization for Visual-Inertial SLAM Systems
| null | null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Visual-inertial initialization can be classified into joint and disjoint
approaches. Joint approaches tackle both the visual and the inertial parameters
together by aligning observations from feature-bearing points based on IMU
integration then use a closed-form solution with visual and acceleration
observations to find initial velocity and gravity. In contrast, disjoint
approaches independently solve the Structure from Motion (SFM) problem and
determine inertial parameters from up-to-scale camera poses obtained from pure
monocular SLAM. However, previous disjoint methods have limitations, like
assuming negligible acceleration bias impact or accurate rotation estimation by
pure monocular SLAM. To address these issues, we propose EDI, a novel approach
for fast, accurate, and robust visual-inertial initialization. Our method
incorporates an Error-state Kalman Filter (ESKF) to estimate gyroscope bias and
correct rotation estimates from monocular SLAM, overcoming dependence on pure
monocular SLAM for rotation estimation. To estimate the scale factor without
prior information, we offer a closed-form solution for initial velocity, scale,
gravity, and acceleration bias estimation. To address gravity and acceleration
bias coupling, we introduce weights in the linear least-squares equations,
ensuring acceleration bias observability and handling outliers. Extensive
evaluation on the EuRoC dataset shows that our method achieves an average scale
error of 5.8% in less than 3 seconds, outperforming other state-of-the-art
disjoint visual-inertial initialization approaches, even in challenging
environments and with artificial noise corruption.
|
[
{
"version": "v1",
"created": "Fri, 4 Aug 2023 19:06:58 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Wang",
"Weihan",
""
],
[
"Li",
"Jiani",
""
],
[
"Ming",
"Yuhang",
""
],
[
"Mordohai",
"Philippos",
""
]
] |
new_dataset
| 0.979763 |
2308.02696
|
Mohammad Soleymani
|
Mohammad Soleymani, Ignacio Santamaria, and Eduard Jorswieck
|
NOMA-based Improper Signaling for MIMO STAR-RIS-assisted Broadcast
Channels with Hardware Impairments
|
IEEE GLOBECOM 2023
| null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper proposes schemes to improve the spectral efficiency of a
multiple-input multiple-output (MIMO) broadcast channel (BC) with I/Q imbalance
(IQI) at transceivers by employing a combination of improper Gaussian signaling
(IGS), non-orthogonal multiple access (NOMA) and simultaneously transmit and
reflect (STAR) reconfigurable intelligent surface (RIS). When there exists IQI,
the output RF signal is a widely linear transformation of the input signal,
which may make the output signal improper. To compensate for IQI, we employ
IGS, thus generating a transmit improper signal. We show that IGS alongside
with NOMA can highly increase the minimum rate of the users. Moreover, we
propose schemes for different operational modes of STAR-RIS and show that
STAR-RIS can significantly improve the system performance. Additionally, we
show that IQI can highly degrade the performance especially if it is overlooked
in the design.
|
[
{
"version": "v1",
"created": "Fri, 4 Aug 2023 20:21:17 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Soleymani",
"Mohammad",
""
],
[
"Santamaria",
"Ignacio",
""
],
[
"Jorswieck",
"Eduard",
""
]
] |
new_dataset
| 0.951305 |
2308.02752
|
Dmitry Baranchuk
|
Dmitry Baranchuk, Matthijs Douze, Yash Upadhyay, I. Zeki Yalniz
|
DeDrift: Robust Similarity Search under Content Drift
|
ICCV2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The statistical distribution of content uploaded and searched on media
sharing sites changes over time due to seasonal, sociological and technical
factors. We investigate the impact of this "content drift" for large-scale
similarity search tools, based on nearest neighbor search in embedding space.
Unless a costly index reconstruction is performed frequently, content drift
degrades the search accuracy and efficiency. The degradation is especially
severe since, in general, both the query and database distributions change.
We introduce and analyze real-world image and video datasets for which
temporal information is available over a long time period. Based on the
learnings, we devise DeDrift, a method that updates embedding quantizers to
continuously adapt large-scale indexing structures on-the-fly. DeDrift almost
eliminates the accuracy degradation due to the query and database content drift
while being up to 100x faster than a full index reconstruction.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 00:12:39 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Baranchuk",
"Dmitry",
""
],
[
"Douze",
"Matthijs",
""
],
[
"Upadhyay",
"Yash",
""
],
[
"Yalniz",
"I. Zeki",
""
]
] |
new_dataset
| 0.999019 |
2308.02764
|
Md Naimul Hoque
|
Md Naimul Hoque and Niklas Elmqvist
|
Dataopsy: Scalable and Fluid Visual Exploration using Aggregate Query
Sculpting
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We present aggregate query sculpting (AQS), a faceted visual query technique
for large-scale multidimensional data. As a "born scalable" query technique,
AQS starts visualization with a single visual mark representing an aggregation
of the entire dataset. The user can then progressively explore the dataset
through a sequence of operations abbreviated as P6: pivot (facet an aggregate
based on an attribute), partition (lay out a facet in space), peek (see inside
a subset using an aggregate visual representation), pile (merge two or more
subsets), project (extracting a subset into a new substrate), and prune
(discard an aggregate not currently of interest). We validate AQS with
Dataopsy, a prototype implementation of AQS that has been designed for fluid
interaction on desktop and touch-based mobile devices. We demonstrate AQS and
Dataopsy using two case studies and three application examples.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 01:51:22 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Hoque",
"Md Naimul",
""
],
[
"Elmqvist",
"Niklas",
""
]
] |
new_dataset
| 0.967238 |
2308.02767
|
Alex James Dr
|
Rajalekshmi TR, Rinku Rani Das, Chithra R, Alex James
|
Graphene-based RRAM devices for neural computing
|
Last revision - 04 Jul 2023
| null | null | null |
cs.ET
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Resistive random access memory (RRAM) is very well known for its potential
application in in-memory and neural computing. However, they often have
different types of device-to-device and cycle-to-cycle variability. This makes
it harder to build highly accurate crossbar arrays.Traditional RRAM designs
make use of various filament-based oxide materials for creating a channel which
is sandwiched between two electrodes to form a two-terminal structure. They are
often subjected to mechanical and electrical stress over repeated
read-and-write cycles. The behavior of these devices often varies in practice
across wafer arrays over these stress when fabricated. The use of emerging 2D
materials is explored to improve electrical endurance, long retention In review
time, high switching speed, and fewer power losses. This study provides an
in-depth exploration of neuro-memristive computing and its potential
applications, focusing specifically on the utilization of graphene and 2D
materials in resistive random-access memory (RRAM) for neural computing. The
paper presents a comprehensive analysis of the structural and design aspects of
graphene-based RRAM, along with a thorough examination of commercially
available RRAM models and their fabrication techniques. Furthermore, the study
investigates the diverse range of applications that can benefit from
graphene-based RRAM devices.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 02:10:33 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"TR",
"Rajalekshmi",
""
],
[
"Das",
"Rinku Rani",
""
],
[
"R",
"Chithra",
""
],
[
"James",
"Alex",
""
]
] |
new_dataset
| 0.999796 |
2308.02768
|
Yuhui Hao
|
Yuhui Hao and Bo Yu and Qiang Liu and Shao-Shan Liu
|
FGLQR: Factor Graph Accelerator of LQR Control for Autonomous Machines
| null | null | null | null |
cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Factor graph represents the factorization of a probability distribution
function and serves as an effective abstraction in various autonomous machine
computing tasks. Control is one of the core applications in autonomous machine
computing stacks. Among all control algorithms, Linear Quadratic Regulator
(LQR) offers one of the best trade-offs between efficiency and accuracy.
However, due to the inherent iterative process and extensive computation, it is
a challenging task for the autonomous systems with real-time limits and energy
constrained.
In this paper, we present FGLQR, an accelerator of LQR control for autonomous
machines using the abstraction of a factor graph. By transforming the dynamic
equation constraints into least squares constraints, the factor graph solving
process is more hardware friendly and accelerated with almost no loss in
accuracy. With a domain specific parallel solving pattern, FGLQR achieves 10.2x
speed up and 32.9x energy reduction compared to the software implementation on
an advanced Intel CPU.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 02:19:04 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Hao",
"Yuhui",
""
],
[
"Yu",
"Bo",
""
],
[
"Liu",
"Qiang",
""
],
[
"Liu",
"Shao-Shan",
""
]
] |
new_dataset
| 0.99746 |
2308.02773
|
Jie Zhou
|
Yuhao Dan, Zhikai Lei, Yiyang Gu, Yong Li, Jianghao Yin, Jiaju Lin,
Linhao Ye, Zhiyan Tie, Yougen Zhou, Yilei Wang, Aimin Zhou, Ze Zhou, Qin
Chen, Jie Zhou, Liang He, Xipeng Qiu
|
EduChat: A Large-Scale Language Model-based Chatbot System for
Intelligent Education
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
EduChat (https://www.educhat.top/) is a large-scale language model
(LLM)-based chatbot system in the education domain. Its goal is to support
personalized, fair, and compassionate intelligent education, serving teachers,
students, and parents. Guided by theories from psychology and education, it
further strengthens educational functions such as open question answering,
essay assessment, Socratic teaching, and emotional support based on the
existing basic LLMs. Particularly, we learn domain-specific knowledge by
pre-training on the educational corpus and stimulate various skills with tool
use by fine-tuning on designed system prompts and instructions. Currently,
EduChat is available online as an open-source project, with its code, data, and
model parameters available on platforms (e.g., GitHub
https://github.com/icalk-nlp/EduChat, Hugging Face
https://huggingface.co/ecnu-icalk ). We also prepare a demonstration of its
capabilities online (https://vimeo.com/851004454). This initiative aims to
promote research and applications of LLMs for intelligent education.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 02:55:35 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Dan",
"Yuhao",
""
],
[
"Lei",
"Zhikai",
""
],
[
"Gu",
"Yiyang",
""
],
[
"Li",
"Yong",
""
],
[
"Yin",
"Jianghao",
""
],
[
"Lin",
"Jiaju",
""
],
[
"Ye",
"Linhao",
""
],
[
"Tie",
"Zhiyan",
""
],
[
"Zhou",
"Yougen",
""
],
[
"Wang",
"Yilei",
""
],
[
"Zhou",
"Aimin",
""
],
[
"Zhou",
"Ze",
""
],
[
"Chen",
"Qin",
""
],
[
"Zhou",
"Jie",
""
],
[
"He",
"Liang",
""
],
[
"Qiu",
"Xipeng",
""
]
] |
new_dataset
| 0.995975 |
2308.02792
|
Sudipta Paria
|
Sudipta Paria and Swarup Bhunia
|
DiSPEL: Distributed Security Policy Enforcement for Bus-based SoC
|
14 Pages, 9 Figures
| null | null | null |
cs.CR cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The current zero trust model adopted in System-on-Chip (SoC) design is
vulnerable to various malicious entities, and modern SoC designs must
incorporate various security policies to protect sensitive assets from
unauthorized access. These policies involve complex interactions between
multiple IP blocks, which poses challenges for SoC designers and security
experts when implementing these policies and for system validators when
ensuring compliance. Difficulties arise when upgrading policies, reusing IPs
for systems targeting different security requirements, and the subsequent
increase in design time and time-to-market. This paper proposes a generic and
flexible framework, called DiSPEL, for enforcing security policies defined by
the user represented in a formal way for any bus-based SoC design. It employs a
distributed deployment strategy while ensuring trusted bus operations despite
the presence of untrusted IPs. It relies on incorporating a dedicated,
centralized module capable of implementing diverse security policies involving
bus-level interactions while generating the necessary logic and appending in
the bus-level wrapper for IP-level policies. The proposed architecture is
generic and independent of specific security policy types supporting both
synthesizable and non-synthesizable solutions. The experimental results
demonstrate its effectiveness and correctness in enforcing the security
requirements and viability due to low overhead in terms of area, delay, and
power consumption tested on open-source standard SoC benchmarks.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 05:15:22 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Paria",
"Sudipta",
""
],
[
"Bhunia",
"Swarup",
""
]
] |
new_dataset
| 0.998061 |
2308.02795
|
Md Amjad Hossain
|
Md Amjad Hossain, Javed I. Khan
|
ZePoP: A Distributed Leader Election Protocol using the Delay-based
Closeness Centrality for Peer-to-Peer Applications
| null | null | null | null |
cs.DC cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents ZePoP, a leader election protocol for distributed
systems, optimizing a delay-based closeness centrality. We design the protocol
specifically for the Peer to Peer(P2P) applications, where the leader peer
(node) is responsible for collecting, processing, and redistributing data or
control signals satisfying some timing constraints. The protocol elects an
optimal leader node in the dynamically changing network and constructs a Data
Collection and Distribution Tree (DCDT) rooted at the leader node. The elected
optimal leader is closest to all nodes in the system compared to other nodes.
