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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2206.00717
|
Yue Qi
|
Yue Qi, Mojtaba Vaezi, H. Vincent Poor
|
K-Receiver Wiretap Channel: Optimal Encoding Order and Signaling Design
|
arXiv admin note: substantial text overlap with arXiv:2205.06412. The
paper will appear in TWC
| null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The K-receiver wiretap channel is a channel model where a transmitter
broadcasts K independent messages to K intended receivers while keeping them
secret from an eavesdropper. The capacity region of the K-receiver
multiple-input multiple-output (MIMO) wiretap channel has been characterized by
using dirty-paper coding and stochastic encoding. However, K factorial encoding
orders may need to be enumerated to evaluate the capacity region, which makes
the problem intractable. In addition, even though the capacity region is known,
the optimal signaling to achieve the capacity region is unknown. In this paper,
we determine one optimal encoding order to achieve every point on the capacity
region, and thus reduce the encoding complexity K factorial times. We prove
that the optimal decoding order for the K-receiver MIMO wiretap channel is the
same as that for the MIMO broadcast channel without secrecy. To be specific,
the descending weight ordering in the weighted sum-rate (WSR) maximization
problem determines the optimal encoding order. Next, to reach the border of the
secrecy capacity region, we form a WSR maximization problem and apply the block
successive maximization method to solve this nonconvex problem and find the
input covariance matrices corresponding to each message. Numerical results are
used to verify the optimality of the encoding order and to demonstrate the
efficacy of the proposed signaling design.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 18:58:08 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Apr 2023 21:36:58 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Qi",
"Yue",
""
],
[
"Vaezi",
"Mojtaba",
""
],
[
"Poor",
"H. Vincent",
""
]
] |
new_dataset
| 0.977845 |
2206.15476
|
Marius Dragoi
|
Marius Dragoi, Elena Burceanu, Emanuela Haller, Andrei Manolache and
Florin Brad
|
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly
Detection
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Analyzing the distribution shift of data is a growing research direction in
nowadays Machine Learning (ML), leading to emerging new benchmarks that focus
on providing a suitable scenario for studying the generalization properties of
ML models. The existing benchmarks are focused on supervised learning, and to
the best of our knowledge, there is none for unsupervised learning. Therefore,
we introduce an unsupervised anomaly detection benchmark with data that shifts
over time, built over Kyoto-2006+, a traffic dataset for network intrusion
detection. This type of data meets the premise of shifting the input
distribution: it covers a large time span ($10$ years), with naturally
occurring changes over time (eg users modifying their behavior patterns, and
software updates). We first highlight the non-stationary nature of the data,
using a basic per-feature analysis, t-SNE, and an Optimal Transport approach
for measuring the overall distribution distances between years. Next, we
propose AnoShift, a protocol splitting the data in IID, NEAR, and FAR testing
splits. We validate the performance degradation over time with diverse models,
ranging from classical approaches to deep learning. Finally, we show that by
acknowledging the distribution shift problem and properly addressing it, the
performance can be improved compared to the classical training which assumes
independent and identically distributed data (on average, by up to $3\%$ for
our approach). Dataset and code are available at
https://github.com/bit-ml/AnoShift/.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 17:59:22 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Oct 2022 18:23:23 GMT"
},
{
"version": "v3",
"created": "Mon, 17 Oct 2022 16:14:03 GMT"
},
{
"version": "v4",
"created": "Mon, 3 Apr 2023 16:00:22 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Dragoi",
"Marius",
""
],
[
"Burceanu",
"Elena",
""
],
[
"Haller",
"Emanuela",
""
],
[
"Manolache",
"Andrei",
""
],
[
"Brad",
"Florin",
""
]
] |
new_dataset
| 0.999674 |
2207.08562
|
Haoran Luo
|
Haoran Luo, Haihong E, Ling Tan, Gengxian Zhou, Tianyu Yao, Kaiyang
Wan
|
DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link
Prediction and Entity Typing
|
Accepted by AAAI 2023
| null | null | null |
cs.AI cs.CL cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In the field of representation learning on knowledge graphs (KGs), a
hyper-relational fact consists of a main triple and several auxiliary
attribute-value descriptions, which is considered more comprehensive and
specific than a triple-based fact. However, currently available
hyper-relational KG embedding methods in a single view are limited in
application because they weaken the hierarchical structure that represents the
affiliation between entities. To overcome this limitation, we propose a
dual-view hyper-relational KG structure (DH-KG) that contains a
hyper-relational instance view for entities and a hyper-relational ontology
view for concepts that are abstracted hierarchically from the entities. This
paper defines link prediction and entity typing tasks on DH-KG for the first
time and constructs two DH-KG datasets, JW44K-6K, extracted from Wikidata, and
HTDM based on medical data. Furthermore, we propose DHGE, a DH-KG embedding
model based on GRAN encoders, HGNNs, and joint learning. DHGE outperforms
baseline models on DH-KG, according to experimental results. Finally, we
provide an example of how this technology can be used to treat hypertension.
Our model and new datasets are publicly available.
|
[
{
"version": "v1",
"created": "Mon, 18 Jul 2022 12:44:59 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Nov 2022 08:24:38 GMT"
},
{
"version": "v3",
"created": "Fri, 24 Feb 2023 15:57:49 GMT"
},
{
"version": "v4",
"created": "Fri, 31 Mar 2023 21:56:20 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Luo",
"Haoran",
""
],
[
"E",
"Haihong",
""
],
[
"Tan",
"Ling",
""
],
[
"Zhou",
"Gengxian",
""
],
[
"Yao",
"Tianyu",
""
],
[
"Wan",
"Kaiyang",
""
]
] |
new_dataset
| 0.952082 |
2210.03117
|
Hanoona Bangalath Rasheed Ms
|
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan,
Fahad Shahbaz Khan
|
MaPLe: Multi-modal Prompt Learning
|
Accepted at CVPR2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Pre-trained vision-language (V-L) models such as CLIP have shown excellent
generalization ability to downstream tasks. However, they are sensitive to the
choice of input text prompts and require careful selection of prompt templates
to perform well. Inspired by the Natural Language Processing (NLP) literature,
recent CLIP adaptation approaches learn prompts as the textual inputs to
fine-tune CLIP for downstream tasks. We note that using prompting to adapt
representations in a single branch of CLIP (language or vision) is sub-optimal
since it does not allow the flexibility to dynamically adjust both
representation spaces on a downstream task. In this work, we propose
Multi-modal Prompt Learning (MaPLe) for both vision and language branches to
improve alignment between the vision and language representations. Our design
promotes strong coupling between the vision-language prompts to ensure mutual
synergy and discourages learning independent uni-modal solutions. Further, we
learn separate prompts across different early stages to progressively model the
stage-wise feature relationships to allow rich context learning. We evaluate
the effectiveness of our approach on three representative tasks of
generalization to novel classes, new target datasets and unseen domain shifts.
Compared with the state-of-the-art method Co-CoOp, MaPLe exhibits favorable
performance and achieves an absolute gain of 3.45% on novel classes and 2.72%
on overall harmonic-mean, averaged over 11 diverse image recognition datasets.
Our code and pre-trained models are available at
https://github.com/muzairkhattak/multimodal-prompt-learning.
|
[
{
"version": "v1",
"created": "Thu, 6 Oct 2022 17:59:56 GMT"
},
{
"version": "v2",
"created": "Sat, 25 Mar 2023 22:10:13 GMT"
},
{
"version": "v3",
"created": "Sat, 1 Apr 2023 06:47:44 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Khattak",
"Muhammad Uzair",
""
],
[
"Rasheed",
"Hanoona",
""
],
[
"Maaz",
"Muhammad",
""
],
[
"Khan",
"Salman",
""
],
[
"Khan",
"Fahad Shahbaz",
""
]
] |
new_dataset
| 0.998073 |
2210.16579
|
Aditya Agarwal
|
Bipasha Sen, Aditya Agarwal, Vinay P Namboodiri, C. V. Jawahar
|
INR-V: A Continuous Representation Space for Video-based Generative
Tasks
|
Published in Transactions on Machine Learning Research (10/2022);
https://openreview.net/forum?id=aIoEkwc2oB
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Generating videos is a complex task that is accomplished by generating a set
of temporally coherent images frame-by-frame. This limits the expressivity of
videos to only image-based operations on the individual video frames needing
network designs to obtain temporally coherent trajectories in the underlying
image space. We propose INR-V, a video representation network that learns a
continuous space for video-based generative tasks. INR-V parameterizes videos
using implicit neural representations (INRs), a multi-layered perceptron that
predicts an RGB value for each input pixel location of the video. The INR is
predicted using a meta-network which is a hypernetwork trained on neural
representations of multiple video instances. Later, the meta-network can be
sampled to generate diverse novel videos enabling many downstream video-based
generative tasks. Interestingly, we find that conditional regularization and
progressive weight initialization play a crucial role in obtaining INR-V. The
representation space learned by INR-V is more expressive than an image space
showcasing many interesting properties not possible with the existing works.
For instance, INR-V can smoothly interpolate intermediate videos between known
video instances (such as intermediate identities, expressions, and poses in
face videos). It can also in-paint missing portions in videos to recover
temporally coherent full videos. In this work, we evaluate the space learned by
INR-V on diverse generative tasks such as video interpolation, novel video
generation, video inversion, and video inpainting against the existing
baselines. INR-V significantly outperforms the baselines on several of these
demonstrated tasks, clearly showcasing the potential of the proposed
representation space.
|
[
{
"version": "v1",
"created": "Sat, 29 Oct 2022 11:54:58 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 02:58:58 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Sen",
"Bipasha",
""
],
[
"Agarwal",
"Aditya",
""
],
[
"Namboodiri",
"Vinay P",
""
],
[
"Jawahar",
"C. V.",
""
]
] |
new_dataset
| 0.974979 |
2211.00895
|
Jongho Choi
|
Jongho Choi, Kyogu Lee
|
Pop2Piano : Pop Audio-based Piano Cover Generation
| null | null | null | null |
cs.SD cs.LG eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Piano covers of pop music are enjoyed by many people. However, the task of
automatically generating piano covers of pop music is still understudied. This
is partly due to the lack of synchronized {Pop, Piano Cover} data pairs, which
made it challenging to apply the latest data-intensive deep learning-based
methods. To leverage the power of the data-driven approach, we make a large
amount of paired and synchronized {Pop, Piano Cover} data using an automated
pipeline. In this paper, we present Pop2Piano, a Transformer network that
generates piano covers given waveforms of pop music. To the best of our
knowledge, this is the first model to generate a piano cover directly from pop
audio without using melody and chord extraction modules. We show that
Pop2Piano, trained with our dataset, is capable of producing plausible piano
covers.
|
[
{
"version": "v1",
"created": "Wed, 2 Nov 2022 05:42:22 GMT"
},
{
"version": "v2",
"created": "Sat, 1 Apr 2023 06:02:16 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Choi",
"Jongho",
""
],
[
"Lee",
"Kyogu",
""
]
] |
new_dataset
| 0.998866 |
2211.05776
|
Lu Qi
|
Lu Qi, Jason Kuen, Weidong Guo, Tiancheng Shen, Jiuxiang Gu, Jiaya
Jia, Zhe Lin, Ming-Hsuan Yang
|
High-Quality Entity Segmentation
|
The project webiste: http://luqi.info/entityv2.github.io/
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Dense image segmentation tasks e.g., semantic, panoptic) are useful for image
editing, but existing methods can hardly generalize well in an in-the-wild
setting where there are unrestricted image domains, classes, and image
resolution and quality variations. Motivated by these observations, we
construct a new entity segmentation dataset, with a strong focus on
high-quality dense segmentation in the wild. The dataset contains images
spanning diverse image domains and entities, along with plentiful
high-resolution images and high-quality mask annotations for training and
testing. Given the high-quality and -resolution nature of the dataset, we
propose CropFormer which is designed to tackle the intractability of
instance-level segmentation on high-resolution images. It improves mask
prediction by fusing high-res image crops that provide more fine-grained image
details and the full image. CropFormer is the first query-based Transformer
architecture that can effectively fuse mask predictions from multiple image
views, by learning queries that effectively associate the same entities across
the full image and its crop. With CropFormer, we achieve a significant AP gain
of $1.9$ on the challenging entity segmentation task. Furthermore, CropFormer
consistently improves the accuracy of traditional segmentation tasks and
datasets. The dataset and code will be released at
http://luqi.info/entityv2.github.io/.
|
[
{
"version": "v1",
"created": "Thu, 10 Nov 2022 18:58:22 GMT"
},
{
"version": "v2",
"created": "Sat, 12 Nov 2022 04:10:32 GMT"
},
{
"version": "v3",
"created": "Sun, 2 Apr 2023 22:01:17 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Qi",
"Lu",
""
],
[
"Kuen",
"Jason",
""
],
[
"Guo",
"Weidong",
""
],
[
"Shen",
"Tiancheng",
""
],
[
"Gu",
"Jiuxiang",
""
],
[
"Jia",
"Jiaya",
""
],
[
"Lin",
"Zhe",
""
],
[
"Yang",
"Ming-Hsuan",
""
]
] |
new_dataset
| 0.966317 |
2211.10624
|
Jiaxin Deng
|
Jiaxin Deng, Dong Shen, Haojie Pan, Xiangyu Wu, Ximan Liu, Gaofeng
Meng, Fan Yang, Size Li, Ruiji Fu, Zhongyuan Wang
|
A Unified Model for Video Understanding and Knowledge Embedding with
Heterogeneous Knowledge Graph Dataset
|
Accepted by ICMR 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Video understanding is an important task in short video business platforms
and it has a wide application in video recommendation and classification. Most
of the existing video understanding works only focus on the information that
appeared within the video content, including the video frames, audio and text.
However, introducing common sense knowledge from the external Knowledge Graph
(KG) dataset is essential for video understanding when referring to the content
which is less relevant to the video. Owing to the lack of video knowledge graph
dataset, the work which integrates video understanding and KG is rare. In this
paper, we propose a heterogeneous dataset that contains the multi-modal video
entity and fruitful common sense relations. This dataset also provides multiple
novel video inference tasks like the Video-Relation-Tag (VRT) and
Video-Relation-Video (VRV) tasks. Furthermore, based on this dataset, we
propose an end-to-end model that jointly optimizes the video understanding
objective with knowledge graph embedding, which can not only better inject
factual knowledge into video understanding but also generate effective
multi-modal entity embedding for KG. Comprehensive experiments indicate that
combining video understanding embedding with factual knowledge benefits the
content-based video retrieval performance. Moreover, it also helps the model
generate better knowledge graph embedding which outperforms traditional
KGE-based methods on VRT and VRV tasks with at least 42.36% and 17.73%
improvement in HITS@10.
|
[
{
"version": "v1",
"created": "Sat, 19 Nov 2022 09:00:45 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Apr 2023 03:10:21 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Deng",
"Jiaxin",
""
],
[
"Shen",
"Dong",
""
],
[
"Pan",
"Haojie",
""
],
[
"Wu",
"Xiangyu",
""
],
[
"Liu",
"Ximan",
""
],
[
"Meng",
"Gaofeng",
""
],
[
"Yang",
"Fan",
""
],
[
"Li",
"Size",
""
],
[
"Fu",
"Ruiji",
""
],
[
"Wang",
"Zhongyuan",
""
]
] |
new_dataset
| 0.995514 |
2211.12886
|
Haim Sawdayee
|
Haim Sawdayee, Amir Vaxman, Amit H. Bermano
|
OReX: Object Reconstruction from Planar Cross-sections Using Neural
Fields
|
CVPR 2023
| null | null | null |
cs.CV cs.GR cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Reconstructing 3D shapes from planar cross-sections is a challenge inspired
by downstream applications like medical imaging and geographic informatics. The
input is an in/out indicator function fully defined on a sparse collection of
planes in space, and the output is an interpolation of the indicator function
to the entire volume. Previous works addressing this sparse and ill-posed
problem either produce low quality results, or rely on additional priors such
as target topology, appearance information, or input normal directions. In this
paper, we present OReX, a method for 3D shape reconstruction from slices alone,
featuring a Neural Field as the interpolation prior. A modest neural network is
trained on the input planes to return an inside/outside estimate for a given 3D
coordinate, yielding a powerful prior that induces smoothness and
self-similarities. The main challenge for this approach is high-frequency
details, as the neural prior is overly smoothing. To alleviate this, we offer
an iterative estimation architecture and a hierarchical input sampling scheme
that encourage coarse-to-fine training, allowing the training process to focus
on high frequencies at later stages. In addition, we identify and analyze a
ripple-like effect stemming from the mesh extraction step. We mitigate it by
regularizing the spatial gradients of the indicator function around input
in/out boundaries during network training, tackling the problem at the root.
Through extensive qualitative and quantitative experimentation, we demonstrate
our method is robust, accurate, and scales well with the size of the input. We
report state-of-the-art results compared to previous approaches and recent
potential solutions, and demonstrate the benefit of our individual
contributions through analysis and ablation studies.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 11:44:35 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Mar 2023 08:18:42 GMT"
},
{
"version": "v3",
"created": "Sun, 2 Apr 2023 09:31:02 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Sawdayee",
"Haim",
""
],
[
"Vaxman",
"Amir",
""
],
[
"Bermano",
"Amit H.",
""
]
] |
new_dataset
| 0.998791 |
2211.17260
|
Minjung Son
|
Minjung Son, Jeong Joon Park, Leonidas Guibas, Gordon Wetzstein
|
SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene
|
CVPR 2023. Project page:
https://www.computationalimaging.org/publications/singraf/
| null | null | null |
cs.CV cs.AI cs.GR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Generative models have shown great promise in synthesizing photorealistic 3D
objects, but they require large amounts of training data. We introduce SinGRAF,
a 3D-aware generative model that is trained with a few input images of a single
scene. Once trained, SinGRAF generates different realizations of this 3D scene
that preserve the appearance of the input while varying scene layout. For this
purpose, we build on recent progress in 3D GAN architectures and introduce a
novel progressive-scale patch discrimination approach during training. With
several experiments, we demonstrate that the results produced by SinGRAF
outperform the closest related works in both quality and diversity by a large
margin.
|
[
{
"version": "v1",
"created": "Wed, 30 Nov 2022 18:55:27 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Apr 2023 14:26:57 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Son",
"Minjung",
""
],
[
"Park",
"Jeong Joon",
""
],
[
"Guibas",
"Leonidas",
""
],
[
"Wetzstein",
"Gordon",
""
]
] |
new_dataset
| 0.988767 |
2212.00776
|
Rui Tian
|
Rui Tian, Zuxuan Wu, Qi Dai, Han Hu, Yu Qiao, Yu-Gang Jiang
|
ResFormer: Scaling ViTs with Multi-Resolution Training
|
CVPR 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Vision Transformers (ViTs) have achieved overwhelming success, yet they
suffer from vulnerable resolution scalability, i.e., the performance drops
drastically when presented with input resolutions that are unseen during
training. We introduce, ResFormer, a framework that is built upon the seminal
idea of multi-resolution training for improved performance on a wide spectrum
of, mostly unseen, testing resolutions. In particular, ResFormer operates on
replicated images of different resolutions and enforces a scale consistency
loss to engage interactive information across different scales. More
importantly, to alternate among varying resolutions effectively, especially
novel ones in testing, we propose a global-local positional embedding strategy
that changes smoothly conditioned on input sizes. We conduct extensive
experiments for image classification on ImageNet. The results provide strong
quantitative evidence that ResFormer has promising scaling abilities towards a
wide range of resolutions. For instance, ResFormer-B-MR achieves a Top-1
accuracy of 75.86% and 81.72% when evaluated on relatively low and high
resolutions respectively (i.e., 96 and 640), which are 48% and 7.49% better
than DeiT-B. We also demonstrate, moreover, ResFormer is flexible and can be
easily extended to semantic segmentation, object detection and video action
recognition. Code is available at https://github.com/ruitian12/resformer.
