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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2209.08430
|
Shihao Shen
|
Shihao Shen and Yilin Cai and Wenshan Wang and Sebastian Scherer
|
DytanVO: Joint Refinement of Visual Odometry and Motion Segmentation in
Dynamic Environments
|
Accepted to ICRA 2023
| null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Learning-based visual odometry (VO) algorithms achieve remarkable performance
on common static scenes, benefiting from high-capacity models and massive
annotated data, but tend to fail in dynamic, populated environments. Semantic
segmentation is largely used to discard dynamic associations before estimating
camera motions but at the cost of discarding static features and is hard to
scale up to unseen categories. In this paper, we leverage the mutual dependence
between camera ego-motion and motion segmentation and show that both can be
jointly refined in a single learning-based framework. In particular, we present
DytanVO, the first supervised learning-based VO method that deals with dynamic
environments. It takes two consecutive monocular frames in real-time and
predicts camera ego-motion in an iterative fashion. Our method achieves an
average improvement of 27.7% in ATE over state-of-the-art VO solutions in
real-world dynamic environments, and even performs competitively among dynamic
visual SLAM systems which optimize the trajectory on the backend. Experiments
on plentiful unseen environments also demonstrate our method's
generalizability.
|
[
{
"version": "v1",
"created": "Sat, 17 Sep 2022 23:56:03 GMT"
},
{
"version": "v2",
"created": "Sat, 24 Sep 2022 21:04:07 GMT"
},
{
"version": "v3",
"created": "Tue, 17 Jan 2023 09:33:21 GMT"
},
{
"version": "v4",
"created": "Sat, 29 Apr 2023 04:37:57 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Shen",
"Shihao",
""
],
[
"Cai",
"Yilin",
""
],
[
"Wang",
"Wenshan",
""
],
[
"Scherer",
"Sebastian",
""
]
] |
new_dataset
| 0.980791 |
2209.08752
|
Yiye Chen
|
Yiye Chen, Yunzhi Lin, Ruinian Xu, Patricio Vela
|
Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the
Monocular RGB-D input
|
Accepted by ICRA2023. Final version. Code is available at:
https://github.com/ivalab/KGN
| null | null | null |
cs.RO cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Great success has been achieved in the 6-DoF grasp learning from the point
cloud input, yet the computational cost due to the point set orderlessness
remains a concern. Alternatively, we explore the grasp generation from the
RGB-D input in this paper. The proposed solution, Keypoint-GraspNet, detects
the projection of the gripper keypoints in the image space and then recover the
SE(3) poses with a PnP algorithm. A synthetic dataset based on the primitive
shape and the grasp family is constructed to examine our idea. Metric-based
evaluation reveals that our method outperforms the baselines in terms of the
grasp proposal accuracy, diversity, and the time cost. Finally, robot
experiments show high success rate, demonstrating the potential of the idea in
the real-world applications.
|
[
{
"version": "v1",
"created": "Mon, 19 Sep 2022 04:23:20 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Jan 2023 18:51:50 GMT"
},
{
"version": "v3",
"created": "Thu, 16 Mar 2023 18:10:43 GMT"
},
{
"version": "v4",
"created": "Mon, 1 May 2023 17:53:42 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Chen",
"Yiye",
""
],
[
"Lin",
"Yunzhi",
""
],
[
"Xu",
"Ruinian",
""
],
[
"Vela",
"Patricio",
""
]
] |
new_dataset
| 0.976065 |
2209.12726
|
Ayan Biswas
|
Arijit Saha, Ayan Biswas, Supriya Dhabal, Palaniandavar Venkateswaran
|
An Improved PMOS-Based Low Dropout Regulator Design for Large Loads
| null | null | null | null |
cs.AR
|
http://creativecommons.org/licenses/by/4.0/
|
A stable low dropout (LDO) voltage regulator topology is presented in this
paper. LDOs are linear voltage regulators that do not produce ripples in the DC
voltage. Despite the close proximity of the supply input voltage to the output,
this regulator will maintain the desired output voltage. Based on a detailed
comparison between NMOS and PMOS-based LDOs, we decided to opt for a PMOS
design because it does not require an additional charge pump as compared to
NMOS. A demonstration of how Miller capacitance enhances overall design
stability is also presented here. Multiple pass elements are arranged in
parallel in order to increase the current carrying capacity of the pass
network.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 14:29:54 GMT"
},
{
"version": "v2",
"created": "Tue, 24 Jan 2023 10:23:27 GMT"
},
{
"version": "v3",
"created": "Fri, 27 Jan 2023 10:31:38 GMT"
},
{
"version": "v4",
"created": "Tue, 31 Jan 2023 16:25:49 GMT"
},
{
"version": "v5",
"created": "Mon, 1 May 2023 14:31:08 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Saha",
"Arijit",
""
],
[
"Biswas",
"Ayan",
""
],
[
"Dhabal",
"Supriya",
""
],
[
"Venkateswaran",
"Palaniandavar",
""
]
] |
new_dataset
| 0.998677 |
2210.12527
|
Victor Adewopo
|
Victor Adewopo, Nelly Elsayed, Kelly Anderson
|
Baby Physical Safety Monitoring in Smart Home Using Action Recognition
System
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Humans are able to intuitively deduce actions that took place between two
states in observations via deductive reasoning. This is because the brain
operates on a bidirectional communication model, which has radically improved
the accuracy of recognition and prediction based on features connected to
previous experiences. During the past decade, deep learning models for action
recognition have significantly improved. However, deep neural networks struggle
with these tasks on a smaller dataset for specific Action Recognition (AR)
tasks. As with most action recognition tasks, the ambiguity of accurately
describing activities in spatial-temporal data is a drawback that can be
overcome by curating suitable datasets, including careful annotations and
preprocessing of video data for analyzing various recognition tasks. In this
study, we present a novel lightweight framework combining transfer learning
techniques with a Conv2D LSTM layer to extract features from the pre-trained
I3D model on the Kinetics dataset for a new AR task (Smart Baby Care) that
requires a smaller dataset and less computational resources. Furthermore, we
developed a benchmark dataset and an automated model that uses LSTM convolution
with I3D (ConvLSTM-I3D) for recognizing and predicting baby activities in a
smart baby room. Finally, we implemented video augmentation to improve model
performance on the smart baby care task. Compared to other benchmark models,
our experimental framework achieved better performance with less computational
resources.
|
[
{
"version": "v1",
"created": "Sat, 22 Oct 2022 19:00:14 GMT"
},
{
"version": "v2",
"created": "Sun, 30 Apr 2023 01:17:01 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Adewopo",
"Victor",
""
],
[
"Elsayed",
"Nelly",
""
],
[
"Anderson",
"Kelly",
""
]
] |
new_dataset
| 0.988332 |
2211.17256
|
Yael Vinker
|
Yael Vinker, Yuval Alaluf, Daniel Cohen-Or, Ariel Shamir
|
CLIPascene: Scene Sketching with Different Types and Levels of
Abstraction
|
Project page available at https://clipascene.github.io/CLIPascene/
| null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this paper, we present a method for converting a given scene image into a
sketch using different types and multiple levels of abstraction. We distinguish
between two types of abstraction. The first considers the fidelity of the
sketch, varying its representation from a more precise portrayal of the input
to a looser depiction. The second is defined by the visual simplicity of the
sketch, moving from a detailed depiction to a sparse sketch. Using an explicit
disentanglement into two abstraction axes -- and multiple levels for each one
-- provides users additional control over selecting the desired sketch based on
their personal goals and preferences. To form a sketch at a given level of
fidelity and simplification, we train two MLP networks. The first network
learns the desired placement of strokes, while the second network learns to
gradually remove strokes from the sketch without harming its recognizability
and semantics. Our approach is able to generate sketches of complex scenes
including those with complex backgrounds (e.g., natural and urban settings) and
subjects (e.g., animals and people) while depicting gradual abstractions of the
input scene in terms of fidelity and simplicity.
|
[
{
"version": "v1",
"created": "Wed, 30 Nov 2022 18:54:32 GMT"
},
{
"version": "v2",
"created": "Mon, 1 May 2023 15:33:55 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Vinker",
"Yael",
""
],
[
"Alaluf",
"Yuval",
""
],
[
"Cohen-Or",
"Daniel",
""
],
[
"Shamir",
"Ariel",
""
]
] |
new_dataset
| 0.999241 |
2302.03840
|
Tang Jiankai
|
Jiankai Tang, Kequan Chen, Yuntao Wang, Yuanchun Shi, Shwetak Patel,
Daniel McDuff, Xin Liu
|
MMPD: Multi-Domain Mobile Video Physiology Dataset
|
GitHub : https://github.com/McJackTang/MMPD_rPPG_dataset
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Remote photoplethysmography (rPPG) is an attractive method for noninvasive,
convenient and concomitant measurement of physiological vital signals. Public
benchmark datasets have served a valuable role in the development of this
technology and improvements in accuracy over recent years.However, there remain
gaps in the public datasets.First, despite the ubiquity of cameras on mobile
devices, there are few datasets recorded specifically with mobile phone
cameras. Second, most datasets are relatively small and therefore are limited
in diversity, both in appearance (e.g., skin tone), behaviors (e.g., motion)
and environment (e.g., lighting conditions). In an effort to help the field
advance, we present the Multi-domain Mobile Video Physiology Dataset (MMPD),
comprising 11 hours of recordings from mobile phones of 33 subjects. The
dataset is designed to capture videos with greater representation across skin
tone, body motion, and lighting conditions. MMPD is comprehensive with eight
descriptive labels and can be used in conjunction with the rPPG-toolbox. The
reliability of the dataset is verified by mainstream unsupervised methods and
neural methods. The GitHub repository of our dataset:
https://github.com/THU-CS-PI/MMPD_rPPG_dataset.
|
[
{
"version": "v1",
"created": "Wed, 8 Feb 2023 02:20:01 GMT"
},
{
"version": "v2",
"created": "Mon, 1 May 2023 01:43:36 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Tang",
"Jiankai",
""
],
[
"Chen",
"Kequan",
""
],
[
"Wang",
"Yuntao",
""
],
[
"Shi",
"Yuanchun",
""
],
[
"Patel",
"Shwetak",
""
],
[
"McDuff",
"Daniel",
""
],
[
"Liu",
"Xin",
""
]
] |
new_dataset
| 0.999829 |
2302.08217
|
Thomas P\"ahtz
|
Zhiguo He, Yang Yang, Pengcheng Jiao, Haipeng Wang, Guanzheng Lin,
Thomas P\"ahtz
|
Copebot: Underwater soft robot with copepod-like locomotion
| null |
Soft Robotics 10 (2), 314-325 (2023)
|
10.1089/soro.2021.0158
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
It has been a great challenge to develop robots that are able to perform
complex movement patterns with high speed and, simultaneously, high accuracy.
Copepods are animals found in freshwater and saltwater habitats that can have
extremely fast escape responses when a predator is sensed by performing
explosive curved jumps. Here, we present a design and build prototypes of a
combustion-driven underwater soft robot, the "copebot", that, like copepods, is
able to accurately reach nearby predefined locations in space within a single
curved jump. Because of an improved thrust force transmission unit, causing a
large initial acceleration peak (850 Bodylength*s-2), the copebot is 8 times
faster than previous combustion-driven underwater soft robots, whilst able to
perform a complete 360{\deg} rotation during the jump. Thrusts generated by the
copebot are tested to quantitatively determine the actuation performance, and
parametric studies are conducted to investigate the sensitivities of the input
parameters to the kinematic performance of the copebot. We demonstrate the
utility of our design by building a prototype that rapidly jumps out of the
water, accurately lands on its feet on a small platform, wirelessly transmits
data, and jumps back into the water. Our copebot design opens the way toward
high-performance biomimetic robots for multifunctional applications.
|
[
{
"version": "v1",
"created": "Thu, 16 Feb 2023 11:02:10 GMT"
},
{
"version": "v2",
"created": "Sat, 29 Apr 2023 11:33:46 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"He",
"Zhiguo",
""
],
[
"Yang",
"Yang",
""
],
[
"Jiao",
"Pengcheng",
""
],
[
"Wang",
"Haipeng",
""
],
[
"Lin",
"Guanzheng",
""
],
[
"Pähtz",
"Thomas",
""
]
] |
new_dataset
| 0.999189 |
2302.08956
|
Idris Abdulmumin
|
Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Abinew Ali Ayele,
Nedjma Ousidhoum, David Ifeoluwa Adelani, Seid Muhie Yimam, Ibrahim Sa'id
Ahmad, Meriem Beloucif, Saif M. Mohammad, Sebastian Ruder, Oumaima Hourrane,
Pavel Brazdil, Felermino D\'ario M\'ario Ant\'onio Ali, Davis David, Salomey
Osei, Bello Shehu Bello, Falalu Ibrahim, Tajuddeen Gwadabe, Samuel Rutunda,
Tadesse Belay, Wendimu Baye Messelle, Hailu Beshada Balcha, Sisay Adugna
Chala, Hagos Tesfahun Gebremichael, Bernard Opoku, Steven Arthur
|
AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
|
16 pages, 6 Figures, 9 Tables
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Africa is home to over 2000 languages from over six language families and has
the highest linguistic diversity among all continents. This includes 75
languages with at least one million speakers each. Yet, there is little NLP
research conducted on African languages. Crucial in enabling such research is
the availability of high-quality annotated datasets. In this paper, we
introduce AfriSenti, which consists of 14 sentiment datasets of 110,000+ tweets
in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda,
Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili,
Tigrinya, Twi, Xitsonga, and Yor\`ub\'a) from four language families annotated
by native speakers. The data is used in SemEval 2023 Task 12, the first
Afro-centric SemEval shared task. We describe the data collection methodology,
annotation process, and related challenges when curating each of the datasets.
We conduct experiments with different sentiment classification baselines and
discuss their usefulness. We hope AfriSenti enables new work on
under-represented languages. The dataset is available at
https://github.com/afrisenti-semeval/afrisent-semeval-2023 and can also be
loaded as a huggingface datasets
(https://huggingface.co/datasets/shmuhammad/AfriSenti).
|
[
{
"version": "v1",
"created": "Fri, 17 Feb 2023 15:40:12 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Apr 2023 14:43:02 GMT"
},
{
"version": "v3",
"created": "Mon, 24 Apr 2023 13:57:08 GMT"
},
{
"version": "v4",
"created": "Fri, 28 Apr 2023 19:46:51 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Muhammad",
"Shamsuddeen Hassan",
""
],
[
"Abdulmumin",
"Idris",
""
],
[
"Ayele",
"Abinew Ali",
""
],
[
"Ousidhoum",
"Nedjma",
""
],
[
"Adelani",
"David Ifeoluwa",
""
],
[
"Yimam",
"Seid Muhie",
""
],
[
"Ahmad",
"Ibrahim Sa'id",
""
],
[
"Beloucif",
"Meriem",
""
],
[
"Mohammad",
"Saif M.",
""
],
[
"Ruder",
"Sebastian",
""
],
[
"Hourrane",
"Oumaima",
""
],
[
"Brazdil",
"Pavel",
""
],
[
"Ali",
"Felermino Dário Mário António",
""
],
[
"David",
"Davis",
""
],
[
"Osei",
"Salomey",
""
],
[
"Bello",
"Bello Shehu",
""
],
[
"Ibrahim",
"Falalu",
""
],
[
"Gwadabe",
"Tajuddeen",
""
],
[
"Rutunda",
"Samuel",
""
],
[
"Belay",
"Tadesse",
""
],
[
"Messelle",
"Wendimu Baye",
""
],
[
"Balcha",
"Hailu Beshada",
""
],
[
"Chala",
"Sisay Adugna",
""
],
[
"Gebremichael",
"Hagos Tesfahun",
""
],
[
"Opoku",
"Bernard",
""
],
[
"Arthur",
"Steven",
""
]
] |
new_dataset
| 0.999842 |
2302.14705
|
Shikhar Tuli
|
Shikhar Tuli and Niraj K. Jha
|
AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference with
Transformers
| null | null | null | null |
cs.AR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Self-attention-based transformer models have achieved tremendous success in
the domain of natural language processing. Despite their efficacy, accelerating
the transformer is challenging due to its quadratic computational complexity
and large activation sizes. Existing transformer accelerators attempt to prune
its tokens to reduce memory access, albeit with high compute overheads.
Moreover, previous works directly operate on large matrices involved in the
attention operation, which limits hardware utilization. In order to address
these challenges, this work proposes a novel dynamic inference scheme,
DynaTran, which prunes activations at runtime with low overhead, substantially
reducing the number of ineffectual operations. This improves the throughput of
transformer inference. We further propose tiling the matrices in transformer
operations along with diverse dataflows to improve data reuse, thus enabling
higher energy efficiency. To effectively implement these methods, we propose
AccelTran, a novel accelerator architecture for transformers. Extensive
experiments with different models and benchmarks demonstrate that DynaTran
achieves higher accuracy than the state-of-the-art top-k hardware-aware pruning
strategy while attaining up to 1.2$\times$ higher sparsity. One of our proposed
accelerators, AccelTran-Edge, achieves 330K$\times$ higher throughput with
93K$\times$ lower energy requirement when compared to a Raspberry Pi device. On
the other hand, AccelTran-Server achieves 5.73$\times$ higher throughput and
3.69$\times$ lower energy consumption compared to the state-of-the-art
transformer co-processor, Energon. The simulation source code is available at
https://github.com/jha-lab/acceltran.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 16:17:23 GMT"
},
{
"version": "v2",
"created": "Mon, 1 May 2023 16:21:21 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Tuli",
"Shikhar",
""
],
[
"Jha",
"Niraj K.",
""
]
] |
new_dataset
| 0.991211 |
2303.12445
|
Leo Milecki
|
Leo Milecki, Vicky Kalogeiton, Sylvain Bodard, Dany Anglicheau,
Jean-Michel Correas, Marc-Olivier Timsit, Maria Vakalopoulou
|
MEDIMP: 3D Medical Images with clinical Prompts from limited tabular
data for renal transplantation
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Renal transplantation emerges as the most effective solution for end-stage
renal disease. Occurring from complex causes, a substantial risk of transplant
chronic dysfunction persists and may lead to graft loss. Medical imaging plays
a substantial role in renal transplant monitoring in clinical practice.
However, graft supervision is multi-disciplinary, notably joining nephrology,
urology, and radiology, while identifying robust biomarkers from such
high-dimensional and complex data for prognosis is challenging. In this work,
taking inspiration from the recent success of Large Language Models (LLMs), we
propose MEDIMP -- Medical Images with clinical Prompts -- a model to learn
meaningful multi-modal representations of renal transplant Dynamic
Contrast-Enhanced Magnetic Resonance Imaging (DCE MRI) by incorporating
structural clinicobiological data after translating them into text prompts.
MEDIMP is based on contrastive learning from joint text-image paired embeddings
to perform this challenging task. Moreover, we propose a framework that
generates medical prompts using automatic textual data augmentations from LLMs.
