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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1908.07899 | Tobias Hinz | Marcus Soll, Tobias Hinz, Sven Magg, Stefan Wermter | Evaluating Defensive Distillation For Defending Text Processing Neural
Networks Against Adversarial Examples | Published at the International Conference on Artificial Neural
Networks (ICANN) 2019 | null | null | null | cs.CL cs.CR cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Adversarial examples are artificially modified input samples which lead to
misclassifications, while not being detectable by humans. These adversarial
examples are a challenge for many tasks such as image and text classification,
especially as research shows that many adversarial examples are transferable
between different classifiers. In this work, we evaluate the performance of a
popular defensive strategy for adversarial examples called defensive
distillation, which can be successful in hardening neural networks against
adversarial examples in the image domain. However, instead of applying
defensive distillation to networks for image classification, we examine, for
the first time, its performance on text classification tasks and also evaluate
its effect on the transferability of adversarial text examples. Our results
indicate that defensive distillation only has a minimal impact on text
classifying neural networks and does neither help with increasing their
robustness against adversarial examples nor prevent the transferability of
adversarial examples between neural networks.
| [
{
"created": "Wed, 21 Aug 2019 14:50:13 GMT",
"version": "v1"
}
] | 2019-08-22 | [
[
"Soll",
"Marcus",
""
],
[
"Hinz",
"Tobias",
""
],
[
"Magg",
"Sven",
""
],
[
"Wermter",
"Stefan",
""
]
] | Adversarial examples are artificially modified input samples which lead to misclassifications, while not being detectable by humans. These adversarial examples are a challenge for many tasks such as image and text classification, especially as research shows that many adversarial examples are transferable between different classifiers. In this work, we evaluate the performance of a popular defensive strategy for adversarial examples called defensive distillation, which can be successful in hardening neural networks against adversarial examples in the image domain. However, instead of applying defensive distillation to networks for image classification, we examine, for the first time, its performance on text classification tasks and also evaluate its effect on the transferability of adversarial text examples. Our results indicate that defensive distillation only has a minimal impact on text classifying neural networks and does neither help with increasing their robustness against adversarial examples nor prevent the transferability of adversarial examples between neural networks. |
2204.03738 | Felipe Oviedo | Felipe Oviedo, Srinivas Vinnakota, Eugene Seleznev, Hemant Malhotra,
Saqib Shaikh, Juan Lavista Ferres | BankNote-Net: Open dataset for assistive universal currency recognition | Pre-print | null | null | null | cs.CV cs.HC cs.LG | http://creativecommons.org/licenses/by/4.0/ | Millions of people around the world have low or no vision. Assistive software
applications have been developed for a variety of day-to-day tasks, including
optical character recognition, scene identification, person recognition, and
currency recognition. This last task, the recognition of banknotes from
different denominations, has been addressed by the use of computer vision
models for image recognition. However, the datasets and models available for
this task are limited, both in terms of dataset size and in variety of
currencies covered. In this work, we collect a total of 24,826 images of
banknotes in variety of assistive settings, spanning 17 currencies and 112
denominations. Using supervised contrastive learning, we develop a machine
learning model for universal currency recognition. This model learns compliant
embeddings of banknote images in a variety of contexts, which can be shared
publicly (as a compressed vector representation), and can be used to train and
test specialized downstream models for any currency, including those not
covered by our dataset or for which only a few real images per denomination are
available (few-shot learning). We deploy a variation of this model for public
use in the last version of the Seeing AI app developed by Microsoft. We share
our encoder model and the embeddings as an open dataset in our BankNote-Net
repository.
| [
{
"created": "Thu, 7 Apr 2022 21:16:54 GMT",
"version": "v1"
}
] | 2022-04-11 | [
[
"Oviedo",
"Felipe",
""
],
[
"Vinnakota",
"Srinivas",
""
],
[
"Seleznev",
"Eugene",
""
],
[
"Malhotra",
"Hemant",
""
],
[
"Shaikh",
"Saqib",
""
],
[
"Ferres",
"Juan Lavista",
""
]
] | Millions of people around the world have low or no vision. Assistive software applications have been developed for a variety of day-to-day tasks, including optical character recognition, scene identification, person recognition, and currency recognition. This last task, the recognition of banknotes from different denominations, has been addressed by the use of computer vision models for image recognition. However, the datasets and models available for this task are limited, both in terms of dataset size and in variety of currencies covered. In this work, we collect a total of 24,826 images of banknotes in variety of assistive settings, spanning 17 currencies and 112 denominations. Using supervised contrastive learning, we develop a machine learning model for universal currency recognition. This model learns compliant embeddings of banknote images in a variety of contexts, which can be shared publicly (as a compressed vector representation), and can be used to train and test specialized downstream models for any currency, including those not covered by our dataset or for which only a few real images per denomination are available (few-shot learning). We deploy a variation of this model for public use in the last version of the Seeing AI app developed by Microsoft. We share our encoder model and the embeddings as an open dataset in our BankNote-Net repository. |
2004.07610 | Roshan Singh | Roshan Singh, Pranav Kumar Singh | Connecting the Dots of COVID-19 Transmissions in India | Withdrawing for improving research scope | null | null | null | cs.SI physics.soc-ph q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Witnessing its first case in late January 2020 India has seen a sharp rise in
the number of positive cases of COVID-19. 34 States/UT (s) of the country have
been found to be affected by the pandemic to date. We in this work, study the
progress of COVID-19 pandemic in India. We aim to create transmission network
visualization (s) of COVID-19 in India and perform analysis upon them. Using
the transmission networks obtained we attempt to find the possible Super
Spreader Individual (s) and Super Spreader Events (SSE) responsible for the
outbreak in their respective regions. We discuss the potentials of network
analysis in mitigating the further spread of the disease. This is one of the
initial studies of the outbreak in India and the first attempt to study the
pandemic in the country from a transmission network perspective.
| [
{
"created": "Thu, 16 Apr 2020 11:40:04 GMT",
"version": "v1"
},
{
"created": "Sat, 25 Jul 2020 13:18:39 GMT",
"version": "v2"
}
] | 2020-07-28 | [
[
"Singh",
"Roshan",
""
],
[
"Singh",
"Pranav Kumar",
""
]
] | Witnessing its first case in late January 2020 India has seen a sharp rise in the number of positive cases of COVID-19. 34 States/UT (s) of the country have been found to be affected by the pandemic to date. We in this work, study the progress of COVID-19 pandemic in India. We aim to create transmission network visualization (s) of COVID-19 in India and perform analysis upon them. Using the transmission networks obtained we attempt to find the possible Super Spreader Individual (s) and Super Spreader Events (SSE) responsible for the outbreak in their respective regions. We discuss the potentials of network analysis in mitigating the further spread of the disease. This is one of the initial studies of the outbreak in India and the first attempt to study the pandemic in the country from a transmission network perspective. |
2408.01163 | Alexander Olza | Alexander Olza, David Soto, Roberto Santana | Domain Adaptation-Enhanced Searchlight: Enabling brain decoding from
visual perception to mental imagery | null | null | null | null | cs.LG q-bio.NC | http://creativecommons.org/licenses/by-sa/4.0/ | In cognitive neuroscience and brain-computer interface research, accurately
predicting imagined stimuli is crucial. This study investigates the
effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using
primarily visual data from fMRI scans of 18 subjects. Initially, we train a
baseline model on visual stimuli to predict imagined stimuli, utilizing data
from 14 brain regions. We then develop several models to improve imagery
prediction, comparing different DA methods. Our results demonstrate that DA
significantly enhances imagery prediction, especially with the Regular Transfer
approach. We then conduct a DA-enhanced searchlight analysis using Regular
Transfer, followed by permutation-based statistical tests to identify brain
regions where imagery decoding is consistently above chance across subjects.
Our DA-enhanced searchlight predicts imagery contents in a highly distributed
set of brain regions, including the visual cortex and the frontoparietal
cortex, thereby outperforming standard cross-domain classification methods. The
complete code and data for this paper have been made openly available for the
use of the scientific community.
| [
{
"created": "Fri, 2 Aug 2024 10:25:19 GMT",
"version": "v1"
}
] | 2024-08-05 | [
[
"Olza",
"Alexander",
""
],
[
"Soto",
"David",
""
],
[
"Santana",
"Roberto",
""
]
] | In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we train a baseline model on visual stimuli to predict imagined stimuli, utilizing data from 14 brain regions. We then develop several models to improve imagery prediction, comparing different DA methods. Our results demonstrate that DA significantly enhances imagery prediction, especially with the Regular Transfer approach. We then conduct a DA-enhanced searchlight analysis using Regular Transfer, followed by permutation-based statistical tests to identify brain regions where imagery decoding is consistently above chance across subjects. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex, thereby outperforming standard cross-domain classification methods. The complete code and data for this paper have been made openly available for the use of the scientific community. |
2007.03680 | Ioannis Mavromatis Dr | Ioannis Mavromatis, Robert J. Piechocki, Mahesh Sooriyabandara, Arjun
Parekh | DRIVE: A Digital Network Oracle for Cooperative Intelligent
Transportation Systems | Accepted for publication at IEEE ISCC 2020 | null | 10.1109/ISCC50000.2020.9219683 | null | cs.NI cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a world where Artificial Intelligence revolutionizes inference, prediction
and decision-making tasks, Digital Twins emerge as game-changing tools. A case
in point is the development and optimization of Cooperative Intelligent
Transportation Systems (C-ITSs): a confluence of cyber-physical digital
infrastructure and (semi)automated mobility. Herein we introduce Digital Twin
for self-dRiving Intelligent VEhicles (DRIVE). The developed framework tackles
shortcomings of traditional vehicular and network simulators. It provides a
flexible, modular, and scalable implementation to ensure large-scale, city-wide
experimentation with a moderate computational cost. The defining feature of our
Digital Twin is a unique architecture allowing for submission of sequential
queries, to which the Digital Twin provides instantaneous responses with the
"state of the world", and hence is an Oracle. With such bidirectional
interaction with external intelligent agents and realistic mobility traces,
DRIVE provides the environment for development, training and optimization of
Machine Learning based C-ITS solutions.