We validate the proposed protocol through theoretical proofs as well as
experimental results.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 05:55:18 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Hossain",
"Md Amjad",
""
],
[
"Khan",
"Javed I.",
""
]
] |
new_dataset
| 0.998918 |
2308.02812
|
Oliver Keszocze
|
Max Bartunik, Jens Kirchner, Oliver Keszocze
|
Artificial Intelligence for Molecular Communication
|
The abstract was slightly altered compared to the journal version in
order to meet arXiv's requirements
| null |
10.1515/itit-2023-0029
| null |
cs.ET cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Molecular communication is a novel approach for data transmission between
miniaturized devices, especially in contexts where electrical signals are to be
avoided. The communication is based on sending molecules (or other particles)
at nano scale through channel instead sending electrons over a wire. Molecular
communication devices have a large potential in medical applications as they
offer an alternative to antenna-based transmission systems that may not be
applicable due to size, temperature, or radiation constraints. The
communication is achieved by transforming a digital signal into concentrations
of molecules. These molecules are then detected at the other end of the
communication channel and transformed back into a digital signal. Accurately
modeling the transmission channel is often not possible which may be due to a
lack of data or time-varying parameters of the channel (e. g., the movements of
a person wearing a medical device). This makes demodulation of the signal very
difficult. Many approaches for demodulation have been discussed with one
particular approach having tremendous success: artificial neural networks.
These networks imitate the decision process in the human brain and are capable
of reliably classifying noisy input data. Training such a network relies on a
large set of training data. As molecular communication as a technology is still
in its early development phase, this data is not always readily available. We
discuss neural network-based demodulation approaches relying on synthetic data
based on theoretical channel models as well as works using actual measurements
produced by a prototype test bed. In this work, we give a general overview over
the field molecular communication, discuss the challenges in the demodulations
process of transmitted signals, and present approaches to these challenges that
are based on artificial neural networks.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 07:07:02 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Bartunik",
"Max",
""
],
[
"Kirchner",
"Jens",
""
],
[
"Keszocze",
"Oliver",
""
]
] |
new_dataset
| 0.992202 |
2308.02816
|
Hongwei Yao
|
Hongwei Yao, Jian Lou, Kui Ren and Zhan Qin
|
PromptCARE: Prompt Copyright Protection by Watermark Injection and
Verification
| null | null | null | null |
cs.MM cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Large language models (LLMs) have witnessed a meteoric rise in popularity
among the general public users over the past few months, facilitating diverse
downstream tasks with human-level accuracy and proficiency. Prompts play an
essential role in this success, which efficiently adapt pre-trained LLMs to
task-specific applications by simply prepending a sequence of tokens to the
query texts. However, designing and selecting an optimal prompt can be both
expensive and demanding, leading to the emergence of Prompt-as-a-Service
providers who profit by providing well-designed prompts for authorized use.
With the growing popularity of prompts and their indispensable role in
LLM-based services, there is an urgent need to protect the copyright of prompts
against unauthorized use.
In this paper, we propose PromptCARE, the first framework for prompt
copyright protection through watermark injection and verification. Prompt
watermarking presents unique challenges that render existing watermarking
techniques developed for model and dataset copyright verification ineffective.
PromptCARE overcomes these hurdles by proposing watermark injection and
verification schemes tailor-made for prompts and NLP characteristics. Extensive
experiments on six well-known benchmark datasets, using three prevalent
pre-trained LLMs (BERT, RoBERTa, and Facebook OPT-1.3b), demonstrate the
effectiveness, harmlessness, robustness, and stealthiness of PromptCARE.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 08:12:34 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Yao",
"Hongwei",
""
],
[
"Lou",
"Jian",
""
],
[
"Ren",
"Kui",
""
],
[
"Qin",
"Zhan",
""
]
] |
new_dataset
| 0.999364 |
2308.02827
|
Tianxing Li
|
Tianxing Li, Rui Shi, Qing Zhu, Takashi Kanai
|
SwinGar: Spectrum-Inspired Neural Dynamic Deformation for Free-Swinging
Garments
| null | null | null | null |
cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Our work presents a novel spectrum-inspired learning-based approach for
generating clothing deformations with dynamic effects and personalized details.
Existing methods in the field of clothing animation are limited to either
static behavior or specific network models for individual garments, which
hinders their applicability in real-world scenarios where diverse animated
garments are required. Our proposed method overcomes these limitations by
providing a unified framework that predicts dynamic behavior for different
garments with arbitrary topology and looseness, resulting in versatile and
realistic deformations. First, we observe that the problem of bias towards low
frequency always hampers supervised learning and leads to overly smooth
deformations. To address this issue, we introduce a frequency-control strategy
from a spectral perspective that enhances the generation of high-frequency
details of the deformation. In addition, to make the network highly
generalizable and able to learn various clothing deformations effectively, we
propose a spectral descriptor to achieve a generalized description of the
global shape information. Building on the above strategies, we develop a
dynamic clothing deformation estimator that integrates frequency-controllable
attention mechanisms with long short-term memory. The estimator takes as input
expressive features from garments and human bodies, allowing it to
automatically output continuous deformations for diverse clothing types,
independent of mesh topology or vertex count. Finally, we present a neural
collision handling method to further enhance the realism of garments. Our
experimental results demonstrate the effectiveness of our approach on a variety
of free-swinging garments and its superiority over state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 09:09:50 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Li",
"Tianxing",
""
],
[
"Shi",
"Rui",
""
],
[
"Zhu",
"Qing",
""
],
[
"Kanai",
"Takashi",
""
]
] |
new_dataset
| 0.999708 |
2308.02828
|
Shuyin Ouyang
|
Shuyin Ouyang, Jie M. Zhang, Mark Harman, Meng Wang
|
LLM is Like a Box of Chocolates: the Non-determinism of ChatGPT in Code
Generation
| null | null | null | null |
cs.SE
|
http://creativecommons.org/publicdomain/zero/1.0/
|
There has been a recent explosion of research on Large Language Models (LLMs)
for software engineering tasks, in particular code generation. However, results
from LLMs can be highly unstable; nondeterministically returning very different
codes for the same prompt. Non-determinism is a potential menace to scientific
conclusion validity. When non-determinism is high, scientific conclusions
simply cannot be relied upon unless researchers change their behaviour to
control for it in their empirical analyses. This paper conducts an empirical
study to demonstrate that non-determinism is, indeed, high, thereby underlining
the need for this behavioural change. We choose to study ChatGPT because it is
already highly prevalent in the code generation research literature. We report
results from a study of 829 code generation problems from three code generation
benchmarks (i.e., CodeContests, APPS, and HumanEval). Our results reveal high
degrees of non-determinism: the ratio of coding tasks with zero equal test
output across different requests is 72.73%, 60.40%, and 65.85% for
CodeContests, APPS, and HumanEval, respectively. In addition, we find that
setting the temperature to 0 does not guarantee determinism in code generation,
although it indeed brings less non-determinism than the default configuration
(temperature=1). These results confirm that there is, currently, a significant
threat to scientific conclusion validity. In order to put LLM-based research on
firmer scientific foundations, researchers need to take into account
non-determinism in drawing their conclusions.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 09:30:33 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Ouyang",
"Shuyin",
""
],
[
"Zhang",
"Jie M.",
""
],
[
"Harman",
"Mark",
""
],
[
"Wang",
"Meng",
""
]
] |
new_dataset
| 0.967974 |
2308.02838
|
Nihir Vedd
|
Nihir Vedd and Paul Riga
|
feather -- a Python SDK to share and deploy models
|
Accepted to ICML 2023 Workshop AI&HCI. 8 pages, 3 figures and 1
figure
| null | null | null |
cs.AI cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
At its core, feather was a tool that allowed model developers to build
shareable user interfaces for their models in under 20 lines of code. Using the
Python SDK, developers specified visual components that users would interact
with. (e.g. a FileUpload component to allow users to upload a file). Our
service then provided 1) a URL that allowed others to access and use the model
visually via a user interface; 2) an API endpoint to allow programmatic
requests to a model. In this paper, we discuss feather's motivations and the
value we intended to offer AI researchers and developers. For example, the SDK
can support multi-step models and can be extended to run automatic evaluation
against held out datasets. We additionally provide comprehensive technical and
implementation details.