Our goal is to learn meaningful manifolds of renal transplant DCE MRI,
interesting for the prognosis of the transplant or patient status (2, 3, and 4
years after the transplant), fully exploiting the limited available multi-modal
data most efficiently. Extensive experiments and comparisons with other renal
transplant representation learning methods with limited data prove the
effectiveness of MEDIMP in a relevant clinical setting, giving new directions
toward medical prompts. Our code is available at
https://github.com/leomlck/MEDIMP.
|
[
{
"version": "v1",
"created": "Wed, 22 Mar 2023 10:30:43 GMT"
},
{
"version": "v2",
"created": "Sat, 29 Apr 2023 15:42:49 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Milecki",
"Leo",
""
],
[
"Kalogeiton",
"Vicky",
""
],
[
"Bodard",
"Sylvain",
""
],
[
"Anglicheau",
"Dany",
""
],
[
"Correas",
"Jean-Michel",
""
],
[
"Timsit",
"Marc-Olivier",
""
],
[
"Vakalopoulou",
"Maria",
""
]
] |
new_dataset
| 0.999397 |
2303.14152
|
Nikolas Lamb
|
Nikolas Lamb, Cameron Palmer, Benjamin Molloy, Sean Banerjee, Natasha
Kholgade Banerjee
|
Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken
Objects and Their Complete Counterparts
|
To be published at CVPR 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Automated shape repair approaches currently lack access to datasets that
describe real-world damaged geometry. We present Fantastic Breaks (and Where to
Find Them:
https://terascale-all-sensing-research-studio.github.io/FantasticBreaks), a
dataset containing scanned, waterproofed, and cleaned 3D meshes for 150 broken
objects, paired and geometrically aligned with complete counterparts. Fantastic
Breaks contains class and material labels, proxy repair parts that join to
broken meshes to generate complete meshes, and manually annotated fracture
boundaries. Through a detailed analysis of fracture geometry, we reveal
differences between Fantastic Breaks and synthetic fracture datasets generated
using geometric and physics-based methods. We show experimental shape repair
evaluation with Fantastic Breaks using multiple learning-based approaches
pre-trained with synthetic datasets and re-trained with subset of Fantastic
Breaks.
|
[
{
"version": "v1",
"created": "Fri, 24 Mar 2023 17:03:40 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Mar 2023 13:13:35 GMT"
},
{
"version": "v3",
"created": "Thu, 30 Mar 2023 20:16:26 GMT"
},
{
"version": "v4",
"created": "Mon, 1 May 2023 12:58:51 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Lamb",
"Nikolas",
""
],
[
"Palmer",
"Cameron",
""
],
[
"Molloy",
"Benjamin",
""
],
[
"Banerjee",
"Sean",
""
],
[
"Banerjee",
"Natasha Kholgade",
""
]
] |
new_dataset
| 0.999751 |
2304.06845
|
Idris Abdulmumin
|
Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Seid Muhie Yimam, David
Ifeoluwa Adelani, Ibrahim Sa'id Ahmad, Nedjma Ousidhoum, Abinew Ayele, Saif
M. Mohammad, Meriem Beloucif, Sebastian Ruder
|
SemEval-2023 Task 12: Sentiment Analysis for African Languages
(AfriSenti-SemEval)
|
19 pages, 5 figures, 6 tables
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present the first Africentric SemEval Shared task, Sentiment Analysis for
African Languages (AfriSenti-SemEval) - The dataset is available at
https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval
is a sentiment classification challenge in 14 African languages: Amharic,
Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican
Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and
Yor\`ub\'a (Muhammad et al., 2023), using data labeled with 3 sentiment
classes. We present three subtasks: (1) Task A: monolingual classification,
which received 44 submissions; (2) Task B: multilingual classification, which
received 32 submissions; and (3) Task C: zero-shot classification, which
received 34 submissions. The best performance for tasks A and B was achieved by
NLNDE team with 71.31 and 75.06 weighted F1, respectively. UCAS-IIE-NLP
achieved the best average score for task C with 58.15 weighted F1. We describe
the various approaches adopted by the top 10 systems and their approaches.
|
[
{
"version": "v1",
"created": "Thu, 13 Apr 2023 22:26:10 GMT"
},
{
"version": "v2",
"created": "Mon, 1 May 2023 10:18:04 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Muhammad",
"Shamsuddeen Hassan",
""
],
[
"Abdulmumin",
"Idris",
""
],
[
"Yimam",
"Seid Muhie",
""
],
[
"Adelani",
"David Ifeoluwa",
""
],
[
"Ahmad",
"Ibrahim Sa'id",
""
],
[
"Ousidhoum",
"Nedjma",
""
],
[
"Ayele",
"Abinew",
""
],
[
"Mohammad",
"Saif M.",
""
],
[
"Beloucif",
"Meriem",
""
],
[
"Ruder",
"Sebastian",
""
]
] |
new_dataset
| 0.999774 |
2304.09548
|
Ashutosh Modi
|
Ashutosh Modi and Prathamesh Kalamkar and Saurabh Karn and Aman Tiwari
and Abhinav Joshi and Sai Kiran Tanikella and Shouvik Kumar Guha and Sachin
Malhan and Vivek Raghavan
|
SemEval 2023 Task 6: LegalEval - Understanding Legal Texts
|
13 Pages (9 Pages + References), Accepted at SemEval 2023 at ACL 2023
| null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In populous countries, pending legal cases have been growing exponentially.
There is a need for developing NLP-based techniques for processing and
automatically understanding legal documents. To promote research in the area of
Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at
SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles
Labeling) is about automatically structuring legal documents into semantically
coherent units, Task-B (Legal Named Entity Recognition) deals with identifying
relevant entities in a legal document and Task-C (Court Judgement Prediction
with Explanation) explores the possibility of automatically predicting the
outcome of a legal case along with providing an explanation for the prediction.
In total 26 teams (approx. 100 participants spread across the world) submitted
systems paper. In each of the sub-tasks, the proposed systems outperformed the
baselines; however, there is a lot of scope for improvement. This paper
describes the tasks, and analyzes techniques proposed by various teams.
|
[
{
"version": "v1",
"created": "Wed, 19 Apr 2023 10:28:32 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Apr 2023 12:13:15 GMT"
},
{
"version": "v3",
"created": "Mon, 1 May 2023 11:33:08 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Modi",
"Ashutosh",
""
],
[
"Kalamkar",
"Prathamesh",
""
],
[
"Karn",
"Saurabh",
""
],
[
"Tiwari",
"Aman",
""
],
[
"Joshi",
"Abhinav",
""
],
[
"Tanikella",
"Sai Kiran",
""
],
[
"Guha",
"Shouvik Kumar",
""
],
[
"Malhan",
"Sachin",
""
],
[
"Raghavan",
"Vivek",
""
]
] |
new_dataset
| 0.999064 |
2304.09989
|
Abdesslem Layeb
|
Abdesslem Layeb
|
CKmeans and FCKmeans : Two deterministic initialization procedures for
Kmeans algorithm using a modified crowding distance
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents two novel deterministic initialization procedures for
K-means clustering based on a modified crowding distance. The procedures, named
CKmeans and FCKmeans, use more crowded points as initial centroids.
Experimental studies on multiple datasets demonstrate that the proposed
approach outperforms Kmeans and Kmeans++ in terms of clustering accuracy. The
effectiveness of CKmeans and FCKmeans is attributed to their ability to select
better initial centroids based on the modified crowding distance. Overall, the
proposed approach provides a promising alternative for improving K-means
clustering.
|
[
{
"version": "v1",
"created": "Wed, 19 Apr 2023 21:46:02 GMT"
},
{
"version": "v2",
"created": "Mon, 1 May 2023 17:13:38 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Layeb",
"Abdesslem",
""
]
] |
new_dataset
| 0.978858 |
2304.12749
|
Liyi Zhou
|
Yu Gai, Liyi Zhou, Kaihua Qin, Dawn Song, Arthur Gervais
|
Blockchain Large Language Models
| null | null | null | null |
cs.CR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a dynamic, real-time approach to detecting anomalous
blockchain transactions. The proposed tool, BlockGPT, generates tracing
representations of blockchain activity and trains from scratch a large language
model to act as a real-time Intrusion Detection System. Unlike traditional
methods, BlockGPT is designed to offer an unrestricted search space and does
not rely on predefined rules or patterns, enabling it to detect a broader range
of anomalies. We demonstrate the effectiveness of BlockGPT through its use as
an anomaly detection tool for Ethereum transactions. In our experiments, it
effectively identifies abnormal transactions among a dataset of 68M
transactions and has a batched throughput of 2284 transactions per second on
average. Our results show that, BlockGPT identifies abnormal transactions by
ranking 49 out of 124 attacks among the top-3 most abnormal transactions
interacting with their victim contracts. This work makes contributions to the
field of blockchain transaction analysis by introducing a custom data encoding
compatible with the transformer architecture, a domain-specific tokenization
technique, and a tree encoding method specifically crafted for the Ethereum
Virtual Machine (EVM) trace representation.
|
[
{
"version": "v1",
"created": "Tue, 25 Apr 2023 11:56:18 GMT"
},
{
"version": "v2",
"created": "Sat, 29 Apr 2023 16:26:40 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Gai",
"Yu",
""
],
[
"Zhou",
"Liyi",
""
],
[
"Qin",
"Kaihua",
""
],
[
"Song",
"Dawn",
""
],
[
"Gervais",
"Arthur",
""
]
] |
new_dataset
| 0.997224 |
2304.14407
|
Dongdong Chen
|
Junke Wang and Dongdong Chen and Chong Luo and Xiyang Dai and Lu Yuan
and Zuxuan Wu and Yu-Gang Jiang
|
ChatVideo: A Tracklet-centric Multimodal and Versatile Video
Understanding System
|
work in progress
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Existing deep video models are limited by specific tasks, fixed input-output
spaces, and poor generalization capabilities, making it difficult to deploy
them in real-world scenarios. In this paper, we present our vision for
multimodal and versatile video understanding and propose a prototype system,
\system. Our system is built upon a tracklet-centric paradigm, which treats
tracklets as the basic video unit and employs various Video Foundation Models
(ViFMs) to annotate their properties e.g., appearance, motion, \etc. All the
detected tracklets are stored in a database and interact with the user through
a database manager. We have conducted extensive case studies on different types
of in-the-wild videos, which demonstrates the effectiveness of our method in
answering various video-related problems. Our project is available at
https://www.wangjunke.info/ChatVideo/
|
[
{
"version": "v1",
"created": "Thu, 27 Apr 2023 17:59:58 GMT"
},
{
"version": "v2",
"created": "Sat, 29 Apr 2023 03:48:26 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Wang",
"Junke",
""
],
[
"Chen",
"Dongdong",
""
],
[
"Luo",
"Chong",
""
],
[
"Dai",
"Xiyang",
""
],
[
"Yuan",
"Lu",
""
],
[
"Wu",
"Zuxuan",
""
],
[
"Jiang",
"Yu-Gang",
""
]
] |
new_dataset
| 0.999262 |
2304.14931
|
Abdurahman Maarouf
|
Abdurahman Maarouf, Dominik B\"ar, Dominique Geissler, Stefan
Feuerriegel
|
HQP: A Human-Annotated Dataset for Detecting Online Propaganda
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Online propaganda poses a severe threat to the integrity of societies.
However, existing datasets for detecting online propaganda have a key
limitation: they were annotated using weak labels that can be noisy and even
incorrect. To address this limitation, our work makes the following
contributions: (1) We present HQP: a novel dataset (N=30,000) for detecting
online propaganda with high-quality labels. To the best of our knowledge, HQP
is the first dataset for detecting online propaganda that was created through
human annotation. (2) We show empirically that state-of-the-art language models
fail in detecting online propaganda when trained with weak labels (AUC: 64.03).
In contrast, state-of-the-art language models can accurately detect online
propaganda when trained with our high-quality labels (AUC: 92.25), which is an
improvement of ~44%. (3) To address the cost of labeling, we extend our work to
few-shot learning. Specifically, we show that prompt-based learning using a
small sample of high-quality labels can still achieve a reasonable performance
(AUC: 80.27). Finally, we discuss implications for the NLP community to balance
the cost and quality of labeling. Crucially, our work highlights the importance
of high-quality labels for sensitive NLP tasks such as propaganda detection.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 15:42:55 GMT"
},
{
"version": "v2",
"created": "Mon, 1 May 2023 08:29:51 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Maarouf",
"Abdurahman",
""
],
[
"Bär",
"Dominik",
""
],
[
"Geissler",
"Dominique",
""
],
[
"Feuerriegel",
"Stefan",
""
]
] |
new_dataset
| 0.999802 |
2305.00039
|
Luigi Capogrosso
|
Luigi Capogrosso, Luca Geretti, Marco Cristani, Franco Fummi, Tiziano
Villa
|
HermesBDD: A Multi-Core and Multi-Platform Binary Decision Diagram
Package
|
26th International Symposium on Design and Diagnostics of Electronic
Circuits and Systems (DDECS)
| null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
BDDs are representations of a Boolean expression in the form of a directed
acyclic graph. BDDs are widely used in several fields, particularly in model
checking and hardware verification. There are several implementations for BDD
manipulation, where each package differs depending on the application. This
paper presents HermesBDD: a novel multi-core and multi-platform binary decision
diagram package focused on high performance and usability. HermesBDD supports a
static and dynamic memory management mechanism, the possibility to exploit
lock-free hash tables, and a simple parallel implementation of the If-Then-Else
procedure based on a higher-level wrapper for threads and futures. HermesBDD is
completely written in C++ with no need to rely on external libraries and is
developed according to software engineering principles for reliability and easy
maintenance over time. We provide experimental results on the n-Queens problem,
the de-facto SAT solver benchmark for BDDs, demonstrating a significant speedup
of 18.73x over our non-parallel baselines, and a remarkable performance boost
w.r.t. other state-of-the-art BDDs packages.
|
[
{
"version": "v1",
"created": "Wed, 22 Mar 2023 11:15:27 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Capogrosso",
"Luigi",
""
],
[
"Geretti",
"Luca",
""
],
[
"Cristani",
"Marco",
""
],
[
"Fummi",
"Franco",
""
],
[
"Villa",
"Tiziano",
""
]
] |
new_dataset
| 0.999326 |
2305.00061
|
Zhengzhong Liang
|
Zhengzhong Liang, Zeyu Zhang, Steven Bethard, Mihai Surdeanu
|
Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning
Framework that Supports Diverse Compositional Reasoning
| null | null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Languages models have been successfully applied to a variety of reasoning
tasks in NLP, yet the language models still suffer from compositional
generalization. In this paper we present Explainable Verbal Reasoner Plus
(EVR+), a reasoning framework that enhances language models' compositional
reasoning ability by (1) allowing the model to explicitly generate and execute
symbolic operators, and (2) allowing the model to decompose a complex task into
several simpler ones in a flexible manner. Compared with its predecessor
Explainable Verbal Reasoner (EVR) and other previous approaches adopting
similar ideas, our framework supports more diverse types of reasoning such as
nested loops and different types of recursion. To evaluate our reasoning
framework, we build a synthetic dataset with five tasks that require
compositional reasoning. Results show that our reasoning framework can enhance
the language model's compositional generalization performance on the five
tasks, using a fine-tuned language model. We also discussed the possibility and
the challenges to combine our reasoning framework with a few-shot prompted
language model.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 19:27:26 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Liang",
"Zhengzhong",
""
],
[
"Zhang",
"Zeyu",
""
],
[
"Bethard",
"Steven",
""
],
[
"Surdeanu",
"Mihai",
""
]
] |
new_dataset
| 0.995846 |
2305.00076
|
Saminu Mohammad Aliyu
|
Saminu Mohammad Aliyu, Idris Abdulmumin, Shamsuddeen Hassan Muhammad,
Ibrahim Said Ahmad, Saheed Abdullahi Salahudeen, Aliyu Yusuf, Falalu Ibrahim
Lawan
|
HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and
Side-Information for Multi-Level Sexism Classification
|
5 pages, 3 figures
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present the findings of our participation in the SemEval-2023 Task 10:
Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive
language (sexism) detection on English Gab and Reddit dataset. We investigated
the effects of transferring two language models: XLM-T (sentiment
classification) and HateBERT (same domain -- Reddit) for multi-level
classification into Sexist or not Sexist, and other subsequent
sub-classifications of the sexist data. We also use synthetic classification of
unlabelled dataset and intermediary class information to maximize the
performance of our models. We submitted a system in Task A, and it ranked 49th
with F1-score of 0.82. This result showed to be competitive as it only
under-performed the best system by 0.052% F1-score.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 20:03:46 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Aliyu",
"Saminu Mohammad",
""
],
[
"Abdulmumin",
"Idris",
""
],
[
"Muhammad",
"Shamsuddeen Hassan",
""
],
[
"Ahmad",
"Ibrahim Said",
""
],
[
"Salahudeen",
"Saheed Abdullahi",
""
],
[
"Yusuf",
"Aliyu",
""
],
[
"Lawan",
"Falalu Ibrahim",
""
]
] |
new_dataset
| 0.998919 |
2305.00084
|
Wanwan Li
|
Dang Bui, Wanwan Li, Hong Huang
|
CarGameAR: An Integrated AR Car Game Authoring Interface for
Custom-Built Car Programed on Arduino Board
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this paper, we present CarGameAR: An Integrated AR Car Game Authoring
Interface for Custom-Built Car Programed on Arduino Board. The car consists of
an Arduino board, an H-bridge, and motors. The objective of the project is to
create a system that can move a car in different directions using a computer
application. The system uses Unity software to create a virtual environment
where the user can control the car using keyboard commands. The car's motion is
achieved by sending signals from the computer to the Arduino board, which then
drives the motors through the H-bridge. The project provides a cost-effective
and efficient way to build a car, which can be used for educational purposes,
such as teaching programming. Moreover, this project is not limited to the
control of the car through keyboard commands in a virtual environment. The
system can be adapted to support augmented reality (AR) technology, providing
an even more immersive and engaging user experience. By integrating the car
with AR, the user can control the car's motion using physical gestures and
movements, adding an extra layer of interactivity to the system. This makes the
car an ideal platform for game development in AR, allowing the user to create
driving games that blend the physical and virtual worlds seamlessly.
Additionally, the car's affordability and ease of construction make it an
accessible and valuable tool for teaching programming and principles in a fun
and interactive way. Overall, this project demonstrates the versatility and
potential of the car system, highlighting the various applications and
possibilities it offers for both education and entertainment.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 20:36:24 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Bui",
"Dang",
""
],
[
"Li",
"Wanwan",
""
],
[
"Huang",
"Hong",
""
]
] |
new_dataset
| 0.999549 |
2305.00104
|
Yuchen Liu
|
Yuchen Liu, Natasha Ong, Kaiyan Peng, Bo Xiong, Qifan Wang, Rui Hou,
Madian Khabsa, Kaiyue Yang, David Liu, Donald S. Williamson, Hanchao Yu
|
MMViT: Multiscale Multiview Vision Transformers
| null | null | null | null |
cs.CV eess.AS eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present Multiscale Multiview Vision Transformers (MMViT), which introduces
multiscale feature maps and multiview encodings to transformer models. Our
model encodes different views of the input signal and builds several
channel-resolution feature stages to process the multiple views of the input at
different resolutions in parallel. At each scale stage, we use a
cross-attention block to fuse information across different views. This enables
the MMViT model to acquire complex high-dimensional representations of the
input at different resolutions. The proposed model can serve as a backbone
model in multiple domains. We demonstrate the effectiveness of MMViT on audio
and image classification tasks, achieving state-of-the-art results.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 21:51:41 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Liu",
"Yuchen",
""
],
[
"Ong",
"Natasha",
""
],
[
"Peng",
"Kaiyan",
""
],
[
"Xiong",
"Bo",
""
],
[
"Wang",
"Qifan",
""
],
[
"Hou",
"Rui",
""
],
[
"Khabsa",
"Madian",
""
],
[
"Yang",
"Kaiyue",
""
],
[
"Liu",
"David",
""
],
[
"Williamson",
"Donald S.",
""
],
[
"Yu",
"Hanchao",
""
]
] |
new_dataset
| 0.999782 |
2305.00126
|
Zhuyun Zhou Ms.