|
[
{
"version": "v1",
"created": "Thu, 1 Dec 2022 18:57:20 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 06:55:09 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Tian",
"Rui",
""
],
[
"Wu",
"Zuxuan",
""
],
[
"Dai",
"Qi",
""
],
[
"Hu",
"Han",
""
],
[
"Qiao",
"Yu",
""
],
[
"Jiang",
"Yu-Gang",
""
]
] |
new_dataset
| 0.986199 |
2212.04808
|
Muhammad Anwaar Khalid
|
Muhammad Anwaar Khalid, Kanwal Zulfiqar, Ulfat Bashir, Areeba Shaheen,
Rida Iqbal, Zarnab Rizwan, Ghina Rizwan, Muhammad Moazam Fraz
|
CEPHA29: Automatic Cephalometric Landmark Detection Challenge 2023
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Quantitative cephalometric analysis is the most widely used clinical and
research tool in modern orthodontics. Accurate localization of cephalometric
landmarks enables the quantification and classification of anatomical
abnormalities, however, the traditional manual way of marking these landmarks
is a very tedious job. Endeavours have constantly been made to develop
automated cephalometric landmark detection systems but they are inadequate for
orthodontic applications. The fundamental reason for this is that the amount of
publicly available datasets as well as the images provided for training in
these datasets are insufficient for an AI model to perform well. To facilitate
the development of robust AI solutions for morphometric analysis, we organise
the CEPHA29 Automatic Cephalometric Landmark Detection Challenge in conjunction
with IEEE International Symposium on Biomedical Imaging (ISBI 2023). In this
context, we provide the largest known publicly available dataset, consisting of
1000 cephalometric X-ray images. We hope that our challenge will not only
derive forward research and innovation in automatic cephalometric landmark
identification but will also signal the beginning of a new era in the
discipline.
|
[
{
"version": "v1",
"created": "Fri, 9 Dec 2022 12:25:58 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 10:27:21 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Khalid",
"Muhammad Anwaar",
""
],
[
"Zulfiqar",
"Kanwal",
""
],
[
"Bashir",
"Ulfat",
""
],
[
"Shaheen",
"Areeba",
""
],
[
"Iqbal",
"Rida",
""
],
[
"Rizwan",
"Zarnab",
""
],
[
"Rizwan",
"Ghina",
""
],
[
"Fraz",
"Muhammad Moazam",
""
]
] |
new_dataset
| 0.990823 |
2212.04843
|
Bruno Rossi
|
Martin Macak, Matus Stovcik, Tomas Rebok, Mouzhi Ge, Bruno Rossi,
Barbora Buhnova
|
CopAS: A Big Data Forensic Analytics System
| null | null | null | null |
cs.CR cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the advancing digitization of our society, network security has become
one of the critical concerns for most organizations. In this paper, we present
CopAS, a system targeted at Big Data forensics analysis, allowing network
operators to comfortably analyze and correlate large amounts of network data to
get insights about potentially malicious and suspicious events. We demonstrate
the practical usage of CopAS for insider attack detection on a publicly
available PCAP dataset and show how the system can be used to detect insiders
hiding their malicious activity in the large amounts of data streams generated
during the operations of an organization within the network.
|
[
{
"version": "v1",
"created": "Fri, 9 Dec 2022 13:22:41 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 08:51:18 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Macak",
"Martin",
""
],
[
"Stovcik",
"Matus",
""
],
[
"Rebok",
"Tomas",
""
],
[
"Ge",
"Mouzhi",
""
],
[
"Rossi",
"Bruno",
""
],
[
"Buhnova",
"Barbora",
""
]
] |
new_dataset
| 0.997923 |
2212.05923
|
So Yeon Min
|
So Yeon Min, Yao-Hung Hubert Tsai, Wei Ding, Ali Farhadi, Ruslan
Salakhutdinov, Yonatan Bisk, Jian Zhang
|
Self-Supervised Object Goal Navigation with In-Situ Finetuning
| null | null | null | null |
cs.RO cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
A household robot should be able to navigate to target objects without
requiring users to first annotate everything in their home. Most current
approaches to object navigation do not test on real robots and rely solely on
reconstructed scans of houses and their expensively labeled semantic 3D meshes.
In this work, our goal is to build an agent that builds self-supervised models
of the world via exploration, the same as a child might - thus we (1) eschew
the expense of labeled 3D mesh and (2) enable self-supervised in-situ
finetuning in the real world. We identify a strong source of self-supervision
(Location Consistency - LocCon) that can train all components of an ObjectNav
agent, using unannotated simulated houses. Our key insight is that embodied
agents can leverage location consistency as a self-supervision signal -
collecting images from different views/angles and applying contrastive
learning. We show that our agent can perform competitively in the real world
and simulation. Our results also indicate that supervised training with 3D mesh
annotations causes models to learn simulation artifacts, which are not
transferrable to the real world. In contrast, our LocCon shows the most robust
transfer in the real world among the set of models we compare to, and that the
real-world performance of all models can be further improved with
self-supervised LocCon in-situ training.
|
[
{
"version": "v1",
"created": "Fri, 9 Dec 2022 03:41:40 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Apr 2023 01:39:47 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Min",
"So Yeon",
""
],
[
"Tsai",
"Yao-Hung Hubert",
""
],
[
"Ding",
"Wei",
""
],
[
"Farhadi",
"Ali",
""
],
[
"Salakhutdinov",
"Ruslan",
""
],
[
"Bisk",
"Yonatan",
""
],
[
"Zhang",
"Jian",
""
]
] |
new_dataset
| 0.977203 |
2212.06250
|
Ahmed Abdelreheem Mr.
|
Ahmed Abdelreheem, Kyle Olszewski, Hsin-Ying Lee, Peter Wonka, Panos
Achlioptas
|
ScanEnts3D: Exploiting Phrase-to-3D-Object Correspondences for Improved
Visio-Linguistic Models in 3D Scenes
|
The project's webpage is https://scanents3d.github.io/
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The two popular datasets ScanRefer [16] and ReferIt3D [3] connect natural
language to real-world 3D data. In this paper, we curate a large-scale and
complementary dataset extending both the aforementioned ones by associating all
objects mentioned in a referential sentence to their underlying instances
inside a 3D scene. Specifically, our Scan Entities in 3D (ScanEnts3D) dataset
provides explicit correspondences between 369k objects across 84k natural
referential sentences, covering 705 real-world scenes. Crucially, we show that
by incorporating intuitive losses that enable learning from this novel dataset,
we can significantly improve the performance of several recently introduced
neural listening architectures, including improving the SoTA in both the Nr3D
and ScanRefer benchmarks by 4.3% and 5.0%, respectively. Moreover, we
experiment with competitive baselines and recent methods for the task of
language generation and show that, as with neural listeners, 3D neural speakers
can also noticeably benefit by training with ScanEnts3D, including improving
the SoTA by 13.2 CIDEr points on the Nr3D benchmark. Overall, our carefully
conducted experimental studies strongly support the conclusion that, by
learning on ScanEnts3D, commonly used visio-linguistic 3D architectures can
become more efficient and interpretable in their generalization without needing
to provide these newly collected annotations at test time. The project's
webpage is https://scanents3d.github.io/ .
|
[
{
"version": "v1",
"created": "Mon, 12 Dec 2022 21:25:58 GMT"
},
{
"version": "v2",
"created": "Sat, 1 Apr 2023 12:13:27 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Abdelreheem",
"Ahmed",
""
],
[
"Olszewski",
"Kyle",
""
],
[
"Lee",
"Hsin-Ying",
""
],
[
"Wonka",
"Peter",
""
],
[
"Achlioptas",
"Panos",
""
]
] |
new_dataset
| 0.997187 |
2212.08045
|
Michael Tschannen
|
Michael Tschannen, Basil Mustafa, Neil Houlsby
|
CLIPPO: Image-and-Language Understanding from Pixels Only
|
CVPR 2023. Code and pretrained models are available at
https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/clippo/README.md
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multimodal models are becoming increasingly effective, in part due to unified
components, such as the Transformer architecture. However, multimodal models
still often consist of many task- and modality-specific pieces and training
procedures. For example, CLIP (Radford et al., 2021) trains independent text
and image towers via a contrastive loss. We explore an additional unification:
the use of a pure pixel-based model to perform image, text, and multimodal
tasks. Our model is trained with contrastive loss alone, so we call it
CLIP-Pixels Only (CLIPPO). CLIPPO uses a single encoder that processes both
regular images and text rendered as images. CLIPPO performs image-based tasks
such as retrieval and zero-shot image classification almost as well as
CLIP-style models, with half the number of parameters and no text-specific
tower or embedding. When trained jointly via image-text contrastive learning
and next-sentence contrastive learning, CLIPPO can perform well on natural
language understanding tasks, without any word-level loss (language modelling
or masked language modelling), outperforming pixel-based prior work.
Surprisingly, CLIPPO can obtain good accuracy in visual question answering,
simply by rendering the question and image together. Finally, we exploit the
fact that CLIPPO does not require a tokenizer to show that it can achieve
strong performance on multilingual multimodal retrieval without modifications.
|
[
{
"version": "v1",
"created": "Thu, 15 Dec 2022 18:52:08 GMT"
},
{
"version": "v2",
"created": "Sat, 1 Apr 2023 21:01:36 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Tschannen",
"Michael",
""
],
[
"Mustafa",
"Basil",
""
],
[
"Houlsby",
"Neil",
""
]
] |
new_dataset
| 0.999113 |
2212.08067
|
Yufan Ren
|
Yufan Ren, Fangjinhua Wang, Tong Zhang, Marc Pollefeys and Sabine
S\"usstrunk
|
VolRecon: Volume Rendering of Signed Ray Distance Functions for
Generalizable Multi-View Reconstruction
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The success of the Neural Radiance Fields (NeRF) in novel view synthesis has
inspired researchers to propose neural implicit scene reconstruction. However,
most existing neural implicit reconstruction methods optimize per-scene
parameters and therefore lack generalizability to new scenes. We introduce
VolRecon, a novel generalizable implicit reconstruction method with Signed Ray
Distance Function (SRDF). To reconstruct the scene with fine details and little
noise, VolRecon combines projection features aggregated from multi-view
features, and volume features interpolated from a coarse global feature volume.
Using a ray transformer, we compute SRDF values of sampled points on a ray and
then render color and depth. On DTU dataset, VolRecon outperforms SparseNeuS by
about 30% in sparse view reconstruction and achieves comparable accuracy as
MVSNet in full view reconstruction. Furthermore, our approach exhibits good
generalization performance on the large-scale ETH3D benchmark.
|
[
{
"version": "v1",
"created": "Thu, 15 Dec 2022 18:59:54 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 06:54:50 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Ren",
"Yufan",
""
],
[
"Wang",
"Fangjinhua",
""
],
[
"Zhang",
"Tong",
""
],
[
"Pollefeys",
"Marc",
""
],
[
"Süsstrunk",
"Sabine",
""
]
] |
new_dataset
| 0.97656 |
2212.14704
|
Jiale Xu
|
Jiale Xu, Xintao Wang, Weihao Cheng, Yan-Pei Cao, Ying Shan, Xiaohu
Qie, Shenghua Gao
|
Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and
Text-to-Image Diffusion Models
|
Accepted by CVPR 2023. Project page:
https://bluestyle97.github.io/dream3d/
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Recent CLIP-guided 3D optimization methods, such as DreamFields and
PureCLIPNeRF, have achieved impressive results in zero-shot text-to-3D
synthesis. However, due to scratch training and random initialization without
prior knowledge, these methods often fail to generate accurate and faithful 3D
structures that conform to the input text. In this paper, we make the first
attempt to introduce explicit 3D shape priors into the CLIP-guided 3D
optimization process. Specifically, we first generate a high-quality 3D shape
from the input text in the text-to-shape stage as a 3D shape prior. We then use
it as the initialization of a neural radiance field and optimize it with the
full prompt. To address the challenging text-to-shape generation task, we
present a simple yet effective approach that directly bridges the text and
image modalities with a powerful text-to-image diffusion model. To narrow the
style domain gap between the images synthesized by the text-to-image diffusion
model and shape renderings used to train the image-to-shape generator, we
further propose to jointly optimize a learnable text prompt and fine-tune the
text-to-image diffusion model for rendering-style image generation. Our method,
Dream3D, is capable of generating imaginative 3D content with superior visual
quality and shape accuracy compared to state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Wed, 28 Dec 2022 18:23:47 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 15:55:40 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Xu",
"Jiale",
""
],
[
"Wang",
"Xintao",
""
],
[
"Cheng",
"Weihao",
""
],
[
"Cao",
"Yan-Pei",
""
],
[
"Shan",
"Ying",
""
],
[
"Qie",
"Xiaohu",
""
],
[
"Gao",
"Shenghua",
""
]
] |
new_dataset
| 0.998853 |
2301.02379
|
Jinbo Xing
|
Jinbo Xing, Menghan Xia, Yuechen Zhang, Xiaodong Cun, Jue Wang,
Tien-Tsin Wong
|
CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior
|
CVPR2023 Camera-Ready. Project Page:
https://doubiiu.github.io/projects/codetalker/, Code:
https://github.com/Doubiiu/CodeTalker
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Speech-driven 3D facial animation has been widely studied, yet there is still
a gap to achieving realism and vividness due to the highly ill-posed nature and
scarcity of audio-visual data. Existing works typically formulate the
cross-modal mapping into a regression task, which suffers from the
regression-to-mean problem leading to over-smoothed facial motions. In this
paper, we propose to cast speech-driven facial animation as a code query task
in a finite proxy space of the learned codebook, which effectively promotes the
vividness of the generated motions by reducing the cross-modal mapping
uncertainty. The codebook is learned by self-reconstruction over real facial
motions and thus embedded with realistic facial motion priors. Over the
discrete motion space, a temporal autoregressive model is employed to
sequentially synthesize facial motions from the input speech signal, which
guarantees lip-sync as well as plausible facial expressions. We demonstrate
that our approach outperforms current state-of-the-art methods both
qualitatively and quantitatively. Also, a user study further justifies our
superiority in perceptual quality.
|
[
{
"version": "v1",
"created": "Fri, 6 Jan 2023 05:04:32 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 15:58:43 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Xing",
"Jinbo",
""
],
[
"Xia",
"Menghan",
""
],
[
"Zhang",
"Yuechen",
""
],
[
"Cun",
"Xiaodong",
""
],
[
"Wang",
"Jue",
""
],
[
"Wong",
"Tien-Tsin",
""
]
] |
new_dataset
| 0.99868 |
2301.02778
|
Gongyang Li
|
Gongyang Li, Zhi Liu, Xinpeng Zhang, Weisi Lin
|
Lightweight Salient Object Detection in Optical Remote-Sensing Images
via Semantic Matching and Edge Alignment
|
11 pages, 4 figures, Accepted by IEEE Transactions on Geoscience and
Remote Sensing 2023
| null |
10.1109/TGRS.2023.3235717
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Recently, relying on convolutional neural networks (CNNs), many methods for
salient object detection in optical remote sensing images (ORSI-SOD) are
proposed. However, most methods ignore the huge parameters and computational
cost brought by CNNs, and only a few pay attention to the portability and
mobility. To facilitate practical applications, in this paper, we propose a
novel lightweight network for ORSI-SOD based on semantic matching and edge
alignment, termed SeaNet. Specifically, SeaNet includes a lightweight
MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM)
for high-level features, an edge self-alignment module (ESAM) for low-level
features, and a portable decoder for inference. First, the high-level features
are compressed into semantic kernels. Then, semantic kernels are used to
activate salient object locations in two groups of high-level features through
dynamic convolution operations in DSMM. Meanwhile, in ESAM, cross-scale edge
information extracted from two groups of low-level features is self-aligned
through L2 loss and used for detail enhancement. Finally, starting from the
highest-level features, the decoder infers salient objects based on the
accurate locations and fine details contained in the outputs of the two
modules. Extensive experiments on two public datasets demonstrate that our
lightweight SeaNet not only outperforms most state-of-the-art lightweight
methods but also yields comparable accuracy with state-of-the-art conventional
methods, while having only 2.76M parameters and running with 1.7G FLOPs for
288x288 inputs. Our code and results are available at
https://github.com/MathLee/SeaNet.
|
[
{
"version": "v1",
"created": "Sat, 7 Jan 2023 04:33:51 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 05:02:47 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Li",
"Gongyang",
""
],
[
"Liu",
"Zhi",
""
],
[
"Zhang",
"Xinpeng",
""
],
[
"Lin",
"Weisi",
""
]
] |
new_dataset
| 0.998414 |
2303.07945
|
Heeseung Kim
|
Chaehun Shin, Heeseung Kim, Che Hyun Lee, Sang-gil Lee, Sungroh Yoon
|
Edit-A-Video: Single Video Editing with Object-Aware Consistency
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Despite the fact that text-to-video (TTV) model has recently achieved
remarkable success, there have been few approaches on TTV for its extension to
video editing. Motivated by approaches on TTV models adapting from
diffusion-based text-to-image (TTI) models, we suggest the video editing
framework given only a pretrained TTI model and a single <text, video> pair,
which we term Edit-A-Video. The framework consists of two stages: (1) inflating
the 2D model into the 3D model by appending temporal modules and tuning on the
source video (2) inverting the source video into the noise and editing with
target text prompt and attention map injection. Each stage enables the temporal
modeling and preservation of semantic attributes of the source video. One of
the key challenges for video editing include a background inconsistency
problem, where the regions not included for the edit suffer from undesirable
and inconsistent temporal alterations. To mitigate this issue, we also
introduce a novel mask blending method, termed as sparse-causal blending (SC
Blending). We improve previous mask blending methods to reflect the temporal
consistency so that the area where the editing is applied exhibits smooth
transition while also achieving spatio-temporal consistency of the unedited
regions. We present extensive experimental results over various types of text
and videos, and demonstrate the superiority of the proposed method compared to
baselines in terms of background consistency, text alignment, and video editing
quality.
|
[
{
"version": "v1",
"created": "Tue, 14 Mar 2023 14:35:59 GMT"
},
{
"version": "v2",
"created": "Thu, 23 Mar 2023 03:04:45 GMT"
},
{
"version": "v3",
"created": "Sat, 1 Apr 2023 01:45:15 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Shin",
"Chaehun",
""
],
[
"Kim",
"Heeseung",
""
],
[
"Lee",
"Che Hyun",
""
],
[
"Lee",
"Sang-gil",
""
],
[
"Yoon",
"Sungroh",
""
]
] |
new_dataset
| 0.994755 |
2303.08594
|
Junjie He
|
Junjie He, Pengyu Li, Yifeng Geng, Xuansong Xie
|
FastInst: A Simple Query-Based Model for Real-Time Instance Segmentation
|
CVPR 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent attention in instance segmentation has focused on query-based models.