N.B. feather is presently a dormant project. We have open sourced our code
for research purposes: https://github.com/feather-ai/
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 10:27:50 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Vedd",
"Nihir",
""
],
[
"Riga",
"Paul",
""
]
] |
new_dataset
| 0.997894 |
2308.02866
|
Jianfeng Wang
|
Jianfeng Wang, Daniela Massiceti, Xiaolin Hu, Vladimir Pavlovic,
Thomas Lukasiewicz
|
NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic
Segmentation
|
Appear at ICML2023. Source codes are available at:
https://github.com/Jianf-Wang/NP-SemiSeg
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Semi-supervised semantic segmentation involves assigning pixel-wise labels to
unlabeled images at training time. This is useful in a wide range of real-world
applications where collecting pixel-wise labels is not feasible in time or
cost. Current approaches to semi-supervised semantic segmentation work by
predicting pseudo-labels for each pixel from a class-wise probability
distribution output by a model. If the predicted probability distribution is
incorrect, however, this leads to poor segmentation results, which can have
knock-on consequences in safety critical systems, like medical images or
self-driving cars. It is, therefore, important to understand what a model does
not know, which is mainly achieved by uncertainty quantification. Recently,
neural processes (NPs) have been explored in semi-supervised image
classification, and they have been a computationally efficient and effective
method for uncertainty quantification. In this work, we move one step forward
by adapting NPs to semi-supervised semantic segmentation, resulting in a new
model called NP-SemiSeg. We experimentally evaluated NP-SemiSeg on the public
benchmarks PASCAL VOC 2012 and Cityscapes, with different training settings,
and the results verify its effectiveness.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 12:42:15 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Wang",
"Jianfeng",
""
],
[
"Massiceti",
"Daniela",
""
],
[
"Hu",
"Xiaolin",
""
],
[
"Pavlovic",
"Vladimir",
""
],
[
"Lukasiewicz",
"Thomas",
""
]
] |
new_dataset
| 0.990649 |
2308.02905
|
Alloy Das
|
Alloy Das, Prasun Roy, Saumik Bhattacharya, Subhankar Ghosh, Umapada
Pal, Michael Blumenstein
|
FAST: Font-Agnostic Scene Text Editing
|
13 pages, in submission
| null | null | null |
cs.CV cs.MM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Scene Text Editing (STE) is a challenging research problem, and it aims to
modify existing texts in an image while preserving the background and the font
style of the original text of the image. Due to its various real-life
applications, researchers have explored several approaches toward STE in recent
years. However, most of the existing STE methods show inferior editing
performance because of (1) complex image backgrounds, (2) various font styles,
and (3) varying word lengths within the text. To address such inferior editing
performance issues, in this paper, we propose a novel font-agnostic scene text
editing framework, named FAST, for simultaneously generating text in arbitrary
styles and locations while preserving a natural and realistic appearance
through combined mask generation and style transfer. The proposed approach
differs from the existing methods as they directly modify all image pixels.
Instead, the proposed method has introduced a filtering mechanism to remove
background distractions, allowing the network to focus solely on the text
regions where editing is required. Additionally, a text-style transfer module
has been designed to mitigate the challenges posed by varying word lengths.
Extensive experiments and ablations have been conducted, and the results
demonstrate that the proposed method outperforms the existing methods both
qualitatively and quantitatively.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 15:54:06 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Das",
"Alloy",
""
],
[
"Roy",
"Prasun",
""
],
[
"Bhattacharya",
"Saumik",
""
],
[
"Ghosh",
"Subhankar",
""
],
[
"Pal",
"Umapada",
""
],
[
"Blumenstein",
"Michael",
""
]
] |
new_dataset
| 0.977319 |
2308.02907
|
Kasra EdalatNejad
|
Kasra EdalatNejad, Wouter Lueks, Justinas Sukaitis, Vincent Graf
Narbel, Massimo Marelli, Carmela Troncoso
|
Janus: Safe Biometric Deduplication for Humanitarian Aid Distribution
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Humanitarian organizations provide aid to people in need. To use their
limited budget efficiently, their distribution processes must ensure that
legitimate recipients cannot receive more aid than they are entitled to. Thus,
it is essential that recipients can register at most once per aid program.
Taking the International Committee of the Red Cross's aid distribution
registration process as a use case, we identify the requirements to detect
double registration without creating new risks for aid recipients. We then
design Janus, which combines privacy-enhancing technologies with biometrics to
prevent double registration in a safe manner. Janus does not create plaintext
biometric databases and reveals only one bit of information at registration
time (whether the user registering is present in the database or not). We
implement and evaluate three instantiations of Janus based on secure multiparty
computation, somewhat homomorphic encryption, and trusted execution
environments. We demonstrate that they support the privacy, accuracy, and
performance needs of humanitarian organizations. We compare Janus with existing
alternatives and show it is the first system that provides the accuracy our
scenario requires while providing strong protection.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 15:59:13 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"EdalatNejad",
"Kasra",
""
],
[
"Lueks",
"Wouter",
""
],
[
"Sukaitis",
"Justinas",
""
],
[
"Narbel",
"Vincent Graf",
""
],
[
"Marelli",
"Massimo",
""
],
[
"Troncoso",
"Carmela",
""
]
] |
new_dataset
| 0.99525 |
2308.02915
|
Le Zhuo
|
Qiaosong Qi, Le Zhuo, Aixi Zhang, Yue Liao, Fei Fang, Si Liu,
Shuicheng Yan
|
DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation
|
Accepted at ACM MM 2023
| null |
10.1145/3581783.3612307
| null |
cs.GR cs.CV cs.SD eess.AS
|
http://creativecommons.org/licenses/by-sa/4.0/
|
When hearing music, it is natural for people to dance to its rhythm.
Automatic dance generation, however, is a challenging task due to the physical
constraints of human motion and rhythmic alignment with target music.
Conventional autoregressive methods introduce compounding errors during
sampling and struggle to capture the long-term structure of dance sequences. To
address these limitations, we present a novel cascaded motion diffusion model,
DiffDance, designed for high-resolution, long-form dance generation. This model
comprises a music-to-dance diffusion model and a sequence super-resolution
diffusion model. To bridge the gap between music and motion for conditional
generation, DiffDance employs a pretrained audio representation learning model
to extract music embeddings and further align its embedding space to motion via
contrastive loss. During training our cascaded diffusion model, we also
incorporate multiple geometric losses to constrain the model outputs to be
physically plausible and add a dynamic loss weight that adaptively changes over
diffusion timesteps to facilitate sample diversity. Through comprehensive
experiments performed on the benchmark dataset AIST++, we demonstrate that
DiffDance is capable of generating realistic dance sequences that align
effectively with the input music. These results are comparable to those
achieved by state-of-the-art autoregressive methods.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 16:18:57 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Qi",
"Qiaosong",
""
],
[
"Zhuo",
"Le",
""
],
[
"Zhang",
"Aixi",
""
],
[
"Liao",
"Yue",
""
],
[
"Fang",
"Fei",
""
],
[
"Liu",
"Si",
""
],
[
"Yan",
"Shuicheng",
""
]
] |
new_dataset
| 0.998302 |
2308.02944
|
Renato Geh
|
Renato Lui Geh, Jonas Gon\c{c}alves, Igor Cataneo Silveira, Denis
Deratani Mau\'a, Fabio Gagliardi Cozman
|
dPASP: A Comprehensive Differentiable Probabilistic Answer Set
Programming Environment For Neurosymbolic Learning and Reasoning
|
12 pages, 1 figure
| null | null | null |
cs.AI cs.LG cs.LO cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
We present dPASP, a novel declarative probabilistic logic programming
framework for differentiable neuro-symbolic reasoning. The framework allows for
the specification of discrete probabilistic models with neural predicates,
logic constraints and interval-valued probabilistic choices, thus supporting
models that combine low-level perception (images, texts, etc), common-sense
reasoning, and (vague) statistical knowledge. To support all such features, we
discuss the several semantics for probabilistic logic programs that can express
nondeterministic, contradictory, incomplete and/or statistical knowledge. We
also discuss how gradient-based learning can be performed with neural
predicates and probabilistic choices under selected semantics. We then describe
an implemented package that supports inference and learning in the language,
along with several example programs. The package requires minimal user
knowledge of deep learning system's inner workings, while allowing end-to-end
training of rather sophisticated models and loss functions.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 19:36:58 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Geh",
"Renato Lui",
""
],
[
"Gonçalves",
"Jonas",
""
],
[
"Silveira",
"Igor Cataneo",
""
],
[
"Mauá",
"Denis Deratani",
""
],
[
"Cozman",
"Fabio Gagliardi",
""
]
] |
new_dataset
| 0.983087 |
2308.02945
|
Yonghae Kim
|
Yonghae Kim, Anurag Kar, Jaewon Lee, Jaekyu Lee, Hyesoon Kim
|
RV-CURE: A RISC-V Capability Architecture for Full Memory Safety
| null | null | null | null |
cs.AR cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Despite decades of efforts to resolve, memory safety violations are still
persistent and problematic in modern systems. Various defense mechanisms have
been proposed, but their deployment in real systems remains challenging because
of performance, security, or compatibility concerns. In this paper, we propose
RV-CURE, a RISC-V capability architecture that implements full-system support
for full memory safety. For capability enforcement, we first propose a compiler
technique, data-pointer tagging (DPT), applicable to protecting all memory
types. It inserts a pointer tag in a pointer address and associates that tag
with the pointer's capability metadata. DPT enforces a capability check for
every memory access by a tagged pointer and thereby prevents illegitimate
memory accesses. Furthermore, we investigate and present lightweight hardware
extensions for DPT based on the open-source RISC-V BOOM processor. We observe
that a capability-execution pipeline can be implemented in parallel with the
existing memory-execution pipeline without intrusive modifications. With our
seamless hardware integration, we achieve low-cost capability checks
transparently performed in hardware. Altogether, we prototype RV-CURE as a
synthesized RTL processor and conduct full-system evaluations on FPGAs running
Linux OS. Our evaluations show that RV-CURE achieves strong memory safety at a
10.8% slowdown across the SPEC 2017 C/C++ workloads.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 19:45:18 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Kim",
"Yonghae",
""
],
[
"Kar",
"Anurag",
""
],
[
"Lee",
"Jaewon",
""
],
[
"Lee",
"Jaekyu",
""
],
[
"Kim",
"Hyesoon",
""
]
] |
new_dataset
| 0.995193 |
2308.02992
|
Zian Liu
|
Zian Liu
|
Binary Code Similarity Detection
|
4 pages, conference paper
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Binary code similarity detection is to detect the similarity of code at
binary (assembly) level without source code. Existing works have their
limitations when dealing with mutated binary code generated by different
compiling options. In this paper, we propose a novel approach to addressing
this problem. By inspecting the binary code, we found that generally, within a
function, some instructions aim to calculate (prepare) values for other
instructions. The latter instructions are defined by us as key instructions.