| [
{
"created": "Tue, 7 Jul 2020 09:34:09 GMT",
"version": "v1"
}
] | 2022-09-05 | [
[
"Mavromatis",
"Ioannis",
""
],
[
"Piechocki",
"Robert J.",
""
],
[
"Sooriyabandara",
"Mahesh",
""
],
[
"Parekh",
"Arjun",
""
]
] | In a world where Artificial Intelligence revolutionizes inference, prediction and decision-making tasks, Digital Twins emerge as game-changing tools. A case in point is the development and optimization of Cooperative Intelligent Transportation Systems (C-ITSs): a confluence of cyber-physical digital infrastructure and (semi)automated mobility. Herein we introduce Digital Twin for self-dRiving Intelligent VEhicles (DRIVE). The developed framework tackles shortcomings of traditional vehicular and network simulators. It provides a flexible, modular, and scalable implementation to ensure large-scale, city-wide experimentation with a moderate computational cost. The defining feature of our Digital Twin is a unique architecture allowing for submission of sequential queries, to which the Digital Twin provides instantaneous responses with the "state of the world", and hence is an Oracle. With such bidirectional interaction with external intelligent agents and realistic mobility traces, DRIVE provides the environment for development, training and optimization of Machine Learning based C-ITS solutions. |
2304.05895 | Damien Dablain | Damien A. Dablain and Nitesh V. Chawla | Towards Understanding How Data Augmentation Works with Imbalanced Data | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Data augmentation forms the cornerstone of many modern machine learning
training pipelines; yet, the mechanisms by which it works are not clearly
understood. Much of the research on data augmentation (DA) has focused on
improving existing techniques, examining its regularization effects in the
context of neural network over-fitting, or investigating its impact on
features. Here, we undertake a holistic examination of the effect of DA on
three different classifiers, convolutional neural networks, support vector
machines, and logistic regression models, which are commonly used in supervised
classification of imbalanced data. We support our examination with testing on
three image and five tabular datasets. Our research indicates that DA, when
applied to imbalanced data, produces substantial changes in model weights,
support vectors and feature selection; even though it may only yield relatively
modest changes to global metrics, such as balanced accuracy or F1 measure. We
hypothesize that DA works by facilitating variances in data, so that machine
learning models can associate changes in the data with labels. By diversifying
the range of feature amplitudes that a model must recognize to predict a label,
DA improves a model's capacity to generalize when learning with imbalanced
data.
| [
{
"created": "Wed, 12 Apr 2023 15:01:22 GMT",
"version": "v1"
}
] | 2023-04-13 | [
[
"Dablain",
"Damien A.",
""
],
[
"Chawla",
"Nitesh V.",
""
]
] | Data augmentation forms the cornerstone of many modern machine learning training pipelines; yet, the mechanisms by which it works are not clearly understood. Much of the research on data augmentation (DA) has focused on improving existing techniques, examining its regularization effects in the context of neural network over-fitting, or investigating its impact on features. Here, we undertake a holistic examination of the effect of DA on three different classifiers, convolutional neural networks, support vector machines, and logistic regression models, which are commonly used in supervised classification of imbalanced data. We support our examination with testing on three image and five tabular datasets. Our research indicates that DA, when applied to imbalanced data, produces substantial changes in model weights, support vectors and feature selection; even though it may only yield relatively modest changes to global metrics, such as balanced accuracy or F1 measure. We hypothesize that DA works by facilitating variances in data, so that machine learning models can associate changes in the data with labels. By diversifying the range of feature amplitudes that a model must recognize to predict a label, DA improves a model's capacity to generalize when learning with imbalanced data. |
2001.05581 | Andreas Z\"ufle | Tobias Emrich, Hans-Peter Kriegel, Andreas Z\"ufle, Peer Kr\"oger,
Matthias Renz | Complete and Sufficient Spatial Domination of Multidimensional
Rectangles | null | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rectangles are used to approximate objects, or sets of objects, in a plethora
of applications, systems and index structures. Many tasks, such as nearest
neighbor search and similarity ranking, require to decide if objects in one
rectangle A may, must, or must not be closer to objects in a second rectangle
B, than objects in a third rectangle R. To decide this relation of "Spatial
Domination" it can be shown that using minimum and maximum distances it is
often impossible to detect spatial domination. This spatial gem provides a
necessary and sufficient decision criterion for spatial domination that can be
computed efficiently even in higher dimensional space. In addition, this
spatial gem provides an example, pseudocode and an implementation in Python.
| [
{
"created": "Wed, 15 Jan 2020 22:24:40 GMT",
"version": "v1"
}
] | 2020-01-17 | [
[
"Emrich",
"Tobias",
""
],
[
"Kriegel",
"Hans-Peter",
""
],
[
"Züfle",
"Andreas",
""
],
[
"Kröger",
"Peer",
""
],
[
"Renz",
"Matthias",
""
]
] | Rectangles are used to approximate objects, or sets of objects, in a plethora of applications, systems and index structures. Many tasks, such as nearest neighbor search and similarity ranking, require to decide if objects in one rectangle A may, must, or must not be closer to objects in a second rectangle B, than objects in a third rectangle R. To decide this relation of "Spatial Domination" it can be shown that using minimum and maximum distances it is often impossible to detect spatial domination. This spatial gem provides a necessary and sufficient decision criterion for spatial domination that can be computed efficiently even in higher dimensional space. In addition, this spatial gem provides an example, pseudocode and an implementation in Python. |
1306.5838 | Elena Khramtcova | Panagiotis Cheilaris, Elena Khramtcova, Evanthia Papadopoulou | Randomized incremental construction of the Hausdorff Voronoi diagram of
non-crossing clusters | This paper has been withdrawn by the author because the substantially
updated version (improved results, major text revision) is now submitted
(arXiv:1312.3904) | null | null | null | cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the Hausdorff Voronoi diagram of a set of clusters of points in the plane,
the distance between a point t and a cluster P is the maximum Euclidean
distance between t and a point in P. This diagram has direct applications in
VLSI design. We consider so-called "non-crossing" clusters. The complexity of
the Hausdorff diagram of m such clusters is linear in the total number n of
points in the convex hulls of all clusters. We present randomized incremental
constructions for computing efficiently the diagram, improving considerably
previous results. Our best complexity algorithm runs in expected time O((n +
m(log log(n))^2)log^2(n)) and worst-case space O(n). We also provide a more
practical algorithm whose expected running time is O((n + m log(n))log^2(n))
and expected space complexity is O(n). To achieve these bounds, we augment the
randomized incremental paradigm for the construction of Voronoi diagrams with
the ability to efficiently handle non-standard characteristics of generalized
Voronoi diagrams, such as sites of non-constant complexity, sites that are not
enclosed in their Voronoi regions, and empty Voronoi regions.
| [
{
"created": "Tue, 25 Jun 2013 03:12:56 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Dec 2013 12:31:12 GMT",
"version": "v2"
}
] | 2013-12-17 | [
[
"Cheilaris",
"Panagiotis",
""
],
[
"Khramtcova",
"Elena",
""
],
[
"Papadopoulou",
"Evanthia",
""
]
] | In the Hausdorff Voronoi diagram of a set of clusters of points in the plane, the distance between a point t and a cluster P is the maximum Euclidean distance between t and a point in P. This diagram has direct applications in VLSI design. We consider so-called "non-crossing" clusters. The complexity of the Hausdorff diagram of m such clusters is linear in the total number n of points in the convex hulls of all clusters. We present randomized incremental constructions for computing efficiently the diagram, improving considerably previous results. Our best complexity algorithm runs in expected time O((n + m(log log(n))^2)log^2(n)) and worst-case space O(n). We also provide a more practical algorithm whose expected running time is O((n + m log(n))log^2(n)) and expected space complexity is O(n). To achieve these bounds, we augment the randomized incremental paradigm for the construction of Voronoi diagrams with the ability to efficiently handle non-standard characteristics of generalized Voronoi diagrams, such as sites of non-constant complexity, sites that are not enclosed in their Voronoi regions, and empty Voronoi regions. |
2008.06471 | Gal Metzer | Gal Metzer, Rana Hanocka, Raja Giryes, Daniel Cohen-Or | Self-Sampling for Neural Point Cloud Consolidation | TOG 2021 | null | null | null | cs.GR cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a novel technique for neural point cloud consolidation which
learns from only the input point cloud. Unlike other point upsampling methods
which analyze shapes via local patches, in this work, we learn from global
subsets. We repeatedly self-sample the input point cloud with global subsets
that are used to train a deep neural network. Specifically, we define source
and target subsets according to the desired consolidation criteria (e.g.,
generating sharp points or points in sparse regions). The network learns a
mapping from source to target subsets, and implicitly learns to consolidate the
point cloud. During inference, the network is fed with random subsets of points
from the input, which it displaces to synthesize a consolidated point set. We
leverage the inductive bias of neural networks to eliminate noise and outliers,
a notoriously difficult problem in point cloud consolidation. The shared
weights of the network are optimized over the entire shape, learning non-local
statistics and exploiting the recurrence of local-scale geometries.
Specifically, the network encodes the distribution of the underlying shape
surface within a fixed set of local kernels, which results in the best
explanation of the underlying shape surface. We demonstrate the ability to
consolidate point sets from a variety of shapes, while eliminating outliers and
noise.
| [
{
"created": "Fri, 14 Aug 2020 17:16:02 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Mar 2021 17:09:31 GMT",
"version": "v2"
},
{
"created": "Fri, 13 May 2022 09:19:55 GMT",
"version": "v3"
}
] | 2022-05-16 | [
[
"Metzer",
"Gal",
""
],
[
"Hanocka",
"Rana",
""
],
[
"Giryes",
"Raja",
""
],
[
"Cohen-Or",
"Daniel",
""
]
] | We introduce a novel technique for neural point cloud consolidation which learns from only the input point cloud. Unlike other point upsampling methods which analyze shapes via local patches, in this work, we learn from global subsets. We repeatedly self-sample the input point cloud with global subsets that are used to train a deep neural network. Specifically, we define source and target subsets according to the desired consolidation criteria (e.g., generating sharp points or points in sparse regions). The network learns a mapping from source to target subsets, and implicitly learns to consolidate the point cloud. During inference, the network is fed with random subsets of points from the input, which it displaces to synthesize a consolidated point set. We leverage the inductive bias of neural networks to eliminate noise and outliers, a notoriously difficult problem in point cloud consolidation. The shared weights of the network are optimized over the entire shape, learning non-local statistics and exploiting the recurrence of local-scale geometries. Specifically, the network encodes the distribution of the underlying shape surface within a fixed set of local kernels, which results in the best explanation of the underlying shape surface. We demonstrate the ability to consolidate point sets from a variety of shapes, while eliminating outliers and noise. |
1708.09540 | Sukhdev Singh | Ayush Sharma, Piyush Bajpai, Sukhdev Singh and Kiran Khatter | Virtual Reality: Blessings and Risk Assessment | 22 page and 1 Table | null | null | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objectives: This paper presents an up-to-date overview of research performed
in the Virtual Reality (VR) environment ranging from definitions, its presence
in the various fields, and existing market players and their projects in the VR
technology. Further an attempt is made to gain an insight on the psychological
mechanism underlying experience in using VR device. Methods: Our literature
survey is based on the research articles, analysis of the projects of various
companies and their findings for different areas of interest. Findings: In our
literature survey we observed that the recent advances in virtual reality
enabling technologies have led to variety of virtual devices that facilitate
people to interact with the digital world. In fact in the past two decades
researchers have tried to integrate reality and VR in the form of intuitive
computer interface. Improvements: This has led to variety of potential benefits
of VR in many applications such as News, Healthcare, Entertainment, Tourism,
Military and Defence etc. However despite the extensive research efforts in
creating virtual system environments it is yet to become apparent in normal
daily life.