Despite being non-maximum suppression (NMS)-free and end-to-end, the
superiority of these models on high-accuracy real-time benchmarks has not been
well demonstrated. In this paper, we show the strong potential of query-based
models on efficient instance segmentation algorithm designs. We present
FastInst, a simple, effective query-based framework for real-time instance
segmentation. FastInst can execute at a real-time speed (i.e., 32.5 FPS) while
yielding an AP of more than 40 (i.e., 40.5 AP) on COCO test-dev without bells
and whistles. Specifically, FastInst follows the meta-architecture of recently
introduced Mask2Former. Its key designs include instance activation-guided
queries, dual-path update strategy, and ground truth mask-guided learning,
which enable us to use lighter pixel decoders, fewer Transformer decoder
layers, while achieving better performance. The experiments show that FastInst
outperforms most state-of-the-art real-time counterparts, including strong
fully convolutional baselines, in both speed and accuracy. Code can be found at
https://github.com/junjiehe96/FastInst .
|
[
{
"version": "v1",
"created": "Wed, 15 Mar 2023 13:06:30 GMT"
},
{
"version": "v2",
"created": "Sat, 1 Apr 2023 17:55:21 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"He",
"Junjie",
""
],
[
"Li",
"Pengyu",
""
],
[
"Geng",
"Yifeng",
""
],
[
"Xie",
"Xuansong",
""
]
] |
new_dataset
| 0.995837 |
2303.11240
|
Patrick Gerard
|
Patrick Gerard, Nicholas Botzer, Tim Weninger
|
Truth Social Dataset
|
7 pages, 5 figures, ICWSM 2023
| null | null | null |
cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Formally announced to the public following former President Donald Trump's
bans and suspensions from mainstream social networks in early 2022 after his
role in the January 6 Capitol Riots, Truth Social was launched as an
"alternative" social media platform that claims to be a refuge for free speech,
offering a platform for those disaffected by the content moderation policies of
the existing, mainstream social networks. The subsequent rise of Truth Social
has been driven largely by hard-line supporters of the former president as well
as those affected by the content moderation of other social networks. These
distinct qualities combined with its status as the main mouthpiece of the
former president positions Truth Social as a particularly influential social
media platform and give rise to several research questions. However, outside of
a handful of news reports, little is known about the new social media platform
partially due to a lack of well-curated data. In the current work, we describe
a dataset of over 823,000 posts to Truth Social and and social network with
over 454,000 distinct users. In addition to the dataset itself, we also present
some basic analysis of its content, certain temporal features, and its network.
|
[
{
"version": "v1",
"created": "Mon, 20 Mar 2023 16:26:24 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Gerard",
"Patrick",
""
],
[
"Botzer",
"Nicholas",
""
],
[
"Weninger",
"Tim",
""
]
] |
new_dataset
| 0.999891 |
2303.12570
|
Fengji Zhang
|
Fengji Zhang, Bei Chen, Yue Zhang, Jin Liu, Daoguang Zan, Yi Mao,
Jian-Guang Lou, Weizhu Chen
|
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval
and Generation
| null | null | null | null |
cs.CL cs.AI cs.PL cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The task of repository-level code completion is to continue writing the
unfinished code based on a broader context of the repository. While for
automated code completion tools, it is difficult to utilize the useful
information scattered in different files. We propose RepoCoder, a simple,
generic, and effective framework to address the challenge. It streamlines the
repository-level code completion process by incorporating a similarity-based
retriever and a pre-trained code language model, which allows for the effective
utilization of repository-level information for code completion and grants the
ability to generate code at various levels of granularity. Furthermore,
RepoCoder utilizes a novel iterative retrieval-generation paradigm that bridges
the gap between retrieval context and the intended completion target. We also
propose a new benchmark RepoEval, which consists of the latest and high-quality
real-world repositories covering line, API invocation, and function body
completion scenarios. We test the performance of RepoCoder by using various
combinations of code retrievers and generators. Experimental results indicate
that RepoCoder significantly improves the zero-shot code completion baseline by
over 10% in all settings and consistently outperforms the vanilla
retrieval-augmented code completion approach. Furthermore, we validate the
effectiveness of RepoCoder through comprehensive analysis, providing valuable
insights for future research.
|
[
{
"version": "v1",
"created": "Wed, 22 Mar 2023 13:54:46 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 08:07:16 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Zhang",
"Fengji",
""
],
[
"Chen",
"Bei",
""
],
[
"Zhang",
"Yue",
""
],
[
"Liu",
"Jin",
""
],
[
"Zan",
"Daoguang",
""
],
[
"Mao",
"Yi",
""
],
[
"Lou",
"Jian-Guang",
""
],
[
"Chen",
"Weizhu",
""
]
] |
new_dataset
| 0.985557 |
2303.13962
|
Jianzhu Huai
|
Yuan Zhuang, Binliang Wang, Jianzhu Huai, Miao Li
|
4D iRIOM: 4D Imaging Radar Inertial Odometry and Mapping
|
8 pages, 8 figures, 4 tables, the proofread version will appear on
RA-L soon
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Millimeter wave radar can measure distances, directions, and Doppler velocity
for objects in harsh conditions such as fog. The 4D imaging radar with both
vertical and horizontal data resembling an image can also measure objects'
height. Previous studies have used 3D radars for ego-motion estimation. But few
methods leveraged the rich data of imaging radars, and they usually omitted the
mapping aspect, thus leading to inferior odometry accuracy. This paper presents
a real-time imaging radar inertial odometry and mapping method, iRIOM, based on
the submap concept. To deal with moving objects and multipath reflections, we
use the graduated non-convexity method to robustly and efficiently estimate
ego-velocity from a single scan. To measure the agreement between sparse
non-repetitive radar scan points and submap points, the
distribution-to-multi-distribution distance for matches is adopted. The
ego-velocity, scan-to-submap matches are fused with the 6D inertial data by an
iterative extended Kalman filter to get the platform's 3D position and
orientation. A loop closure module is also developed to curb the odometry
module's drift. To our knowledge, iRIOM based on the two modules is the first
4D radar inertial SLAM system. On our and third-party data, we show iRIOM's
favorable odometry accuracy and mapping consistency against the FastLIO-SLAM
and the EKFRIO. Also, the ablation study reveal the benefit of inertial data
versus the constant velocity model, and scan-to-submap matching versus
scan-to-scan matching.
|
[
{
"version": "v1",
"created": "Fri, 24 Mar 2023 12:36:26 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 03:53:59 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Zhuang",
"Yuan",
""
],
[
"Wang",
"Binliang",
""
],
[
"Huai",
"Jianzhu",
""
],
[
"Li",
"Miao",
""
]
] |
new_dataset
| 0.997827 |
2303.17774
|
Gengxin Liu
|
Gengxin Liu, Qian Sun, Haibin Huang, Chongyang Ma, Yulan Guo, Li Yi,
Hui Huang, Ruizhen Hu
|
Semi-Weakly Supervised Object Kinematic Motion Prediction
|
CVPR 2023
| null | null | null |
cs.CV cs.AI cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Given a 3D object, kinematic motion prediction aims to identify the mobile
parts as well as the corresponding motion parameters. Due to the large
variations in both topological structure and geometric details of 3D objects,
this remains a challenging task and the lack of large scale labeled data also
constrain the performance of deep learning based approaches. In this paper, we
tackle the task of object kinematic motion prediction problem in a semi-weakly
supervised manner. Our key observations are two-fold. First, although 3D
dataset with fully annotated motion labels is limited, there are existing
datasets and methods for object part semantic segmentation at large scale.
Second, semantic part segmentation and mobile part segmentation is not always
consistent but it is possible to detect the mobile parts from the underlying 3D
structure. Towards this end, we propose a graph neural network to learn the map
between hierarchical part-level segmentation and mobile parts parameters, which
are further refined based on geometric alignment. This network can be first
trained on PartNet-Mobility dataset with fully labeled mobility information and
then applied on PartNet dataset with fine-grained and hierarchical part-level
segmentation. The network predictions yield a large scale of 3D objects with
pseudo labeled mobility information and can further be used for
weakly-supervised learning with pre-existing segmentation. Our experiments show
there are significant performance boosts with the augmented data for previous
method designed for kinematic motion prediction on 3D partial scans.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 02:37:36 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 02:36:17 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Liu",
"Gengxin",
""
],
[
"Sun",
"Qian",
""
],
[
"Huang",
"Haibin",
""
],
[
"Ma",
"Chongyang",
""
],
[
"Guo",
"Yulan",
""
],
[
"Yi",
"Li",
""
],
[
"Huang",
"Hui",
""
],
[
"Hu",
"Ruizhen",
""
]
] |
new_dataset
| 0.995991 |
2304.00111
|
Yonghui Wu
|
Aokun Chen, Daniel Paredes, Zehao Yu, Xiwei Lou, Roberta Brunson,
Jamie N. Thomas, Kimberly A. Martinez, Robert J. Lucero, Tanja Magoc,
Laurence M. Solberg, Urszula A. Snigurska, Sarah E. Ser, Mattia Prosperi,
Jiang Bian, Ragnhildur I. Bjarnadottir, Yonghui Wu
|
Identifying Symptoms of Delirium from Clinical Narratives Using Natural
Language Processing
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Delirium is an acute decline or fluctuation in attention, awareness, or other
cognitive function that can lead to serious adverse outcomes. Despite the
severe outcomes, delirium is frequently unrecognized and uncoded in patients'
electronic health records (EHRs) due to its transient and diverse nature.
Natural language processing (NLP), a key technology that extracts medical
concepts from clinical narratives, has shown great potential in studies of
delirium outcomes and symptoms. To assist in the diagnosis and phenotyping of
delirium, we formed an expert panel to categorize diverse delirium symptoms,
composed annotation guidelines, created a delirium corpus with diverse delirium
symptoms, and developed NLP methods to extract delirium symptoms from clinical
notes. We compared 5 state-of-the-art transformer models including 2 models
(BERT and RoBERTa) from the general domain and 3 models (BERT_MIMIC,
RoBERTa_MIMIC, and GatorTron) from the clinical domain. GatorTron achieved the
best strict and lenient F1 scores of 0.8055 and 0.8759, respectively. We
conducted an error analysis to identify challenges in annotating delirium
symptoms and developing NLP systems. To the best of our knowledge, this is the
first large language model-based delirium symptom extraction system. Our study
lays the foundation for the future development of computable phenotypes and
diagnosis methods for delirium.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 20:16:44 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Chen",
"Aokun",
""
],
[
"Paredes",
"Daniel",
""
],
[
"Yu",
"Zehao",
""
],
[
"Lou",
"Xiwei",
""
],
[
"Brunson",
"Roberta",
""
],
[
"Thomas",
"Jamie N.",
""
],
[
"Martinez",
"Kimberly A.",
""
],
[
"Lucero",
"Robert J.",
""
],
[
"Magoc",
"Tanja",
""
],
[
"Solberg",
"Laurence M.",
""
],
[
"Snigurska",
"Urszula A.",
""
],
[
"Ser",
"Sarah E.",
""
],
[
"Prosperi",
"Mattia",
""
],
[
"Bian",
"Jiang",
""
],
[
"Bjarnadottir",
"Ragnhildur I.",
""
],
[
"Wu",
"Yonghui",
""
]
] |
new_dataset
| 0.99305 |
2304.00122
|
Harish Karunakaran
|
Harish Karunakaran, Gopeshh Raaj Subbaraj
|
Trajectory Control for Differential Drive Mobile Manipulators
|
9 pages
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Mobile manipulator systems are comprised of a mobile platform with one or
more manipulators and are of great interest in a number of applications such as
indoor warehouses, mining, construction, forestry etc. We present an approach
for computing actuator commands for such systems so that they can follow
desired end-effector and platform trajectories without the violation of the
nonholonomic constraints of the system in an indoor warehouse environment. We
work with the Fetch robot which consists of a 7-DOF manipulator with a
differential drive mobile base to validate our method. The major contributions
of our project are, writing the dynamics of the system, Trajectory planning for
the manipulator and the mobile base, state machine for the pick and place task
and the inverse kinematics of the manipulator. Our results indicate that we are
able to successfully implement trajectory control on the mobile base and the
manipulator of the Fetch robot.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 20:47:32 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Karunakaran",
"Harish",
""
],
[
"Subbaraj",
"Gopeshh Raaj",
""
]
] |
new_dataset
| 0.996611 |
2304.00235
|
Suman Adhya
|
Suman Adhya, Debarshi Kumar Sanyal
|
What Does the Indian Parliament Discuss? An Exploratory Analysis of the
Question Hour in the Lok Sabha
|
Accepted at the workshop PoliticalNLP co-located with the conference
LREC 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The TCPD-IPD dataset is a collection of questions and answers discussed in
the Lower House of the Parliament of India during the Question Hour between
1999 and 2019. Although it is difficult to analyze such a huge collection
manually, modern text analysis tools can provide a powerful means to navigate
it. In this paper, we perform an exploratory analysis of the dataset. In
particular, we present insightful corpus-level statistics and a detailed
analysis of three subsets of the dataset. In the latter analysis, the focus is
on understanding the temporal evolution of topics using a dynamic topic model.
We observe that the parliamentary conversation indeed mirrors the political and
socio-economic tensions of each period.
|
[
{
"version": "v1",
"created": "Sat, 1 Apr 2023 05:43:22 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Adhya",
"Suman",
""
],
[
"Sanyal",
"Debarshi Kumar",
""
]
] |
new_dataset
| 0.998921 |
2304.00265
|
Masayuki Tezuka
|
Masayuki Tezuka, Keisuke Tanaka
|
Pointcheval-Sanders Signature-Based Synchronized Aggregate Signature
| null |
ICISC 2022
|
10.1007/978-3-031-29371-9_16
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Synchronized aggregate signature is a special type of signature that all
signers have a synchronized time period and allows aggregating signatures which
are generated in the same period. This signature has a wide range of
applications for systems that have a natural reporting period such as log and
sensor data, or blockchain protocol. In CT-RSA 2016, Pointcheval and Sanders
proposed the new randomizable signature scheme. Since this signature scheme is
based on type-3 pairing, this signature achieves a short signature size and
efficient signature verification. In this paper, we design the
Pointchcval-Sanders signature-based synchronized aggregate signature scheme and
prove its security under the generalized Pointcheval-Sanders assumption in the
random oracle model. Our scheme offers the most efficient aggregate signature
verification among synchronized aggregate signature schemes based on bilinear
groups.
|
[
{
"version": "v1",
"created": "Sat, 1 Apr 2023 09:12:41 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Tezuka",
"Masayuki",
""
],
[
"Tanaka",
"Keisuke",
""
]
] |
new_dataset
| 0.992955 |
2304.00350
|
Won Ik Cho
|
Won Ik Cho, Yoon Kyung Lee, Seoyeon Bae, Jihwan Kim, Sangah Park,
Moosung Kim, Sowon Hahn, Nam Soo Kim
|
When Crowd Meets Persona: Creating a Large-Scale Open-Domain Persona
Dialogue Corpus
|
Presented at HCOMP 2022 as Works-in-Progress
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Building a natural language dataset requires caution since word semantics is
vulnerable to subtle text change or the definition of the annotated concept.
Such a tendency can be seen in generative tasks like question-answering and
dialogue generation and also in tasks that create a categorization-based
corpus, like topic classification or sentiment analysis. Open-domain
conversations involve two or more crowdworkers freely conversing about any
topic, and collecting such data is particularly difficult for two reasons: 1)
the dataset should be ``crafted" rather than ``obtained" due to privacy
concerns, and 2) paid creation of such dialogues may differ from how
crowdworkers behave in real-world settings. In this study, we tackle these
issues when creating a large-scale open-domain persona dialogue corpus, where
persona implies that the conversation is performed by several actors with a
fixed persona and user-side workers from an unspecified crowd.
|
[
{
"version": "v1",
"created": "Sat, 1 Apr 2023 16:10:36 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Cho",
"Won Ik",
""
],
[
"Lee",
"Yoon Kyung",
""
],
[
"Bae",
"Seoyeon",
""
],
[
"Kim",
"Jihwan",
""
],
[
"Park",
"Sangah",
""
],
[
"Kim",
"Moosung",
""
],
[
"Hahn",
"Sowon",
""
],
[
"Kim",
"Nam Soo",
""
]
] |
new_dataset
| 0.99978 |
2304.00358
|
Steven Obua
|
Steven Obua
|
Logic is Algebra
| null | null | null | null |
cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Logic really is just algebra, given one uses the right kind of algebra, and
the right kind of logic. The right kind of algebra is abstraction algebra, and
the right kind of logic is abstraction logic.
|
[
{
"version": "v1",
"created": "Sat, 1 Apr 2023 16:51:57 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Obua",
"Steven",
""
]
] |
new_dataset
| 0.999882 |
2304.00359
|
Yukang Cao
|
Yukang Cao, Kai Han, Kwan-Yee K. Wong
|
SeSDF: Self-evolved Signed Distance Field for Implicit 3D Clothed Human
Reconstruction
|
25 pages, 21 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We address the problem of clothed human reconstruction from a single image or
uncalibrated multi-view images. Existing methods struggle with reconstructing
detailed geometry of a clothed human and often require a calibrated setting for
multi-view reconstruction. We propose a flexible framework which, by leveraging
the parametric SMPL-X model, can take an arbitrary number of input images to
reconstruct a clothed human model under an uncalibrated setting. At the core of
our framework is our novel self-evolved signed distance field (SeSDF) module
which allows the framework to learn to deform the signed distance field (SDF)
derived from the fitted SMPL-X model, such that detailed geometry reflecting
the actual clothed human can be encoded for better reconstruction. Besides, we
propose a simple method for self-calibration of multi-view images via the
fitted SMPL-X parameters. This lifts the requirement of tedious manual
calibration and largely increases the flexibility of our method. Further, we
introduce an effective occlusion-aware feature fusion strategy to account for
the most useful features to reconstruct the human model. We thoroughly evaluate
our framework on public benchmarks, demonstrating significant superiority over
the state-of-the-arts both qualitatively and quantitatively.
|
[
{
"version": "v1",
"created": "Sat, 1 Apr 2023 16:58:19 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Cao",
"Yukang",
""
],
[
"Han",
"Kai",
""
],
[
"Wong",
"Kwan-Yee K.",
""
]
] |
new_dataset
| 0.988144 |
2304.00378
|
Xiou Ge
|
Xiou Ge, Yun-Cheng Wang, Bin Wang, C.-C. Jay Kuo
|
Knowledge Graph Embedding with 3D Compound Geometric Transformations
| null | null | null | null |
cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The cascade of 2D geometric transformations were exploited to model relations
between entities in a knowledge graph (KG), leading to an effective KG
embedding (KGE) model, CompoundE. Furthermore, the rotation in the 3D space was
proposed as a new KGE model, Rotate3D, by leveraging its non-commutative
property. Inspired by CompoundE and Rotate3D, we leverage 3D compound geometric
transformations, including translation, rotation, scaling, reflection, and
shear and propose a family of KGE models, named CompoundE3D, in this work.
CompoundE3D allows multiple design variants to match rich underlying
characteristics of a KG. Since each variant has its own advantages on a subset
of relations, an ensemble of multiple variants can yield superior performance.
The effectiveness and flexibility of CompoundE3D are experimentally verified on
four popular link prediction datasets.
|
[
{
"version": "v1",
"created": "Sat, 1 Apr 2023 19:56:51 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Ge",
"Xiou",
""
],
[
"Wang",
"Yun-Cheng",
""
],
[
"Wang",
"Bin",
""
],
[
"Kuo",
"C. -C. Jay",
""
]
] |
new_dataset
| 0.973438 |
2304.00411
|
Tomoya Sasaki
|
Tomoya Sasaki, Narin Okazaki, Takatoshi Yoshida, Alfonso Balandra,
Zendai Kashino and Masahiko Inami
|
SolefulTap: Augmenting Tap Dancing Experience using a Floor-Type Impact
Display
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We propose SolefulTap for a novel tap dancing experience. It allows users to
feel as if they are tap dancing or appreciate a tap dancing performance using
the sensations of their own feet. SolefulTap uses a method called Step
Augmentation that provides audio-haptic feedback to users, generating impacts
in response to users' simple step motions. Our prototype uses a floor-type
impact display consisting of pressure sensors, which detect users' steps, and
solenoids, which generate feedback through impact. Through a preliminary user
study, we confirmed that the system can provide untrained users with the
experience of tap dancing. This study serves as a case study that provides
insight into how a reactive environment can affect the human capabilities of
physical expression and the sensation experienced.
|
[
{
"version": "v1",
"created": "Sat, 1 Apr 2023 23:53:42 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Sasaki",
"Tomoya",
""
],
[
"Okazaki",
"Narin",
""
],
[
"Yoshida",
"Takatoshi",
""
],
[
"Balandra",
"Alfonso",
""
],
[
"Kashino",
"Zendai",
""
],
[
"Inami",
"Masahiko",
""
]
] |
new_dataset
| 0.998316 |
2304.00460
|
Yibo Yan
|
Yibo Yan, Seth Frey, Amy Zhang, Vladimir Filkov, Likang Yin
|
GitHub OSS Governance File Dataset
|
5 pages, 1 figure, 1 table, to be published in MSR 2023 Data and Tool
Showcase Track
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Open-source Software (OSS) has become a valuable resource in both industry
and academia over the last few decades. Despite the innovative structures they
develop to support the projects, OSS projects and their communities have
complex needs and face risks such as getting abandoned. To manage the internal
social dynamics and community evolution, OSS developer communities have started
relying on written governance documents that assign roles and responsibilities
to different community actors. To facilitate the study of the impact and
effectiveness of formal governance documents on OSS projects and communities,
we present a longitudinal dataset of 710 GitHub-hosted OSS projects with
\path{GOVERNANCE.MD} governance files. This dataset includes all commits made
to the repository, all issues and comments created on GitHub, and all revisions
made to the governance file. We hope its availability will foster more research
interest in studying how OSS communities govern their projects and the impact
of governance files on communities.
|
[
{
"version": "v1",
"created": "Sun, 2 Apr 2023 06:07:00 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Yan",
"Yibo",
""
],
[
"Frey",
"Seth",
""
],
[
"Zhang",
"Amy",
""
],
[
"Filkov",
"Vladimir",
""
],
[
"Yin",
"Likang",
""
]
] |
new_dataset
| 0.999417 |
2304.00467
|
Haiping Wang
|
Haiping Wang, Yuan Liu, Zhen Dong, Yulan Guo, Yu-Shen Liu, Wenping
Wang, Bisheng Yang
|
Robust Multiview Point Cloud Registration with Reliable Pose Graph
Initialization and History Reweighting
|
Accepted by CVPR 2023; Code at https://github.com/WHU-USI3DV/SGHR
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we present a new method for the multiview registration of
point cloud. Previous multiview registration methods rely on exhaustive
pairwise registration to construct a densely-connected pose graph and apply
Iteratively Reweighted Least Square (IRLS) on the pose graph to compute the
scan poses. However, constructing a densely-connected graph is time-consuming
and contains lots of outlier edges, which makes the subsequent IRLS struggle to
find correct poses. To address the above problems, we first propose to use a
neural network to estimate the overlap between scan pairs, which enables us to
construct a sparse but reliable pose graph. Then, we design a novel history
reweighting function in the IRLS scheme, which has strong robustness to outlier
edges on the graph. In comparison with existing multiview registration methods,
our method achieves 11% higher registration recall on the 3DMatch dataset and
~13% lower registration errors on the ScanNet dataset while reducing ~70%
required pairwise registrations. Comprehensive ablation studies are conducted
to demonstrate the effectiveness of our designs.