Currently, we define four categories of key instructions: calling subfunctions,
comparing instruction, returning instruction, and memory-store instruction.
Thus if we symbolically execute similar binary codes, symbolic values at these
key instructions are expected to be similar. As such, we implement a prototype
tool, which has three steps. First, it symbolically executes binary code;
Second, it extracts symbolic values at defined key instructions into a graph;
Last, it compares the symbolic graph similarity. In our implementation, we also
address some problems, including path explosion and loop handling.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 02:24:42 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Liu",
"Zian",
""
]
] |
new_dataset
| 0.989028 |
2308.03000
|
Xianyi Liu
|
Peiguang Jing, Xianyi Liu, Ji Wang, Yinwei Wei, Liqiang Nie, Yuting Su
|
StyleEDL: Style-Guided High-order Attention Network for Image Emotion
Distribution Learning
|
8 pages, 5 figures, conference
| null | null | null |
cs.CV cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
Emotion distribution learning has gained increasing attention with the
tendency to express emotions through images. As for emotion ambiguity arising
from humans' subjectivity, substantial previous methods generally focused on
learning appropriate representations from the holistic or significant part of
images. However, they rarely consider establishing connections with the
stylistic information although it can lead to a better understanding of images.
In this paper, we propose a style-guided high-order attention network for image
emotion distribution learning termed StyleEDL, which interactively learns
stylistic-aware representations of images by exploring the hierarchical
stylistic information of visual contents. Specifically, we consider exploring
the intra- and inter-layer correlations among GRAM-based stylistic
representations, and meanwhile exploit an adversary-constrained high-order
attention mechanism to capture potential interactions between subtle visual
parts. In addition, we introduce a stylistic graph convolutional network to
dynamically generate the content-dependent emotion representations to benefit
the final emotion distribution learning. Extensive experiments conducted on
several benchmark datasets demonstrate the effectiveness of our proposed
StyleEDL compared to state-of-the-art methods. The implementation is released
at: https://github.com/liuxianyi/StyleEDL.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 03:22:46 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Jing",
"Peiguang",
""
],
[
"Liu",
"Xianyi",
""
],
[
"Wang",
"Ji",
""
],
[
"Wei",
"Yinwei",
""
],
[
"Nie",
"Liqiang",
""
],
[
"Su",
"Yuting",
""
]
] |
new_dataset
| 0.976921 |
2308.03004
|
Namyoon Lee
|
Geon Choi and Namyoon Lee
|
Deep Polar Codes
| null | null | null | null |
cs.IT cs.LG math.IT
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this paper, we introduce a novel class of pre-transformed polar codes,
termed as deep polar codes. We first present a deep polar encoder that
harnesses a series of multi-layered polar transformations with varying sizes.
Our approach to encoding enables a low-complexity implementation while
significantly enhancing the weight distribution of the code. Moreover, our
encoding method offers flexibility in rate-profiling, embracing a wide range of
code rates and blocklengths. Next, we put forth a low-complexity decoding
algorithm called successive cancellation list with backpropagation parity
checks (SCL-BPC). This decoding algorithm leverages the parity check equations
in the reverse process of the multi-layered pre-transformed encoding for SCL
decoding. Additionally, we present a low-latency decoding algorithm that
employs parallel-SCL decoding by treating partially pre-transformed bit
patterns as additional frozen bits. Through simulations, we demonstrate that
deep polar codes outperform existing pre-transformed polar codes in terms of
block error rates across various code rates under short block lengths, while
maintaining low encoding and decoding complexity. Furthermore, we show that
concatenating deep polar codes with cyclic-redundancy-check codes can achieve
the meta-converse bound of the finite block length capacity within 0.4 dB in
some instances.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 03:29:18 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Choi",
"Geon",
""
],
[
"Lee",
"Namyoon",
""
]
] |
new_dataset
| 0.971682 |
2308.03006
|
Xiao Liang
|
Kareem Eltouny, Seyedomid Sajedi, and Xiao Liang
|
High-Resolution Vision Transformers for Pixel-Level Identification of
Structural Components and Damage
| null | null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Visual inspection is predominantly used to evaluate the state of civil
structures, but recent developments in unmanned aerial vehicles (UAVs) and
artificial intelligence have increased the speed, safety, and reliability of
the inspection process. In this study, we develop a semantic segmentation
network based on vision transformers and Laplacian pyramids scaling networks
for efficiently parsing high-resolution visual inspection images. The massive
amounts of collected high-resolution images during inspections can slow down
the investigation efforts. And while there have been extensive studies
dedicated to the use of deep learning models for damage segmentation,
processing high-resolution visual data can pose major computational
difficulties. Traditionally, images are either uniformly downsampled or
partitioned to cope with computational demands. However, the input is at risk
of losing local fine details, such as thin cracks, or global contextual
information. Inspired by super-resolution architectures, our vision transformer
model learns to resize high-resolution images and masks to retain both the
valuable local features and the global semantics without sacrificing
computational efficiency. The proposed framework has been evaluated through
comprehensive experiments on a dataset of bridge inspection report images using
multiple metrics for pixel-wise materials detection.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 03:34:25 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Eltouny",
"Kareem",
""
],
[
"Sajedi",
"Seyedomid",
""
],
[
"Liang",
"Xiao",
""
]
] |
new_dataset
| 0.9889 |
2308.03065
|
Mohamadreza Delbari
|
Alejandro Jim\'enez-S\'aez, Arash Asadi, Robin Neuder, Mohamadreza
Delbari, and Vahid Jamali
|
Reconfigurable Intelligent Surfaces with Liquid Crystal Technology: A
Hardware Design and Communication Perspective
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
With the surge of theoretical work investigating Reconfigurable Intelligent
Surfaces (RISs) for wireless communication and sensing, there exists an urgent
need of hardware solutions for the evaluation of these theoretical results and
further advancing the field. The most common solutions proposed in the
literature are based on varactors, Positive Intrinsic-Negative (PIN) diodes,
and Micro-Electro-Mechanical Systems (MEMS). This paper presents the use of
Liquid Crystal (LC) technology for the realization of continuously tunable
extremely large millimeter-wave RISs. We review the basic physical principles
of LC theory, introduce two different realizations of LC-RISs, namely
reflect-array and phased-array, and highlight their key properties that have an
impact on the system design and RIS reconfiguration strategy. Moreover, the LC
technology is compared with the competing technologies in terms of feasibility,
cost, power consumption, reconfiguration speed, and bandwidth. Furthermore,
several important open problems for both theoretical and experimental research
on LC-RISs are presented.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 09:20:15 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Jiménez-Sáez",
"Alejandro",
""
],
[
"Asadi",
"Arash",
""
],
[
"Neuder",
"Robin",
""
],
[
"Delbari",
"Mohamadreza",
""
],
[
"Jamali",
"Vahid",
""
]
] |
new_dataset
| 0.998189 |
2308.03108
|
Amira Guesmi
|
Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni, Muhammad Shafique
|
SAAM: Stealthy Adversarial Attack on Monoculor Depth Estimation
| null | null | null | null |
cs.CV cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we investigate the vulnerability of MDE to adversarial
patches. We propose a novel \underline{S}tealthy \underline{A}dversarial
\underline{A}ttacks on \underline{M}DE (SAAM) that compromises MDE by either
corrupting the estimated distance or causing an object to seamlessly blend into
its surroundings. Our experiments, demonstrate that the designed stealthy patch
successfully causes a DNN-based MDE to misestimate the depth of objects. In
fact, our proposed adversarial patch achieves a significant 60\% depth error
with 99\% ratio of the affected region. Importantly, despite its adversarial
nature, the patch maintains a naturalistic appearance, making it inconspicuous
to human observers. We believe that this work sheds light on the threat of
adversarial attacks in the context of MDE on edge devices. We hope it raises
awareness within the community about the potential real-life harm of such
attacks and encourages further research into developing more robust and
adaptive defense mechanisms.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 13:29:42 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Guesmi",
"Amira",
""
],
[
"Hanif",
"Muhammad Abdullah",
""
],
[
"Ouni",
"Bassem",
""
],
[
"Shafique",
"Muhammad",
""
]
] |
new_dataset
| 0.997925 |
2308.03120
|
Conrad Sanderson
|
Ryan R. Curtin, Marcus Edel, Conrad Sanderson
|
Bandicoot: C++ Library for GPU Linear Algebra and Scientific Computing
| null | null | null | null |
cs.MS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This report provides an introduction to the Bandicoot C++ library for linear
algebra and scientific computing on GPUs, overviewing its user interface and
performance characteristics, as well as the technical details of its internal
design. Bandicoot is the GPU-enabled counterpart to the well-known Armadillo
C++ linear algebra library, aiming to allow users to take advantage of
GPU-accelerated computation for their existing codebases without significant
changes. Adapting the same internal template meta-programming techniques that
Armadillo uses, Bandicoot is able to provide compile-time optimisation of
mathematical expressions within user code. The library is ready for production
use and is available at https://coot.sourceforge.io. Bandicoot is distributed
under the Apache 2.0 License.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 14:01:12 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Curtin",
"Ryan R.",
""
],
[
"Edel",
"Marcus",
""
],
[
"Sanderson",
"Conrad",
""
]
] |
new_dataset
| 0.999465 |
2308.03121
|
Yuan Tong
|
Yuan Tong, Mengshun Hu, Zheng Wang
|
NNVISR: Bring Neural Network Video Interpolation and Super Resolution
into Video Processing Framework
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We present NNVISR - an open-source filter plugin for the VapourSynth video
processing framework, which facilitates the application of neural networks for
various kinds of video enhancing tasks, including denoising, super resolution,
interpolation, and spatio-temporal super-resolution. NNVISR fills the gap
between video enhancement neural networks and video processing pipelines, by
accepting any network that enhances a group of frames, and handling all other
network agnostic details during video processing. NNVISR is publicly released
at https://github.com/tongyuantongyu/vs-NNVISR.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 14:09:00 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Tong",
"Yuan",
""
],
[
"Hu",
"Mengshun",
""
],
[
"Wang",
"Zheng",
""
]
] |
new_dataset
| 0.997854 |
2308.03122
|
Prerak Gandhi
|
Prerak Gandhi, Vishal Pramanik, Pushpak Bhattacharyya
|
"Kurosawa": A Script Writer's Assistant
|
6 pages, 9 figures, 1 table
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Storytelling is the lifeline of the entertainment industry -- movies, TV
shows, and stand-up comedies, all need stories. A good and gripping script is
the lifeline of storytelling and demands creativity and resource investment.