| [
{
"created": "Thu, 31 Aug 2017 02:34:22 GMT",
"version": "v1"
}
] | 2017-09-01 | [
[
"Sharma",
"Ayush",
""
],
[
"Bajpai",
"Piyush",
""
],
[
"Singh",
"Sukhdev",
""
],
[
"Khatter",
"Kiran",
""
]
] | Objectives: This paper presents an up-to-date overview of research performed in the Virtual Reality (VR) environment ranging from definitions, its presence in the various fields, and existing market players and their projects in the VR technology. Further an attempt is made to gain an insight on the psychological mechanism underlying experience in using VR device. Methods: Our literature survey is based on the research articles, analysis of the projects of various companies and their findings for different areas of interest. Findings: In our literature survey we observed that the recent advances in virtual reality enabling technologies have led to variety of virtual devices that facilitate people to interact with the digital world. In fact in the past two decades researchers have tried to integrate reality and VR in the form of intuitive computer interface. Improvements: This has led to variety of potential benefits of VR in many applications such as News, Healthcare, Entertainment, Tourism, Military and Defence etc. However despite the extensive research efforts in creating virtual system environments it is yet to become apparent in normal daily life. |
0905.1307 | Michael Goodrich | Michael T. Goodrich, Roberto Tamassia, Jasminka Hasic | An Efficient Dynamic and Distributed RSA Accumulator | Expanded version of a paper appearing in the 5th International
Information Security Conference (ISC) | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show how to use the RSA one-way accumulator to realize an efficient and
dynamic authenticated dictionary, where untrusted directories provide
cryptographically verifiable answers to membership queries on a set maintained
by a trusted source. Our accumulator-based scheme for authenticated
dictionaries supports efficient incremental updates of the underlying set by
insertions and deletions of elements. Also, the user can optimally verify in
constant time the authenticity of the answer provided by a directory with a
simple and practical algorithm. We have also implemented this scheme and we
give empirical results that can be used to determine the best strategy for
systems implementation with respect to resources that are available. This work
has applications to certificate revocation in public key infrastructure and
end-to-end integrity of data collections published by third parties on the
Internet.
| [
{
"created": "Fri, 8 May 2009 17:49:57 GMT",
"version": "v1"
}
] | 2009-05-11 | [
[
"Goodrich",
"Michael T.",
""
],
[
"Tamassia",
"Roberto",
""
],
[
"Hasic",
"Jasminka",
""
]
] | We show how to use the RSA one-way accumulator to realize an efficient and dynamic authenticated dictionary, where untrusted directories provide cryptographically verifiable answers to membership queries on a set maintained by a trusted source. Our accumulator-based scheme for authenticated dictionaries supports efficient incremental updates of the underlying set by insertions and deletions of elements. Also, the user can optimally verify in constant time the authenticity of the answer provided by a directory with a simple and practical algorithm. We have also implemented this scheme and we give empirical results that can be used to determine the best strategy for systems implementation with respect to resources that are available. This work has applications to certificate revocation in public key infrastructure and end-to-end integrity of data collections published by third parties on the Internet. |
2202.13114 | Hoang Lam Nguyen | Hoang Lam Nguyen, Lars Grunske | BeDivFuzz: Integrating Behavioral Diversity into Generator-based Fuzzing | To appear in the proceedings of the 44th International Conference on
Software Engineering (ICSE 2022) | null | 10.1145/3510003.3510182 | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A popular metric to evaluate the performance of fuzzers is branch coverage.
However, we argue that focusing solely on covering many different branches
(i.e., the richness) is not sufficient since the majority of the covered
branches may have been exercised only once, which does not inspire a high
confidence in the reliability of the covered code. Instead, the distribution of
the executed branches (i.e., the evenness) should also be considered. That is,
behavioral diversity is only given if the generated inputs not only trigger
many different branches, but also trigger them evenly often with diverse
inputs. We introduce BeDivFuzz, a feedback-driven fuzzing technique for
generator-based fuzzers. BeDivFuzz distinguishes between structure-preserving
and structure-changing mutations in the space of syntactically valid inputs,
and biases its mutation strategy towards validity and behavioral diversity
based on the received program feedback. We have evaluated BeDivFuzz on Ant,
Maven, Rhino, Closure, Nashorn, and Tomcat. The results show that BeDivFuzz
achieves better behavioral diversity than the state of the art, measured by
established biodiversity metrics, namely the Hill numbers, from the field of
ecology.
| [
{
"created": "Sat, 26 Feb 2022 11:03:35 GMT",
"version": "v1"
}
] | 2022-03-01 | [
[
"Nguyen",
"Hoang Lam",
""
],
[
"Grunske",
"Lars",
""
]
] | A popular metric to evaluate the performance of fuzzers is branch coverage. However, we argue that focusing solely on covering many different branches (i.e., the richness) is not sufficient since the majority of the covered branches may have been exercised only once, which does not inspire a high confidence in the reliability of the covered code. Instead, the distribution of the executed branches (i.e., the evenness) should also be considered. That is, behavioral diversity is only given if the generated inputs not only trigger many different branches, but also trigger them evenly often with diverse inputs. We introduce BeDivFuzz, a feedback-driven fuzzing technique for generator-based fuzzers. BeDivFuzz distinguishes between structure-preserving and structure-changing mutations in the space of syntactically valid inputs, and biases its mutation strategy towards validity and behavioral diversity based on the received program feedback. We have evaluated BeDivFuzz on Ant, Maven, Rhino, Closure, Nashorn, and Tomcat. The results show that BeDivFuzz achieves better behavioral diversity than the state of the art, measured by established biodiversity metrics, namely the Hill numbers, from the field of ecology. |
2311.08552 | Mahmoud Salem | Mahmoud G. Salem, Jiayu Ye, Chu-Cheng Lin, Frederick Liu | UT5: Pretraining Non autoregressive T5 with unrolled denoising | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Recent advances in Transformer-based Large Language Models have made great
strides in natural language generation. However, to decode K tokens, an
autoregressive model needs K sequential forward passes, which may be a
performance bottleneck for large language models. Many non-autoregressive (NAR)
research are aiming to address this sequentiality bottleneck, albeit many have
focused on a dedicated architecture in supervised benchmarks. In this work, we
studied unsupervised pretraining for non auto-regressive T5 models via unrolled
denoising and shown its SoTA results in downstream generation tasks such as
SQuAD question generation and XSum.
| [
{
"created": "Tue, 14 Nov 2023 21:28:10 GMT",
"version": "v1"
}
] | 2023-11-16 | [
[
"Salem",
"Mahmoud G.",
""
],
[
"Ye",
"Jiayu",
""
],
[
"Lin",
"Chu-Cheng",
""
],
[
"Liu",
"Frederick",
""
]
] | Recent advances in Transformer-based Large Language Models have made great strides in natural language generation. However, to decode K tokens, an autoregressive model needs K sequential forward passes, which may be a performance bottleneck for large language models. Many non-autoregressive (NAR) research are aiming to address this sequentiality bottleneck, albeit many have focused on a dedicated architecture in supervised benchmarks. In this work, we studied unsupervised pretraining for non auto-regressive T5 models via unrolled denoising and shown its SoTA results in downstream generation tasks such as SQuAD question generation and XSum. |
1706.08675 | Dorien Herremans | Dorien Herremans, Ching-Hua Chuan | Proceedings of the First International Workshop on Deep Learning and
Music | null | Proceedings of the First International Workshop on Deep Learning
and Music, joint with IJCNN, Anchorage, US, May 17-18, 2017 | 10.13140/RG.2.2.22227.99364/1 | null | cs.NE cs.LG cs.MM cs.SD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proceedings of the First International Workshop on Deep Learning and Music,
joint with IJCNN, Anchorage, US, May 17-18, 2017
| [
{
"created": "Tue, 27 Jun 2017 05:28:06 GMT",
"version": "v1"
}
] | 2017-06-28 | [
[
"Herremans",
"Dorien",
""
],
[
"Chuan",
"Ching-Hua",
""
]
] | Proceedings of the First International Workshop on Deep Learning and Music, joint with IJCNN, Anchorage, US, May 17-18, 2017 |
2210.07474 | Xiaojian Ma | Xiaojian Ma, Silong Yong, Zilong Zheng, Qing Li, Yitao Liang,
Song-Chun Zhu, Siyuan Huang | SQA3D: Situated Question Answering in 3D Scenes | ICLR 2023. First two authors contributed equally. Project website:
https://sqa3d.github.io | null | null | null | cs.CV cs.AI cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | We propose a new task to benchmark scene understanding of embodied agents:
Situated Question Answering in 3D Scenes (SQA3D). Given a scene context (e.g.,
3D scan), SQA3D requires the tested agent to first understand its situation
(position, orientation, etc.) in the 3D scene as described by text, then reason
about its surrounding environment and answer a question under that situation.
