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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2402.03989
|
Anton Backhaus
|
Anton Backhaus, Thorsten Luettel, Hans-Joachim Wuensche
|
YOLOPoint Joint Keypoint and Object Detection
|
12 pages, 5 figures
|
Proceedings of Advanced Concepts for Intelligent Vision Systems,
14124, 112-123 (2023)
| null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Intelligent vehicles of the future must be capable of understanding and
navigating safely through their surroundings. Camera-based vehicle systems can
use keypoints as well as objects as low- and high-level landmarks for
GNSS-independent SLAM and visual odometry. To this end we propose YOLOPoint, a
convolutional neural network model that simultaneously detects keypoints and
objects in an image by combining YOLOv5 and SuperPoint to create a single
forward-pass network that is both real-time capable and accurate. By using a
shared backbone and a light-weight network structure, YOLOPoint is able to
perform competitively on both the HPatches and KITTI benchmarks.
|
[
{
"created": "Tue, 6 Feb 2024 13:31:45 GMT",
"version": "v1"
}
] |
2024-02-07
|
[
[
"Backhaus",
"Anton",
""
],
[
"Luettel",
"Thorsten",
""
],
[
"Wuensche",
"Hans-Joachim",
""
]
] |
Intelligent vehicles of the future must be capable of understanding and navigating safely through their surroundings. Camera-based vehicle systems can use keypoints as well as objects as low- and high-level landmarks for GNSS-independent SLAM and visual odometry. To this end we propose YOLOPoint, a convolutional neural network model that simultaneously detects keypoints and objects in an image by combining YOLOv5 and SuperPoint to create a single forward-pass network that is both real-time capable and accurate. By using a shared backbone and a light-weight network structure, YOLOPoint is able to perform competitively on both the HPatches and KITTI benchmarks.
|
1603.02813
|
Emre Erturk
|
Emre Erturk
|
Using a Cloud Based Collaboration Technology in a Systems Analysis and
Design Course
| null | null | null | null |
cs.CY cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In order to effectively prepare the next generation of IT professionals and
systems analysts, it is important to incorporate cloud based online
collaboration tools into the coursework for developing the students'
cooperative skills as well as for storing and sharing content. For these
pedagogical and practical reasons, Google Drive has been used at a medium-sized
institution of higher education in New Zealand during the Systems Analysis and
Design course. Ongoing and successful use of any learning technology requires
gathering meaningful feedback from students, and acting as a mentor during
their learning journey. This study has been developed and implemented to help
students enjoy the collaborative technology and to help increase their
satisfaction and commitment. In order to overcome the obstacles that may
prevent students from using Google Drive optimally, an initial survey has been
conducted to better understand the influential factors and issues. Furthermore,
this study aims at promoting various types of collaboration and sharing: seeing
and learning from other students' work, receiving direct suggestions from
others, and allowing others to edit documents that belong to them. Following
the results of the first quantitative survey, numerous teaching strategies were
formulated and implemented. A final qualitative survey was done at the end of
the course for students to evaluate their project work. The results of this
study also provide original practical and theoretical implications that may be
of interest to other researchers, course designers, and teachers.
|
[
{
"created": "Wed, 9 Mar 2016 08:51:33 GMT",
"version": "v1"
}
] |
2016-03-10
|
[
[
"Erturk",
"Emre",
""
]
] |
In order to effectively prepare the next generation of IT professionals and systems analysts, it is important to incorporate cloud based online collaboration tools into the coursework for developing the students' cooperative skills as well as for storing and sharing content. For these pedagogical and practical reasons, Google Drive has been used at a medium-sized institution of higher education in New Zealand during the Systems Analysis and Design course. Ongoing and successful use of any learning technology requires gathering meaningful feedback from students, and acting as a mentor during their learning journey. This study has been developed and implemented to help students enjoy the collaborative technology and to help increase their satisfaction and commitment. In order to overcome the obstacles that may prevent students from using Google Drive optimally, an initial survey has been conducted to better understand the influential factors and issues. Furthermore, this study aims at promoting various types of collaboration and sharing: seeing and learning from other students' work, receiving direct suggestions from others, and allowing others to edit documents that belong to them. Following the results of the first quantitative survey, numerous teaching strategies were formulated and implemented. A final qualitative survey was done at the end of the course for students to evaluate their project work. The results of this study also provide original practical and theoretical implications that may be of interest to other researchers, course designers, and teachers.
|
1905.02906
|
Jaehwan Lee
|
Jaehwan Lee and Donggeon Yoo and Jung Yin Huh and Hyo-Eun Kim
|
Photometric Transformer Networks and Label Adjustment for Breast Density
Prediction
|
miccai 2019 submission
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Grading breast density is highly sensitive to normalization settings of
digital mammogram as the density is tightly correlated with the distribution of
pixel intensity. Also, the grade varies with readers due to uncertain grading
criteria. These issues are inherent in the density assessment of digital
mammography. They are problematic when designing a computer-aided prediction
model for breast density and become worse if the data comes from multiple
sites. In this paper, we proposed two novel deep learning techniques for breast
density prediction: 1) photometric transformation which adaptively normalizes
the input mammograms, and 2) label distillation which adjusts the label by
using its output prediction. The photometric transformer network predicts
optimal parameters for photometric transformation on the fly, learned jointly
with the main prediction network. The label distillation, a type of
pseudo-label techniques, is intended to mitigate the grading variation. We
experimentally showed that the proposed methods are beneficial in terms of
breast density prediction, resulting in significant performance improvement
compared to various previous approaches.
|
[
{
"created": "Wed, 8 May 2019 04:32:34 GMT",
"version": "v1"
}
] |
2019-05-09
|
[
[
"Lee",
"Jaehwan",
""
],
[
"Yoo",
"Donggeon",
""
],
[
"Huh",
"Jung Yin",
""
],
[
"Kim",
"Hyo-Eun",
""
]
] |
Grading breast density is highly sensitive to normalization settings of digital mammogram as the density is tightly correlated with the distribution of pixel intensity. Also, the grade varies with readers due to uncertain grading criteria. These issues are inherent in the density assessment of digital mammography. They are problematic when designing a computer-aided prediction model for breast density and become worse if the data comes from multiple sites. In this paper, we proposed two novel deep learning techniques for breast density prediction: 1) photometric transformation which adaptively normalizes the input mammograms, and 2) label distillation which adjusts the label by using its output prediction. The photometric transformer network predicts optimal parameters for photometric transformation on the fly, learned jointly with the main prediction network. The label distillation, a type of pseudo-label techniques, is intended to mitigate the grading variation. We experimentally showed that the proposed methods are beneficial in terms of breast density prediction, resulting in significant performance improvement compared to various previous approaches.
|
2002.10916
|
Rodrigo de Lamare
|
S. B. Pinto and R. C. de Lamare
|
Study of Coarse Quantization-Aware Block Diagonalization Algorithms for
MIMO Systems with Low Resolution
|
3 figures, 9 pages. arXiv admin note: text overlap with
arXiv:1707.00953
| null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
It is known that the estimated energy consumption of digital-to analog
converters (DACs) is around 30\% of the energy consumed by analog-to-digital
converters (ADCs) keeping fixed the sampling rate and bit resolution. Assuming
that similarly to ADC, DAC dissipation doubles with every extra bit of
resolution, a decrease in two resolution bits, for instance from 4 to 2 bits,
represents a 75$\% $ lower dissipation. The current limitations in sum-rates of
1-bit quantization have motivated researchers to consider extra bits in
resolution to obtain higher levels of sum-rates. Following this, we devise
coarse quantization-aware precoding using few bits for the broadcast channel of
multiple-antenna systems based on the Bussgang theorem. In particular, we
consider block diagonalization algorithms, which have not been considered in
the literature so far. The sum-rates achieved by the proposed Coarse
Quantization-Aware Block Diagonalization (CQA-BD) and its regularized version
(CQA-RBD) are superior to those previously reported in the literature.
Simulations illustrate the performance of the proposed CQA-BD and CGA-RBD
algorithms against existing approaches.
|
[
{
"created": "Sun, 23 Feb 2020 01:45:50 GMT",
"version": "v1"
}
] |
2020-02-26
|
[
[
"Pinto",
"S. B.",
""
],
[
"de Lamare",
"R. C.",
""
]
] |
It is known that the estimated energy consumption of digital-to analog converters (DACs) is around 30\% of the energy consumed by analog-to-digital converters (ADCs) keeping fixed the sampling rate and bit resolution. Assuming that similarly to ADC, DAC dissipation doubles with every extra bit of resolution, a decrease in two resolution bits, for instance from 4 to 2 bits, represents a 75$\% $ lower dissipation. The current limitations in sum-rates of 1-bit quantization have motivated researchers to consider extra bits in resolution to obtain higher levels of sum-rates. Following this, we devise coarse quantization-aware precoding using few bits for the broadcast channel of multiple-antenna systems based on the Bussgang theorem. In particular, we consider block diagonalization algorithms, which have not been considered in the literature so far. The sum-rates achieved by the proposed Coarse Quantization-Aware Block Diagonalization (CQA-BD) and its regularized version (CQA-RBD) are superior to those previously reported in the literature. Simulations illustrate the performance of the proposed CQA-BD and CGA-RBD algorithms against existing approaches.
|
1910.07963
|
Nicolas Tremblay
|
Yusuf Y. Pilavci, Pierre-Olivier Amblard, Simon Barthelm\'e, Nicolas
Tremblay
|
Smoothing graph signals via random spanning forests
| null | null | null | null |
cs.DM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Another facet of the elegant link between random processes on graphs and
Laplacian-based numerical linear algebra is uncovered: based on random spanning
forests, novel Monte-Carlo estimators for graph signal smoothing are proposed.
These random forests are sampled efficiently via a variant of Wilson's
algorithm --in time linear in the number of edges. The theoretical variance of
the proposed estimators are analyzed, and their application to several problems
are considered, such as Tikhonov denoising of graph signals or semi-supervised
learning for node classification on graphs.
|
[
{
"created": "Thu, 17 Oct 2019 15:11:03 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Feb 2020 15:26:33 GMT",
"version": "v2"
}
] |
2020-02-06
|
[
[
"Pilavci",
"Yusuf Y.",
""
],
[
"Amblard",
"Pierre-Olivier",
""
],
[
"Barthelmé",
"Simon",
""
],
[
"Tremblay",
"Nicolas",
""
]
] |
Another facet of the elegant link between random processes on graphs and Laplacian-based numerical linear algebra is uncovered: based on random spanning forests, novel Monte-Carlo estimators for graph signal smoothing are proposed. These random forests are sampled efficiently via a variant of Wilson's algorithm --in time linear in the number of edges. The theoretical variance of the proposed estimators are analyzed, and their application to several problems are considered, such as Tikhonov denoising of graph signals or semi-supervised learning for node classification on graphs.
|
1605.02324
|
Charles Jeon
|
Charles Jeon, Arian Maleki, and Christoph Studer
|
On the Performance of Mismatched Data Detection in Large MIMO Systems
|
Will be presented at the 2016 IEEE International Symposium on
Information Theory
| null |
10.1109/ISIT.2016.7541285
| null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate the performance of mismatched data detection in large
multiple-input multiple-output (MIMO) systems, where the prior distribution of
the transmit signal used in the data detector differs from the true prior. To
minimize the performance loss caused by this prior mismatch, we include a
tuning stage into our recently-proposed large MIMO approximate message passing
(LAMA) algorithm, which allows us to develop mismatched LAMA algorithms with
optimal as well as sub-optimal tuning. We show that carefully-selected priors
often enable simpler and computationally more efficient algorithms compared to
LAMA with the true prior while achieving near-optimal performance. A
performance analysis of our algorithms for a Gaussian prior and a uniform prior
within a hypercube covering the QAM constellation recovers classical and recent
results on linear and non-linear MIMO data detection, respectively.
|
[
{
"created": "Sun, 8 May 2016 14:37:45 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Jun 2016 16:35:30 GMT",
"version": "v2"
}
] |
2018-11-12
|
[
[
"Jeon",
"Charles",
""
],
[
"Maleki",
"Arian",
""
],
[
"Studer",
"Christoph",
""
]
] |
We investigate the performance of mismatched data detection in large multiple-input multiple-output (MIMO) systems, where the prior distribution of the transmit signal used in the data detector differs from the true prior. To minimize the performance loss caused by this prior mismatch, we include a tuning stage into our recently-proposed large MIMO approximate message passing (LAMA) algorithm, which allows us to develop mismatched LAMA algorithms with optimal as well as sub-optimal tuning. We show that carefully-selected priors often enable simpler and computationally more efficient algorithms compared to LAMA with the true prior while achieving near-optimal performance. A performance analysis of our algorithms for a Gaussian prior and a uniform prior within a hypercube covering the QAM constellation recovers classical and recent results on linear and non-linear MIMO data detection, respectively.
|
2210.02231
|
Yue Zhu
|
Yue Zhu, David Picard
|
Decanus to Legatus: Synthetic training for 2D-3D human pose lifting
|
Accepted by ACCV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
3D human pose estimation is a challenging task because of the difficulty to
acquire ground-truth data outside of controlled environments. A number of
further issues have been hindering progress in building a universal and robust
model for this task, including domain gaps between different datasets, unseen
actions between train and test datasets, various hardware settings and high
cost of annotation, etc. In this paper, we propose an algorithm to generate
infinite 3D synthetic human poses (Legatus) from a 3D pose distribution based
on 10 initial handcrafted 3D poses (Decanus) during the training of a 2D to 3D
human pose lifter neural network. Our results show that we can achieve 3D pose
estimation performance comparable to methods using real data from specialized
datasets but in a zero-shot setup, showing the generalization potential of our
framework.
|
[
{
"created": "Wed, 5 Oct 2022 13:10:19 GMT",
"version": "v1"
}
] |
2022-10-06
|
[
[
"Zhu",
"Yue",
""
],
[
"Picard",
"David",
""
]
] |
3D human pose estimation is a challenging task because of the difficulty to acquire ground-truth data outside of controlled environments. A number of further issues have been hindering progress in building a universal and robust model for this task, including domain gaps between different datasets, unseen actions between train and test datasets, various hardware settings and high cost of annotation, etc. In this paper, we propose an algorithm to generate infinite 3D synthetic human poses (Legatus) from a 3D pose distribution based on 10 initial handcrafted 3D poses (Decanus) during the training of a 2D to 3D human pose lifter neural network. Our results show that we can achieve 3D pose estimation performance comparable to methods using real data from specialized datasets but in a zero-shot setup, showing the generalization potential of our framework.
|
1807.11580
|
Ryo Yoshinaka
|
Yuki Nozaki, Diptarama Hendrian, Ryo Yoshinaka, Takashi Horiyama,
Ayumi Shinohara
|
Enumerating Cryptarithms Using Deterministic Finite Automata
| null | null | null | null |
cs.FL cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A cryptarithm is a mathematical puzzle where given an arithmetic equation
written with letters rather than numerals, a player must discover an assignment
of numerals on letters that makes the equation hold true. In this paper, we
propose a method to construct a DFA that accepts cryptarithms that admit
(unique) solutions for each base. We implemented the method and constructed a
DFA for bases $k \le 7$. Those DFAs can be used as complete catalogues of
cryptarithms,whose applications include enumeration of and counting the exact
numbers $G_k(n)$ of cryptarithm instances with $n$ digits that admit base-$k$
solutions. Moreover, explicit formulas for $G_2(n)$ and $G_3(n)$ are given.
|
[
{
"created": "Fri, 27 Jul 2018 01:37:45 GMT",
"version": "v1"
}
] |
2018-08-01
|
[
[
"Nozaki",
"Yuki",
""
],
[
"Hendrian",
"Diptarama",
""
],
[
"Yoshinaka",
"Ryo",
""
],
[
"Horiyama",
"Takashi",
""
],
[
"Shinohara",
"Ayumi",
""
]
] |
A cryptarithm is a mathematical puzzle where given an arithmetic equation written with letters rather than numerals, a player must discover an assignment of numerals on letters that makes the equation hold true. In this paper, we propose a method to construct a DFA that accepts cryptarithms that admit (unique) solutions for each base. We implemented the method and constructed a DFA for bases $k \le 7$. Those DFAs can be used as complete catalogues of cryptarithms,whose applications include enumeration of and counting the exact numbers $G_k(n)$ of cryptarithm instances with $n$ digits that admit base-$k$ solutions. Moreover, explicit formulas for $G_2(n)$ and $G_3(n)$ are given.
|
1011.0640
|
M. Emre Celebi
|
M. Emre Celebi, Hitoshi Iyatomi, Gerald Schaefer, William V. Stoecker
|
Lesion Border Detection in Dermoscopy Images
|
10 pages, 1 figure, 3 tables
|
Computerized Medical Imaging and Graphics 33 (2009) 148--153
|
10.1016/j.compmedimag.2008.11.002
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Background: Dermoscopy is one of the major imaging modalities used in the
diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty
and subjectivity of human interpretation, computerized analysis of dermoscopy
images has become an important research area. One of the most important steps
in dermoscopy image analysis is the automated detection of lesion borders.
Methods: In this article, we present a systematic overview of the recent border
detection methods in the literature paying particular attention to
computational issues and evaluation aspects. Conclusion: Common problems with
the existing approaches include the acquisition, size, and diagnostic
distribution of the test image set, the evaluation of the results, and the
inadequate description of the employed methods. Border determination by
dermatologists appears to depend upon higher-level knowledge, therefore it is
likely that the incorporation of domain knowledge in automated methods will
enable them to perform better, especially in sets of images with a variety of
diagnoses.
|
[
{
"created": "Sat, 30 Oct 2010 17:17:02 GMT",
"version": "v1"
}
] |
2010-11-13
|
[
[
"Celebi",
"M. Emre",
""
],
[
"Iyatomi",
"Hitoshi",
""
],
[
"Schaefer",
"Gerald",
""
],
[
"Stoecker",
"William V.",
""
]
] |
Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. Methods: In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects. Conclusion: Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses.
|
2309.16237
|
Jiaman Li
|
Jiaman Li, Jiajun Wu, C. Karen Liu
|
Object Motion Guided Human Motion Synthesis
|
SIGGRAPH Asia 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Modeling human behaviors in contextual environments has a wide range of
applications in character animation, embodied AI, VR/AR, and robotics. In
real-world scenarios, humans frequently interact with the environment and
manipulate various objects to complete daily tasks. In this work, we study the
problem of full-body human motion synthesis for the manipulation of large-sized
objects. We propose Object MOtion guided human MOtion synthesis (OMOMO), a
conditional diffusion framework that can generate full-body manipulation
behaviors from only the object motion. Since naively applying diffusion models
fails to precisely enforce contact constraints between the hands and the
object, OMOMO learns two separate denoising processes to first predict hand
positions from object motion and subsequently synthesize full-body poses based
on the predicted hand positions. By employing the hand positions as an
intermediate representation between the two denoising processes, we can
explicitly enforce contact constraints, resulting in more physically plausible
manipulation motions. With the learned model, we develop a novel system that
captures full-body human manipulation motions by simply attaching a smartphone
to the object being manipulated. Through extensive experiments, we demonstrate
the effectiveness of our proposed pipeline and its ability to generalize to
unseen objects. Additionally, as high-quality human-object interaction datasets
are scarce, we collect a large-scale dataset consisting of 3D object geometry,
object motion, and human motion. Our dataset contains human-object interaction
motion for 15 objects, with a total duration of approximately 10 hours.
|
[
{
"created": "Thu, 28 Sep 2023 08:22:00 GMT",
"version": "v1"
}
] |
2023-09-29
|
[
[
"Li",
"Jiaman",
""
],
[
"Wu",
"Jiajun",
""
],
[
"Liu",
"C. Karen",
""
]
] |
Modeling human behaviors in contextual environments has a wide range of applications in character animation, embodied AI, VR/AR, and robotics. In real-world scenarios, humans frequently interact with the environment and manipulate various objects to complete daily tasks. In this work, we study the problem of full-body human motion synthesis for the manipulation of large-sized objects. We propose Object MOtion guided human MOtion synthesis (OMOMO), a conditional diffusion framework that can generate full-body manipulation behaviors from only the object motion. Since naively applying diffusion models fails to precisely enforce contact constraints between the hands and the object, OMOMO learns two separate denoising processes to first predict hand positions from object motion and subsequently synthesize full-body poses based on the predicted hand positions. By employing the hand positions as an intermediate representation between the two denoising processes, we can explicitly enforce contact constraints, resulting in more physically plausible manipulation motions. With the learned model, we develop a novel system that captures full-body human manipulation motions by simply attaching a smartphone to the object being manipulated. Through extensive experiments, we demonstrate the effectiveness of our proposed pipeline and its ability to generalize to unseen objects. Additionally, as high-quality human-object interaction datasets are scarce, we collect a large-scale dataset consisting of 3D object geometry, object motion, and human motion. Our dataset contains human-object interaction motion for 15 objects, with a total duration of approximately 10 hours.
|
1907.02765
|
Zhi Wang
|
Xueshuo Xie and Zhi Wang and Xuhang Xiao and Lei Yang and Shenwei
Huang and Tao Li
|
A Pvalue-guided Anomaly Detection Approach Combining Multiple
Heterogeneous Log Parser Algorithms on IIoT Systems
|
7 pages, 3 figures
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Industrial Internet of Things (IIoT) is becoming an attack target of advanced
persistent threat (APT). Currently, IIoT logs have not been effectively used
for anomaly detection. In this paper, we use blockchain to prevent logs from
being tampered with and propose a pvalue-guided anomaly detection approach.
This approach uses statistical pvalues to combine multiple heterogeneous log
parser algorithms. The weighted edit distance is selected as a score function
to calculate the nonconformity score between a log and a predefined event. The
pvalue is calculated based on the non-conformity scores which indicate how well
a log matches an event. This approach is tested on a large number of real-world
HDFS logs and IIoT logs. The experiment results show that abnormal events could
be effectively recognized by our pvalue-guided approach.
|
[
{
"created": "Fri, 5 Jul 2019 10:44:37 GMT",
"version": "v1"
}
] |
2019-07-08
|
[
[
"Xie",
"Xueshuo",
""
],
[
"Wang",
"Zhi",
""
],
[
"Xiao",
"Xuhang",
""
],
[
"Yang",
"Lei",
""
],
[
"Huang",
"Shenwei",
""
],
[
"Li",
"Tao",
""
]
] |
Industrial Internet of Things (IIoT) is becoming an attack target of advanced persistent threat (APT). Currently, IIoT logs have not been effectively used for anomaly detection. In this paper, we use blockchain to prevent logs from being tampered with and propose a pvalue-guided anomaly detection approach. This approach uses statistical pvalues to combine multiple heterogeneous log parser algorithms. The weighted edit distance is selected as a score function to calculate the nonconformity score between a log and a predefined event. The pvalue is calculated based on the non-conformity scores which indicate how well a log matches an event. This approach is tested on a large number of real-world HDFS logs and IIoT logs. The experiment results show that abnormal events could be effectively recognized by our pvalue-guided approach.
|
1806.04713
|
Hanan Aldarmaki
|
Hanan Aldarmaki and Mona Diab
|
Evaluation of Unsupervised Compositional Representations
|
12 pages, 5 figures. COLING 2018
|
Proceedings of the 27th International Conference on Computational
Linguistics (2018)
| null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We evaluated various compositional models, from bag-of-words representations
to compositional RNN-based models, on several extrinsic supervised and
unsupervised evaluation benchmarks. Our results confirm that weighted vector
averaging can outperform context-sensitive models in most benchmarks, but
structural features encoded in RNN models can also be useful in certain
classification tasks. We analyzed some of the evaluation datasets to identify
the aspects of meaning they measure and the characteristics of the various
models that explain their performance variance.
|
[
{
"created": "Tue, 12 Jun 2018 18:53:14 GMT",
"version": "v1"
},
{
"created": "Thu, 14 Jun 2018 16:43:27 GMT",
"version": "v2"
}
] |
2018-11-30
|
[
[
"Aldarmaki",
"Hanan",
""
],
[
"Diab",
"Mona",
""
]
] |
We evaluated various compositional models, from bag-of-words representations to compositional RNN-based models, on several extrinsic supervised and unsupervised evaluation benchmarks. Our results confirm that weighted vector averaging can outperform context-sensitive models in most benchmarks, but structural features encoded in RNN models can also be useful in certain classification tasks. We analyzed some of the evaluation datasets to identify the aspects of meaning they measure and the characteristics of the various models that explain their performance variance.
|
1805.02716
|
Eliu Huerta
|
E. A. Huerta, Daniel George, Zhizhen Zhao and Gabrielle Allen
|
Real-time regression analysis with deep convolutional neural networks
|
3 pages. Position Paper accepted to SciML2018: DOE ASCR Workshop on
Scientific Machine Learning. North Bethesda, MD, United States, January
30-February 1, 2018
| null | null | null |
cs.LG astro-ph.IM cs.AI stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We discuss the development of novel deep learning algorithms to enable
real-time regression analysis for time series data. We showcase the application
of this new method with a timely case study, and then discuss the applicability
of this approach to tackle similar challenges across science domains.
|
[
{
"created": "Mon, 7 May 2018 19:43:26 GMT",
"version": "v1"
}
] |
2018-05-09
|
[
[
"Huerta",
"E. A.",
""
],
[
"George",
"Daniel",
""
],
[
"Zhao",
"Zhizhen",
""
],
[
"Allen",
"Gabrielle",
""
]
] |
We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data. We showcase the application of this new method with a timely case study, and then discuss the applicability of this approach to tackle similar challenges across science domains.
|
2205.07314
|
Pradeep Gupta Dr.
|
Raghav Dalmia, Aryaman Sinha, Ruchi Verma, P. K. Gupta
|
Dynamic Ready Queue Based Process Priority Scheduling Algorithm
|
5 pages, 7 Figures, 5 Tables
| null | null | null |
cs.OS
|
http://creativecommons.org/licenses/by/4.0/
|
CPU scheduling is the reason behind the performance of multiprocessing and in
time-shared operating systems. Different scheduling criteria are used to
evaluate Central Processing Unit Scheduling algorithms which are based on
different properties of the system. Round Robin is known to be the most
recurrent pre-emptive algorithm used in an environment where processes are
allotted a unit of time and multiprocessing operating systems. In this paper, a
reformed variation of the Round Robin algorithm has been introduced to minimise
the completion time, turnaround time, waiting time and number of context
switches that results in the better performance of the system. The proposed
work consists of calculation of priority on the basis of the difference between
time spent in ready upto the moment and arrival time of the process, to ease up
the burden on the ready queue. We have also evaluated the performance of the
proposed approach on different datasets and measured the different scheduling
criteria.
|
[
{
"created": "Sun, 15 May 2022 15:38:59 GMT",
"version": "v1"
}
] |
2022-05-17
|
[
[
"Dalmia",
"Raghav",
""
],
[
"Sinha",
"Aryaman",
""
],
[
"Verma",
"Ruchi",
""
],
[
"Gupta",
"P. K.",
""
]
] |
CPU scheduling is the reason behind the performance of multiprocessing and in time-shared operating systems. Different scheduling criteria are used to evaluate Central Processing Unit Scheduling algorithms which are based on different properties of the system. Round Robin is known to be the most recurrent pre-emptive algorithm used in an environment where processes are allotted a unit of time and multiprocessing operating systems. In this paper, a reformed variation of the Round Robin algorithm has been introduced to minimise the completion time, turnaround time, waiting time and number of context switches that results in the better performance of the system. The proposed work consists of calculation of priority on the basis of the difference between time spent in ready upto the moment and arrival time of the process, to ease up the burden on the ready queue. We have also evaluated the performance of the proposed approach on different datasets and measured the different scheduling criteria.
|
2210.15377
|
Miriam Ansch\"utz
|
Miriam Ansch\"utz, Tobias Eder, Georg Groh
|
Retrieving Users' Opinions on Social Media with Multimodal Aspect-Based
Sentiment Analysis
|
8 pages, 5 figures, published at 2023 IEEE 17th International
Conference on Semantic Computing (ICSC)
| null | null | null |
cs.IR cs.AI cs.CL cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
People post their opinions and experiences on social media, yielding rich
databases of end-users' sentiments. This paper shows to what extent machine
learning can analyze and structure these databases. An automated data analysis
pipeline is deployed to provide insights into user-generated content for
researchers in other domains. First, the domain expert can select an image and
a term of interest. Then, the pipeline uses image retrieval to find all images
showing similar content and applies aspect-based sentiment analysis to outline
users' opinions about the selected term. As part of an interdisciplinary
project between architecture and computer science researchers, an empirical
study of Hamburg's Elbphilharmonie was conveyed. Therefore, we selected 300
thousand posts with the hashtag \enquote{\texttt{hamburg}} from the platform
Flickr. Image retrieval methods generated a subset of slightly more than 1.5
thousand images displaying the Elbphilharmonie. We found that these posts
mainly convey a neutral or positive sentiment towards it. With this pipeline,
we suggest a new semantic computing method that offers novel insights into
end-users opinions, e.g., for architecture domain experts.
|
[
{
"created": "Thu, 27 Oct 2022 12:38:10 GMT",
"version": "v1"
},
{
"created": "Mon, 9 Jan 2023 07:40:32 GMT",
"version": "v2"
}
] |
2023-01-10
|
[
[
"Anschütz",
"Miriam",
""
],
[
"Eder",
"Tobias",
""
],
[
"Groh",
"Georg",
""
]
] |
People post their opinions and experiences on social media, yielding rich databases of end-users' sentiments. This paper shows to what extent machine learning can analyze and structure these databases. An automated data analysis pipeline is deployed to provide insights into user-generated content for researchers in other domains. First, the domain expert can select an image and a term of interest. Then, the pipeline uses image retrieval to find all images showing similar content and applies aspect-based sentiment analysis to outline users' opinions about the selected term. As part of an interdisciplinary project between architecture and computer science researchers, an empirical study of Hamburg's Elbphilharmonie was conveyed. Therefore, we selected 300 thousand posts with the hashtag \enquote{\texttt{hamburg}} from the platform Flickr. Image retrieval methods generated a subset of slightly more than 1.5 thousand images displaying the Elbphilharmonie. We found that these posts mainly convey a neutral or positive sentiment towards it. With this pipeline, we suggest a new semantic computing method that offers novel insights into end-users opinions, e.g., for architecture domain experts.
|
0811.4483
|
Ga\"etan Le Guelvouit
|
Ga\"etan Le Guelvouit, St\'ephane Pateux
|
Wide spread spectrum watermarking with side information and interference
cancellation
|
12 pages, 8 figures
|
Proc. IS&T/SPIE Electronic Imaging, vol. 5020, Santa Clara, CA,
Jan. 2003
|
10.1117/12.476839
| null |
cs.MM cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Nowadays, a popular method used for additive watermarking is wide spread
spectrum. It consists in adding a spread signal into the host document. This
signal is obtained by the sum of a set of carrier vectors, which are modulated
by the bits to be embedded. To extract these embedded bits, weighted
correlations between the watermarked document and the carriers are computed.