|
Zhuyun Zhou, Zongwei Wu, R\'emi Boutteau, Fan Yang, Dominique Ginhac
|
DSEC-MOS: Segment Any Moving Object with Moving Ego Vehicle
| null | null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Moving Object Segmentation (MOS), a crucial task in computer vision, has
numerous applications such as surveillance, autonomous driving, and video
analytics. Existing datasets for moving object segmentation mainly focus on RGB
or Lidar videos, but lack additional event information that can enhance the
understanding of dynamic scenes. To address this limitation, we propose a novel
dataset, called DSEC-MOS. Our dataset includes frames captured by RGB cameras
embedded on moving vehicules and incorporates event data, which provide high
temporal resolution and low-latency information about changes in the scenes. To
generate accurate segmentation mask annotations for moving objects, we apply
the recently emerged large model SAM - Segment Anything Model - with moving
object bounding boxes from DSEC-MOD serving as prompts and calibrated RGB
frames, then further revise the results. Our DSEC-MOS dataset contains in total
16 sequences (13314 images). To the best of our knowledge, DSEC-MOS is also the
first moving object segmentation dataset that includes event camera in
autonomous driving. Project Page: https://github.com/ZZY-Zhou/DSEC-MOS.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 23:43:10 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Zhou",
"Zhuyun",
""
],
[
"Wu",
"Zongwei",
""
],
[
"Boutteau",
"Rémi",
""
],
[
"Yang",
"Fan",
""
],
[
"Ginhac",
"Dominique",
""
]
] |
new_dataset
| 0.999737 |
2305.00182
|
David Alonso Del Barrio
|
David Alonso del Barrio and Daniel Gatica-Perez
|
Examining European Press Coverage of the Covid-19 No-Vax Movement: An
NLP Framework
| null | null |
10.1145/3592572.3592845
| null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper examines how the European press dealt with the no-vax reactions
against the Covid-19 vaccine and the dis- and misinformation associated with
this movement. Using a curated dataset of 1786 articles from 19 European
newspapers on the anti-vaccine movement over a period of 22 months in
2020-2021, we used Natural Language Processing techniques including topic
modeling, sentiment analysis, semantic relationship with word embeddings,
political analysis, named entity recognition, and semantic networks, to
understand the specific role of the European traditional press in the
disinformation ecosystem. The results of this multi-angle analysis demonstrate
that the European well-established press actively opposed a variety of hoaxes
mainly spread on social media, and was critical of the anti-vax trend,
regardless of the political orientation of the newspaper. This confirms the
relevance of studying the role of high-quality press in the disinformation
ecosystem.
|
[
{
"version": "v1",
"created": "Sat, 29 Apr 2023 06:26:03 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"del Barrio",
"David Alonso",
""
],
[
"Gatica-Perez",
"Daniel",
""
]
] |
new_dataset
| 0.992578 |
2305.00201
|
Yuzhong Chen
|
Zhenxiang Xiao, Yuzhong Chen, Lu Zhang, Junjie Yao, Zihao Wu, Xiaowei
Yu, Yi Pan, Lin Zhao, Chong Ma, Xinyu Liu, Wei Liu, Xiang Li, Yixuan Yuan,
Dinggang Shen, Dajiang Zhu, Tianming Liu, Xi Jiang
|
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Prompts have been proven to play a crucial role in large language models, and
in recent years, vision models have also been using prompts to improve
scalability for multiple downstream tasks. In this paper, we focus on adapting
prompt design based on instruction tuning into a visual transformer model for
image classification which we called Instruction-ViT. The key idea is to
implement multi-modal prompts (text or image prompt) related to category
information to guide the fine-tuning of the model. Based on the experiments of
several image captionining tasks, the performance and domain adaptability were
improved. Our work provided an innovative strategy to fuse multi-modal prompts
with better performance and faster adaptability for visual classification
models.
|
[
{
"version": "v1",
"created": "Sat, 29 Apr 2023 08:59:12 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Xiao",
"Zhenxiang",
""
],
[
"Chen",
"Yuzhong",
""
],
[
"Zhang",
"Lu",
""
],
[
"Yao",
"Junjie",
""
],
[
"Wu",
"Zihao",
""
],
[
"Yu",
"Xiaowei",
""
],
[
"Pan",
"Yi",
""
],
[
"Zhao",
"Lin",
""
],
[
"Ma",
"Chong",
""
],
[
"Liu",
"Xinyu",
""
],
[
"Liu",
"Wei",
""
],
[
"Li",
"Xiang",
""
],
[
"Yuan",
"Yixuan",
""
],
[
"Shen",
"Dinggang",
""
],
[
"Zhu",
"Dajiang",
""
],
[
"Liu",
"Tianming",
""
],
[
"Jiang",
"Xi",
""
]
] |
new_dataset
| 0.995303 |
2305.00204
|
Maciej Wielgosz
|
Maciej Wielgosz and Antonio M. L\'opez and Muhammad Naveed Riaz
|
CARLA-BSP: a simulated dataset with pedestrians
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We present a sample dataset featuring pedestrians generated using the ARCANE
framework, a new framework for generating datasets in CARLA (0.9.13). We
provide use cases for pedestrian detection, autoencoding, pose estimation, and
pose lifting. We also showcase baseline results. For more information, visit
https://project-arcane.eu/.
|
[
{
"version": "v1",
"created": "Sat, 29 Apr 2023 09:10:32 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Wielgosz",
"Maciej",
""
],
[
"López",
"Antonio M.",
""
],
[
"Riaz",
"Muhammad Naveed",
""
]
] |
new_dataset
| 0.999869 |
2305.00210
|
Shahroz Khan
|
Shahroz Khan, Kosa Goucher-Lambert, Konstantinos Kostas, Panagiotis
Kaklis
|
ShipHullGAN: A generic parametric modeller for ship hull design using
deep convolutional generative model
| null |
Volume 411, 1 June 2023, 116051
|
10.1016/j.cma.2023.116051
| null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this work, we introduce ShipHullGAN, a generic parametric modeller built
using deep convolutional generative adversarial networks (GANs) for the
versatile representation and generation of ship hulls. At a high level, the new
model intends to address the current conservatism in the parametric ship design
paradigm, where parametric modellers can only handle a particular ship type. We
trained ShipHullGAN on a large dataset of 52,591 \textit{physically validated}
designs from a wide range of existing ship types, including container ships,
tankers, bulk carriers, tugboats, and crew supply vessels. We developed a new
shape extraction and representation strategy to convert all training designs
into a common geometric representation of the same resolution, as typically
GANs can only accept vectors of fixed dimension as input. A space-filling layer
is placed right after the generator component to ensure that the trained
generator can cover all design classes. During training, designs are provided
in the form of a shape-signature tensor (SST) which harnesses the compact
geometric representation using geometric moments that further enable the
inexpensive incorporation of physics-informed elements in ship design. We have
shown through extensive comparative studies and optimisation cases that
ShipHullGAN can generate designs with augmented features resulting in versatile
design spaces that produce traditional and novel designs with geometrically
valid and practically feasible shapes.
|
[
{
"version": "v1",
"created": "Sat, 29 Apr 2023 09:31:20 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Khan",
"Shahroz",
""
],
[
"Goucher-Lambert",
"Kosa",
""
],
[
"Kostas",
"Konstantinos",
""
],
[
"Kaklis",
"Panagiotis",
""
]
] |
new_dataset
| 0.999084 |
2305.00278
|
Chaoning Zhang
|
Dongsheng Han, Chaoning Zhang, Yu Qiao, Maryam Qamar, Yuna Jung,
SeungKyu Lee, Sung-Ho Bae, Choong Seon Hong
|
Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects
Cannot Be Easily Detected
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Meta AI Research has recently released SAM (Segment Anything Model) which is
trained on a large segmentation dataset of over 1 billion masks. As a
foundation model in the field of computer vision, SAM (Segment Anything Model)
has gained attention for its impressive performance in generic object
segmentation. Despite its strong capability in a wide range of zero-shot
transfer tasks, it remains unknown whether SAM can detect things in challenging
setups like transparent objects. In this work, we perform an empirical
evaluation of two glass-related challenging scenarios: mirror and transparent
objects. We found that SAM often fails to detect the glass in both scenarios,
which raises concern for deploying the SAM in safety-critical situations that
have various forms of glass.
|
[
{
"version": "v1",
"created": "Sat, 29 Apr 2023 15:27:57 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Han",
"Dongsheng",
""
],
[
"Zhang",
"Chaoning",
""
],
[
"Qiao",
"Yu",
""
],
[
"Qamar",
"Maryam",
""
],
[
"Jung",
"Yuna",
""
],
[
"Lee",
"SeungKyu",
""
],
[
"Bae",
"Sung-Ho",
""
],
[
"Hong",
"Choong Seon",
""
]
] |
new_dataset
| 0.999123 |
2305.00314
|
Walter Zimmer
|
Walter Zimmer, Joseph Birkner, Marcel Brucker, Huu Tung Nguyen, Stefan
Petrovski, Bohan Wang, Alois C. Knoll
|
InfraDet3D: Multi-Modal 3D Object Detection based on Roadside
Infrastructure Camera and LiDAR Sensors
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Current multi-modal object detection approaches focus on the vehicle domain
and are limited in the perception range and the processing capabilities.
Roadside sensor units (RSUs) introduce a new domain for perception systems and
leverage altitude to observe traffic. Cameras and LiDARs mounted on gantry
bridges increase the perception range and produce a full digital twin of the
traffic. In this work, we introduce InfraDet3D, a multi-modal 3D object
detector for roadside infrastructure sensors. We fuse two LiDARs using early
fusion and further incorporate detections from monocular cameras to increase
the robustness and to detect small objects. Our monocular 3D detection module
uses HD maps to ground object yaw hypotheses, improving the final perception
results. The perception framework is deployed on a real-world intersection that
is part of the A9 Test Stretch in Munich, Germany. We perform several ablation
studies and experiments and show that fusing two LiDARs with two cameras leads
to an improvement of +1.90 mAP compared to a camera-only solution. We evaluate
our results on the A9 infrastructure dataset and achieve 68.48 mAP on the test
set. The dataset and code will be available at https://a9-dataset.com to allow
the research community to further improve the perception results and make
autonomous driving safer.
|
[
{
"version": "v1",
"created": "Sat, 29 Apr 2023 17:59:55 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Zimmer",
"Walter",
""
],
[
"Birkner",
"Joseph",
""
],
[
"Brucker",
"Marcel",
""
],
[
"Nguyen",
"Huu Tung",
""
],
[
"Petrovski",
"Stefan",
""
],
[
"Wang",
"Bohan",
""
],
[
"Knoll",
"Alois C.",
""
]
] |
new_dataset
| 0.999781 |
2305.00347
|
Pierre Ohlmann
|
Pierre Ohlmann
|
Positionality of mean-payoff games on infinite graphs
|
4 pages, 2 figures
| null | null | null |
cs.LO cs.GT
|
http://creativecommons.org/licenses/by/4.0/
|
This short note establishes positionality of mean-payoff games over infinite
game graphs by constructing a well-founded monotone universal graph.
|
[
{
"version": "v1",
"created": "Sat, 29 Apr 2023 21:43:31 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Ohlmann",
"Pierre",
""
]
] |
new_dataset
| 0.950867 |
2305.00366
|
Yuze Lou
|
Yuze Lou, Bailey Kuehl, Erin Bransom, Sergey Feldman, Aakanksha Naik,
Doug Downey
|
S2abEL: A Dataset for Entity Linking from Scientific Tables
| null | null | null | null |
cs.CL cs.IR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Entity linking (EL) is the task of linking a textual mention to its
corresponding entry in a knowledge base, and is critical for many
knowledge-intensive NLP applications. When applied to tables in scientific
papers, EL is a step toward large-scale scientific knowledge bases that could
enable advanced scientific question answering and analytics. We present the
first dataset for EL in scientific tables. EL for scientific tables is
especially challenging because scientific knowledge bases can be very
incomplete, and disambiguating table mentions typically requires understanding
the papers's tet in addition to the table. Our dataset, S2abEL, focuses on EL
in machine learning results tables and includes hand-labeled cell types,
attributed sources, and entity links from the PaperswithCode taxonomy for 8,429
cells from 732 tables. We introduce a neural baseline method designed for EL on
scientific tables containing many out-of-knowledge-base mentions, and show that
it significantly outperforms a state-of-the-art generic table EL method. The
best baselines fall below human performance, and our analysis highlights
avenues for improvement.
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 02:07:22 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Lou",
"Yuze",
""
],
[
"Kuehl",
"Bailey",
""
],
[
"Bransom",
"Erin",
""
],
[
"Feldman",
"Sergey",
""
],
[
"Naik",
"Aakanksha",
""
],
[
"Downey",
"Doug",
""
]
] |
new_dataset
| 0.999295 |
2305.00367
|
Cong Nguyen
|
Cong T. Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Yong Xiao, Dusit
Niyato, Eryk Dutkiewicz
|
MetaShard: A Novel Sharding Blockchain Platform for Metaverse
Applications
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Due to its security, transparency, and flexibility in verifying virtual
assets, blockchain has been identified as one of the key technologies for
Metaverse. Unfortunately, blockchain-based Metaverse faces serious challenges
such as massive resource demands, scalability, and security concerns. To
address these issues, this paper proposes a novel sharding-based blockchain
framework, namely MetaShard, for Metaverse applications. Particularly, we first
develop an effective consensus mechanism, namely Proof-of-Engagement, that can
incentivize MUs' data and computing resource contribution. Moreover, to improve
the scalability of MetaShard, we propose an innovative sharding management
scheme to maximize the network's throughput while protecting the shards from
51% attacks. Since the optimization problem is NP-complete, we develop a hybrid
approach that decomposes the problem (using the binary search method) into
sub-problems that can be solved effectively by the Lagrangian method. As a
result, the proposed approach can obtain solutions in polynomial time, thereby
enabling flexible shard reconfiguration and reducing the risk of corruption
from the adversary. Extensive numerical experiments show that, compared to the
state-of-the-art commercial solvers, our proposed approach can achieve up to
66.6% higher throughput in less than 1/30 running time. Moreover, the proposed
approach can achieve global optimal solutions in most experiments.
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 02:11:35 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Nguyen",
"Cong T.",
""
],
[
"Hoang",
"Dinh Thai",
""
],
[
"Nguyen",
"Diep N.",
""
],
[
"Xiao",
"Yong",
""
],
[
"Niyato",
"Dusit",
""
],
[
"Dutkiewicz",
"Eryk",
""
]
] |
new_dataset
| 0.96012 |
2305.00397
|
Su Pang
|
Su Pang, Daniel Morris, Hayder Radha
|
TransCAR: Transformer-based Camera-And-Radar Fusion for 3D Object
Detection
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Despite radar's popularity in the automotive industry, for fusion-based 3D
object detection, most existing works focus on LiDAR and camera fusion. In this
paper, we propose TransCAR, a Transformer-based Camera-And-Radar fusion
solution for 3D object detection. Our TransCAR consists of two modules. The
first module learns 2D features from surround-view camera images and then uses
a sparse set of 3D object queries to index into these 2D features. The
vision-updated queries then interact with each other via transformer
self-attention layer. The second module learns radar features from multiple
radar scans and then applies transformer decoder to learn the interactions
between radar features and vision-updated queries. The cross-attention layer
within the transformer decoder can adaptively learn the soft-association
between the radar features and vision-updated queries instead of
hard-association based on sensor calibration only. Finally, our model estimates
a bounding box per query using set-to-set Hungarian loss, which enables the
method to avoid non-maximum suppression. TransCAR improves the velocity
estimation using the radar scans without temporal information. The superior
experimental results of our TransCAR on the challenging nuScenes datasets
illustrate that our TransCAR outperforms state-of-the-art Camera-Radar
fusion-based 3D object detection approaches.
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 05:35:03 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Pang",
"Su",
""
],
[
"Morris",
"Daniel",
""
],
[
"Radha",
"Hayder",
""
]
] |
new_dataset
| 0.998449 |
2305.00405
|
Graham H. Norton
|
Graham H. Norton
|
On Rueppel's Linear Complexity Conjecture
| null | null | null | null |
cs.SC
|
http://creativecommons.org/licenses/by/4.0/
|
Rueppel's conjecture on the linear complexity of the first $n$ terms of the
sequence $(1,1,0,1,0^3,1,0^7,1,0^{15},\ldots)$ was first proved by Dai using
the Euclidean algorithm. We have previously shown that we can attach a
homogeneous (annihilator) ideal of $F[x,z]$ to the first $n$ terms of a
sequence over a field $F$ and construct a pair of generating forms for it. This
approach gives another proof of Rueppel's conjecture. We also prove additional
properties of these forms and deduce the outputs of the LFSR synthesis
algorithm applied to the first $n$ terms. Further, dehomogenising the leading
generators yields the minimal polynomials of Dai.
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 06:39:29 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Norton",
"Graham H.",
""
]
] |
new_dataset
| 0.972151 |
2305.00412
|
Zhe Chen
|
Zhe Chen, Yang Yang, Anne Bettens, Youngho Eun, Xiaofeng Wu
|
A Simulation-Augmented Benchmarking Framework for Automatic RSO Streak
Detection in Single-Frame Space Images
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Detecting Resident Space Objects (RSOs) and preventing collisions with other
satellites is crucial. Recently, deep convolutional neural networks (DCNNs)
have shown superior performance in object detection when large-scale datasets
are available. However, collecting rich data of RSOs is difficult due to very
few occurrences in the space images. Without sufficient data, it is challenging
to comprehensively train DCNN detectors and make them effective for detecting
RSOs in space images, let alone to estimate whether a detector is sufficiently
robust. The lack of meaningful evaluation of different detectors could further
affect the design and application of detection methods. To tackle this issue,
we propose that the space images containing RSOs can be simulated to complement
the shortage of raw data for better benchmarking. Accordingly, we introduce a
novel simulation-augmented benchmarking framework for RSO detection (SAB-RSOD).
In our framework, by making the best use of the hardware parameters of the
sensor that captures real-world space images, we first develop a high-fidelity
RSO simulator that can generate various realistic space images. Then, we use
this simulator to generate images that contain diversified RSOs in space and
annotate them automatically. Later, we mix the synthetic images with the
real-world images, obtaining around 500 images for training with only the
real-world images for evaluation. Under SAB-RSOD, we can train different
popular object detectors like Yolo and Faster RCNN effectively, enabling us to
evaluate their performance thoroughly. The evaluation results have shown that
the amount of available data and image resolution are two key factors for
robust RSO detection. Moreover, if using a lower resolution for higher
efficiency, we demonstrated that a simple UNet-based detection method can
already access high detection accuracy.
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 07:00:16 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Chen",
"Zhe",
""
],
[
"Yang",
"Yang",
""
],
[
"Bettens",
"Anne",
""
],
[
"Eun",
"Youngho",
""
],
[
"Wu",
"Xiaofeng",
""
]
] |
new_dataset
| 0.980194 |
2305.00446
|
Hiuching Hung
|
Hiuchung Hung, Andreas Maier, Thorsten Piske
|
Building a Non-native Speech Corpus Featuring Chinese-English Bilingual
Children: Compilation and Rationale
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper introduces a non-native speech corpus consisting of narratives
from fifty 5- to 6-year-old Chinese-English children. Transcripts totaling 6.5
hours of children taking a narrative comprehension test in English (L2) are
presented, along with human-rated scores and annotations of grammatical and
pronunciation errors. The children also completed the parallel MAIN tests in
Chinese (L1) for reference purposes. For all tests we recorded audio and video
with our innovative self-developed remote collection methods. The video
recordings serve to mitigate the challenge of low intelligibility in L2
narratives produced by young children during the transcription process. This
corpus offers valuable resources for second language teaching and has the
potential to enhance the overall performance of automatic speech recognition
(ASR).