Based upon 650 scenes from ScanNet, we provide a dataset centered around 6.8k
unique situations, along with 20.4k descriptions and 33.4k diverse reasoning
questions for these situations. These questions examine a wide spectrum of
reasoning capabilities for an intelligent agent, ranging from spatial relation
comprehension to commonsense understanding, navigation, and multi-hop
reasoning. SQA3D imposes a significant challenge to current multi-modal
especially 3D reasoning models. We evaluate various state-of-the-art approaches
and find that the best one only achieves an overall score of 47.20%, while
amateur human participants can reach 90.06%. We believe SQA3D could facilitate
future embodied AI research with stronger situation understanding and reasoning
capability.
| [
{
"created": "Fri, 14 Oct 2022 02:52:26 GMT",
"version": "v1"
},
{
"created": "Sat, 22 Oct 2022 15:25:26 GMT",
"version": "v2"
},
{
"created": "Sat, 11 Feb 2023 01:57:41 GMT",
"version": "v3"
},
{
"created": "Wed, 22 Feb 2023 08:25:24 GMT",
"version": "v4"
},
{
"created": "Wed, 12 Apr 2023 20:05:41 GMT",
"version": "v5"
}
] | 2023-04-14 | [
[
"Ma",
"Xiaojian",
""
],
[
"Yong",
"Silong",
""
],
[
"Zheng",
"Zilong",
""
],
[
"Li",
"Qing",
""
],
[
"Liang",
"Yitao",
""
],
[
"Zhu",
"Song-Chun",
""
],
[
"Huang",
"Siyuan",
""
]
] | We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D). Given a scene context (e.g., 3D scan), SQA3D requires the tested agent to first understand its situation (position, orientation, etc.) in the 3D scene as described by text, then reason about its surrounding environment and answer a question under that situation. Based upon 650 scenes from ScanNet, we provide a dataset centered around 6.8k unique situations, along with 20.4k descriptions and 33.4k diverse reasoning questions for these situations. These questions examine a wide spectrum of reasoning capabilities for an intelligent agent, ranging from spatial relation comprehension to commonsense understanding, navigation, and multi-hop reasoning. SQA3D imposes a significant challenge to current multi-modal especially 3D reasoning models. We evaluate various state-of-the-art approaches and find that the best one only achieves an overall score of 47.20%, while amateur human participants can reach 90.06%. We believe SQA3D could facilitate future embodied AI research with stronger situation understanding and reasoning capability. |
2308.02874 | Zijie Wu | Zijie Wu, Yaonan Wang, Mingtao Feng, He Xie, Ajmal Mian | Sketch and Text Guided Diffusion Model for Colored Point Cloud
Generation | Accepted by ICCV 2023 | null | null | null | cs.CV cs.MM | http://creativecommons.org/licenses/by/4.0/ | Diffusion probabilistic models have achieved remarkable success in text
guided image generation. However, generating 3D shapes is still challenging due
to the lack of sufficient data containing 3D models along with their
descriptions. Moreover, text based descriptions of 3D shapes are inherently
ambiguous and lack details. In this paper, we propose a sketch and text guided
probabilistic diffusion model for colored point cloud generation that
conditions the denoising process jointly with a hand drawn sketch of the object
and its textual description. We incrementally diffuse the point coordinates and
color values in a joint diffusion process to reach a Gaussian distribution.
Colored point cloud generation thus amounts to learning the reverse diffusion
process, conditioned by the sketch and text, to iteratively recover the desired
shape and color. Specifically, to learn effective sketch-text embedding, our
model adaptively aggregates the joint embedding of text prompt and the sketch
based on a capsule attention network. Our model uses staged diffusion to
generate the shape and then assign colors to different parts conditioned on the
appearance prompt while preserving precise shapes from the first stage. This
gives our model the flexibility to extend to multiple tasks, such as appearance
re-editing and part segmentation. Experimental results demonstrate that our
model outperforms recent state-of-the-art in point cloud generation.
| [
{
"created": "Sat, 5 Aug 2023 13:10:43 GMT",
"version": "v1"
}
] | 2023-08-08 | [
[
"Wu",
"Zijie",
""
],
[
"Wang",
"Yaonan",
""
],
[
"Feng",
"Mingtao",
""
],
[
"Xie",
"He",
""
],
[
"Mian",
"Ajmal",
""
]
] | Diffusion probabilistic models have achieved remarkable success in text guided image generation. However, generating 3D shapes is still challenging due to the lack of sufficient data containing 3D models along with their descriptions. Moreover, text based descriptions of 3D shapes are inherently ambiguous and lack details. In this paper, we propose a sketch and text guided probabilistic diffusion model for colored point cloud generation that conditions the denoising process jointly with a hand drawn sketch of the object and its textual description. We incrementally diffuse the point coordinates and color values in a joint diffusion process to reach a Gaussian distribution. Colored point cloud generation thus amounts to learning the reverse diffusion process, conditioned by the sketch and text, to iteratively recover the desired shape and color. Specifically, to learn effective sketch-text embedding, our model adaptively aggregates the joint embedding of text prompt and the sketch based on a capsule attention network. Our model uses staged diffusion to generate the shape and then assign colors to different parts conditioned on the appearance prompt while preserving precise shapes from the first stage. This gives our model the flexibility to extend to multiple tasks, such as appearance re-editing and part segmentation. Experimental results demonstrate that our model outperforms recent state-of-the-art in point cloud generation. |
1102.5197 | Moez Hizem | Moez Hizem and Ridha Bouallegue | Fine Synchronization through UWB TH-PPM Impulse Radios | 11 pages, 7 figures | International Journal of Wireless & Mobile Networks (IJWMN) Vol.
3, No. 1, February 2011 | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, a novel fine timing algorithm has been tested and developed to
synchronize Ultra-Wideband (UWB) signals with pulse position modulation (PPM).
By applying this algorithm, we evaluate timing algorithms in both data-aided
(DA) and non-data-aided (NDA) modes. Based on correlation operations, our
algorithm remains operational in practical UWB settings. The proposed timing
scheme consists of two complementary floors or steps. The first floor consists
on a coarse synchronization which is founded on the recently proposed
acquisition scheme based on dirty templates (TDT). In the second floor, we
investigate a new fine synchronization algorithm which gives an improved
estimate of timing offset. Simulations confirm performance improvement of our
timing synchronization compared to the original TDT algorithm in terms of mean
square error.
| [
{
"created": "Fri, 25 Feb 2011 09:31:28 GMT",
"version": "v1"
}
] | 2011-02-28 | [
[
"Hizem",
"Moez",
""
],
[
"Bouallegue",
"Ridha",
""
]
] | In this paper, a novel fine timing algorithm has been tested and developed to synchronize Ultra-Wideband (UWB) signals with pulse position modulation (PPM). By applying this algorithm, we evaluate timing algorithms in both data-aided (DA) and non-data-aided (NDA) modes. Based on correlation operations, our algorithm remains operational in practical UWB settings. The proposed timing scheme consists of two complementary floors or steps. The first floor consists on a coarse synchronization which is founded on the recently proposed acquisition scheme based on dirty templates (TDT). In the second floor, we investigate a new fine synchronization algorithm which gives an improved estimate of timing offset. Simulations confirm performance improvement of our timing synchronization compared to the original TDT algorithm in terms of mean square error. |
1304.0270 | Jun Zhu Professor | Fan Wang, Jun Zhu and Lin Zhang | An optimal problem for relative entropy | 10 page | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relative entropy is an essential tool in quantum information theory. There
are so many problems which are related to relative entropy. In this article,
the optimal values which are defined by $\displaystyle\max_{U\in{U(\cX_{d})}}
S(U\rho{U^{\ast}}\parallel\sigma)$ and $\displaystyle\min_{U\in{U(\cX_{d})}}
S(U\rho{U^{\ast}}\parallel\sigma)$ for two positive definite operators
$\rho,\sigma\in{\textmd{Pd}(\cX)}$ are obtained. And the set of
$S(U\rho{U^{\ast}}\parallel\sigma)$ for every unitary operator $U$ is full of
the interval $[\displaystyle\min_{U\in{U(\cX_{d})}}
S(U\rho{U^{\ast}}\parallel\sigma),\displaystyle\max_{U\in{U(\cX_{d})}}
S(U\rho{U^{\ast}}\parallel\sigma)]$
| [
{
"created": "Mon, 1 Apr 2013 00:53:08 GMT",
"version": "v1"
}
] | 2013-04-02 | [
[
"Wang",
"Fan",
""
],
[
"Zhu",
"Jun",
""
],
[
"Zhang",
"Lin",
""
]
] | Relative entropy is an essential tool in quantum information theory. There are so many problems which are related to relative entropy. In this article, the optimal values which are defined by $\displaystyle\max_{U\in{U(\cX_{d})}} S(U\rho{U^{\ast}}\parallel\sigma)$ and $\displaystyle\min_{U\in{U(\cX_{d})}} S(U\rho{U^{\ast}}\parallel\sigma)$ for two positive definite operators $\rho,\sigma\in{\textmd{Pd}(\cX)}$ are obtained. And the set of $S(U\rho{U^{\ast}}\parallel\sigma)$ for every unitary operator $U$ is full of the interval $[\displaystyle\min_{U\in{U(\cX_{d})}} S(U\rho{U^{\ast}}\parallel\sigma),\displaystyle\max_{U\in{U(\cX_{d})}} S(U\rho{U^{\ast}}\parallel\sigma)]$ |
0909.5649 | Vitaly Osipov | Nikolaj Leischner, Vitaly Osipov, Peter Sanders | GPU sample sort | null | null | null | null | cs.DS cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present the design of a sample sort algorithm for manycore
GPUs. Despite being one of the most efficient comparison-based sorting
algorithms for distributed memory architectures its performance on GPUs was
previously unknown. For uniformly distributed keys our sample sort is at least
25% and on average 68% faster than the best comparison-based sorting algorithm,
GPU Thrust merge sort, and on average more than 2 times faster than GPU
quicksort. Moreover, for 64-bit integer keys it is at least 63% and on average
2 times faster than the highly optimized GPU Thrust radix sort that directly
manipulates the binary representation of keys. Our implementation is robust to
different distributions and entropy levels of keys and scales almost linearly
with the input size. These results indicate that multi-way techniques in
general and sample sort in particular achieve substantially better performance
than two-way merge sort and quicksort.