Unfortunately, even without any attack, the obtained set of bits can be
corrupted due to the interference with the host signal (host interference) and
also due to the interference with the others carriers (inter-symbols
interference (ISI) due to the non-orthogonality of the carriers). Some recent
watermarking algorithms deal with host interference using side informed
methods, but inter-symbols interference problem is still open. In this paper,
we deal with interference cancellation methods, and we propose to consider ISI
as side information and to integrate it into the host signal. This leads to a
great improvement of extraction performance in term of signal-to-noise ratio
and/or watermark robustness.
|
[
{
"created": "Fri, 28 Nov 2008 16:28:59 GMT",
"version": "v1"
}
] |
2008-12-01
|
[
[
"Guelvouit",
"Gaëtan Le",
""
],
[
"Pateux",
"Stéphane",
""
]
] |
Nowadays, a popular method used for additive watermarking is wide spread spectrum. It consists in adding a spread signal into the host document. This signal is obtained by the sum of a set of carrier vectors, which are modulated by the bits to be embedded. To extract these embedded bits, weighted correlations between the watermarked document and the carriers are computed. Unfortunately, even without any attack, the obtained set of bits can be corrupted due to the interference with the host signal (host interference) and also due to the interference with the others carriers (inter-symbols interference (ISI) due to the non-orthogonality of the carriers). Some recent watermarking algorithms deal with host interference using side informed methods, but inter-symbols interference problem is still open. In this paper, we deal with interference cancellation methods, and we propose to consider ISI as side information and to integrate it into the host signal. This leads to a great improvement of extraction performance in term of signal-to-noise ratio and/or watermark robustness.
|
2309.15646
|
Devin Kuang
|
Xiangyu Zhang, Zongqiang Kuang, Zehao Zhang, Fan Huang, Xianfeng Tan
|
Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems
| null | null | null | null |
cs.IR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cold-start recommendation is one of the major challenges faced by recommender
systems (RS). Herein, we focus on the user cold-start problem. Recently,
methods utilizing side information or meta-learning have been used to model
cold-start users. However, it is difficult to deploy these methods to
industrial RS. There has not been much research that pays attention to the user
cold-start problem in the matching stage. In this paper, we propose Cold & Warm
Net based on expert models who are responsible for modeling cold-start and
warm-up users respectively. A gate network is applied to incorporate the
results from two experts. Furthermore, dynamic knowledge distillation acting as
a teacher selector is introduced to assist experts in better learning user
representation. With comprehensive mutual information, features highly relevant
to user behavior are selected for the bias net which explicitly models user
behavior bias. Finally, we evaluate our Cold & Warm Net on public datasets in
comparison to models commonly applied in the matching stage and it outperforms
other models on all user types. The proposed model has also been deployed on an
industrial short video platform and achieves a significant increase in app
dwell time and user retention rate.
|
[
{
"created": "Wed, 27 Sep 2023 13:31:43 GMT",
"version": "v1"
}
] |
2023-09-28
|
[
[
"Zhang",
"Xiangyu",
""
],
[
"Kuang",
"Zongqiang",
""
],
[
"Zhang",
"Zehao",
""
],
[
"Huang",
"Fan",
""
],
[
"Tan",
"Xianfeng",
""
]
] |
Cold-start recommendation is one of the major challenges faced by recommender systems (RS). Herein, we focus on the user cold-start problem. Recently, methods utilizing side information or meta-learning have been used to model cold-start users. However, it is difficult to deploy these methods to industrial RS. There has not been much research that pays attention to the user cold-start problem in the matching stage. In this paper, we propose Cold & Warm Net based on expert models who are responsible for modeling cold-start and warm-up users respectively. A gate network is applied to incorporate the results from two experts. Furthermore, dynamic knowledge distillation acting as a teacher selector is introduced to assist experts in better learning user representation. With comprehensive mutual information, features highly relevant to user behavior are selected for the bias net which explicitly models user behavior bias. Finally, we evaluate our Cold & Warm Net on public datasets in comparison to models commonly applied in the matching stage and it outperforms other models on all user types. The proposed model has also been deployed on an industrial short video platform and achieves a significant increase in app dwell time and user retention rate.
|
2203.07407
|
Manon Blanc
|
Manon Blanc and Kristoffer Arnsfelt Hansen
|
Computational Complexity of Multi-Player Evolutionarily Stable
Strategies
| null | null | null | null |
cs.CC
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper we study the computational complexity of computing an
evolutionary stable strategy (ESS) in multi-player symmetric games. For
two-player games, deciding existence of an ESS is complete for {\Sigma} 2 , the
second level of the polynomial time hierarchy. We show that deciding existence
of an ESS of a multi-player game is closely connected to the second level of
the real polynomial time hierarchy. Namely, we show that the problem is hard
for a complexity class we denote as \exists D . \forall R and is a member of
\exists\forall R, where the former class restrict the latter by having the
existentially quantified variables be Boolean rather then real-valued. As a
special case of our results it follows that deciding whether a given strategy
is an ESS is complete for \forall R. A concept strongly related to ESS is that
of a locally superior strategy (LSS). We extend our results about ESS and show
that deciding existence of an LSS of a multiplayer game is likewise hard for
\exists D \forall R and a member of \exists\forall R, and as a special case
that deciding whether a given strategy is an LSS is complete for \forall R.
|
[
{
"created": "Mon, 14 Mar 2022 18:13:59 GMT",
"version": "v1"
}
] |
2022-03-16
|
[
[
"Blanc",
"Manon",
""
],
[
"Hansen",
"Kristoffer Arnsfelt",
""
]
] |
In this paper we study the computational complexity of computing an evolutionary stable strategy (ESS) in multi-player symmetric games. For two-player games, deciding existence of an ESS is complete for {\Sigma} 2 , the second level of the polynomial time hierarchy. We show that deciding existence of an ESS of a multi-player game is closely connected to the second level of the real polynomial time hierarchy. Namely, we show that the problem is hard for a complexity class we denote as \exists D . \forall R and is a member of \exists\forall R, where the former class restrict the latter by having the existentially quantified variables be Boolean rather then real-valued. As a special case of our results it follows that deciding whether a given strategy is an ESS is complete for \forall R. A concept strongly related to ESS is that of a locally superior strategy (LSS). We extend our results about ESS and show that deciding existence of an LSS of a multiplayer game is likewise hard for \exists D \forall R and a member of \exists\forall R, and as a special case that deciding whether a given strategy is an LSS is complete for \forall R.
|
0802.3267
|
Amitabh Trehan
|
Tom Hayes and Navin Rustagi and Jared Saia and Amitabh Trehan
|
The Forgiving Tree: A Self-Healing Distributed Data Structure
|
Submitted to Principles of Distributed Computing (PODC) 2008
|
PODC '08: Proceedings of the twenty-seventh ACM symposium on
Principles of distributed computing. 2008, pages 203--212
| null | null |
cs.DC cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider the problem of self-healing in peer-to-peer networks that are
under repeated attack by an omniscient adversary. We assume that the following
process continues for up to n rounds where n is the total number of nodes
initially in the network: the adversary deletes an arbitrary node from the
network, then the network responds by quickly adding a small number of new
edges.
We present a distributed data structure that ensures two key properties.
First, the diameter of the network is never more than $O(\log \Delta)$ times
its original diameter, where $\Delta$ is the maximum degree of the network
initially. We note that for many peer-to-peer systems, $\Delta$ is
polylogarithmic, so the diameter increase would be a O(log log n)
multiplicative factor. Second, the degree of any node never increases by more
than 3 over its original degree. Our data structure is fully distributed, has
O(1) latency per round and requires each node to send and receive O(1) messages
per round. The data structure requires an initial setup phase that has latency
equal to the diameter of the original network, and requires, with high
probability, each node v to send O(log n) messages along every edge incident to
v. Our approach is orthogonal and complementary to traditional topology-based
approaches to defending against attack.
|
[
{
"created": "Fri, 22 Feb 2008 08:22:33 GMT",
"version": "v1"
}
] |
2009-02-15
|
[
[
"Hayes",
"Tom",
""
],
[
"Rustagi",
"Navin",
""
],
[
"Saia",
"Jared",
""
],
[
"Trehan",
"Amitabh",
""
]
] |
We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that the following process continues for up to n rounds where n is the total number of nodes initially in the network: the adversary deletes an arbitrary node from the network, then the network responds by quickly adding a small number of new edges. We present a distributed data structure that ensures two key properties. First, the diameter of the network is never more than $O(\log \Delta)$ times its original diameter, where $\Delta$ is the maximum degree of the network initially. We note that for many peer-to-peer systems, $\Delta$ is polylogarithmic, so the diameter increase would be a O(log log n) multiplicative factor. Second, the degree of any node never increases by more than 3 over its original degree. Our data structure is fully distributed, has O(1) latency per round and requires each node to send and receive O(1) messages per round. The data structure requires an initial setup phase that has latency equal to the diameter of the original network, and requires, with high probability, each node v to send O(log n) messages along every edge incident to v. Our approach is orthogonal and complementary to traditional topology-based approaches to defending against attack.
|
2310.03003
|
Vijay Gadepally
|
Siddharth Samsi, Dan Zhao, Joseph McDonald, Baolin Li, Adam Michaleas,
Michael Jones, William Bergeron, Jeremy Kepner, Devesh Tiwari, Vijay
Gadepally
|
From Words to Watts: Benchmarking the Energy Costs of Large Language
Model Inference
| null | null | null | null |
cs.CL cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Large language models (LLMs) have exploded in popularity due to their new
generative capabilities that go far beyond prior state-of-the-art. These
technologies are increasingly being leveraged in various domains such as law,
finance, and medicine. However, these models carry significant computational
challenges, especially the compute and energy costs required for inference.
Inference energy costs already receive less attention than the energy costs of
training LLMs -- despite how often these large models are called on to conduct
inference in reality (e.g., ChatGPT). As these state-of-the-art LLMs see
increasing usage and deployment in various domains, a better understanding of
their resource utilization is crucial for cost-savings, scaling performance,
efficient hardware usage, and optimal inference strategies.
In this paper, we describe experiments conducted to study the computational
and energy utilization of inference with LLMs. We benchmark and conduct a
preliminary analysis of the inference performance and inference energy costs of
different sizes of LLaMA -- a recent state-of-the-art LLM -- developed by Meta
AI on two generations of popular GPUs (NVIDIA V100 \& A100) and two datasets
(Alpaca and GSM8K) to reflect the diverse set of tasks/benchmarks for LLMs in
research and practice. We present the results of multi-node, multi-GPU
inference using model sharding across up to 32 GPUs. To our knowledge, our work
is the one of the first to study LLM inference performance from the perspective
of computational and energy resources at this scale.
|
[
{
"created": "Wed, 4 Oct 2023 17:41:59 GMT",
"version": "v1"
}
] |
2023-10-05
|
[
[
"Samsi",
"Siddharth",
""
],
[
"Zhao",
"Dan",
""
],
[
"McDonald",
"Joseph",
""
],
[
"Li",
"Baolin",
""
],
[
"Michaleas",
"Adam",
""
],
[
"Jones",
"Michael",
""
],
[
"Bergeron",
"William",
""
],
[
"Kepner",
"Jeremy",
""
],
[
"Tiwari",
"Devesh",
""
],
[
"Gadepally",
"Vijay",
""
]
] |
Large language models (LLMs) have exploded in popularity due to their new generative capabilities that go far beyond prior state-of-the-art. These technologies are increasingly being leveraged in various domains such as law, finance, and medicine. However, these models carry significant computational challenges, especially the compute and energy costs required for inference. Inference energy costs already receive less attention than the energy costs of training LLMs -- despite how often these large models are called on to conduct inference in reality (e.g., ChatGPT). As these state-of-the-art LLMs see increasing usage and deployment in various domains, a better understanding of their resource utilization is crucial for cost-savings, scaling performance, efficient hardware usage, and optimal inference strategies. In this paper, we describe experiments conducted to study the computational and energy utilization of inference with LLMs. We benchmark and conduct a preliminary analysis of the inference performance and inference energy costs of different sizes of LLaMA -- a recent state-of-the-art LLM -- developed by Meta AI on two generations of popular GPUs (NVIDIA V100 \& A100) and two datasets (Alpaca and GSM8K) to reflect the diverse set of tasks/benchmarks for LLMs in research and practice. We present the results of multi-node, multi-GPU inference using model sharding across up to 32 GPUs. To our knowledge, our work is the one of the first to study LLM inference performance from the perspective of computational and energy resources at this scale.
|
2010.05446
|
Bo Chen
|
Jing Zhang, Bo Chen, Lingxi Zhang, Xirui Ke, Haipeng Ding
|
Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs
|
29 pages, AI Open Journal 2021
| null | null | null |
cs.AI cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Knowledge graph reasoning is the fundamental component to support machine
learning applications such as information extraction, information retrieval,
and recommendation. Since knowledge graphs can be viewed as the discrete
symbolic representations of knowledge, reasoning on knowledge graphs can
naturally leverage the symbolic techniques. However, symbolic reasoning is
intolerant of the ambiguous and noisy data. On the contrary, the recent
advances of deep learning promote neural reasoning on knowledge graphs, which
is robust to the ambiguous and noisy data, but lacks interpretability compared
to symbolic reasoning. Considering the advantages and disadvantages of both
methodologies, recent efforts have been made on combining the two reasoning
methods. In this survey, we take a thorough look at the development of the
symbolic, neural and hybrid reasoning on knowledge graphs. We survey two
specific reasoning tasks, knowledge graph completion and question answering on
knowledge graphs, and explain them in a unified reasoning framework. We also
briefly discuss the future directions for knowledge graph reasoning.
|
[
{
"created": "Mon, 12 Oct 2020 04:28:57 GMT",
"version": "v1"
},
{
"created": "Wed, 28 Oct 2020 06:47:45 GMT",
"version": "v2"
},
{
"created": "Thu, 29 Oct 2020 04:49:30 GMT",
"version": "v3"
},
{
"created": "Fri, 26 Mar 2021 06:46:16 GMT",
"version": "v4"
},
{
"created": "Wed, 31 Mar 2021 02:53:48 GMT",
"version": "v5"
}
] |
2021-04-01
|
[
[
"Zhang",
"Jing",
""
],
[
"Chen",
"Bo",
""
],
[
"Zhang",
"Lingxi",
""
],
[
"Ke",
"Xirui",
""
],
[
"Ding",
"Haipeng",
""
]
] |
Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval, and recommendation. Since knowledge graphs can be viewed as the discrete symbolic representations of knowledge, reasoning on knowledge graphs can naturally leverage the symbolic techniques. However, symbolic reasoning is intolerant of the ambiguous and noisy data. On the contrary, the recent advances of deep learning promote neural reasoning on knowledge graphs, which is robust to the ambiguous and noisy data, but lacks interpretability compared to symbolic reasoning. Considering the advantages and disadvantages of both methodologies, recent efforts have been made on combining the two reasoning methods. In this survey, we take a thorough look at the development of the symbolic, neural and hybrid reasoning on knowledge graphs. We survey two specific reasoning tasks, knowledge graph completion and question answering on knowledge graphs, and explain them in a unified reasoning framework. We also briefly discuss the future directions for knowledge graph reasoning.
|
2108.08874
|
Zu Kim
|
Zu Kim, Andr\'e Araujo, Bingyi Cao, Cam Askew, Jack Sim, Mike Green,
N'Mah Fodiatu Yilla, Tobias Weyand
|
Towards A Fairer Landmark Recognition Dataset
|
Please cite the full detailed version of the paper instead: Improving
Fairness in Large-Scale Object Recognition by CrowdSourced Demographic
Information arXiv:2206.01326
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We introduce a new landmark recognition dataset, which is created with a
focus on fair worldwide representation. While previous work proposes to collect
as many images as possible from web repositories, we instead argue that such
approaches can lead to biased data. To create a more comprehensive and
equitable dataset, we start by defining the fair relevance of a landmark to the
world population. These relevances are estimated by combining anonymized Google
Maps user contribution statistics with the contributors' demographic
information. We present a stratification approach and analysis which leads to a
much fairer coverage of the world, compared to existing datasets. The resulting
datasets are used to evaluate computer vision models as part of the the Google
Landmark Recognition and RetrievalChallenges 2021.
|
[
{
"created": "Thu, 19 Aug 2021 18:42:22 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Jun 2022 15:36:36 GMT",
"version": "v2"
}
] |
2022-06-07
|
[
[
"Kim",
"Zu",
""
],
[
"Araujo",
"André",
""
],
[
"Cao",
"Bingyi",
""
],
[
"Askew",
"Cam",
""
],
[
"Sim",
"Jack",
""
],
[
"Green",
"Mike",
""
],
[
"Yilla",
"N'Mah Fodiatu",
""
],
[
"Weyand",
"Tobias",
""
]
] |
We introduce a new landmark recognition dataset, which is created with a focus on fair worldwide representation. While previous work proposes to collect as many images as possible from web repositories, we instead argue that such approaches can lead to biased data. To create a more comprehensive and equitable dataset, we start by defining the fair relevance of a landmark to the world population. These relevances are estimated by combining anonymized Google Maps user contribution statistics with the contributors' demographic information. We present a stratification approach and analysis which leads to a much fairer coverage of the world, compared to existing datasets. The resulting datasets are used to evaluate computer vision models as part of the the Google Landmark Recognition and RetrievalChallenges 2021.
|
2306.02632
|
Catie Cuan
|
Catie Cuan, Emre Fisher, Allison Okamura, and Tom Engbersen
|
Music Mode: Transforming Robot Movement into Music Increases Likability
and Perceived Intelligence
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As robots enter everyday spaces like offices, the sounds they create affect
how they are perceived. We present Music Mode, a novel mapping between a
robot's joint motions and sounds, programmed by artists and engineers to make
the robot generate music as it moves. Two experiments were designed to
characterize the effect of this musical augmentation on human users. In the
first experiment, a robot performed three tasks while playing three different
sound mappings. Results showed that participants observing the robot perceived
it as more safe, animate, intelligent, anthropomorphic, and likable when
playing the Music Mode Orchestra software. To test whether the results of the
first experiment were due to the Music Mode algorithm, rather than music alone,
we conducted a second experiment. Here the robot performed the same three
tasks, while a participant observed via video, but the Orchestra music was
either linked to its movement or random. Participants rated the robots as more
intelligent when the music was linked to the movement. Robots using Music Mode
logged approximately two hundred hours of operation while navigating, wiping
tables, and sorting trash, and bystander comments made during this operating
time served as an embedded case study. This paper has both designerly
contributions and engineering contributions. The contributions are: (1) an
interdisciplinary choreographic, musical, and coding design process to develop
a real-world robot sound feature, (2) a technical implementation for
movement-based sound generation, and (3) two experiments and an embedded case
study of robots running this feature during daily work activities that resulted
in increased likeability and perceived intelligence of the robot.
|
[
{
"created": "Mon, 5 Jun 2023 07:04:45 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Apr 2024 01:40:59 GMT",
"version": "v2"
}
] |
2024-04-02
|
[
[
"Cuan",
"Catie",
""
],
[
"Fisher",
"Emre",
""
],
[
"Okamura",
"Allison",
""
],
[
"Engbersen",
"Tom",
""
]
] |
As robots enter everyday spaces like offices, the sounds they create affect how they are perceived. We present Music Mode, a novel mapping between a robot's joint motions and sounds, programmed by artists and engineers to make the robot generate music as it moves. Two experiments were designed to characterize the effect of this musical augmentation on human users. In the first experiment, a robot performed three tasks while playing three different sound mappings. Results showed that participants observing the robot perceived it as more safe, animate, intelligent, anthropomorphic, and likable when playing the Music Mode Orchestra software. To test whether the results of the first experiment were due to the Music Mode algorithm, rather than music alone, we conducted a second experiment. Here the robot performed the same three tasks, while a participant observed via video, but the Orchestra music was either linked to its movement or random. Participants rated the robots as more intelligent when the music was linked to the movement. Robots using Music Mode logged approximately two hundred hours of operation while navigating, wiping tables, and sorting trash, and bystander comments made during this operating time served as an embedded case study. This paper has both designerly contributions and engineering contributions. The contributions are: (1) an interdisciplinary choreographic, musical, and coding design process to develop a real-world robot sound feature, (2) a technical implementation for movement-based sound generation, and (3) two experiments and an embedded case study of robots running this feature during daily work activities that resulted in increased likeability and perceived intelligence of the robot.
|
2003.08747
|
Laura Rieger
|
Laura Rieger, Lars Kai Hansen
|
IROF: a low resource evaluation metric for explanation methods
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The adoption of machine learning in health care hinges on the transparency of
the used algorithms, necessitating the need for explanation methods. However,
despite a growing literature on explaining neural networks, no consensus has
been reached on how to evaluate those explanation methods. We propose IROF, a
new approach to evaluating explanation methods that circumvents the need for
manual evaluation. Compared to other recent work, our approach requires several
orders of magnitude less computational resources and no human input, making it
accessible to lower resource groups and robust to human bias.
|
[
{
"created": "Mon, 9 Mar 2020 13:01:30 GMT",
"version": "v1"
}
] |
2020-03-20
|
[
[
"Rieger",
"Laura",
""
],
[
"Hansen",
"Lars Kai",
""
]
] |
The adoption of machine learning in health care hinges on the transparency of the used algorithms, necessitating the need for explanation methods. However, despite a growing literature on explaining neural networks, no consensus has been reached on how to evaluate those explanation methods. We propose IROF, a new approach to evaluating explanation methods that circumvents the need for manual evaluation. Compared to other recent work, our approach requires several orders of magnitude less computational resources and no human input, making it accessible to lower resource groups and robust to human bias.
|
1704.02537
|
Nikhil Mande
|
Arkadev Chattopadhyay, Nikhil S. Mande
|
Dual polynomials and communication complexity of $\textsf{XOR}$
functions
| null | null | null | null |
cs.CC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We show a new duality between the polynomial margin complexity of $f$ and the
discrepancy of the function $f \circ \textsf{XOR}$, called an $\textsf{XOR}$
function. Using this duality, we develop polynomial based techniques for
understanding the bounded error ($\textsf{BPP}$) and the weakly-unbounded error
($\textsf{PP}$) communication complexities of $\textsf{XOR}$ functions. We show
the following.
A weak form of an interesting conjecture of Zhang and Shi (Quantum
Information and Computation, 2009) (The full conjecture has just been reported
to be independently settled by Hatami and Qian (Arxiv, 2017). However, their
techniques are quite different and are not known to yield many of the results
we obtain here). Zhang and Shi assert that for symmetric functions $f : \{0,
1\}^n \rightarrow \{-1, 1\}$, the weakly unbounded-error complexity of $f \circ
\textsf{XOR}$ is essentially characterized by the number of points $i$ in the
set $\{0,1, \dots,n-2\}$ for which $D_f(i) \neq D_f(i+2)$, where $D_f$ is the
predicate corresponding to $f$. The number of such points is called the
odd-even degree of $f$. We show that the $\textsf{PP}$ complexity of $f \circ
\textsf{XOR}$ is $\Omega(k/ \log(n/k))$.
We resolve a conjecture of a different Zhang characterizing the Threshold of
Parity circuit size of symmetric functions in terms of their odd-even degree.
We obtain a new proof of the exponential separation between
$\textsf{PP}^{cc}$ and $\textsf{UPP}^{cc}$ via an $\textsf{XOR}$ function.
We provide a characterization of the approximate spectral norm of symmetric
functions, affirming a conjecture of Ada et al. (APPROX-RANDOM, 2012) which has
several consequences.
Additionally, we prove strong $\textsf{UPP}$ lower bounds for $f \circ
\textsf{XOR}$, when $f$ is symmetric and periodic with period
$O(n^{1/2-\epsilon})$, for any constant $\epsilon > 0$.
|
[
{
"created": "Sat, 8 Apr 2017 21:27:11 GMT",
"version": "v1"
}
] |
2017-04-11
|
[
[
"Chattopadhyay",
"Arkadev",
""
],
[
"Mande",
"Nikhil S.",
""
]
] |
We show a new duality between the polynomial margin complexity of $f$ and the discrepancy of the function $f \circ \textsf{XOR}$, called an $\textsf{XOR}$ function. Using this duality, we develop polynomial based techniques for understanding the bounded error ($\textsf{BPP}$) and the weakly-unbounded error ($\textsf{PP}$) communication complexities of $\textsf{XOR}$ functions. We show the following. A weak form of an interesting conjecture of Zhang and Shi (Quantum Information and Computation, 2009) (The full conjecture has just been reported to be independently settled by Hatami and Qian (Arxiv, 2017). However, their techniques are quite different and are not known to yield many of the results we obtain here). Zhang and Shi assert that for symmetric functions $f : \{0, 1\}^n \rightarrow \{-1, 1\}$, the weakly unbounded-error complexity of $f \circ \textsf{XOR}$ is essentially characterized by the number of points $i$ in the set $\{0,1, \dots,n-2\}$ for which $D_f(i) \neq D_f(i+2)$, where $D_f$ is the predicate corresponding to $f$. The number of such points is called the odd-even degree of $f$. We show that the $\textsf{PP}$ complexity of $f \circ \textsf{XOR}$ is $\Omega(k/ \log(n/k))$. We resolve a conjecture of a different Zhang characterizing the Threshold of Parity circuit size of symmetric functions in terms of their odd-even degree. We obtain a new proof of the exponential separation between $\textsf{PP}^{cc}$ and $\textsf{UPP}^{cc}$ via an $\textsf{XOR}$ function. We provide a characterization of the approximate spectral norm of symmetric functions, affirming a conjecture of Ada et al. (APPROX-RANDOM, 2012) which has several consequences. Additionally, we prove strong $\textsf{UPP}$ lower bounds for $f \circ \textsf{XOR}$, when $f$ is symmetric and periodic with period $O(n^{1/2-\epsilon})$, for any constant $\epsilon > 0$.
|
2102.03520
|
Jie Mei
|
Jie Mei, Jenq-Neng Hwang, Suzanne Romain, Craig Rose, Braden Moore,
and Kelsey Magrane
|
Video-based Hierarchical Species Classification for Longline Fishing
Monitoring
|
To be published in CVAUI2020 in conjunction with ICPR2020
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The goal of electronic monitoring (EM) of longline fishing is to monitor the
fish catching activities on fishing vessels, either for the regulatory
compliance or catch counting. Hierarchical classification based on videos
allows for inexpensive and efficient fish species identification of catches
from longline fishing, where fishes are under severe deformation and
self-occlusion during the catching process. More importantly, the flexibility
of hierarchical classification mitigates the laborious efforts of human reviews
by providing confidence scores in different hierarchical levels. Some related
works either use cascaded models for hierarchical classification or make
predictions per image or predict one overlapping hierarchical data structure of
the dataset in advance. However, with a known non-overlapping hierarchical data
structure provided by fisheries scientists, our method enforces the
hierarchical data structure and introduces an efficient training and inference
strategy for video-based fisheries data. Our experiments show that the proposed
method outperforms the classic flat classification system significantly and our
ablation study justifies our contributions in CNN model design, training
strategy, and the video-based inference schemes for the hierarchical fish
species classification task.
|
[
{
"created": "Sat, 6 Feb 2021 06:10:52 GMT",
"version": "v1"
}
] |
2021-02-09
|
[
[
"Mei",
"Jie",
""
],
[
"Hwang",
"Jenq-Neng",
""
],
[
"Romain",
"Suzanne",
""
],
[
"Rose",
"Craig",
""
],
[
"Moore",
"Braden",
""
],
[
"Magrane",
"Kelsey",
""
]
] |
The goal of electronic monitoring (EM) of longline fishing is to monitor the fish catching activities on fishing vessels, either for the regulatory compliance or catch counting. Hierarchical classification based on videos allows for inexpensive and efficient fish species identification of catches from longline fishing, where fishes are under severe deformation and self-occlusion during the catching process. More importantly, the flexibility of hierarchical classification mitigates the laborious efforts of human reviews by providing confidence scores in different hierarchical levels. Some related works either use cascaded models for hierarchical classification or make predictions per image or predict one overlapping hierarchical data structure of the dataset in advance. However, with a known non-overlapping hierarchical data structure provided by fisheries scientists, our method enforces the hierarchical data structure and introduces an efficient training and inference strategy for video-based fisheries data. Our experiments show that the proposed method outperforms the classic flat classification system significantly and our ablation study justifies our contributions in CNN model design, training strategy, and the video-based inference schemes for the hierarchical fish species classification task.
|
2405.20527
|
Francesco Ronzano
|
Francesco Ronzano and Jay Nanavati
|
Towards Ontology-Enhanced Representation Learning for Large Language
Models
|
14 pages, 1 figure
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Taking advantage of the widespread use of ontologies to organise and
harmonize knowledge across several distinct domains, this paper proposes a
novel approach to improve an embedding-Large Language Model (embedding-LLM) of
interest by infusing the knowledge formalized by a reference ontology:
ontological knowledge infusion aims at boosting the ability of the considered
LLM to effectively model the knowledge domain described by the infused
ontology. The linguistic information (i.e. concept synonyms and descriptions)
and structural information (i.e. is-a relations) formalized by the ontology are
utilized to compile a comprehensive set of concept definitions, with the
assistance of a powerful generative LLM (i.e. GPT-3.5-turbo). These concept
definitions are then employed to fine-tune the target embedding-LLM using a
contrastive learning framework. To demonstrate and evaluate the proposed
approach, we utilize the biomedical disease ontology MONDO. The results show
that embedding-LLMs enhanced by ontological disease knowledge exhibit an
improved capability to effectively evaluate the similarity of in-domain
sentences from biomedical documents mentioning diseases, without compromising
their out-of-domain performance.
|
[
{
"created": "Thu, 30 May 2024 23:01:10 GMT",
"version": "v1"
}
] |
2024-06-03
|
[
[
"Ronzano",
"Francesco",
""
],
[
"Nanavati",
"Jay",
""
]
] |
Taking advantage of the widespread use of ontologies to organise and harmonize knowledge across several distinct domains, this paper proposes a novel approach to improve an embedding-Large Language Model (embedding-LLM) of interest by infusing the knowledge formalized by a reference ontology: ontological knowledge infusion aims at boosting the ability of the considered LLM to effectively model the knowledge domain described by the infused ontology. The linguistic information (i.e. concept synonyms and descriptions) and structural information (i.e. is-a relations) formalized by the ontology are utilized to compile a comprehensive set of concept definitions, with the assistance of a powerful generative LLM (i.e. GPT-3.5-turbo). These concept definitions are then employed to fine-tune the target embedding-LLM using a contrastive learning framework. To demonstrate and evaluate the proposed approach, we utilize the biomedical disease ontology MONDO. The results show that embedding-LLMs enhanced by ontological disease knowledge exhibit an improved capability to effectively evaluate the similarity of in-domain sentences from biomedical documents mentioning diseases, without compromising their out-of-domain performance.
|
2308.04761
|
Zijian Li
|
Zijian Li, Yuchang Sun, Jiawei Shao, Yuyi Mao, Jessie Hui Wang, Jun
Zhang
|
Feature Matching Data Synthesis for Non-IID Federated Learning
|
16 pages
| null | null | null |
cs.LG cs.AI cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Federated learning (FL) has emerged as a privacy-preserving paradigm that
trains neural networks on edge devices without collecting data at a central
server. However, FL encounters an inherent challenge in dealing with
non-independent and identically distributed (non-IID) data among devices. To
address this challenge, this paper proposes a hard feature matching data
synthesis (HFMDS) method to share auxiliary data besides local models.