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 10:41:43 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Hung",
"Hiuchung",
""
],
[
"Maier",
"Andreas",
""
],
[
"Piske",
"Thorsten",
""
]
] |
new_dataset
| 0.960678 |
2305.00517
|
Lakmal Meegahapola
|
Yasith Amarasinghe, Darshana Sandaruwan, Thilina Madusanka, Indika
Perera, Lakmal Meegahapola
|
Multimodal Earable Sensing for Human Energy Expenditure Estimation
|
IEEE EMBC 2023 (45th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society)
| null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Energy Expenditure Estimation (EEE) is vital for maintaining weight, managing
chronic diseases, achieving fitness goals, and improving overall health and
well-being. Gold standard measurements for energy expenditure are expensive and
time-consuming, hence limiting utility and adoption. Prior work has used
wearable sensors for EEE as a workaround. Moreover, earables (ear-worn sensing
devices such as earbuds) have recently emerged as a sub-category of wearables
with unique characteristics (i.e., small form factor, high adoption) and
positioning on the human body (i.e., robust to motion, high stability, facing
thin skin), opening up a novel sensing opportunity. However, earables with
multimodal sensors have rarely been used for EEE, with data collected in
multiple activity types. Further, it is unknown how earable sensors perform
compared to standard wearable sensors worn on other body positions. In this
study, using a publicly available dataset gathered from 17 participants, we
evaluate the EEE performance using multimodal sensors of earable devices to
show that an MAE of 0.5 MET (RMSE = 0.67) can be achieved. Furthermore, we
compare the EEE performance of three commercial wearable devices with the
earable, demonstrating competitive performance of earables
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 16:06:06 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Amarasinghe",
"Yasith",
""
],
[
"Sandaruwan",
"Darshana",
""
],
[
"Madusanka",
"Thilina",
""
],
[
"Perera",
"Indika",
""
],
[
"Meegahapola",
"Lakmal",
""
]
] |
new_dataset
| 0.992025 |
2305.00521
|
Ki Taekyung
|
Taekyung Ki and Dongchan Min
|
StyleLipSync: Style-based Personalized Lip-sync Video Generation
|
Our project page: https://stylelipsync.github.io
| null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this paper, we present StyleLipSync, a style-based personalized lip-sync
video generative model that can generate identity-agnostic lip-synchronizing
video from arbitrary audio. To generate a video of arbitrary identities, we
leverage expressive lip prior from the semantically rich latent space of a
pre-trained StyleGAN, where we can also design a video consistency with a
linear transformation. In contrast to the previous lip-sync methods, we
introduce pose-aware masking that dynamically locates the mask to improve the
naturalness over frames by utilizing a 3D parametric mesh predictor frame by
frame. Moreover, we propose a few-shot lip-sync adaptation method for an
arbitrary person by introducing a sync regularizer that preserves lips-sync
generalization while enhancing the person-specific visual information.
Extensive experiments demonstrate that our model can generate accurate lip-sync
videos even with the zero-shot setting and enhance characteristics of an unseen
face using a few seconds of target video through the proposed adaptation
method. Please refer to our project page.
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 16:38:42 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Ki",
"Taekyung",
""
],
[
"Min",
"Dongchan",
""
]
] |
new_dataset
| 0.997588 |
2305.00522
|
Steven Piantadosi
|
Steven T. Piantadosi
|
How to enumerate trees from a context-free grammar
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
I present a simple algorithm for enumerating the trees generated by a Context
Free Grammar (CFG). The algorithm uses a pairing function to form a bijection
between CFG derivations and natural numbers, so that trees can be uniquely
decoded from counting. This provides a general way to number expressions in
natural logical languages, and potentially can be extended to other
combinatorial problems. I also show how this algorithm may be generalized to
more general forms of derivation, including analogs of Lempel-Ziv coding on
trees.
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 16:40:54 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Piantadosi",
"Steven T.",
""
]
] |
new_dataset
| 0.995551 |
2305.00538
|
Yanfang Le
|
Yanfang Le, Jeongkeun Lee, Jeremias Blendin, Jiayi Chen, Georgios
Nikolaidis, Rong Pan, Robert Soule, Aditya Akella, Pedro Yebenes Segura,
Arjun singhvi, Yuliang Li, Qingkai Meng, Changhoon Kim, Serhat Arslan
|
SFC: Near-Source Congestion Signaling and Flow Control
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
State-of-the-art congestion control algorithms for data centers alone do not
cope well with transient congestion and high traffic bursts. To help with
these, we revisit the concept of direct \emph{backward} feedback from switches
and propose Back-to-Sender (BTS) signaling to many concurrent incast senders.
Combining it with our novel approach to in-network caching, we achieve
near-source sub-RTT congestion signaling. Source Flow Control (SFC) combines
these two simple signaling mechanisms to instantly pause traffic sources, hence
avoiding the head-of-line blocking problem of conventional hop-by-hop flow
control. Our prototype system and scale simulations demonstrate that
near-source signaling can significantly reduce the message completion time of
various workloads in the presence of incast, complementing existing congestion
control algorithms. Our results show that SFC can reduce the
$99^{th}$-percentile flow completion times by $1.2-6\times$ and the peak switch
buffer usage by $2-3\times$ compared to the recent incast solutions.
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 17:33:50 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Le",
"Yanfang",
""
],
[
"Lee",
"Jeongkeun",
""
],
[
"Blendin",
"Jeremias",
""
],
[
"Chen",
"Jiayi",
""
],
[
"Nikolaidis",
"Georgios",
""
],
[
"Pan",
"Rong",
""
],
[
"Soule",
"Robert",
""
],
[
"Akella",
"Aditya",
""
],
[
"Segura",
"Pedro Yebenes",
""
],
[
"singhvi",
"Arjun",
""
],
[
"Li",
"Yuliang",
""
],
[
"Meng",
"Qingkai",
""
],
[
"Kim",
"Changhoon",
""
],
[
"Arslan",
"Serhat",
""
]
] |
new_dataset
| 0.996951 |
2305.00546
|
Lesley Frew
|
Lesley Frew, Michael L. Nelson, Michele C. Weigle
|
Making Changes in Webpages Discoverable: A Change-Text Search Interface
for Web Archives
|
In Proceedings of JCDL 2023; 20 pages, 11 figures, 2 tables
| null | null | null |
cs.IR cs.DL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Webpages change over time, and web archives hold copies of historical
versions of webpages. Users of web archives, such as journalists, want to find
and view changes on webpages over time. However, the current search interfaces
for web archives do not support this task. For the web archives that include a
full-text search feature, multiple versions of the same webpage that match the
search query are shown individually without enumerating changes, or are grouped
together in a way that hides changes. We present a change text search engine
that allows users to find changes in webpages. We describe the implementation
of the search engine backend and frontend, including a tool that allows users
to view the changes between two webpage versions in context as an animation. We
evaluate the search engine with U.S. federal environmental webpages that
changed between 2016 and 2020. The change text search results page can clearly
show when terms and phrases were added or removed from webpages. The inverted
index can also be queried to identify salient and frequently deleted terms in a
corpus.
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 18:16:06 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Frew",
"Lesley",
""
],
[
"Nelson",
"Michael L.",
""
],
[
"Weigle",
"Michele C.",
""
]
] |
new_dataset
| 0.964485 |
2305.00582
|
Augustine Musukwa
|
Augustine Musukwa
|
On APN functions and their derivatives
|
16
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
We determine a connection between the weight of a Boolean function and the
total weight of its first-order derivatives. The relationship established is
used to study some cryptographic properties of Boolean functions. We establish
a characterization of APN permutations in terms of the weight of the
first-order derivatives of their components. We also characterize APN functions
by the total weight of the second-order derivatives of their components. The
total weight of the first-order and second-order derivatives for functions such
as permutations, bent, partially-bent, quadratic, plateaued and balanced
functions is determined.
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 21:22:36 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Musukwa",
"Augustine",
""
]
] |
new_dataset
| 0.997526 |
2305.00598
|
Antonio Abelem
|
Antonio Abelem, Don Towsley, Gayane Vardoyan
|
Quantum Internet: The Future of Internetworking
|
Shortcourse presented in the XXXVIII Brazilian Symposium on Computer
Networks and Distributed Systems (SBRC 2020). arXiv admin note: text overlap
with arXiv:1912.06642, arXiv:1810.08421, arXiv:quant-ph/0607065,
arXiv:1610.05238 by other authors
|
Shortcourses' Book of the XXXVIII Brazilian Symposium on Computer
Networks and Distributed Systems (SBRC 2020). 1ed.: SBC, 2020, v.1, ISBN-13:
978-65-87003-33-7, p. 48-90
|
10.5753/sbc.5033.7.2
| null |
cs.NI quant-ph
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Quantum information, computation and communication, will have a great impact
on our world. One important subfield will be quantum networking and the quantum
Internet. The purpose of a quantum Internet is to enable applications that are
fundamentally out of reach for the classical Internet. Quantum networks enable
new capabilities to communication systems. This allows the parties to generate
long distance quantum entanglement, which serves a number of tasks including
the generation of multiparty shared secrets whose security relies only on the
laws of physics, distributed quantum computing, improved sensing, quantum
computing on encrypted data, and secure private-bid auctions. However, quantum
signals are fragile, and, in general, cannot be copied or amplified. In order
to enable widespread use and application development, it is essential to
develop methods that allow quantum protocols to connect to the underlying
hardware implementation transparently and to make fast and reactive decisions
for generating entanglement in the network to mitigate limited qubit lifetimes.
Architectures for large-scale quantum internetworking are in development,
paralleling theoretical and experimental work on physical layers and low-level
error management and connection technologies. This chapter aims to present the
main concepts, challenges, and opportunities for research in quantum
information, quantum computing and quantum networking.
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 23:17:47 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Abelem",
"Antonio",
""
],
[
"Towsley",
"Don",
""
],
[
"Vardoyan",
"Gayane",
""
]
] |
new_dataset
| 0.955259 |
2305.00603
|
Tianxiang Hao
|
Tianxiang Hao, Hui Chen, Yuchen Guo and Guiguang Ding
|
Consolidator: Mergeable Adapter with Grouped Connections for Visual
Adaptation
|
ICLR 2023
| null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, transformers have shown strong ability as visual feature
extractors, surpassing traditional convolution-based models in various
scenarios. However, the success of vision transformers largely owes to their
capacity to accommodate numerous parameters. As a result, new challenges for
adapting large models to downstream tasks arise. On the one hand, classic
fine-tuning tunes all parameters in a huge model for every task and thus easily
falls into overfitting, leading to inferior performance. On the other hand, on
resource-limited devices, fine-tuning stores a full copy of parameters and thus
is usually impracticable for the shortage of storage space. However, few works
have focused on how to efficiently and effectively transfer knowledge in a
vision transformer. Existing methods did not dive into the properties of visual
features, leading to inferior performance. Moreover, some of them bring heavy
inference cost though benefiting storage. To tackle these problems, we propose
consolidator to modify the pre-trained model with the addition of a small set
of tunable parameters to temporarily store the task-specific knowledge while
freezing the backbone model. Motivated by the success of group-wise
convolution, we adopt grouped connections across the features extracted by
fully connected layers to construct tunable parts in a consolidator. To further
enhance the model's capacity to transfer knowledge under a constrained storage
budget and keep inference efficient, we consolidate the parameters in two
stages: 1. between adaptation and storage, and 2. between loading and
inference. On a series of downstream visual tasks, our consolidator can reach
up to 7.56 better accuracy than full fine-tuning with merely 0.35% parameters,
and outperform state-of-the-art parameter-efficient tuning methods by a clear
margin. Code is available at https://github.com/beyondhtx/Consolidator.
|
[
{
"version": "v1",
"created": "Sun, 30 Apr 2023 23:59:02 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Hao",
"Tianxiang",
""
],
[
"Chen",
"Hui",
""
],
[
"Guo",
"Yuchen",
""
],
[
"Ding",
"Guiguang",
""
]
] |
new_dataset
| 0.961165 |
2305.00604
|
Felix Petersen
|
Felix Petersen, Tobias Sutter, Christian Borgelt, Dongsung Huh, Hilde
Kuehne, Yuekai Sun, Oliver Deussen
|
ISAAC Newton: Input-based Approximate Curvature for Newton's Method
|
Published at ICLR 2023, Code @
https://github.com/Felix-Petersen/isaac, Video @ https://youtu.be/7RKRX-MdwqM
| null | null | null |
cs.LG cs.CV math.OC stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present ISAAC (Input-baSed ApproximAte Curvature), a novel method that
conditions the gradient using selected second-order information and has an
asymptotically vanishing computational overhead, assuming a batch size smaller
than the number of neurons. We show that it is possible to compute a good
conditioner based on only the input to a respective layer without a substantial
computational overhead. The proposed method allows effective training even in
small-batch stochastic regimes, which makes it competitive to first-order as
well as second-order methods.
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 00:00:04 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Petersen",
"Felix",
""
],
[
"Sutter",
"Tobias",
""
],
[
"Borgelt",
"Christian",
""
],
[
"Huh",
"Dongsung",
""
],
[
"Kuehne",
"Hilde",
""
],
[
"Sun",
"Yuekai",
""
],
[
"Deussen",
"Oliver",
""
]
] |
new_dataset
| 0.988133 |
2305.00606
|
Derguene Mbaye
|
Derguene Mbaye, Moussa Diallo, Thierno Ibrahima Diop
|
Low-Resourced Machine Translation for Senegalese Wolof Language
|
14 pages, 5 figures, 2 Tables, 8th International Congress on
Information and Communication Technology (ICICT 2023)
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Natural Language Processing (NLP) research has made great advancements in
recent years with major breakthroughs that have established new benchmarks.
However, these advances have mainly benefited a certain group of languages
commonly referred to as resource-rich such as English and French. Majority of
other languages with weaker resources are then left behind which is the case
for most African languages including Wolof. In this work, we present a parallel
Wolof/French corpus of 123,000 sentences on which we conducted experiments on
machine translation models based on Recurrent Neural Networks (RNN) in
different data configurations. We noted performance gains with the models
trained on subworded data as well as those trained on the French-English
language pair compared to those trained on the French-Wolof pair under the same
experimental conditions.
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 00:04:19 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Mbaye",
"Derguene",
""
],
[
"Diallo",
"Moussa",
""
],
[
"Diop",
"Thierno Ibrahima",
""
]
] |
new_dataset
| 0.98662 |
2305.00645
|
Qifan Wang
|
Qifan Wang, Shujie Cui, Lei Zhou, Ye Dong, Jianli Bai, Yun Sing Koh
and Giovanni Russello
|
GTree: GPU-Friendly Privacy-preserving Decision Tree Training and
Inference
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Decision tree (DT) is a widely used machine learning model due to its
versatility, speed, and interpretability. However, for privacy-sensitive
applications, outsourcing DT training and inference to cloud platforms raise
concerns about data privacy. Researchers have developed privacy-preserving
approaches for DT training and inference using cryptographic primitives, such
as Secure Multi-Party Computation (MPC). While these approaches have shown
progress, they still suffer from heavy computation and communication overheads.
Few recent works employ Graphical Processing Units (GPU) to improve the
performance of MPC-protected deep learning. This raises a natural question:
\textit{can MPC-protected DT training and inference be accelerated by GPU?}
We present GTree, the first scheme that uses GPU to accelerate MPC-protected
secure DT training and inference. GTree is built across 3 parties who securely
and jointly perform each step of DT training and inference with GPU. Each MPC
protocol in GTree is designed in a GPU-friendly version. The performance
evaluation shows that GTree achieves ${\thicksim}11{\times}$ and
${\thicksim}21{\times}$ improvements in training SPECT and Adult datasets,
compared to the prior most efficient CPU-based work. For inference, GTree shows
its superior efficiency when the DT has less than 10 levels, which is
$126\times$ faster than the prior most efficient work when inferring $10^4$
instances with a tree of 7 levels. GTree also achieves a stronger security
guarantee than prior solutions, which only leaks the tree depth and size of
data samples while prior solutions also leak the tree structure. With
\textit{oblivious array access}, the access pattern on GPU is also protected.
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 03:35:43 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Wang",
"Qifan",
""
],
[
"Cui",
"Shujie",
""
],
[
"Zhou",
"Lei",
""
],
[
"Dong",
"Ye",
""
],
[
"Bai",
"Jianli",
""
],
[
"Koh",
"Yun Sing",
""
],
[
"Russello",
"Giovanni",
""
]
] |
new_dataset
| 0.973189 |
2305.00671
|
Fangjian Lin
|
Yizhe Ma, Fangjian Lin, Sitong Wu, Shengwei Tian, Long Yu
|
PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation
|
Accepted by IEEE TCSVT
| null |
10.1109/TCSVT.2023.3271523
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The lightweight MLP-based decoder has become increasingly promising for
semantic segmentation. However, the channel-wise MLP cannot expand the
receptive fields, lacking the context modeling capacity, which is critical to
semantic segmentation. In this paper, we propose a parametric-free patch rotate
operation to reorganize the pixels spatially. It first divides the feature map
into multiple groups and then rotates the patches within each group. Based on
the proposed patch rotate operation, we design a novel segmentation network,
named PRSeg, which includes an off-the-shelf backbone and a lightweight Patch
Rotate MLP decoder containing multiple Dynamic Patch Rotate Blocks
(DPR-Blocks). In each DPR-Block, the fully connected layer is performed
following a Patch Rotate Module (PRM) to exchange spatial information between
pixels. Specifically, in PRM, the feature map is first split into the reserved
part and rotated part along the channel dimension according to the predicted
probability of the Dynamic Channel Selection Module (DCSM), and our proposed
patch rotate operation is only performed on the rotated part. Extensive
experiments on ADE20K, Cityscapes and COCO-Stuff 10K datasets prove the
effectiveness of our approach. We expect that our PRSeg can promote the
development of MLP-based decoder in semantic segmentation.
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 06:03:16 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Ma",
"Yizhe",
""
],
[
"Lin",
"Fangjian",
""
],
[
"Wu",
"Sitong",
""
],
[
"Tian",
"Shengwei",
""
],
[
"Yu",
"Long",
""
]
] |
new_dataset
| 0.998668 |
2305.00696
|
Litao Yang
|
Litao Yang, Deval Mehta, Sidong Liu, Dwarikanath Mahapatra, Antonio Di
Ieva, Zongyuan Ge
|
TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole
Slide Image Classification
|
Accepted for MIDL 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Digital pathology based on whole slide images (WSIs) plays a key role in
cancer diagnosis and clinical practice. Due to the high resolution of the WSI
and the unavailability of patch-level annotations, WSI classification is
usually formulated as a weakly supervised problem, which relies on multiple
instance learning (MIL) based on patches of a WSI. In this paper, we aim to
learn an optimal patch-level feature space by integrating prototype learning
with MIL. To this end, we develop a Trainable Prototype enhanced deep MIL
(TPMIL) framework for weakly supervised WSI classification. In contrast to the
conventional methods which rely on a certain number of selected patches for
feature space refinement, we softly cluster all the instances by allocating
them to their corresponding prototypes. Additionally, our method is able to
reveal the correlations between different tumor subtypes through distances
between corresponding trained prototypes. More importantly, TPMIL also enables
to provide a more accurate interpretability based on the distance of the
instances from the trained prototypes which serves as an alternative to the
conventional attention score-based interpretability. We test our method on two
WSI datasets and it achieves a new SOTA. GitHub repository:
https://github.com/LitaoYang-Jet/TPMIL
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 07:39:19 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Yang",
"Litao",
""
],
[
"Mehta",
"Deval",
""
],
[
"Liu",
"Sidong",
""
],
[
"Mahapatra",
"Dwarikanath",
""
],
[
"Di Ieva",
"Antonio",
""
],
[
"Ge",
"Zongyuan",
""
]
] |
new_dataset
| 0.999636 |
2305.00767
|
Cong Cao
|
Huanjing Yue, Cong Cao, Lei Liao, and Jingyu Yang
|
RViDeformer: Efficient Raw Video Denoising Transformer with a Larger
Benchmark Dataset
|
16 pages,15 figures
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, raw video denoising has garnered increased attention due to
the consistency with the imaging process and well-studied noise modeling in the
raw domain. However, two problems still hinder the denoising performance.
Firstly, there is no large dataset with realistic motions for supervised raw
video denoising, as capturing noisy and clean frames for real dynamic scenes is
difficult. To address this, we propose recapturing existing high-resolution
videos displayed on a 4K screen with high-low ISO settings to construct
noisy-clean paired frames. In this way, we construct a video denoising dataset
(named as ReCRVD) with 120 groups of noisy-clean videos, whose ISO values
ranging from 1600 to 25600. Secondly, while non-local temporal-spatial
attention is beneficial for denoising, it often leads to heavy computation
costs. We propose an efficient raw video denoising transformer network
(RViDeformer) that explores both short and long-distance correlations.
Specifically, we propose multi-branch spatial and temporal attention modules,
which explore the patch correlations from local window, local low-resolution
window, global downsampled window, and neighbor-involved window, and then they
are fused together. We employ reparameterization to reduce computation costs.
Our network is trained in both supervised and unsupervised manners, achieving
the best performance compared with state-of-the-art methods. Additionally, the
model trained with our proposed dataset (ReCRVD) outperforms the model trained
with previous benchmark dataset (CRVD) when evaluated on the real-world outdoor
noisy videos. Our code and dataset will be released after the acceptance of
this work.