Good scriptwriters are rare to find and often work under severe time pressure.
Consequently, entertainment media are actively looking for automation. In this
paper, we present an AI-based script-writing workbench called KUROSAWA which
addresses the tasks of plot generation and script generation. Plot generation
aims to generate a coherent and creative plot (600-800 words) given a prompt
(15-40 words). Script generation, on the other hand, generates a scene (200-500
words) in a screenplay format from a brief description (15-40 words). Kurosawa
needs data to train. We use a 4-act structure of storytelling to annotate the
plot dataset manually. We create a dataset of 1000 manually annotated plots and
their corresponding prompts/storylines and a gold-standard dataset of 1000
scenes with four main elements -- scene headings, action lines, dialogues, and
character names -- tagged individually. We fine-tune GPT-3 with the above
datasets to generate plots and scenes. These plots and scenes are first
evaluated and then used by the scriptwriters of a large and famous media
platform ErosNow. We release the annotated datasets and the models trained on
these datasets as a working benchmark for automatic movie plot and script
generation.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 14:09:02 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Gandhi",
"Prerak",
""
],
[
"Pramanik",
"Vishal",
""
],
[
"Bhattacharyya",
"Pushpak",
""
]
] |
new_dataset
| 0.999891 |
2308.03151
|
Zheng Ma
|
Zheng Ma, Mianzhi Pan, Wenhan Wu, Kanzhi Cheng, Jianbing Zhang,
Shujian Huang and Jiajun Chen
|
Food-500 Cap: A Fine-Grained Food Caption Benchmark for Evaluating
Vision-Language Models
|
Accepted at ACM Multimedia (ACMMM) 2023
| null | null | null |
cs.CV cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision-language models (VLMs) have shown impressive performance in
substantial downstream multi-modal tasks. However, only comparing the
fine-tuned performance on downstream tasks leads to the poor interpretability
of VLMs, which is adverse to their future improvement. Several prior works have
identified this issue and used various probing methods under a zero-shot
setting to detect VLMs' limitations, but they all examine VLMs using general
datasets instead of specialized ones. In practical applications, VLMs are
usually applied to specific scenarios, such as e-commerce and news fields, so
the generalization of VLMs in specific domains should be given more attention.
In this paper, we comprehensively investigate the capabilities of popular VLMs
in a specific field, the food domain. To this end, we build a food caption
dataset, Food-500 Cap, which contains 24,700 food images with 494 categories.
Each image is accompanied by a detailed caption, including fine-grained
attributes of food, such as the ingredient, shape, and color. We also provide a
culinary culture taxonomy that classifies each food category based on its
geographic origin in order to better analyze the performance differences of VLM
in different regions. Experiments on our proposed datasets demonstrate that
popular VLMs underperform in the food domain compared with their performance in
the general domain. Furthermore, our research reveals severe bias in VLMs'
ability to handle food items from different geographic regions. We adopt
diverse probing methods and evaluate nine VLMs belonging to different
architectures to verify the aforementioned observations. We hope that our study
will bring researchers' attention to VLM's limitations when applying them to
the domain of food or culinary cultures, and spur further investigations to
address this issue.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 15:56:31 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Ma",
"Zheng",
""
],
[
"Pan",
"Mianzhi",
""
],
[
"Wu",
"Wenhan",
""
],
[
"Cheng",
"Kanzhi",
""
],
[
"Zhang",
"Jianbing",
""
],
[
"Huang",
"Shujian",
""
],
[
"Chen",
"Jiajun",
""
]
] |
new_dataset
| 0.993999 |
2308.03163
|
Md Farhamdur Reza
|
Md Farhamdur Reza, Ali Rahmati, Tianfu Wu, Huaiyu Dai
|
CGBA: Curvature-aware Geometric Black-box Attack
|
This paper is accepted to publish in ICCV
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Decision-based black-box attacks often necessitate a large number of queries
to craft an adversarial example. Moreover, decision-based attacks based on
querying boundary points in the estimated normal vector direction often suffer
from inefficiency and convergence issues. In this paper, we propose a novel
query-efficient curvature-aware geometric decision-based black-box attack
(CGBA) that conducts boundary search along a semicircular path on a restricted
2D plane to ensure finding a boundary point successfully irrespective of the
boundary curvature. While the proposed CGBA attack can work effectively for an
arbitrary decision boundary, it is particularly efficient in exploiting the low
curvature to craft high-quality adversarial examples, which is widely seen and
experimentally verified in commonly used classifiers under non-targeted
attacks. In contrast, the decision boundaries often exhibit higher curvature
under targeted attacks. Thus, we develop a new query-efficient variant, CGBA-H,
that is adapted for the targeted attack. In addition, we further design an
algorithm to obtain a better initial boundary point at the expense of some
extra queries, which considerably enhances the performance of the targeted
attack. Extensive experiments are conducted to evaluate the performance of our
proposed methods against some well-known classifiers on the ImageNet and
CIFAR10 datasets, demonstrating the superiority of CGBA and CGBA-H over
state-of-the-art non-targeted and targeted attacks, respectively. The source
code is available at https://github.com/Farhamdur/CGBA.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 17:18:04 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Reza",
"Md Farhamdur",
""
],
[
"Rahmati",
"Ali",
""
],
[
"Wu",
"Tianfu",
""
],
[
"Dai",
"Huaiyu",
""
]
] |
new_dataset
| 0.991623 |
2308.03164
|
Yue Hu
|
Yue Hu, Xinan Ye, Yifei Liu, Souvik Kundu, Gourav Datta, Srikar
Mutnuri, Namo Asavisanu, Nora Ayanian, Konstantinos Psounis, Peter Beerel
|
FireFly A Synthetic Dataset for Ember Detection in Wildfire
|
Artificial Intelligence (AI) and Humanitarian Assistance and Disaster
Recovery (HADR) workshop, ICCV 2023 in Paris, France
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents "FireFly", a synthetic dataset for ember detection
created using Unreal Engine 4 (UE4), designed to overcome the current lack of
ember-specific training resources. To create the dataset, we present a tool
that allows the automated generation of the synthetic labeled dataset with
adjustable parameters, enabling data diversity from various environmental
conditions, making the dataset both diverse and customizable based on user
requirements. We generated a total of 19,273 frames that have been used to
evaluate FireFly on four popular object detection models. Further to minimize
human intervention, we leveraged a trained model to create a semi-automatic
labeling process for real-life ember frames. Moreover, we demonstrated an up to
8.57% improvement in mean Average Precision (mAP) in real-world wildfire
scenarios compared to models trained exclusively on a small real dataset.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 17:19:51 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Hu",
"Yue",
""
],
[
"Ye",
"Xinan",
""
],
[
"Liu",
"Yifei",
""
],
[
"Kundu",
"Souvik",
""
],
[
"Datta",
"Gourav",
""
],
[
"Mutnuri",
"Srikar",
""
],
[
"Asavisanu",
"Namo",
""
],
[
"Ayanian",
"Nora",
""
],
[
"Psounis",
"Konstantinos",
""
],
[
"Beerel",
"Peter",
""
]
] |
new_dataset
| 0.999695 |
2308.03165
|
Wei Cai
|
Zhonghao Lin, Haihan Duan, Jiaye Li, Xinyao Sun, Wei Cai
|
MetaCast: A Self-Driven Metaverse Announcer Architecture Based on
Quality of Experience Evaluation Model
| null | null |
10.1145/3581783.3613761
| null |
cs.MM cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Metaverse provides users with a novel experience through immersive multimedia
technologies. Along with the rapid user growth, numerous events bursting in the
metaverse necessitate an announcer to help catch and monitor ongoing events.
However, systems on the market primarily serve for esports competitions and
rely on human directors, making it challenging to provide 24-hour delivery in
the metaverse persistent world. To fill the blank, we proposed a three-stage
architecture for metaverse announcers, which is designed to identify events,
position cameras, and blend between shots. Based on the architecture, we
introduced a Metaverse Announcer User Experience (MAUE) model to identify the
factors affecting the users' Quality of Experience (QoE) from a human-centered
perspective. In addition, we implemented \textit{MetaCast}, a practical
self-driven metaverse announcer in a university campus metaverse prototype, to
conduct user studies for MAUE model. The experimental results have effectively
achieved satisfactory announcer settings that align with the preferences of
most users, encompassing parameters such as video transition rate, repetition
rate, importance threshold value, and image composition.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 17:21:31 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Lin",
"Zhonghao",
""
],
[
"Duan",
"Haihan",
""
],
[
"Li",
"Jiaye",
""
],
[
"Sun",
"Xinyao",
""
],
[
"Cai",
"Wei",
""
]
] |
new_dataset
| 0.993496 |
2308.03166
|
Chunming He
|
Chunming He, Kai Li, Yachao Zhang, Yulun Zhang, Zhenhua Guo, Xiu Li,
Martin Danelljan, Fisher Yu
|
Strategic Preys Make Acute Predators: Enhancing Camouflaged Object
Detectors by Generating Camouflaged Objects
|
10 pages, 7 figures, 4 tables
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Camouflaged object detection (COD) is the challenging task of identifying
camouflaged objects visually blended into surroundings. Albeit achieving
remarkable success, existing COD detectors still struggle to obtain precise
results in some challenging cases. To handle this problem, we draw inspiration
from the prey-vs-predator game that leads preys to develop better camouflage
and predators to acquire more acute vision systems and develop algorithms from
both the prey side and the predator side. On the prey side, we propose an
adversarial training framework, Camouflageator, which introduces an auxiliary
generator to generate more camouflaged objects that are harder for a COD method
to detect. Camouflageator trains the generator and detector in an adversarial
way such that the enhanced auxiliary generator helps produce a stronger
detector. On the predator side, we introduce a novel COD method, called
Internal Coherence and Edge Guidance (ICEG), which introduces a camouflaged
feature coherence module to excavate the internal coherence of camouflaged
objects, striving to obtain more complete segmentation results. Additionally,
ICEG proposes a novel edge-guided separated calibration module to remove false
predictions to avoid obtaining ambiguous boundaries. Extensive experiments show
that ICEG outperforms existing COD detectors and Camouflageator is flexible to
improve various COD detectors, including ICEG, which brings state-of-the-art
COD performance.