| [
{
"created": "Wed, 30 Sep 2009 15:58:53 GMT",
"version": "v1"
}
] | 2009-10-01 | [
[
"Leischner",
"Nikolaj",
""
],
[
"Osipov",
"Vitaly",
""
],
[
"Sanders",
"Peter",
""
]
] | In this paper, we present the design of a sample sort algorithm for manycore GPUs. Despite being one of the most efficient comparison-based sorting algorithms for distributed memory architectures its performance on GPUs was previously unknown. For uniformly distributed keys our sample sort is at least 25% and on average 68% faster than the best comparison-based sorting algorithm, GPU Thrust merge sort, and on average more than 2 times faster than GPU quicksort. Moreover, for 64-bit integer keys it is at least 63% and on average 2 times faster than the highly optimized GPU Thrust radix sort that directly manipulates the binary representation of keys. Our implementation is robust to different distributions and entropy levels of keys and scales almost linearly with the input size. These results indicate that multi-way techniques in general and sample sort in particular achieve substantially better performance than two-way merge sort and quicksort. |
2408.05746 | Nianzu Li | Nianzu Li, Weidong Mei, Boyu Ning, Peiran Wu | Movable Antenna Enhanced AF Relaying: Two-Stage Antenna Position
Optimization | null | null | null | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The movable antenna (MA) technology has attracted increasing attention in
wireless communications due to its capability for flexibly adjusting the
positions of multiple antennas in a local region to reconfigure channel
conditions. In this paper, we investigate its application in an
amplify-and-forward (AF) relay system, where a multi-MA AF relay is deployed to
assist in the wireless communications from a source to a destination. In
particular, we aim to maximize the achievable rate at the destination, by
jointly optimizing the AF weight matrix at the relay and its MAs' positions in
two stages for receiving the signal from the source and transmitting its
amplified version to the destination, respectively. However, compared to the
existing one-stage antenna position optimization, the two-stage position
optimization is more challenging due to its intricate coupling in the
achievable rate at the destination. To tackle this challenge, we decompose the
considered problem into several subproblems by invoking the alternating
optimization (AO) and solve them by using the semidefinite programming and the
gradient ascent. Numerical results demonstrate the superiority of our proposed
system over the conventional relaying system with fixed-position antennas
(FPAs) and also drive essential insights.
| [
{
"created": "Sun, 11 Aug 2024 10:58:13 GMT",
"version": "v1"
}
] | 2024-08-13 | [
[
"Li",
"Nianzu",
""
],
[
"Mei",
"Weidong",
""
],
[
"Ning",
"Boyu",
""
],
[
"Wu",
"Peiran",
""
]
] | The movable antenna (MA) technology has attracted increasing attention in wireless communications due to its capability for flexibly adjusting the positions of multiple antennas in a local region to reconfigure channel conditions. In this paper, we investigate its application in an amplify-and-forward (AF) relay system, where a multi-MA AF relay is deployed to assist in the wireless communications from a source to a destination. In particular, we aim to maximize the achievable rate at the destination, by jointly optimizing the AF weight matrix at the relay and its MAs' positions in two stages for receiving the signal from the source and transmitting its amplified version to the destination, respectively. However, compared to the existing one-stage antenna position optimization, the two-stage position optimization is more challenging due to its intricate coupling in the achievable rate at the destination. To tackle this challenge, we decompose the considered problem into several subproblems by invoking the alternating optimization (AO) and solve them by using the semidefinite programming and the gradient ascent. Numerical results demonstrate the superiority of our proposed system over the conventional relaying system with fixed-position antennas (FPAs) and also drive essential insights. |
2002.08859 | Eitan Richardson | Eitan Richardson and Yair Weiss | A Bayes-Optimal View on Adversarial Examples | Minor revision per journal review, 28 pages | null | null | null | cs.LG cs.CR cs.CV stat.ML | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Since the discovery of adversarial examples - the ability to fool modern CNN
classifiers with tiny perturbations of the input, there has been much
discussion whether they are a "bug" that is specific to current neural
architectures and training methods or an inevitable "feature" of high
dimensional geometry. In this paper, we argue for examining adversarial
examples from the perspective of Bayes-Optimal classification. We construct
realistic image datasets for which the Bayes-Optimal classifier can be
efficiently computed and derive analytic conditions on the distributions under
which these classifiers are provably robust against any adversarial attack even
in high dimensions. Our results show that even when these "gold standard"
optimal classifiers are robust, CNNs trained on the same datasets consistently
learn a vulnerable classifier, indicating that adversarial examples are often
an avoidable "bug". We further show that RBF SVMs trained on the same data
consistently learn a robust classifier. The same trend is observed in
experiments with real images in different datasets.
| [
{
"created": "Thu, 20 Feb 2020 16:43:47 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Mar 2021 09:47:10 GMT",
"version": "v2"
}
] | 2021-03-18 | [
[
"Richardson",
"Eitan",
""
],
[
"Weiss",
"Yair",
""
]
] | Since the discovery of adversarial examples - the ability to fool modern CNN classifiers with tiny perturbations of the input, there has been much discussion whether they are a "bug" that is specific to current neural architectures and training methods or an inevitable "feature" of high dimensional geometry. In this paper, we argue for examining adversarial examples from the perspective of Bayes-Optimal classification. We construct realistic image datasets for which the Bayes-Optimal classifier can be efficiently computed and derive analytic conditions on the distributions under which these classifiers are provably robust against any adversarial attack even in high dimensions. Our results show that even when these "gold standard" optimal classifiers are robust, CNNs trained on the same datasets consistently learn a vulnerable classifier, indicating that adversarial examples are often an avoidable "bug". We further show that RBF SVMs trained on the same data consistently learn a robust classifier. The same trend is observed in experiments with real images in different datasets. |
2111.12077 | Jonathan Barron | Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan,
Peter Hedman | Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields | https://jonbarron.info/mipnerf360/ | null | null | null | cs.CV cs.GR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Though neural radiance fields (NeRF) have demonstrated impressive view
synthesis results on objects and small bounded regions of space, they struggle
on "unbounded" scenes, where the camera may point in any direction and content
may exist at any distance. In this setting, existing NeRF-like models often
produce blurry or low-resolution renderings (due to the unbalanced detail and
scale of nearby and distant objects), are slow to train, and may exhibit
artifacts due to the inherent ambiguity of the task of reconstructing a large
scene from a small set of images. We present an extension of mip-NeRF (a NeRF
variant that addresses sampling and aliasing) that uses a non-linear scene
parameterization, online distillation, and a novel distortion-based regularizer
to overcome the challenges presented by unbounded scenes. Our model, which we
dub "mip-NeRF 360" as we target scenes in which the camera rotates 360 degrees
around a point, reduces mean-squared error by 57% compared to mip-NeRF, and is
able to produce realistic synthesized views and detailed depth maps for highly
intricate, unbounded real-world scenes.
| [
{
"created": "Tue, 23 Nov 2021 18:51:18 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Nov 2021 18:51:06 GMT",
"version": "v2"
},
{
"created": "Fri, 25 Mar 2022 23:05:20 GMT",
"version": "v3"
}
] | 2022-03-29 | [
[
"Barron",
"Jonathan T.",
""
],
[
"Mildenhall",
"Ben",
""
],
[
"Verbin",
"Dor",
""
],
[
"Srinivasan",
"Pratul P.",
""
],
[
"Hedman",
"Peter",
""
]
] | Though neural radiance fields (NeRF) have demonstrated impressive view synthesis results on objects and small bounded regions of space, they struggle on "unbounded" scenes, where the camera may point in any direction and content may exist at any distance. In this setting, existing NeRF-like models often produce blurry or low-resolution renderings (due to the unbalanced detail and scale of nearby and distant objects), are slow to train, and may exhibit artifacts due to the inherent ambiguity of the task of reconstructing a large scene from a small set of images. We present an extension of mip-NeRF (a NeRF variant that addresses sampling and aliasing) that uses a non-linear scene parameterization, online distillation, and a novel distortion-based regularizer to overcome the challenges presented by unbounded scenes. Our model, which we dub "mip-NeRF 360" as we target scenes in which the camera rotates 360 degrees around a point, reduces mean-squared error by 57% compared to mip-NeRF, and is able to produce realistic synthesized views and detailed depth maps for highly intricate, unbounded real-world scenes. |
1103.4080 | Armel Ulrich Kemloh Wagoum | A. U. Kemloh Wagoum, A. Seyfried and S. Holl | Modelling dynamic route choice of pedestrians to assess the criticality
of building evacuation | 15 pages, 34 figures | null | null | null | cs.OH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an event-driven way finding algorithm for pedestrians in
an evacuation scenario, which operates on a graph-based structure. The
motivation of each pedestrian is to leave the facility. The events used to
redirect pedestrians include the identification of a jam situation and/or
identification of a better route than the current. This study considers two
types of pedestrians: familiar and unfamiliar with the facility. Four
strategies are modelled to cover those groups. The modelled strategies are the
shortest path (local and global); They are combined with a quickest path
approach, which is based on an observation principle. In the quickest path
approach, pedestrians take their decisions based on the observed environment
and are routed dynamically in the network using an appropriate cost benefit
analysis function. The dynamic modelling of route choice with different
strategies and types of pedestrians considers the manifold of in uences which
appears in the real system and raises questions about the criticality of an
evacuation process. To address this question criteria are elaborated. The
criteria we focus on in this contribution are the evacuation time, the
individual times spent in jam, the jam size evolution and the overall jam size
itself. The in uences of the different strategies on those evaluation criteria
are investigated. The sensibility of the system to disturbances (e.g. broken
escape route) is also analysed. Keywords: pedestrian dynamics, routing,
quickest path, evacuation, jam, critical state
| [
{
"created": "Mon, 21 Mar 2011 17:04:08 GMT",
"version": "v1"
}
] | 2011-03-22 | [
[
"Wagoum",
"A. U. Kemloh",
""
],
[
"Seyfried",
"A.",
""
],
[
"Holl",
"S.",
""
]
] | This paper presents an event-driven way finding algorithm for pedestrians in an evacuation scenario, which operates on a graph-based structure. The motivation of each pedestrian is to leave the facility. The events used to redirect pedestrians include the identification of a jam situation and/or identification of a better route than the current. This study considers two types of pedestrians: familiar and unfamiliar with the facility. Four strategies are modelled to cover those groups. The modelled strategies are the shortest path (local and global); They are combined with a quickest path approach, which is based on an observation principle. In the quickest path approach, pedestrians take their decisions based on the observed environment and are routed dynamically in the network using an appropriate cost benefit analysis function. The dynamic modelling of route choice with different strategies and types of pedestrians considers the manifold of in uences which appears in the real system and raises questions about the criticality of an evacuation process. To address this question criteria are elaborated. The criteria we focus on in this contribution are the evacuation time, the individual times spent in jam, the jam size evolution and the overall jam size itself. The in uences of the different strategies on those evaluation criteria are investigated. The sensibility of the system to disturbances (e.g. broken escape route) is also analysed. Keywords: pedestrian dynamics, routing, quickest path, evacuation, jam, critical state |
2211.10409 | Chi Zhang | Chi Zhang, Paul Scheffler, Thomas Benz, Matteo Perotti, Luca Benini | AXI-Pack: Near-Memory Bus Packing for Bandwidth-Efficient Irregular
Workloads | 6 pages, 5 figures. Submitted to DATE 2023 | null | null | null | cs.AR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data-intensive applications involving irregular memory streams are
inefficiently handled by modern processors and memory systems highly optimized
for regular, contiguous data. Recent work tackles these inefficiencies in
hardware through core-side stream extensions or memory-side prefetchers and
accelerators, but fails to provide end-to-end solutions which also achieve high
efficiency in on-chip interconnects. We propose AXI-Pack, an extension to ARM's
AXI4 protocol introducing bandwidth-efficient strided and indirect bursts to
enable end-to-end irregular streams. AXI-Pack adds irregular stream semantics
to memory requests and avoids inefficient narrow-bus transfers by packing
multiple narrow data elements onto a wide bus. It retains full compatibility
with AXI4 and does not require modifications to non-burst-reshaping
interconnect IPs. To demonstrate our approach end-to-end, we extend an
open-source RISC-V vector processor to leverage AXI-Pack at its memory
interface for strided and indexed accesses. On the memory side, we design a
banked memory controller efficiently handling AXI-Pack requests. On a system
with a 256-bit-wide interconnect running FP32 workloads, AXI-Pack achieves
near-ideal peak on-chip bus utilizations of 87% and 39%, speedups of 5.4x and
2.4x, and energy efficiency improvements of 5.3x and 2.1x over a baseline using
an AXI4 bus on strided and indirect benchmarks, respectively.