Specifically, synthetic data are generated by learning the essential
class-relevant features of real samples and discarding the redundant features,
which helps to effectively tackle the non-IID issue. For better privacy
preservation, we propose a hard feature augmentation method to transfer real
features towards the decision boundary, with which the synthetic data not only
improve the model generalization but also erase the information of real
features. By integrating the proposed HFMDS method with FL, we present a novel
FL framework with data augmentation to relieve data heterogeneity. The
theoretical analysis highlights the effectiveness of our proposed data
synthesis method in solving the non-IID challenge. Simulation results further
demonstrate that our proposed HFMDS-FL algorithm outperforms the baselines in
terms of accuracy, privacy preservation, and computational cost on various
benchmark datasets.
|
[
{
"created": "Wed, 9 Aug 2023 07:49:39 GMT",
"version": "v1"
}
] |
2023-08-10
|
[
[
"Li",
"Zijian",
""
],
[
"Sun",
"Yuchang",
""
],
[
"Shao",
"Jiawei",
""
],
[
"Mao",
"Yuyi",
""
],
[
"Wang",
"Jessie Hui",
""
],
[
"Zhang",
"Jun",
""
]
] |
Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural networks on edge devices without collecting data at a central server. However, FL encounters an inherent challenge in dealing with non-independent and identically distributed (non-IID) data among devices. To address this challenge, this paper proposes a hard feature matching data synthesis (HFMDS) method to share auxiliary data besides local models. Specifically, synthetic data are generated by learning the essential class-relevant features of real samples and discarding the redundant features, which helps to effectively tackle the non-IID issue. For better privacy preservation, we propose a hard feature augmentation method to transfer real features towards the decision boundary, with which the synthetic data not only improve the model generalization but also erase the information of real features. By integrating the proposed HFMDS method with FL, we present a novel FL framework with data augmentation to relieve data heterogeneity. The theoretical analysis highlights the effectiveness of our proposed data synthesis method in solving the non-IID challenge. Simulation results further demonstrate that our proposed HFMDS-FL algorithm outperforms the baselines in terms of accuracy, privacy preservation, and computational cost on various benchmark datasets.
|
2312.14211
|
Sergi Blanco-Cuaresma
|
Sergi Blanco-Cuaresma, Ioana Ciuc\u{a}, Alberto Accomazzi, Michael J.
Kurtz, Edwin A. Henneken, Kelly E. Lockhart, Felix Grezes, Thomas Allen,
Golnaz Shapurian, Carolyn S. Grant, Donna M. Thompson, Timothy W. Hostetler,
Matthew R. Templeton, Shinyi Chen, Jennifer Koch, Taylor Jacovich, Daniel
Chivvis, Fernanda de Macedo Alves, Jean-Claude Paquin, Jennifer Bartlett,
Mugdha Polimera, and Stephanie Jarmak
|
Experimenting with Large Language Models and vector embeddings in NASA
SciX
|
To appear in the proceedings of the 33th annual international
Astronomical Data Analysis Software & Systems (ADASS XXXIII)
| null | null | null |
cs.CL astro-ph.IM cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Open-source Large Language Models enable projects such as NASA SciX (i.e.,
NASA ADS) to think out of the box and try alternative approaches for
information retrieval and data augmentation, while respecting data copyright
and users' privacy. However, when large language models are directly prompted
with questions without any context, they are prone to hallucination. At NASA
SciX we have developed an experiment where we created semantic vectors for our
large collection of abstracts and full-text content, and we designed a prompt
system to ask questions using contextual chunks from our system. Based on a
non-systematic human evaluation, the experiment shows a lower degree of
hallucination and better responses when using Retrieval Augmented Generation.
Further exploration is required to design new features and data augmentation
processes at NASA SciX that leverages this technology while respecting the high
level of trust and quality that the project holds.
|
[
{
"created": "Thu, 21 Dec 2023 10:19:58 GMT",
"version": "v1"
}
] |
2023-12-25
|
[
[
"Blanco-Cuaresma",
"Sergi",
""
],
[
"Ciucă",
"Ioana",
""
],
[
"Accomazzi",
"Alberto",
""
],
[
"Kurtz",
"Michael J.",
""
],
[
"Henneken",
"Edwin A.",
""
],
[
"Lockhart",
"Kelly E.",
""
],
[
"Grezes",
"Felix",
""
],
[
"Allen",
"Thomas",
""
],
[
"Shapurian",
"Golnaz",
""
],
[
"Grant",
"Carolyn S.",
""
],
[
"Thompson",
"Donna M.",
""
],
[
"Hostetler",
"Timothy W.",
""
],
[
"Templeton",
"Matthew R.",
""
],
[
"Chen",
"Shinyi",
""
],
[
"Koch",
"Jennifer",
""
],
[
"Jacovich",
"Taylor",
""
],
[
"Chivvis",
"Daniel",
""
],
[
"Alves",
"Fernanda de Macedo",
""
],
[
"Paquin",
"Jean-Claude",
""
],
[
"Bartlett",
"Jennifer",
""
],
[
"Polimera",
"Mugdha",
""
],
[
"Jarmak",
"Stephanie",
""
]
] |
Open-source Large Language Models enable projects such as NASA SciX (i.e., NASA ADS) to think out of the box and try alternative approaches for information retrieval and data augmentation, while respecting data copyright and users' privacy. However, when large language models are directly prompted with questions without any context, they are prone to hallucination. At NASA SciX we have developed an experiment where we created semantic vectors for our large collection of abstracts and full-text content, and we designed a prompt system to ask questions using contextual chunks from our system. Based on a non-systematic human evaluation, the experiment shows a lower degree of hallucination and better responses when using Retrieval Augmented Generation. Further exploration is required to design new features and data augmentation processes at NASA SciX that leverages this technology while respecting the high level of trust and quality that the project holds.
|
1501.07429
|
Nans Lefebvre
|
Nans Lefebvre
|
Convergence law for hyper-graphs with prescribed degree sequences
|
10 pages, 6 figures
| null | null | null |
cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We view hyper-graphs as incidence graphs, i.e. bipartite graphs with a set of
nodes representing vertices and a set of nodes representing hyper-edges, with
two nodes being adjacent if the corresponding vertex belongs to the
corresponding hyper-edge. It defines a random hyper-multigraph specified by two
distributions, one for the degrees of the vertices, and one for the sizes of
the hyper-edges. We develop the logical analysis of this framework and first
prove a convergence law for first-order logic, then characterise the limit
first-order theories defined by a wide class of degree distributions.
Convergence laws of other models follow, and in particular for the classical
Erd\H{o}s-R\'enyi graphs and $k$-uniform hyper-graphs.
|
[
{
"created": "Thu, 29 Jan 2015 12:07:25 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Feb 2015 01:02:47 GMT",
"version": "v2"
},
{
"created": "Wed, 6 May 2015 21:36:34 GMT",
"version": "v3"
}
] |
2015-05-08
|
[
[
"Lefebvre",
"Nans",
""
]
] |
We view hyper-graphs as incidence graphs, i.e. bipartite graphs with a set of nodes representing vertices and a set of nodes representing hyper-edges, with two nodes being adjacent if the corresponding vertex belongs to the corresponding hyper-edge. It defines a random hyper-multigraph specified by two distributions, one for the degrees of the vertices, and one for the sizes of the hyper-edges. We develop the logical analysis of this framework and first prove a convergence law for first-order logic, then characterise the limit first-order theories defined by a wide class of degree distributions. Convergence laws of other models follow, and in particular for the classical Erd\H{o}s-R\'enyi graphs and $k$-uniform hyper-graphs.
|
2404.11757
|
Mike Merrill
|
Mike A. Merrill and Mingtian Tan and Vinayak Gupta and Tom Hartvigsen
and Tim Althoff
|
Language Models Still Struggle to Zero-shot Reason about Time Series
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Time series are critical for decision-making in fields like finance and
healthcare. Their importance has driven a recent influx of works passing time
series into language models, leading to non-trivial forecasting on some
datasets. But it remains unknown whether non-trivial forecasting implies that
language models can reason about time series. To address this gap, we generate
a first-of-its-kind evaluation framework for time series reasoning, including
formal tasks and a corresponding dataset of multi-scale time series paired with
text captions across ten domains. Using these data, we probe whether language
models achieve three forms of reasoning: (1) Etiological Reasoning - given an
input time series, can the language model identify the scenario that most
likely created it? (2) Question Answering - can a language model answer factual
questions about time series? (3) Context-Aided Forecasting - does highly
relevant textual context improve a language model's time series forecasts?
We find that otherwise highly-capable language models demonstrate
surprisingly limited time series reasoning: they score marginally above random
on etiological and question answering tasks (up to 30 percentage points worse
than humans) and show modest success in using context to improve forecasting.
These weakness showcase that time series reasoning is an impactful, yet deeply
underdeveloped direction for language model research. We also make our datasets
and code public at to support further research in this direction at
https://github.com/behavioral-data/TSandLanguage
|
[
{
"created": "Wed, 17 Apr 2024 21:27:33 GMT",
"version": "v1"
}
] |
2024-04-19
|
[
[
"Merrill",
"Mike A.",
""
],
[
"Tan",
"Mingtian",
""
],
[
"Gupta",
"Vinayak",
""
],
[
"Hartvigsen",
"Tom",
""
],
[
"Althoff",
"Tim",
""
]
] |
Time series are critical for decision-making in fields like finance and healthcare. Their importance has driven a recent influx of works passing time series into language models, leading to non-trivial forecasting on some datasets. But it remains unknown whether non-trivial forecasting implies that language models can reason about time series. To address this gap, we generate a first-of-its-kind evaluation framework for time series reasoning, including formal tasks and a corresponding dataset of multi-scale time series paired with text captions across ten domains. Using these data, we probe whether language models achieve three forms of reasoning: (1) Etiological Reasoning - given an input time series, can the language model identify the scenario that most likely created it? (2) Question Answering - can a language model answer factual questions about time series? (3) Context-Aided Forecasting - does highly relevant textual context improve a language model's time series forecasts? We find that otherwise highly-capable language models demonstrate surprisingly limited time series reasoning: they score marginally above random on etiological and question answering tasks (up to 30 percentage points worse than humans) and show modest success in using context to improve forecasting. These weakness showcase that time series reasoning is an impactful, yet deeply underdeveloped direction for language model research. We also make our datasets and code public at to support further research in this direction at https://github.com/behavioral-data/TSandLanguage
|
2205.11194
|
Tao Shen
|
Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Guodong Long, Kai Zhang,
Daxin Jiang
|
UnifieR: A Unified Retriever for Large-Scale Retrieval
|
To appear at KDD ADS 2023
| null | null | null |
cs.IR cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Large-scale retrieval is to recall relevant documents from a huge collection
given a query. It relies on representation learning to embed documents and
queries into a common semantic encoding space. According to the encoding space,
recent retrieval methods based on pre-trained language models (PLM) can be
coarsely categorized into either dense-vector or lexicon-based paradigms. These
two paradigms unveil the PLMs' representation capability in different
granularities, i.e., global sequence-level compression and local word-level
contexts, respectively. Inspired by their complementary global-local
contextualization and distinct representing views, we propose a new learning
framework, UnifieR which unifies dense-vector and lexicon-based retrieval in
one model with a dual-representing capability. Experiments on passage retrieval
benchmarks verify its effectiveness in both paradigms. A uni-retrieval scheme
is further presented with even better retrieval quality. We lastly evaluate the
model on BEIR benchmark to verify its transferability.
|
[
{
"created": "Mon, 23 May 2022 11:01:59 GMT",
"version": "v1"
},
{
"created": "Sun, 4 Jun 2023 12:59:36 GMT",
"version": "v2"
}
] |
2023-06-06
|
[
[
"Shen",
"Tao",
""
],
[
"Geng",
"Xiubo",
""
],
[
"Tao",
"Chongyang",
""
],
[
"Xu",
"Can",
""
],
[
"Long",
"Guodong",
""
],
[
"Zhang",
"Kai",
""
],
[
"Jiang",
"Daxin",
""
]
] |
Large-scale retrieval is to recall relevant documents from a huge collection given a query. It relies on representation learning to embed documents and queries into a common semantic encoding space. According to the encoding space, recent retrieval methods based on pre-trained language models (PLM) can be coarsely categorized into either dense-vector or lexicon-based paradigms. These two paradigms unveil the PLMs' representation capability in different granularities, i.e., global sequence-level compression and local word-level contexts, respectively. Inspired by their complementary global-local contextualization and distinct representing views, we propose a new learning framework, UnifieR which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability. Experiments on passage retrieval benchmarks verify its effectiveness in both paradigms. A uni-retrieval scheme is further presented with even better retrieval quality. We lastly evaluate the model on BEIR benchmark to verify its transferability.
|
2006.05935
|
Dieter B\"uchler
|
Dieter B\"uchler, Simon Guist, Roberto Calandra, Vincent Berenz,
Bernhard Sch\"olkopf, Jan Peters
|
Learning to Play Table Tennis From Scratch using Muscular Robots
|
11 pages, 8 figures. Submitted to T-RO. For more information visit
https://muscularTT.embodied.ml
| null | null | null |
cs.RO cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dynamic tasks like table tennis are relatively easy to learn for humans but
pose significant challenges to robots. Such tasks require accurate control of
fast movements and precise timing in the presence of imprecise state estimation
of the flying ball and the robot. Reinforcement Learning (RL) has shown promise
in learning of complex control tasks from data. However, applying step-based RL
to dynamic tasks on real systems is safety-critical as RL requires exploring
and failing safely for millions of time steps in high-speed regimes. In this
paper, we demonstrate that safe learning of table tennis using model-free
Reinforcement Learning can be achieved by using robot arms driven by pneumatic
artificial muscles (PAMs). Softness and back-drivability properties of PAMs
prevent the system from leaving the safe region of its state space. In this
manner, RL empowers the robot to return and smash real balls with 5 m\s and
12m\s on average to a desired landing point. Our setup allows the agent to
learn this safety-critical task (i) without safety constraints in the
algorithm, (ii) while maximizing the speed of returned balls directly in the
reward function (iii) using a stochastic policy that acts directly on the
low-level controls of the real system and (iv) trains for thousands of trials
(v) from scratch without any prior knowledge. Additionally, we present HYSR, a
practical hybrid sim and real training that avoids playing real balls during
training by randomly replaying recorded ball trajectories in simulation and
applying actions to the real robot. This work is the first to (a) fail-safe
learn of a safety-critical dynamic task using anthropomorphic robot arms, (b)
learn a precision-demanding problem with a PAM-driven system despite the
control challenges and (c) train robots to play table tennis without real
balls. Videos and datasets are available at muscularTT.embodied.ml.
|
[
{
"created": "Wed, 10 Jun 2020 16:43:27 GMT",
"version": "v1"
}
] |
2020-06-11
|
[
[
"Büchler",
"Dieter",
""
],
[
"Guist",
"Simon",
""
],
[
"Calandra",
"Roberto",
""
],
[
"Berenz",
"Vincent",
""
],
[
"Schölkopf",
"Bernhard",
""
],
[
"Peters",
"Jan",
""
]
] |
Dynamic tasks like table tennis are relatively easy to learn for humans but pose significant challenges to robots. Such tasks require accurate control of fast movements and precise timing in the presence of imprecise state estimation of the flying ball and the robot. Reinforcement Learning (RL) has shown promise in learning of complex control tasks from data. However, applying step-based RL to dynamic tasks on real systems is safety-critical as RL requires exploring and failing safely for millions of time steps in high-speed regimes. In this paper, we demonstrate that safe learning of table tennis using model-free Reinforcement Learning can be achieved by using robot arms driven by pneumatic artificial muscles (PAMs). Softness and back-drivability properties of PAMs prevent the system from leaving the safe region of its state space. In this manner, RL empowers the robot to return and smash real balls with 5 m\s and 12m\s on average to a desired landing point. Our setup allows the agent to learn this safety-critical task (i) without safety constraints in the algorithm, (ii) while maximizing the speed of returned balls directly in the reward function (iii) using a stochastic policy that acts directly on the low-level controls of the real system and (iv) trains for thousands of trials (v) from scratch without any prior knowledge. Additionally, we present HYSR, a practical hybrid sim and real training that avoids playing real balls during training by randomly replaying recorded ball trajectories in simulation and applying actions to the real robot. This work is the first to (a) fail-safe learn of a safety-critical dynamic task using anthropomorphic robot arms, (b) learn a precision-demanding problem with a PAM-driven system despite the control challenges and (c) train robots to play table tennis without real balls. Videos and datasets are available at muscularTT.embodied.ml.
|
2102.00434
|
Gilad Yehudai
|
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
|
The Connection Between Approximation, Depth Separation and Learnability
in Neural Networks
|
COLT 2021 camera ready version
| null | null | null |
cs.LG cs.NE stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Several recent works have shown separation results between deep neural
networks, and hypothesis classes with inferior approximation capacity such as
shallow networks or kernel classes. On the other hand, the fact that deep
networks can efficiently express a target function does not mean that this
target function can be learned efficiently by deep neural networks. In this
work we study the intricate connection between learnability and approximation
capacity. We show that learnability with deep networks of a target function
depends on the ability of simpler classes to approximate the target.
Specifically, we show that a necessary condition for a function to be learnable
by gradient descent on deep neural networks is to be able to approximate the
function, at least in a weak sense, with shallow neural networks. We also show
that a class of functions can be learned by an efficient statistical query
algorithm if and only if it can be approximated in a weak sense by some kernel
class. We give several examples of functions which demonstrate depth
separation, and conclude that they cannot be efficiently learned, even by a
hypothesis class that can efficiently approximate them.
|
[
{
"created": "Sun, 31 Jan 2021 11:32:30 GMT",
"version": "v1"
},
{
"created": "Sun, 18 Jul 2021 12:32:55 GMT",
"version": "v2"
}
] |
2021-07-20
|
[
[
"Malach",
"Eran",
""
],
[
"Yehudai",
"Gilad",
""
],
[
"Shalev-Shwartz",
"Shai",
""
],
[
"Shamir",
"Ohad",
""
]
] |
Several recent works have shown separation results between deep neural networks, and hypothesis classes with inferior approximation capacity such as shallow networks or kernel classes. On the other hand, the fact that deep networks can efficiently express a target function does not mean that this target function can be learned efficiently by deep neural networks. In this work we study the intricate connection between learnability and approximation capacity. We show that learnability with deep networks of a target function depends on the ability of simpler classes to approximate the target. Specifically, we show that a necessary condition for a function to be learnable by gradient descent on deep neural networks is to be able to approximate the function, at least in a weak sense, with shallow neural networks. We also show that a class of functions can be learned by an efficient statistical query algorithm if and only if it can be approximated in a weak sense by some kernel class. We give several examples of functions which demonstrate depth separation, and conclude that they cannot be efficiently learned, even by a hypothesis class that can efficiently approximate them.
|
1909.01383
|
Elena Voita
|
Elena Voita, Rico Sennrich, Ivan Titov
|
Context-Aware Monolingual Repair for Neural Machine Translation
|
EMNLP 2019 (camera-ready)
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modern sentence-level NMT systems often produce plausible translations of
isolated sentences. However, when put in context, these translations may end up
being inconsistent with each other. We propose a monolingual DocRepair model to
correct inconsistencies between sentence-level translations. DocRepair performs
automatic post-editing on a sequence of sentence-level translations, refining
translations of sentences in context of each other. For training, the DocRepair
model requires only monolingual document-level data in the target language. It
is trained as a monolingual sequence-to-sequence model that maps inconsistent
groups of sentences into consistent ones. The consistent groups come from the
original training data; the inconsistent groups are obtained by sampling
round-trip translations for each isolated sentence. We show that this approach
successfully imitates inconsistencies we aim to fix: using contrastive
evaluation, we show large improvements in the translation of several contextual
phenomena in an English-Russian translation task, as well as improvements in
the BLEU score. We also conduct a human evaluation and show a strong preference
of the annotators to corrected translations over the baseline ones. Moreover,
we analyze which discourse phenomena are hard to capture using monolingual data
only.
|
[
{
"created": "Tue, 3 Sep 2019 18:12:36 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Oct 2019 11:13:52 GMT",
"version": "v2"
}
] |
2019-10-16
|
[
[
"Voita",
"Elena",
""
],
[
"Sennrich",
"Rico",
""
],
[
"Titov",
"Ivan",
""
]
] |
Modern sentence-level NMT systems often produce plausible translations of isolated sentences. However, when put in context, these translations may end up being inconsistent with each other. We propose a monolingual DocRepair model to correct inconsistencies between sentence-level translations. DocRepair performs automatic post-editing on a sequence of sentence-level translations, refining translations of sentences in context of each other. For training, the DocRepair model requires only monolingual document-level data in the target language. It is trained as a monolingual sequence-to-sequence model that maps inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence. We show that this approach successfully imitates inconsistencies we aim to fix: using contrastive evaluation, we show large improvements in the translation of several contextual phenomena in an English-Russian translation task, as well as improvements in the BLEU score. We also conduct a human evaluation and show a strong preference of the annotators to corrected translations over the baseline ones. Moreover, we analyze which discourse phenomena are hard to capture using monolingual data only.
|
1406.1273
|
Will Rosenbaum
|
Rafail Ostrovsky and Will Rosenbaum
|
On The Communication Complexity of Finding an (Approximate) Stable
Marriage
|
This paper has been subsumed by arXiv:1405.7709
| null | null | null |
cs.CC cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we consider the communication complexity of protocols that
compute stable matchings. We work within the context of Gale and Shapley's
original stable marriage problem\cite{GS62}: $n$ men and $n$ women each
privately hold a total and strict ordering on all of the members of the
opposite gender. They wish to collaborate in order to find a stable
matching---a pairing of the men and women such that no unmatched pair mutually
prefer each other to their assigned partners in the matching. We show that any
communication protocol (deterministic, nondeterministic, or randomized) that
correctly ouputs a stable matching requires $\Omega(n^2)$ bits of
communication. Thus, the original algorithm of Gale and Shapley is
communication-optimal up to a logarithmic factor. We then introduce a "divorce
metric" on the set of all matchings, which allows us to consider approximately
stable matchings. We describe an efficient algorithm to compute the "distance
to stability" of a given matching. We then show that even under the relaxed
requirement that a protocol only yield an approximate stable matching, the
$\Omega(n^2)$ communication lower bound still holds.
|
[
{
"created": "Thu, 5 Jun 2014 05:33:15 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Oct 2014 04:17:30 GMT",
"version": "v2"
}
] |
2014-10-10
|
[
[
"Ostrovsky",
"Rafail",
""
],
[
"Rosenbaum",
"Will",
""
]
] |
In this paper, we consider the communication complexity of protocols that compute stable matchings. We work within the context of Gale and Shapley's original stable marriage problem\cite{GS62}: $n$ men and $n$ women each privately hold a total and strict ordering on all of the members of the opposite gender. They wish to collaborate in order to find a stable matching---a pairing of the men and women such that no unmatched pair mutually prefer each other to their assigned partners in the matching. We show that any communication protocol (deterministic, nondeterministic, or randomized) that correctly ouputs a stable matching requires $\Omega(n^2)$ bits of communication. Thus, the original algorithm of Gale and Shapley is communication-optimal up to a logarithmic factor. We then introduce a "divorce metric" on the set of all matchings, which allows us to consider approximately stable matchings. We describe an efficient algorithm to compute the "distance to stability" of a given matching. We then show that even under the relaxed requirement that a protocol only yield an approximate stable matching, the $\Omega(n^2)$ communication lower bound still holds.
|
2311.03367
|
Felix Hoops
|
Felix Hoops, Alexander M\"uhle, Florian Matthes, Christoph Meinel
|
A Taxonomy of Decentralized Identifier Methods for Practitioners
| null |
2023 IEEE International Conference on Decentralized Applications
and Infrastructures (DAPPS), Athens, Greece, 2023, pp. 57-65
|
10.1109/DAPPS57946.2023.00017
| null |
cs.SE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
A core part of the new identity management paradigm of Self-Sovereign
Identity (SSI) is the W3C Decentralized Identifiers (DIDs) standard. The
diversity of interoperable implementations encouraged by the paradigm is key
for a less centralized future, and it is made possible by the concept of DIDs.
However, this leads to a kind of dilemma of choices, where practitioners are
faced with the difficult decision of which methods to choose and support in
their applications. Due to the decentralized development of DID method
specifications and the overwhelming number of different choices, it is hard to
get an overview. In this paper, we propose a taxonomy of DID methods with the
goal to empower practitioners to make informed decisions when selecting DID
methods. To that end, our taxonomy is designed to provide an overview of the
current landscape while providing adoption-relevant characteristics. For this
purpose, we rely on the Nickerson et al. methodology for taxonomy creation,
utilizing both conceptual-to-empirical and empirical-to-conceptual approaches.
During the iterative process, we collect and survey an extensive and
potentially exhaustive list of around 160 DID methods from various sources. The
taxonomy we arrive at uses a total of 7 dimensions and 22 characteristics to
span the contemporary design space of DID methods from the perspective of a
practitioner. In addition to elaborating on these characteristics, we also
discuss how a practitioner can use the taxonomy to select suitable DID methods
for a specific use case.
|
[
{
"created": "Wed, 18 Oct 2023 13:01:40 GMT",
"version": "v1"
}
] |
2023-11-08
|
[
[
"Hoops",
"Felix",
""
],
[
"Mühle",
"Alexander",
""
],
[
"Matthes",
"Florian",
""
],
[
"Meinel",
"Christoph",
""
]
] |
A core part of the new identity management paradigm of Self-Sovereign Identity (SSI) is the W3C Decentralized Identifiers (DIDs) standard. The diversity of interoperable implementations encouraged by the paradigm is key for a less centralized future, and it is made possible by the concept of DIDs. However, this leads to a kind of dilemma of choices, where practitioners are faced with the difficult decision of which methods to choose and support in their applications. Due to the decentralized development of DID method specifications and the overwhelming number of different choices, it is hard to get an overview. In this paper, we propose a taxonomy of DID methods with the goal to empower practitioners to make informed decisions when selecting DID methods. To that end, our taxonomy is designed to provide an overview of the current landscape while providing adoption-relevant characteristics. For this purpose, we rely on the Nickerson et al. methodology for taxonomy creation, utilizing both conceptual-to-empirical and empirical-to-conceptual approaches. During the iterative process, we collect and survey an extensive and potentially exhaustive list of around 160 DID methods from various sources. The taxonomy we arrive at uses a total of 7 dimensions and 22 characteristics to span the contemporary design space of DID methods from the perspective of a practitioner. In addition to elaborating on these characteristics, we also discuss how a practitioner can use the taxonomy to select suitable DID methods for a specific use case.
|
2005.06213
|
Giulio Ermanno Pibiri
|
Simon Gog, Giulio Ermanno Pibiri, and Rossano Venturini
|
Efficient and Effective Query Auto-Completion
|
Published in SIGIR 2020
|
SIGIR 2020: Proceedings of the 43rd International ACM SIGIR
Conference on Research and Development in Information Retrieval. July 2020.