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 17:27:08 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"He",
"Chunming",
""
],
[
"Li",
"Kai",
""
],
[
"Zhang",
"Yachao",
""
],
[
"Zhang",
"Yulun",
""
],
[
"Guo",
"Zhenhua",
""
],
[
"Li",
"Xiu",
""
],
[
"Danelljan",
"Martin",
""
],
[
"Yu",
"Fisher",
""
]
] |
new_dataset
| 0.985945 |
2308.03193
|
Rohit Mohan
|
Rohit Mohan, Jos\'e Arce, Sassan Mokhtar, Daniele Cattaneo and Abhinav
Valada
|
Syn-Mediverse: A Multimodal Synthetic Dataset for Intelligent Scene
Understanding of Healthcare Facilities
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Safety and efficiency are paramount in healthcare facilities where the lives
of patients are at stake. Despite the adoption of robots to assist medical
staff in challenging tasks such as complex surgeries, human expertise is still
indispensable. The next generation of autonomous healthcare robots hinges on
their capacity to perceive and understand their complex and frenetic
environments. While deep learning models are increasingly used for this
purpose, they require extensive annotated training data which is impractical to
obtain in real-world healthcare settings. To bridge this gap, we present
Syn-Mediverse, the first hyper-realistic multimodal synthetic dataset of
diverse healthcare facilities. Syn-Mediverse contains over \num{48000} images
from a simulated industry-standard optical tracking camera and provides more
than 1.5M annotations spanning five different scene understanding tasks
including depth estimation, object detection, semantic segmentation, instance
segmentation, and panoptic segmentation. We demonstrate the complexity of our
dataset by evaluating the performance on a broad range of state-of-the-art
baselines for each task. To further advance research on scene understanding of
healthcare facilities, along with the public dataset we provide an online
evaluation benchmark available at \url{http://syn-mediverse.cs.uni-freiburg.de}
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 19:20:18 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Mohan",
"Rohit",
""
],
[
"Arce",
"José",
""
],
[
"Mokhtar",
"Sassan",
""
],
[
"Cattaneo",
"Daniele",
""
],
[
"Valada",
"Abhinav",
""
]
] |
new_dataset
| 0.998302 |
2308.03262
|
Jianqi Ma
|
Jianqi Ma, Zhetong Liang, Wangmeng Xiang, Xi Yang, Lei Zhang
|
A Benchmark for Chinese-English Scene Text Image Super-resolution
|
Accepted by ICCV2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Scene Text Image Super-resolution (STISR) aims to recover high-resolution
(HR) scene text images with visually pleasant and readable text content from
the given low-resolution (LR) input. Most existing works focus on recovering
English texts, which have relatively simple character structures, while little
work has been done on the more challenging Chinese texts with diverse and
complex character structures. In this paper, we propose a real-world
Chinese-English benchmark dataset, namely Real-CE, for the task of STISR with
the emphasis on restoring structurally complex Chinese characters. The
benchmark provides 1,935/783 real-world LR-HR text image pairs~(contains 33,789
text lines in total) for training/testing in 2$\times$ and 4$\times$ zooming
modes, complemented by detailed annotations, including detection boxes and text
transcripts. Moreover, we design an edge-aware learning method, which provides
structural supervision in image and feature domains, to effectively reconstruct
the dense structures of Chinese characters. We conduct experiments on the
proposed Real-CE benchmark and evaluate the existing STISR models with and
without our edge-aware loss. The benchmark, including data and source code, is
available at https://github.com/mjq11302010044/Real-CE.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 02:57:48 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Ma",
"Jianqi",
""
],
[
"Liang",
"Zhetong",
""
],
[
"Xiang",
"Wangmeng",
""
],
[
"Yang",
"Xi",
""
],
[
"Zhang",
"Lei",
""
]
] |
new_dataset
| 0.999886 |
2308.03349
|
Shengzhi Li
|
Shengzhi Li, Nima Tajbakhsh
|
SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering
Dataset for Scientific Graphs
| null | null | null | null |
cs.CL cs.AI cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
In this work, we present SciGraphQA, a synthetic multi-turn question-answer
dataset related to academic graphs. SciGraphQA is 13 times larger than
ChartVQA, the previously largest chart-visual question-answering dataset. It is
also the largest open-sourced chart VQA dataset with non-synthetic charts. To
build our dataset, we selected 290,000 Computer Science or Machine Learning
ArXiv papers published between 2010 and 2020, and then used Palm-2 to generate
295K samples of open-vocabulary multi-turn question-answering dialogues about
the graphs. As context, we provided the text-only Palm-2 with paper title,
abstract, paragraph mentioning the graph, and rich text contextual data from
the graph itself, obtaining dialogues with an average 2.23 question-answer
turns for each graph. We asked GPT-4 to assess the matching quality of our
question-answer turns given the paper's context, obtaining an average rating of
8.7/10 on our 3K test set. We evaluated the 0-shot capability of the most
popular MLLM models such as LLaVa, mPLUGowl, BLIP-2, and openFlamingo's on our
dataset, finding LLaVA-13B being the most performant with a CIDEr score of
0.08. We further enriched the question prompts for LLAVA by including the
serialized data tables extracted from the graphs using the DePlot model,
boosting LLaVA's 0-shot CIDEr to 0.15. To verify the validity of our dataset,
we also fine-tuned LLaVa using our dataset, reaching a substantially higher
CIDEr score of 0.26. We anticipate further accuracy improvement by including
segmentation mask tokens and leveraging larger LLM backbones coupled with
emergent prompting techniques. Our code and data are open-sourced.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 07:03:49 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Li",
"Shengzhi",
""
],
[
"Tajbakhsh",
"Nima",
""
]
] |
new_dataset
| 0.999819 |
2308.03357
|
Yoshiki Obinata
|
Yoshiki Obinata, Naoaki Kanazawa, Kento Kawaharazuka, Iori Yanokura,
Soonhyo Kim, Kei Okada and Masayuki Inaba
|
Foundation Model based Open Vocabulary Task Planning and Executive
System for General Purpose Service Robots
|
In review
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper describes a strategy for implementing a robotic system capable of
performing General Purpose Service Robot (GPSR) tasks in robocup@home. The GPSR
task is that a real robot hears a variety of commands in spoken language and
executes a task in a daily life environment. To achieve the task, we integrate
foundation models based inference system and a state machine task executable.
The foundation models plan the task and detect objects with open vocabulary,
and a state machine task executable manages each robot's actions. This system
works stable, and we took first place in the RoboCup@home Japan Open 2022's
GPSR with 130 points, more than 85 points ahead of the other teams.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 07:26:50 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Obinata",
"Yoshiki",
""
],
[
"Kanazawa",
"Naoaki",
""
],
[
"Kawaharazuka",
"Kento",
""
],
[
"Yanokura",
"Iori",
""
],
[
"Kim",
"Soonhyo",
""
],
[
"Okada",
"Kei",
""
],
[
"Inaba",
"Masayuki",
""
]
] |
new_dataset
| 0.998905 |
2308.03375
|
Maximilian Neidhardt
|
M. Neidhardt, S. Gerlach F. N. Schmidt, I. A. K. Fiedler, S. Grube, B.
Busse, and A. Schlaefer
|
VR-based body tracking to stimulate musculoskeletal training
|
Conference
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Training helps to maintain and improve sufficient muscle function, body
control, and body coordination. These are important to reduce the risk of
fracture incidents caused by falls, especially for the elderly or people
recovering from injury. Virtual reality training can offer a cost-effective and
individualized training experience. We present an application for the HoloLens
2 to enable musculoskeletal training for elderly and impaired persons to allow
for autonomous training and automatic progress evaluation. We designed a
virtual downhill skiing scenario that is controlled by body movement to
stimulate balance and body control. By adapting the parameters of the ski
slope, we can tailor the intensity of the training to individual users. In this
work, we evaluate whether the movement data of the HoloLens 2 alone is
sufficient to control and predict body movement and joint angles during
musculoskeletal training. We record the movements of 10 healthy volunteers with
external tracking cameras and track a set of body and joint angles of the
participant during training. We estimate correlation coefficients and
systematically analyze whether whole body movement can be derived from the
movement data of the HoloLens 2. No participant reports movement sickness
effects and all were able to quickly interact and control their movement during
skiing. Our results show a high correlation between HoloLens 2 movement data
and the external tracking of the upper body movement and joint angles of the
lower limbs.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 07:54:32 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Neidhardt",
"M.",
""
],
[
"Schmidt",
"S. Gerlach F. N.",
""
],
[
"Fiedler",
"I. A. K.",
""
],
[
"Grube",
"S.",
""
],
[
"Busse",
"B.",
""
],
[
"Schlaefer",
"A.",
""
]
] |
new_dataset
| 0.976478 |
2308.03424
|
Matthias Urban
|
Matthias Urban and Carsten Binnig
|
CAESURA: Language Models as Multi-Modal Query Planners
|
6 pages, 4 figures
| null | null | null |
cs.DB
|
http://creativecommons.org/licenses/by/4.0/
|
Traditional query planners translate SQL queries into query plans to be
executed over relational data. However, it is impossible to query other data
modalities, such as images, text, or video stored in modern data systems such
as data lakes using these query planners. In this paper, we propose
Language-Model-Driven Query Planning, a new paradigm of query planning that
uses Language Models to translate natural language queries into executable
query plans. Different from relational query planners, the resulting query
plans can contain complex operators that are able to process arbitrary
modalities. As part of this paper, we present a first GPT-4 based prototype
called CEASURA and show the general feasibility of this idea on two datasets.
Finally, we discuss several ideas to improve the query planning capabilities of
today's Language Models.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 09:20:32 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Urban",
"Matthias",
""
],
[
"Binnig",
"Carsten",
""
]
] |
new_dataset
| 0.999806 |
2308.03425
|
Federico Rossi
|
Federico Rossi, Francesco Urbani, Marco Cococcioni, Emanuele Ruffaldi,
Sergio Saponara
|
FPPU: Design and Implementation of a Pipelined Full Posit Processing
Unit
| null | null | null | null |
cs.AR cs.PF
|
http://creativecommons.org/licenses/by/4.0/
|
By exploiting the modular RISC-V ISA this paper presents the customization of
instruction set with posit\textsuperscript{\texttrademark} arithmetic
instructions to provide improved numerical accuracy, well-defined behavior and
increased range of representable numbers while keeping the flexibility and
benefits of open-source ISA, like no licensing and royalty fee and community
development. In this work we present the design, implementation and integration
into the low-power Ibex RISC-V core of a full posit processing unit capable to
directly implement in hardware the four arithmetic operations (add, sub, mul,
div and fma), the inversion, the float-to-posit and posit-to-float conversions.