| [
{
"created": "Fri, 18 Nov 2022 18:23:47 GMT",
"version": "v1"
}
] | 2022-11-21 | [
[
"Zhang",
"Chi",
""
],
[
"Scheffler",
"Paul",
""
],
[
"Benz",
"Thomas",
""
],
[
"Perotti",
"Matteo",
""
],
[
"Benini",
"Luca",
""
]
] | Data-intensive applications involving irregular memory streams are inefficiently handled by modern processors and memory systems highly optimized for regular, contiguous data. Recent work tackles these inefficiencies in hardware through core-side stream extensions or memory-side prefetchers and accelerators, but fails to provide end-to-end solutions which also achieve high efficiency in on-chip interconnects. We propose AXI-Pack, an extension to ARM's AXI4 protocol introducing bandwidth-efficient strided and indirect bursts to enable end-to-end irregular streams. AXI-Pack adds irregular stream semantics to memory requests and avoids inefficient narrow-bus transfers by packing multiple narrow data elements onto a wide bus. It retains full compatibility with AXI4 and does not require modifications to non-burst-reshaping interconnect IPs. To demonstrate our approach end-to-end, we extend an open-source RISC-V vector processor to leverage AXI-Pack at its memory interface for strided and indexed accesses. On the memory side, we design a banked memory controller efficiently handling AXI-Pack requests. On a system with a 256-bit-wide interconnect running FP32 workloads, AXI-Pack achieves near-ideal peak on-chip bus utilizations of 87% and 39%, speedups of 5.4x and 2.4x, and energy efficiency improvements of 5.3x and 2.1x over a baseline using an AXI4 bus on strided and indirect benchmarks, respectively. |
2303.13514 | Mehmet Ayg\"un | Mehmet Ayg\"un and Oisin Mac Aodha | SAOR: Single-View Articulated Object Reconstruction | Accepted to CVPR 2024, website: https://mehmetaygun.github.io/saor | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | We introduce SAOR, a novel approach for estimating the 3D shape, texture, and
viewpoint of an articulated object from a single image captured in the wild.
Unlike prior approaches that rely on pre-defined category-specific 3D templates
or tailored 3D skeletons, SAOR learns to articulate shapes from single-view
image collections with a skeleton-free part-based model without requiring any
3D object shape priors. To prevent ill-posed solutions, we propose a
cross-instance consistency loss that exploits disentangled object shape
deformation and articulation. This is helped by a new silhouette-based sampling
mechanism to enhance viewpoint diversity during training. Our method only
requires estimated object silhouettes and relative depth maps from
off-the-shelf pre-trained networks during training. At inference time, given a
single-view image, it efficiently outputs an explicit mesh representation. We
obtain improved qualitative and quantitative results on challenging quadruped
animals compared to relevant existing work.
| [
{
"created": "Thu, 23 Mar 2023 17:59:35 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Dec 2023 19:25:10 GMT",
"version": "v2"
},
{
"created": "Mon, 8 Apr 2024 11:22:05 GMT",
"version": "v3"
}
] | 2024-04-09 | [
[
"Aygün",
"Mehmet",
""
],
[
"Mac Aodha",
"Oisin",
""
]
] | We introduce SAOR, a novel approach for estimating the 3D shape, texture, and viewpoint of an articulated object from a single image captured in the wild. Unlike prior approaches that rely on pre-defined category-specific 3D templates or tailored 3D skeletons, SAOR learns to articulate shapes from single-view image collections with a skeleton-free part-based model without requiring any 3D object shape priors. To prevent ill-posed solutions, we propose a cross-instance consistency loss that exploits disentangled object shape deformation and articulation. This is helped by a new silhouette-based sampling mechanism to enhance viewpoint diversity during training. Our method only requires estimated object silhouettes and relative depth maps from off-the-shelf pre-trained networks during training. At inference time, given a single-view image, it efficiently outputs an explicit mesh representation. We obtain improved qualitative and quantitative results on challenging quadruped animals compared to relevant existing work. |
1611.03453 | Abhishek Gupta | Abhishek Gupta, M. Farhan Habib, Uttam Mandal, Pulak Chowdhury,
Massimo Tornatore, and Biswanath Mukherjee | On Service-Chaining Strategies using Virtual Network Functions in
Operator Networks | null | https://doi.org/10.1016/j.comnet.2018.01.028 | 10.1016/j.comnet.2018.01.028 | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network functions (e.g., firewalls, load balancers, etc.) have been
traditionally provided through proprietary hardware appliances. Often, hardware
appliances need to be hardwired back to back to form a service chain providing
chained network functions. Hardware appliances cannot be provisioned on demand
since they are statically embedded in the network topology, making creation,
insertion, modification, upgrade, and removal of service chains complex, and
also slowing down service innovation. Hence, network operators are starting to
deploy Virtual Network Functions (VNFs), which are virtualized over commodity
hardware. VNFs can be deployed in Data Centers (DCs) or in Network Function
Virtualization (NFV) capable network elements (nodes) such as routers and
switches. NFV capable nodes and DCs together form a Network enabled Cloud (NeC)
that helps to facilitate the dynamic service chaining required to support
evolving network traffic and its service demands. In this study, we focus on
the VNF service chain placement and traffic routing problem, and build a model
for placing a VNF service chain while minimizing network resource consumption.
Our results indicate that a NeC having a DC and NFV capable nodes can
significantly reduce network-resource consumption.
| [
{
"created": "Thu, 10 Nov 2016 19:15:45 GMT",
"version": "v1"
}
] | 2018-04-23 | [
[
"Gupta",
"Abhishek",
""
],
[
"Habib",
"M. Farhan",
""
],
[
"Mandal",
"Uttam",
""
],
[
"Chowdhury",
"Pulak",
""
],
[
"Tornatore",
"Massimo",
""
],
[
"Mukherjee",
"Biswanath",
""
]
] | Network functions (e.g., firewalls, load balancers, etc.) have been traditionally provided through proprietary hardware appliances. Often, hardware appliances need to be hardwired back to back to form a service chain providing chained network functions. Hardware appliances cannot be provisioned on demand since they are statically embedded in the network topology, making creation, insertion, modification, upgrade, and removal of service chains complex, and also slowing down service innovation. Hence, network operators are starting to deploy Virtual Network Functions (VNFs), which are virtualized over commodity hardware. VNFs can be deployed in Data Centers (DCs) or in Network Function Virtualization (NFV) capable network elements (nodes) such as routers and switches. NFV capable nodes and DCs together form a Network enabled Cloud (NeC) that helps to facilitate the dynamic service chaining required to support evolving network traffic and its service demands. In this study, we focus on the VNF service chain placement and traffic routing problem, and build a model for placing a VNF service chain while minimizing network resource consumption. Our results indicate that a NeC having a DC and NFV capable nodes can significantly reduce network-resource consumption. |
2209.09668 | Max Klimm | Max Klimm and Martin Knaack | Maximizing a Submodular Function with Bounded Curvature under an Unknown
Knapsack Constraint | null | null | null | null | cs.DS cs.DM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies the problem of maximizing a monotone submodular function
under an unknown knapsack constraint. A solution to this problem is a policy
that decides which item to pack next based on the past packing history. The
robustness factor of a policy is the worst case ratio of the solution obtained
by following the policy and an optimal solution that knows the knapsack
capacity. We develop an algorithm with a robustness factor that is decreasing
in the curvature $B$ of the submodular function. For the extreme cases $c=0$
corresponding to a modular objective, it matches a previously known and best
possible robustness factor of $1/2$. For the other extreme case of $c=1$ it
yields a robustness factor of $\approx 0.35$ improving over the best previously
known robustness factor of $\approx 0.06$.
| [
{
"created": "Tue, 20 Sep 2022 12:04:59 GMT",
"version": "v1"
}
] | 2022-09-21 | [
[
"Klimm",
"Max",
""
],
[
"Knaack",
"Martin",
""
]
] | This paper studies the problem of maximizing a monotone submodular function under an unknown knapsack constraint. A solution to this problem is a policy that decides which item to pack next based on the past packing history. The robustness factor of a policy is the worst case ratio of the solution obtained by following the policy and an optimal solution that knows the knapsack capacity. We develop an algorithm with a robustness factor that is decreasing in the curvature $B$ of the submodular function. For the extreme cases $c=0$ corresponding to a modular objective, it matches a previously known and best possible robustness factor of $1/2$. For the other extreme case of $c=1$ it yields a robustness factor of $\approx 0.35$ improving over the best previously known robustness factor of $\approx 0.06$. |
1511.05768 | Andreas Bulling | Marc Tonsen, Xucong Zhang, Yusuke Sugano, Andreas Bulling | Labeled pupils in the wild: A dataset for studying pupil detection in
unconstrained environments | null | null | 10.1145/2857491.2857520 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present labelled pupils in the wild (LPW), a novel dataset of 66
high-quality, high-speed eye region videos for the development and evaluation
of pupil detection algorithms. The videos in our dataset were recorded from 22
participants in everyday locations at about 95 FPS using a state-of-the-art
dark-pupil head-mounted eye tracker. They cover people with different
ethnicities, a diverse set of everyday indoor and outdoor illumination
environments, as well as natural gaze direction distributions. The dataset also
includes participants wearing glasses, contact lenses, as well as make-up. We
benchmark five state-of-the-art pupil detection algorithms on our dataset with
respect to robustness and accuracy. We further study the influence of image
resolution, vision aids, as well as recording location (indoor, outdoor) on
pupil detection performance. Our evaluations provide valuable insights into the
general pupil detection problem and allow us to identify key challenges for
robust pupil detection on head-mounted eye trackers.