Pages 2271-2280
|
10.1145/3397271.3401432
| null |
cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Query Auto-Completion (QAC) is an ubiquitous feature of modern textual search
systems, suggesting possible ways of completing the query being typed by the
user. Efficiency is crucial to make the system have a real-time responsiveness
when operating in the million-scale search space. Prior work has extensively
advocated the use of a trie data structure for fast prefix-search operations in
compact space. However, searching by prefix has little discovery power in that
only completions that are prefixed by the query are returned. This may impact
negatively the effectiveness of the QAC system, with a consequent monetary loss
for real applications like Web Search Engines and eCommerce. In this work we
describe the implementation that empowers a new QAC system at eBay, and discuss
its efficiency/effectiveness in relation to other approaches at the
state-of-the-art. The solution is based on the combination of an inverted index
with succinct data structures, a much less explored direction in the
literature. This system is replacing the previous implementation based on
Apache SOLR that was not always able to meet the required
service-level-agreement.
|
[
{
"created": "Wed, 13 May 2020 09:07:43 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Jun 2020 08:28:57 GMT",
"version": "v2"
}
] |
2022-02-08
|
[
[
"Gog",
"Simon",
""
],
[
"Pibiri",
"Giulio Ermanno",
""
],
[
"Venturini",
"Rossano",
""
]
] |
Query Auto-Completion (QAC) is an ubiquitous feature of modern textual search systems, suggesting possible ways of completing the query being typed by the user. Efficiency is crucial to make the system have a real-time responsiveness when operating in the million-scale search space. Prior work has extensively advocated the use of a trie data structure for fast prefix-search operations in compact space. However, searching by prefix has little discovery power in that only completions that are prefixed by the query are returned. This may impact negatively the effectiveness of the QAC system, with a consequent monetary loss for real applications like Web Search Engines and eCommerce. In this work we describe the implementation that empowers a new QAC system at eBay, and discuss its efficiency/effectiveness in relation to other approaches at the state-of-the-art. The solution is based on the combination of an inverted index with succinct data structures, a much less explored direction in the literature. This system is replacing the previous implementation based on Apache SOLR that was not always able to meet the required service-level-agreement.
|
1803.00005
|
Longquan Dai
|
Longquan Dai, Mengke Yuan, Zechao Li, Xiaopeng Zhang, Jinhui Tang
|
Hardware-Efficient Guided Image Filtering For Multi-Label Problem
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Guided Filter (GF) is well-known for its linear complexity. However, when
filtering an image with an n-channel guidance, GF needs to invert an n x n
matrix for each pixel. To the best of our knowledge existing matrix inverse
algorithms are inefficient on current hardwares. This shortcoming limits
applications of multichannel guidance in computation intensive system such as
multi-label system. We need a new GF-like filter that can perform fast
multichannel image guided filtering. Since the optimal linear complexity of GF
cannot be minimized further, the only way thus is to bring all potentialities
of current parallel computing hardwares into full play. In this paper we
propose a hardware-efficient Guided Filter (HGF), which solves the efficiency
problem of multichannel guided image filtering and yields competent results
when applying it to multi-label problems with synthesized polynomial
multichannel guidance. Specifically, in order to boost the filtering
performance, HGF takes a new matrix inverse algorithm which only involves two
hardware-efficient operations: element-wise arithmetic calculations and box
filtering. In order to break the linear model restriction, HGF synthesizes a
polynomial multichannel guidance to introduce nonlinearity. Benefiting from our
polynomial guidance and hardware-efficient matrix inverse algorithm, HGF not
only is more sensitive to the underlying structure of guidance but also
achieves the fastest computing speed. Due to these merits, HGF obtains
state-of-the-art results in terms of accuracy and efficiency in the computation
intensive multi-label
|
[
{
"created": "Wed, 28 Feb 2018 07:27:43 GMT",
"version": "v1"
}
] |
2018-03-02
|
[
[
"Dai",
"Longquan",
""
],
[
"Yuan",
"Mengke",
""
],
[
"Li",
"Zechao",
""
],
[
"Zhang",
"Xiaopeng",
""
],
[
"Tang",
"Jinhui",
""
]
] |
The Guided Filter (GF) is well-known for its linear complexity. However, when filtering an image with an n-channel guidance, GF needs to invert an n x n matrix for each pixel. To the best of our knowledge existing matrix inverse algorithms are inefficient on current hardwares. This shortcoming limits applications of multichannel guidance in computation intensive system such as multi-label system. We need a new GF-like filter that can perform fast multichannel image guided filtering. Since the optimal linear complexity of GF cannot be minimized further, the only way thus is to bring all potentialities of current parallel computing hardwares into full play. In this paper we propose a hardware-efficient Guided Filter (HGF), which solves the efficiency problem of multichannel guided image filtering and yields competent results when applying it to multi-label problems with synthesized polynomial multichannel guidance. Specifically, in order to boost the filtering performance, HGF takes a new matrix inverse algorithm which only involves two hardware-efficient operations: element-wise arithmetic calculations and box filtering. In order to break the linear model restriction, HGF synthesizes a polynomial multichannel guidance to introduce nonlinearity. Benefiting from our polynomial guidance and hardware-efficient matrix inverse algorithm, HGF not only is more sensitive to the underlying structure of guidance but also achieves the fastest computing speed. Due to these merits, HGF obtains state-of-the-art results in terms of accuracy and efficiency in the computation intensive multi-label
|
2405.08760
|
Akhila Yerukola
|
Akhila Yerukola, Saujas Vaduguru, Daniel Fried, Maarten Sap
|
Is the Pope Catholic? Yes, the Pope is Catholic. Generative Evaluation
of Non-Literal Intent Resolution in LLMs
| null | null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Humans often express their communicative intents indirectly or non-literally,
which requires their interlocutors -- human or AI -- to understand beyond the
literal meaning of words. While most existing work has focused on
discriminative evaluations, we present a new approach to generatively evaluate
large language models' (LLMs') intention understanding by examining their
responses to non-literal utterances. Ideally, an LLM should respond in line
with the true intention of a non-literal utterance, not its literal
interpretation. Our findings show that LLMs struggle to generate pragmatically
relevant responses to non-literal language, achieving only 50-55% accuracy on
average. While explicitly providing oracle intentions significantly improves
performance (e.g., 75% for Mistral-Instruct), this still indicates challenges
in leveraging given intentions to produce appropriate responses. Using
chain-of-thought to make models spell out intentions yields much smaller gains
(60% for Mistral-Instruct). These findings suggest that LLMs are not yet
effective pragmatic interlocutors, highlighting the need for better approaches
for modeling intentions and utilizing them for pragmatic generation.
|
[
{
"created": "Tue, 14 May 2024 16:48:56 GMT",
"version": "v1"
},
{
"created": "Wed, 19 Jun 2024 19:07:47 GMT",
"version": "v2"
}
] |
2024-06-21
|
[
[
"Yerukola",
"Akhila",
""
],
[
"Vaduguru",
"Saujas",
""
],
[
"Fried",
"Daniel",
""
],
[
"Sap",
"Maarten",
""
]
] |
Humans often express their communicative intents indirectly or non-literally, which requires their interlocutors -- human or AI -- to understand beyond the literal meaning of words. While most existing work has focused on discriminative evaluations, we present a new approach to generatively evaluate large language models' (LLMs') intention understanding by examining their responses to non-literal utterances. Ideally, an LLM should respond in line with the true intention of a non-literal utterance, not its literal interpretation. Our findings show that LLMs struggle to generate pragmatically relevant responses to non-literal language, achieving only 50-55% accuracy on average. While explicitly providing oracle intentions significantly improves performance (e.g., 75% for Mistral-Instruct), this still indicates challenges in leveraging given intentions to produce appropriate responses. Using chain-of-thought to make models spell out intentions yields much smaller gains (60% for Mistral-Instruct). These findings suggest that LLMs are not yet effective pragmatic interlocutors, highlighting the need for better approaches for modeling intentions and utilizing them for pragmatic generation.
|
2301.01221
|
Ali Rahimi
|
Vesal Ahsani, Ali Rahimi, Mehdi Letafati, Babak Hossein Khalaj
|
Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and
Security, Edge Computing, and Blockchain
|
21 pages, 4 figures, added references for section 3-A
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this article, the authors provide a comprehensive overview on three core
pillars of metaverse-as-a-service (MaaS) platforms; privacy and security, edge
computing, and blockchain technology. The article starts by investigating
security aspects for the wireless access to the metaverse. Then it goes through
the privacy and security issues inside the metaverse from data-centric,
learning-centric, and human-centric points-of-view. The authors address private
and secure mechanisms for privatizing sensitive data attributes and securing
machine learning algorithms running in a distributed manner within the
metaverse platforms. Novel visions and less-investigated methods are reviewed
to help mobile network operators and metaverse service providers facilitate the
realization of secure and private MaaS through different layers of the
metaverse, ranging from the access layer to the social interactions among
clients. Later in the article, it has been explained how the paradigm of edge
computing can strengthen different aspects of the metaverse. Along with that,
the challenges of using edge computing in the metaverse have been
comprehensively investigated. Additionally, the paper has comprehensively
investigated and analyzed 10 main challenges of MaaS platforms and thoroughly
discussed how blockchain technology provides solutions for these constraints.
At the final, future vision and directions, such as content-centric security
and zero-trust metaverse, some blockchain's unsolved challenges are also
discussed to bring further insights for the network designers in the metaverse
era.
|
[
{
"created": "Sun, 1 Jan 2023 15:34:18 GMT",
"version": "v1"
},
{
"created": "Wed, 11 Jan 2023 22:38:32 GMT",
"version": "v2"
}
] |
2023-01-13
|
[
[
"Ahsani",
"Vesal",
""
],
[
"Rahimi",
"Ali",
""
],
[
"Letafati",
"Mehdi",
""
],
[
"Khalaj",
"Babak Hossein",
""
]
] |
In this article, the authors provide a comprehensive overview on three core pillars of metaverse-as-a-service (MaaS) platforms; privacy and security, edge computing, and blockchain technology. The article starts by investigating security aspects for the wireless access to the metaverse. Then it goes through the privacy and security issues inside the metaverse from data-centric, learning-centric, and human-centric points-of-view. The authors address private and secure mechanisms for privatizing sensitive data attributes and securing machine learning algorithms running in a distributed manner within the metaverse platforms. Novel visions and less-investigated methods are reviewed to help mobile network operators and metaverse service providers facilitate the realization of secure and private MaaS through different layers of the metaverse, ranging from the access layer to the social interactions among clients. Later in the article, it has been explained how the paradigm of edge computing can strengthen different aspects of the metaverse. Along with that, the challenges of using edge computing in the metaverse have been comprehensively investigated. Additionally, the paper has comprehensively investigated and analyzed 10 main challenges of MaaS platforms and thoroughly discussed how blockchain technology provides solutions for these constraints. At the final, future vision and directions, such as content-centric security and zero-trust metaverse, some blockchain's unsolved challenges are also discussed to bring further insights for the network designers in the metaverse era.
|
2003.04826
|
Syed Mohammed Arshad Zaidi
|
Anuj Sharma, Syed Mohammed Arshad Zaidi
|
Optimizations to the Parallel Breath First Search on Distributed Memory
|
5 pages
| null | null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Graphs and their traversal is becoming significant as it is applicable to
various areas of mathematics, science and technology. Various problems in
fields as varied as biochemistry (genomics), electrical engineering
(communication networks), computer science (algorithms and computation) can be
modeled as Graph problems. Real world scenarios including communities their
interconnections and related properties can be studied using graphs. So fast,
scalable, low-cost execution of parallel graph algorithms is very important. In
this implementation of parallel breadth first search of graphs, we implemented
Parallel BFS algorithm with 1-D partitioning of graph as described in [2] and
have reduced execution time by optimizing communication for local buffers.
|
[
{
"created": "Tue, 10 Mar 2020 16:11:46 GMT",
"version": "v1"
}
] |
2020-03-11
|
[
[
"Sharma",
"Anuj",
""
],
[
"Zaidi",
"Syed Mohammed Arshad",
""
]
] |
Graphs and their traversal is becoming significant as it is applicable to various areas of mathematics, science and technology. Various problems in fields as varied as biochemistry (genomics), electrical engineering (communication networks), computer science (algorithms and computation) can be modeled as Graph problems. Real world scenarios including communities their interconnections and related properties can be studied using graphs. So fast, scalable, low-cost execution of parallel graph algorithms is very important. In this implementation of parallel breadth first search of graphs, we implemented Parallel BFS algorithm with 1-D partitioning of graph as described in [2] and have reduced execution time by optimizing communication for local buffers.
|
1211.4206
|
Pascal Frossard
|
Enrico Magli, Mea Wang, Pascal Frossard, Athina Markopoulou
|
Network Coding Meets Multimedia: a Review
|
Part of this work is under publication in IEEE Transactions on
Multimedia
| null |
10.1109/TMM.2013.2241415
| null |
cs.MM cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While every network node only relays messages in a traditional communication
system, the recent network coding (NC) paradigm proposes to implement simple
in-network processing with packet combinations in the nodes. NC extends the
concept of "encoding" a message beyond source coding (for compression) and
channel coding (for protection against errors and losses). It has been shown to
increase network throughput compared to traditional networks implementation, to
reduce delay and to provide robustness to transmission errors and network
dynamics. These features are so appealing for multimedia applications that they
have spurred a large research effort towards the development of
multimedia-specific NC techniques. This paper reviews the recent work in NC for
multimedia applications and focuses on the techniques that fill the gap between
NC theory and practical applications. It outlines the benefits of NC and
presents the open challenges in this area. The paper initially focuses on
multimedia-specific aspects of network coding, in particular delay, in-network
error control, and media-specific error control. These aspects permit to handle
varying network conditions as well as client heterogeneity, which are critical
to the design and deployment of multimedia systems. After introducing these
general concepts, the paper reviews in detail two applications that lend
themselves naturally to NC via the cooperation and broadcast models, namely
peer-to-peer multimedia streaming and wireless networking.
|
[
{
"created": "Sun, 18 Nov 2012 10:43:54 GMT",
"version": "v1"
}
] |
2016-11-15
|
[
[
"Magli",
"Enrico",
""
],
[
"Wang",
"Mea",
""
],
[
"Frossard",
"Pascal",
""
],
[
"Markopoulou",
"Athina",
""
]
] |
While every network node only relays messages in a traditional communication system, the recent network coding (NC) paradigm proposes to implement simple in-network processing with packet combinations in the nodes. NC extends the concept of "encoding" a message beyond source coding (for compression) and channel coding (for protection against errors and losses). It has been shown to increase network throughput compared to traditional networks implementation, to reduce delay and to provide robustness to transmission errors and network dynamics. These features are so appealing for multimedia applications that they have spurred a large research effort towards the development of multimedia-specific NC techniques. This paper reviews the recent work in NC for multimedia applications and focuses on the techniques that fill the gap between NC theory and practical applications. It outlines the benefits of NC and presents the open challenges in this area. The paper initially focuses on multimedia-specific aspects of network coding, in particular delay, in-network error control, and media-specific error control. These aspects permit to handle varying network conditions as well as client heterogeneity, which are critical to the design and deployment of multimedia systems. After introducing these general concepts, the paper reviews in detail two applications that lend themselves naturally to NC via the cooperation and broadcast models, namely peer-to-peer multimedia streaming and wireless networking.
|
2205.06360
|
William Schultz
|
William Schultz, Ian Dardik, Stavros Tripakis
|
Plain and Simple Inductive Invariant Inference for Distributed Protocols
in TLA+
| null | null | null | null |
cs.LO cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
We present a new technique for automatically inferring inductive invariants
of parameterized distributed protocols specified in TLA+. Ours is the first
such invariant inference technique to work directly on TLA+, an expressive,
high level specification language. To achieve this, we present a new algorithm
for invariant inference that is based around a core procedure for generating
plain, potentially non-inductive lemma invariants that are used as candidate
conjuncts of an overall inductive invariant. We couple this with a greedy lemma
invariant selection procedure that selects lemmas that eliminate the largest
number of counterexamples to induction at each round of our inference
procedure. We have implemented our algorithm in a tool, endive, and evaluate it
on a diverse set of distributed protocol benchmarks, demonstrating competitive
performance and ability to uniquely solve an industrial scale reconfiguration
protocol.
|
[
{
"created": "Thu, 12 May 2022 20:53:44 GMT",
"version": "v1"
},
{
"created": "Sat, 1 Oct 2022 15:43:09 GMT",
"version": "v2"
}
] |
2022-10-04
|
[
[
"Schultz",
"William",
""
],
[
"Dardik",
"Ian",
""
],
[
"Tripakis",
"Stavros",
""
]
] |
We present a new technique for automatically inferring inductive invariants of parameterized distributed protocols specified in TLA+. Ours is the first such invariant inference technique to work directly on TLA+, an expressive, high level specification language. To achieve this, we present a new algorithm for invariant inference that is based around a core procedure for generating plain, potentially non-inductive lemma invariants that are used as candidate conjuncts of an overall inductive invariant. We couple this with a greedy lemma invariant selection procedure that selects lemmas that eliminate the largest number of counterexamples to induction at each round of our inference procedure. We have implemented our algorithm in a tool, endive, and evaluate it on a diverse set of distributed protocol benchmarks, demonstrating competitive performance and ability to uniquely solve an industrial scale reconfiguration protocol.
|
2303.13015
|
Marc Katzef
|
Marc Katzef, Andrew C. Cullen, Tansu Alpcan, Christopher Leckie,
Justin Kopacz
|
Failure-tolerant Distributed Learning for Anomaly Detection in Wireless
Networks
| null | null | null | null |
cs.LG cs.AI cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
The analysis of distributed techniques is often focused upon their
efficiency, without considering their robustness (or lack thereof). Such a
consideration is particularly important when devices or central servers can
fail, which can potentially cripple distributed systems. When such failures
arise in wireless communications networks, important services that they
use/provide (like anomaly detection) can be left inoperable and can result in a
cascade of security problems. In this paper, we present a novel method to
address these risks by combining both flat- and star-topologies, combining the
performance and reliability benefits of both. We refer to this method as
"Tol-FL", due to its increased failure-tolerance as compared to the technique
of Federated Learning. Our approach both limits device failure risks while
outperforming prior methods by up to 8% in terms of anomaly detection AUROC in
a range of realistic settings that consider client as well as server failure,
all while reducing communication costs. This performance demonstrates that
Tol-FL is a highly suitable method for distributed model training for anomaly
detection, especially in the domain of wireless networks.
|
[
{
"created": "Thu, 23 Mar 2023 03:39:12 GMT",
"version": "v1"
}
] |
2023-03-24
|
[
[
"Katzef",
"Marc",
""
],
[
"Cullen",
"Andrew C.",
""
],
[
"Alpcan",
"Tansu",
""
],
[
"Leckie",
"Christopher",
""
],
[
"Kopacz",
"Justin",
""
]
] |
The analysis of distributed techniques is often focused upon their efficiency, without considering their robustness (or lack thereof). Such a consideration is particularly important when devices or central servers can fail, which can potentially cripple distributed systems. When such failures arise in wireless communications networks, important services that they use/provide (like anomaly detection) can be left inoperable and can result in a cascade of security problems. In this paper, we present a novel method to address these risks by combining both flat- and star-topologies, combining the performance and reliability benefits of both. We refer to this method as "Tol-FL", due to its increased failure-tolerance as compared to the technique of Federated Learning. Our approach both limits device failure risks while outperforming prior methods by up to 8% in terms of anomaly detection AUROC in a range of realistic settings that consider client as well as server failure, all while reducing communication costs. This performance demonstrates that Tol-FL is a highly suitable method for distributed model training for anomaly detection, especially in the domain of wireless networks.
|
2301.06549
|
Muhammad Mahboob Ur Rahman
|
Rabia Ahmed, Ahsan Mehmood, Muhammad Mahboob Ur Rahman, Octavia A.
Dobre
|
A Deep Learning & Fast Wavelet Transform-based Hybrid Approach for
Denoising of PPG Signals
|
4 pages, 8 figures
| null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
This letter presents a novel hybrid method that leverages deep learning to
exploit the multi-resolution analysis capability of the wavelets, in order to
denoise a photoplethysmography (PPG) signal. Under the proposed method, a noisy
PPG sequence of length N is first decomposed into L detailed coefficients using
the fast wavelet transform (FWT). Then, the clean PPG sequence is reconstructed
as follows. A custom feedforward neural network (FFNN) provides the binary
weights for each of the wavelet sub-signals outputted by the inverse-FWT block.
This way, all those sub-signals which correspond to noise or artefacts are
discarded during reconstruction. The FFNN is trained on the Beth Israel
Deaconess Medical Center (BIDMC) dataset under the supervised learning
framework, whereby we compute the mean squared-error (MSE) between the denoised
sequence and the reference clean PPG signal, and compute the gradient of the
MSE for the back-propagation. Numerical results show that the proposed method
effectively denoises the corrupted PPG and video-PPG signal.
|
[
{
"created": "Mon, 16 Jan 2023 18:27:12 GMT",
"version": "v1"
}
] |
2023-01-18
|
[
[
"Ahmed",
"Rabia",
""
],
[
"Mehmood",
"Ahsan",
""
],
[
"Rahman",
"Muhammad Mahboob Ur",
""
],
[
"Dobre",
"Octavia A.",
""
]
] |
This letter presents a novel hybrid method that leverages deep learning to exploit the multi-resolution analysis capability of the wavelets, in order to denoise a photoplethysmography (PPG) signal. Under the proposed method, a noisy PPG sequence of length N is first decomposed into L detailed coefficients using the fast wavelet transform (FWT). Then, the clean PPG sequence is reconstructed as follows. A custom feedforward neural network (FFNN) provides the binary weights for each of the wavelet sub-signals outputted by the inverse-FWT block. This way, all those sub-signals which correspond to noise or artefacts are discarded during reconstruction. The FFNN is trained on the Beth Israel Deaconess Medical Center (BIDMC) dataset under the supervised learning framework, whereby we compute the mean squared-error (MSE) between the denoised sequence and the reference clean PPG signal, and compute the gradient of the MSE for the back-propagation. Numerical results show that the proposed method effectively denoises the corrupted PPG and video-PPG signal.
|
2212.00548
|
Ting Zhang
|
Ting Zhang, DongGyun Han, Venkatesh Vinayakarao, Ivana Clairine Irsan,
Bowen Xu, Ferdian Thung, David Lo, Lingxiao Jiang
|
Duplicate Bug Report Detection: How Far Are We?
|
Accepted by ACM Transactions on Software Engineering and Methodology
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many Duplicate Bug Report Detection (DBRD) techniques have been proposed in
the research literature. The industry uses some other techniques.
Unfortunately, there is insufficient comparison among them, and it is unclear
how far we have been. This work fills this gap by comparing the aforementioned
techniques. To compare them, we first need a benchmark that can estimate how a
tool would perform if applied in a realistic setting today. Thus, we first
investigated potential biases that affect the fair comparison of the accuracy
of DBRD techniques. Our experiments suggest that data age and issue tracking
system choice cause a significant difference. Based on these findings, we
prepared a new benchmark. We then used it to evaluate DBRD techniques to
estimate better how far we have been. Surprisingly, a simpler technique
outperforms recently proposed sophisticated techniques on most projects in our
benchmark. In addition, we compared the DBRD techniques proposed in research
with those used in Mozilla and VSCode. Surprisingly, we observe that a simple
technique already adopted in practice can achieve comparable results as a
recently proposed research tool. Our study gives reflections on the current
state of DBRD, and we share our insights to benefit future DBRD research.
|
[
{
"created": "Thu, 1 Dec 2022 14:54:45 GMT",
"version": "v1"
}
] |
2022-12-02
|
[
[
"Zhang",
"Ting",
""
],
[
"Han",
"DongGyun",
""
],
[
"Vinayakarao",
"Venkatesh",
""
],
[
"Irsan",
"Ivana Clairine",
""
],
[
"Xu",
"Bowen",
""
],
[
"Thung",
"Ferdian",
""
],
[
"Lo",
"David",
""
],
[
"Jiang",
"Lingxiao",
""
]
] |
Many Duplicate Bug Report Detection (DBRD) techniques have been proposed in the research literature. The industry uses some other techniques. Unfortunately, there is insufficient comparison among them, and it is unclear how far we have been. This work fills this gap by comparing the aforementioned techniques. To compare them, we first need a benchmark that can estimate how a tool would perform if applied in a realistic setting today. Thus, we first investigated potential biases that affect the fair comparison of the accuracy of DBRD techniques. Our experiments suggest that data age and issue tracking system choice cause a significant difference. Based on these findings, we prepared a new benchmark. We then used it to evaluate DBRD techniques to estimate better how far we have been. Surprisingly, a simpler technique outperforms recently proposed sophisticated techniques on most projects in our benchmark. In addition, we compared the DBRD techniques proposed in research with those used in Mozilla and VSCode. Surprisingly, we observe that a simple technique already adopted in practice can achieve comparable results as a recently proposed research tool. Our study gives reflections on the current state of DBRD, and we share our insights to benefit future DBRD research.
|
2109.00833
|
Dominik Martin
|
Dominik Martin, Niklas K\"uhl, Marcel Schwenk
|
Towards a Reference Architecture for Future Industrial Internet of
Things Networks
| null |
Proceedings of the 2021 IEEE 23rd Conference on Business
Informatics (CBI) Vol. 2
|
10.1109/CBI52690.2021.10049
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the continuing decrease of sensor technology prices as well as the
increase of communication and analytical capabilities of modern internet of
things devices, the continuously generated amount of data is constantly
growing. Various use cases show the untapped potential of this data for new
business models. However, conventional industrial IT networks of traditional
manufacturing companies can hardly meet the modern requirements emerging with
today's and future industrial internet of things applications. Outdated and
rigid network infrastructures are one of the main reasons for hesitant
innovation efforts and cross-organizational collaborations as well as the slow
adoption of modern business models by traditional manufacturing companies.
Following the design science research paradigm, our work contributes by
elaborating on a comprehensive list of requirements for future industrial
internet of things networks from a theoretical and practical perspective as
well as a proposed reference architecture acting as a blueprint for future
implementations.
|
[
{
"created": "Thu, 2 Sep 2021 10:33:53 GMT",
"version": "v1"
}
] |
2021-09-03
|
[
[
"Martin",
"Dominik",
""
],
[
"Kühl",
"Niklas",
""
],
[
"Schwenk",
"Marcel",
""
]
] |
With the continuing decrease of sensor technology prices as well as the increase of communication and analytical capabilities of modern internet of things devices, the continuously generated amount of data is constantly growing. Various use cases show the untapped potential of this data for new business models. However, conventional industrial IT networks of traditional manufacturing companies can hardly meet the modern requirements emerging with today's and future industrial internet of things applications. Outdated and rigid network infrastructures are one of the main reasons for hesitant innovation efforts and cross-organizational collaborations as well as the slow adoption of modern business models by traditional manufacturing companies. Following the design science research paradigm, our work contributes by elaborating on a comprehensive list of requirements for future industrial internet of things networks from a theoretical and practical perspective as well as a proposed reference architecture acting as a blueprint for future implementations.
|
1509.03614
|
Karla Saur
|
Karla Saur and Joseph Collard and Nate Foster and Arjun Guha and
Laurent Vanbever and Michael Hicks
|
Morpheus: Safe and Flexible Dynamic Updates for SDNs
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
SDN controllers must be periodically modified to add features, improve
performance, and fix bugs, but current techniques for implementing dynamic
updates are inadequate. Simply halting old controllers and bringing up new ones
can cause state to be lost, which often leads to incorrect behavior-e.g., if
the state represents hosts blacklisted by a firewall, then traffic that should
be blocked may be allowed to pass through. Techniques based on record and
replay can reconstruct state automatically, but they are expensive to deploy
and can lead to incorrect behavior. Problematic scenarios are especially likely
to arise in distributed controllers and with semantics-altering updates.