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 11:06:58 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Yue",
"Huanjing",
""
],
[
"Cao",
"Cong",
""
],
[
"Liao",
"Lei",
""
],
[
"Yang",
"Jingyu",
""
]
] |
new_dataset
| 0.989443 |
2305.00813
|
Kaushik Roy
|
Amit Sheth, Kaushik Roy, Manas Gaur
|
Neurosymbolic AI - Why, What, and How
|
To appear in IEEE Intelligent Systems
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Humans interact with the environment using a combination of perception -
transforming sensory inputs from their environment into symbols, and cognition
- mapping symbols to knowledge about the environment for supporting
abstraction, reasoning by analogy, and long-term planning. Human
perception-inspired machine perception, in the context of AI, refers to
large-scale pattern recognition from raw data using neural networks trained
using self-supervised learning objectives such as next-word prediction or
object recognition. On the other hand, machine cognition encompasses more
complex computations, such as using knowledge of the environment to guide
reasoning, analogy, and long-term planning. Humans can also control and explain
their cognitive functions. This seems to require the retention of symbolic
mappings from perception outputs to knowledge about their environment. For
example, humans can follow and explain the guidelines and safety constraints
driving their decision-making in safety-critical applications such as
healthcare, criminal justice, and autonomous driving. This article introduces
the rapidly emerging paradigm of Neurosymbolic AI combines neural networks and
knowledge-guided symbolic approaches to create more capable and flexible AI
systems. These systems have immense potential to advance both algorithm-level
(e.g., abstraction, analogy, reasoning) and application-level (e.g.,
explainable and safety-constrained decision-making) capabilities of AI systems.
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 13:27:22 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Sheth",
"Amit",
""
],
[
"Roy",
"Kaushik",
""
],
[
"Gaur",
"Manas",
""
]
] |
new_dataset
| 0.992788 |
2305.00818
|
Joseph Chow
|
Bingqing Liu, Joseph Y. J. Chow
|
On-demand Mobility-as-a-Service platform assignment games with
guaranteed stable outcomes
| null | null | null | null |
cs.GT cs.CY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Mobility-as-a-Service (MaaS) systems are two-sided markets, with two mutually
exclusive sets of agents, i.e., travelers/users and operators, forming a
mobility ecosystem in which multiple operators compete or cooperate to serve
customers under a governing platform provider. This study proposes a MaaS
platform equilibrium model based on many-to-many assignment games incorporating
both fixed-route transit services and mobility-on-demand (MOD) services. The
matching problem is formulated as a multicommodity flow network design problem
under congestion. The local stability conditions reflect a generalization of
Wardrop's principles that include operator decisions. A subsidy mechanism from
the platform is proposed to guarantee local stability. An exact solution
algorithm is proposed based on a branch and bound framework with a Frank-Wolfe
algorithm integrated with Lagrangian relaxation and subgradient optimization,
which guarantees the optimality of the matching problem but not stability. A
heuristic which integrates stability conditions and subsidy design is proposed,
which reaches either the optimal MaaS platform equilibrium solution with global
stability, or a feasible locally stable solution that may require subsidy. A
worst-case bound and condition for obtaining an exact solution are both
identified. Two sets of reproducible numerical experiments are conducted. The
first, on a toy network, verifies the model and algorithm, and illustrates the
differences between local and global stability. The second, on an expanded
Sioux Falls network with 82 nodes and 748 links, derives generalizable insights
about the model for coopetitive interdependencies between operators sharing the
platform, handling congestion effects in MOD services, effects of local
stability on investment impacts, and illustrating inequities that may arise
under heterogeneous populations.
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 13:33:16 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Liu",
"Bingqing",
""
],
[
"Chow",
"Joseph Y. J.",
""
]
] |
new_dataset
| 0.997278 |
2305.00885
|
Glaucia Melo Dos Santos
|
Glaucia Melo, Luis Fernando Lins, Paulo Alencar, Donald Cowan
|
Supporting Contextual Conversational Agent-Based Software Development
|
Accepted on BotSE Workshop 2023
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Software Development (SD) is remarkably dynamic and is critically dependent
on the knowledge acquired by the project's software developers as the project
progresses. Software developers need to understand large amounts of information
related to the tasks at hand. This information (context) is often not explicit,
as it can be lost in large documentation repositories, a team member's brain,
or beyond their cognitive memory capacity. These contexts include tool
features, integration strategies, data structures, code syntax, approaches to
tasks, project definitions, and even implicit or tacit contexts, which add
significant complexity to the SD process. Current software development
practices still lack sufficient techniques using the existing SD execution
information and context to provide developers with relevant process guidance,
augmenting their capacity to do their job using available applicable
information. This paper presents ongoing and future research on an approach to
support conversational agent-based knowledge-augmented software development.
Developers benefit by receiving recommendations about task-related information
and workflows they need to execute. This work advances human-computer
interaction patterns in workflow engines, from graphical user interfaces to
conversational patterns in software engineering.
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 15:34:21 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Melo",
"Glaucia",
""
],
[
"Lins",
"Luis Fernando",
""
],
[
"Alencar",
"Paulo",
""
],
[
"Cowan",
"Donald",
""
]
] |
new_dataset
| 0.997769 |
2305.00911
|
Jai Prakash
|
Jai Prakash, Michele Vignati and Edoardo Sabbioni
|
SRPT vs Smith Predictor for Vehicle Teleoperation
|
This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible
| null | null | null |
cs.RO cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
Vehicle teleoperation has potential applications in fallback solutions for
autonomous vehicles, remote delivery services, and hazardous operations.
However, network delays and limited situational awareness can compromise
teleoperation performance and increase the cognitive workload of human
operators. To address these issues, we previously introduced the novel
successive reference pose tracking (SRPT) approach, which transmits successive
reference poses to the vehicle instead of steering commands. This paper
compares the stability and performance of SRPT with Smith predictor-based
approaches for direct vehicle teleoperation in challenging scenarios. The Smith
predictor approach is further categorized, one with Lookahead driver and second
with Stanley driver. Simulations are conducted in a Simulink environment,
considering variable network delays and different vehicle speeds, and include
maneuvers such as tight corners, slalom, low-adhesion roads, and strong
crosswinds. The results show that the SRPT approach significantly improves
stability and reference tracking performance, with negligible effect of network
delays on path tracking. Our findings demonstrate the effectiveness of SRPT in
eliminating the detrimental effect of network delays in vehicle teleoperation.
|
[
{
"version": "v1",
"created": "Thu, 27 Apr 2023 17:57:38 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Prakash",
"Jai",
""
],
[
"Vignati",
"Michele",
""
],
[
"Sabbioni",
"Edoardo",
""
]
] |
new_dataset
| 0.998195 |
2305.00925
|
Joseph Bao
|
Joseph Bao, Murat Kantarcioglu, Yevgeniy Vorobeychik, Charles Kamhoua
|
IoTFlowGenerator: Crafting Synthetic IoT Device Traffic Flows for Cyber
Deception
|
FLAIRS-36
| null | null | null |
cs.CR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Over the years, honeypots emerged as an important security tool to understand
attacker intent and deceive attackers to spend time and resources. Recently,
honeypots are being deployed for Internet of things (IoT) devices to lure
attackers, and learn their behavior. However, most of the existing IoT
honeypots, even the high interaction ones, are easily detected by an attacker
who can observe honeypot traffic due to lack of real network traffic
originating from the honeypot. This implies that, to build better honeypots and
enhance cyber deception capabilities, IoT honeypots need to generate realistic
network traffic flows. To achieve this goal, we propose a novel deep learning
based approach for generating traffic flows that mimic real network traffic due
to user and IoT device interactions. A key technical challenge that our
approach overcomes is scarcity of device-specific IoT traffic data to
effectively train a generator. We address this challenge by leveraging a core
generative adversarial learning algorithm for sequences along with domain
specific knowledge common to IoT devices. Through an extensive experimental
evaluation with 18 IoT devices, we demonstrate that the proposed synthetic IoT
traffic generation tool significantly outperforms state of the art sequence and
packet generators in remaining indistinguishable from real traffic even to an
adaptive attacker.
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 16:24:07 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Bao",
"Joseph",
""
],
[
"Kantarcioglu",
"Murat",
""
],
[
"Vorobeychik",
"Yevgeniy",
""
],
[
"Kamhoua",
"Charles",
""
]
] |
new_dataset
| 0.993258 |
2305.00936
|
Sihun Cha
|
Sihun Cha, Kwanggyoon Seo, Amirsaman Ashtari, Junyong Noh
|
Generating Texture for 3D Human Avatar from a Single Image using
Sampling and Refinement Networks
| null | null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
There has been significant progress in generating an animatable 3D human
avatar from a single image. However, recovering texture for the 3D human avatar
from a single image has been relatively less addressed. Because the generated
3D human avatar reveals the occluded texture of the given image as it moves, it
is critical to synthesize the occluded texture pattern that is unseen from the
source image. To generate a plausible texture map for 3D human avatars, the
occluded texture pattern needs to be synthesized with respect to the visible
texture from the given image. Moreover, the generated texture should align with
the surface of the target 3D mesh. In this paper, we propose a texture
synthesis method for a 3D human avatar that incorporates geometry information.
The proposed method consists of two convolutional networks for the sampling and
refining process. The sampler network fills in the occluded regions of the
source image and aligns the texture with the surface of the target 3D mesh
using the geometry information. The sampled texture is further refined and
adjusted by the refiner network. To maintain the clear details in the given
image, both sampled and refined texture is blended to produce the final texture
map. To effectively guide the sampler network to achieve its goal, we designed
a curriculum learning scheme that starts from a simple sampling task and
gradually progresses to the task where the alignment needs to be considered. We
conducted experiments to show that our method outperforms previous methods
qualitatively and quantitatively.
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 16:44:02 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Cha",
"Sihun",
""
],
[
"Seo",
"Kwanggyoon",
""
],
[
"Ashtari",
"Amirsaman",
""
],
[
"Noh",
"Junyong",
""
]
] |
new_dataset
| 0.989041 |
2305.00942
|
Lizhen Wang
|
Lizhen Wang, Xiaochen Zhao, Jingxiang Sun, Yuxiang Zhang, Hongwen
Zhang, Tao Yu, Yebin Liu
|
StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single
Video
|
8 pages, 5 figures, SIGGRAPH 2023 Conference Proceedings
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Face reenactment methods attempt to restore and re-animate portrait videos as
realistically as possible. Existing methods face a dilemma in quality versus
controllability: 2D GAN-based methods achieve higher image quality but suffer
in fine-grained control of facial attributes compared with 3D counterparts. In
this work, we propose StyleAvatar, a real-time photo-realistic portrait avatar
reconstruction method using StyleGAN-based networks, which can generate
high-fidelity portrait avatars with faithful expression control. We expand the
capabilities of StyleGAN by introducing a compositional representation and a
sliding window augmentation method, which enable faster convergence and improve
translation generalization. Specifically, we divide the portrait scenes into
three parts for adaptive adjustments: facial region, non-facial foreground
region, and the background. Besides, our network leverages the best of UNet,
StyleGAN and time coding for video learning, which enables high-quality video
generation. Furthermore, a sliding window augmentation method together with a
pre-training strategy are proposed to improve translation generalization and
training performance, respectively. The proposed network can converge within
two hours while ensuring high image quality and a forward rendering time of
only 20 milliseconds. Furthermore, we propose a real-time live system, which
further pushes research into applications. Results and experiments demonstrate
the superiority of our method in terms of image quality, full portrait video
generation, and real-time re-animation compared to existing facial reenactment
methods. Training and inference code for this paper are at
https://github.com/LizhenWangT/StyleAvatar.
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 16:54:35 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Wang",
"Lizhen",
""
],
[
"Zhao",
"Xiaochen",
""
],
[
"Sun",
"Jingxiang",
""
],
[
"Zhang",
"Yuxiang",
""
],
[
"Zhang",
"Hongwen",
""
],
[
"Yu",
"Tao",
""
],
[
"Liu",
"Yebin",
""
]
] |
new_dataset
| 0.993377 |
2305.00956
|
Debarnab Mitra
|
Debarnab Mitra, Lev Tauz, Murat Can Sarihan, Chee Wei Wong, and Lara
Dolecek
|
Non-Binary LDPC Code Design for Energy-Time Entanglement Quantum Key
Distribution
|
5 pages, 4 figures, submitted to International Symposium on Topics in
Coding
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
In energy-time entanglement Quantum Key Distribution (QKD), two users extract
a shared secret key from the arrival times (discretized as symbols) of
entangled photon pairs. In prior work, Zhou et al. proposed a multi-level
coding (MLC) scheme that splits the observed symbols into bit layers and
utilizes binary Low-Density Parity-Check (LDPC) codes for reconciliation of the
symbols. While binary LDPC codes offer low latency for key generation,
splitting the symbols into bits results in a loss of key generation rate due to
error propagation. Additionally, existing LDPC codes do not fully utilize the
properties of the QKD channel to optimize the key rates. In this paper, we
mitigate the above issues by first generalizing the MLC scheme to a
non-binary(NB) MLC scheme that has layers with non-binary symbols and utilizes
NB-LDPC codes. We show the NB-MLC scheme offers flexibility in system design.
Additionally, we show that the NB-MLC scheme with a small symbol size per layer
offers the best trade-off between latency and key rate. We then propose a
framework to jointly optimize the rate and degree profile of the NB-LDPC codes
that is tailored towards the QKD channel resulting in higher key rates than
prior work.
|
[
{
"version": "v1",
"created": "Mon, 1 May 2023 17:39:02 GMT"
}
] | 2023-05-02T00:00:00 |
[
[
"Mitra",
"Debarnab",
""
],
[
"Tauz",
"Lev",
""
],
[
"Sarihan",
"Murat Can",
""
],
[
"Wong",
"Chee Wei",
""
],
[
"Dolecek",
"Lara",
""
]
] |
new_dataset
| 0.997402 |
2103.03133
|
\v{S}imon Bil\'ik
|
Simon Bilik, Lukas Kratochvila, Adam Ligocki, Ondrej Bostik, Tomas
Zemcik, Matous Hybl, Karel Horak, Ludek Zalud
|
Visual diagnosis of the Varroa destructor parasitic mite in honeybees
using object detector techniques
| null |
Sensors, 21-8 (2021), 2764-2780
|
10.3390/s21082764
|
BUT171160
|
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The Varroa destructor mite is one of the most dangerous Honey Bee (Apis
mellifera) parasites worldwide and the bee colonies have to be regularly
monitored in order to control its spread. Here we present an object detector
based method for health state monitoring of bee colonies. This method has the
potential for online measurement and processing. In our experiment, we compare
the YOLO and SSD object detectors along with the Deep SVDD anomaly detector.
Based on the custom dataset with 600 ground-truth images of healthy and
infected bees in various scenes, the detectors reached a high F1 score up to
0.874 in the infected bee detection and up to 0.727 in the detection of the
Varroa Destructor mite itself. The results demonstrate the potential of this
approach, which will be later used in the real-time computer vision based honey
bee inspection system. To the best of our knowledge, this study is the first
one using object detectors for this purpose. We expect that performance of
those object detectors will enable us to inspect the health status of the honey
bee colonies.
|
[
{
"version": "v1",
"created": "Fri, 26 Feb 2021 11:01:31 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Bilik",
"Simon",
""
],
[
"Kratochvila",
"Lukas",
""
],
[
"Ligocki",
"Adam",
""
],
[
"Bostik",
"Ondrej",
""
],
[
"Zemcik",
"Tomas",
""
],
[
"Hybl",
"Matous",
""
],
[
"Horak",
"Karel",
""
],
[
"Zalud",
"Ludek",
""
]
] |
new_dataset
| 0.99844 |
2209.11864
|
Yizhou Huang
|
Yizhou Huang, Hamza Dugmag, Timothy D. Barfoot, and Florian Shkurti
|
Stochastic Planning for ASV Navigation Using Satellite Images
|
7 pages, 5 figures
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Autonomous surface vessels (ASV) represent a promising technology to automate
water-quality monitoring of lakes. In this work, we use satellite images as a
coarse map and plan sampling routes for the robot. However, inconsistency
between the satellite images and the actual lake, as well as environmental
disturbances such as wind, aquatic vegetation, and changing water levels can
make it difficult for robots to visit places suggested by the prior map. This
paper presents a robust route-planning algorithm that minimizes the expected
total travel distance given these environmental disturbances, which induce
uncertainties in the map. We verify the efficacy of our algorithm in
simulations of over a thousand Canadian lakes and demonstrate an application of
our algorithm in a 3.7 km-long real-world robot experiment on a lake in
Northern Ontario, Canada. Videos are available on our website
https://pcctp.github.io/.
|
[
{
"version": "v1",
"created": "Fri, 23 Sep 2022 21:25:48 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Apr 2023 15:27:36 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Huang",
"Yizhou",
""
],
[
"Dugmag",
"Hamza",
""
],
[
"Barfoot",
"Timothy D.",
""
],
[
"Shkurti",
"Florian",
""
]
] |
new_dataset
| 0.994372 |
2210.04476
|
Albert Yu
|
Albert Yu, Raymond J. Mooney
|
Using Both Demonstrations and Language Instructions to Efficiently Learn
Robotic Tasks
|
24 pages, 10 figures. Project website at
https://deltaco-robot.github.io/
| null | null | null |
cs.RO cs.CL cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Demonstrations and natural language instructions are two common ways to
specify and teach robots novel tasks. However, for many complex tasks, a
demonstration or language instruction alone contains ambiguities, preventing
tasks from being specified clearly. In such cases, a combination of both a
demonstration and an instruction more concisely and effectively conveys the
task to the robot than either modality alone. To instantiate this problem
setting, we train a single multi-task policy on a few hundred challenging
robotic pick-and-place tasks and propose DeL-TaCo (Joint Demo-Language Task
Conditioning), a method for conditioning a robotic policy on task embeddings
comprised of two components: a visual demonstration and a language instruction.
By allowing these two modalities to mutually disambiguate and clarify each
other during novel task specification, DeL-TaCo (1) substantially decreases the
teacher effort needed to specify a new task and (2) achieves better
generalization performance on novel objects and instructions over previous
task-conditioning methods. To our knowledge, this is the first work to show
that simultaneously conditioning a multi-task robotic manipulation policy on
both demonstration and language embeddings improves sample efficiency and
generalization over conditioning on either modality alone. See additional
materials at https://deltaco-robot.github.io/
|
[
{
"version": "v1",
"created": "Mon, 10 Oct 2022 08:06:58 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Apr 2023 09:38:07 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Yu",
"Albert",
""
],
[
"Mooney",
"Raymond J.",
""
]
] |
new_dataset
| 0.994442 |
2301.13441
|
Xu Wen
|
Xu Wen, Wanling Gao, Anzheng Li, Lei Wang, Zihan Jiang, Jianfeng Zhan
|
CMLCompiler: A Unified Compiler for Classical Machine Learning
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Classical machine learning (CML) occupies nearly half of machine learning
pipelines in production applications. Unfortunately, it fails to utilize the
state-of-the-practice devices fully and performs poorly. Without a unified
framework, the hybrid deployments of deep learning (DL) and CML also suffer
from severe performance and portability issues. This paper presents the design
of a unified compiler, called CMLCompiler, for CML inference. We propose two
unified abstractions: operator representations and extended computational
graphs. The CMLCompiler framework performs the conversion and graph
optimization based on two unified abstractions, then outputs an optimized
computational graph to DL compilers or frameworks. We implement CMLCompiler on
TVM. The evaluation shows CMLCompiler's portability and superior performance.