| [
{
"created": "Wed, 18 Nov 2015 13:30:55 GMT",
"version": "v1"
}
] | 2017-02-07 | [
[
"Tonsen",
"Marc",
""
],
[
"Zhang",
"Xucong",
""
],
[
"Sugano",
"Yusuke",
""
],
[
"Bulling",
"Andreas",
""
]
] | We present labelled pupils in the wild (LPW), a novel dataset of 66 high-quality, high-speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted eye tracker. They cover people with different ethnicities, a diverse set of everyday indoor and outdoor illumination environments, as well as natural gaze direction distributions. The dataset also includes participants wearing glasses, contact lenses, as well as make-up. We benchmark five state-of-the-art pupil detection algorithms on our dataset with respect to robustness and accuracy. We further study the influence of image resolution, vision aids, as well as recording location (indoor, outdoor) on pupil detection performance. Our evaluations provide valuable insights into the general pupil detection problem and allow us to identify key challenges for robust pupil detection on head-mounted eye trackers. |
2407.03596 | Xuerong Zhang | Xuerong Zhang, Li Huang, Jing Lv, Ming Yang | Self Adaptive Threshold Pseudo-labeling and Unreliable Sample
Contrastive Loss for Semi-supervised Image Classification | ICANN24 accepted | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Semi-supervised learning is attracting blooming attention, due to its success
in combining unlabeled data. However, pseudo-labeling-based semi-supervised
approaches suffer from two problems in image classification: (1) Existing
methods might fail to adopt suitable thresholds since they either use a
pre-defined/fixed threshold or an ad-hoc threshold adjusting scheme, resulting
in inferior performance and slow convergence. (2) Discarding unlabeled data
with confidence below the thresholds results in the loss of discriminating
information. To solve these issues, we develop an effective method to make
sufficient use of unlabeled data. Specifically, we design a self adaptive
threshold pseudo-labeling strategy, which thresholds for each class can be
dynamically adjusted to increase the number of reliable samples. Meanwhile, in
order to effectively utilise unlabeled data with confidence below the
thresholds, we propose an unreliable sample contrastive loss to mine the
discriminative information in low-confidence samples by learning the
similarities and differences between sample features. We evaluate our method on
several classification benchmarks under partially labeled settings and
demonstrate its superiority over the other approaches.
| [
{
"created": "Thu, 4 Jul 2024 03:04:56 GMT",
"version": "v1"
}
] | 2024-07-08 | [
[
"Zhang",
"Xuerong",
""
],
[
"Huang",
"Li",
""
],
[
"Lv",
"Jing",
""
],
[
"Yang",
"Ming",
""
]
] | Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. However, pseudo-labeling-based semi-supervised approaches suffer from two problems in image classification: (1) Existing methods might fail to adopt suitable thresholds since they either use a pre-defined/fixed threshold or an ad-hoc threshold adjusting scheme, resulting in inferior performance and slow convergence. (2) Discarding unlabeled data with confidence below the thresholds results in the loss of discriminating information. To solve these issues, we develop an effective method to make sufficient use of unlabeled data. Specifically, we design a self adaptive threshold pseudo-labeling strategy, which thresholds for each class can be dynamically adjusted to increase the number of reliable samples. Meanwhile, in order to effectively utilise unlabeled data with confidence below the thresholds, we propose an unreliable sample contrastive loss to mine the discriminative information in low-confidence samples by learning the similarities and differences between sample features. We evaluate our method on several classification benchmarks under partially labeled settings and demonstrate its superiority over the other approaches. |
2303.04392 | Amaael Antonini | Amaael Antonini, Rita Gimelshein, and Richard Wesel | Achievable Rates and Low-Complexity Encoding of Posterior Matching for
the BSC | This paper consists of 26 pages and contains 6 figures. An earlier
version of the algorithm included in this paper was published at the 2020
IEEE International Symposium on Information Theory (ISIT), (DOI:
10.1109/ISIT44484.2020.9174232) | null | null | null | cs.IT cs.IR math.IT | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Horstein, Burnashev, Shayevitz and Feder, Naghshvar et al. and others have
studied sequential transmission of a K-bit message over the binary symmetric
channel (BSC) with full, noiseless feedback using posterior matching. Yang et
al. provide an improved lower bound on the achievable rate using martingale
analysis that relies on the small-enough difference (SED) partitioning
introduced by Naghshvar et al. SED requires a relatively complex encoder and
decoder. To reduce complexity, this paper replaces SED with relaxed constraints
that admit the small enough absolute difference (SEAD) partitioning rule. The
main analytical results show that achievable-rate bounds higher than those
found by Yang et al. are possible even under the new constraints, which are
less restrictive than SED. The new analysis does not use martingale theory for
the confirmation phase and applies a surrogate channel technique to tighten the
results. An initial systematic transmission further increases the achievable
rate bound. The simplified encoder associated with SEAD has a complexity below
order O(K^2) and allows simulations for message sizes of at least 1000 bits.
For example, simulations achieve 99% of of the channel's 0.50-bit capacity with
an average block size of 200 bits for a target codeword error rate of 10^(-3).
| [
{
"created": "Wed, 8 Mar 2023 05:53:33 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Mar 2023 01:31:11 GMT",
"version": "v2"
}
] | 2023-03-13 | [
[
"Antonini",
"Amaael",
""
],
[
"Gimelshein",
"Rita",
""
],
[
"Wesel",
"Richard",
""
]
] | Horstein, Burnashev, Shayevitz and Feder, Naghshvar et al. and others have studied sequential transmission of a K-bit message over the binary symmetric channel (BSC) with full, noiseless feedback using posterior matching. Yang et al. provide an improved lower bound on the achievable rate using martingale analysis that relies on the small-enough difference (SED) partitioning introduced by Naghshvar et al. SED requires a relatively complex encoder and decoder. To reduce complexity, this paper replaces SED with relaxed constraints that admit the small enough absolute difference (SEAD) partitioning rule. The main analytical results show that achievable-rate bounds higher than those found by Yang et al. are possible even under the new constraints, which are less restrictive than SED. The new analysis does not use martingale theory for the confirmation phase and applies a surrogate channel technique to tighten the results. An initial systematic transmission further increases the achievable rate bound. The simplified encoder associated with SEAD has a complexity below order O(K^2) and allows simulations for message sizes of at least 1000 bits. For example, simulations achieve 99% of of the channel's 0.50-bit capacity with an average block size of 200 bits for a target codeword error rate of 10^(-3). |
2401.10353 | Mian Zhang | Mian Zhang, Lifeng Jin, Linfeng Song, Haitao Mi and Dong Yu | Inconsistent dialogue responses and how to recover from them | Accepted in EACL 2024. Code and dataset available at
https://github.com/mianzhang/CIDER | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | One critical issue for chat systems is to stay consistent about preferences,
opinions, beliefs and facts of itself, which has been shown a difficult
problem. In this work, we study methods to assess and bolster utterance
consistency of chat systems. A dataset is first developed for studying the
inconsistencies, where inconsistent dialogue responses, explanations of the
inconsistencies, and recovery utterances are authored by annotators. This
covers the life span of inconsistencies, namely introduction, understanding,
and resolution. Building on this, we introduce a set of tasks centered on
dialogue consistency, specifically focused on its detection and resolution. Our
experimental findings indicate that our dataset significantly helps the
progress in identifying and resolving conversational inconsistencies, and
current popular large language models like ChatGPT which are good at resolving
inconsistencies however still struggle with detection.
| [
{
"created": "Thu, 18 Jan 2024 19:46:04 GMT",
"version": "v1"
}
] | 2024-01-22 | [
[
"Zhang",
"Mian",
""
],
[
"Jin",
"Lifeng",
""
],
[
"Song",
"Linfeng",
""
],
[
"Mi",
"Haitao",
""
],
[
"Yu",
"Dong",
""
]
] | One critical issue for chat systems is to stay consistent about preferences, opinions, beliefs and facts of itself, which has been shown a difficult problem. In this work, we study methods to assess and bolster utterance consistency of chat systems. A dataset is first developed for studying the inconsistencies, where inconsistent dialogue responses, explanations of the inconsistencies, and recovery utterances are authored by annotators. This covers the life span of inconsistencies, namely introduction, understanding, and resolution. Building on this, we introduce a set of tasks centered on dialogue consistency, specifically focused on its detection and resolution. Our experimental findings indicate that our dataset significantly helps the progress in identifying and resolving conversational inconsistencies, and current popular large language models like ChatGPT which are good at resolving inconsistencies however still struggle with detection. |
2307.10447 | Yumeng Xue | Yumeng Xue, Patrick Paetzold, Rebecca Kehlbeck, Bin Chen, Kin Chung
Kwan, Yunhai Wang, and Oliver Deussen | Reducing Ambiguities in Line-based Density Plots by Image-space
Colorization | Published in IEEE Transactions on Visualization and Computer Graphics
(Supplementary Material: https://osf.io/jm5yz/) | null | 10.1109/TVCG.2023.3327149 | null | cs.GR | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Line-based density plots are used to reduce visual clutter in line charts
with a multitude of individual lines. However, these traditional density plots
are often perceived ambiguously, which obstructs the user's identification of
underlying trends in complex datasets. Thus, we propose a novel image space
coloring method for line-based density plots that enhances their
interpretability. Our method employs color not only to visually communicate
data density but also to highlight similar regions in the plot, allowing users
to identify and distinguish trends easily. We achieve this by performing
hierarchical clustering based on the lines passing through each region and
mapping the identified clusters to the hue circle using circular MDS.