This paper presents a new approach to implementing dynamic controller updates
based on explicit state transfer. Instead of attempting to infer state changes
automatically-an approach that is expensive and fundamentally incomplete-our
framework gives programmers effective tools for implementing correct updates
that avoid major disruptions. We develop primitives that enable programmers to
directly (and easily, in most cases) initialize the new controller's state as a
function of old state and we design protocols that ensure consistent behavior
during the transition. We also present a prototype implementation called
Morpheus, and evaluate its effectiveness on representative case studies.
|
[
{
"created": "Fri, 11 Sep 2015 19:10:43 GMT",
"version": "v1"
}
] |
2015-09-14
|
[
[
"Saur",
"Karla",
""
],
[
"Collard",
"Joseph",
""
],
[
"Foster",
"Nate",
""
],
[
"Guha",
"Arjun",
""
],
[
"Vanbever",
"Laurent",
""
],
[
"Hicks",
"Michael",
""
]
] |
SDN controllers must be periodically modified to add features, improve performance, and fix bugs, but current techniques for implementing dynamic updates are inadequate. Simply halting old controllers and bringing up new ones can cause state to be lost, which often leads to incorrect behavior-e.g., if the state represents hosts blacklisted by a firewall, then traffic that should be blocked may be allowed to pass through. Techniques based on record and replay can reconstruct state automatically, but they are expensive to deploy and can lead to incorrect behavior. Problematic scenarios are especially likely to arise in distributed controllers and with semantics-altering updates. This paper presents a new approach to implementing dynamic controller updates based on explicit state transfer. Instead of attempting to infer state changes automatically-an approach that is expensive and fundamentally incomplete-our framework gives programmers effective tools for implementing correct updates that avoid major disruptions. We develop primitives that enable programmers to directly (and easily, in most cases) initialize the new controller's state as a function of old state and we design protocols that ensure consistent behavior during the transition. We also present a prototype implementation called Morpheus, and evaluate its effectiveness on representative case studies.
|
2109.02199
|
Nan Xue
|
Rujiao Long and Wen Wang and Nan Xue and Feiyu Gao and Zhibo Yang and
Yongpan Wang and Gui-Song Xia
|
Parsing Table Structures in the Wild
|
Accepted to ICCV 2021
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
This paper tackles the problem of table structure parsing (TSP) from images
in the wild. In contrast to existing studies that mainly focus on parsing
well-aligned tabular images with simple layouts from scanned PDF documents, we
aim to establish a practical table structure parsing system for real-world
scenarios where tabular input images are taken or scanned with severe
deformation, bending or occlusions. For designing such a system, we propose an
approach named Cycle-CenterNet on the top of CenterNet with a novel
cycle-pairing module to simultaneously detect and group tabular cells into
structured tables. In the cycle-pairing module, a new pairing loss function is
proposed for the network training. Alongside with our Cycle-CenterNet, we also
present a large-scale dataset, named Wired Table in the Wild (WTW), which
includes well-annotated structure parsing of multiple style tables in several
scenes like the photo, scanning files, web pages, \emph{etc.}. In experiments,
we demonstrate that our Cycle-CenterNet consistently achieves the best accuracy
of table structure parsing on the new WTW dataset by 24.6\% absolute
improvement evaluated by the TEDS metric. A more comprehensive experimental
analysis also validates the advantages of our proposed methods for the TSP
task.
|
[
{
"created": "Mon, 6 Sep 2021 01:05:48 GMT",
"version": "v1"
}
] |
2021-09-07
|
[
[
"Long",
"Rujiao",
""
],
[
"Wang",
"Wen",
""
],
[
"Xue",
"Nan",
""
],
[
"Gao",
"Feiyu",
""
],
[
"Yang",
"Zhibo",
""
],
[
"Wang",
"Yongpan",
""
],
[
"Xia",
"Gui-Song",
""
]
] |
This paper tackles the problem of table structure parsing (TSP) from images in the wild. In contrast to existing studies that mainly focus on parsing well-aligned tabular images with simple layouts from scanned PDF documents, we aim to establish a practical table structure parsing system for real-world scenarios where tabular input images are taken or scanned with severe deformation, bending or occlusions. For designing such a system, we propose an approach named Cycle-CenterNet on the top of CenterNet with a novel cycle-pairing module to simultaneously detect and group tabular cells into structured tables. In the cycle-pairing module, a new pairing loss function is proposed for the network training. Alongside with our Cycle-CenterNet, we also present a large-scale dataset, named Wired Table in the Wild (WTW), which includes well-annotated structure parsing of multiple style tables in several scenes like the photo, scanning files, web pages, \emph{etc.}. In experiments, we demonstrate that our Cycle-CenterNet consistently achieves the best accuracy of table structure parsing on the new WTW dataset by 24.6\% absolute improvement evaluated by the TEDS metric. A more comprehensive experimental analysis also validates the advantages of our proposed methods for the TSP task.
|
2303.14603
|
Hina Qayyum
|
Hina Qayyum, Benjamin Zi Hao Zhao, Ian D. Wood, Muhammad Ikram,
Mohamed Ali Kaafar, Nicolas Kourtellis
|
A longitudinal study of the top 1% toxic Twitter profiles
| null | null |
10.1145/3578503.3583619
| null |
cs.SI cs.CY
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Toxicity is endemic to online social networks including Twitter. It follows a
Pareto like distribution where most of the toxicity is generated by a very
small number of profiles and as such, analyzing and characterizing these toxic
profiles is critical. Prior research has largely focused on sporadic, event
centric toxic content to characterize toxicity on the platform. Instead, we
approach the problem of characterizing toxic content from a profile centric
point of view. We study 143K Twitter profiles and focus on the behavior of the
top 1 percent producers of toxic content on Twitter, based on toxicity scores
of their tweets availed by Perspective API. With a total of 293M tweets,
spanning 16 years of activity, the longitudinal data allow us to reconstruct
the timelines of all profiles involved. We use these timelines to gauge the
behavior of the most toxic Twitter profiles compared to the rest of the Twitter
population. We study the pattern of tweet posting from highly toxic accounts,
based on the frequency and how prolific they are, the nature of hashtags and
URLs, profile metadata, and Botometer scores. We find that the highly toxic
profiles post coherent and well articulated content, their tweets keep to a
narrow theme with lower diversity in hashtags, URLs, and domains, they are
thematically similar to each other, and have a high likelihood of bot like
behavior, likely to have progenitors with intentions to influence, based on
high fake followers score. Our work contributes insight into the top 1 percent
of toxic profiles on Twitter and establishes the profile centric approach to
investigate toxicity on Twitter to be beneficial.
|
[
{
"created": "Sun, 26 Mar 2023 01:55:28 GMT",
"version": "v1"
}
] |
2023-03-28
|
[
[
"Qayyum",
"Hina",
""
],
[
"Zhao",
"Benjamin Zi Hao",
""
],
[
"Wood",
"Ian D.",
""
],
[
"Ikram",
"Muhammad",
""
],
[
"Kaafar",
"Mohamed Ali",
""
],
[
"Kourtellis",
"Nicolas",
""
]
] |
Toxicity is endemic to online social networks including Twitter. It follows a Pareto like distribution where most of the toxicity is generated by a very small number of profiles and as such, analyzing and characterizing these toxic profiles is critical. Prior research has largely focused on sporadic, event centric toxic content to characterize toxicity on the platform. Instead, we approach the problem of characterizing toxic content from a profile centric point of view. We study 143K Twitter profiles and focus on the behavior of the top 1 percent producers of toxic content on Twitter, based on toxicity scores of their tweets availed by Perspective API. With a total of 293M tweets, spanning 16 years of activity, the longitudinal data allow us to reconstruct the timelines of all profiles involved. We use these timelines to gauge the behavior of the most toxic Twitter profiles compared to the rest of the Twitter population. We study the pattern of tweet posting from highly toxic accounts, based on the frequency and how prolific they are, the nature of hashtags and URLs, profile metadata, and Botometer scores. We find that the highly toxic profiles post coherent and well articulated content, their tweets keep to a narrow theme with lower diversity in hashtags, URLs, and domains, they are thematically similar to each other, and have a high likelihood of bot like behavior, likely to have progenitors with intentions to influence, based on high fake followers score. Our work contributes insight into the top 1 percent of toxic profiles on Twitter and establishes the profile centric approach to investigate toxicity on Twitter to be beneficial.
|
1904.05173
|
Stanis{\l}aw Ambroszkiewicz
|
Stanislaw Ambroszkiewicz
|
Combinatorial constructions of intrinsic geometries
|
The final version (hopefully)
| null | null | null |
cs.CG math.DG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A generic method for combinatorial constructions of intrinsic geometrical
spaces is presented. It is based on the well known inverse sequences of finite
graphs that determine (in the limit) topological spaces. If a pattern of the
construction is sufficiently regular and uniform, then the notions of metric,
geodesic and curvature can be defined in the space as the limits of their
finite versions in the graphs. This gives rise to consider the graphs with
metrics as finite approximations of the geometry of the space. On the basis of
simple and generic examples, several nonstandard and novel notions are proposed
for the Foundations of Geometry. They may be considered as a subject of a
critical discussion.
|
[
{
"created": "Wed, 10 Apr 2019 13:21:08 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Jul 2019 20:07:11 GMT",
"version": "v2"
},
{
"created": "Mon, 23 Sep 2019 19:14:57 GMT",
"version": "v3"
},
{
"created": "Wed, 6 Nov 2019 15:36:29 GMT",
"version": "v4"
},
{
"created": "Sat, 7 Dec 2019 11:12:42 GMT",
"version": "v5"
},
{
"created": "Mon, 27 Apr 2020 11:10:15 GMT",
"version": "v6"
},
{
"created": "Thu, 8 Oct 2020 15:57:59 GMT",
"version": "v7"
}
] |
2020-10-09
|
[
[
"Ambroszkiewicz",
"Stanislaw",
""
]
] |
A generic method for combinatorial constructions of intrinsic geometrical spaces is presented. It is based on the well known inverse sequences of finite graphs that determine (in the limit) topological spaces. If a pattern of the construction is sufficiently regular and uniform, then the notions of metric, geodesic and curvature can be defined in the space as the limits of their finite versions in the graphs. This gives rise to consider the graphs with metrics as finite approximations of the geometry of the space. On the basis of simple and generic examples, several nonstandard and novel notions are proposed for the Foundations of Geometry. They may be considered as a subject of a critical discussion.
|
1407.5718
|
Kamal Rahimi Malekshan
|
K. Rahimi Malekshan, F. Lahouti
|
Distributed Cross-layer Dynamic Route Selection in Wireless Multiuser
Multihop Networks
|
Submitted to IEEE Transaction on Wireless Comunications
| null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In wireless ad-hoc networks, forwarding data through intermediate relays
extends the coverage area and enhances the network throughput. We consider a
general wireless multiuser multihop transmission, where each data flow is
subject to a constraint on the end-to-end buffering delay and the associated
packet drop rate as a quality of service (QoS) requirement. The objective is to
maximize the weighted sum-rate between source destination pairs, while the
corresponding QoS requirements are satisfied. We introduce two new distributed
cross-layer dynamic route selection schemes in this setting that are designed
involving physical, MAC, and network layers. In the proposed opportunistic
cross-layer dynamic route selection scheme, routes are assigned dynamically
based on the state of network nodes' buffers and the instantaneous state of
fading channels. In the same setting, the proposed time division cross layer
dynamic route selection scheme utilizes the average quality of channels instead
for more efficient implementation. Detailed results and comparisons are
provided, which demonstrate the superior performance of the proposed
cross-layer dynamic route selection schemes.
|
[
{
"created": "Tue, 22 Jul 2014 03:08:49 GMT",
"version": "v1"
}
] |
2014-07-23
|
[
[
"Malekshan",
"K. Rahimi",
""
],
[
"Lahouti",
"F.",
""
]
] |
In wireless ad-hoc networks, forwarding data through intermediate relays extends the coverage area and enhances the network throughput. We consider a general wireless multiuser multihop transmission, where each data flow is subject to a constraint on the end-to-end buffering delay and the associated packet drop rate as a quality of service (QoS) requirement. The objective is to maximize the weighted sum-rate between source destination pairs, while the corresponding QoS requirements are satisfied. We introduce two new distributed cross-layer dynamic route selection schemes in this setting that are designed involving physical, MAC, and network layers. In the proposed opportunistic cross-layer dynamic route selection scheme, routes are assigned dynamically based on the state of network nodes' buffers and the instantaneous state of fading channels. In the same setting, the proposed time division cross layer dynamic route selection scheme utilizes the average quality of channels instead for more efficient implementation. Detailed results and comparisons are provided, which demonstrate the superior performance of the proposed cross-layer dynamic route selection schemes.
|
2308.04689
|
Piyush Vyas
|
Piyush Vyas, Akhilesh Chauhan, Tushar Mandge, Surbhi Hardikar
|
Web crawler strategies for web pages under robot.txt restriction
| null | null | null | null |
cs.AI cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the present time, all know about World Wide Web and work over the Internet
daily. In this paper, we introduce the search engines working for keywords that
are entered by users to find something. The search engine uses different search
algorithms for convenient results for providing to the net surfer. Net surfers
go with the top search results but how did the results of web pages get higher
ranks over search engines? how the search engine got that all the web pages in
the database? This paper gives the answers to all these kinds of basic
questions. Web crawlers working for search engines and robot exclusion protocol
rules for web crawlers are also addressed in this research paper. Webmaster
uses different restriction facts in robot.txt file to instruct web crawler,
some basic formats of robot.txt are also mentioned in this paper.
|
[
{
"created": "Wed, 9 Aug 2023 03:52:48 GMT",
"version": "v1"
},
{
"created": "Wed, 28 Feb 2024 21:29:43 GMT",
"version": "v2"
}
] |
2024-03-01
|
[
[
"Vyas",
"Piyush",
""
],
[
"Chauhan",
"Akhilesh",
""
],
[
"Mandge",
"Tushar",
""
],
[
"Hardikar",
"Surbhi",
""
]
] |
In the present time, all know about World Wide Web and work over the Internet daily. In this paper, we introduce the search engines working for keywords that are entered by users to find something. The search engine uses different search algorithms for convenient results for providing to the net surfer. Net surfers go with the top search results but how did the results of web pages get higher ranks over search engines? how the search engine got that all the web pages in the database? This paper gives the answers to all these kinds of basic questions. Web crawlers working for search engines and robot exclusion protocol rules for web crawlers are also addressed in this research paper. Webmaster uses different restriction facts in robot.txt file to instruct web crawler, some basic formats of robot.txt are also mentioned in this paper.
|
1909.02384
|
Yukuan Yang
|
Yukuan Yang, Shuang Wu, Lei Deng, Tianyi Yan, Yuan Xie, Guoqi Li
|
Training High-Performance and Large-Scale Deep Neural Networks with Full
8-bit Integers
| null | null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep neural network (DNN) quantization converting floating-point (FP) data in
the network to integers (INT) is an effective way to shrink the model size for
memory saving and simplify the operations for compute acceleration. Recently,
researches on DNN quantization develop from inference to training, laying a
foundation for the online training on accelerators. However, existing schemes
leaving batch normalization (BN) untouched during training are mostly
incomplete quantization that still adopts high precision FP in some parts of
the data paths. Currently, there is no solution that can use only low bit-width
INT data during the whole training process of large-scale DNNs with acceptable
accuracy. In this work, through decomposing all the computation steps in DNNs
and fusing three special quantization functions to satisfy the different
precision requirements, we propose a unified complete quantization framework
termed as ``WAGEUBN'' to quantize DNNs involving all data paths including W
(Weights), A (Activation), G (Gradient), E (Error), U (Update), and BN.
Moreover, the Momentum optimizer is also quantized to realize a completely
quantized framework. Experiments on ResNet18/34/50 models demonstrate that
WAGEUBN can achieve competitive accuracy on the ImageNet dataset. For the first
time, the study of quantization in large-scale DNNs is advanced to the full
8-bit INT level. In this way, all the operations in the training and inference
can be bit-wise operations, pushing towards faster processing speed, decreased
memory cost, and higher energy efficiency. Our throughout quantization
framework has great potential for future efficient portable devices with online
learning ability.
|
[
{
"created": "Thu, 5 Sep 2019 13:17:38 GMT",
"version": "v1"
},
{
"created": "Tue, 31 Dec 2019 14:31:20 GMT",
"version": "v2"
}
] |
2020-01-01
|
[
[
"Yang",
"Yukuan",
""
],
[
"Wu",
"Shuang",
""
],
[
"Deng",
"Lei",
""
],
[
"Yan",
"Tianyi",
""
],
[
"Xie",
"Yuan",
""
],
[
"Li",
"Guoqi",
""
]
] |
Deep neural network (DNN) quantization converting floating-point (FP) data in the network to integers (INT) is an effective way to shrink the model size for memory saving and simplify the operations for compute acceleration. Recently, researches on DNN quantization develop from inference to training, laying a foundation for the online training on accelerators. However, existing schemes leaving batch normalization (BN) untouched during training are mostly incomplete quantization that still adopts high precision FP in some parts of the data paths. Currently, there is no solution that can use only low bit-width INT data during the whole training process of large-scale DNNs with acceptable accuracy. In this work, through decomposing all the computation steps in DNNs and fusing three special quantization functions to satisfy the different precision requirements, we propose a unified complete quantization framework termed as ``WAGEUBN'' to quantize DNNs involving all data paths including W (Weights), A (Activation), G (Gradient), E (Error), U (Update), and BN. Moreover, the Momentum optimizer is also quantized to realize a completely quantized framework. Experiments on ResNet18/34/50 models demonstrate that WAGEUBN can achieve competitive accuracy on the ImageNet dataset. For the first time, the study of quantization in large-scale DNNs is advanced to the full 8-bit INT level. In this way, all the operations in the training and inference can be bit-wise operations, pushing towards faster processing speed, decreased memory cost, and higher energy efficiency. Our throughout quantization framework has great potential for future efficient portable devices with online learning ability.
|
2304.02811
|
Haoyang Zheng
|
Haoyang Zheng, Yao Huang, Ziyang Huang, Wenrui Hao, Guang Lin
|
HomPINNs: homotopy physics-informed neural networks for solving the
inverse problems of nonlinear differential equations with multiple solutions
|
20 pages, 15 figures, 7 tables
|
Volume 500, 2024
|
10.1016/j.jcp.2023.112751
| null |
cs.LG cs.AI cs.NA math.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Due to the complex behavior arising from non-uniqueness, symmetry, and
bifurcations in the solution space, solving inverse problems of nonlinear
differential equations (DEs) with multiple solutions is a challenging task. To
address this, we propose homotopy physics-informed neural networks (HomPINNs),
a novel framework that leverages homotopy continuation and neural networks
(NNs) to solve inverse problems. The proposed framework begins with the use of
NNs to simultaneously approximate unlabeled observations across diverse
solutions while adhering to DE constraints. Through homotopy continuation, the
proposed method solves the inverse problem by tracing the observations and
identifying multiple solutions. The experiments involve testing the performance
of the proposed method on one-dimensional DEs and applying it to solve a
two-dimensional Gray-Scott simulation. Our findings demonstrate that the
proposed method is scalable and adaptable, providing an effective solution for
solving DEs with multiple solutions and unknown parameters. Moreover, it has
significant potential for various applications in scientific computing, such as
modeling complex systems and solving inverse problems in physics, chemistry,
biology, etc.
|
[
{
"created": "Thu, 6 Apr 2023 01:20:23 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Jan 2024 18:14:20 GMT",
"version": "v2"
}
] |
2024-01-18
|
[
[
"Zheng",
"Haoyang",
""
],
[
"Huang",
"Yao",
""
],
[
"Huang",
"Ziyang",
""
],
[
"Hao",
"Wenrui",
""
],
[
"Lin",
"Guang",
""
]
] |
Due to the complex behavior arising from non-uniqueness, symmetry, and bifurcations in the solution space, solving inverse problems of nonlinear differential equations (DEs) with multiple solutions is a challenging task. To address this, we propose homotopy physics-informed neural networks (HomPINNs), a novel framework that leverages homotopy continuation and neural networks (NNs) to solve inverse problems. The proposed framework begins with the use of NNs to simultaneously approximate unlabeled observations across diverse solutions while adhering to DE constraints. Through homotopy continuation, the proposed method solves the inverse problem by tracing the observations and identifying multiple solutions. The experiments involve testing the performance of the proposed method on one-dimensional DEs and applying it to solve a two-dimensional Gray-Scott simulation. Our findings demonstrate that the proposed method is scalable and adaptable, providing an effective solution for solving DEs with multiple solutions and unknown parameters. Moreover, it has significant potential for various applications in scientific computing, such as modeling complex systems and solving inverse problems in physics, chemistry, biology, etc.
|
2105.11210
|
Chenliang Li
|
Chenliang Li, Bin Bi, Ming Yan, Wei Wang, Songfang Huang, Fei Huang
and Luo Si
|
StructuralLM: Structural Pre-training for Form Understanding
|
Accepted by ACL2021 main conference
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large pre-trained language models achieve state-of-the-art results when
fine-tuned on downstream NLP tasks. However, they almost exclusively focus on
text-only representation, while neglecting cell-level layout information that
is important for form image understanding. In this paper, we propose a new
pre-training approach, StructuralLM, to jointly leverage cell and layout
information from scanned documents. Specifically, we pre-train StructuralLM
with two new designs to make the most of the interactions of cell and layout
information: 1) each cell as a semantic unit; 2) classification of cell
positions. The pre-trained StructuralLM achieves new state-of-the-art results
in different types of downstream tasks, including form understanding (from
78.95 to 85.14), document visual question answering (from 72.59 to 83.94) and
document image classification (from 94.43 to 96.08).
|
[
{
"created": "Mon, 24 May 2021 11:33:20 GMT",
"version": "v1"
}
] |
2021-05-25
|
[
[
"Li",
"Chenliang",
""
],
[
"Bi",
"Bin",
""
],
[
"Yan",
"Ming",
""
],
[
"Wang",
"Wei",
""
],
[
"Huang",
"Songfang",
""
],
[
"Huang",
"Fei",
""
],
[
"Si",
"Luo",
""
]
] |
Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, they almost exclusively focus on text-only representation, while neglecting cell-level layout information that is important for form image understanding. In this paper, we propose a new pre-training approach, StructuralLM, to jointly leverage cell and layout information from scanned documents. Specifically, we pre-train StructuralLM with two new designs to make the most of the interactions of cell and layout information: 1) each cell as a semantic unit; 2) classification of cell positions. The pre-trained StructuralLM achieves new state-of-the-art results in different types of downstream tasks, including form understanding (from 78.95 to 85.14), document visual question answering (from 72.59 to 83.94) and document image classification (from 94.43 to 96.08).
|
1906.02461
|
Yichong Leng
|
Yichong Leng, Xu Tan, Tao Qin, Xiang-Yang Li and Tie-Yan Liu
|
Unsupervised Pivot Translation for Distant Languages
|
Accepted by ACL-2019
| null | null | null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unsupervised neural machine translation (NMT) has attracted a lot of
attention recently. While state-of-the-art methods for unsupervised translation
usually perform well between similar languages (e.g., English-German
translation), they perform poorly between distant languages, because
unsupervised alignment does not work well for distant languages. In this work,
we introduce unsupervised pivot translation for distant languages, which
translates a language to a distant language through multiple hops, and the
unsupervised translation on each hop is relatively easier than the original
direct translation. We propose a learning to route (LTR) method to choose the
translation path between the source and target languages. LTR is trained on
language pairs whose best translation path is available and is applied on the
unseen language pairs for path selection. Experiments on 20 languages and 294
distant language pairs demonstrate the advantages of the unsupervised pivot
translation for distant languages, as well as the effectiveness of the proposed
LTR for path selection. Specifically, in the best case, LTR achieves an
improvement of 5.58 BLEU points over the conventional direct unsupervised
method.
|
[
{
"created": "Thu, 6 Jun 2019 07:48:36 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Jun 2019 05:07:08 GMT",
"version": "v2"
},
{
"created": "Tue, 25 Jun 2019 02:58:06 GMT",
"version": "v3"
}
] |
2019-06-26
|
[
[
"Leng",
"Yichong",
""
],
[
"Tan",
"Xu",
""
],
[
"Qin",
"Tao",
""
],
[
"Li",
"Xiang-Yang",
""
],
[
"Liu",
"Tie-Yan",
""
]
] |
Unsupervised neural machine translation (NMT) has attracted a lot of attention recently. While state-of-the-art methods for unsupervised translation usually perform well between similar languages (e.g., English-German translation), they perform poorly between distant languages, because unsupervised alignment does not work well for distant languages. In this work, we introduce unsupervised pivot translation for distant languages, which translates a language to a distant language through multiple hops, and the unsupervised translation on each hop is relatively easier than the original direct translation. We propose a learning to route (LTR) method to choose the translation path between the source and target languages. LTR is trained on language pairs whose best translation path is available and is applied on the unseen language pairs for path selection. Experiments on 20 languages and 294 distant language pairs demonstrate the advantages of the unsupervised pivot translation for distant languages, as well as the effectiveness of the proposed LTR for path selection. Specifically, in the best case, LTR achieves an improvement of 5.58 BLEU points over the conventional direct unsupervised method.
|
2210.15173
|
Gasper Begus
|
Ga\v{s}per Begu\v{s}, Alan Zhou, Peter Wu, Gopala K Anumanchipalli
|
Articulation GAN: Unsupervised modeling of articulatory learning
|
ICASSP 2023
|
ICASSP 2023 - 2023 IEEE International Conference on Acoustics,
Speech and Signal Processing
|
10.1109/ICASSP49357.2023.10096800
| null |
cs.SD cs.AI cs.CL eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Generative deep neural networks are widely used for speech synthesis, but
most existing models directly generate waveforms or spectral outputs. Humans,
however, produce speech by controlling articulators, which results in the
production of speech sounds through physical properties of sound propagation.
We introduce the Articulatory Generator to the Generative Adversarial Network
paradigm, a new unsupervised generative model of speech production/synthesis.
The Articulatory Generator more closely mimics human speech production by
learning to generate articulatory representations (electromagnetic
articulography or EMA) in a fully unsupervised manner. A separate pre-trained
physical model (ema2wav) then transforms the generated EMA representations to
speech waveforms, which get sent to the Discriminator for evaluation.
Articulatory analysis suggests that the network learns to control articulators
in a similar manner to humans during speech production. Acoustic analysis of
the outputs suggests that the network learns to generate words that are both
present and absent in the training distribution. We additionally discuss
implications of articulatory representations for cognitive models of human
language and speech technology in general.
|
[
{
"created": "Thu, 27 Oct 2022 05:07:04 GMT",
"version": "v1"
},
{
"created": "Sun, 12 Mar 2023 20:28:46 GMT",
"version": "v2"
}
] |
2023-05-10
|
[
[
"Beguš",
"Gašper",
""
],
[
"Zhou",
"Alan",
""
],
[
"Wu",
"Peter",
""
],
[
"Anumanchipalli",
"Gopala K",
""
]
] |
Generative deep neural networks are widely used for speech synthesis, but most existing models directly generate waveforms or spectral outputs. Humans, however, produce speech by controlling articulators, which results in the production of speech sounds through physical properties of sound propagation. We introduce the Articulatory Generator to the Generative Adversarial Network paradigm, a new unsupervised generative model of speech production/synthesis. The Articulatory Generator more closely mimics human speech production by learning to generate articulatory representations (electromagnetic articulography or EMA) in a fully unsupervised manner. A separate pre-trained physical model (ema2wav) then transforms the generated EMA representations to speech waveforms, which get sent to the Discriminator for evaluation. Articulatory analysis suggests that the network learns to control articulators in a similar manner to humans during speech production. Acoustic analysis of the outputs suggests that the network learns to generate words that are both present and absent in the training distribution. We additionally discuss implications of articulatory representations for cognitive models of human language and speech technology in general.
|
1709.07957
|
Zackory Erickson
|
Zackory Erickson, Maggie Collier, Ariel Kapusta, and Charles C. Kemp
|
Tracking Human Pose During Robot-Assisted Dressing using Single-Axis
Capacitive Proximity Sensing
|
8 pages, 13 figures, 2018 IEEE Robotics and Automation Letters (RA-L)
| null |
10.1109/LRA.2018.2812912
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dressing is a fundamental task of everyday living and robots offer an
opportunity to assist people with motor impairments. While several robotic
systems have explored robot-assisted dressing, few have considered how a robot
can manage errors in human pose estimation, or adapt to human motion in real
time during dressing assistance. In addition, estimating pose changes due to
human motion can be challenging with vision-based techniques since dressing is
often intended to visually occlude the body with clothing. We present a method
to track a person's pose in real time using capacitive proximity sensing. This
sensing approach gives direct estimates of distance with low latency, has a
high signal-to-noise ratio, and has low computational requirements. Using our
method, a robot can adjust for errors in the estimated pose of a person and
physically follow the contours and movements of the person while providing
dressing assistance. As part of an evaluation of our method, the robot
successfully pulled the sleeve of a hospital gown and a cardigan onto the right
arms of 10 human participants, despite arm motions and large errors in the
initially estimated pose of the person's arm. We also show that a capacitive
sensor is unaffected by visual occlusion of the body and can sense a person's
body through cotton clothing.
|
[
{
"created": "Fri, 22 Sep 2017 21:26:49 GMT",
"version": "v1"
},
{
"created": "Fri, 24 May 2019 21:41:53 GMT",
"version": "v2"
}
] |
2019-05-28
|
[
[
"Erickson",
"Zackory",
""
],
[
"Collier",
"Maggie",
""
],
[
"Kapusta",
"Ariel",
""
],
[
"Kemp",
"Charles C.",
""
]
] |
Dressing is a fundamental task of everyday living and robots offer an opportunity to assist people with motor impairments. While several robotic systems have explored robot-assisted dressing, few have considered how a robot can manage errors in human pose estimation, or adapt to human motion in real time during dressing assistance. In addition, estimating pose changes due to human motion can be challenging with vision-based techniques since dressing is often intended to visually occlude the body with clothing. We present a method to track a person's pose in real time using capacitive proximity sensing. This sensing approach gives direct estimates of distance with low latency, has a high signal-to-noise ratio, and has low computational requirements. Using our method, a robot can adjust for errors in the estimated pose of a person and physically follow the contours and movements of the person while providing dressing assistance. As part of an evaluation of our method, the robot successfully pulled the sleeve of a hospital gown and a cardigan onto the right arms of 10 human participants, despite arm motions and large errors in the initially estimated pose of the person's arm. We also show that a capacitive sensor is unaffected by visual occlusion of the body and can sense a person's body through cotton clothing.
|
2208.10695
|
Jinpeng Li
|
Lingfeng Li, Huaiwei Cong, Gangming Zhao, Junran Peng, Zheng Zhang,
and Jinpeng Li
|
Structure Regularized Attentive Network for Automatic Femoral Head
Necrosis Diagnosis and Localization
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, several works have adopted the convolutional neural network
(CNN) to diagnose the avascular necrosis of the femoral head (AVNFH) based on
X-ray images or magnetic resonance imaging (MRI). However, due to the tissue
overlap, X-ray images are difficult to provide fine-grained features for early
diagnosis. MRI, on the other hand, has a long imaging time, is more expensive,
making it impractical in mass screening. Computed tomography (CT) shows
layer-wise tissues, is faster to image, and is less costly than MRI. However,
to our knowledge, there is no work on CT-based automated diagnosis of AVNFH. In
this work, we collected and labeled a large-scale dataset for AVNFH ranking. In
addition, existing end-to-end CNNs only yields the classification result and
are difficult to provide more information for doctors in diagnosis. To address
this issue, we propose the structure regularized attentive network (SRANet),
which is able to highlight the necrotic regions during classification based on
patch attention. SRANet extracts features in chunks of images, obtains weight
via the attention mechanism to aggregate the features, and constrains them by a
structural regularizer with prior knowledge to improve the generalization.