|
[
{
"version": "v1",
"created": "Sun, 2 Apr 2023 06:43:40 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Wang",
"Haiping",
""
],
[
"Liu",
"Yuan",
""
],
[
"Dong",
"Zhen",
""
],
[
"Guo",
"Yulan",
""
],
[
"Liu",
"Yu-Shen",
""
],
[
"Wang",
"Wenping",
""
],
[
"Yang",
"Bisheng",
""
]
] |
new_dataset
| 0.995995 |
2304.00592
|
Cheng Deng
|
Cheng Deng, Bo Tong, Luoyi Fu, Jiaxin Ding, Dexing Cao, Xinbing Wang,
Chenghu Zhou
|
PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue
Model
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the research of end-to-end dialogue systems, using real-world knowledge to
generate natural, fluent, and human-like utterances with correct answers is
crucial. However, domain-specific conversational dialogue systems may be
incoherent and introduce erroneous external information to answer questions due
to the out-of-vocabulary issue or the wrong knowledge from the parameters of
the neural network. In this work, we propose PK-Chat, a Pointer network guided
Knowledge-driven generative dialogue model, incorporating a unified pretrained
language model and a pointer network over knowledge graphs. The words generated
by PK-Chat in the dialogue are derived from the prediction of word lists and
the direct prediction of the external knowledge graph knowledge. Moreover,
based on the PK-Chat, a dialogue system is built for academic scenarios in the
case of geosciences. Finally, an academic dialogue benchmark is constructed to
evaluate the quality of dialogue systems in academic scenarios and the source
code is available online.
|
[
{
"version": "v1",
"created": "Sun, 2 Apr 2023 18:23:13 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Deng",
"Cheng",
""
],
[
"Tong",
"Bo",
""
],
[
"Fu",
"Luoyi",
""
],
[
"Ding",
"Jiaxin",
""
],
[
"Cao",
"Dexing",
""
],
[
"Wang",
"Xinbing",
""
],
[
"Zhou",
"Chenghu",
""
]
] |
new_dataset
| 0.958816 |
2304.00634
|
Dwip Dalal
|
Dwip Dalal, Vivek Srivastava, Mayank Singh
|
MMT: A Multilingual and Multi-Topic Indian Social Media Dataset
| null |
EACL Workshop C3NLP 2023
| null | null |
cs.CL cs.LG cs.SI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Social media plays a significant role in cross-cultural communication. A vast
amount of this occurs in code-mixed and multilingual form, posing a significant
challenge to Natural Language Processing (NLP) tools for processing such
information, like language identification, topic modeling, and named-entity
recognition. To address this, we introduce a large-scale multilingual, and
multi-topic dataset (MMT) collected from Twitter (1.7 million Tweets),
encompassing 13 coarse-grained and 63 fine-grained topics in the Indian
context. We further annotate a subset of 5,346 tweets from the MMT dataset with
various Indian languages and their code-mixed counterparts. Also, we
demonstrate that the currently existing tools fail to capture the linguistic
diversity in MMT on two downstream tasks, i.e., topic modeling and language
identification. To facilitate future research, we will make the anonymized and
annotated dataset available in the public domain.
|
[
{
"version": "v1",
"created": "Sun, 2 Apr 2023 21:39:00 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Dalal",
"Dwip",
""
],
[
"Srivastava",
"Vivek",
""
],
[
"Singh",
"Mayank",
""
]
] |
new_dataset
| 0.999892 |
2304.00676
|
Zilin Huang
|
Zilin Huang, Sikai Chen, Yuzhuang Pian, Zihao Sheng, Soyoung Ahn, and
David A. Noyce
|
CV2X-LOCA: Roadside Unit-Enabled Cooperative Localization Framework for
Autonomous Vehicles
| null | null | null | null |
cs.RO cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An accurate and robust localization system is crucial for autonomous vehicles
(AVs) to enable safe driving in urban scenes. While existing global navigation
satellite system (GNSS)-based methods are effective at locating vehicles in
open-sky regions, achieving high-accuracy positioning in urban canyons such as
lower layers of multi-layer bridges, streets beside tall buildings, tunnels,
etc., remains a challenge. In this paper, we investigate the potential of
cellular-vehicle-to-everything (C-V2X) wireless communications in improving the
localization performance of AVs under GNSS-denied environments. Specifically,
we propose the first roadside unit (RSU)-enabled cooperative localization
framework, namely CV2X-LOCA, that only uses C-V2X channel state information to
achieve lane-level positioning accuracy. CV2X-LOCA consists of four key parts:
data processing module, coarse positioning module, environment parameter
correcting module, and vehicle trajectory filtering module. These modules
jointly handle challenges present in dynamic C-V2X networks. Extensive
simulation and field experiments show that CV2X-LOCA achieves state-of-the-art
performance for vehicle localization even under noisy conditions with
high-speed movement and sparse RSUs coverage environments. The study results
also provide insights into future investment decisions for transportation
agencies regarding deploying RSUs cost-effectively.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 01:35:54 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Huang",
"Zilin",
""
],
[
"Chen",
"Sikai",
""
],
[
"Pian",
"Yuzhuang",
""
],
[
"Sheng",
"Zihao",
""
],
[
"Ahn",
"Soyoung",
""
],
[
"Noyce",
"David A.",
""
]
] |
new_dataset
| 0.999678 |
2304.00717
|
Ziqing Yang
|
Xin Yao, Ziqing Yang, Yiming Cui, Shijin Wang
|
MiniRBT: A Two-stage Distilled Small Chinese Pre-trained Model
|
4 pages
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In natural language processing, pre-trained language models have become
essential infrastructures. However, these models often suffer from issues such
as large size, long inference time, and challenging deployment. Moreover, most
mainstream pre-trained models focus on English, and there are insufficient
studies on small Chinese pre-trained models. In this paper, we introduce
MiniRBT, a small Chinese pre-trained model that aims to advance research in
Chinese natural language processing. MiniRBT employs a narrow and deep student
model and incorporates whole word masking and two-stage distillation during
pre-training to make it well-suited for most downstream tasks. Our experiments
on machine reading comprehension and text classification tasks reveal that
MiniRBT achieves 94% performance relative to RoBERTa, while providing a 6.8x
speedup, demonstrating its effectiveness and efficiency.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 04:45:57 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Yao",
"Xin",
""
],
[
"Yang",
"Ziqing",
""
],
[
"Cui",
"Yiming",
""
],
[
"Wang",
"Shijin",
""
]
] |
new_dataset
| 0.998692 |
2304.00736
|
Linhan Yang
|
Linhan Yang, Bidan Huang, Qingbiao Li, Ya-Yen Tsai, Wang Wei Lee,
Chaoyang Song, Jia Pan
|
TacGNN:Learning Tactile-based In-hand Manipulation with a Blind Robot
|
8 pages, 4 figures, accepted by RAL
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a novel framework for tactile-based dexterous
manipulation learning with a blind anthropomorphic robotic hand, i.e. without
visual sensing. First, object-related states were extracted from the raw
tactile signals by a graph-based perception model - TacGNN. The resulting
tactile features were then utilized in the policy learning of an in-hand
manipulation task in the second stage. This method was examined by a Baoding
ball task - simultaneously manipulating two spheres around each other by 180
degrees in hand. We conducted experiments on object states prediction and
in-hand manipulation using a reinforcement learning algorithm (PPO). Results
show that TacGNN is effective in predicting object-related states during
manipulation by decreasing the RMSE of prediction to 0.096cm comparing to other
methods, such as MLP, CNN, and GCN. Finally, the robot hand could finish an
in-hand manipulation task solely relying on the robotic own perception -
tactile sensing and proprioception. In addition, our methods are tested on
three tasks with different difficulty levels and transferred to the real robot
without further training.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 06:15:46 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Yang",
"Linhan",
""
],
[
"Huang",
"Bidan",
""
],
[
"Li",
"Qingbiao",
""
],
[
"Tsai",
"Ya-Yen",
""
],
[
"Lee",
"Wang Wei",
""
],
[
"Song",
"Chaoyang",
""
],
[
"Pan",
"Jia",
""
]
] |
new_dataset
| 0.978344 |
2304.00757
|
Khalid Alnujaidi
|
Khalid Alnujaidi, Ghada Alhabib, Abdulaziz Alodhieb
|
Spot-the-Camel: Computer Vision for Safer Roads
|
arXiv admin note: text overlap with arXiv:2301.09339
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
As the population grows and more land is being used for urbanization,
ecosystems are disrupted by our roads and cars. This expansion of
infrastructure cuts through wildlife territories, leading to many instances of
Wildlife-Vehicle Collision (WVC). These instances of WVC are a global issue
that is having a global socio-economic impact, resulting in billions of dollars
in property damage and, at times, fatalities for vehicle occupants. In Saudi
Arabia, this issue is similar, with instances of Camel-Vehicle Collision (CVC)
being particularly deadly due to the large size of camels, which results in a
25% fatality rate [1]. The focus of this work is to test different object
detection models on the task of detecting camels on the road. The Deep Learning
(DL) object detection models used in the experiments are: Center Net, Efficient
Det, Faster R-CNN, SSD, and YOLOv8. Results of the experiments show that YOLOv8
performed the best in terms of accuracy and was the most efficient in training.
In the future, the plan is to expand on this work by developing a system to
make countryside roads safer.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 07:16:14 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Alnujaidi",
"Khalid",
""
],
[
"Alhabib",
"Ghada",
""
],
[
"Alodhieb",
"Abdulaziz",
""
]
] |
new_dataset
| 0.999609 |
2304.00763
|
Jerome White
|
Jerome White, Chandan Agrawal, Anmol Ojha, Apoorv Agnihotri, Makkunda
Sharma, Jigar Doshi
|
BOLLWM: A real-world dataset for bollworm pest monitoring from cotton
fields in India
| null |
ICLR 2023 workshop on Practical Machine Learning for Developing
Countries
| null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a dataset of agricultural pest images captured over five
years by thousands of small holder farmers and farming extension workers across
India. The dataset has been used to support a mobile application that relies on
artificial intelligence to assist farmers with pest management decisions.
Creation came from a mix of organized data collection, and from mobile
application usage that was less controlled. This makes the dataset unique
within the pest detection community, exhibiting a number of characteristics
that place it closer to other non-agricultural objected detection datasets.
This not only makes the dataset applicable to future pest management
applications, it opens the door for a wide variety of other research agendas.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 07:31:30 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"White",
"Jerome",
""
],
[
"Agrawal",
"Chandan",
""
],
[
"Ojha",
"Anmol",
""
],
[
"Agnihotri",
"Apoorv",
""
],
[
"Sharma",
"Makkunda",
""
],
[
"Doshi",
"Jigar",
""
]
] |
new_dataset
| 0.9999 |
2304.00804
|
Michael Maravgakis
|
Despina-Ekaterini Argiropoulos, Dimitrios Papageorgiou, Michael
Maravgakis, Drosakis Drosakis and Panos Trahanias
|
Two-layer adaptive trajectory tracking controller for quadruped robots
on slippery terrains
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Task space trajectory tracking for quadruped robots plays a crucial role on
achieving dexterous maneuvers in unstructured environments. To fulfill the
control objective, the robot should apply forces through the contact of the
legs with the supporting surface, while maintaining its stability and
controllability. In order to ensure the operation of the robot under these
conditions, one has to account for the possibility of unstable contact of the
legs that arises when the robot operates on partially or globally slippery
terrains. In this work, we propose an adaptive trajectory tracking controller
for quadruped robots, which involves two prioritized layers of adaptation for
avoiding possible slippage of one or multiple legs. The adaptive framework is
evaluated through simulations and validated through experiments.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 08:53:35 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Argiropoulos",
"Despina-Ekaterini",
""
],
[
"Papageorgiou",
"Dimitrios",
""
],
[
"Maravgakis",
"Michael",
""
],
[
"Drosakis",
"Drosakis",
""
],
[
"Trahanias",
"Panos",
""
]
] |
new_dataset
| 0.985737 |
2304.00827
|
Qichao Ying
|
Yangming Zhou, Yuzhou Yang, Qichao Ying, Zhenxing Qian and Xinpeng
Zhang
|
Multi-modal Fake News Detection on Social Media via Multi-grained
Information Fusion
|
Accepted by ICMR 2023
| null | null | null |
cs.CV cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
The easy sharing of multimedia content on social media has caused a rapid
dissemination of fake news, which threatens society's stability and security.
Therefore, fake news detection has garnered extensive research interest in the
field of social forensics. Current methods primarily concentrate on the
integration of textual and visual features but fail to effectively exploit
multi-modal information at both fine-grained and coarse-grained levels.
Furthermore, they suffer from an ambiguity problem due to a lack of correlation
between modalities or a contradiction between the decisions made by each
modality. To overcome these challenges, we present a Multi-grained Multi-modal
Fusion Network (MMFN) for fake news detection. Inspired by the multi-grained
process of human assessment of news authenticity, we respectively employ two
Transformer-based pre-trained models to encode token-level features from text
and images. The multi-modal module fuses fine-grained features, taking into
account coarse-grained features encoded by the CLIP encoder. To address the
ambiguity problem, we design uni-modal branches with similarity-based weighting
to adaptively adjust the use of multi-modal features. Experimental results
demonstrate that the proposed framework outperforms state-of-the-art methods on
three prevalent datasets.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 09:13:59 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Zhou",
"Yangming",
""
],
[
"Yang",
"Yuzhou",
""
],
[
"Ying",
"Qichao",
""
],
[
"Qian",
"Zhenxing",
""
],
[
"Zhang",
"Xinpeng",
""
]
] |
new_dataset
| 0.99107 |
2304.00869
|
Iakovos Evdaimon
|
Iakovos Evdaimon, Hadi Abdine, Christos Xypolopoulos, Stamatis
Outsios, Michalis Vazirgiannis, Giorgos Stamou
|
GreekBART: The First Pretrained Greek Sequence-to-Sequence Model
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The era of transfer learning has revolutionized the fields of Computer Vision
and Natural Language Processing, bringing powerful pretrained models with
exceptional performance across a variety of tasks. Specifically, Natural
Language Processing tasks have been dominated by transformer-based language
models. In Natural Language Inference and Natural Language Generation tasks,
the BERT model and its variants, as well as the GPT model and its successors,
demonstrated exemplary performance. However, the majority of these models are
pretrained and assessed primarily for the English language or on a multilingual
corpus. In this paper, we introduce GreekBART, the first Seq2Seq model based on
BART-base architecture and pretrained on a large-scale Greek corpus. We
evaluate and compare GreekBART against BART-random, Greek-BERT, and XLM-R on a
variety of discriminative tasks. In addition, we examine its performance on two
NLG tasks from GreekSUM, a newly introduced summarization dataset for the Greek
language. The model, the code, and the new summarization dataset will be
publicly available.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 10:48:51 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Evdaimon",
"Iakovos",
""
],
[
"Abdine",
"Hadi",
""
],
[
"Xypolopoulos",
"Christos",
""
],
[
"Outsios",
"Stamatis",
""
],
[
"Vazirgiannis",
"Michalis",
""
],
[
"Stamou",
"Giorgos",
""
]
] |
new_dataset
| 0.99702 |
2304.00892
|
Brahim Tamadazte
|
Maxime Adjigble and Brahim Tamadazte and Cristiana de Farias and
Rustam Stolkin and Naresh Marturi
|
Asservissement visuel 3D direct dans le domaine spectral
|
8 pages, 5 figures
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents a direct 3D visual servo scheme for the automatic
alignment of point clouds (respectively, objects) using visual information in
the spectral domain. Specifically, we propose an alignment method for 3D
models/point clouds that works by estimating the global transformation between
a reference point cloud and a target point cloud using harmonic domain data
analysis. A 3D discrete Fourier transform (DFT) in $\mathbb{R}^3$ is used for
translation estimation and real spherical harmonics in $SO(3)$ are used for
rotation estimation. This approach allows us to derive a decoupled visual servo
controller with 6 degrees of freedom. We then show how this approach can be
used as a controller for a robotic arm to perform a positioning task. Unlike
existing 3D visual servo methods, our method works well with partial point
clouds and in cases of large initial transformations between the initial and
desired position. Additionally, using spectral data (instead of spatial data)
for the transformation estimation makes our method robust to sensor-induced
noise and partial occlusions. Our method has been successfully validated
experimentally on point clouds obtained with a depth camera mounted on a
robotic arm.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 11:28:02 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Adjigble",
"Maxime",
""
],
[
"Tamadazte",
"Brahim",
""
],
[
"de Farias",
"Cristiana",
""
],
[
"Stolkin",
"Rustam",
""
],
[
"Marturi",
"Naresh",
""
]
] |
new_dataset
| 0.997978 |
2304.00906
|
Dan Saattrup Nielsen
|
Dan Saattrup Nielsen
|
ScandEval: A Benchmark for Scandinavian Natural Language Processing
|
17 pages, 11 figures, camera-ready NoDaLiDa 2023 submission
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
This paper introduces a Scandinavian benchmarking platform, ScandEval, which
can benchmark any pretrained model on four different tasks in the Scandinavian
languages. The datasets used in two of the tasks, linguistic acceptability and
question answering, are new. We develop and release a Python package and
command-line interface, scandeval, which can benchmark any model that has been
uploaded to the Hugging Face Hub, with reproducible results. Using this
package, we benchmark more than 100 Scandinavian or multilingual models and
present the results of these in an interactive online leaderboard, as well as
provide an analysis of the results. The analysis shows that there is
substantial cross-lingual transfer among the Mainland Scandinavian languages
(Danish, Swedish and Norwegian), with limited cross-lingual transfer between
the group of Mainland Scandinavian languages and the group of Insular
Scandinavian languages (Icelandic and Faroese). The benchmarking results also
show that the investment in language technology in Norway, Sweden and Denmark
has led to language models that outperform massively multilingual models such
as XLM-RoBERTa and mDeBERTaV3. We release the source code for both the package
and leaderboard.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 11:51:46 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Nielsen",
"Dan Saattrup",
""
]
] |
new_dataset
| 0.999833 |
2304.00913
|
Ankit Yadav
|
Ankit Yadav, Shubham Chandel, Sushant Chatufale and Anil Bandhakavi
|
LAHM : Large Annotated Dataset for Multi-Domain and Multilingual Hate
Speech Identification
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Current research on hate speech analysis is typically oriented towards
monolingual and single classification tasks. In this paper, we present a new
multilingual hate speech analysis dataset for English, Hindi, Arabic, French,
German and Spanish languages for multiple domains across hate speech - Abuse,
Racism, Sexism, Religious Hate and Extremism. To the best of our knowledge,
this paper is the first to address the problem of identifying various types of
hate speech in these five wide domains in these six languages. In this work, we
describe how we created the dataset, created annotations at high level and low
level for different domains and how we use it to test the current
state-of-the-art multilingual and multitask learning approaches. We evaluate
our dataset in various monolingual, cross-lingual and machine translation
classification settings and compare it against open source English datasets
that we aggregated and merged for this task. Then we discuss how this approach
can be used to create large scale hate-speech datasets and how to leverage our
annotations in order to improve hate speech detection and classification in
general.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 12:03:45 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Yadav",
"Ankit",
""
],
[
"Chandel",
"Shubham",
""
],
[
"Chatufale",
"Sushant",
""
],
[
"Bandhakavi",
"Anil",
""
]
] |
new_dataset
| 0.999873 |
2304.00946
|
Xiang Wang
|
Xiang Wang, Shiwei Zhang, Zhiwu Qing, Changxin Gao, Yingya Zhang, Deli
Zhao, Nong Sang
|
MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot
Action Recognition
|
Accepted by CVPR-2023. Code:
https://github.com/alibaba-mmai-research/MoLo
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current state-of-the-art approaches for few-shot action recognition achieve
promising performance by conducting frame-level matching on learned visual
features. However, they generally suffer from two limitations: i) the matching
procedure between local frames tends to be inaccurate due to the lack of
guidance to force long-range temporal perception; ii) explicit motion learning
is usually ignored, leading to partial information loss. To address these
issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo)
method that contains two crucial components, including a long-short contrastive
objective and a motion autodecoder. Specifically, the long-short contrastive
objective is to endow local frame features with long-form temporal awareness by
maximizing their agreement with the global token of videos belonging to the
same class. The motion autodecoder is a lightweight architecture to reconstruct
pixel motions from the differential features, which explicitly embeds the
network with motion dynamics. By this means, MoLo can simultaneously learn
long-range temporal context and motion cues for comprehensive few-shot
matching. To demonstrate the effectiveness, we evaluate MoLo on five standard
benchmarks, and the results show that MoLo favorably outperforms recent
advanced methods. The source code is available at
https://github.com/alibaba-mmai-research/MoLo.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 13:09:39 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Wang",
"Xiang",
""
],
[
"Zhang",
"Shiwei",
""
],
[
"Qing",
"Zhiwu",
""
],
[
"Gao",
"Changxin",
""
],
[
"Zhang",
"Yingya",
""
],
[
"Zhao",
"Deli",
""
],
[
"Sang",
"Nong",
""
]
] |
new_dataset
| 0.999335 |
2304.00954
|
Fnu Aryan
|
Aryan, Bowen Li, Sebastian Scherer, Yun-Jou Lin, Chen Wang
|
AirLoc: Object-based Indoor Relocalization
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Indoor relocalization is vital for both robotic tasks like autonomous
exploration and civil applications such as navigation with a cell phone in a
shopping mall. Some previous approaches adopt geometrical information such as
key-point features or local textures to carry out indoor relocalization, but
they either easily fail in an environment with visually similar scenes or
require many database images. Inspired by the fact that humans often remember
places by recognizing unique landmarks, we resort to objects, which are more
informative than geometry elements. In this work, we propose a simple yet
effective object-based indoor relocalization approach, dubbed AirLoc. To
overcome the critical challenges of object reidentification and remembering
object relationships, we extract object-wise appearance embedding and
inter-object geometric relationships. The geometry and appearance features are
integrated to generate cumulative scene features. This results in a robust,
accurate, and portable indoor relocalization system, which outperforms the
state-of-the-art methods in room-level relocalization by 9.5% of PR-AUC and 7%
of accuracy. In addition to exhaustive evaluation, we also carry out real-world
tests, where AirLoc shows robustness in challenges like severe occlusion,
perceptual aliasing, viewpoint shift, and deformation.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 13:16:47 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Aryan",
"",
""
],
[
"Li",
"Bowen",
""
],
[
"Scherer",
"Sebastian",
""
],
[
"Lin",
"Yun-Jou",
""
],
[
"Wang",
"Chen",
""
]
] |
new_dataset
| 0.998891 |
2304.00979
|
Xinwei Liu
|
Xinwei Liu, Kiran Raja, Renfang Wang, Hong Qiu, Hucheng Wu, Dechao
Sun, Qiguang Zheng, Nian Liu, Xiaoxia Wang, Gehang Huang, Raghavendra
Ramachandra, Christoph Busch
|
A Latent Fingerprint in the Wild Database
|
Submitted to IEEE Transactions on Information Forensics and Security
(under review)
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Latent fingerprints are among the most important and widely used evidence in
crime scenes, digital forensics and law enforcement worldwide. Despite the
number of advancements reported in recent works, we note that significant open
issues such as independent benchmarking and lack of large-scale evaluation
databases for improving the algorithms are inadequately addressed. The
available databases are mostly of semi-public nature, lack of acquisition in
the wild environment, and post-processing pipelines. Moreover, they do not
represent a realistic capture scenario similar to real crime scenes, to
benchmark the robustness of the algorithms. Further, existing databases for
latent fingerprint recognition do not have a large number of unique
subjects/fingerprint instances or do not provide ground truth/reference
fingerprint images to conduct a cross-comparison against the latent. In this
paper, we introduce a new wild large-scale latent fingerprint database that
includes five different acquisition scenarios: reference fingerprints from (1)
optical and (2) capacitive sensors, (3) smartphone fingerprints, latent
fingerprints captured from (4) wall surface, (5) Ipad surface, and (6)
aluminium foil surface. The new database consists of 1,318 unique fingerprint
instances captured in all above mentioned settings. A total of 2,636 reference
fingerprints from optical and capacitive sensors, 1,318 fingerphotos from
smartphones, and 9,224 latent fingerprints from each of the 132 subjects were
provided in this work. The dataset is constructed considering various age
groups, equal representations of genders and backgrounds. In addition, we
provide an extensive set of analysis of various subset evaluations to highlight
open challenges for future directions in latent fingerprint recognition
research.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 13:47:38 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Liu",
"Xinwei",
""
],
[
"Raja",
"Kiran",
""
],
[
"Wang",
"Renfang",
""
],
[
"Qiu",
"Hong",
""
],
[
"Wu",
"Hucheng",
""
],
[
"Sun",
"Dechao",
""
],
[
"Zheng",
"Qiguang",
""
],
[
"Liu",
"Nian",
""
],
[
"Wang",
"Xiaoxia",
""
],
[
"Huang",
"Gehang",
""
],
[
"Ramachandra",
"Raghavendra",
""
],
[
"Busch",
"Christoph",
""
]
] |
new_dataset
| 0.995288 |
2304.01003
|
Ivano Lauriola
|
Stefano Campese, Ivano Lauriola, Alessandro Moschitti
|
QUADRo: Dataset and Models for QUestion-Answer Database Retrieval
| null | null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
An effective paradigm for building Automated Question Answering systems is
the re-use of previously answered questions, e.g., for FAQs or forum
applications. Given a database (DB) of question/answer (q/a) pairs, it is
possible to answer a target question by scanning the DB for similar questions.