It achieves up to 4.38$\times$ speedup on CPU, 3.31$\times$ speedup on GPU, and
5.09$\times$ speedup on IoT devices, compared to the state-of-the-art solutions
-- scikit-learn, intel sklearn, and hummingbird. Our performance of CML and DL
mixed pipelines achieves up to 3.04x speedup compared with cross-framework
implementations. The project documents and source code are available at
https://www.computercouncil.org/cmlcompiler.
|
[
{
"version": "v1",
"created": "Tue, 31 Jan 2023 06:38:05 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Feb 2023 02:49:12 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Apr 2023 06:44:50 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Wen",
"Xu",
""
],
[
"Gao",
"Wanling",
""
],
[
"Li",
"Anzheng",
""
],
[
"Wang",
"Lei",
""
],
[
"Jiang",
"Zihan",
""
],
[
"Zhan",
"Jianfeng",
""
]
] |
new_dataset
| 0.996974 |
2302.01039
|
Chao Wang
|
Chao Wang, Anna Belardinelli, Stephan Hasler, Theodoros Stouraitis,
Daniel Tanneberg, Michael Gienger
|
Explainable Human-Robot Training and Cooperation with Augmented Reality
| null | null |
10.1145/3544549.3583889
| null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The current spread of social and assistive robotics applications is
increasingly highlighting the need for robots that can be easily taught and
interacted with, even by users with no technical background. Still, it is often
difficult to grasp what such robots know or to assess if a correct
representation of the task is being formed. Augmented Reality (AR) has the
potential to bridge this gap. We demonstrate three use cases where AR design
elements enhance the explainability and efficiency of human-robot interaction:
1) a human teaching a robot some simple kitchen tasks by demonstration, 2) the
robot showing its plan for solving novel tasks in AR to a human for validation,
and 3) a robot communicating its intentions via AR while assisting people with
limited mobility during daily activities.
|
[
{
"version": "v1",
"created": "Thu, 2 Feb 2023 12:07:34 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Wang",
"Chao",
""
],
[
"Belardinelli",
"Anna",
""
],
[
"Hasler",
"Stephan",
""
],
[
"Stouraitis",
"Theodoros",
""
],
[
"Tanneberg",
"Daniel",
""
],
[
"Gienger",
"Michael",
""
]
] |
new_dataset
| 0.999083 |
2302.07363
|
Haoran Wang
|
Haoran Wang, Yingtong Dou, Canyu Chen, Lichao Sun, Philip S. Yu, Kai
Shu
|
Attacking Fake News Detectors via Manipulating News Social Engagement
|
ACM Web Conference 2023 (WWW'23)
| null | null | null |
cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Social media is one of the main sources for news consumption, especially
among the younger generation. With the increasing popularity of news
consumption on various social media platforms, there has been a surge of
misinformation which includes false information or unfounded claims. As various
text- and social context-based fake news detectors are proposed to detect
misinformation on social media, recent works start to focus on the
vulnerabilities of fake news detectors. In this paper, we present the first
adversarial attack framework against Graph Neural Network (GNN)-based fake news
detectors to probe their robustness. Specifically, we leverage a multi-agent
reinforcement learning (MARL) framework to simulate the adversarial behavior of
fraudsters on social media. Research has shown that in real-world settings,
fraudsters coordinate with each other to share different news in order to evade
the detection of fake news detectors. Therefore, we modeled our MARL framework
as a Markov Game with bot, cyborg, and crowd worker agents, which have their
own distinctive cost, budget, and influence. We then use deep Q-learning to
search for the optimal policy that maximizes the rewards. Extensive
experimental results on two real-world fake news propagation datasets
demonstrate that our proposed framework can effectively sabotage the GNN-based
fake news detector performance. We hope this paper can provide insights for
future research on fake news detection.
|
[
{
"version": "v1",
"created": "Tue, 14 Feb 2023 21:51:56 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Feb 2023 19:05:42 GMT"
},
{
"version": "v3",
"created": "Thu, 27 Apr 2023 19:39:43 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Wang",
"Haoran",
""
],
[
"Dou",
"Yingtong",
""
],
[
"Chen",
"Canyu",
""
],
[
"Sun",
"Lichao",
""
],
[
"Yu",
"Philip S.",
""
],
[
"Shu",
"Kai",
""
]
] |
new_dataset
| 0.969754 |
2302.11097
|
Ming-Liang Zhang
|
Ming-Liang Zhang, Fei Yin, Cheng-Lin Liu
|
A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from
Diagram
|
Accepted to IJCAI 2023
| null | null | null |
cs.AI cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Geometry problem solving (GPS) is a high-level mathematical reasoning
requiring the capacities of multi-modal fusion and geometric knowledge
application. Recently, neural solvers have shown great potential in GPS but
still be short in diagram presentation and modal fusion. In this work, we
convert diagrams into basic textual clauses to describe diagram features
effectively, and propose a new neural solver called PGPSNet to fuse multi-modal
information efficiently. Combining structural and semantic pre-training, data
augmentation and self-limited decoding, PGPSNet is endowed with rich knowledge
of geometry theorems and geometric representation, and therefore promotes
geometric understanding and reasoning. In addition, to facilitate the research
of GPS, we build a new large-scale and fine-annotated GPS dataset named PGPS9K,
labeled with both fine-grained diagram annotation and interpretable solution
program. Experiments on PGPS9K and an existing dataset Geometry3K validate the
superiority of our method over the state-of-the-art neural solvers. Our code,
dataset and appendix material are available at
\url{https://github.com/mingliangzhang2018/PGPS}.
|
[
{
"version": "v1",
"created": "Wed, 22 Feb 2023 02:38:25 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Apr 2023 10:04:17 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Zhang",
"Ming-Liang",
""
],
[
"Yin",
"Fei",
""
],
[
"Liu",
"Cheng-Lin",
""
]
] |
new_dataset
| 0.995143 |
2303.06880
|
Bo Zhang
|
Bo Zhang, Jiakang Yuan, Botian Shi, Tao Chen, Yikang Li, Yu Qiao
|
Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection
|
Accepted by CVPR-2023, and our code is available at
https://github.com/PJLab-ADG/3DTrans
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current 3D object detection models follow a single dataset-specific training
and testing paradigm, which often faces a serious detection accuracy drop when
they are directly deployed in another dataset. In this paper, we study the task
of training a unified 3D detector from multiple datasets. We observe that this
appears to be a challenging task, which is mainly due to that these datasets
present substantial data-level differences and taxonomy-level variations caused
by different LiDAR types and data acquisition standards. Inspired by such
observation, we present a Uni3D which leverages a simple data-level correction
operation and a designed semantic-level coupling-and-recoupling module to
alleviate the unavoidable data-level and taxonomy-level differences,
respectively. Our method is simple and easily combined with many 3D object
detection baselines such as PV-RCNN and Voxel-RCNN, enabling them to
effectively learn from multiple off-the-shelf 3D datasets to obtain more
discriminative and generalizable representations. Experiments are conducted on
many dataset consolidation settings including Waymo-nuScenes, nuScenes-KITTI,
Waymo-KITTI, and Waymo-nuScenes-KITTI consolidations. Their results demonstrate
that Uni3D exceeds a series of individual detectors trained on a single
dataset, with a 1.04x parameter increase over a selected baseline detector. We
expect this work will inspire the research of 3D generalization since it will
push the limits of perceptual performance.
|
[
{
"version": "v1",
"created": "Mon, 13 Mar 2023 05:54:13 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Apr 2023 05:25:22 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Zhang",
"Bo",
""
],
[
"Yuan",
"Jiakang",
""
],
[
"Shi",
"Botian",
""
],
[
"Chen",
"Tao",
""
],
[
"Li",
"Yikang",
""
],
[
"Qiao",
"Yu",
""
]
] |
new_dataset
| 0.999658 |
2303.15811
|
Florian M\"uller
|
Florian M\"uller (LMU Munich), Daniel Schmitt (TU Darmstadt), Andrii
Matviienko (KTH Royal Institute of Technology), Dominik Sch\"on (TU
Darmstadt), Sebastian G\"unther (TU Darmstadt), Thomas Kosch (HU Berlin),
Martin Schmitz (Saarland University)
|
TicTacToes: Assessing Toe Movements as an Input Modality
|
To appear in Proceedings of the 2023 CHI Conference on Human Factors
in Computing Systems (CHI 23), April 23-28, 2023, Hamburg, Germany. ACM, New
York, NY, USA, 17 pages
|
In Proceedings of the 2023 CHI Conference on Human Factors in
Computing Systems (CHI '23). Association for Computing Machinery, New York,
NY, USA, Article 520, 1-17
|
10.1145/3544548.3580954
| null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
From carrying grocery bags to holding onto handles on the bus, there are a
variety of situations where one or both hands are busy, hindering the vision of
ubiquitous interaction with technology. Voice commands, as a popular hands-free
alternative, struggle with ambient noise and privacy issues. As an alternative
approach, research explored movements of various body parts (e.g., head, arms)
as input modalities, with foot-based techniques proving particularly suitable
for hands-free interaction. Whereas previous research only considered the
movement of the foot as a whole, in this work, we argue that our toes offer
further degrees of freedom that can be leveraged for interaction. To explore
the viability of toe-based interaction, we contribute the results of a
controlled experiment with 18 participants assessing the impact of five factors
on the accuracy, efficiency and user experience of such interfaces. Based on
the findings, we provide design recommendations for future toe-based
interfaces.
|
[
{
"version": "v1",
"created": "Tue, 28 Mar 2023 08:30:05 GMT"
},
{
"version": "v2",
"created": "Thu, 6 Apr 2023 08:14:47 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Müller",
"Florian",
"",
"LMU Munich"
],
[
"Schmitt",
"Daniel",
"",
"TU Darmstadt"
],
[
"Matviienko",
"Andrii",
"",
"KTH Royal Institute of Technology"
],
[
"Schön",
"Dominik",
"",
"TU\n Darmstadt"
],
[
"Günther",
"Sebastian",
"",
"TU Darmstadt"
],
[
"Kosch",
"Thomas",
"",
"HU Berlin"
],
[
"Schmitz",
"Martin",
"",
"Saarland University"
]
] |
new_dataset
| 0.998823 |
2304.05351
|
Qianqian Xie
|
Qianqian Xie, Weiguang Han, Yanzhao Lai, Min Peng, Jimin Huang
|
The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over
MultiModal Stock Movement Prediction Challenges
|
13 pages
| null | null | null |
cs.CL cs.LG q-fin.ST
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, large language models (LLMs) like ChatGPT have demonstrated
remarkable performance across a variety of natural language processing tasks.
However, their effectiveness in the financial domain, specifically in
predicting stock market movements, remains to be explored. In this paper, we
conduct an extensive zero-shot analysis of ChatGPT's capabilities in multimodal
stock movement prediction, on three tweets and historical stock price datasets.
Our findings indicate that ChatGPT is a "Wall Street Neophyte" with limited
success in predicting stock movements, as it underperforms not only
state-of-the-art methods but also traditional methods like linear regression
using price features. Despite the potential of Chain-of-Thought prompting
strategies and the inclusion of tweets, ChatGPT's performance remains subpar.
Furthermore, we observe limitations in its explainability and stability,
suggesting the need for more specialized training or fine-tuning. This research
provides insights into ChatGPT's capabilities and serves as a foundation for
future work aimed at improving financial market analysis and prediction by
leveraging social media sentiment and historical stock data.
|
[
{
"version": "v1",
"created": "Mon, 10 Apr 2023 04:31:00 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Apr 2023 12:06:43 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Xie",
"Qianqian",
""
],
[
"Han",
"Weiguang",
""
],
[
"Lai",
"Yanzhao",
""
],
[
"Peng",
"Min",
""
],
[
"Huang",
"Jimin",
""
]
] |
new_dataset
| 0.998923 |
2304.10637
|
Iker Garc\'ia-Ferrero
|
Iker Garc\'ia-Ferrero, Jon Ander Campos, Oscar Sainz, Ander
Salaberria, Dan Roth
|
IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named
Entity Recognition using Knowledge Bases
|
SemEval 2023
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Named Entity Recognition (NER) is a core natural language processing task in
which pre-trained language models have shown remarkable performance. However,
standard benchmarks like CoNLL 2003 do not address many of the challenges that
deployed NER systems face, such as having to classify emerging or complex
entities in a fine-grained way. In this paper we present a novel NER cascade
approach comprising three steps: first, identifying candidate entities in the
input sentence; second, linking the each candidate to an existing knowledge
base; third, predicting the fine-grained category for each entity candidate. We
empirically demonstrate the significance of external knowledge bases in
accurately classifying fine-grained and emerging entities. Our system exhibits
robust performance in the MultiCoNER2 shared task, even in the low-resource
language setting where we leverage knowledge bases of high-resource languages.
|
[
{
"version": "v1",
"created": "Thu, 20 Apr 2023 20:30:34 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Apr 2023 10:21:20 GMT"
},
{
"version": "v3",
"created": "Thu, 27 Apr 2023 20:51:36 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"García-Ferrero",
"Iker",
""
],
[
"Campos",
"Jon Ander",
""
],
[
"Sainz",
"Oscar",
""
],
[
"Salaberria",
"Ander",
""
],
[
"Roth",
"Dan",
""
]
] |
new_dataset
| 0.991666 |
2304.11639
|
Guangji Chen
|
Guangji Chen, Qingqing Wu, Celimuge Wu, Mengnan Jian, Yijian Chen, Wen
Chen
|
Static IRS Meets Distributed MIMO: A New Architecture for Dynamic
Beamforming
|
Submitted to IEEE WCL for possible publication
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Intelligent reflecting surface (IRS) has been considered as a revolutionary
technology to enhance the wireless communication performance. To cater for
multiple mobile users, adjusting IRS beamforming patterns over time, i.e.,
dynamic IRS beamforming (DIBF), is generally needed for achieving satisfactory
performance, which results in high controlling power consumption and overhead.
To avoid such cost, we propose a new architecture based on the static regulated
IRS for wireless coverage enhancement, where the principle of distributed
multiple-input multiple-output (D-MIMO) is integrated into the system to
exploite the diversity of spatial directions provided by multiple access points
(APs). For this new D-MIMO empowered static IRS architecture, the total target
area is partitioned into several subareas and each subarea is served by an
assigned AP. We consider to maximize the worst-case received power over all
locations in the target area by jointly optimizing a single set of IRS
beamforming pattern and AP-subarea association. Then, a two-step algorithm is
proposed to obtain its high-quality solution. Theoretical analysis unveils that
the fundamental squared power gain can still be achieved over all locations in
the target area. The performance gap relative to the DIBF scheme is also
analytically quantified. Numerical results validate our theoretical findings
and demonstrate the effectiveness of our proposed design over benchmark
schemes.
|
[
{
"version": "v1",
"created": "Sun, 23 Apr 2023 12:44:00 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Apr 2023 08:17:25 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Chen",
"Guangji",
""
],
[
"Wu",
"Qingqing",
""
],
[
"Wu",
"Celimuge",
""
],
[
"Jian",
"Mengnan",
""
],
[
"Chen",
"Yijian",
""
],
[
"Chen",
"Wen",
""
]
] |
new_dataset
| 0.999149 |
2304.11968
|
Zhe Li
|
Jinyu Yang, Mingqi Gao, Zhe Li, Shang Gao, Fangjing Wang, Feng Zheng
|
Track Anything: Segment Anything Meets Videos
|
Tech-report
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, the Segment Anything Model (SAM) gains lots of attention rapidly
due to its impressive segmentation performance on images. Regarding its strong
ability on image segmentation and high interactivity with different prompts, we
found that it performs poorly on consistent segmentation in videos. Therefore,
in this report, we propose Track Anything Model (TAM), which achieves
high-performance interactive tracking and segmentation in videos. To be
detailed, given a video sequence, only with very little human participation,
i.e., several clicks, people can track anything they are interested in, and get
satisfactory results in one-pass inference. Without additional training, such
an interactive design performs impressively on video object tracking and
segmentation. All resources are available on
{https://github.com/gaomingqi/Track-Anything}. We hope this work can facilitate
related research.
|
[
{
"version": "v1",
"created": "Mon, 24 Apr 2023 10:04:06 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Apr 2023 03:21:27 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Yang",
"Jinyu",
""
],
[
"Gao",
"Mingqi",
""
],
[
"Li",
"Zhe",
""
],
[
"Gao",
"Shang",
""
],
[
"Wang",
"Fangjing",
""
],
[
"Zheng",
"Feng",
""
]
] |
new_dataset
| 0.973078 |
2304.12041
|
Anand Agrawal
|
Anand Agrawal and Rajib Ranjan Maiti
|
iTieProbe: Is Your IoT Setup Secure against (Modern) Evil Twin?
|
To do the responsible vulnerability disclosure of our findings
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Evil twin attack on Wi-Fi network has been a challenging security problem and
several solutions have been proposed to this problem. In general, evil twin
attack aims to exfiltrate data, like Wi-Fi and service credentials, from the
client devices and considered as a serious threat at MAC layer. IoT devices
with its companion apps provides different pairing methods for provisioning.
The "SmartConfig Mode", the one proposed by Texas Instrument (TI) and the
"Access Point pairing mode (AP mode)" are the most common pairing modes
provided by the application developer and vendor of the IoT devices.
Especially, AP mode use Wi-Fi connectivity to setup IoT devices where a device
activates an access point to which the mobile device running the corresponding
mobile application is required to connect. In this paper, we have used evil
twin attack as a weapon to test the security posture of IoT devices that use
Wi-Fi network to set them up. We have designed, implemented and applied a
system, called iTieProbe, that can be used in ethical hacking for discovering
certain vulnerabilities during such setup. AP mode successfully completes when
the mobile device is able to communicate with the IoT device via a home router
over a Wi-Fi network. Our proposed system, iTieProbe, is capable of discovering
several serious vulnerabilities in the commercial IoT devices that use AP mode
or similar approach. We evaluated iTieProbe's efficacy on 9 IoT devices, like
IoT cameras, smart plugs, Echo Dot and smart bulbs, and discovered that several
of these IoT devices have certain serious threats, like leaking Wi-Fi
credential of home router and creating fake IoT device, during the setup of the
IoT devices.
|
[
{
"version": "v1",
"created": "Mon, 24 Apr 2023 12:38:06 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Apr 2023 06:42:20 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Agrawal",
"Anand",
""
],
[
"Maiti",
"Rajib Ranjan",
""
]
] |
new_dataset
| 0.995234 |
2304.12412
|
Arya Rachman
|
Arya Rachman, J\"urgen Seiler, and Andr\'e Kaup
|
End-to-End Lidar-Camera Self-Calibration for Autonomous Vehicles
|
Accepted for The 35th IEEE Intelligent Vehicles Symposium (IV 2023)
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Autonomous vehicles are equipped with a multi-modal sensor setup to enable
the car to drive safely. The initial calibration of such perception sensors is
a highly matured topic and is routinely done in an automated factory
environment. However, an intriguing question arises on how to maintain the
calibration quality throughout the vehicle's operating duration. Another
challenge is to calibrate multiple sensors jointly to ensure no propagation of
systemic errors. In this paper, we propose CaLiCa, an end-to-end deep
self-calibration network which addresses the automatic calibration problem for
pinhole camera and Lidar. We jointly predict the camera intrinsic parameters
(focal length and distortion) as well as Lidar-Camera extrinsic parameters
(rotation and translation), by regressing feature correlation between the
camera image and the Lidar point cloud. The network is arranged in a
Siamese-twin structure to constrain the network features learning to a mutually
shared feature in both point cloud and camera (Lidar-camera constraint).
Evaluation using KITTI datasets shows that we achieve 0.154 {\deg} and 0.059 m
accuracy with a reprojection error of 0.028 pixel with a single-pass inference.