We evaluate speed, power and area of this unit (that we have called Full Posit
Processing Unit). The FPPU has been prototyped on Alveo and Kintex FPGAs, and
its impact on the metrics of the full-RISC-V core have been evaluated, showing
that we can provide real number processing capabilities to the mentioned core
with an increase in area limited to $7\%$ for 8-bit posits and to $15\%$ for
16-bit posits. Finally we present tests one the use of posits for deep neural
networks with different network models and datasets, showing minimal drop in
accuracy when using 16-bit posits instead of 32-bit IEEE floats.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 09:20:49 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Rossi",
"Federico",
""
],
[
"Urbani",
"Francesco",
""
],
[
"Cococcioni",
"Marco",
""
],
[
"Ruffaldi",
"Emanuele",
""
],
[
"Saponara",
"Sergio",
""
]
] |
new_dataset
| 0.991531 |
2308.03427
|
Jingqing Ruan
|
Jingqing Ruan, Yihong Chen, Bin Zhang, Zhiwei Xu, Tianpeng Bao,
Guoqing Du, Shiwei Shi, Hangyu Mao, Xingyu Zeng, Rui Zhao
|
TPTU: Task Planning and Tool Usage of Large Language Model-based AI
Agents
| null | null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With recent advancements in natural language processing, Large Language
Models (LLMs) have emerged as powerful tools for various real-world
applications. Despite their prowess, the intrinsic generative abilities of LLMs
may prove insufficient for handling complex tasks which necessitate a
combination of task planning and the usage of external tools. In this paper, we
first propose a structured framework tailored for LLM-based AI Agents and
discuss the crucial capabilities necessary for tackling intricate problems.
Within this framework, we design two distinct types of agents (i.e., one-step
agent and sequential agent) to execute the inference process. Subsequently, we
instantiate the framework using various LLMs and evaluate their Task Planning
and Tool Usage (TPTU) abilities on typical tasks. By highlighting key findings
and challenges, our goal is to provide a helpful resource for researchers and
practitioners to leverage the power of LLMs in their AI applications. Our study
emphasizes the substantial potential of these models, while also identifying
areas that need more investigation and improvement.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 09:22:03 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Ruan",
"Jingqing",
""
],
[
"Chen",
"Yihong",
""
],
[
"Zhang",
"Bin",
""
],
[
"Xu",
"Zhiwei",
""
],
[
"Bao",
"Tianpeng",
""
],
[
"Du",
"Guoqing",
""
],
[
"Shi",
"Shiwei",
""
],
[
"Mao",
"Hangyu",
""
],
[
"Zeng",
"Xingyu",
""
],
[
"Zhao",
"Rui",
""
]
] |
new_dataset
| 0.996414 |
2308.03429
|
Herman Sugiharto
|
Herman Sugiharto, Aradea, Husni Mubarok
|
RCMHA: Relative Convolutional Multi-Head Attention for Natural Language
Modelling
|
13 pages, 13 figures, 6 tables
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The Attention module finds common usage in language modeling, presenting
distinct challenges within the broader scope of Natural Language Processing.
Multi-Head Attention (MHA) employs an absolute positional encoding, which
imposes limitations on token length and entails substantial memory consumption
during the processing of embedded inputs. The current remedy proposed by
researchers involves the utilization of relative positional encoding, similar
to the approach adopted in Transformer-XL or Relative Multi-Head Attention
(RMHA), albeit the employed architecture consumes considerable memory
resources. To address these challenges, this study endeavors to refine MHA,
leveraging relative positional encoding in conjunction with the Depth-Wise
Convolutional Layer architecture, which promises heightened accuracy coupled
with minimized memory usage. The proposed RCMHA framework entails the
modification of two integral components: firstly, the application of the
Depth-Wise Convolutional Layer to the input embedding, encompassing Query, Key,
and Value parameters; secondly, the incorporation of Relative Positional
Encoding into the attention scoring phase, harmoniously integrated with Scaled
Dot-Product Attention. Empirical experiments underscore the advantages of
RCMHA, wherein it exhibits superior accuracy, boasting a score of 0.572 in
comparison to alternative attention modules such as MHA, Multi-DConv-Head
Attention (MDHA), and RMHA. Concerning memory utilization, RMHA emerges as the
most frugal, demonstrating an average consumption of 2.98 GB, surpassing RMHA
which necessitates 3.5 GB.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 09:24:24 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Sugiharto",
"Herman",
""
],
[
"Aradea",
"",
""
],
[
"Mubarok",
"Husni",
""
]
] |
new_dataset
| 0.981423 |
2308.03467
|
Guruprasad Parasnis
|
Guruprasad Parasnis, Anmol Chokshi, Kailas Devadkar
|
RoadScan: A Novel and Robust Transfer Learning Framework for Autonomous
Pothole Detection in Roads
|
6 pages, 5 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This research paper presents a novel approach to pothole detection using Deep
Learning and Image Processing techniques. The proposed system leverages the
VGG16 model for feature extraction and utilizes a custom Siamese network with
triplet loss, referred to as RoadScan. The system aims to address the critical
issue of potholes on roads, which pose significant risks to road users.
Accidents due to potholes on the roads have led to numerous accidents. Although
it is necessary to completely remove potholes, it is a time-consuming process.
Hence, a general road user should be able to detect potholes from a safe
distance in order to avoid damage. Existing methods for pothole detection
heavily rely on object detection algorithms which tend to have a high chance of
failure owing to the similarity in structures and textures of a road and a
pothole. Additionally, these systems utilize millions of parameters thereby
making the model difficult to use in small-scale applications for the general
citizen. By analyzing diverse image processing methods and various
high-performing networks, the proposed model achieves remarkable performance in
accurately detecting potholes. Evaluation metrics such as accuracy, EER,
precision, recall, and AUROC validate the effectiveness of the system.
Additionally, the proposed model demonstrates computational efficiency and
cost-effectiveness by utilizing fewer parameters and data for training. The
research highlights the importance of technology in the transportation sector
and its potential to enhance road safety and convenience. The network proposed
in this model performs with a 96.12 % accuracy, 3.89 % EER, and a 0.988 AUROC
value, which is highly competitive with other state-of-the-art works.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 10:47:08 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Parasnis",
"Guruprasad",
""
],
[
"Chokshi",
"Anmol",
""
],
[
"Devadkar",
"Kailas",
""
]
] |
new_dataset
| 0.958106 |
2308.03487
|
Stephanie Jean-Daubias
|
St\'ephanie Jean-Daubias (LIRIS, TWEAK, UCBL)
|
JADE: a board game to teach software ergonomics
| null |
Interaction Design and Architecture(s) Journal, 2023, 56, pp.29-52
|
10.55612/s-5002-056-002
| null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
JADE is an educational game we have imagined, designed, built, and used
successfully in various contexts. This board game enables learning and
practicing software ergonomics concepts. It is intended for beginners. We use
it every year during several hours with our second-year computer science
students at Lyon 1 University. In this paper, we present the classical version
of the game, as well as the design and evaluation process that we applied. We
also present the hybrid version of JADE, which relies on the use of QR codes
and videos. We also present its use in our teaching (with about 850 learners
for a total duration of 54 hours, which totals more than 2500 student-hours).
We then discuss the results obtained and present the considered evolutions.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 11:29:34 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Jean-Daubias",
"Stéphanie",
"",
"LIRIS, TWEAK, UCBL"
]
] |
new_dataset
| 0.999401 |
2308.03514
|
Sungho Suh
|
Sungho Suh, Vitor Fortes Rey, Sizhen Bian, Yu-Chi Huang, Jo\v{z}e M.
Ro\v{z}anec, Hooman Tavakoli Ghinani, Bo Zhou, Paul Lukowicz
|
Worker Activity Recognition in Manufacturing Line Using Near-body
Electric Field
| null | null | null | null |
cs.LG eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Manufacturing industries strive to improve production efficiency and product
quality by deploying advanced sensing and control systems. Wearable sensors are
emerging as a promising solution for achieving this goal, as they can provide
continuous and unobtrusive monitoring of workers' activities in the
manufacturing line. This paper presents a novel wearable sensing prototype that
combines IMU and body capacitance sensing modules to recognize worker
activities in the manufacturing line. To handle these multimodal sensor data,
we propose and compare early, and late sensor data fusion approaches for
multi-channel time-series convolutional neural networks and deep convolutional
LSTM. We evaluate the proposed hardware and neural network model by collecting
and annotating sensor data using the proposed sensing prototype and Apple
Watches in the testbed of the manufacturing line. Experimental results
demonstrate that our proposed methods achieve superior performance compared to
the baseline methods, indicating the potential of the proposed approach for
real-world applications in manufacturing industries. Furthermore, the proposed
sensing prototype with a body capacitive sensor and feature fusion method
improves by 6.35%, yielding a 9.38% higher macro F1 score than the proposed
sensing prototype without a body capacitive sensor and Apple Watch data,
respectively.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 12:10:13 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Suh",
"Sungho",
""
],
[
"Rey",
"Vitor Fortes",
""
],
[
"Bian",
"Sizhen",
""
],
[
"Huang",
"Yu-Chi",
""
],
[
"Rožanec",
"Jože M.",
""
],
[
"Ghinani",
"Hooman Tavakoli",
""
],
[
"Zhou",
"Bo",
""
],
[
"Lukowicz",
"Paul",
""
]
] |
new_dataset
| 0.998415 |
2308.03526
|
Michael Mathieu
|
Micha\"el Mathieu, Sherjil Ozair, Srivatsan Srinivasan, Caglar
Gulcehre, Shangtong Zhang, Ray Jiang, Tom Le Paine, Richard Powell, Konrad
\.Zo{\l}na, Julian Schrittwieser, David Choi, Petko Georgiev, Daniel Toyama,
Aja Huang, Roman Ring, Igor Babuschkin, Timo Ewalds, Mahyar Bordbar, Sarah
Henderson, Sergio G\'omez Colmenarejo, A\"aron van den Oord, Wojciech Marian
Czarnecki, Nando de Freitas, Oriol Vinyals
|
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
|
32 pages, 13 figures, previous version published as a NeurIPS 2021
workshop: https://openreview.net/forum?id=Np8Pumfoty
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
StarCraft II is one of the most challenging simulated reinforcement learning
environments; it is partially observable, stochastic, multi-agent, and
mastering StarCraft II requires strategic planning over long time horizons with
real-time low-level execution. It also has an active professional competitive
scene. StarCraft II is uniquely suited for advancing offline RL algorithms,
both because of its challenging nature and because Blizzard has released a
massive dataset of millions of StarCraft II games played by human players. This
paper leverages that and establishes a benchmark, called AlphaStar Unplugged,
introducing unprecedented challenges for offline reinforcement learning. We
define a dataset (a subset of Blizzard's release), tools standardizing an API
for machine learning methods, and an evaluation protocol. We also present
baseline agents, including behavior cloning, offline variants of actor-critic
and MuZero. We improve the state of the art of agents using only offline data,
and we achieve 90% win rate against previously published AlphaStar behavior
cloning agent.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 12:21:37 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Mathieu",
"Michaël",
""
],
[
"Ozair",
"Sherjil",
""
],
[
"Srinivasan",
"Srivatsan",
""
],
[
"Gulcehre",
"Caglar",
""
],
[
"Zhang",
"Shangtong",
""
],
[
"Jiang",
"Ray",
""
],
[
"Paine",
"Tom Le",
""
],
[
"Powell",
"Richard",
""
],
[
"Żołna",
"Konrad",
""
],
[
"Schrittwieser",
"Julian",
""
],
[
"Choi",
"David",
""
],
[
"Georgiev",
"Petko",
""
],
[
"Toyama",
"Daniel",
""
],
[
"Huang",
"Aja",
""
],
[
"Ring",
"Roman",
""
],
[
"Babuschkin",
"Igor",
""
],
[
"Ewalds",
"Timo",
""
],
[
"Bordbar",
"Mahyar",
""
],
[
"Henderson",
"Sarah",
""
],
[
"Colmenarejo",
"Sergio Gómez",
""
],
[
"Oord",
"Aäron van den",
""
],
[
"Czarnecki",
"Wojciech Marian",
""
],
[
"de Freitas",
"Nando",
""
],
[
"Vinyals",
"Oriol",
""
]
] |
new_dataset
| 0.998634 |
2308.03558
|
Wai Man Si
|
Wai Man Si, Michael Backes, Yang Zhang
|
Mondrian: Prompt Abstraction Attack Against Large Language Models for
Cheaper API Pricing
| null | null | null | null |
cs.CR cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The Machine Learning as a Service (MLaaS) market is rapidly expanding and
becoming more mature. For example, OpenAI's ChatGPT is an advanced large
language model (LLM) that generates responses for various queries with
associated fees. Although these models can deliver satisfactory performance,
they are far from perfect. Researchers have long studied the vulnerabilities
and limitations of LLMs, such as adversarial attacks and model toxicity.