In this paper, we scale this approach to open domain, making it competitive
with other standard methods, e.g., unstructured document or graph based. For
this purpose, we (i) build a large scale DB of 6.3M q/a pairs, using public
questions, (ii) design a new system based on neural IR and a q/a pair reranker,
and (iii) construct training and test data to perform comparative experiments
with our models. We demonstrate that Transformer-based models using (q,a) pairs
outperform models only based on question representation, for both neural search
and reranking. Additionally, we show that our DB-based approach is competitive
with Web-based methods, i.e., a QA system built on top the BING search engine,
demonstrating the challenge of finding relevant information. Finally, we make
our data and models available for future research.
|
[
{
"version": "v1",
"created": "Thu, 30 Mar 2023 00:42:07 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Campese",
"Stefano",
""
],
[
"Lauriola",
"Ivano",
""
],
[
"Moschitti",
"Alessandro",
""
]
] |
new_dataset
| 0.999435 |
2304.01073
|
Mona Wang
|
Watson Jia, Mona Wang, Liang Wang, and Prateek Mittal
|
QUICstep: Circumventing QUIC-based Censorship
| null | null | null | null |
cs.CR cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Governments around the world limit free and open communication on the
Internet through censorship. To reliably identify and block access to certain
web domains, censors inspect the plaintext TLS SNI field sent in TLS
handshakes. With QUIC rapidly displacing TCP as the dominant transport-layer
protocol on the web, censorship regimes have already begun prosecuting network
traffic delivered over QUIC. With QUIC censorship poised to expand, censorship
circumvention tools must similarly adapt. We present QUICstep, a
censorship-resilient, application-agnostic, performant, and easy-to-implement
approach to censorship circumvention in the QUIC era. QUICstep circumvents TLS
SNI censorship by conducting a QUIC-TLS handshake over an encrypted tunnel to
hide the SNI field from censors and performs connection migration to resume the
QUIC session in plain sight of the censor. Our evaluation finds that QUICstep
successfully establishes QUIC sessions in the presence of a proof-of-concept
censor with minimal latency overhead.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 15:31:58 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Jia",
"Watson",
""
],
[
"Wang",
"Mona",
""
],
[
"Wang",
"Liang",
""
],
[
"Mittal",
"Prateek",
""
]
] |
new_dataset
| 0.996455 |
2304.01080
|
Alejandro Linares-Barranco A. Linares-Barranco
|
Antonio Rios-Navarro, Enrique Pi\~nero-Fuentes, Salvador Canas-Moreno,
Aqib Javed, Jin Harkin, Alejandro Linares-Barranco
|
LIPSFUS: A neuromorphic dataset for audio-visual sensory fusion of lip
reading
|
Submitted to ISCAS2023, 4 pages, plus references, github link
provided
| null | null | null |
cs.SD cs.RO eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents a sensory fusion neuromorphic dataset collected with
precise temporal synchronization using a set of Address-Event-Representation
sensors and tools. The target application is the lip reading of several
keywords for different machine learning applications, such as digits, robotic
commands, and auxiliary rich phonetic short words. The dataset is enlarged with
a spiking version of an audio-visual lip reading dataset collected with
frame-based cameras. LIPSFUS is publicly available and it has been validated
with a deep learning architecture for audio and visual classification. It is
intended for sensory fusion architectures based on both artificial and spiking
neural network algorithms.
|
[
{
"version": "v1",
"created": "Tue, 28 Mar 2023 12:27:43 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Rios-Navarro",
"Antonio",
""
],
[
"Piñero-Fuentes",
"Enrique",
""
],
[
"Canas-Moreno",
"Salvador",
""
],
[
"Javed",
"Aqib",
""
],
[
"Harkin",
"Jin",
""
],
[
"Linares-Barranco",
"Alejandro",
""
]
] |
new_dataset
| 0.999793 |
2304.01102
|
Julian Aron Prenner
|
Julian Aron Prenner and Romain Robbes
|
RunBugRun -- An Executable Dataset for Automated Program Repair
| null | null | null | null |
cs.SE cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, we can notice a transition to data-driven techniques in Automated
Program Repair (APR), in particular towards deep neural networks. This entails
training on hundreds of thousands or even millions of non-executable code
fragments. We would like to bring more attention to an aspect of code often
neglected in Neural Program Repair (NPR), namely its execution. Code execution
has several significant advantages. It allows for test-based evaluation of
candidate fixes and can provide valuable information to aid repair. In this
work we present a fully executable dataset of 450,000 small buggy/fixed program
pairs originally submitted to programming competition websites written in eight
different programming languages. Along with the dataset we provide
infrastructure to compile, safely execute and test programs as well as
fine-grained bug-type labels. To give a point of reference, we provide basic
evaluation results for two baselines, one based on a generate-and-validate
approach and one on deep learning. With this dataset we follow several goals:
we want to lift Neural Program Repair beyond fully static code representations,
foster the use of execution-based features and, by including several different
languages, counterbalance the predominance of Java in the current landscape of
APR datasets and benchmarks.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 16:02:00 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Prenner",
"Julian Aron",
""
],
[
"Robbes",
"Romain",
""
]
] |
new_dataset
| 0.997328 |
2304.01179
|
Nadav Schneider
|
Nadav Schneider, Shimon Shouei, Saleem Ghantous, Elad Feldman
|
Hate Speech Targets Detection in Parler using BERT
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Online social networks have become a fundamental component of our everyday
life. Unfortunately, these platforms are also a stage for hate speech. Popular
social networks have regularized rules against hate speech. Consequently,
social networks like Parler and Gab advocating and claiming to be free speech
platforms have evolved. These platforms have become a district for hate speech
against diverse targets. We present in our paper a pipeline for detecting hate
speech and its targets and use it for creating Parler hate targets'
distribution. The pipeline consists of two models; one for hate speech
detection and the second for target classification, both based on BERT with
Back-Translation and data pre-processing for improved results. The source code
used in this work, as well as other relevant sources, are available at:
https://github.com/NadavSc/HateRecognition.git
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 17:49:04 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Schneider",
"Nadav",
""
],
[
"Shouei",
"Shimon",
""
],
[
"Ghantous",
"Saleem",
""
],
[
"Feldman",
"Elad",
""
]
] |
new_dataset
| 0.999262 |
2304.01186
|
Yue Ma
|
Yue Ma, Yingqing He, Xiaodong Cun, Xintao Wang, Ying Shan, Xiu Li,
Qifeng Chen
|
Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free
Videos
|
Project page: https://follow-your-pose.github.io/; Github repository:
https://github.com/mayuelala/FollowYourPose
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Generating text-editable and pose-controllable character videos have an
imperious demand in creating various digital human. Nevertheless, this task has
been restricted by the absence of a comprehensive dataset featuring paired
video-pose captions and the generative prior models for videos. In this work,
we design a novel two-stage training scheme that can utilize easily obtained
datasets (i.e.,image pose pair and pose-free video) and the pre-trained
text-to-image (T2I) model to obtain the pose-controllable character videos.
Specifically, in the first stage, only the keypoint-image pairs are used only
for a controllable text-to-image generation. We learn a zero-initialized
convolu- tional encoder to encode the pose information. In the second stage, we
finetune the motion of the above network via a pose-free video dataset by
adding the learnable temporal self-attention and reformed cross-frame
self-attention blocks. Powered by our new designs, our method successfully
generates continuously pose-controllable character videos while keeps the
editing and concept composition ability of the pre-trained T2I model. The code
and models will be made publicly available.
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 17:55:14 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Ma",
"Yue",
""
],
[
"He",
"Yingqing",
""
],
[
"Cun",
"Xiaodong",
""
],
[
"Wang",
"Xintao",
""
],
[
"Shan",
"Ying",
""
],
[
"Li",
"Xiu",
""
],
[
"Chen",
"Qifeng",
""
]
] |
new_dataset
| 0.99892 |
2304.01194
|
Akshay Dudhane
|
Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan,
Ming-Hsuan Yang
|
Burstormer: Burst Image Restoration and Enhancement Transformer
|
Accepted at CVPR 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
On a shutter press, modern handheld cameras capture multiple images in rapid
succession and merge them to generate a single image. However, individual
frames in a burst are misaligned due to inevitable motions and contain multiple
degradations. The challenge is to properly align the successive image shots and
merge their complimentary information to achieve high-quality outputs. Towards
this direction, we propose Burstormer: a novel transformer-based architecture
for burst image restoration and enhancement. In comparison to existing works,
our approach exploits multi-scale local and non-local features to achieve
improved alignment and feature fusion. Our key idea is to enable inter-frame
communication in the burst neighborhoods for information aggregation and
progressive fusion while modeling the burst-wide context. However, the input
burst frames need to be properly aligned before fusing their information.
Therefore, we propose an enhanced deformable alignment module for aligning
burst features with regards to the reference frame. Unlike existing methods,
the proposed alignment module not only aligns burst features but also exchanges
feature information and maintains focused communication with the reference
frame through the proposed reference-based feature enrichment mechanism, which
facilitates handling complex motions. After multi-level alignment and
enrichment, we re-emphasize on inter-frame communication within burst using a
cyclic burst sampling module. Finally, the inter-frame information is
aggregated using the proposed burst feature fusion module followed by
progressive upsampling. Our Burstormer outperforms state-of-the-art methods on
burst super-resolution, burst denoising and burst low-light enhancement. Our
codes and pretrained models are available at https://
github.com/akshaydudhane16/Burstormer
|
[
{
"version": "v1",
"created": "Mon, 3 Apr 2023 17:58:44 GMT"
}
] | 2023-04-04T00:00:00 |
[
[
"Dudhane",
"Akshay",
""
],
[
"Zamir",
"Syed Waqas",
""
],
[
"Khan",
"Salman",
""
],
[
"Khan",
"Fahad Shahbaz",
""
],
[
"Yang",
"Ming-Hsuan",
""
]
] |
new_dataset
| 0.99884 |
2101.00784
|
Zekun Wang
|
Zekun Wang, Pengwei Wang, Peter C. Louis, Lee E. Wheless, Yuankai Huo
|
WearMask: Fast In-browser Face Mask Detection with Serverless Edge
Computing for COVID-19
| null |
Electronic Imaging, 2023, pp 229-1 - 229-6
|
10.2352/EI.2023.35.11.HPCI-229
| null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The COVID-19 epidemic has been a significant healthcare challenge in the
United States. According to the Centers for Disease Control and Prevention
(CDC), COVID-19 infection is transmitted predominately by respiratory droplets
generated when people breathe, talk, cough, or sneeze. Wearing a mask is the
primary, effective, and convenient method of blocking 80% of all respiratory
infections. Therefore, many face mask detection and monitoring systems have
been developed to provide effective supervision for hospitals, airports,
publication transportation, sports venues, and retail locations. However, the
current commercial face mask detection systems are typically bundled with
specific software or hardware, impeding public accessibility. In this paper, we
propose an in-browser serverless edge-computing based face mask detection
solution, called Web-based efficient AI recognition of masks (WearMask), which
can be deployed on any common devices (e.g., cell phones, tablets, computers)
that have internet connections using web browsers, without installing any
software. The serverless edge-computing design minimizes the extra hardware
costs (e.g., specific devices or cloud computing servers). The contribution of
the proposed method is to provide a holistic edge-computing framework of
integrating (1) deep learning models (YOLO), (2) high-performance neural
network inference computing framework (NCNN), and (3) a stack-based virtual
machine (WebAssembly). For end-users, our web-based solution has advantages of
(1) serverless edge-computing design with minimal device limitation and privacy
risk, (2) installation free deployment, (3) low computing requirements, and (4)
high detection speed. Our WearMask application has been launched with public
access at facemask-detection.com.
|
[
{
"version": "v1",
"created": "Mon, 4 Jan 2021 05:50:48 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Wang",
"Zekun",
""
],
[
"Wang",
"Pengwei",
""
],
[
"Louis",
"Peter C.",
""
],
[
"Wheless",
"Lee E.",
""
],
[
"Huo",
"Yuankai",
""
]
] |
new_dataset
| 0.999706 |
2203.00806
|
Simon Le Cleac'h
|
Taylor A. Howell and Simon Le Cleac'h and Jan Br\"udigam and J. Zico
Kolter and Mac Schwager and Zachary Manchester
|
Dojo: A Differentiable Physics Engine for Robotics
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present Dojo, a differentiable physics engine for robotics that
prioritizes stable simulation, accurate contact physics, and differentiability
with respect to states, actions, and system parameters. Dojo achieves stable
simulation at low sample rates and conserves energy and momentum by employing a
variational integrator. A nonlinear complementarity problem with second-order
cones for friction models hard contact, and is reliably solved using a custom
primal-dual interior-point method. Special properties of the interior-point
method are exploited using implicit differentiation to efficiently compute
smooth gradients that provide useful information through contact events. We
demonstrate Dojo with a number of examples including: planning, policy
optimization, and system identification, that demonstrate the engine's unique
ability to simulate hard contact while providing smooth, analytic gradients.
|
[
{
"version": "v1",
"created": "Wed, 2 Mar 2022 00:56:23 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Mar 2022 06:12:42 GMT"
},
{
"version": "v3",
"created": "Mon, 27 Jun 2022 18:09:13 GMT"
},
{
"version": "v4",
"created": "Fri, 31 Mar 2023 01:31:26 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Howell",
"Taylor A.",
""
],
[
"Cleac'h",
"Simon Le",
""
],
[
"Brüdigam",
"Jan",
""
],
[
"Kolter",
"J. Zico",
""
],
[
"Schwager",
"Mac",
""
],
[
"Manchester",
"Zachary",
""
]
] |
new_dataset
| 0.992743 |
2203.16799
|
Sreyan Ghosh
|
Sreyan Ghosh and S Ramaneswaran and Utkarsh Tyagi and Harshvardhan
Srivastava and Samden Lepcha and S Sakshi and Dinesh Manocha
|
M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in
Conversations
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Expression of emotions is a crucial part of daily human communication.
Emotion recognition in conversations (ERC) is an emerging field of study, where
the primary task is to identify the emotion behind each utterance in a
conversation. Though a lot of work has been done on ERC in the past, these
works only focus on ERC in the English language, thereby ignoring any other
languages. In this paper, we present Multilingual MELD (M-MELD), where we
extend the Multimodal EmotionLines Dataset (MELD) \cite{poria2018meld} to 4
other languages beyond English, namely Greek, Polish, French, and Spanish.