We also provide an ablative study of how our end-to-end learning architecture
offers lower terminal loss (21% decrease in rotation loss) compared to isolated
calibration
|
[
{
"version": "v1",
"created": "Mon, 24 Apr 2023 19:44:23 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Apr 2023 01:12:36 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Rachman",
"Arya",
""
],
[
"Seiler",
"Jürgen",
""
],
[
"Kaup",
"André",
""
]
] |
new_dataset
| 0.995424 |
2304.14418
|
Madhusudhanan Balasubramanian
|
Fisseha Admasu Ferede, Madhusudhanan Balasubramanian
|
SSTM: Spatiotemporal Recurrent Transformers for Multi-frame Optical Flow
Estimation
|
5 tables, 7 figures, MS thesis
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Inaccurate optical flow estimates in and near occluded regions, and
out-of-boundary regions are two of the current significant limitations of
optical flow estimation algorithms. Recent state-of-the-art optical flow
estimation algorithms are two-frame based methods where optical flow is
estimated sequentially for each consecutive image pair in a sequence. While
this approach gives good flow estimates, it fails to generalize optical flows
in occluded regions mainly due to limited local evidence regarding moving
elements in a scene. In this work, we propose a learning-based multi-frame
optical flow estimation method that estimates two or more consecutive optical
flows in parallel from multi-frame image sequences. Our underlying hypothesis
is that by understanding temporal scene dynamics from longer sequences with
more than two frames, we can characterize pixel-wise dependencies in a larger
spatiotemporal domain, generalize complex motion patterns and thereby improve
the accuracy of optical flow estimates in occluded regions. We present
learning-based spatiotemporal recurrent transformers for multi-frame based
optical flow estimation (SSTMs). Our method utilizes 3D Convolutional Gated
Recurrent Units (3D-ConvGRUs) and spatiotemporal transformers to learn
recurrent space-time motion dynamics and global dependencies in the scene and
provide a generalized optical flow estimation. When compared with recent
state-of-the-art two-frame and multi-frame methods on real world and synthetic
datasets, performance of the SSTMs were significantly higher in occluded and
out-of-boundary regions. Among all published state-of-the-art multi-frame
methods, SSTM achieved state-of the-art results on the Sintel Final and
KITTI2015 benchmark datasets.
|
[
{
"version": "v1",
"created": "Wed, 26 Apr 2023 23:39:40 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Ferede",
"Fisseha Admasu",
""
],
[
"Balasubramanian",
"Madhusudhanan",
""
]
] |
new_dataset
| 0.982904 |
2304.14444
|
\v{S}imon Bil\'ik
|
Jakub Nevlacil, Simon Bilik, Karel Horak
|
Raspberry Pi Bee Health Monitoring Device
| null | null | null | null |
cs.CV cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
A declining honeybee population could pose a threat to a food resources of
the whole world one of the latest trend in beekeeping is an effort to monitor a
health of the honeybees using various sensors and devices. This paper
participates on a development on one of these devices. The aim of this paper is
to make an upgrades and improvement of an in-development bee health monitoring
device and propose a remote data logging solution for a continual monitoring of
a beehive.
|
[
{
"version": "v1",
"created": "Thu, 27 Apr 2023 18:05:52 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Nevlacil",
"Jakub",
""
],
[
"Bilik",
"Simon",
""
],
[
"Horak",
"Karel",
""
]
] |
new_dataset
| 0.999039 |
2304.14466
|
Hamam Mokayed Dr
|
Hamam Mokayed and Amirhossein Nayebiastaneh and Kanjar De and Stergios
Sozos and Olle Hagner and Bjorn Backe
|
Nordic Vehicle Dataset (NVD): Performance of vehicle detectors using
newly captured NVD from UAV in different snowy weather conditions
| null | null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Vehicle detection and recognition in drone images is a complex problem that
has been used for different safety purposes. The main challenge of these images
is captured at oblique angles and poses several challenges like non-uniform
illumination effect, degradations, blur, occlusion, loss of visibility, etc.
Additionally, weather conditions play a crucial role in causing safety concerns
and add another high level of challenge to the collected data. Over the past
few decades, various techniques have been employed to detect and track vehicles
in different weather conditions. However, detecting vehicles in heavy snow is
still in the early stages because of a lack of available data. Furthermore,
there has been no research on detecting vehicles in snowy weather using real
images captured by unmanned aerial vehicles (UAVs). This study aims to address
this gap by providing the scientific community with data on vehicles captured
by UAVs in different settings and under various snow cover conditions in the
Nordic region. The data covers different adverse weather conditions like
overcast with snowfall, low light and low contrast conditions with patchy snow
cover, high brightness, sunlight, fresh snow, and the temperature reaching far
below -0 degrees Celsius. The study also evaluates the performance of commonly
used object detection methods such as Yolo v8, Yolo v5, and fast RCNN.
Additionally, data augmentation techniques are explored, and those that enhance
the detectors' performance in such scenarios are proposed. The code and the
dataset will be available at https://nvd.ltu-ai.dev
|
[
{
"version": "v1",
"created": "Thu, 27 Apr 2023 18:55:43 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Mokayed",
"Hamam",
""
],
[
"Nayebiastaneh",
"Amirhossein",
""
],
[
"De",
"Kanjar",
""
],
[
"Sozos",
"Stergios",
""
],
[
"Hagner",
"Olle",
""
],
[
"Backe",
"Bjorn",
""
]
] |
new_dataset
| 0.999773 |
2304.14492
|
Mohammed Al-Rawi
|
Mohammed Al-Rawi
|
Ultra-Fast Zernike Moments using FFT and GPU
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Zernike moments can be used to generate invariant features that are applied
in various machine vision applications. They, however, suffer from slow
implementation and numerical stability problems. We propose a novel method for
computing Zernike using Fast Fourier Transform (FFT) and GPU computing. The
method can be used to generate accurate moments up to high orders, and can
compute Zernike moments of 4K resolution images in real-time. Numerical
accuracies of Zernike moments computed with the proposed FFT approach have been
analyzed using the orthogonality property and the results show that they beat
other methods in numerical stability. The proposed method is simple and fast
and can make use of the huge GPU-FFT libraries that are available in several
programming frameworks.
|
[
{
"version": "v1",
"created": "Thu, 6 Apr 2023 14:39:08 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Al-Rawi",
"Mohammed",
""
]
] |
new_dataset
| 0.995465 |
2304.14500
|
Fang Chen
|
Fang Chen, Heiko Balzter, Peng Ren and Huiyu Zhou
|
SRCNet: Seminal Representation Collaborative Network for Marine Oil
Spill Segmentation
|
arXiv admin note: substantial text overlap with arXiv:2301.01202
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Effective oil spill segmentation in Synthetic Aperture Radar (SAR) images is
critical for marine oil pollution cleanup, and proper image representation is
helpful for accurate image segmentation. In this paper, we propose an effective
oil spill image segmentation network named SRCNet by leveraging SAR image
representation and the training for oil spill segmentation simultaneously.
Specifically, our proposed segmentation network is constructed with a pair of
deep neural nets with the collaboration of the seminal representation that
describes SAR images, where one deep neural net is the generative net which
strives to produce oil spill segmentation maps, and the other is the
discriminative net which trys its best to distinguish between the produced and
the true segmentations, and they thus built a two-player game. Particularly,
the seminal representation exploited in our proposed SRCNet originates from SAR
imagery, modelling with the internal characteristics of SAR images. Thus, in
the training process, the collaborated seminal representation empowers the
mapped generative net to produce accurate oil spill segmentation maps
efficiently with small amount of training data, promoting the discriminative
net reaching its optimal solution at a fast speed. Therefore, our proposed
SRCNet operates effective oil spill segmentation in an economical and efficient
manner. Additionally, to increase the segmentation capability of the proposed
segmentation network in terms of accurately delineating oil spill details in
SAR images, a regularisation term that penalises the segmentation loss is
devised. This encourages our proposed SRCNet for accurately segmenting oil
spill areas from SAR images. Empirical experimental evaluations from different
metrics validate the effectiveness of our proposed SRCNet for oil spill image
segmentation.
|
[
{
"version": "v1",
"created": "Mon, 17 Apr 2023 13:23:03 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Chen",
"Fang",
""
],
[
"Balzter",
"Heiko",
""
],
[
"Ren",
"Peng",
""
],
[
"Zhou",
"Huiyu",
""
]
] |
new_dataset
| 0.994226 |
2304.14501
|
Jiafei Duan
|
Jiafei Duan, Samson Yu, Nicholas Tan, Yi Ru Wang, Cheston Tan
|
Read My Mind: A Multi-Modal Dataset for Human Belief Prediction
|
Accepted to ICRA 2023 Communicating Robot Learning Across Human-Robot
Interaction Workshop
| null | null | null |
cs.CV cs.AI cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Understanding human intentions is key to enabling effective and efficient
human-robot interaction (HRI) in collaborative settings. To enable developments
and evaluation of the ability of artificial intelligence (AI) systems to infer
human beliefs, we introduce a large-scale multi-modal video dataset for intent
prediction based on object-context relations.
|
[
{
"version": "v1",
"created": "Tue, 7 Mar 2023 06:19:38 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Duan",
"Jiafei",
""
],
[
"Yu",
"Samson",
""
],
[
"Tan",
"Nicholas",
""
],
[
"Wang",
"Yi Ru",
""
],
[
"Tan",
"Cheston",
""
]
] |
new_dataset
| 0.998736 |
2304.14507
|
Bala Murugan MS
|
Vrinda Agarwal, Aaron George Pichappa, Manideep Ramisetty, Bala
Murugan MS, Manoj kumar Rajagopal
|
Suspicious Vehicle Detection Using Licence Plate Detection And Facial
Feature Recognition
|
eight pages and three figures
| null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the increasing need to strengthen vehicle safety and detection, the
availability of pre-existing methods of catching criminals and identifying
vehicles manually through the various traffic surveillance cameras is not only
time-consuming but also inefficient. With the advancement of technology in
every field the use of real-time traffic surveillance models will help
facilitate an easy approach. Keeping this in mind, the main focus of our paper
is to develop a combined face recognition and number plate recognition model to
ensure vehicle safety and real-time tracking of running-away criminals and
stolen vehicles.
|
[
{
"version": "v1",
"created": "Tue, 18 Apr 2023 06:44:08 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Agarwal",
"Vrinda",
""
],
[
"Pichappa",
"Aaron George",
""
],
[
"Ramisetty",
"Manideep",
""
],
[
"MS",
"Bala Murugan",
""
],
[
"Rajagopal",
"Manoj kumar",
""
]
] |
new_dataset
| 0.997272 |
2304.14510
|
Martina Paccini
|
Martina Paccini, Giuseppe Patan\`e, Michela Spagnuolo
|
3D Patient-specific Modelling and Characterisation of Muscle-Skeletal
Districts
|
arXiv admin note: substantial text overlap with arXiv:2208.08983
| null | null | null |
cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work addresses the patient-specific characterisation of the morphology
and pathologies of muscle-skeletal districts (e.g., wrist, spine) to support
diagnostic activities and follow-up exams through the integration of
morphological and tissue information. We propose different methods for the
integration of morphological information, retrieved from the geometrical
analysis of 3D surface models, with tissue information extracted from volume
images. For the qualitative and quantitative validation, we will discuss the
localisation of bone erosion sites on the wrists to monitor rheumatic diseases
and the characterisation of the three functional regions of the spinal
vertebrae to study the presence of osteoporotic fractures. The proposed
approach supports the quantitative and visual evaluation of possible damages,
surgery planning, and early diagnosis or follow-up studies. Finally, our
analysis is general enough to be applied to different districts.
|
[
{
"version": "v1",
"created": "Tue, 18 Apr 2023 21:46:42 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Paccini",
"Martina",
""
],
[
"Patanè",
"Giuseppe",
""
],
[
"Spagnuolo",
"Michela",
""
]
] |
new_dataset
| 0.960758 |
2304.14516
|
Valdecy Pereira
|
Valdecy Pereira, Marcio Pereira Basilio, Carlos Henrique Tarjano
Santos
|
pyBibX -- A Python Library for Bibliometric and Scientometric Analysis
Powered with Artificial Intelligence Tools
|
30 pages, 12 figures, 6 tables
| null | null | null |
cs.DL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Bibliometric and Scientometric analyses offer invaluable perspectives on the
complex research terrain and collaborative dynamics spanning diverse academic
disciplines. This paper presents pyBibX, a python library devised to conduct
comprehensive bibliometric and scientometric analyses on raw data files sourced
from Scopus, Web of Science, and PubMed, seamlessly integrating state of the
art AI capabilities into its core functionality. The library executes a
comprehensive EDA, presenting outcomes via visually appealing graphical
illustrations. Network capabilities have been deftly integrated, encompassing
Citation, Collaboration, and Similarity Analysis. Furthermore, the library
incorporates AI capabilities, including Embedding vectors, Topic Modeling, Text
Summarization, and other general Natural Language Processing tasks, employing
models such as Sentence-BERT, BerTopic, BERT, chatGPT, and PEGASUS. As a
demonstration, we have analyzed 184 documents associated with multiple-criteria
decision analysis published between 1984 and 2023. The EDA emphasized a growing
fascination with decision-making and fuzzy logic methodologies. Next, Network
Analysis further accentuated the significance of central authors and
intra-continental collaboration, identifying Canada and China as crucial
collaboration hubs. Finally, AI Analysis distinguished two primary topics and
chatGPT preeminence in Text Summarization. It also proved to be an
indispensable instrument for interpreting results, as our library enables
researchers to pose inquiries to chatGPT regarding bibliometric outcomes. Even
so, data homogeneity remains a daunting challenge due to database
inconsistencies. PyBibX is the first application integrating cutting-edge AI
capabilities for analyzing scientific publications, enabling researchers to
examine and interpret these outcomes more effectively.
|
[
{
"version": "v1",
"created": "Thu, 27 Apr 2023 20:06:07 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Pereira",
"Valdecy",
""
],
[
"Basilio",
"Marcio Pereira",
""
],
[
"Santos",
"Carlos Henrique Tarjano",
""
]
] |
new_dataset
| 0.997869 |
2304.14539
|
Marco Peressotti
|
Lu\'is Cruz-Filipe, Eva Graversen, Fabrizio Montesi, Marco Peressotti
|
Reasoning about Choreographic Programs
| null | null | null | null |
cs.PL cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Choreographic programming is a paradigm where a concurrent or distributed
system is developed in a top-down fashion. Programs, called choreographies,
detail the desired interactions between processes, and can be compiled to
distributed implementations based on message passing. Choreographic languages
usually guarantee deadlock-freedom and provide an operational correspondence
between choreographies and their compiled implementations, but until now little
work has been done on verifying other properties.
This paper presents a Hoare-style logic for reasoning about the behaviour of
choreographies, and illustrate its usage in representative examples. We show
that this logic is sound and complete, and discuss decidability of its
judgements. Using existing results from choreographic programming, we show that
any functional correctness property proven for a choreography also holds for
its compiled implementation.
|
[
{
"version": "v1",
"created": "Thu, 27 Apr 2023 21:37:29 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Cruz-Filipe",
"Luís",
""
],
[
"Graversen",
"Eva",
""
],
[
"Montesi",
"Fabrizio",
""
],
[
"Peressotti",
"Marco",
""
]
] |
new_dataset
| 0.992137 |
2304.14571
|
Yousef Yeganeh
|
Yousef Yeganeh, Azade Farshad, Peter Weinberger, Seyed-Ahmad Ahmadi,
Ehsan Adeli, Nassir Navab
|
DIAMANT: Dual Image-Attention Map Encoders For Medical Image
Segmentation
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Although purely transformer-based architectures showed promising performance
in many computer vision tasks, many hybrid models consisting of CNN and
transformer blocks are introduced to fit more specialized tasks. Nevertheless,
despite the performance gain of both pure and hybrid transformer-based
architectures compared to CNNs in medical imaging segmentation, their high
training cost and complexity make it challenging to use them in real scenarios.
In this work, we propose simple architectures based on purely convolutional
layers, and show that by just taking advantage of the attention map
visualizations obtained from a self-supervised pretrained vision transformer
network (e.g., DINO) one can outperform complex transformer-based networks with
much less computation costs. The proposed architecture is composed of two
encoder branches with the original image as input in one branch and the
attention map visualizations of the same image from multiple self-attention
heads from a pre-trained DINO model (as multiple channels) in the other branch.
The results of our experiments on two publicly available medical imaging
datasets show that the proposed pipeline outperforms U-Net and the
state-of-the-art medical image segmentation models.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 00:11:18 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Yeganeh",
"Yousef",
""
],
[
"Farshad",
"Azade",
""
],
[
"Weinberger",
"Peter",
""
],
[
"Ahmadi",
"Seyed-Ahmad",
""
],
[
"Adeli",
"Ehsan",
""
],
[
"Navab",
"Nassir",
""
]
] |
new_dataset
| 0.998163 |
2304.14581
|
Mao Yang
|
Ze Liu, Bo Li, Mao Yang, ZhongJiang Yan
|
An Adaptive Channel Reservation MAC Protocol Based on Forwarding Traffic
of Key Nodes
|
17 pages, 14 figures
| null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Ad Hoc networks with multi-hop topology are widely used in military and
civilian applications. One challenge for Ad Hoc networks is to design efficient
Media Access Control (MAC) protocols to ensure the quality of service (QoS). In
Ad Hoc networks, there is a kind of node called key node, which undertakes more
forwarding traffic than other surrounding nodes. The number of neighbor nodes
around key nodes is often large, and the surrounding channel environment and
interference are often more complex. Thus, the key nodes can hardly get enough
channel access opportunities, resulting in poor end-to-end performance.
Therefore, we propose an adaptive channel reservation MAC protocol based on
forwarding traffic of key nodes, which is aimed at alleviating the congestion
for key nodes. Nodes initiate reservations for future transmission time
according to the buffer status before sending packets and then calculate the
Weight of Reservation Ability (WRA). The node adaptively adjusts its
reservation opportunity by comparing the WRA with neighbor nodes, thus
improving the channel access efficiency and ensuring the transmission
opportunity of key nodes. Extensive simulation confirms that our proposed
FTKN-CRM provides significant improvements in end-to-end performance over the
IEEE 802.11ax protocol and other reservation access protocols.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 00:50:48 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Liu",
"Ze",
""
],
[
"Li",
"Bo",
""
],
[
"Yang",
"Mao",
""
],
[
"Yan",
"ZhongJiang",
""
]
] |
new_dataset
| 0.988395 |
2304.14599
|
Gunther Jikeli Jr.
|
Gunther Jikeli, Sameer Karali, Daniel Miehling, and Katharina Soemer
|
Antisemitic Messages? A Guide to High-Quality Annotation and a Labeled
Dataset of Tweets
| null | null | null | null |
cs.CL cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
One of the major challenges in automatic hate speech detection is the lack of
datasets that cover a wide range of biased and unbiased messages and that are
consistently labeled. We propose a labeling procedure that addresses some of
the common weaknesses of labeled datasets. We focus on antisemitic speech on
Twitter and create a labeled dataset of 6,941 tweets that cover a wide range of
topics common in conversations about Jews, Israel, and antisemitism between
January 2019 and December 2021 by drawing from representative samples with
relevant keywords. Our annotation process aims to strictly apply a commonly
used definition of antisemitism by forcing annotators to specify which part of
the definition applies, and by giving them the option to personally disagree
with the definition on a case-by-case basis. Labeling tweets that call out
antisemitism, report antisemitism, or are otherwise related to antisemitism
(such as the Holocaust) but are not actually antisemitic can help reduce false
positives in automated detection. The dataset includes 1,250 tweets (18%) that
are antisemitic according to the International Holocaust Remembrance Alliance
(IHRA) definition of antisemitism. It is important to note, however, that the
dataset is not comprehensive. Many topics are still not covered, and it only
includes tweets collected from Twitter between January 2019 and December 2021.
Additionally, the dataset only includes tweets that were written in English.