SRANet was evaluated on our AVNFH-CT dataset. Experimental results show that
SRANet is superior to CNNs for AVNFH classification, moreover, it can localize
lesions and provide more information to assist doctors in diagnosis. Our codes
are made public at https://github.com/tomas-lilingfeng/SRANet.
|
[
{
"created": "Tue, 23 Aug 2022 02:31:38 GMT",
"version": "v1"
}
] |
2022-08-24
|
[
[
"Li",
"Lingfeng",
""
],
[
"Cong",
"Huaiwei",
""
],
[
"Zhao",
"Gangming",
""
],
[
"Peng",
"Junran",
""
],
[
"Zhang",
"Zheng",
""
],
[
"Li",
"Jinpeng",
""
]
] |
In recent years, several works have adopted the convolutional neural network (CNN) to diagnose the avascular necrosis of the femoral head (AVNFH) based on X-ray images or magnetic resonance imaging (MRI). However, due to the tissue overlap, X-ray images are difficult to provide fine-grained features for early diagnosis. MRI, on the other hand, has a long imaging time, is more expensive, making it impractical in mass screening. Computed tomography (CT) shows layer-wise tissues, is faster to image, and is less costly than MRI. However, to our knowledge, there is no work on CT-based automated diagnosis of AVNFH. In this work, we collected and labeled a large-scale dataset for AVNFH ranking. In addition, existing end-to-end CNNs only yields the classification result and are difficult to provide more information for doctors in diagnosis. To address this issue, we propose the structure regularized attentive network (SRANet), which is able to highlight the necrotic regions during classification based on patch attention. SRANet extracts features in chunks of images, obtains weight via the attention mechanism to aggregate the features, and constrains them by a structural regularizer with prior knowledge to improve the generalization. SRANet was evaluated on our AVNFH-CT dataset. Experimental results show that SRANet is superior to CNNs for AVNFH classification, moreover, it can localize lesions and provide more information to assist doctors in diagnosis. Our codes are made public at https://github.com/tomas-lilingfeng/SRANet.
|
2305.14779
|
Nikita Srivatsan
|
Nikita Srivatsan, Sofia Samaniego, Omar Florez, Taylor
Berg-Kirkpatrick
|
Alt-Text with Context: Improving Accessibility for Images on Twitter
|
ICLR 2024
| null | null | null |
cs.CV cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In this work we present an approach for generating alternative text (or
alt-text) descriptions for images shared on social media, specifically Twitter.
More than just a special case of image captioning, alt-text is both more
literally descriptive and context-specific. Also critically, images posted to
Twitter are often accompanied by user-written text that despite not necessarily
describing the image may provide useful context that if properly leveraged can
be informative. We address this task with a multimodal model that conditions on
both textual information from the associated social media post as well as
visual signal from the image, and demonstrate that the utility of these two
information sources stacks. We put forward a new dataset of 371k images paired
with alt-text and tweets scraped from Twitter and evaluate on it across a
variety of automated metrics as well as human evaluation. We show that our
approach of conditioning on both tweet text and visual information
significantly outperforms prior work, by more than 2x on BLEU@4.
|
[
{
"created": "Wed, 24 May 2023 06:35:26 GMT",
"version": "v1"
},
{
"created": "Tue, 3 Oct 2023 23:01:05 GMT",
"version": "v2"
},
{
"created": "Thu, 29 Feb 2024 22:31:53 GMT",
"version": "v3"
}
] |
2024-03-04
|
[
[
"Srivatsan",
"Nikita",
""
],
[
"Samaniego",
"Sofia",
""
],
[
"Florez",
"Omar",
""
],
[
"Berg-Kirkpatrick",
"Taylor",
""
]
] |
In this work we present an approach for generating alternative text (or alt-text) descriptions for images shared on social media, specifically Twitter. More than just a special case of image captioning, alt-text is both more literally descriptive and context-specific. Also critically, images posted to Twitter are often accompanied by user-written text that despite not necessarily describing the image may provide useful context that if properly leveraged can be informative. We address this task with a multimodal model that conditions on both textual information from the associated social media post as well as visual signal from the image, and demonstrate that the utility of these two information sources stacks. We put forward a new dataset of 371k images paired with alt-text and tweets scraped from Twitter and evaluate on it across a variety of automated metrics as well as human evaluation. We show that our approach of conditioning on both tweet text and visual information significantly outperforms prior work, by more than 2x on BLEU@4.
|
2208.00231
|
Samuel Yang
|
Alexander Liu, Samuel Yang
|
Masked Autoencoders As The Unified Learners For Pre-Trained Sentence
Representation
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Despite the progresses on pre-trained language models, there is a lack of
unified frameworks for pre-trained sentence representation. As such, it calls
for different pre-training methods for specific scenarios, and the pre-trained
models are likely to be limited by their universality and representation
quality. In this work, we extend the recently proposed MAE style pre-training
strategy, RetroMAE, such that it may effectively support a wide variety of
sentence representation tasks. The extended framework consists of two stages,
with RetroMAE conducted throughout the process. The first stage performs
RetroMAE over generic corpora, like Wikipedia, BookCorpus, etc., from which the
base model is learned. The second stage takes place on domain-specific data,
e.g., MS MARCO and NLI, where the base model is continuingly trained based on
RetroMAE and contrastive learning. The pre-training outputs at the two stages
may serve different applications, whose effectiveness are verified with
comprehensive experiments. Concretely, the base model are proved to be
effective for zero-shot retrieval, with remarkable performances achieved on
BEIR benchmark. The continuingly pre-trained models further benefit more
downstream tasks, including the domain-specific dense retrieval on MS MARCO,
Natural Questions, and the sentence embeddings' quality for standard STS and
transfer tasks in SentEval. The empirical insights of this work may inspire the
future design of sentence representation pre-training. Our pre-trained models
and source code will be released to the public communities.
|
[
{
"created": "Sat, 30 Jul 2022 14:34:55 GMT",
"version": "v1"
}
] |
2022-08-02
|
[
[
"Liu",
"Alexander",
""
],
[
"Yang",
"Samuel",
""
]
] |
Despite the progresses on pre-trained language models, there is a lack of unified frameworks for pre-trained sentence representation. As such, it calls for different pre-training methods for specific scenarios, and the pre-trained models are likely to be limited by their universality and representation quality. In this work, we extend the recently proposed MAE style pre-training strategy, RetroMAE, such that it may effectively support a wide variety of sentence representation tasks. The extended framework consists of two stages, with RetroMAE conducted throughout the process. The first stage performs RetroMAE over generic corpora, like Wikipedia, BookCorpus, etc., from which the base model is learned. The second stage takes place on domain-specific data, e.g., MS MARCO and NLI, where the base model is continuingly trained based on RetroMAE and contrastive learning. The pre-training outputs at the two stages may serve different applications, whose effectiveness are verified with comprehensive experiments. Concretely, the base model are proved to be effective for zero-shot retrieval, with remarkable performances achieved on BEIR benchmark. The continuingly pre-trained models further benefit more downstream tasks, including the domain-specific dense retrieval on MS MARCO, Natural Questions, and the sentence embeddings' quality for standard STS and transfer tasks in SentEval. The empirical insights of this work may inspire the future design of sentence representation pre-training. Our pre-trained models and source code will be released to the public communities.
|
2304.07674
|
Nathan Klein
|
Nathan Klein and Neil Olver
|
Thin trees for laminar families
| null | null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the laminar-constrained spanning tree problem, the goal is to find a
minimum-cost spanning tree which respects upper bounds on the number of times
each cut in a given laminar family is crossed. This generalizes the
well-studied degree-bounded spanning tree problem, as well as a previously
studied setting where a chain of cuts is given. We give the first
constant-factor approximation algorithm; in particular we show how to obtain a
multiplicative violation of the crossing bounds of less than 22 while losing
less than a factor of 5 in terms of cost.
Our result compares to the natural LP relaxation. As a consequence, our
results show that given a $k$-edge-connected graph and a laminar family
$\mathcal{L} \subseteq 2^V$ of cuts, there exists a spanning tree which
contains only an $O(1/k)$ fraction of the edges across every cut in
$\mathcal{L}$. This can be viewed as progress towards the Thin Tree Conjecture,
which (in a strong form) states that this guarantee can be obtained for all
cuts simultaneously.
|
[
{
"created": "Sun, 16 Apr 2023 02:33:04 GMT",
"version": "v1"
}
] |
2023-04-18
|
[
[
"Klein",
"Nathan",
""
],
[
"Olver",
"Neil",
""
]
] |
In the laminar-constrained spanning tree problem, the goal is to find a minimum-cost spanning tree which respects upper bounds on the number of times each cut in a given laminar family is crossed. This generalizes the well-studied degree-bounded spanning tree problem, as well as a previously studied setting where a chain of cuts is given. We give the first constant-factor approximation algorithm; in particular we show how to obtain a multiplicative violation of the crossing bounds of less than 22 while losing less than a factor of 5 in terms of cost. Our result compares to the natural LP relaxation. As a consequence, our results show that given a $k$-edge-connected graph and a laminar family $\mathcal{L} \subseteq 2^V$ of cuts, there exists a spanning tree which contains only an $O(1/k)$ fraction of the edges across every cut in $\mathcal{L}$. This can be viewed as progress towards the Thin Tree Conjecture, which (in a strong form) states that this guarantee can be obtained for all cuts simultaneously.
|
2203.04524
|
Arundhati Banerjee
|
Arundhati Banerjee, Ramina Ghods, Jeff Schneider
|
Multi-Agent Active Search using Detection and Location Uncertainty
|
Accepted to ICRA 2023
| null | null | null |
cs.RO cs.LG cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Active search, in applications like environment monitoring or disaster
response missions, involves autonomous agents detecting targets in a search
space using decision making algorithms that adapt to the history of their
observations. Active search algorithms must contend with two types of
uncertainty: detection uncertainty and location uncertainty. The more common
approach in robotics is to focus on location uncertainty and remove detection
uncertainty by thresholding the detection probability to zero or one. In
contrast, it is common in the sparse signal processing literature to assume the
target location is accurate and instead focus on the uncertainty of its
detection. In this work, we first propose an inference method to jointly handle
both target detection and location uncertainty. We then build a decision making
algorithm on this inference method that uses Thompson sampling to enable
decentralized multi-agent active search. We perform simulation experiments to
show that our algorithms outperform competing baselines that only account for
either target detection or location uncertainty. We finally demonstrate the
real world transferability of our algorithms using a realistic simulation
environment we created on the Unreal Engine 4 platform with an AirSim plugin.
|
[
{
"created": "Wed, 9 Mar 2022 04:53:37 GMT",
"version": "v1"
},
{
"created": "Mon, 22 May 2023 06:09:36 GMT",
"version": "v2"
}
] |
2023-05-23
|
[
[
"Banerjee",
"Arundhati",
""
],
[
"Ghods",
"Ramina",
""
],
[
"Schneider",
"Jeff",
""
]
] |
Active search, in applications like environment monitoring or disaster response missions, involves autonomous agents detecting targets in a search space using decision making algorithms that adapt to the history of their observations. Active search algorithms must contend with two types of uncertainty: detection uncertainty and location uncertainty. The more common approach in robotics is to focus on location uncertainty and remove detection uncertainty by thresholding the detection probability to zero or one. In contrast, it is common in the sparse signal processing literature to assume the target location is accurate and instead focus on the uncertainty of its detection. In this work, we first propose an inference method to jointly handle both target detection and location uncertainty. We then build a decision making algorithm on this inference method that uses Thompson sampling to enable decentralized multi-agent active search. We perform simulation experiments to show that our algorithms outperform competing baselines that only account for either target detection or location uncertainty. We finally demonstrate the real world transferability of our algorithms using a realistic simulation environment we created on the Unreal Engine 4 platform with an AirSim plugin.
|
2204.00656
|
Samrudhdhi Bharatkumar Rangrej
|
Samrudhdhi B. Rangrej, Chetan L. Srinidhi, James J. Clark
|
Consistency driven Sequential Transformers Attention Model for Partially
Observable Scenes
|
Accepted to CVPR 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most hard attention models initially observe a complete scene to locate and
sense informative glimpses, and predict class-label of a scene based on
glimpses. However, in many applications (e.g., aerial imaging), observing an
entire scene is not always feasible due to the limited time and resources
available for acquisition. In this paper, we develop a Sequential Transformers
Attention Model (STAM) that only partially observes a complete image and
predicts informative glimpse locations solely based on past glimpses. We design
our agent using DeiT-distilled and train it with a one-step actor-critic
algorithm. Furthermore, to improve classification performance, we introduce a
novel training objective, which enforces consistency between the class
distribution predicted by a teacher model from a complete image and the class
distribution predicted by our agent using glimpses. When the agent senses only
4% of the total image area, the inclusion of the proposed consistency loss in
our training objective yields 3% and 8% higher accuracy on ImageNet and fMoW
datasets, respectively. Moreover, our agent outperforms previous
state-of-the-art by observing nearly 27% and 42% fewer pixels in glimpses on
ImageNet and fMoW.
|
[
{
"created": "Fri, 1 Apr 2022 18:51:55 GMT",
"version": "v1"
}
] |
2022-04-05
|
[
[
"Rangrej",
"Samrudhdhi B.",
""
],
[
"Srinidhi",
"Chetan L.",
""
],
[
"Clark",
"James J.",
""
]
] |
Most hard attention models initially observe a complete scene to locate and sense informative glimpses, and predict class-label of a scene based on glimpses. However, in many applications (e.g., aerial imaging), observing an entire scene is not always feasible due to the limited time and resources available for acquisition. In this paper, we develop a Sequential Transformers Attention Model (STAM) that only partially observes a complete image and predicts informative glimpse locations solely based on past glimpses. We design our agent using DeiT-distilled and train it with a one-step actor-critic algorithm. Furthermore, to improve classification performance, we introduce a novel training objective, which enforces consistency between the class distribution predicted by a teacher model from a complete image and the class distribution predicted by our agent using glimpses. When the agent senses only 4% of the total image area, the inclusion of the proposed consistency loss in our training objective yields 3% and 8% higher accuracy on ImageNet and fMoW datasets, respectively. Moreover, our agent outperforms previous state-of-the-art by observing nearly 27% and 42% fewer pixels in glimpses on ImageNet and fMoW.
|
2004.12615
|
Jingjing Li
|
Li Jingjing, Chen Erpeng, Ding Zhengming, Zhu Lei, Lu Ke, Shen Heng
Tao
|
Maximum Density Divergence for Domain Adaptation
|
Published on IEEE Transactions on Pattern Analysis and Machine
Intelligence (TPAMI)
| null |
10.1109/TPAMI.2020.2991050
| null |
cs.CV cs.LG cs.MM stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unsupervised domain adaptation addresses the problem of transferring
knowledge from a well-labeled source domain to an unlabeled target domain where
the two domains have distinctive data distributions. Thus, the essence of
domain adaptation is to mitigate the distribution divergence between the two
domains. The state-of-the-art methods practice this very idea by either
conducting adversarial training or minimizing a metric which defines the
distribution gaps. In this paper, we propose a new domain adaptation method
named Adversarial Tight Match (ATM) which enjoys the benefits of both
adversarial training and metric learning. Specifically, at first, we propose a
novel distance loss, named Maximum Density Divergence (MDD), to quantify the
distribution divergence. MDD minimizes the inter-domain divergence ("match" in
ATM) and maximizes the intra-class density ("tight" in ATM). Then, to address
the equilibrium challenge issue in adversarial domain adaptation, we consider
leveraging the proposed MDD into adversarial domain adaptation framework. At
last, we tailor the proposed MDD as a practical learning loss and report our
ATM. Both empirical evaluation and theoretical analysis are reported to verify
the effectiveness of the proposed method. The experimental results on four
benchmarks, both classical and large-scale, show that our method is able to
achieve new state-of-the-art performance on most evaluations. Codes and
datasets used in this paper are available at {\it github.com/lijin118/ATM}.
|
[
{
"created": "Mon, 27 Apr 2020 07:35:06 GMT",
"version": "v1"
}
] |
2020-04-28
|
[
[
"Jingjing",
"Li",
""
],
[
"Erpeng",
"Chen",
""
],
[
"Zhengming",
"Ding",
""
],
[
"Lei",
"Zhu",
""
],
[
"Ke",
"Lu",
""
],
[
"Tao",
"Shen Heng",
""
]
] |
Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing a metric which defines the distribution gaps. In this paper, we propose a new domain adaptation method named Adversarial Tight Match (ATM) which enjoys the benefits of both adversarial training and metric learning. Specifically, at first, we propose a novel distance loss, named Maximum Density Divergence (MDD), to quantify the distribution divergence. MDD minimizes the inter-domain divergence ("match" in ATM) and maximizes the intra-class density ("tight" in ATM). Then, to address the equilibrium challenge issue in adversarial domain adaptation, we consider leveraging the proposed MDD into adversarial domain adaptation framework. At last, we tailor the proposed MDD as a practical learning loss and report our ATM. Both empirical evaluation and theoretical analysis are reported to verify the effectiveness of the proposed method. The experimental results on four benchmarks, both classical and large-scale, show that our method is able to achieve new state-of-the-art performance on most evaluations. Codes and datasets used in this paper are available at {\it github.com/lijin118/ATM}.
|
1905.13352
|
Hafiz Muhammad Mohsin Bashir
|
Hafiz Mohsin Bashir, Abdullah Bin Faisal, Muhammad Asim Jamshed, Peter
Vondras, Ali Musa Iftikhar, Ihsan Ayyub Qazi, Fahad R. Dogar
|
Reducing Tail Latency via Safe and Simple Duplication
| null | null | null | null |
cs.NI cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Duplication can be a powerful strategy for overcoming stragglers in cloud
services, but is often used conservatively because of the risk of overloading
the system. We present duplicate-aware scheduling or DAS, which makes
duplication safe and easy to use, by leveraging the two well-known primitives
of prioritization and purging. To support DAS across diverse layers of a cloud
system (e.g., network, storage, etc), we propose the D-Stage abstraction, which
decouples the duplication policy from the mechanism, and facilitates working
with legacy layers of a system. Using this abstraction, we evaluate the
benefits of DAS for two data parallel applications (HDFS, an in-memory workload
generator) and a network function (snort-based IDS cluster). Our experiments on
the public cloud and Emulab show that DAS is safe to use, and the tail latency
improvement holds across a wide range of workloads
|
[
{
"created": "Thu, 30 May 2019 23:30:56 GMT",
"version": "v1"
}
] |
2019-06-03
|
[
[
"Bashir",
"Hafiz Mohsin",
""
],
[
"Faisal",
"Abdullah Bin",
""
],
[
"Jamshed",
"Muhammad Asim",
""
],
[
"Vondras",
"Peter",
""
],
[
"Iftikhar",
"Ali Musa",
""
],
[
"Qazi",
"Ihsan Ayyub",
""
],
[
"Dogar",
"Fahad R.",
""
]
] |
Duplication can be a powerful strategy for overcoming stragglers in cloud services, but is often used conservatively because of the risk of overloading the system. We present duplicate-aware scheduling or DAS, which makes duplication safe and easy to use, by leveraging the two well-known primitives of prioritization and purging. To support DAS across diverse layers of a cloud system (e.g., network, storage, etc), we propose the D-Stage abstraction, which decouples the duplication policy from the mechanism, and facilitates working with legacy layers of a system. Using this abstraction, we evaluate the benefits of DAS for two data parallel applications (HDFS, an in-memory workload generator) and a network function (snort-based IDS cluster). Our experiments on the public cloud and Emulab show that DAS is safe to use, and the tail latency improvement holds across a wide range of workloads
|
2111.13091
|
Bas Van Den Heuvel
|
Bas van den Heuvel and Jorge A. P\'erez
|
Asynchronous Session-Based Concurrency: Deadlock-freedom in Cyclic
Process Networks
|
Extended version of arXiv:2110.00146, doi:10.4204/EPTCS.347.3 and
arXiv:2209.06820, doi:10.4204/EPTCS.368.5
| null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
We tackle the challenge of ensuring the deadlock-freedom property for
message-passing processes that communicate asynchronously in cyclic process
networks. Our contributions are twofold. First, we present Asynchronous
Priority-based Classical Processes (APCP), a session-typed process framework
that supports asynchronous communication, delegation, and recursion in cyclic
process networks. Building upon the Curry-Howard correspondences between linear
logic and session types, we establish essential meta-theoretical results for
APCP, most notably deadlock freedom. Second, we present a new concurrent
$\lambda$-calculus with asynchronous session types, dubbed LASTn. We illustrate
LASTn by example and establish its meta-theoretical results; in particular, we
show how to soundly transfer the deadlock-freedom guarantee from APCP. To this
end, we develop a translation of terms in LASTn into processes in APCP that
satisfies a strong formulation of operational correspondence.
|
[
{
"created": "Thu, 25 Nov 2021 14:00:40 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Jul 2022 14:46:22 GMT",
"version": "v2"
},
{
"created": "Thu, 6 Oct 2022 14:20:44 GMT",
"version": "v3"
},
{
"created": "Mon, 22 Jan 2024 14:46:38 GMT",
"version": "v4"
},
{
"created": "Thu, 4 Jul 2024 13:52:30 GMT",
"version": "v5"
}
] |
2024-07-08
|
[
[
"Heuvel",
"Bas van den",
""
],
[
"Pérez",
"Jorge A.",
""
]
] |
We tackle the challenge of ensuring the deadlock-freedom property for message-passing processes that communicate asynchronously in cyclic process networks. Our contributions are twofold. First, we present Asynchronous Priority-based Classical Processes (APCP), a session-typed process framework that supports asynchronous communication, delegation, and recursion in cyclic process networks. Building upon the Curry-Howard correspondences between linear logic and session types, we establish essential meta-theoretical results for APCP, most notably deadlock freedom. Second, we present a new concurrent $\lambda$-calculus with asynchronous session types, dubbed LASTn. We illustrate LASTn by example and establish its meta-theoretical results; in particular, we show how to soundly transfer the deadlock-freedom guarantee from APCP. To this end, we develop a translation of terms in LASTn into processes in APCP that satisfies a strong formulation of operational correspondence.
|
2112.12589
|
Fateme Golivand Darvishvand
|
Moein Latifi, Fateme Golivand Darvishvand, Omid Khandel, Mobin Latifi
Nowsoud
|
A deep reinforcement learning model for predictive maintenance planning
of road assets: Integrating LCA and LCCA
| null | null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Road maintenance planning is an integral part of road asset management. One
of the main challenges in Maintenance and Rehabilitation (M&R) practices is to
determine maintenance type and timing. This research proposes a framework using
Reinforcement Learning (RL) based on the Long Term Pavement Performance (LTPP)
database to determine the type and timing of M&R practices. A predictive DNN
model is first developed in the proposed algorithm, which serves as the
Environment for the RL algorithm. For the Policy estimation of the RL model,
both DQN and PPO models are developed. However, PPO has been selected in the
end due to better convergence and higher sample efficiency. Indicators used in
this study are International Roughness Index (IRI) and Rutting Depth (RD).
Initially, we considered Cracking Metric (CM) as the third indicator, but it
was then excluded due to the much fewer data compared to other indicators,
which resulted in lower accuracy of the results. Furthermore, in
cost-effectiveness calculation (reward), we considered both the economic and
environmental impacts of M&R treatments. Costs and environmental impacts have
been evaluated with paLATE 2.0 software. Our method is tested on a hypothetical
case study of a six-lane highway with 23 kilometers length located in Texas,
which has a warm and wet climate. The results propose a 20-year M&R plan in
which road condition remains in an excellent condition range. Because the early
state of the road is at a good level of service, there is no need for heavy
maintenance practices in the first years. Later, after heavy M&R actions, there
are several 1-2 years of no need for treatments. All of these show that the
proposed plan has a logical result. Decision-makers and transportation agencies
can use this scheme to conduct better maintenance practices that can prevent
budget waste and, at the same time, minimize the environmental impacts.
|
[
{
"created": "Mon, 20 Dec 2021 13:46:39 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Dec 2021 18:17:08 GMT",
"version": "v2"
},
{
"created": "Mon, 27 Nov 2023 18:29:31 GMT",
"version": "v3"
}
] |
2023-11-28
|
[
[
"Latifi",
"Moein",
""
],
[
"Darvishvand",
"Fateme Golivand",
""
],
[
"Khandel",
"Omid",
""
],
[
"Nowsoud",
"Mobin Latifi",
""
]
] |
Road maintenance planning is an integral part of road asset management. One of the main challenges in Maintenance and Rehabilitation (M&R) practices is to determine maintenance type and timing. This research proposes a framework using Reinforcement Learning (RL) based on the Long Term Pavement Performance (LTPP) database to determine the type and timing of M&R practices. A predictive DNN model is first developed in the proposed algorithm, which serves as the Environment for the RL algorithm. For the Policy estimation of the RL model, both DQN and PPO models are developed. However, PPO has been selected in the end due to better convergence and higher sample efficiency. Indicators used in this study are International Roughness Index (IRI) and Rutting Depth (RD). Initially, we considered Cracking Metric (CM) as the third indicator, but it was then excluded due to the much fewer data compared to other indicators, which resulted in lower accuracy of the results. Furthermore, in cost-effectiveness calculation (reward), we considered both the economic and environmental impacts of M&R treatments. Costs and environmental impacts have been evaluated with paLATE 2.0 software. Our method is tested on a hypothetical case study of a six-lane highway with 23 kilometers length located in Texas, which has a warm and wet climate. The results propose a 20-year M&R plan in which road condition remains in an excellent condition range. Because the early state of the road is at a good level of service, there is no need for heavy maintenance practices in the first years. Later, after heavy M&R actions, there are several 1-2 years of no need for treatments. All of these show that the proposed plan has a logical result. Decision-makers and transportation agencies can use this scheme to conduct better maintenance practices that can prevent budget waste and, at the same time, minimize the environmental impacts.
|
1708.02497
|
Janne Lepp\"a-aho
|
Janne Lepp\"a-aho, Santeri R\"ais\"anen, Xiao Yang, Teemu Roos
|
Learning non-parametric Markov networks with mutual information
| null | null | null | null |
cs.LG cs.IT math.IT stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a method for learning Markov network structures for continuous
data without invoking any assumptions about the distribution of the variables.