Beyond just establishing strong baselines for all of these 4 languages, we also
propose a novel architecture, DiscLSTM, that uses both sequential and
conversational discourse context in a conversational dialogue for ERC. Our
proposed approach is computationally efficient, can transfer across languages
using just a cross-lingual encoder, and achieves better performance than most
uni-modal text approaches in the literature on both MELD and M-MELD. We make
our data and code publicly on GitHub.
|
[
{
"version": "v1",
"created": "Thu, 31 Mar 2022 05:07:16 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Apr 2022 04:38:19 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Nov 2022 21:07:06 GMT"
},
{
"version": "v4",
"created": "Fri, 31 Mar 2023 13:25:05 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Ghosh",
"Sreyan",
""
],
[
"Ramaneswaran",
"S",
""
],
[
"Tyagi",
"Utkarsh",
""
],
[
"Srivastava",
"Harshvardhan",
""
],
[
"Lepcha",
"Samden",
""
],
[
"Sakshi",
"S",
""
],
[
"Manocha",
"Dinesh",
""
]
] |
new_dataset
| 0.999784 |
2205.13489
|
Wang Zhihua
|
Zhihua Wang, Keshuo Xu, Yang Yang, Jianlei Dong, Shuhang Gu, Lihao Xu,
Yuming Fang, and Kede Ma
|
Measuring Perceptual Color Differences of Smartphone Photographs
|
10 figures, 8 tables, 14 pages
| null | null | null |
cs.CV cs.GR eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Measuring perceptual color differences (CDs) is of great importance in modern
smartphone photography. Despite the long history, most CD measures have been
constrained by psychophysical data of homogeneous color patches or a limited
number of simplistic natural photographic images. It is thus questionable
whether existing CD measures generalize in the age of smartphone photography
characterized by greater content complexities and learning-based image signal
processors. In this paper, we put together so far the largest image dataset for
perceptual CD assessment, in which the photographic images are 1) captured by
six flagship smartphones, 2) altered by Photoshop, 3) post-processed by
built-in filters of the smartphones, and 4) reproduced with incorrect color
profiles. We then conduct a large-scale psychophysical experiment to gather
perceptual CDs of 30,000 image pairs in a carefully controlled laboratory
environment. Based on the newly established dataset, we make one of the first
attempts to construct an end-to-end learnable CD formula based on a lightweight
neural network, as a generalization of several previous metrics. Extensive
experiments demonstrate that the optimized formula outperforms 33 existing CD
measures by a large margin, offers reasonable local CD maps without the use of
dense supervision, generalizes well to homogeneous color patch data, and
empirically behaves as a proper metric in the mathematical sense. Our dataset
and code are publicly available at https://github.com/hellooks/CDNet.
|
[
{
"version": "v1",
"created": "Thu, 26 May 2022 16:57:04 GMT"
},
{
"version": "v2",
"created": "Fri, 31 Mar 2023 15:07:28 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Wang",
"Zhihua",
""
],
[
"Xu",
"Keshuo",
""
],
[
"Yang",
"Yang",
""
],
[
"Dong",
"Jianlei",
""
],
[
"Gu",
"Shuhang",
""
],
[
"Xu",
"Lihao",
""
],
[
"Fang",
"Yuming",
""
],
[
"Ma",
"Kede",
""
]
] |
new_dataset
| 0.998739 |
2206.02241
|
Fabian Peller-Konrad
|
Fabian Peller-Konrad, Rainer Kartmann, Christian R. G. Dreher, Andre
Meixner, Fabian Reister, Markus Grotz, Tamim Asfour
|
A Memory System of a Robot Cognitive Architecture and its Implementation
in ArmarX
|
35 pages, 19 figures, submitted to RAS
|
Robotics and Autonomous Systems (2023)
|
10.1016/j.robot.2023.104415
|
ROBOT: 104415
|
cs.AI cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Cognitive agents such as humans and robots perceive their environment through
an abundance of sensors producing streams of data that need to be processed to
generate intelligent behavior. A key question of cognition-enabled and
AI-driven robotics is how to organize and manage knowledge efficiently in a
cognitive robot control architecture. We argue, that memory is a central active
component of such architectures that mediates between semantic and sensorimotor
representations, orchestrates the flow of data streams and events between
different processes and provides the components of a cognitive architecture
with data-driven services for the abstraction of semantics from sensorimotor
data, the parametrization of symbolic plans for execution and prediction of
action effects.
Based on related work, and the experience gained in developing our ARMAR
humanoid robot systems, we identified conceptual and technical requirements of
a memory system as central component of cognitive robot control architecture
that facilitate the realization of high-level cognitive abilities such as
explaining, reasoning, prospection, simulation and augmentation. Conceptually,
a memory should be active, support multi-modal data representations, associate
knowledge, be introspective, and have an inherently episodic structure.
Technically, the memory should support a distributed design, be
access-efficient and capable of long-term data storage. We introduce the memory
system for our cognitive robot control architecture and its implementation in
the robot software framework ArmarX. We evaluate the efficiency of the memory
system with respect to transfer speeds, compression, reproduction and
prediction capabilities.
|
[
{
"version": "v1",
"created": "Sun, 5 Jun 2022 19:15:29 GMT"
},
{
"version": "v2",
"created": "Fri, 17 Jun 2022 09:42:05 GMT"
},
{
"version": "v3",
"created": "Tue, 31 Jan 2023 12:33:13 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Peller-Konrad",
"Fabian",
""
],
[
"Kartmann",
"Rainer",
""
],
[
"Dreher",
"Christian R. G.",
""
],
[
"Meixner",
"Andre",
""
],
[
"Reister",
"Fabian",
""
],
[
"Grotz",
"Markus",
""
],
[
"Asfour",
"Tamim",
""
]
] |
new_dataset
| 0.994781 |
2208.01765
|
Dianne O'Leary
|
Jennifer Head and Dianne P. O'Leary
|
Mary Kenneth Keller: First US PhD in Computer Science
|
This revision expands the abstract, adds a reference to a condensed
version of this paper published in a journal, references Keller's work on ACM
curricula, and notes an IEEE prize in her honor
|
IEEE Annals of the History of Computing 45(1):55--63,
January-March 2023
|
10.1109/MAHC.2022.3231763
| null |
cs.GL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In June 1965, Sister Mary Kenneth Keller, BVM, received the first US PhD in
Computer Science, and this paper outlines her life and accomplishments. As a
scholar, she has the distinction of being an early advocate of
learning-by-example in artificial intelligence. Her main scholarly contribution
was in shaping computer science education in high schools and small colleges.
She was an evangelist for viewing the computer as a symbol manipulator, for
providing computer literacy to everyone, and for the use of computers in
service to humanity. She was far ahead of her time in working to ensure a place
for women in technology and in eliminating barriers preventing their
participation, such as poor access to education and daycare. She was a strong
and spirited woman, a visionary in seeing how computers would revolutionize our
lives. A condensation of this paper appeared as, ``The Legacy of Mary Kenneth
Keller, First U.S. Ph.D. in Computer Science," Jennifer Head and Dianne P.
O'Leary, IEEE Annals of the History of Computing 45(1):55--63, January-March
2023.
|
[
{
"version": "v1",
"created": "Tue, 2 Aug 2022 21:42:01 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Mar 2023 18:18:18 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Head",
"Jennifer",
""
],
[
"O'Leary",
"Dianne P.",
""
]
] |
new_dataset
| 0.999177 |
2210.10094
|
Yasas Seneviratne
|
Yasas Seneviratne, Korakit Seemakhupt, Sihang Liu, Samira Khan
|
NearPM: A Near-Data Processing System for Storage-Class Applications
| null | null | null | null |
cs.CE cs.GT
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Persistent Memory (PM) technologies enable program recovery to a consistent
state in a case of failure. To ensure this crash-consistent behavior, programs
need to enforce persist ordering by employing mechanisms, such as logging and
checkpointing, which introduce additional data movement. The emerging near-data
processing (NDP) architec-tures can effectively reduce this data movement
overhead. In this work we propose NearPM, a near data processor that supports
accelerable primitives in crash consistent programs. Using these primitives
NearPM accelerate commonly used crash consistency mechanisms logging,
checkpointing, and shadow-paging. NearPM further reduces the synchronization
overheads between the NDP and the CPU to guarantee persistent ordering by
moving ordering handling near memory. We ensures a correct persist ordering
between CPU and NDP devices, as well as among multiple NDP devices with
Partitioned Persist Ordering (PPO). We prototype NearPM on an FPGA platform.1
NearPM executes data-intensive operations in crash consistency mechanisms with
correct ordering guarantees while the rest of the program runs on the CPU. We
evaluate nine PM workloads, where each work load supports three crash
consistency mechanisms -logging, checkpointing, and shadow paging. Overall,
NearPM achieves 4.3-9.8X speedup in the NDP-offloaded operations and 1.22-1.35X
speedup in end-to-end execution.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 18:45:54 GMT"
},
{
"version": "v2",
"created": "Fri, 31 Mar 2023 15:24:27 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Seneviratne",
"Yasas",
""
],
[
"Seemakhupt",
"Korakit",
""
],
[
"Liu",
"Sihang",
""
],
[
"Khan",
"Samira",
""
]
] |
new_dataset
| 0.969305 |
2211.03726
|
Carl Doersch
|
Carl Doersch, Ankush Gupta, Larisa Markeeva, Adri\`a Recasens, Lucas
Smaira, Yusuf Aytar, Jo\~ao Carreira, Andrew Zisserman, Yi Yang
|
TAP-Vid: A Benchmark for Tracking Any Point in a Video
|
Published in NeurIPS Datasets and Benchmarks track, 2022
| null | null | null |
cs.CV stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Generic motion understanding from video involves not only tracking objects,
but also perceiving how their surfaces deform and move. This information is
useful to make inferences about 3D shape, physical properties and object
interactions. While the problem of tracking arbitrary physical points on
surfaces over longer video clips has received some attention, no dataset or
benchmark for evaluation existed, until now. In this paper, we first formalize
the problem, naming it tracking any point (TAP). We introduce a companion
benchmark, TAP-Vid, which is composed of both real-world videos with accurate
human annotations of point tracks, and synthetic videos with perfect
ground-truth point tracks. Central to the construction of our benchmark is a
novel semi-automatic crowdsourced pipeline which uses optical flow estimates to
compensate for easier, short-term motion like camera shake, allowing annotators
to focus on harder sections of video. We validate our pipeline on synthetic
data and propose a simple end-to-end point tracking model TAP-Net, showing that
it outperforms all prior methods on our benchmark when trained on synthetic
data.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 17:57:02 GMT"
},
{
"version": "v2",
"created": "Fri, 31 Mar 2023 11:51:40 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Doersch",
"Carl",
""
],
[
"Gupta",
"Ankush",
""
],
[
"Markeeva",
"Larisa",
""
],
[
"Recasens",
"Adrià",
""
],
[
"Smaira",
"Lucas",
""
],
[
"Aytar",
"Yusuf",
""
],
[
"Carreira",
"João",
""
],
[
"Zisserman",
"Andrew",
""
],
[
"Yang",
"Yi",
""
]
] |
new_dataset
| 0.999819 |
2211.07021
|
Eddie Bkheet
|
Eddie Bkheet, Anne-Lise D'Angelo, Adam Goldbraikh, Shlomi Laufer
|
Using Hand Pose Estimation To Automate Open Surgery Training Feedback
|
Accepted to IPCAI 2023, 12 pages, 5 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Purpose: This research aims to facilitate the use of state-of-the-art
computer vision algorithms for the automated training of surgeons and the
analysis of surgical footage. By estimating 2D hand poses, we model the
movement of the practitioner's hands, and their interaction with surgical
instruments, to study their potential benefit for surgical training.
Methods: We leverage pre-trained models on a publicly-available hands dataset
to create our own in-house dataset of 100 open surgery simulation videos with
2D hand poses. We also assess the ability of pose estimations to segment
surgical videos into gestures and tool-usage segments and compare them to
kinematic sensors and I3D features. Furthermore, we introduce 6 novel surgical
dexterity proxies stemming from domain experts' training advice, all of which
our framework can automatically detect given raw video footage.
Results: State-of-the-art gesture segmentation accuracy of 88.35\% on the
Open Surgery Simulation dataset is achieved with the fusion of 2D poses and I3D
features from multiple angles. The introduced surgical skill proxies presented
significant differences for novices compared to experts and produced actionable
feedback for improvement.
Conclusion: This research demonstrates the benefit of pose estimations for
open surgery by analyzing their effectiveness in gesture segmentation and skill
assessment. Gesture segmentation using pose estimations achieved comparable
results to physical sensors while being remote and markerless. Surgical
dexterity proxies that rely on pose estimation proved they can be used to work
towards automated training feedback. We hope our findings encourage additional
collaboration on novel skill proxies to make surgical training more efficient.
|
[
{
"version": "v1",
"created": "Sun, 13 Nov 2022 21:47:31 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Mar 2023 19:14:54 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Bkheet",
"Eddie",
""
],
[
"D'Angelo",
"Anne-Lise",
""
],
[
"Goldbraikh",
"Adam",
""
],
[
"Laufer",
"Shlomi",
""
]
] |
new_dataset
| 0.999553 |
2211.11417
|
Ehsan Pajouheshgar
|
Ehsan Pajouheshgar, Yitao Xu, Tong Zhang, Sabine S\"usstrunk
|
DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular
Automata
|
Link to the demo: https://dynca.github.io/
| null | null | null |
cs.CV cs.GR cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic
videos. However, they require a slow iterative optimization process to
synthesize a single fixed-size short video, and they do not offer any
post-training control over the synthesis process. We propose Dynamic Neural
Cellular Automata (DyNCA), a framework for real-time and controllable dynamic
texture synthesis. Our method is built upon the recently introduced NCA models
and can synthesize infinitely long and arbitrary-sized realistic video textures
in real time. We quantitatively and qualitatively evaluate our model and show
that our synthesized videos appear more realistic than the existing results. We
improve the SOTA DyTS performance by $2\sim 4$ orders of magnitude. Moreover,
our model offers several real-time video controls including motion speed,
motion direction, and an editing brush tool. We exhibit our trained models in
an online interactive demo that runs on local hardware and is accessible on
personal computers and smartphones.
|
[
{
"version": "v1",
"created": "Mon, 21 Nov 2022 13:01:52 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Mar 2023 21:56:33 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Pajouheshgar",
"Ehsan",
""
],
[
"Xu",
"Yitao",
""
],
[
"Zhang",
"Tong",
""
],
[
"Süsstrunk",
"Sabine",
""
]
] |
new_dataset
| 0.999639 |
2211.11525
|
Bruno Spilak
|
Raul Bag, Bruno Spilak, Julian Winkel, Wolfgang Karl H\"ardle
|
Quantinar: a blockchain p2p ecosystem for honest scientific research
| null | null | null | null |
cs.CY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Living in the Information Age, the power of data and correct statistical
analysis has never been more prevalent. Academics and practitioners require
nowadays an accurate application of quantitative methods. Yet many branches are
subject to a crisis of integrity, which is shown in an improper use of
statistical models, $p$-hacking, HARKing, or failure to replicate results. We
propose the use of a Peer-to-Peer (P2P) ecosystem based on a blockchain
network, Quantinar (quantinar.com), to support quantitative analytics knowledge
paired with code in the form of Quantlets (quantlet.com) or software snippets.
The integration of blockchain technology makes Quantinar a decentralized
autonomous organization (DAO) that ensures fully transparent and reproducible
scientific research.
|
[
{
"version": "v1",
"created": "Sun, 13 Nov 2022 11:28:04 GMT"
},
{
"version": "v2",
"created": "Fri, 31 Mar 2023 14:29:58 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Bag",
"Raul",
""
],
[
"Spilak",
"Bruno",
""
],
[
"Winkel",
"Julian",
""
],
[
"Härdle",
"Wolfgang Karl",
""
]
] |
new_dataset
| 0.99238 |
2301.05570
|
Mike Sharples PhD
|
Mike Sharples
|
John Clark's Latin Verse Machine: 19th Century Computational Creativity
|
13 pages, 5 figures, 1 table. Submitted to IEEE Annals of the History
of Computing
|
IEEE Annals of the History of Computing, 45, 1, 31-42 (2023)
|
10.1109/MAHC.2023.3241258
| null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
John Clark was inventor of the Eureka machine to generate hexameter Latin
verse. He labored for 13 years from 1832 to implement the device that could
compose at random over 26 million different lines of well-formed verse. This
paper proposes that Clark should be regarded as an early cognitive scientist.
Clark described his machine as an illustration of a theory of "kaleidoscopic
evolution" whereby the Latin verse is "conceived in the mind of the machine"
then mechanically produced and displayed. We describe the background to
automated generation of verse, the design and mechanics of Eureka, its
reception in London in 1845 and its place in the history of language generation
by machine. The article interprets Clark's theory of kaleidoscopic evolution in
terms of modern cognitive science. It suggests that Clark has not been given
the recognition he deserves as a pioneer of computational creativity.
|
[
{
"version": "v1",
"created": "Fri, 13 Jan 2023 14:20:04 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Jan 2023 15:21:58 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Sharples",
"Mike",
""
]
] |
new_dataset
| 0.999569 |
2302.09665
|
Zirong Chen
|
Zirong Chen, Issa Li, Haoxiang Zhang, Sarah Preum, John A. Stankovic,
Meiyi Ma
|
CitySpec with Shield: A Secure Intelligent Assistant for Requirement
Formalization
|
arXiv admin note: substantial text overlap with arXiv:2206.03132
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
An increasing number of monitoring systems have been developed in smart
cities to ensure that the real-time operations of a city satisfy safety and
performance requirements. However, many existing city requirements are written
in English with missing, inaccurate, or ambiguous information. There is a high
demand for assisting city policymakers in converting human-specified
requirements to machine-understandable formal specifications for monitoring
systems. To tackle this limitation, we build CitySpec, the first intelligent
assistant system for requirement specification in smart cities. To create
CitySpec, we first collect over 1,500 real-world city requirements across
different domains (e.g., transportation and energy) from over 100 cities and
extract city-specific knowledge to generate a dataset of city vocabulary with
3,061 words. We also build a translation model and enhance it through
requirement synthesis and develop a novel online learning framework with
shielded validation. The evaluation results on real-world city requirements
show that CitySpec increases the sentence-level accuracy of requirement
specification from 59.02% to 86.64%, and has strong adaptability to a new city
and a new domain (e.g., the F1 score for requirements in Seattle increases from
77.6% to 93.75% with online learning). After the enhancement from the shield
function, CitySpec is now immune to most known textual adversarial inputs
(e.g., the attack success rate of DeepWordBug after the shield function is
reduced to 0% from 82.73%). We test the CitySpec with 18 participants from
different domains. CitySpec shows its strong usability and adaptability to
different domains, and also its robustness to malicious inputs.
|
[
{
"version": "v1",
"created": "Sun, 19 Feb 2023 20:11:06 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Mar 2023 23:25:57 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Chen",
"Zirong",
""
],
[
"Li",
"Issa",
""
],
[
"Zhang",
"Haoxiang",
""
],
[
"Preum",
"Sarah",
""
],
[
"Stankovic",
"John A.",
""
],
[
"Ma",
"Meiyi",
""
]
] |
new_dataset
| 0.997534 |
2303.15616
|
Xuyang Shen
|
Xuyang Shen and Dong Li and Jinxing Zhou and Zhen Qin and Bowen He and
Xiaodong Han and Aixuan Li and Yuchao Dai and Lingpeng Kong and Meng Wang and
Yu Qiao and Yiran Zhong
|
Fine-grained Audible Video Description
|
accepted to CVPR 2023, Xuyang Shen, Dong Li and Jinxing Zhou
contribute equally, code link: github.com/OpenNLPLab/FAVDBench, dataset link:
www.avlbench.opennlplab.cn
| null | null |
17
|
cs.CV
|
http://creativecommons.org/publicdomain/zero/1.0/
|
We explore a new task for audio-visual-language modeling called fine-grained
audible video description (FAVD). It aims to provide detailed textual
descriptions for the given audible videos, including the appearance and spatial
locations of each object, the actions of moving objects, and the sounds in
videos. Existing visual-language modeling tasks often concentrate on visual
cues in videos while undervaluing the language and audio modalities. On the
other hand, FAVD requires not only audio-visual-language modeling skills but
also paragraph-level language generation abilities. We construct the first
fine-grained audible video description benchmark (FAVDBench) to facilitate this
research. For each video clip, we first provide a one-sentence summary of the
video, ie, the caption, followed by 4-6 sentences describing the visual details
and 1-2 audio-related descriptions at the end. The descriptions are provided in
both English and Chinese. We create two new metrics for this task: an
EntityScore to gauge the completeness of entities in the visual descriptions,
and an AudioScore to assess the audio descriptions. As a preliminary approach
to this task, we propose an audio-visual-language transformer that extends
existing video captioning model with an additional audio branch. We combine the
masked language modeling and auto-regressive language modeling losses to
optimize our model so that it can produce paragraph-level descriptions. We
illustrate the efficiency of our model in audio-visual-language modeling by
evaluating it against the proposed benchmark using both conventional captioning
metrics and our proposed metrics. We further put our benchmark to the test in
video generation models, demonstrating that employing fine-grained video
descriptions can create more intricate videos than using captions.
|
[
{
"version": "v1",
"created": "Mon, 27 Mar 2023 22:03:48 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Shen",
"Xuyang",
""
],
[
"Li",
"Dong",
""
],
[
"Zhou",
"Jinxing",
""
],
[
"Qin",
"Zhen",
""
],
[
"He",
"Bowen",
""
],
[
"Han",
"Xiaodong",
""
],
[
"Li",
"Aixuan",
""
],
[
"Dai",
"Yuchao",
""
],
[
"Kong",
"Lingpeng",
""
],
[
"Wang",
"Meng",
""
],
[
"Qiao",
"Yu",
""
],
[
"Zhong",
"Yiran",
""
]
] |
new_dataset
| 0.999774 |
2303.17582
|
Ahmad Amine
|
Ahmad Amine, Mostafa Aldilati, Hadi Hasan, Noel Maalouf, Imad H.