Despite these limitations, we hope that this is a meaningful contribution to
improving the automated detection of antisemitic speech.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 02:52:38 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Jikeli",
"Gunther",
""
],
[
"Karali",
"Sameer",
""
],
[
"Miehling",
"Daniel",
""
],
[
"Soemer",
"Katharina",
""
]
] |
new_dataset
| 0.998768 |
2304.14622
|
Babar Shahzaad
|
Babar Shahzaad, Balsam Alkouz, Jermaine Janszen, Athman Bouguettaya
|
Optimizing Drone Delivery in Smart Cities
|
8 pages, 3 figures. This is an accepted paper and it is going to
appear in IEEE Internet Computing magazine
| null | null | null |
cs.RO cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a novel context-aware drone delivery framework for optimizing
package delivery through skyway networks in smart cities. We reformulate the
problem of finding an optimal drone service delivery pathway as a more
congruent and elegant drone delivery service composition problem. In this
respect, we propose a novel line-of-sight heuristic-based context-aware
composition algorithm that selects and composes near-optimal drone delivery
services. We conducted an extensive experiment using a real dataset to show the
robustness of our proposed approach.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 04:32:26 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Shahzaad",
"Babar",
""
],
[
"Alkouz",
"Balsam",
""
],
[
"Janszen",
"Jermaine",
""
],
[
"Bouguettaya",
"Athman",
""
]
] |
new_dataset
| 0.99347 |
2304.14653
|
Murugeshwari B
|
B. Murugeshwari, D. Saral Jeeva Jothi, B. Hemalatha, S. Neelavathy
Pari
|
Trust Aware Privacy Preserving Routing Protocol for Wireless Adhoc
Network
| null | null |
10.14445/22315381/IJETT-V70I9P236
| null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Wireless Ad-Hoc Networks are especially helpful and quite well for essential
circumstances such as defense, public safety, and disaster recovery. MANETs
require communication privacy and security, notably in core routing protocols,
when functioning in hostile or suspicious environments. The Trust Aware
Privacy-Preserving Protocol (TAP3) is a mechanism for supporting the origin in
proactively selecting a trust-able target and doing privacy-preserving route
verification. We suggest TAP3 using the fellow recommendation model for MANETs
in this work. Nodes use their features to discover their fellow node and use
the trust to create strong connections with the random node via a multi-hop
trusting chain by identifying the secure location. The verification duties are
then spread among the nodes and validate the log updates without exposing the
nodes' details. Unlike previous models that uncover node vulnerabilities or
misconduct after an attack, TAP3 may guarantee the origin node to prevent data
from being transferred through malicious nodes from the beginning and do
verification without needing a third party. Our results show that this approach
can locate problematic nodes with minimal overhead than the conventional
routing protocol.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 06:49:53 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Murugeshwari",
"B.",
""
],
[
"Jothi",
"D. Saral Jeeva",
""
],
[
"Hemalatha",
"B.",
""
],
[
"Pari",
"S. Neelavathy",
""
]
] |
new_dataset
| 0.992332 |
2304.14657
|
Jieting Chen
|
Jieting Chen, Junkai Ding, Wenping Chen, Qin Jin
|
Knowledge Enhanced Model for Live Video Comment Generation
| null | null | null | null |
cs.CV cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
Live video commenting is popular on video media platforms, as it can create a
chatting atmosphere and provide supplementary information for users while
watching videos. Automatically generating live video comments can improve user
experience and enable human-like generation for bot chatting. Existing works
mostly focus on short video datasets while ignoring other important video types
such as long videos like movies. In this work, we collect a new Movie Live
Comments (MovieLC) dataset to support research on live video comment generation
for long videos. We also propose a knowledge enhanced generation model inspired
by the divergent and informative nature of live video comments. Our model
adopts a pre-training encoder-decoder framework and incorporates external
knowledge. Extensive experiments show that both objective metrics and human
evaluation demonstrate the effectiveness of our proposed model. The MovieLC
dataset and our code will be released.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 07:03:50 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Chen",
"Jieting",
""
],
[
"Ding",
"Junkai",
""
],
[
"Chen",
"Wenping",
""
],
[
"Jin",
"Qin",
""
]
] |
new_dataset
| 0.998123 |
2304.14659
|
Alexandre Quemy
|
Alexandre Quemy, Marc Schoenauer, Johann Dreo
|
MultiZenoTravel: a Tunable Benchmark for Multi-Objective Planning with
Known Pareto Front
| null | null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Multi-objective AI planning suffers from a lack of benchmarks exhibiting
known Pareto Fronts. In this work, we propose a tunable benchmark generator,
together with a dedicated solver that provably computes the true Pareto front
of the resulting instances. First, we prove a proposition allowing us to
characterize the optimal plans for a constrained version of the problem, and
then show how to reduce the general problem to the constrained one. Second, we
provide a constructive way to find all the Pareto-optimal plans and discuss the
complexity of the algorithm. We provide an implementation that allows the
solver to handle realistic instances in a reasonable time. Finally, as a
practical demonstration, we used this solver to find all Pareto-optimal plans
between the two largest airports in the world, considering the routes between
the 50 largest airports, spherical distances between airports and a made-up
risk.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 07:09:23 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Quemy",
"Alexandre",
""
],
[
"Schoenauer",
"Marc",
""
],
[
"Dreo",
"Johann",
""
]
] |
new_dataset
| 0.998683 |
2304.14662
|
Tong Zhu
|
Tong Zhu, Guoliang Zhang, Zechang Li, Zijian Yu, Junfei Ren, Mengsong
Wu, Zhefeng Wang, Baoxing Huai, Pingfu Chao, Wenliang Chen
|
CED: Catalog Extraction from Documents
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Sentence-by-sentence information extraction from long documents is an
exhausting and error-prone task. As the indicator of document skeleton,
catalogs naturally chunk documents into segments and provide informative
cascade semantics, which can help to reduce the search space. Despite their
usefulness, catalogs are hard to be extracted without the assist from external
knowledge. For documents that adhere to a specific template, regular
expressions are practical to extract catalogs. However, handcrafted heuristics
are not applicable when processing documents from different sources with
diverse formats. To address this problem, we build a large manually annotated
corpus, which is the first dataset for the Catalog Extraction from Documents
(CED) task. Based on this corpus, we propose a transition-based framework for
parsing documents into catalog trees. The experimental results demonstrate that
our proposed method outperforms baseline systems and shows a good ability to
transfer. We believe the CED task could fill the gap between raw text segments
and information extraction tasks on extremely long documents. Data and code are
available at \url{https://github.com/Spico197/CatalogExtraction}
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 07:32:00 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Zhu",
"Tong",
""
],
[
"Zhang",
"Guoliang",
""
],
[
"Li",
"Zechang",
""
],
[
"Yu",
"Zijian",
""
],
[
"Ren",
"Junfei",
""
],
[
"Wu",
"Mengsong",
""
],
[
"Wang",
"Zhefeng",
""
],
[
"Huai",
"Baoxing",
""
],
[
"Chao",
"Pingfu",
""
],
[
"Chen",
"Wenliang",
""
]
] |
new_dataset
| 0.999419 |
2304.14678
|
Wen Zhang
|
Wen Zhang, Zhen Yao, Mingyang Chen, Zhiwei Huang and Huajun Chen
|
NeuralKG-ind: A Python Library for Inductive Knowledge Graph
Representation Learning
|
Accepted by SIGIR2023 Demonstration Track
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Since the dynamic characteristics of knowledge graphs, many inductive
knowledge graph representation learning (KGRL) works have been proposed in
recent years, focusing on enabling prediction over new entities. NeuralKG-ind
is the first library of inductive KGRL as an important update of NeuralKG
library. It includes standardized processes, rich existing methods, decoupled
modules, and comprehensive evaluation metrics. With NeuralKG-ind, it is easy
for researchers and engineers to reproduce, redevelop, and compare inductive
KGRL methods. The library, experimental methodologies, and model
re-implementing results of NeuralKG-ind are all publicly released at
https://github.com/zjukg/NeuralKG/tree/ind .
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 08:09:08 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Zhang",
"Wen",
""
],
[
"Yao",
"Zhen",
""
],
[
"Chen",
"Mingyang",
""
],
[
"Huang",
"Zhiwei",
""
],
[
"Chen",
"Huajun",
""
]
] |
new_dataset
| 0.987897 |
2304.14714
|
Binqiang Wang
|
Binqiang Wang and Gang Dong and Yaqian Zhao and Rengang Li and Lu Cao
and Lihua Lu
|
SGED: A Benchmark dataset for Performance Evaluation of Spiking Gesture
Emotion Recognition
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the field of affective computing, researchers in the community have
promoted the performance of models and algorithms by using the complementarity
of multimodal information. However, the emergence of more and more modal
information makes the development of datasets unable to keep up with the
progress of existing modal sensing equipment. Collecting and studying
multimodal data is a complex and significant work. In order to supplement the
challenge of partial missing of community data. We collected and labeled a new
homogeneous multimodal gesture emotion recognition dataset based on the
analysis of the existing data sets. This data set complements the defects of
homogeneous multimodal data and provides a new research direction for emotion
recognition. Moreover, we propose a pseudo dual-flow network based on this
dataset, and verify the application potential of this dataset in the affective
computing community. The experimental results demonstrate that it is feasible
to use the traditional visual information and spiking visual information based
on homogeneous multimodal data for visual emotion recognition.The dataset is
available at \url{https://github.com/201528014227051/SGED}
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 09:32:09 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Wang",
"Binqiang",
""
],
[
"Dong",
"Gang",
""
],
[
"Zhao",
"Yaqian",
""
],
[
"Li",
"Rengang",
""
],
[
"Cao",
"Lu",
""
],
[
"Lu",
"Lihua",
""
]
] |
new_dataset
| 0.999576 |
2304.14791
|
Naif Mehanna
|
Naif Mehanna (CRIStAL, CNRS, SPIRALS), Walter Rudametkin (UR, IUF,
CNRS, IRISA, DiverSe)
|
Caught in the Game: On the History and Evolution of Web Browser Gaming
| null |
TheWebConference 2023, Apr 2023, Austin (TX), United States
| null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Web browsers have come a long way since their inception, evolving from a
simple means of displaying text documents over the network to complex software
stacks with advanced graphics and network capabilities. As personal computers
grew in popularity, developers jumped at the opportunity to deploy
cross-platform games with centralized management and a low barrier to entry.
Simply going to the right address is now enough to start a game. From
text-based to GPU-powered 3D games, browser gaming has evolved to become a
strong alternative to traditional console and mobile-based gaming, targeting
both casual and advanced gamers. Browser technology has also evolved to
accommodate more demanding applications, sometimes even supplanting functions
typically left to the operating system. Today, websites display rich,
computationally intensive, hardware-accelerated graphics, allowing developers
to build ever-more impressive applications and games.In this paper, we present
the evolution of browser gaming and the technologies that enabled it, from the
release of the first text-based games in the early 1990s to current open-world
and game-engine-powered browser games. We discuss the societal impact of
browser gaming and how it has allowed a new target audience to accessdigital
gaming. Finally, we review the potential future evolution ofthe browser gaming
industry.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 12:02:16 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Mehanna",
"Naif",
"",
"CRIStAL, CNRS, SPIRALS"
],
[
"Rudametkin",
"Walter",
"",
"UR, IUF,\n CNRS, IRISA, DiverSe"
]
] |
new_dataset
| 0.99557 |
2304.14803
|
Elisa Leonardelli
|
Elisa Leonardelli, Alexandra Uma, Gavin Abercrombie, Dina Almanea,
Valerio Basile, Tommaso Fornaciari, Barbara Plank, Verena Rieser, Massimo
Poesio
|
SemEval-2023 Task 11: Learning With Disagreements (LeWiDi)
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
NLP datasets annotated with human judgments are rife with disagreements
between the judges. This is especially true for tasks depending on subjective
judgments such as sentiment analysis or offensive language detection.
Particularly in these latter cases, the NLP community has come to realize that
the approach of 'reconciling' these different subjective interpretations is
inappropriate. Many NLP researchers have therefore concluded that rather than
eliminating disagreements from annotated corpora, we should preserve
them-indeed, some argue that corpora should aim to preserve all annotator
judgments. But this approach to corpus creation for NLP has not yet been widely
accepted. The objective of the LeWiDi series of shared tasks is to promote this
approach to developing NLP models by providing a unified framework for training
and evaluating with such datasets. We report on the second LeWiDi shared task,
which differs from the first edition in three crucial respects: (i) it focuses
entirely on NLP, instead of both NLP and computer vision tasks in its first
edition; (ii) it focuses on subjective tasks, instead of covering different
types of disagreements-as training with aggregated labels for subjective NLP
tasks is a particularly obvious misrepresentation of the data; and (iii) for
the evaluation, we concentrate on soft approaches to evaluation. This second
edition of LeWiDi attracted a wide array of participants resulting in 13 shared
task submission papers.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 12:20:35 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Leonardelli",
"Elisa",
""
],
[
"Uma",
"Alexandra",
""
],
[
"Abercrombie",
"Gavin",
""
],
[
"Almanea",
"Dina",
""
],
[
"Basile",
"Valerio",
""
],
[
"Fornaciari",
"Tommaso",
""
],
[
"Plank",
"Barbara",
""
],
[
"Rieser",
"Verena",
""
],
[
"Poesio",
"Massimo",
""
]
] |
new_dataset
| 0.986403 |
2304.14811
|
Junge Zhang
|
Junge Zhang, Feihu Zhang, Shaochen Kuang, Li Zhang
|
NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance
Fields
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Labeling LiDAR point clouds for training autonomous driving is extremely
expensive and difficult. LiDAR simulation aims at generating realistic LiDAR
data with labels for training and verifying self-driving algorithms more
efficiently. Recently, Neural Radiance Fields (NeRF) have been proposed for
novel view synthesis using implicit reconstruction of 3D scenes. Inspired by
this, we present NeRF-LIDAR, a novel LiDAR simulation method that leverages
real-world information to generate realistic LIDAR point clouds. Different from
existing LiDAR simulators, we use real images and point cloud data collected by
self-driving cars to learn the 3D scene representation, point cloud generation
and label rendering. We verify the effectiveness of our NeRF-LiDAR by training
different 3D segmentation models on the generated LiDAR point clouds. It
reveals that the trained models are able to achieve similar accuracy when
compared with the same model trained on the real LiDAR data. Besides, the
generated data is capable of boosting the accuracy through pre-training which
helps reduce the requirements of the real labeled data.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 12:41:28 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Zhang",
"Junge",
""
],
[
"Zhang",
"Feihu",
""
],
[
"Kuang",
"Shaochen",
""
],
[
"Zhang",
"Li",
""
]
] |
new_dataset
| 0.998836 |
2304.14918
|
Johannes Czech
|
Johannes Czech, Jannis Bl\"uml, Kristian Kersting
|
Representation Matters: The Game of Chess Poses a Challenge to Vision
Transformers
|
11 pages, 5 figures, 8 tables
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
While transformers have gained the reputation as the "Swiss army knife of
AI", no one has challenged them to master the game of chess, one of the
classical AI benchmarks. Simply using vision transformers (ViTs) within
AlphaZero does not master the game of chess, mainly because ViTs are too slow.
Even making them more efficient using a combination of MobileNet and NextViT
does not beat what actually matters: a simple change of the input
representation and value loss, resulting in a greater boost of up to 180 Elo
points over AlphaZero.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 15:33:39 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Czech",
"Johannes",
""
],
[
"Blüml",
"Jannis",
""
],
[
"Kersting",
"Kristian",
""
]
] |
new_dataset
| 0.995215 |
2304.14937
|
David Wong
|
James Bungay, Osasenaga Emokpae, Samuel D. Relton, Jane Alty, Stefan
Williams, Hui Fang, David C. Wong
|
Contactless hand tremor amplitude measurement using smartphones:
development and pilot evaluation
|
Accepted to IEEE EMBC 2023, Sydney (pre-refereed version)
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Background: Physiological tremor is defined as an involuntary and rhythmic
shaking. Tremor of the hand is a key symptom of multiple neurological diseases,
and its frequency and amplitude differs according to both disease type and
disease progression. In routine clinical practice, tremor frequency and
amplitude are assessed by expert rating using a 0 to 4 integer scale. Such
ratings are subjective and have poor inter-rater reliability. There is thus a
clinical need for a practical and accurate method for objectively assessing
hand tremor.
Objective: to develop a proof of principle method to measure hand tremor
amplitude from smartphone videos.
Methods: We created a computer vision pipeline that automatically extracts
salient points on the hand and produces a 1-D time series of movement due to
tremor, in pixels. Using the smartphones' depth measurement, we convert this
measure into real distance units. We assessed the accuracy of the method using
60 videos of simulated tremor of different amplitudes from two healthy adults.
Videos were taken at distances of 50, 75 and 100 cm between hand and camera.
The participants had skin tone II and VI on the Fitzpatrick scale. We compared
our method to a gold-standard measurement from a slide rule. Bland-Altman
methods agreement analysis indicated a bias of 0.04 cm and 95% limits of
agreement from -1.27 to 1.20 cm. Furthermore, we qualitatively observed that
the method was robust to differences in skin tone and limited occlusion, such
as a band-aid affixed to the participant's hand.
Clinical relevance: We have demonstrated how tremor amplitude can be measured
from smartphone videos. In conjunction with tremor frequency, this approach
could be used to help diagnose and monitor neurological diseases
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 15:48:49 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Bungay",
"James",
""
],
[
"Emokpae",
"Osasenaga",
""
],
[
"Relton",
"Samuel D.",
""
],
[
"Alty",
"Jane",
""
],
[
"Williams",
"Stefan",
""
],
[
"Fang",
"Hui",
""
],
[
"Wong",
"David C.",
""
]
] |
new_dataset
| 0.999174 |
2304.14947
|
Mariya Kilina
|
Mariya Kilina, Tommaso Elia, Syed Yusha Kareem, Alessandro Carfi,
Fulvio Mastrogiovanni
|
Embodiment perception of a smart home assistant
|
Published at International Conference on Social Robotics 2022
| null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Demographic growth and rise in the average age of the population is
increasing the demand for the elderly assistance. Health care oriented ambient
intelligence technologies are fundamental to support elderly peoples' autonomy.
In this paper, we present a smart home system that is able to recognize human
activities and is integrated with a proactive vocal assistant. We chose one of
possible user scenarios to show the performance of this smart home system and
to perform a preliminary comparison between users' experience while watching
videos of a volunteer interacting with an embodied versus a not-embodied
assistant. The scenario is recorded from the user's point of view, while the
user interacts with a robot assistant or a simple vocal assistant. The results
of the User Experience Questionnaire show that participants found the robot
assistant considerably more attractive, innovative and stimulating in
comparison to the vocal assistant.
|
[
{
"version": "v1",
"created": "Fri, 28 Apr 2023 16:06:14 GMT"
}
] | 2023-05-01T00:00:00 |
[
[
"Kilina",
"Mariya",
""
],
[
"Elia",
"Tommaso",
""
],
[
"Kareem",
"Syed Yusha",
""
],
[
"Carfi",
"Alessandro",
""
],
[
"Mastrogiovanni",
"Fulvio",
""
]
] |
new_dataset
| 0.998539 |
2203.11667
|
Duc A. Hoang
|
Duc A. Hoang
|
TS-Reconfiguration of $k$-Path Vertex Covers in Caterpillars for $k \geq
4$
|
12 pages, 3 figures, minor revision, update title and abstract
|
Theory and Applications of Graphs: Vol. 10: Iss. 1, Article 8
(2023)
|
10.20429/tag.2023.10108
| null |
cs.DS cs.DM math.CO
|
http://creativecommons.org/licenses/by-sa/4.0/
|
A $k$-path vertex cover ($k$-PVC) of a graph $G$ is a vertex subset $I$ such
that each path on $k$ vertices in $G$ contains at least one member of $I$.
Imagine that a token is placed on each vertex of a $k$-PVC. Given two $k$-PVCs
$I, J$ of a graph $G$, the $k$-Path Vertex Cover Reconfiguration ($k$-PVCR)
under Token Sliding ($\mathsf{TS}$) problem asks if there is a sequence of
$k$-PVCs between $I$ and $J$ where each intermediate member is obtained from
its predecessor by sliding a token from some vertex to one of its unoccupied
neighbors. This problem is known to be $\mathtt{PSPACE}$-complete even for
planar graphs of maximum degree $3$ and bounded treewidth and can be solved in
polynomial time for paths and cycles. Its complexity for trees remains unknown.
In this paper, as a first step toward answering this question, for $k \geq 4$,
we present a polynomial-time algorithm that solves $k$-PVCR under $\mathsf{TS}$
for caterpillars (i.e., trees formed by attaching leaves to a path).
|
[
{
"version": "v1",
"created": "Tue, 22 Mar 2022 12:41:14 GMT"
},
{
"version": "v2",
"created": "Mon, 23 May 2022 01:39:46 GMT"
},
{
"version": "v3",
"created": "Mon, 8 Aug 2022 14:27:15 GMT"
}
] | 2023-04-28T00:00:00 |
[
[
"Hoang",
"Duc A.",
""
]
] |
new_dataset
| 0.995191 |
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