The method makes use of previous work on a non-parametric estimator for mutual
information which is used to create a non-parametric test for multivariate
conditional independence. This independence test is then combined with an
efficient constraint-based algorithm for learning the graph structure. The
performance of the method is evaluated on several synthetic data sets and it is
shown to learn considerably more accurate structures than competing methods
when the dependencies between the variables involve non-linearities.
|
[
{
"created": "Tue, 8 Aug 2017 14:10:55 GMT",
"version": "v1"
}
] |
2017-08-09
|
[
[
"Leppä-aho",
"Janne",
""
],
[
"Räisänen",
"Santeri",
""
],
[
"Yang",
"Xiao",
""
],
[
"Roos",
"Teemu",
""
]
] |
We propose a method for learning Markov network structures for continuous data without invoking any assumptions about the distribution of the variables. The method makes use of previous work on a non-parametric estimator for mutual information which is used to create a non-parametric test for multivariate conditional independence. This independence test is then combined with an efficient constraint-based algorithm for learning the graph structure. The performance of the method is evaluated on several synthetic data sets and it is shown to learn considerably more accurate structures than competing methods when the dependencies between the variables involve non-linearities.
|
1402.6556
|
Csaba P\u{a}tca\c{s}
|
Csaba Patcas and Attila Bartha
|
Evolutionary solving of the debts' clearing problem
|
13 pages, 5 figures
| null | null | null |
cs.NE cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The debts' clearing problem is about clearing all the debts in a group of n
entities (persons, companies etc.) using a minimal number of money transaction
operations. The problem is known to be NP-hard in the strong sense. As for many
intractable problems, techniques from the field of artificial intelligence are
useful in finding solutions close to optimum for large inputs. An evolutionary
algorithm for solving the debts' clearing problem is proposed.
|
[
{
"created": "Wed, 26 Feb 2014 14:39:57 GMT",
"version": "v1"
}
] |
2014-02-27
|
[
[
"Patcas",
"Csaba",
""
],
[
"Bartha",
"Attila",
""
]
] |
The debts' clearing problem is about clearing all the debts in a group of n entities (persons, companies etc.) using a minimal number of money transaction operations. The problem is known to be NP-hard in the strong sense. As for many intractable problems, techniques from the field of artificial intelligence are useful in finding solutions close to optimum for large inputs. An evolutionary algorithm for solving the debts' clearing problem is proposed.
|
1103.2501
|
Anas Chaaban
|
Anas Chaaban, Aydin Sezgin, Bernd Bandemer, Arogyaswami Paulraj
|
On Gaussian Multiple Access Channels with Interference: Achievable Rates
and Upper Bounds
|
submitted to MACOM 2011
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the interaction between two interfering Gaussian 2-user multiple
access channels. The capacity region is characterized under mixed
strong--extremely strong interference and individually very strong
interference. Furthermore, the sum capacity is derived under a less restricting
definition of very strong interference. Finally, a general upper bound on the
sum capacity is provided, which is nearly tight for weak cross links.
|
[
{
"created": "Sun, 13 Mar 2011 06:57:41 GMT",
"version": "v1"
}
] |
2011-03-15
|
[
[
"Chaaban",
"Anas",
""
],
[
"Sezgin",
"Aydin",
""
],
[
"Bandemer",
"Bernd",
""
],
[
"Paulraj",
"Arogyaswami",
""
]
] |
We study the interaction between two interfering Gaussian 2-user multiple access channels. The capacity region is characterized under mixed strong--extremely strong interference and individually very strong interference. Furthermore, the sum capacity is derived under a less restricting definition of very strong interference. Finally, a general upper bound on the sum capacity is provided, which is nearly tight for weak cross links.
|
2309.05317
|
Anthony Frion
|
Anthony Frion, Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon,
Abdeldjalil A\"issa El Bey
|
Neural Koopman prior for data assimilation
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
With the increasing availability of large scale datasets, computational power
and tools like automatic differentiation and expressive neural network
architectures, sequential data are now often treated in a data-driven way, with
a dynamical model trained from the observation data. While neural networks are
often seen as uninterpretable black-box architectures, they can still benefit
from physical priors on the data and from mathematical knowledge. In this
paper, we use a neural network architecture which leverages the long-known
Koopman operator theory to embed dynamical systems in latent spaces where their
dynamics can be described linearly, enabling a number of appealing features. We
introduce methods that enable to train such a model for long-term continuous
reconstruction, even in difficult contexts where the data comes in
irregularly-sampled time series. The potential for self-supervised learning is
also demonstrated, as we show the promising use of trained dynamical models as
priors for variational data assimilation techniques, with applications to e.g.
time series interpolation and forecasting.
|
[
{
"created": "Mon, 11 Sep 2023 09:04:36 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Mar 2024 14:57:23 GMT",
"version": "v2"
},
{
"created": "Fri, 21 Jun 2024 20:14:59 GMT",
"version": "v3"
}
] |
2024-06-25
|
[
[
"Frion",
"Anthony",
""
],
[
"Drumetz",
"Lucas",
""
],
[
"Mura",
"Mauro Dalla",
""
],
[
"Tochon",
"Guillaume",
""
],
[
"Bey",
"Abdeldjalil Aïssa El",
""
]
] |
With the increasing availability of large scale datasets, computational power and tools like automatic differentiation and expressive neural network architectures, sequential data are now often treated in a data-driven way, with a dynamical model trained from the observation data. While neural networks are often seen as uninterpretable black-box architectures, they can still benefit from physical priors on the data and from mathematical knowledge. In this paper, we use a neural network architecture which leverages the long-known Koopman operator theory to embed dynamical systems in latent spaces where their dynamics can be described linearly, enabling a number of appealing features. We introduce methods that enable to train such a model for long-term continuous reconstruction, even in difficult contexts where the data comes in irregularly-sampled time series. The potential for self-supervised learning is also demonstrated, as we show the promising use of trained dynamical models as priors for variational data assimilation techniques, with applications to e.g. time series interpolation and forecasting.
|
2401.06161
|
Pavel Novoa-Hern\'andez Dr.
|
Marcelino Cabrera and Carlos Cruz and Pavel Novoa-Hern\'andez and
David A. Pelta and Jos\'e Luis Verdegay
|
Trustworthy human-centric based Automated Decision-Making Systems
|
16 pages, 1 Table
| null | null | null |
cs.CY cs.AI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Automated Decision-Making Systems (ADS) have become pervasive across various
fields, activities, and occupations, to enhance performance. However, this
widespread adoption introduces potential risks, including the misuse of ADS.
Such misuse may manifest when ADS is employed in situations where it is
unnecessary or when essential requirements, conditions, and terms are
overlooked, leading to unintended consequences. This research paper presents a
thorough examination of the implications, distinctions, and ethical
considerations associated with digitalization, digital transformation, and the
utilization of ADS in contemporary society and future contexts. Emphasis is
placed on the imperative need for regulation, transparency, and ethical conduct
in the deployment of ADS.
|
[
{
"created": "Fri, 22 Dec 2023 11:02:57 GMT",
"version": "v1"
}
] |
2024-01-15
|
[
[
"Cabrera",
"Marcelino",
""
],
[
"Cruz",
"Carlos",
""
],
[
"Novoa-Hernández",
"Pavel",
""
],
[
"Pelta",
"David A.",
""
],
[
"Verdegay",
"José Luis",
""
]
] |
Automated Decision-Making Systems (ADS) have become pervasive across various fields, activities, and occupations, to enhance performance. However, this widespread adoption introduces potential risks, including the misuse of ADS. Such misuse may manifest when ADS is employed in situations where it is unnecessary or when essential requirements, conditions, and terms are overlooked, leading to unintended consequences. This research paper presents a thorough examination of the implications, distinctions, and ethical considerations associated with digitalization, digital transformation, and the utilization of ADS in contemporary society and future contexts. Emphasis is placed on the imperative need for regulation, transparency, and ethical conduct in the deployment of ADS.
|
2212.03519
|
Bowen Xie
|
Bowen Xie, Yuxuan Sun, Sheng Zhou, Zhisheng Niu, Yang Xu, Jingran
Chen, Deniz G\"und\"uz
|
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected
Vehicles
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Federated learning (FL) is a promising approach to enable the future Internet
of vehicles consisting of intelligent connected vehicles (ICVs) with powerful
sensing, computing and communication capabilities. We consider a base station
(BS) coordinating nearby ICVs to train a neural network in a collaborative yet
distributed manner, in order to limit data traffic and privacy leakage.
However, due to the mobility of vehicles, the connections between the BS and
ICVs are short-lived, which affects the resource utilization of ICVs, and thus,
the convergence speed of the training process. In this paper, we propose an
accelerated FL-ICV framework, by optimizing the duration of each training round
and the number of local iterations, for better convergence performance of FL.
We propose a mobility-aware optimization algorithm called MOB-FL, which aims at
maximizing the resource utilization of ICVs under short-lived wireless
connections, so as to increase the convergence speed. Simulation results based
on the beam selection and the trajectory prediction tasks verify the
effectiveness of the proposed solution.
|
[
{
"created": "Wed, 7 Dec 2022 08:53:53 GMT",
"version": "v1"
}
] |
2022-12-08
|
[
[
"Xie",
"Bowen",
""
],
[
"Sun",
"Yuxuan",
""
],
[
"Zhou",
"Sheng",
""
],
[
"Niu",
"Zhisheng",
""
],
[
"Xu",
"Yang",
""
],
[
"Chen",
"Jingran",
""
],
[
"Gündüz",
"Deniz",
""
]
] |
Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities. We consider a base station (BS) coordinating nearby ICVs to train a neural network in a collaborative yet distributed manner, in order to limit data traffic and privacy leakage. However, due to the mobility of vehicles, the connections between the BS and ICVs are short-lived, which affects the resource utilization of ICVs, and thus, the convergence speed of the training process. In this paper, we propose an accelerated FL-ICV framework, by optimizing the duration of each training round and the number of local iterations, for better convergence performance of FL. We propose a mobility-aware optimization algorithm called MOB-FL, which aims at maximizing the resource utilization of ICVs under short-lived wireless connections, so as to increase the convergence speed. Simulation results based on the beam selection and the trajectory prediction tasks verify the effectiveness of the proposed solution.
|
2109.13860
|
Chengcheng Ye
|
Chengcheng Ye
|
Introduce the Result Into Self-Attention
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Traditional self-attention mechanisms in convolutional networks tend to use
only the output of the previous layer as input to the attention network, such
as SENet, CBAM, etc. In this paper, we propose a new attention modification
method that tries to get the output of the classification network in advance
and use it as a part of the input of the attention network. We used the
auxiliary classifier proposed in GoogLeNet to obtain the results in advance and
pass them into attention networks. we added this mechanism to SE-ResNet for our
experiments and achieved a classification accuracy improvement of at most 1.94%
on cifar100.
|
[
{
"created": "Tue, 21 Sep 2021 02:16:00 GMT",
"version": "v1"
}
] |
2021-09-29
|
[
[
"Ye",
"Chengcheng",
""
]
] |
Traditional self-attention mechanisms in convolutional networks tend to use only the output of the previous layer as input to the attention network, such as SENet, CBAM, etc. In this paper, we propose a new attention modification method that tries to get the output of the classification network in advance and use it as a part of the input of the attention network. We used the auxiliary classifier proposed in GoogLeNet to obtain the results in advance and pass them into attention networks. we added this mechanism to SE-ResNet for our experiments and achieved a classification accuracy improvement of at most 1.94% on cifar100.
|
1812.11859
|
Eric Benhamou
|
Eric Benhamou, Jamal Atif, Rida Laraki
|
A discrete version of CMA-ES
|
11 pages
| null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modern machine learning uses more and more advanced optimization techniques
to find optimal hyper parameters. Whenever the objective function is
non-convex, non continuous and with potentially multiple local minima, standard
gradient descent optimization methods fail. A last resource and very different
method is to assume that the optimum(s), not necessarily unique, is/are
distributed according to a distribution and iteratively to adapt the
distribution according to tested points. These strategies originated in the
early 1960s, named Evolution Strategy (ES) have culminated with the CMA-ES
(Covariance Matrix Adaptation) ES. It relies on a multi variate normal
distribution and is supposed to be state of the art for general optimization
program. However, it is far from being optimal for discrete variables. In this
paper, we extend the method to multivariate binomial correlated distributions.
For such a distribution, we show that it shares similar features to the multi
variate normal: independence and correlation is equivalent and correlation is
efficiently modeled by interaction between different variables. We discuss this
distribution in the framework of the exponential family. We prove that the
model can estimate not only pairwise interactions among the two variables but
also is capable of modeling higher order interactions. This allows creating a
version of CMA ES that can accommodate efficiently discrete variables. We
provide the corresponding algorithm and conclude.
|
[
{
"created": "Thu, 27 Dec 2018 23:21:47 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Feb 2019 19:59:07 GMT",
"version": "v2"
}
] |
2019-02-13
|
[
[
"Benhamou",
"Eric",
""
],
[
"Atif",
"Jamal",
""
],
[
"Laraki",
"Rida",
""
]
] |
Modern machine learning uses more and more advanced optimization techniques to find optimal hyper parameters. Whenever the objective function is non-convex, non continuous and with potentially multiple local minima, standard gradient descent optimization methods fail. A last resource and very different method is to assume that the optimum(s), not necessarily unique, is/are distributed according to a distribution and iteratively to adapt the distribution according to tested points. These strategies originated in the early 1960s, named Evolution Strategy (ES) have culminated with the CMA-ES (Covariance Matrix Adaptation) ES. It relies on a multi variate normal distribution and is supposed to be state of the art for general optimization program. However, it is far from being optimal for discrete variables. In this paper, we extend the method to multivariate binomial correlated distributions. For such a distribution, we show that it shares similar features to the multi variate normal: independence and correlation is equivalent and correlation is efficiently modeled by interaction between different variables. We discuss this distribution in the framework of the exponential family. We prove that the model can estimate not only pairwise interactions among the two variables but also is capable of modeling higher order interactions. This allows creating a version of CMA ES that can accommodate efficiently discrete variables. We provide the corresponding algorithm and conclude.
|
1510.03560
|
Julien Duchateau
|
Julien Duchateau, Fran\c{c}ois Rousselle, Nicolas Maquignon, Gilles
Roussel, Christophe Renaud
|
A progressive mesh method for physical simulations using lattice
Boltzmann method on single-node multi-gpu architectures
|
15 pages, International Journal of Distributed and Parallel Systems
(IJDPS) Vol.6, No.5, September 2015
| null |
10.5121/ijdps.2015.6501
| null |
cs.DC physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, a new progressive mesh algorithm is introduced in order to
perform fast physical simulations by the use of a lattice Boltzmann method
(LBM) on a single-node multi-GPU architecture. This algorithm is able to mesh
automatically the simulation domain according to the propagation of fluids.
This method can also be useful in order to perform various types of simulations
on complex geometries. The use of this algorithm combined with the massive
parallelism of GPUs allows to obtain very good performance in comparison with
the static mesh method used in literature. Several simulations are shown in
order to evaluate the algorithm.
|
[
{
"created": "Tue, 13 Oct 2015 07:32:24 GMT",
"version": "v1"
}
] |
2015-10-14
|
[
[
"Duchateau",
"Julien",
""
],
[
"Rousselle",
"François",
""
],
[
"Maquignon",
"Nicolas",
""
],
[
"Roussel",
"Gilles",
""
],
[
"Renaud",
"Christophe",
""
]
] |
In this paper, a new progressive mesh algorithm is introduced in order to perform fast physical simulations by the use of a lattice Boltzmann method (LBM) on a single-node multi-GPU architecture. This algorithm is able to mesh automatically the simulation domain according to the propagation of fluids. This method can also be useful in order to perform various types of simulations on complex geometries. The use of this algorithm combined with the massive parallelism of GPUs allows to obtain very good performance in comparison with the static mesh method used in literature. Several simulations are shown in order to evaluate the algorithm.
|
2305.12559
|
Zsolt Pocze
|
Zsolt Pocze
|
Multi-scale information content measurement method based on Shannon
information
|
12 pages, 5 figures, 4 tables
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we present a new multi-scale information content calculation
method based on Shannon information (and Shannon entropy). The original method
described by Claude E. Shannon and based on the logarithm of the probability of
elements gives an upper limit to the information content of discrete patterns,
but in many cases (for example, in the case of repeating patterns) it is
inaccurate and does not approximate the true information content of the pattern
well enough. The new mathematical method presented here provides a more
accurate estimate of the (internal) information content of any discrete pattern
based on Shannon's original function. The method is tested on different data
sets and the results are compared with the results of other methods like
compression algorithms.
|
[
{
"created": "Sun, 21 May 2023 20:14:03 GMT",
"version": "v1"
}
] |
2023-05-23
|
[
[
"Pocze",
"Zsolt",
""
]
] |
In this paper, we present a new multi-scale information content calculation method based on Shannon information (and Shannon entropy). The original method described by Claude E. Shannon and based on the logarithm of the probability of elements gives an upper limit to the information content of discrete patterns, but in many cases (for example, in the case of repeating patterns) it is inaccurate and does not approximate the true information content of the pattern well enough. The new mathematical method presented here provides a more accurate estimate of the (internal) information content of any discrete pattern based on Shannon's original function. The method is tested on different data sets and the results are compared with the results of other methods like compression algorithms.
|
2103.07054
|
Wei Chen
|
Wei Chen, Xi Jia, Hyung Jin Chang, Jinming Duan, Linlin Shen, Ales
Leonardis
|
FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose
Estimation with Decoupled Rotation Mechanism
|
accepted by CVPR2021, oral
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we focus on category-level 6D pose and size estimation from
monocular RGB-D image. Previous methods suffer from inefficient category-level
pose feature extraction which leads to low accuracy and inference speed. To
tackle this problem, we propose a fast shape-based network (FS-Net) with
efficient category-level feature extraction for 6D pose estimation. First, we
design an orientation aware autoencoder with 3D graph convolution for latent
feature extraction. The learned latent feature is insensitive to point shift
and object size thanks to the shift and scale-invariance properties of the 3D
graph convolution. Then, to efficiently decode category-level rotation
information from the latent feature, we propose a novel decoupled rotation
mechanism that employs two decoders to complementarily access the rotation
information. Meanwhile, we estimate translation and size by two residuals,
which are the difference between the mean of object points and ground truth
translation, and the difference between the mean size of the category and
ground truth size, respectively. Finally, to increase the generalization
ability of FS-Net, we propose an online box-cage based 3D deformation mechanism
to augment the training data. Extensive experiments on two benchmark datasets
show that the proposed method achieves state-of-the-art performance in both
category- and instance-level 6D object pose estimation. Especially in
category-level pose estimation, without extra synthetic data, our method
outperforms existing methods by 6.3% on the NOCS-REAL dataset.
|
[
{
"created": "Fri, 12 Mar 2021 03:07:24 GMT",
"version": "v1"
},
{
"created": "Sun, 6 Jun 2021 09:50:51 GMT",
"version": "v2"
}
] |
2021-06-08
|
[
[
"Chen",
"Wei",
""
],
[
"Jia",
"Xi",
""
],
[
"Chang",
"Hyung Jin",
""
],
[
"Duan",
"Jinming",
""
],
[
"Shen",
"Linlin",
""
],
[
"Leonardis",
"Ales",
""
]
] |
In this paper, we focus on category-level 6D pose and size estimation from monocular RGB-D image. Previous methods suffer from inefficient category-level pose feature extraction which leads to low accuracy and inference speed. To tackle this problem, we propose a fast shape-based network (FS-Net) with efficient category-level feature extraction for 6D pose estimation. First, we design an orientation aware autoencoder with 3D graph convolution for latent feature extraction. The learned latent feature is insensitive to point shift and object size thanks to the shift and scale-invariance properties of the 3D graph convolution. Then, to efficiently decode category-level rotation information from the latent feature, we propose a novel decoupled rotation mechanism that employs two decoders to complementarily access the rotation information. Meanwhile, we estimate translation and size by two residuals, which are the difference between the mean of object points and ground truth translation, and the difference between the mean size of the category and ground truth size, respectively. Finally, to increase the generalization ability of FS-Net, we propose an online box-cage based 3D deformation mechanism to augment the training data. Extensive experiments on two benchmark datasets show that the proposed method achieves state-of-the-art performance in both category- and instance-level 6D object pose estimation. Especially in category-level pose estimation, without extra synthetic data, our method outperforms existing methods by 6.3% on the NOCS-REAL dataset.
|
2403.13658
|
Moahmmod Suvon
|
Mohammod N. I. Suvon, Prasun C. Tripathi, Wenrui Fan, Shuo Zhou,
Xianyuan Liu, Samer Alabed, Venet Osmani, Andrew J. Swift, Chen Chen, Haiping
Lu
|
Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics
Instability Detection
| null | null | null | null |
cs.LG cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent advancements in non-invasive detection of cardiac hemodynamic
instability (CHDI) primarily focus on applying machine learning techniques to a
single data modality, e.g. cardiac magnetic resonance imaging (MRI). Despite
their potential, these approaches often fall short especially when the size of
labeled patient data is limited, a common challenge in the medical domain.
Furthermore, only a few studies have explored multimodal methods to study CHDI,
which mostly rely on costly modalities such as cardiac MRI and echocardiogram.
In response to these limitations, we propose a novel multimodal variational
autoencoder ($\text{CardioVAE}_\text{X,G}$) to integrate low-cost chest X-ray
(CXR) and electrocardiogram (ECG) modalities with pre-training on a large
unlabeled dataset. Specifically, $\text{CardioVAE}_\text{X,G}$ introduces a
novel tri-stream pre-training strategy to learn both shared and
modality-specific features, thus enabling fine-tuning with both unimodal and
multimodal datasets. We pre-train $\text{CardioVAE}_\text{X,G}$ on a large,
unlabeled dataset of $50,982$ subjects from a subset of MIMIC database and then
fine-tune the pre-trained model on a labeled dataset of $795$ subjects from the
ASPIRE registry. Comprehensive evaluations against existing methods show that
$\text{CardioVAE}_\text{X,G}$ offers promising performance (AUROC $=0.79$ and
Accuracy $=0.77$), representing a significant step forward in non-invasive
prediction of CHDI. Our model also excels in producing fine interpretations of
predictions directly associated with clinical features, thereby supporting
clinical decision-making.
|
[
{
"created": "Wed, 20 Mar 2024 15:06:49 GMT",
"version": "v1"
},
{
"created": "Thu, 20 Jun 2024 15:07:51 GMT",
"version": "v2"
},
{
"created": "Fri, 5 Jul 2024 15:42:25 GMT",
"version": "v3"
}
] |
2024-07-08
|
[
[
"Suvon",
"Mohammod N. I.",
""
],
[
"Tripathi",
"Prasun C.",
""
],
[
"Fan",
"Wenrui",
""
],
[
"Zhou",
"Shuo",
""
],
[
"Liu",
"Xianyuan",
""
],
[
"Alabed",
"Samer",
""
],
[
"Osmani",
"Venet",
""
],
[
"Swift",
"Andrew J.",
""
],
[
"Chen",
"Chen",
""
],
[
"Lu",
"Haiping",
""
]
] |
Recent advancements in non-invasive detection of cardiac hemodynamic instability (CHDI) primarily focus on applying machine learning techniques to a single data modality, e.g. cardiac magnetic resonance imaging (MRI). Despite their potential, these approaches often fall short especially when the size of labeled patient data is limited, a common challenge in the medical domain. Furthermore, only a few studies have explored multimodal methods to study CHDI, which mostly rely on costly modalities such as cardiac MRI and echocardiogram. In response to these limitations, we propose a novel multimodal variational autoencoder ($\text{CardioVAE}_\text{X,G}$) to integrate low-cost chest X-ray (CXR) and electrocardiogram (ECG) modalities with pre-training on a large unlabeled dataset. Specifically, $\text{CardioVAE}_\text{X,G}$ introduces a novel tri-stream pre-training strategy to learn both shared and modality-specific features, thus enabling fine-tuning with both unimodal and multimodal datasets. We pre-train $\text{CardioVAE}_\text{X,G}$ on a large, unlabeled dataset of $50,982$ subjects from a subset of MIMIC database and then fine-tune the pre-trained model on a labeled dataset of $795$ subjects from the ASPIRE registry. Comprehensive evaluations against existing methods show that $\text{CardioVAE}_\text{X,G}$ offers promising performance (AUROC $=0.79$ and Accuracy $=0.77$), representing a significant step forward in non-invasive prediction of CHDI. Our model also excels in producing fine interpretations of predictions directly associated with clinical features, thereby supporting clinical decision-making.
|
2012.08761
|
Satoshi Sunada
|
Genki Furuhata, Tomoaki Niiyama, and Satoshi Sunada
|
Physical deep learning based on optimal control of dynamical systems
|
11 pages, 9 figures
|
Phys. Rev. Applied 15, 034092 (2021)
|
10.1103/PhysRevApplied.15.034092
| null |
cs.NE cs.ET physics.app-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep learning is the backbone of artificial intelligence technologies, and it
can be regarded as a kind of multilayer feedforward neural network. An essence
of deep learning is information propagation through layers. This suggests that
there is a connection between deep neural networks and dynamical systems in the
sense that information propagation is explicitly modeled by the time-evolution
of dynamical systems. In this study, we perform pattern recognition based on
the optimal control of continuous-time dynamical systems, which is suitable for
physical hardware implementation. The learning is based on the adjoint method
to optimally control dynamical systems, and the deep (virtual) network
structures based on the time evolution of the systems are used for processing
input information. As a key example, we apply the dynamics-based recognition
approach to an optoelectronic delay system and demonstrate that the use of the
delay system allows for image recognition and nonlinear classifications using
only a few control signals. This is in contrast to conventional multilayer
neural networks, which require a large number of weight parameters to be
trained. The proposed approach provides insight into the mechanisms of deep
network processing in the framework of an optimal control problem and presents
a pathway for realizing physical computing hardware.
|
[
{
"created": "Wed, 16 Dec 2020 06:38:01 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Apr 2021 06:43:47 GMT",
"version": "v2"
}
] |
2021-04-02
|
[
[
"Furuhata",
"Genki",
""
],
[
"Niiyama",
"Tomoaki",
""
],
[
"Sunada",
"Satoshi",
""
]
] |
Deep learning is the backbone of artificial intelligence technologies, and it can be regarded as a kind of multilayer feedforward neural network. An essence of deep learning is information propagation through layers. This suggests that there is a connection between deep neural networks and dynamical systems in the sense that information propagation is explicitly modeled by the time-evolution of dynamical systems. In this study, we perform pattern recognition based on the optimal control of continuous-time dynamical systems, which is suitable for physical hardware implementation. The learning is based on the adjoint method to optimally control dynamical systems, and the deep (virtual) network structures based on the time evolution of the systems are used for processing input information. As a key example, we apply the dynamics-based recognition approach to an optoelectronic delay system and demonstrate that the use of the delay system allows for image recognition and nonlinear classifications using only a few control signals. This is in contrast to conventional multilayer neural networks, which require a large number of weight parameters to be trained. The proposed approach provides insight into the mechanisms of deep network processing in the framework of an optimal control problem and presents a pathway for realizing physical computing hardware.
|
2105.15114
|
Spyridon Doukakis
|
Maria Chionidou-Moskofoglou, Spyridon Doukakis, Amalia Lappa
|
The use of e-portfolios in teaching and assessment
|
8 pages
|
Proceedings of the 7th International Conference on Technology in
Mathematics Teaching, pp. 224-232, 2005
| null | null |
cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we will initially go through the results of assessment in
mathematics according to the international assessment programs PISA, TIMSS
(2003), with respect to students' portfolios. Furthermore, we will present the
forms and the ways of assessment and will focus on that assessment which refers
to the use of eportfolios.
|
[
{
"created": "Fri, 21 May 2021 05:53:11 GMT",
"version": "v1"
}
] |
2021-06-01
|
[
[
"Chionidou-Moskofoglou",
"Maria",
""
],
[
"Doukakis",
"Spyridon",
""
],
[
"Lappa",
"Amalia",
""
]
] |
In this paper, we will initially go through the results of assessment in mathematics according to the international assessment programs PISA, TIMSS (2003), with respect to students' portfolios. Furthermore, we will present the forms and the ways of assessment and will focus on that assessment which refers to the use of eportfolios.
|
1802.07117
|
Ana Paula Appel
|
Ana Paula Appel, Paulo Rodrigo Cavalin, Marisa Affonso Vasconcelos,
Claudio Santos Pinhanez
|
Combining Textual Content and Structure to Improve Dialog Similarity
|
5 pages
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Chatbots, taking advantage of the success of the messaging apps and recent
advances in Artificial Intelligence, have become very popular, from helping
business to improve customer services to chatting to users for the sake of
conversation and engagement (celebrity or personal bots). However, developing
and improving a chatbot requires understanding their data generated by its
users. Dialog data has a different nature of a simple question and answering
interaction, in which context and temporal properties (turn order) creates a
different understanding of such data. In this paper, we propose a novelty
metric to compute dialogs' similarity based not only on the text content but
also on the information related to the dialog structure. Our experimental
results performed over the Switchboard dataset show that using evidence from
both textual content and the dialog structure leads to more accurate results
than using each measure in isolation.
|
[
{
"created": "Tue, 20 Feb 2018 14:05:48 GMT",
"version": "v1"
}
] |
2018-02-21
|
[
[
"Appel",
"Ana Paula",
""
],
[
"Cavalin",
"Paulo Rodrigo",
""
],
[
"Vasconcelos",
"Marisa Affonso",
""
],
[
"Pinhanez",
"Claudio Santos",
""
]
] |
Chatbots, taking advantage of the success of the messaging apps and recent advances in Artificial Intelligence, have become very popular, from helping business to improve customer services to chatting to users for the sake of conversation and engagement (celebrity or personal bots). However, developing and improving a chatbot requires understanding their data generated by its users. Dialog data has a different nature of a simple question and answering interaction, in which context and temporal properties (turn order) creates a different understanding of such data. In this paper, we propose a novelty metric to compute dialogs' similarity based not only on the text content but also on the information related to the dialog structure. Our experimental results performed over the Switchboard dataset show that using evidence from both textual content and the dialog structure leads to more accurate results than using each measure in isolation.
|
2208.03799
|
Martin Nisser
|
Martin Nisser and Yashaswini Makaram and Faraz Faruqi and Ryo Suzuki
and Stefanie Mueller
|
Selective Self-Assembly using Re-Programmable Magnetic Pixels
|
2022 IEEE International Conference on Intelligent Robots and Systems
(IROS)
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
This paper introduces a method to generate highly selective encodings that
can be magnetically "programmed" onto physical modules to enable them to
self-assemble in chosen configurations. We generate these encodings based on
Hadamard matrices, and show how to design the faces of modules to be maximally
attractive to their intended mate, while remaining maximally agnostic to other
faces. We derive guarantees on these bounds, and verify their attraction and
agnosticism experimentally. Using cubic modules whose faces have been covered
in soft magnetic material, we show how inexpensive, passive modules with planar
faces can be used to selectively self-assemble into target shapes without
geometric guides. We show that these modules can be easily re-programmed for
new target shapes using a CNC-based magnetic plotter, and demonstrate
self-assembly of 8 cubes in a water tank.
|
[
{
"created": "Sun, 7 Aug 2022 20:18:12 GMT",
"version": "v1"
}
] |
2022-08-09
|
[
[
"Nisser",
"Martin",
""
],
[
"Makaram",
"Yashaswini",
""
],
[
"Faruqi",
"Faraz",
""
],
[
"Suzuki",
"Ryo",
""
],
[
"Mueller",
"Stefanie",
""
]
] |
This paper introduces a method to generate highly selective encodings that can be magnetically "programmed" onto physical modules to enable them to self-assemble in chosen configurations. We generate these encodings based on Hadamard matrices, and show how to design the faces of modules to be maximally attractive to their intended mate, while remaining maximally agnostic to other faces. We derive guarantees on these bounds, and verify their attraction and agnosticism experimentally. Using cubic modules whose faces have been covered in soft magnetic material, we show how inexpensive, passive modules with planar faces can be used to selectively self-assemble into target shapes without geometric guides. We show that these modules can be easily re-programmed for new target shapes using a CNC-based magnetic plotter, and demonstrate self-assembly of 8 cubes in a water tank.
|
cs/0112024
|
Thomas Schmidt
|
B. Feustel, T.C. Schmidt
|
Media Objects in Time - A Multimedia Streaming System
|
9 pdf pages
|
Computer Networks 37,6 (2001), pp. 729 - 737
| null | null |
cs.NI cs.MM
| null |
The widespread availability of networked multimedia potentials embedded in an
infrastructure of qualitative superior kind gives rise to new approaches in the
areas of teleteaching and internet presentation: The distribution of
professionally styled multimedia streams has fallen in the realm of
possibility. This paper presents a prototype - both model and runtime
environment - of a time directed media system treating any kind of
presentational contribution as reusable media object components. The plug-in
free runtime system is based on a database and allows for a flexible support of
static media types as well as for easy extensions by streaming media servers.