Elhajj
|
Human-Robot Interaction using VAHR: Virtual Assistant, Human, and Robots
in the Loop
|
7 pages, 7 figures
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Robots have become ubiquitous tools in various industries and households,
highlighting the importance of human-robot interaction (HRI). This has
increased the need for easy and accessible communication between humans and
robots. Recent research has focused on the intersection of virtual assistant
technology, such as Amazon's Alexa, with robots and its effect on HRI. This
paper presents the Virtual Assistant, Human, and Robots in the loop (VAHR)
system, which utilizes bidirectional communication to control multiple robots
through Alexa. VAHR's performance was evaluated through a human-subjects
experiment, comparing objective and subjective metrics of traditional keyboard
and mouse interfaces to VAHR. The results showed that VAHR required 41% less
Robot Attention Demand and ensured 91% more Fan-out time compared to the
standard method. Additionally, VAHR led to a 62.5% improvement in
multi-tasking, highlighting the potential for efficient human-robot interaction
in physically- and mentally-demanding scenarios. However, subjective metrics
revealed a need for human operators to build confidence and trust with this new
method of operation.
|
[
{
"version": "v1",
"created": "Thu, 30 Mar 2023 17:49:55 GMT"
},
{
"version": "v2",
"created": "Fri, 31 Mar 2023 15:53:06 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Amine",
"Ahmad",
""
],
[
"Aldilati",
"Mostafa",
""
],
[
"Hasan",
"Hadi",
""
],
[
"Maalouf",
"Noel",
""
],
[
"Elhajj",
"Imad H.",
""
]
] |
new_dataset
| 0.98301 |
2303.17619
|
Pooja Prajod
|
Pooja Prajod, Matteo Lavit Nicora, Matteo Malosio, Elisabeth Andr\'e
|
Gaze-based Attention Recognition for Human-Robot Collaboration
|
Accepted to PETRA 2023
| null | null | null |
cs.HC cs.AI cs.CV cs.RO
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Attention (and distraction) recognition is a key factor in improving
human-robot collaboration. We present an assembly scenario where a human
operator and a cobot collaborate equally to piece together a gearbox. The setup
provides multiple opportunities for the cobot to adapt its behavior depending
on the operator's attention, which can improve the collaboration experience and
reduce psychological strain. As a first step, we recognize the areas in the
workspace that the human operator is paying attention to, and consequently,
detect when the operator is distracted. We propose a novel deep-learning
approach to develop an attention recognition model. First, we train a
convolutional neural network to estimate the gaze direction using a publicly
available image dataset. Then, we use transfer learning with a small dataset to
map the gaze direction onto pre-defined areas of interest. Models trained using
this approach performed very well in leave-one-subject-out evaluation on the
small dataset. We performed an additional validation of our models using the
video snippets collected from participants working as an operator in the
presented assembly scenario. Although the recall for the Distracted class was
lower in this case, the models performed well in recognizing the areas the
operator paid attention to. To the best of our knowledge, this is the first
work that validated an attention recognition model using data from a setting
that mimics industrial human-robot collaboration. Our findings highlight the
need for validation of attention recognition solutions in such full-fledged,
non-guided scenarios.
|
[
{
"version": "v1",
"created": "Thu, 30 Mar 2023 11:55:38 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Prajod",
"Pooja",
""
],
[
"Nicora",
"Matteo Lavit",
""
],
[
"Malosio",
"Matteo",
""
],
[
"André",
"Elisabeth",
""
]
] |
new_dataset
| 0.996754 |
2303.17647
|
Danyang Liu
|
Danyang Liu, Frank Keller
|
Detecting and Grounding Important Characters in Visual Stories
|
AAAI 2023
| null | null | null |
cs.CL cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Characters are essential to the plot of any story. Establishing the
characters before writing a story can improve the clarity of the plot and the
overall flow of the narrative. However, previous work on visual storytelling
tends to focus on detecting objects in images and discovering relationships
between them. In this approach, characters are not distinguished from other
objects when they are fed into the generation pipeline. The result is a
coherent sequence of events rather than a character-centric story. In order to
address this limitation, we introduce the VIST-Character dataset, which
provides rich character-centric annotations, including visual and textual
co-reference chains and importance ratings for characters. Based on this
dataset, we propose two new tasks: important character detection and character
grounding in visual stories. For both tasks, we develop simple, unsupervised
models based on distributional similarity and pre-trained vision-and-language
models. Our new dataset, together with these models, can serve as the
foundation for subsequent work on analysing and generating stories from a
character-centric perspective.
|
[
{
"version": "v1",
"created": "Thu, 30 Mar 2023 18:24:06 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Liu",
"Danyang",
""
],
[
"Keller",
"Frank",
""
]
] |
new_dataset
| 0.977279 |
2303.17667
|
Nicholas Milikich
|
Nicholas Milikich and Joshua Johnson
|
Taureau: A Stock Market Movement Inference Framework Based on Twitter
Sentiment Analysis
| null | null | null | null |
cs.CY cs.SI q-fin.CP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the advent of fast-paced information dissemination and retrieval, it has
become inherently important to resort to automated means of predicting stock
market prices. In this paper, we propose Taureau, a framework that leverages
Twitter sentiment analysis for predicting stock market movement. The aim of our
research is to determine whether Twitter, which is assumed to be representative
of the general public, can give insight into the public perception of a
particular company and has any correlation to that company's stock price
movement. We intend to utilize this correlation to predict stock price
movement. We first utilize Tweepy and getOldTweets to obtain historical tweets
indicating public opinions for a set of top companies during periods of major
events. We filter and label the tweets using standard programming libraries. We
then vectorize and generate word embedding from the obtained tweets. Afterward,
we leverage TextBlob, a state-of-the-art sentiment analytics engine, to assess
and quantify the users' moods based on the tweets. Next, we correlate the
temporal dimensions of the obtained sentiment scores with monthly stock price
movement data. Finally, we design and evaluate a predictive model to forecast
stock price movement from lagged sentiment scores. We evaluate our framework
using actual stock price movement data to assess its ability to predict
movement direction.
|
[
{
"version": "v1",
"created": "Thu, 30 Mar 2023 19:12:08 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Milikich",
"Nicholas",
""
],
[
"Johnson",
"Joshua",
""
]
] |
new_dataset
| 0.992597 |
2303.17717
|
Weimin Jin
|
Fengjiao Zou, Jennifer Ogle, Weimin Jin, Patrick Gerard, Daniel Petty,
and Andrew Robb
|
Pedestrian Behavior Interacting with Autonomous Vehicles during Unmarked
Midblock Multilane Crossings: Role of Infrastructure Design, AV Operations
and Signaling
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
One of the main challenges autonomous vehicles (AVs) will face is interacting
with pedestrians, especially at unmarked midblock locations where the
right-of-way is unspecified. This study investigates pedestrian crossing
behavior given different roadway centerline features (i.e., undivided, two-way
left-turn lane (TWLTL), and median) and various AV operational schemes
portrayed to pedestrians through on-vehicle signals (i.e., no signal, yellow
negotiating indication, and yellow/blue negotiating/no-yield indications). This
study employs virtual reality to simulate an urban unmarked midblock
environment where pedestrians interact with AVs. Results demonstrate that both
roadway centerline design features and AV operations and signaling
significantly impact pedestrian unmarked midblock crossing behavior, including
the waiting time at the curb, waiting time in the middle of the road, and the
total crossing time. But only the roadway centerline features significantly
impact the walking time. Participants in the undivided scene spent a longer
time waiting at the curb and walking on the road than in the median and TWLTL
scenes, but they spent a shorter time waiting in the middle. Compared to the AV
without a signal, the design of yellow signal significantly reduced pedestrian
waiting time at the curb and in the middle. But yellow/blue significantly
increased the pedestrian waiting time. Interaction effects between roadway
centerline design features and AV operations and signaling are significant only
for waiting time in the middle. For middle waiting time, yellow/blue signals
had the most impact on the median road type and the least on the undivided
road. Demographics, past behaviors, and walking exposure are also explored.
Older individuals tend to wait longer, and pedestrian past crossing behaviors
and past walking exposures do not significantly impact pedestrian walking
behavior.
|
[
{
"version": "v1",
"created": "Thu, 30 Mar 2023 21:36:51 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Zou",
"Fengjiao",
""
],
[
"Ogle",
"Jennifer",
""
],
[
"Jin",
"Weimin",
""
],
[
"Gerard",
"Patrick",
""
],
[
"Petty",
"Daniel",
""
],
[
"Robb",
"Andrew",
""
]
] |
new_dataset
| 0.970586 |
2303.17845
|
Ayokunle Ige
|
Ayokunle Olalekan Ige, Mohd Halim Mohd Noor
|
WSense: A Robust Feature Learning Module for Lightweight Human Activity
Recognition
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent times, various modules such as squeeze-and-excitation, and others
have been proposed to improve the quality of features learned from wearable
sensor signals. However, these modules often cause the number of parameters to
be large, which is not suitable for building lightweight human activity
recognition models which can be easily deployed on end devices. In this
research, we propose a feature learning module, termed WSense, which uses two
1D CNN and global max pooling layers to extract similar quality features from
wearable sensor data while ignoring the difference in activity recognition
models caused by the size of the sliding window. Experiments were carried out
using CNN and ConvLSTM feature learning pipelines on a dataset obtained with a
single accelerometer (WISDM) and another obtained using the fusion of
accelerometers, gyroscopes, and magnetometers (PAMAP2) under various sliding
window sizes. A total of nine hundred sixty (960) experiments were conducted to
validate the WSense module against baselines and existing methods on the two
datasets. The results showed that the WSense module aided pipelines in learning
similar quality features and outperformed the baselines and existing models
with a minimal and uniform model size across all sliding window segmentations.
The code is available at https://github.com/AOige/WSense.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 07:12:58 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Ige",
"Ayokunle Olalekan",
""
],
[
"Noor",
"Mohd Halim Mohd",
""
]
] |
new_dataset
| 0.997858 |
2303.17877
|
Kaihua Qin
|
Kaihua Qin, Stefanos Chaliasos, Liyi Zhou, Benjamin Livshits, Dawn
Song, Arthur Gervais
|
The Blockchain Imitation Game
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The use of blockchains for automated and adversarial trading has become
commonplace. However, due to the transparent nature of blockchains, an
adversary is able to observe any pending, not-yet-mined transactions, along
with their execution logic. This transparency further enables a new type of
adversary, which copies and front-runs profitable pending transactions in
real-time, yielding significant financial gains.
Shedding light on such "copy-paste" malpractice, this paper introduces the
Blockchain Imitation Game and proposes a generalized imitation attack
methodology called Ape. Leveraging dynamic program analysis techniques, Ape
supports the automatic synthesis of adversarial smart contracts. Over a
timeframe of one year (1st of August, 2021 to 31st of July, 2022), Ape could
have yielded 148.96M USD in profit on Ethereum, and 42.70M USD on BNB Smart
Chain (BSC).
Not only as a malicious attack, we further show the potential of transaction
and contract imitation as a defensive strategy. Within one year, we find that
Ape could have successfully imitated 13 and 22 known Decentralized Finance
(DeFi) attacks on Ethereum and BSC, respectively. Our findings suggest that
blockchain validators can imitate attacks in real-time to prevent intrusions in
DeFi.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 08:21:43 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Qin",
"Kaihua",
""
],
[
"Chaliasos",
"Stefanos",
""
],
[
"Zhou",
"Liyi",
""
],
[
"Livshits",
"Benjamin",
""
],
[
"Song",
"Dawn",
""
],
[
"Gervais",
"Arthur",
""
]
] |
new_dataset
| 0.998176 |
2303.17881
|
Colin Drewes
|
Colin Drewes, Olivia Weng, Andres Meza, Alric Althoff, David
Kohlbrenner, Ryan Kastner, Dustin Richmond
|
Pentimento: Data Remanence in Cloud FPGAs
|
17 Pages, 8 Figures
| null | null | null |
cs.CR cs.AR
|
http://creativecommons.org/licenses/by/4.0/
|
Cloud FPGAs strike an alluring balance between computational efficiency,
energy efficiency, and cost. It is the flexibility of the FPGA architecture
that enables these benefits, but that very same flexibility that exposes new
security vulnerabilities. We show that a remote attacker can recover "FPGA
pentimenti" - long-removed secret data belonging to a prior user of a cloud
FPGA. The sensitive data constituting an FPGA pentimento is an analog imprint
from bias temperature instability (BTI) effects on the underlying transistors.
We demonstrate how this slight degradation can be measured using a
time-to-digital (TDC) converter when an adversary programs one into the target
cloud FPGA.
This technique allows an attacker to ascertain previously safe information on
cloud FPGAs, even after it is no longer explicitly present. Notably, it can
allow an attacker who knows a non-secret "skeleton" (the physical structure,
but not the contents) of the victim's design to (1) extract proprietary details
from an encrypted FPGA design image available on the AWS marketplace and (2)
recover data loaded at runtime by a previous user of a cloud FPGA using a known
design. Our experiments show that BTI degradation (burn-in) and recovery are
measurable and constitute a security threat to commercial cloud FPGAs.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 08:32:40 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Drewes",
"Colin",
""
],
[
"Weng",
"Olivia",
""
],
[
"Meza",
"Andres",
""
],
[
"Althoff",
"Alric",
""
],
[
"Kohlbrenner",
"David",
""
],
[
"Kastner",
"Ryan",
""
],
[
"Richmond",
"Dustin",
""
]
] |
new_dataset
| 0.999632 |
2303.17892
|
Samy Badreddine
|
Samy Badreddine and Gianluca Apriceno and Andrea Passerini and Luciano
Serafini
|
Interval Logic Tensor Networks
| null | null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic
that interprets knowledge such as sequential properties (traces) and event
properties using sequences of real-featured data. We interpret connectives
using fuzzy logic, event durations using trapezoidal fuzzy intervals, and fuzzy
temporal relations using relationships between the intervals' areas. We propose
Interval Logic Tensor Networks (ILTN), a neuro-symbolic system that learns by
propagating gradients through IRL. In order to support effective learning, ILTN
defines smoothened versions of the fuzzy intervals and temporal relations of
IRL using softplus activations. We show that ILTN can successfully leverage
knowledge expressed in IRL in synthetic tasks that require reasoning about
events to predict their fuzzy durations. Our results show that the system is
capable of making events compliant with background temporal knowledge.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 08:51:44 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Badreddine",
"Samy",
""
],
[
"Apriceno",
"Gianluca",
""
],
[
"Passerini",
"Andrea",
""
],
[
"Serafini",
"Luciano",
""
]
] |
new_dataset
| 0.984405 |
2303.17912
|
Jo\~ao Pedro Ara\'ujo
|
Joao Pedro Araujo, Jiaman Li, Karthik Vetrivel, Rishi Agarwal, Deepak
Gopinath, Jiajun Wu, Alexander Clegg, C. Karen Liu
|
CIRCLE: Capture In Rich Contextual Environments
| null | null | null | null |
cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Synthesizing 3D human motion in a contextual, ecological environment is
important for simulating realistic activities people perform in the real world.
However, conventional optics-based motion capture systems are not suited for
simultaneously capturing human movements and complex scenes. The lack of rich
contextual 3D human motion datasets presents a roadblock to creating
high-quality generative human motion models. We propose a novel motion
acquisition system in which the actor perceives and operates in a highly
contextual virtual world while being motion captured in the real world. Our
system enables rapid collection of high-quality human motion in highly diverse
scenes, without the concern of occlusion or the need for physical scene
construction in the real world. We present CIRCLE, a dataset containing 10
hours of full-body reaching motion from 5 subjects across nine scenes, paired
with ego-centric information of the environment represented in various forms,
such as RGBD videos. We use this dataset to train a model that generates human
motion conditioned on scene information. Leveraging our dataset, the model
learns to use ego-centric scene information to achieve nontrivial reaching
tasks in the context of complex 3D scenes. To download the data please visit
https://stanford-tml.github.io/circle_dataset/.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 09:18:12 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Araujo",
"Joao Pedro",
""
],
[
"Li",
"Jiaman",
""
],
[
"Vetrivel",
"Karthik",
""
],
[
"Agarwal",
"Rishi",
""
],
[
"Gopinath",
"Deepak",
""
],
[
"Wu",
"Jiajun",
""
],
[
"Clegg",
"Alexander",
""
],
[
"Liu",
"C. Karen",
""
]
] |
new_dataset
| 0.999448 |
2303.17930
|
Shiyao Wu
|
Shiyao Wu
|
JobHam-place with smart recommend job options and candidate filtering
options
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Due to the increasing number of graduates, many applicants experience the
situation about finding a job, and employers experience difficulty filtering
job applicants, which might negatively impact their effectiveness. However,
most job-hunting websites lack job recommendation and CV filtering or ranking
functionality, which are not integrated into the system. Thus, a smart job
hunter combined with the above functionality will be conducted in this project,
which contains job recommendations, CV ranking and even a job dashboard for
skills and job applicant functionality. Job recommendation and CV ranking
starts from the automatic keyword extraction and end with the Job/CV ranking
algorithm. Automatic keyword extraction is implemented by Job2Skill and the
CV2Skill model based on Bert. Job2Skill consists of two components, text
encoder and Gru-based layers, while CV2Skill is mainly based on Bert and
fine-tunes the pre-trained model by the Resume- Entity dataset. Besides, to
match skills from CV and job description and rank lists of jobs and candidates,
job/CV ranking algorithms have been provided to compute the occurrence ratio of
skill words based on TFIDF score and match ratio of the total skill numbers.
Besides, some advanced features have been integrated into the website to
improve user experiences, such as the calendar and sweetalert2 plugin. And some
basic features to go through job application processes, such as job application
tracking and interview arrangement.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 09:54:47 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Wu",
"Shiyao",
""
]
] |
new_dataset
| 0.985381 |
2303.17935
|
Sandra Liu
|
Sandra Q. Liu, Leonardo Zamora Ya\~nez, Edward H. Adelson
|
GelSight EndoFlex: A Soft Endoskeleton Hand with Continuous
High-Resolution Tactile Sensing
|
Accepted to IEEE Conference on Soft Robotics (RoboSoft) 2023
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
We describe a novel three-finger robot hand that has high resolution tactile
sensing along the entire length of each finger. The fingers are compliant,
constructed with a soft shell supported with a flexible endoskeleton. Each
finger contains two cameras, allowing tactile data to be gathered along the
front and side surfaces of the fingers. The gripper can perform an enveloping
grasp of an object and extract a large amount of rich tactile data in a single
grasp. By capturing data from many parts of the grasped object at once, we can
do object recognition with a single grasp rather than requiring multiple
touches. We describe our novel design and construction techniques which allow
us to simultaneously satisfy the requirements of compliance and strength, and
high resolution tactile sensing over large areas. The supplementary video can
be found here: https://youtu.be/H1OYADtgj9k
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 10:00:40 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Liu",
"Sandra Q.",
""
],
[
"Yañez",
"Leonardo Zamora",
""
],
[
"Adelson",
"Edward H.",
""
]
] |
new_dataset
| 0.999402 |
2303.17946
|
Luca Pajola
|
Sara Bardi, Mauro Conti, Luca Pajola, Pier Paolo Tricomi
|
Social Honeypot for Humans: Luring People through Self-managed Instagram
Pages
|
Accepted at ACNS2023
| null | null | null |
cs.SI cs.AI cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Social Honeypots are tools deployed in Online Social Networks (OSN) to
attract malevolent activities performed by spammers and bots. To this end,
their content is designed to be of maximum interest to malicious users.
However, by choosing an appropriate content topic, this attractive mechanism
could be extended to any OSN users, rather than only luring malicious actors.
As a result, honeypots can be used to attract individuals interested in a wide
range of topics, from sports and hobbies to more sensitive subjects like
political views and conspiracies. With all these individuals gathered in one
place, honeypot owners can conduct many analyses, from social to marketing
studies.
In this work, we introduce a novel concept of social honeypot for attracting
OSN users interested in a generic target topic. We propose a framework based on
fully-automated content generation strategies and engagement plans to mimic
legit Instagram pages. To validate our framework, we created 21 self-managed
social honeypots (i.e., pages) on Instagram, covering three topics, four
content generation strategies, and three engaging plans. In nine weeks, our
honeypots gathered a total of 753 followers, 5387 comments, and 15739 likes.