Inevitably, commercial ML models are also not exempt from such issues, which
can be problematic as MLaaS continues to grow. In this paper, we discover a new
attack strategy against LLM APIs, namely the prompt abstraction attack.
Specifically, we propose Mondrian, a simple and straightforward method that
abstracts sentences, which can lower the cost of using LLM APIs. In this
approach, the adversary first creates a pseudo API (with a lower established
price) to serve as the proxy of the target API (with a higher established
price). Next, the pseudo API leverages Mondrian to modify the user query,
obtain the abstracted response from the target API, and forward it back to the
end user. Our results show that Mondrian successfully reduces user queries'
token length ranging from 13% to 23% across various tasks, including text
classification, generation, and question answering. Meanwhile, these abstracted
queries do not significantly affect the utility of task-specific and general
language models like ChatGPT. Mondrian also reduces instruction prompts' token
length by at least 11% without compromising output quality. As a result, the
prompt abstraction attack enables the adversary to profit without bearing the
cost of API development and deployment.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 13:10:35 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Si",
"Wai Man",
""
],
[
"Backes",
"Michael",
""
],
[
"Zhang",
"Yang",
""
]
] |
new_dataset
| 0.968351 |
2308.03586
|
Nafiseh Kakhani
|
Nafiseh Kakhani, Moien Rangzan, Ali Jamali, Sara Attarchi, Seyed Kazem
Alavipanah, and Thomas Scholten
|
SoilNet: An Attention-based Spatio-temporal Deep Learning Framework for
Soil Organic Carbon Prediction with Digital Soil Mapping in Europe
|
12 pages
| null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Digital soil mapping (DSM) is an advanced approach that integrates
statistical modeling and cutting-edge technologies, including machine learning
(ML) methods, to accurately depict soil properties and their spatial
distribution. Soil organic carbon (SOC) is a crucial soil attribute providing
valuable insights into soil health, nutrient cycling, greenhouse gas emissions,
and overall ecosystem productivity. This study highlights the significance of
spatial-temporal deep learning (DL) techniques within the DSM framework. A
novel architecture is proposed, incorporating spatial information using a base
convolutional neural network (CNN) model and spatial attention mechanism, along
with climate temporal information using a long short-term memory (LSTM)
network, for SOC prediction across Europe. The model utilizes a comprehensive
set of environmental features, including Landsat-8 images, topography, remote
sensing indices, and climate time series, as input features. Results
demonstrate that the proposed framework outperforms conventional ML approaches
like random forest commonly used in DSM, yielding lower root mean square error
(RMSE). This model is a robust tool for predicting SOC and could be applied to
other soil properties, thereby contributing to the advancement of DSM
techniques and facilitating land management and decision-making processes based
on accurate information.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 13:44:44 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Kakhani",
"Nafiseh",
""
],
[
"Rangzan",
"Moien",
""
],
[
"Jamali",
"Ali",
""
],
[
"Attarchi",
"Sara",
""
],
[
"Alavipanah",
"Seyed Kazem",
""
],
[
"Scholten",
"Thomas",
""
]
] |
new_dataset
| 0.993568 |
2308.03610
|
Liao Qu
|
Huichao Zhang, Bowen Chen, Hao Yang, Liao Qu, Xu Wang, Li Chen, Chao
Long, Feida Zhu, Kang Du, Min Zheng
|
AvatarVerse: High-quality & Stable 3D Avatar Creation from Text and Pose
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Creating expressive, diverse and high-quality 3D avatars from highly
customized text descriptions and pose guidance is a challenging task, due to
the intricacy of modeling and texturing in 3D that ensure details and various
styles (realistic, fictional, etc). We present AvatarVerse, a stable pipeline
for generating expressive high-quality 3D avatars from nothing but text
descriptions and pose guidance. In specific, we introduce a 2D diffusion model
conditioned on DensePose signal to establish 3D pose control of avatars through
2D images, which enhances view consistency from partially observed scenarios.
It addresses the infamous Janus Problem and significantly stablizes the
generation process. Moreover, we propose a progressive high-resolution 3D
synthesis strategy, which obtains substantial improvement over the quality of
the created 3D avatars. To this end, the proposed AvatarVerse pipeline achieves
zero-shot 3D modeling of 3D avatars that are not only more expressive, but also
in higher quality and fidelity than previous works. Rigorous qualitative
evaluations and user studies showcase AvatarVerse's superiority in synthesizing
high-fidelity 3D avatars, leading to a new standard in high-quality and stable
3D avatar creation. Our project page is: https://avatarverse3d.github.io
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 14:09:46 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Zhang",
"Huichao",
""
],
[
"Chen",
"Bowen",
""
],
[
"Yang",
"Hao",
""
],
[
"Qu",
"Liao",
""
],
[
"Wang",
"Xu",
""
],
[
"Chen",
"Li",
""
],
[
"Long",
"Chao",
""
],
[
"Zhu",
"Feida",
""
],
[
"Du",
"Kang",
""
],
[
"Zheng",
"Min",
""
]
] |
new_dataset
| 0.999705 |
2308.03652
|
Ardit Ramadani
|
Ardit Ramadani, Peter Ewert, Heribert Schunkert, Nassir Navab
|
WarpEM: Dynamic Time Warping for Accurate Catheter Registration in
EM-guided Procedures
|
The 26th International Conference on Medical Image Computing and
Computer Assisted Intervention, MICCAI 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Accurate catheter tracking is crucial during minimally invasive endovascular
procedures (MIEP), and electromagnetic (EM) tracking is a widely used
technology that serves this purpose. However, registration between preoperative
images and the EM tracking system is often challenging. Existing registration
methods typically require manual interactions, which can be time-consuming,
increase the risk of errors and change the procedural workflow. Although
several registration methods are available for catheter tracking, such as
marker-based and path-based approaches, their limitations can impact the
accuracy of the resulting tracking solution, consequently, the outcome of the
medical procedure.
This paper introduces a novel automated catheter registration method for
EM-guided MIEP. The method utilizes 3D signal temporal analysis, such as
Dynamic Time Warping (DTW) algorithms, to improve registration accuracy and
reliability compared to existing methods. DTW can accurately warp and match
EM-tracked paths to the vessel's centerline, making it particularly suitable
for registration. The introduced registration method is evaluated for accuracy
in a vascular phantom using a marker-based registration as the ground truth.
The results indicate that the DTW method yields accurate and reliable
registration outcomes, with a mean error of $2.22$mm. The introduced
registration method presents several advantages over state-of-the-art methods,
such as high registration accuracy, no initialization required, and increased
automation.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 15:07:21 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Ramadani",
"Ardit",
""
],
[
"Ewert",
"Peter",
""
],
[
"Schunkert",
"Heribert",
""
],
[
"Navab",
"Nassir",
""
]
] |
new_dataset
| 0.981605 |
2308.03665
|
Felix Chalumeau
|
Felix Chalumeau, Bryan Lim, Raphael Boige, Maxime Allard, Luca
Grillotti, Manon Flageat, Valentin Mac\'e, Arthur Flajolet, Thomas Pierrot,
Antoine Cully
|
QDax: A Library for Quality-Diversity and Population-based Algorithms
with Hardware Acceleration
| null | null | null | null |
cs.AI cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
QDax is an open-source library with a streamlined and modular API for
Quality-Diversity (QD) optimization algorithms in Jax. The library serves as a
versatile tool for optimization purposes, ranging from black-box optimization
to continuous control. QDax offers implementations of popular QD,
Neuroevolution, and Reinforcement Learning (RL) algorithms, supported by
various examples. All the implementations can be just-in-time compiled with
Jax, facilitating efficient execution across multiple accelerators, including
GPUs and TPUs. These implementations effectively demonstrate the framework's
flexibility and user-friendliness, easing experimentation for research
purposes. Furthermore, the library is thoroughly documented and tested with
95\% coverage.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 15:29:44 GMT"
}
] | 2023-08-08T00:00:00 |
[
[
"Chalumeau",
"Felix",
""
],
[
"Lim",
"Bryan",
""
],
[
"Boige",
"Raphael",
""
],
[
"Allard",
"Maxime",
""
],
[
"Grillotti",
"Luca",
""
],
[
"Flageat",
"Manon",
""
],
[
"Macé",
"Valentin",
""
],
[
"Flajolet",
"Arthur",
""
],
[
"Pierrot",
"Thomas",
""
],
[
"Cully",
"Antoine",
""
]
] |
new_dataset
| 0.968428 |
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