The prototypic implementation includes a preliminary Web Authoring platform.
|
[
{
"created": "Fri, 28 Dec 2001 20:19:09 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Feustel",
"B.",
""
],
[
"Schmidt",
"T. C.",
""
]
] |
The widespread availability of networked multimedia potentials embedded in an infrastructure of qualitative superior kind gives rise to new approaches in the areas of teleteaching and internet presentation: The distribution of professionally styled multimedia streams has fallen in the realm of possibility. This paper presents a prototype - both model and runtime environment - of a time directed media system treating any kind of presentational contribution as reusable media object components. The plug-in free runtime system is based on a database and allows for a flexible support of static media types as well as for easy extensions by streaming media servers. The prototypic implementation includes a preliminary Web Authoring platform.
|
2406.07141
|
Avinash Kori
|
Avinash Kori, Francesco Locatello, Ainkaran Santhirasekaram, Francesca
Toni, Ben Glocker, Fabio De Sousa Ribeiro
|
Identifiable Object-Centric Representation Learning via Probabilistic
Slot Attention
| null | null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Learning modular object-centric representations is crucial for systematic
generalization. Existing methods show promising object-binding capabilities
empirically, but theoretical identifiability guarantees remain relatively
underdeveloped. Understanding when object-centric representations can
theoretically be identified is crucial for scaling slot-based methods to
high-dimensional images with correctness guarantees. To that end, we propose a
probabilistic slot-attention algorithm that imposes an aggregate mixture prior
over object-centric slot representations, thereby providing slot
identifiability guarantees without supervision, up to an equivalence relation.
We provide empirical verification of our theoretical identifiability result
using both simple 2-dimensional data and high-resolution imaging datasets.
|
[
{
"created": "Tue, 11 Jun 2024 10:40:54 GMT",
"version": "v1"
}
] |
2024-06-12
|
[
[
"Kori",
"Avinash",
""
],
[
"Locatello",
"Francesco",
""
],
[
"Santhirasekaram",
"Ainkaran",
""
],
[
"Toni",
"Francesca",
""
],
[
"Glocker",
"Ben",
""
],
[
"Ribeiro",
"Fabio De Sousa",
""
]
] |
Learning modular object-centric representations is crucial for systematic generalization. Existing methods show promising object-binding capabilities empirically, but theoretical identifiability guarantees remain relatively underdeveloped. Understanding when object-centric representations can theoretically be identified is crucial for scaling slot-based methods to high-dimensional images with correctness guarantees. To that end, we propose a probabilistic slot-attention algorithm that imposes an aggregate mixture prior over object-centric slot representations, thereby providing slot identifiability guarantees without supervision, up to an equivalence relation. We provide empirical verification of our theoretical identifiability result using both simple 2-dimensional data and high-resolution imaging datasets.
|
2406.02826
|
Yu-Wen Chen
|
Yu-Wen Chen, Julia Hirschberg
|
Exploring Robustness in Doctor-Patient Conversation Summarization: An
Analysis of Out-of-Domain SOAP Notes
|
Clinical NLP Workshop 2024
| null | null | null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Summarizing medical conversations poses unique challenges due to the
specialized domain and the difficulty of collecting in-domain training data. In
this study, we investigate the performance of state-of-the-art doctor-patient
conversation generative summarization models on the out-of-domain data. We
divide the summarization model of doctor-patient conversation into two
configurations: (1) a general model, without specifying subjective (S),
objective (O), and assessment (A) and plan (P) notes; (2) a SOAP-oriented model
that generates a summary with SOAP sections. We analyzed the limitations and
strengths of the fine-tuning language model-based methods and GPTs on both
configurations. We also conducted a Linguistic Inquiry and Word Count analysis
to compare the SOAP notes from different datasets. The results exhibit a strong
correlation for reference notes across different datasets, indicating that
format mismatch (i.e., discrepancies in word distribution) is not the main
cause of performance decline on out-of-domain data. Lastly, a detailed analysis
of SOAP notes is included to provide insights into missing information and
hallucinations introduced by the models.
|
[
{
"created": "Wed, 5 Jun 2024 00:11:20 GMT",
"version": "v1"
}
] |
2024-06-06
|
[
[
"Chen",
"Yu-Wen",
""
],
[
"Hirschberg",
"Julia",
""
]
] |
Summarizing medical conversations poses unique challenges due to the specialized domain and the difficulty of collecting in-domain training data. In this study, we investigate the performance of state-of-the-art doctor-patient conversation generative summarization models on the out-of-domain data. We divide the summarization model of doctor-patient conversation into two configurations: (1) a general model, without specifying subjective (S), objective (O), and assessment (A) and plan (P) notes; (2) a SOAP-oriented model that generates a summary with SOAP sections. We analyzed the limitations and strengths of the fine-tuning language model-based methods and GPTs on both configurations. We also conducted a Linguistic Inquiry and Word Count analysis to compare the SOAP notes from different datasets. The results exhibit a strong correlation for reference notes across different datasets, indicating that format mismatch (i.e., discrepancies in word distribution) is not the main cause of performance decline on out-of-domain data. Lastly, a detailed analysis of SOAP notes is included to provide insights into missing information and hallucinations introduced by the models.
|
2209.09341
|
Nermin Samet
|
Georgy Ponimatkin, Nermin Samet, Yang Xiao, Yuming Du, Renaud Marlet,
Vincent Lepetit
|
A Simple and Powerful Global Optimization for Unsupervised Video Object
Segmentation
|
Accepted to the IEEE Winter Conference on Applications of Computer
Vision (WACV) 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a simple, yet powerful approach for unsupervised object
segmentation in videos. We introduce an objective function whose minimum
represents the mask of the main salient object over the input sequence. It only
relies on independent image features and optical flows, which can be obtained
using off-the-shelf self-supervised methods. It scales with the length of the
sequence with no need for superpixels or sparsification, and it generalizes to
different datasets without any specific training. This objective function can
actually be derived from a form of spectral clustering applied to the entire
video. Our method achieves on-par performance with the state of the art on
standard benchmarks (DAVIS2016, SegTrack-v2, FBMS59), while being conceptually
and practically much simpler. Code is available at
https://ponimatkin.github.io/ssl-vos.
|
[
{
"created": "Mon, 19 Sep 2022 20:41:26 GMT",
"version": "v1"
},
{
"created": "Wed, 19 Oct 2022 14:45:08 GMT",
"version": "v2"
}
] |
2022-10-20
|
[
[
"Ponimatkin",
"Georgy",
""
],
[
"Samet",
"Nermin",
""
],
[
"Xiao",
"Yang",
""
],
[
"Du",
"Yuming",
""
],
[
"Marlet",
"Renaud",
""
],
[
"Lepetit",
"Vincent",
""
]
] |
We propose a simple, yet powerful approach for unsupervised object segmentation in videos. We introduce an objective function whose minimum represents the mask of the main salient object over the input sequence. It only relies on independent image features and optical flows, which can be obtained using off-the-shelf self-supervised methods. It scales with the length of the sequence with no need for superpixels or sparsification, and it generalizes to different datasets without any specific training. This objective function can actually be derived from a form of spectral clustering applied to the entire video. Our method achieves on-par performance with the state of the art on standard benchmarks (DAVIS2016, SegTrack-v2, FBMS59), while being conceptually and practically much simpler. Code is available at https://ponimatkin.github.io/ssl-vos.
|
1409.2073
|
Tobias Kortkamp
|
Tobias Kortkamp
|
An NLP Assistant for Clide
|
Bachelor Report
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/3.0/
|
This report describes an NLP assistant for the collaborative development
environment Clide, that supports the development of NLP applications by
providing easy access to some common NLP data structures. The assistant
visualizes text fragments and their dependencies by displaying the semantic
graph of a sentence, the coreference chain of a paragraph and mined triples
that are extracted from a paragraph's semantic graphs and linked using its
coreference chain. Using this information and a logic programming library, we
create an NLP database which is used by a series of queries to mine the
triples. The algorithm is tested by translating a natural language text
describing a graph to an actual graph that is shown as an annotation in the
text editor.
|
[
{
"created": "Sun, 7 Sep 2014 02:31:03 GMT",
"version": "v1"
}
] |
2014-09-09
|
[
[
"Kortkamp",
"Tobias",
""
]
] |
This report describes an NLP assistant for the collaborative development environment Clide, that supports the development of NLP applications by providing easy access to some common NLP data structures. The assistant visualizes text fragments and their dependencies by displaying the semantic graph of a sentence, the coreference chain of a paragraph and mined triples that are extracted from a paragraph's semantic graphs and linked using its coreference chain. Using this information and a logic programming library, we create an NLP database which is used by a series of queries to mine the triples. The algorithm is tested by translating a natural language text describing a graph to an actual graph that is shown as an annotation in the text editor.
|
1304.3111
|
Randall Smith
|
Randall Smith, Matthew Self, Peter Cheeseman
|
Estimating Uncertain Spatial Relationships in Robotics
|
Appears in Proceedings of the Second Conference on Uncertainty in
Artificial Intelligence (UAI1986)
| null | null |
UAI-P-1986-PG-267-288
|
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we describe a representation for spatial information, called
the stochastic map, and associated procedures for building it, reading
information from it, and revising it incrementally as new information is
obtained. The map contains the estimates of relationships among objects in the
map, and their uncertainties, given all the available information. The
procedures provide a general solution to the problem of estimating uncertain
relative spatial relationships. The estimates are probabilistic in nature, an
advance over the previous, very conservative, worst-case approaches to the
problem. Finally, the procedures are developed in the context of
state-estimation and filtering theory, which provides a solid basis for
numerous extensions.
|
[
{
"created": "Wed, 27 Mar 2013 19:54:21 GMT",
"version": "v1"
}
] |
2013-04-12
|
[
[
"Smith",
"Randall",
""
],
[
"Self",
"Matthew",
""
],
[
"Cheeseman",
"Peter",
""
]
] |
In this paper, we describe a representation for spatial information, called the stochastic map, and associated procedures for building it, reading information from it, and revising it incrementally as new information is obtained. The map contains the estimates of relationships among objects in the map, and their uncertainties, given all the available information. The procedures provide a general solution to the problem of estimating uncertain relative spatial relationships. The estimates are probabilistic in nature, an advance over the previous, very conservative, worst-case approaches to the problem. Finally, the procedures are developed in the context of state-estimation and filtering theory, which provides a solid basis for numerous extensions.
|
0908.4413
|
Laurent Romary
|
Patrice Lopez (IDSL), Laurent Romary (IDSL, INRIA Saclay - Ile de
France)
|
Multiple Retrieval Models and Regression Models for Prior Art Search
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents the system called PATATRAS (PATent and Article Tracking,
Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach
presents three main characteristics: 1. The usage of multiple retrieval models
(KL, Okapi) and term index definitions (lemma, phrase, concept) for the three
languages considered in the present track (English, French, German) producing
ten different sets of ranked results. 2. The merging of the different results
based on multiple regression models using an additional validation set created
from the patent collection. 3. The exploitation of patent metadata and of the
citation structures for creating restricted initial working sets of patents and
for producing a final re-ranking regression model. As we exploit specific
metadata of the patent documents and the citation relations only at the
creation of initial working sets and during the final post ranking step, our
architecture remains generic and easy to extend.
|
[
{
"created": "Sun, 30 Aug 2009 18:50:19 GMT",
"version": "v1"
}
] |
2009-09-01
|
[
[
"Lopez",
"Patrice",
"",
"IDSL"
],
[
"Romary",
"Laurent",
"",
"IDSL, INRIA Saclay - Ile de\n France"
]
] |
This paper presents the system called PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach presents three main characteristics: 1. The usage of multiple retrieval models (KL, Okapi) and term index definitions (lemma, phrase, concept) for the three languages considered in the present track (English, French, German) producing ten different sets of ranked results. 2. The merging of the different results based on multiple regression models using an additional validation set created from the patent collection. 3. The exploitation of patent metadata and of the citation structures for creating restricted initial working sets of patents and for producing a final re-ranking regression model. As we exploit specific metadata of the patent documents and the citation relations only at the creation of initial working sets and during the final post ranking step, our architecture remains generic and easy to extend.
|
1711.01243
|
Mohammad Ghasemzadeh
|
Mohammad Ghasemzadeh, Mohammad Samragh, Farinaz Koushanfar
|
ReBNet: Residual Binarized Neural Network
|
To Appear In The 26th IEEE International Symposium on
Field-Programmable Custom Computing Machines
| null | null | null |
cs.LG cs.CV cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper proposes ReBNet, an end-to-end framework for training
reconfigurable binary neural networks on software and developing efficient
accelerators for execution on FPGA. Binary neural networks offer an intriguing
opportunity for deploying large-scale deep learning models on
resource-constrained devices. Binarization reduces the memory footprint and
replaces the power-hungry matrix-multiplication with light-weight XnorPopcount
operations. However, binary networks suffer from a degraded accuracy compared
to their fixed-point counterparts. We show that the state-of-the-art methods
for optimizing binary networks accuracy, significantly increase the
implementation cost and complexity. To compensate for the degraded accuracy
while adhering to the simplicity of binary networks, we devise the first
reconfigurable scheme that can adjust the classification accuracy based on the
application. Our proposition improves the classification accuracy by
representing features with multiple levels of residual binarization. Unlike
previous methods, our approach does not exacerbate the area cost of the
hardware accelerator. Instead, it provides a tradeoff between throughput and
accuracy while the area overhead of multi-level binarization is negligible.
|
[
{
"created": "Fri, 3 Nov 2017 17:12:15 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Nov 2017 00:45:57 GMT",
"version": "v2"
},
{
"created": "Tue, 27 Mar 2018 20:58:01 GMT",
"version": "v3"
}
] |
2018-03-29
|
[
[
"Ghasemzadeh",
"Mohammad",
""
],
[
"Samragh",
"Mohammad",
""
],
[
"Koushanfar",
"Farinaz",
""
]
] |
This paper proposes ReBNet, an end-to-end framework for training reconfigurable binary neural networks on software and developing efficient accelerators for execution on FPGA. Binary neural networks offer an intriguing opportunity for deploying large-scale deep learning models on resource-constrained devices. Binarization reduces the memory footprint and replaces the power-hungry matrix-multiplication with light-weight XnorPopcount operations. However, binary networks suffer from a degraded accuracy compared to their fixed-point counterparts. We show that the state-of-the-art methods for optimizing binary networks accuracy, significantly increase the implementation cost and complexity. To compensate for the degraded accuracy while adhering to the simplicity of binary networks, we devise the first reconfigurable scheme that can adjust the classification accuracy based on the application. Our proposition improves the classification accuracy by representing features with multiple levels of residual binarization. Unlike previous methods, our approach does not exacerbate the area cost of the hardware accelerator. Instead, it provides a tradeoff between throughput and accuracy while the area overhead of multi-level binarization is negligible.
|
2407.18981
|
Jan Clusmann
|
Jan Clusmann, Dyke Ferber, Isabella C. Wiest, Carolin V. Schneider,
Titus J. Brinker, Sebastian Foersch, Daniel Truhn, Jakob N. Kather
|
Prompt Injection Attacks on Large Language Models in Oncology
|
57 Pages, 5 Figures
| null | null | null |
cs.CR cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Vision-language artificial intelligence models (VLMs) possess medical
knowledge and can be employed in healthcare in numerous ways, including as
image interpreters, virtual scribes, and general decision support systems.
However, here, we demonstrate that current VLMs applied to medical tasks
exhibit a fundamental security flaw: they can be attacked by prompt injection
attacks, which can be used to output harmful information just by interacting
with the VLM, without any access to its parameters. We performed a quantitative
study to evaluate the vulnerabilities to these attacks in four state of the art
VLMs which have been proposed to be of utility in healthcare: Claude 3 Opus,
Claude 3.5 Sonnet, Reka Core, and GPT-4o. Using a set of N=297 attacks, we show
that all of these models are susceptible. Specifically, we show that embedding
sub-visual prompts in medical imaging data can cause the model to provide
harmful output, and that these prompts are non-obvious to human observers.
Thus, our study demonstrates a key vulnerability in medical VLMs which should
be mitigated before widespread clinical adoption.
|
[
{
"created": "Tue, 23 Jul 2024 15:29:57 GMT",
"version": "v1"
}
] |
2024-07-30
|
[
[
"Clusmann",
"Jan",
""
],
[
"Ferber",
"Dyke",
""
],
[
"Wiest",
"Isabella C.",
""
],
[
"Schneider",
"Carolin V.",
""
],
[
"Brinker",
"Titus J.",
""
],
[
"Foersch",
"Sebastian",
""
],
[
"Truhn",
"Daniel",
""
],
[
"Kather",
"Jakob N.",
""
]
] |
Vision-language artificial intelligence models (VLMs) possess medical knowledge and can be employed in healthcare in numerous ways, including as image interpreters, virtual scribes, and general decision support systems. However, here, we demonstrate that current VLMs applied to medical tasks exhibit a fundamental security flaw: they can be attacked by prompt injection attacks, which can be used to output harmful information just by interacting with the VLM, without any access to its parameters. We performed a quantitative study to evaluate the vulnerabilities to these attacks in four state of the art VLMs which have been proposed to be of utility in healthcare: Claude 3 Opus, Claude 3.5 Sonnet, Reka Core, and GPT-4o. Using a set of N=297 attacks, we show that all of these models are susceptible. Specifically, we show that embedding sub-visual prompts in medical imaging data can cause the model to provide harmful output, and that these prompts are non-obvious to human observers. Thus, our study demonstrates a key vulnerability in medical VLMs which should be mitigated before widespread clinical adoption.
|
0802.3441
|
Javier D. Garcia-Lasheras
|
Javier D. Garcia-Lasheras
|
Efficient implementation of GALS systems over commercial synchronous
FPGAs: a new approach
|
English version of the paper presented in the Spanish Workshop on
Reconfigurable Computing and Applications, Zaragoza (2007)
|
"Implementacion eficiente de sistemas GALS sobre FPGAs", Jornadas
de Computacion Reconfigurable y Aplicaciones (JCRA'07), Zaragoza (2007)
| null | null |
cs.AR
| null |
The new vision presented is aimed to overcome the logic overhead issues that
previous works exhibit when applying GALS techniques to programmable logic
devices. The proposed new view relies in a 2-phase, bundled data parity based
protocol for data transfer and clock generation tasks. The ability of the
introduced methodology for smart real-time delay selection allows the
implementation of a variety of new methodologies for electromagnetic
interference mitigation and device environment changes adaptation.
|
[
{
"created": "Sat, 23 Feb 2008 13:11:13 GMT",
"version": "v1"
}
] |
2008-02-26
|
[
[
"Garcia-Lasheras",
"Javier D.",
""
]
] |
The new vision presented is aimed to overcome the logic overhead issues that previous works exhibit when applying GALS techniques to programmable logic devices. The proposed new view relies in a 2-phase, bundled data parity based protocol for data transfer and clock generation tasks. The ability of the introduced methodology for smart real-time delay selection allows the implementation of a variety of new methodologies for electromagnetic interference mitigation and device environment changes adaptation.
|
2211.01751
|
Pavel Andreev
|
Pavel Andreev, Nicholas Babaev, Azat Saginbaev, Ivan Shchekotov, Aibek
Alanov
|
Iterative autoregression: a novel trick to improve your low-latency
speech enhancement model
|
Accepted to Interspeech 2023
| null | null | null |
cs.SD cs.AI eess.AS
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Streaming models are an essential component of real-time speech enhancement
tools. The streaming regime constrains speech enhancement models to use only a
tiny context of future information. As a result, the low-latency streaming
setup is generally considered a challenging task and has a significant negative
impact on the model's quality. However, the sequential nature of streaming
generation offers a natural possibility for autoregression, that is, utilizing
previous predictions while making current ones. The conventional method for
training autoregressive models is teacher forcing, but its primary drawback
lies in the training-inference mismatch that can lead to a substantial
degradation in quality. In this study, we propose a straightforward yet
effective alternative technique for training autoregressive low-latency speech
enhancement models. We demonstrate that the proposed approach leads to stable
improvement across diverse architectures and training scenarios.
|
[
{
"created": "Thu, 3 Nov 2022 12:32:33 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Jun 2023 15:50:00 GMT",
"version": "v2"
},
{
"created": "Tue, 27 Jun 2023 13:54:39 GMT",
"version": "v3"
},
{
"created": "Tue, 5 Dec 2023 11:36:32 GMT",
"version": "v4"
}
] |
2023-12-06
|
[
[
"Andreev",
"Pavel",
""
],
[
"Babaev",
"Nicholas",
""
],
[
"Saginbaev",
"Azat",
""
],
[
"Shchekotov",
"Ivan",
""
],
[
"Alanov",
"Aibek",
""
]
] |
Streaming models are an essential component of real-time speech enhancement tools. The streaming regime constrains speech enhancement models to use only a tiny context of future information. As a result, the low-latency streaming setup is generally considered a challenging task and has a significant negative impact on the model's quality. However, the sequential nature of streaming generation offers a natural possibility for autoregression, that is, utilizing previous predictions while making current ones. The conventional method for training autoregressive models is teacher forcing, but its primary drawback lies in the training-inference mismatch that can lead to a substantial degradation in quality. In this study, we propose a straightforward yet effective alternative technique for training autoregressive low-latency speech enhancement models. We demonstrate that the proposed approach leads to stable improvement across diverse architectures and training scenarios.
|
2401.07314
|
Jiaqi Chen
|
Jiaqi Chen, Bingqian Lin, Ran Xu, Zhenhua Chai, Xiaodan Liang,
Kwan-Yee K. Wong
|
MapGPT: Map-Guided Prompting with Adaptive Path Planning for
Vision-and-Language Navigation
|
LLM/VLM-based VLN Agents. Accepted to ACL 2024. Project:
https://chen-judge.github.io/MapGPT/
| null | null | null |
cs.AI cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Embodied agents equipped with GPT as their brains have exhibited
extraordinary decision-making and generalization abilities across various
tasks. However, existing zero-shot agents for vision-and-language navigation
(VLN) only prompt GPT-4 to select potential locations within localized
environments, without constructing an effective "global-view" for the agent to
understand the overall environment. In this work, we present a novel map-guided
GPT-based agent, dubbed MapGPT, which introduces an online linguistic-formed
map to encourage global exploration. Specifically, we build an online map and
incorporate it into the prompts that include node information and topological
relationships, to help GPT understand the spatial environment. Benefiting from
this design, we further propose an adaptive planning mechanism to assist the
agent in performing multi-step path planning based on a map, systematically
exploring multiple candidate nodes or sub-goals step by step. Extensive
experiments demonstrate that our MapGPT is applicable to both GPT-4 and GPT-4V,
achieving state-of-the-art zero-shot performance on R2R and REVERIE
simultaneously (~10% and ~12% improvements in SR), and showcasing the newly
emergent global thinking and path planning abilities of the GPT.
|
[
{
"created": "Sun, 14 Jan 2024 15:34:48 GMT",
"version": "v1"
},
{
"created": "Sun, 25 Feb 2024 14:39:48 GMT",
"version": "v2"
},
{
"created": "Thu, 20 Jun 2024 07:23:45 GMT",
"version": "v3"
}
] |
2024-06-21
|
[
[
"Chen",
"Jiaqi",
""
],
[
"Lin",
"Bingqian",
""
],
[
"Xu",
"Ran",
""
],
[
"Chai",
"Zhenhua",
""
],
[
"Liang",
"Xiaodan",
""
],
[
"Wong",
"Kwan-Yee K.",
""
]
] |
Embodied agents equipped with GPT as their brains have exhibited extraordinary decision-making and generalization abilities across various tasks. However, existing zero-shot agents for vision-and-language navigation (VLN) only prompt GPT-4 to select potential locations within localized environments, without constructing an effective "global-view" for the agent to understand the overall environment. In this work, we present a novel map-guided GPT-based agent, dubbed MapGPT, which introduces an online linguistic-formed map to encourage global exploration. Specifically, we build an online map and incorporate it into the prompts that include node information and topological relationships, to help GPT understand the spatial environment. Benefiting from this design, we further propose an adaptive planning mechanism to assist the agent in performing multi-step path planning based on a map, systematically exploring multiple candidate nodes or sub-goals step by step. Extensive experiments demonstrate that our MapGPT is applicable to both GPT-4 and GPT-4V, achieving state-of-the-art zero-shot performance on R2R and REVERIE simultaneously (~10% and ~12% improvements in SR), and showcasing the newly emergent global thinking and path planning abilities of the GPT.
|
1406.3693
|
Partha Pratim Ray
|
Partha Pratim Ray
|
Channel Modeling of Human Somatosensory Nanonetwork: Body Discriminative
Touch and Proprioception Perspective
|
11 pages, 6 figures
|
International Journal on Computer Science and Engineering, Vol. 5
No. 10, pp. 874-884, Oct 2013
| null | null |
cs.ET
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Nanonetwork design and analysis has become a very interesting topic in recent
years. Though this area of research is in its formative stage, it definitely
posses a strong integrity in finding out numerous applications in medical and
allied sciences. Nanonetworking is indeed a nature built foundation which
comprises human intra body communications. Somatosensory system is the one of
the critical and must have systems of human body. This literature concentrates
on the body discriminative touch and proprioception mechanism of somatosensory
system. This particular system is well architecture by medial lemniscal
pathway, in human body for transduction of touch and proprioceptive
information. This paper seeks out the novel communication channel model of
somatosensory system. The working principle of the channel model is established
by an equivalent Moore machine. A novel algorithm MLP is proposed after its
name, medial lemniscal pathway. A novel naomachine and appropriate processing
unit are also devised, based on the automaton.
|
[
{
"created": "Sat, 14 Jun 2014 07:04:30 GMT",
"version": "v1"
}
] |
2014-06-17
|
[
[
"Ray",
"Partha Pratim",
""
]
] |
Nanonetwork design and analysis has become a very interesting topic in recent years. Though this area of research is in its formative stage, it definitely posses a strong integrity in finding out numerous applications in medical and allied sciences. Nanonetworking is indeed a nature built foundation which comprises human intra body communications. Somatosensory system is the one of the critical and must have systems of human body. This literature concentrates on the body discriminative touch and proprioception mechanism of somatosensory system. This particular system is well architecture by medial lemniscal pathway, in human body for transduction of touch and proprioceptive information. This paper seeks out the novel communication channel model of somatosensory system. The working principle of the channel model is established by an equivalent Moore machine. A novel algorithm MLP is proposed after its name, medial lemniscal pathway. A novel naomachine and appropriate processing unit are also devised, based on the automaton.
|
cs/0307011
|
Naren Ramakrishnan
|
Atul Shenoy, Naren Ramakrishnan, Manuel A. Perez-Quinones, and
Srinidhi Varadarajan
|
Supporting Out-of-turn Interactions in a Multimodal Web Interface
| null | null | null | null |
cs.IR cs.HC
| null |
Multimodal interfaces are becoming increasingly important with the advent of
mobile devices, accessibility considerations, and novel software technologies
that combine diverse interaction media. This article investigates systems
support for web browsing in a multimodal interface. Specifically, we outline
the design and implementation of a software framework that integrates hyperlink
and speech modes of interaction. Instead of viewing speech as merely an
alternative interaction medium, the framework uses it to support out-of-turn
interaction, providing a flexibility of information access not possible with
hyperlinks alone. This approach enables the creation of websites that adapt to
the needs of users, yet permits the designer fine-grained control over what
interactions to support. Design methodology, implementation details, and two
case studies are presented.
|
[
{
"created": "Fri, 4 Jul 2003 13:44:04 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Shenoy",
"Atul",
""
],
[
"Ramakrishnan",
"Naren",
""
],
[
"Perez-Quinones",
"Manuel A.",
""
],
[
"Varadarajan",
"Srinidhi",
""
]
] |
Multimodal interfaces are becoming increasingly important with the advent of mobile devices, accessibility considerations, and novel software technologies that combine diverse interaction media. This article investigates systems support for web browsing in a multimodal interface. Specifically, we outline the design and implementation of a software framework that integrates hyperlink and speech modes of interaction. Instead of viewing speech as merely an alternative interaction medium, the framework uses it to support out-of-turn interaction, providing a flexibility of information access not possible with hyperlinks alone. This approach enables the creation of websites that adapt to the needs of users, yet permits the designer fine-grained control over what interactions to support. Design methodology, implementation details, and two case studies are presented.
|
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