These results demonstrate the validity of our approach, and through statistical
analysis, we examine the characteristics of effective social honeypots.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 10:20:24 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Bardi",
"Sara",
""
],
[
"Conti",
"Mauro",
""
],
[
"Pajola",
"Luca",
""
],
[
"Tricomi",
"Pier Paolo",
""
]
] |
new_dataset
| 0.981957 |
2303.17948
|
Ming Yan
|
Ming Yan, Xin Wang, Yudi Dai, Siqi Shen, Chenglu Wen, Lan Xu, Yuexin
Ma, Cheng Wang
|
CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene
Interactions
|
CVPR 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Motion capture is a long-standing research problem. Although it has been
studied for decades, the majority of research focus on ground-based movements
such as walking, sitting, dancing, etc. Off-grounded actions such as climbing
are largely overlooked. As an important type of action in sports and
firefighting field, the climbing movements is challenging to capture because of
its complex back poses, intricate human-scene interactions, and difficult
global localization. The research community does not have an in-depth
understanding of the climbing action due to the lack of specific datasets. To
address this limitation, we collect CIMI4D, a large rock
\textbf{C}l\textbf{I}mbing \textbf{M}ot\textbf{I}on dataset from 12 persons
climbing 13 different climbing walls. The dataset consists of around 180,000
frames of pose inertial measurements, LiDAR point clouds, RGB videos,
high-precision static point cloud scenes, and reconstructed scene meshes.
Moreover, we frame-wise annotate touch rock holds to facilitate a detailed
exploration of human-scene interaction. The core of this dataset is a blending
optimization process, which corrects for the pose as it drifts and is affected
by the magnetic conditions. To evaluate the merit of CIMI4D, we perform four
tasks which include human pose estimations (with/without scene constraints),
pose prediction, and pose generation. The experimental results demonstrate that
CIMI4D presents great challenges to existing methods and enables extensive
research opportunities. We share the dataset with the research community in
http://www.lidarhumanmotion.net/cimi4d/.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 10:26:47 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Yan",
"Ming",
""
],
[
"Wang",
"Xin",
""
],
[
"Dai",
"Yudi",
""
],
[
"Shen",
"Siqi",
""
],
[
"Wen",
"Chenglu",
""
],
[
"Xu",
"Lan",
""
],
[
"Ma",
"Yuexin",
""
],
[
"Wang",
"Cheng",
""
]
] |
new_dataset
| 0.99986 |
2303.17974
|
An Mo
|
Nayan Man Singh Pradhan, Patrick Frank, An Mo, Alexander
Badri-Spr\"owitz
|
Upside down: affordable high-performance motion platform
|
For associated videos, see https://youtu.be/thXPA2MYcQw For
open-source files, see
https://github.com/nayan-pradhan/solo-6dof-motion-platform
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Parallel robots are capable of high-speed manipulation and have become
essential tools in the industry. The proximal placement of their motors and the
low weight of their end effectors make them ideal for generating highly dynamic
motion. Therefore, parallel robots can be adopted for motion platform designs,
as long as end effector loads are low. Traditional motion platforms can be
large and powerful to generate multiple g acceleration. However, these designs
tend to be expensive and large. Similar but smaller motion platforms feature a
small work range with reduced degrees of freedom (DoFs) and a limited payload.
Here we seek a medium-sized affordable parallel robot capable of powerful and
high-speed 6-DoF motion in a comparably large workspace. This work explores the
concept of a quadruped robot flipped upside-down, with the motion platform
fixed between its feet. In particular, we exploit the high-power dynamic
brushless actuation and the four-leg redundancy when moving the motion
platform. We characterize the resulting motion platform by tracking sinusoidal
and circular trajectories with varying loads. Dynamic motions in 6 DoFs up to
10 Hz and ~10 mm amplitude are possible when moving a mass of 300 grams. We
demonstrate single-axis end-effector translations up to ~20 mm at 10 Hz for
higher loads of 1.2 kg. The motion platform can be replicated easily by 3D
printing and off-the-shelf components. All motion platform-related hardware and
the custom-written software required to replicate are open-source.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 11:21:03 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Pradhan",
"Nayan Man Singh",
""
],
[
"Frank",
"Patrick",
""
],
[
"Mo",
"An",
""
],
[
"Badri-Spröwitz",
"Alexander",
""
]
] |
new_dataset
| 0.985696 |
2303.17989
|
Panagiotis Agrafiotis
|
Panagiotis Agrafiotis, Anastastios Doulamis, Andreas Georgopoulos
|
Unsupervised crack detection on complex stone masonry surfaces
|
Submitted to the Journal of Cultural Heritage, Elsevier, under review
as of 31st of March 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Computer vision for detecting building pathologies has interested researchers
for quite some time. Vision-based crack detection is a non-destructive
assessment technique, which can be useful especially for Cultural Heritage (CH)
where strict regulations apply and, even simple, interventions are not
permitted. Recently, shallow and deep machine learning architectures applied on
various types of imagery are gaining ground. In this article a crack detection
methodology for stone masonry walls is presented. In the proposed approach,
crack detection is approached as an unsupervised anomaly detection problem on
RGB (Red Green Blue) image patches. Towards this direction, some of the most
popular state of the art CNN (Convolutional Neural Network) architectures are
deployed and modified to binary classify the images or image patches by
predicting a specific class for the tested imagery; 'Crack' or 'No crack', and
detect and localize those cracks on the RGB imagery with high accuracy. Testing
of the model was performed on various test sites and random images retrieved
from the internet and collected by the authors and results suggested the high
performance of specific networks compared to the rest, considering also the
small numbers of epochs required for training. Those results met the accuracy
delivered by more complex and computationally heavy approaches, requiring a
large amount of data for training. Source code is available on GitHub
https://github.com/pagraf/Crack-detection while datasets are available on
Zenodo https://doi.org/10.5281/zenodo.6516913 .
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 12:07:23 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Agrafiotis",
"Panagiotis",
""
],
[
"Doulamis",
"Anastastios",
""
],
[
"Georgopoulos",
"Andreas",
""
]
] |
new_dataset
| 0.996515 |
2303.18021
|
Huu-Thinh Do
|
Huu-Thinh Do, Franco Blanchini, Ionela Prodan
|
A flatness-based saturated controller design for a quadcopter with
experimental validation
| null | null | null | null |
cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Using the properties of differential flatness, a controllable system, such as
a quadcoper model, may be transformed into a linear equivalent system via a
coordinate change and an input mapping. This is a straightforward advantage for
the quadcopter's controller design and its real-time implementation. However,
one significant hindrance is that, while the dynamics become linear in the new
coordinates (the flat output space), the input constraints become convoluted.
This paper addresses an explicit pre-stabilization based control scheme which
handles the input constraints for the quadcopter in the flat output space with
a saturation component. The system's stability is shown to hold by
Lyapunov-stability arguments. Moreover, the practical viability of the proposed
method is validated both in simulation and experiments over a nano-drone
platform. Hence, the flatness-based saturated controller not only ensures
stability and constraints satisfaction, but also requires very low
computational effort, allowing for embedded implementations.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 12:55:44 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Do",
"Huu-Thinh",
""
],
[
"Blanchini",
"Franco",
""
],
[
"Prodan",
"Ionela",
""
]
] |
new_dataset
| 0.957212 |
2303.18094
|
Agapius Bou Ghosn
|
Agapius Bou Ghosn, Marcus Nolte, Philip Polack, Arnaud de La Fortelle
and Markus Maurer
|
Robust LSTM-based Vehicle Velocity Observer for Regular and Near-limits
Applications
| null | null | null | null |
cs.RO eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accurate velocity estimation is key to vehicle control. While the literature
describes how model-based and learning-based observers are able to estimate a
vehicle's velocity in normal driving conditions, the challenge remains to
estimate the velocity in near-limits maneuvers while using only conventional
in-car sensors. In this paper, we introduce a novel neural network architecture
based on Long Short-Term Memory (LSTM) networks to accurately estimate the
vehicle's velocity in different driving conditions, including maneuvers at the
limits of handling. The approach has been tested on real vehicle data and it
provides more accurate estimations than state-of-the-art model-based and
learning-based methods, for both regular and near-limits driving scenarios. Our
approach is robust since the performance of the state-of-the-art observers
deteriorates with higher dynamics, while our method adapts to different
maneuvers, providing accurate estimations even at the vehicle's limits of
handling.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 14:35:08 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Ghosn",
"Agapius Bou",
""
],
[
"Nolte",
"Marcus",
""
],
[
"Polack",
"Philip",
""
],
[
"de La Fortelle",
"Arnaud",
""
],
[
"Maurer",
"Markus",
""
]
] |
new_dataset
| 0.997673 |
2303.18110
|
Ramon Sanabria
|
Ramon Sanabria, Nikolay Bogoychev, Nina Markl, Andrea Carmantini,
Ondrej Klejch, Peter Bell
|
The Edinburgh International Accents of English Corpus: Towards the
Democratization of English ASR
|
Accepted to IEEE ICASSP 2023
| null | null | null |
cs.CL cs.LG cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
English is the most widely spoken language in the world, used daily by
millions of people as a first or second language in many different contexts. As
a result, there are many varieties of English. Although the great many advances
in English automatic speech recognition (ASR) over the past decades, results
are usually reported based on test datasets which fail to represent the
diversity of English as spoken today around the globe. We present the first
release of The Edinburgh International Accents of English Corpus (EdAcc). This
dataset attempts to better represent the wide diversity of English,
encompassing almost 40 hours of dyadic video call conversations between
friends. Unlike other datasets, EdAcc includes a wide range of first and
second-language varieties of English and a linguistic background profile of
each speaker. Results on latest public, and commercial models show that EdAcc
highlights shortcomings of current English ASR models. The best performing
model, trained on 680 thousand hours of transcribed data, obtains an average of
19.7% word error rate (WER) -- in contrast to the 2.7% WER obtained when
evaluated on US English clean read speech. Across all models, we observe a drop
in performance on Indian, Jamaican, and Nigerian English speakers. Recordings,
linguistic backgrounds, data statement, and evaluation scripts are released on
our website (https://groups.inf.ed.ac.uk/edacc/) under CC-BY-SA license.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 14:56:54 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Sanabria",
"Ramon",
""
],
[
"Bogoychev",
"Nikolay",
""
],
[
"Markl",
"Nina",
""
],
[
"Carmantini",
"Andrea",
""
],
[
"Klejch",
"Ondrej",
""
],
[
"Bell",
"Peter",
""
]
] |
new_dataset
| 0.999731 |
2303.18130
|
Mehmet Parlak
|
Mehmet Parlak
|
Blockchain-based Immutable Evidence and Decentralized Loss Adjustment
for Autonomous Vehicle Accidents in Insurance
|
IEEE Global Emerging Technology Blockchain Forum 2022
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In case of an accident between two autonomous vehicles equipped with emerging
technologies, how do we apportion liability among the various players? A
special liability regime has not even yet been established for damages that may
arise due to the accidents of autonomous vehicles. Would the immutable,
time-stamped sensor records of vehicles on distributed ledger help define the
intertwined relations of liability subjects right through the accident? What if
the synthetic media created through deepfake gets involved in the insurance
claims? While integrating AI-powered anomaly or deepfake detection into
automated insurance claims processing helps to prevent insurance fraud, it is
only a matter of time before deepfake becomes nearly undetectable even to
elaborate forensic tools. This paper proposes a blockchain-based insurtech
decentralized application to check the authenticity and provenance of the
accident footage and also to decentralize the loss-adjusting process through a
hybrid of decentralized and centralized databases using smart contracts.
|
[
{
"version": "v1",
"created": "Wed, 29 Mar 2023 21:50:13 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Parlak",
"Mehmet",
""
]
] |
new_dataset
| 0.983685 |
2303.18132
|
Jakub Breier
|
Jakub Breier, Dirmanto Jap, Xiaolu Hou, Shivam Bhasin
|
A Desynchronization-Based Countermeasure Against Side-Channel Analysis
of Neural Networks
|
Accepted to the International Symposium on Cyber Security, Cryptology
and Machine Learning 2023 (CSCML)
| null | null | null |
cs.CR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Model extraction attacks have been widely applied, which can normally be used
to recover confidential parameters of neural networks for multiple layers.
Recently, side-channel analysis of neural networks allows parameter extraction
even for networks with several multiple deep layers with high effectiveness. It
is therefore of interest to implement a certain level of protection against
these attacks. In this paper, we propose a desynchronization-based
countermeasure that makes the timing analysis of activation functions harder.
We analyze the timing properties of several activation functions and design the
desynchronization in a way that the dependency on the input and the activation
type is hidden. We experimentally verify the effectiveness of the
countermeasure on a 32-bit ARM Cortex-M4 microcontroller and employ a t-test to
show the side-channel information leakage. The overhead ultimately depends on
the number of neurons in the fully-connected layer, for example, in the case of
4096 neurons in VGG-19, the overheads are between 2.8% and 11%.
|
[
{
"version": "v1",
"created": "Sat, 25 Mar 2023 12:35:04 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Breier",
"Jakub",
""
],
[
"Jap",
"Dirmanto",
""
],
[
"Hou",
"Xiaolu",
""
],
[
"Bhasin",
"Shivam",
""
]
] |
new_dataset
| 0.989608 |
2303.18142
|
Hideyuki Kawashima
|
Takashi Kambayashi and Takayuki Tanabe and Takashi Hoshino and
Hideyuki Kawashima
|
Shirakami: A Hybrid Concurrency Control Protocol for Tsurugi Relational
Database System
| null | null | null | null |
cs.DB
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Modern real-world transactional workloads such as bills of materials or
telecommunication billing need to process both short transactions and long
transactions. Recent concurrency control protocols do not cope with such
workloads since they assume only classical workloads (i.e., YCSB and TPC-C)
that have relatively short transactions. To this end, we proposed a new
concurrency control protocol Shirakami. Shirakami has two sub-protocols.
Shirakami-LTX protocol is for long transactions based on multiversion
concurrency control and Shirakami-OCC protocol is for short transactions based
on Silo. Shirakami naturally integrates them with write preservation method and
epoch-based synchronization. Shirakami is a module in Tsurugi system, which is
a production-purpose relational database system.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 15:26:42 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Kambayashi",
"Takashi",
""
],
[
"Tanabe",
"Takayuki",
""
],
[
"Hoshino",
"Takashi",
""
],
[
"Kawashima",
"Hideyuki",
""
]
] |
new_dataset
| 0.984268 |
2303.18157
|
Guillermo Bern\'ardez
|
Guillermo Bern\'ardez, Jos\'e Su\'arez-Varela, Albert L\'opez, Xiang
Shi, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, and Albert
Cabellos-Aparicio
|
MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic
Engineering
|
IEEE Transactions on Cognitive Communications and Networking (2023).
arXiv admin note: text overlap with arXiv:2109.01445
| null |
10.1109/TCCN.2023.3235719
| null |
cs.NI cs.LG cs.MA
|
http://creativecommons.org/licenses/by/4.0/
|
Current trends in networking propose the use of Machine Learning (ML) for a
wide variety of network optimization tasks. As such, many efforts have been
made to produce ML-based solutions for Traffic Engineering (TE), which is a
fundamental problem in ISP networks. Nowadays, state-of-the-art TE optimizers
rely on traditional optimization techniques, such as Local search, Constraint
Programming, or Linear programming. In this paper, we present MAGNNETO, a
distributed ML-based framework that leverages Multi-Agent Reinforcement
Learning and Graph Neural Networks for distributed TE optimization. MAGNNETO
deploys a set of agents across the network that learn and communicate in a
distributed fashion via message exchanges between neighboring agents.
Particularly, we apply this framework to optimize link weights in OSPF, with
the goal of minimizing network congestion. In our evaluation, we compare
MAGNNETO against several state-of-the-art TE optimizers in more than 75
topologies (up to 153 nodes and 354 links), including realistic traffic loads.
Our experimental results show that, thanks to its distributed nature, MAGNNETO
achieves comparable performance to state-of-the-art TE optimizers with
significantly lower execution times. Moreover, our ML-based solution
demonstrates a strong generalization capability to successfully operate in new
networks unseen during training.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 15:47:49 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Bernárdez",
"Guillermo",
""
],
[
"Suárez-Varela",
"José",
""
],
[
"López",
"Albert",
""
],
[
"Shi",
"Xiang",
""
],
[
"Xiao",
"Shihan",
""
],
[
"Cheng",
"Xiangle",
""
],
[
"Barlet-Ros",
"Pere",
""
],
[
"Cabellos-Aparicio",
"Albert",
""
]
] |
new_dataset
| 0.998285 |
2303.18162
|
Son T. Luu
|
Son T. Luu, Khoi Trong Hoang, Tuong Quang Pham, Kiet Van Nguyen, Ngan
Luu-Thuy Nguyen
|
A Multiple Choices Reading Comprehension Corpus for Vietnamese Language
Education
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Machine reading comprehension has been an interesting and challenging task in
recent years, with the purpose of extracting useful information from texts. To
attain the computer ability to understand the reading text and answer relevant
information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for
the task of multiple-choice reading comprehension in Vietnamese Textbooks which
contain the reading articles for students from Grade 1 to Grade 12. This
dataset has 699 reading passages which are prose and poems, and 5,273
questions. The questions in the new dataset are not fixed with four options as
in the previous version. Moreover, the difficulty of questions is increased,
which challenges the models to find the correct choice. The computer must
understand the whole context of the reading passage, the question, and the
content of each choice to extract the right answers. Hence, we propose the
multi-stage approach that combines the multi-step attention network (MAN) with
the natural language inference (NLI) task to enhance the performance of the
reading comprehension model. Then, we compare the proposed methodology with the
baseline BERTology models on the new dataset and the ViMMRC 1.0. Our
multi-stage models achieved 58.81% by Accuracy on the test set, which is 5.34%
better than the highest BERTology models. From the results of the error
analysis, we found the challenge of the reading comprehension models is
understanding the implicit context in texts and linking them together in order
to find the correct answers. Finally, we hope our new dataset will motivate
further research in enhancing the language understanding ability of computers
in the Vietnamese language.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 15:54:54 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Luu",
"Son T.",
""
],
[
"Hoang",
"Khoi Trong",
""
],
[
"Pham",
"Tuong Quang",
""
],
[
"Van Nguyen",
"Kiet",
""
],
[
"Nguyen",
"Ngan Luu-Thuy",
""
]
] |
new_dataset
| 0.999119 |
2303.18219
|
Shan Lin
|
Shan Lin, Yuheng Zhi, and Michael C. Yip
|
SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised
Monocular Depth Estimation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Without ground truth supervision, self-supervised depth estimation can be
trapped in a local minimum due to the gradient-locality issue of the
photometric loss. In this paper, we present a framework to enhance depth by
leveraging semantic segmentation to guide the network to jump out of the local
minimum. Prior works have proposed to share encoders between these two tasks or
explicitly align them based on priors like the consistency between edges in the
depth and segmentation maps. Yet, these methods usually require ground truth or
high-quality pseudo labels, which may not be easily accessible in real-world
applications. In contrast, we investigate self-supervised depth estimation
along with a segmentation branch that is supervised with noisy labels provided
by models pre-trained with limited data. We extend parameter sharing from the
encoder to the decoder and study the influence of different numbers of shared
decoder parameters on model performance. Also, we propose to use cross-task
information to refine current depth and segmentation predictions to generate
pseudo-depth and semantic labels for training. The advantages of the proposed
method are demonstrated through extensive experiments on the KITTI benchmark
and a downstream task for endoscopic tissue deformation tracking.
|
[
{
"version": "v1",
"created": "Fri, 31 Mar 2023 17:20:27 GMT"
}
] | 2023-04-03T00:00:00 |
[
[
"Lin",
"Shan",
""
],
[
"Zhi",
"Yuheng",
""
],
[
"Yip",
"Michael C.",
""
]
] |
new_dataset
| 0.98096 |
1801.05544
|
Ankit Parag Shah
|
Benjamin Elizalde, Rohan Badlani, Ankit Shah, Anurag Kumar, Bhiksha
Raj
|
NELS -- Never-Ending Learner of Sounds
|
Accepted at Machine Learning for Audio Signal Processing (ML4Audio),
31st Conference on Neural Information Processing Systems (NIPS 2017), Long
Beach, CA, USA
| null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sounds are essential to how humans perceive and interact with the world and
are captured in recordings and shared on the Internet on a minute-by-minute
basis. These recordings, which are predominantly videos, constitute the largest
archive of sounds we know. However, most of these recordings have undescribed
content making necessary methods for automatic sound analysis, indexing and
retrieval. These methods have to address multiple challenges, such as the
relation between sounds and language, numerous and diverse sound classes, and
large-scale evaluation. We propose a system that continuously learns from the
web relations between sounds and language, improves sound recognition models
over time and evaluates its learning competency in the large-scale without
references. We introduce the Never-Ending Learner of Sounds (NELS), a project
for continuously learning of sounds and their associated knowledge, available
on line in nels.cs.cmu.edu
|
[
{
"version": "v1",
"created": "Wed, 17 Jan 2018 04:29:12 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Mar 2023 19:52:25 GMT"
}
] | 2023-03-31T00:00:00 |
[
[
"Elizalde",
"Benjamin",
""
],
[
"Badlani",
"Rohan",
""
],
[
"Shah",
"Ankit",
""
],
[
"Kumar",
"Anurag",
""
],
[
"Raj",
"Bhiksha",
""
]
] |
new_dataset
| 0.992851 |
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