Additionally, we propose a heuristic approach to assign each line to the most
probable cluster, enabling users to analyze density and individual lines. We
motivate our method by conducting a small-scale user study, demonstrating the
effectiveness of our method using synthetic and real-world datasets, and
providing an interactive online tool for generating colored line-based density
plots.
| [
{
"created": "Sun, 16 Jul 2023 15:15:00 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Nov 2023 13:22:48 GMT",
"version": "v2"
}
] | 2023-11-23 | [
[
"Xue",
"Yumeng",
""
],
[
"Paetzold",
"Patrick",
""
],
[
"Kehlbeck",
"Rebecca",
""
],
[
"Chen",
"Bin",
""
],
[
"Kwan",
"Kin Chung",
""
],
[
"Wang",
"Yunhai",
""
],
[
"Deussen",
"Oliver",
""
]
] | Line-based density plots are used to reduce visual clutter in line charts with a multitude of individual lines. However, these traditional density plots are often perceived ambiguously, which obstructs the user's identification of underlying trends in complex datasets. Thus, we propose a novel image space coloring method for line-based density plots that enhances their interpretability. Our method employs color not only to visually communicate data density but also to highlight similar regions in the plot, allowing users to identify and distinguish trends easily. We achieve this by performing hierarchical clustering based on the lines passing through each region and mapping the identified clusters to the hue circle using circular MDS. Additionally, we propose a heuristic approach to assign each line to the most probable cluster, enabling users to analyze density and individual lines. We motivate our method by conducting a small-scale user study, demonstrating the effectiveness of our method using synthetic and real-world datasets, and providing an interactive online tool for generating colored line-based density plots. |
2401.15289 | Xi Tan | Xi Tan, Zheyuan Ma, Sandro Pinto, Le Guan, Ning Zhang, Jun Xu,
Zhiqiang Lin, Hongxin Hu, Ziming Zhao | SoK: Where's the "up"?! A Comprehensive (bottom-up) Study on the
Security of Arm Cortex-M Systems | To Appear in the 18th USENIX WOOT Conference on Offensive
Technologies, August 12-13, 2024 | null | null | null | cs.CR cs.AR | http://creativecommons.org/licenses/by/4.0/ | Arm Cortex-M processors are the most widely used 32-bit microcontrollers
among embedded and Internet-of-Things devices. Despite the widespread usage,
there has been little effort in summarizing their hardware security features,
characterizing the limitations and vulnerabilities of their hardware and
software stack, and systematizing the research on securing these systems. The
goals and contributions of this paper are multi-fold. First, we analyze the
hardware security limitations and issues of Cortex-M systems. Second, we
conducted a deep study of the software stack designed for Cortex-M and revealed
its limitations, which is accompanied by an empirical analysis of 1,797
real-world firmware. Third, we categorize the reported bugs in Cortex-M
software systems. Finally, we systematize the efforts that aim at securing
Cortex-M systems and evaluate them in terms of the protections they offer,
runtime performance, required hardware features, etc. Based on the insights, we
develop a set of recommendations for the research community and MCU software
developers.
| [
{
"created": "Sat, 27 Jan 2024 04:09:29 GMT",
"version": "v1"
},
{
"created": "Wed, 31 Jan 2024 17:20:26 GMT",
"version": "v2"
},
{
"created": "Mon, 13 May 2024 21:09:28 GMT",
"version": "v3"
}
] | 2024-05-15 | [
[
"Tan",
"Xi",
""
],
[
"Ma",
"Zheyuan",
""
],
[
"Pinto",
"Sandro",
""
],
[
"Guan",
"Le",
""
],
[
"Zhang",
"Ning",
""
],
[
"Xu",
"Jun",
""
],
[
"Lin",
"Zhiqiang",
""
],
[
"Hu",
"Hongxin",
""
],
[
"Zhao",
"Ziming",
""
]
] | Arm Cortex-M processors are the most widely used 32-bit microcontrollers among embedded and Internet-of-Things devices. Despite the widespread usage, there has been little effort in summarizing their hardware security features, characterizing the limitations and vulnerabilities of their hardware and software stack, and systematizing the research on securing these systems. The goals and contributions of this paper are multi-fold. First, we analyze the hardware security limitations and issues of Cortex-M systems. Second, we conducted a deep study of the software stack designed for Cortex-M and revealed its limitations, which is accompanied by an empirical analysis of 1,797 real-world firmware. Third, we categorize the reported bugs in Cortex-M software systems. Finally, we systematize the efforts that aim at securing Cortex-M systems and evaluate them in terms of the protections they offer, runtime performance, required hardware features, etc. Based on the insights, we develop a set of recommendations for the research community and MCU software developers. |
2205.01873 | Yuanfei Dai | Yuanfei Dai, Wenzhong Guo and Carsten Eickhoff | Wasserstein Adversarial Learning based Temporal Knowledge Graph
Embedding | null | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Research on knowledge graph embedding (KGE) has emerged as an active field in
which most existing KGE approaches mainly focus on static structural data and
ignore the influence of temporal variation involved in time-aware triples. In
order to deal with this issue, several temporal knowledge graph embedding
(TKGE) approaches have been proposed to integrate temporal and structural
information in recent years. However, these methods only employ a uniformly
random sampling to construct negative facts. As a consequence, the corrupted
samples are often too simplistic for training an effective model. In this
paper, we propose a new temporal knowledge graph embedding framework by
introducing adversarial learning to further refine the performance of
traditional TKGE models. In our framework, a generator is utilized to construct
high-quality plausible quadruples and a discriminator learns to obtain the
embeddings of entities and relations based on both positive and negative
samples. Meanwhile, we also apply a Gumbel-Softmax relaxation and the
Wasserstein distance to prevent vanishing gradient problems on discrete data;
an inherent flaw in traditional generative adversarial networks. Through
comprehensive experimentation on temporal datasets, the results indicate that
our proposed framework can attain significant improvements based on benchmark
models and also demonstrate the effectiveness and applicability of our
framework.
| [
{
"created": "Wed, 4 May 2022 03:28:49 GMT",
"version": "v1"
}
] | 2022-05-05 | [
[
"Dai",
"Yuanfei",
""
],
[
"Guo",
"Wenzhong",
""
],
[
"Eickhoff",
"Carsten",
""
]
] | Research on knowledge graph embedding (KGE) has emerged as an active field in which most existing KGE approaches mainly focus on static structural data and ignore the influence of temporal variation involved in time-aware triples. In order to deal with this issue, several temporal knowledge graph embedding (TKGE) approaches have been proposed to integrate temporal and structural information in recent years. However, these methods only employ a uniformly random sampling to construct negative facts. As a consequence, the corrupted samples are often too simplistic for training an effective model. In this paper, we propose a new temporal knowledge graph embedding framework by introducing adversarial learning to further refine the performance of traditional TKGE models. In our framework, a generator is utilized to construct high-quality plausible quadruples and a discriminator learns to obtain the embeddings of entities and relations based on both positive and negative samples. Meanwhile, we also apply a Gumbel-Softmax relaxation and the Wasserstein distance to prevent vanishing gradient problems on discrete data; an inherent flaw in traditional generative adversarial networks. Through comprehensive experimentation on temporal datasets, the results indicate that our proposed framework can attain significant improvements based on benchmark models and also demonstrate the effectiveness and applicability of our framework. |
1612.05877 | Zhiwu Huang | Zhiwu Huang, Chengde Wan, Thomas Probst, Luc Van Gool | Deep Learning on Lie Groups for Skeleton-based Action Recognition | Accepted to CVPR 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, skeleton-based action recognition has become a popular 3D
classification problem. State-of-the-art methods typically first represent each
motion sequence as a high-dimensional trajectory on a Lie group with an
additional dynamic time warping, and then shallowly learn favorable Lie group
features. In this paper we incorporate the Lie group structure into a deep
network architecture to learn more appropriate Lie group features for 3D action
recognition. Within the network structure, we design rotation mapping layers to
transform the input Lie group features into desirable ones, which are aligned
better in the temporal domain. To reduce the high feature dimensionality, the
architecture is equipped with rotation pooling layers for the elements on the
Lie group. Furthermore, we propose a logarithm mapping layer to map the
resulting manifold data into a tangent space that facilitates the application
of regular output layers for the final classification. Evaluations of the
proposed network for standard 3D human action recognition datasets clearly
demonstrate its superiority over existing shallow Lie group feature learning
methods as well as most conventional deep learning methods.
| [
{
"created": "Sun, 18 Dec 2016 09:08:29 GMT",
"version": "v1"
},
{
"created": "Tue, 11 Apr 2017 08:47:00 GMT",
"version": "v2"
}
] | 2017-04-12 | [
[
"Huang",
"Zhiwu",
""
],
[
"Wan",
"Chengde",
""
],
[
"Probst",
"Thomas",
""
],
[
"Van Gool",
"Luc",
""
]
] | In recent years, skeleton-based action recognition has become a popular 3D classification problem. State-of-the-art methods typically first represent each motion sequence as a high-dimensional trajectory on a Lie group with an additional dynamic time warping, and then shallowly learn favorable Lie group features. In this paper we incorporate the Lie group structure into a deep network architecture to learn more appropriate Lie group features for 3D action recognition. Within the network structure, we design rotation mapping layers to transform the input Lie group features into desirable ones, which are aligned better in the temporal domain. To reduce the high feature dimensionality, the architecture is equipped with rotation pooling layers for the elements on the Lie group. Furthermore, we propose a logarithm mapping layer to map the resulting manifold data into a tangent space that facilitates the application of regular output layers for the final classification. Evaluations of the proposed network for standard 3D human action recognition datasets clearly demonstrate its superiority over existing shallow Lie group feature learning methods as well as most conventional deep learning methods. |
1806.04836 | Noam Buckman | Noam Buckman, Han-Lim Choi, Jonathan P. How | Partial Replanning for Decentralized Dynamic Task Allocation | 11 pages, Accepted to AIAA GNC 2019 | null | 10.2514/6.2019-0915 | null | cs.MA | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In time-sensitive and dynamic missions, multi-UAV teams must respond quickly
to new information and objectives. This paper presents a dynamic decentralized
task allocation algorithm for allocating new tasks that appear online during
the solving of the task allocation problem. Our algorithm extends the
Consensus-Based Bundle Algorithm (CBBA), a decentralized task allocation
algorithm, allowing for the fast allocation of new tasks without a full
reallocation of existing tasks. CBBA with Partial Replanning (CBBA-PR) enables
the team to trade-off between convergence time and increased coordination by
resetting a portion of their previous allocation at every round of bidding on
tasks. By resetting the last tasks allocated by each agent, we are able to
ensure the convergence of the team to a conflict-free solution. CBBA-PR can be
further improved by reducing the team size involved in the replanning, further
reducing the communication burden of the team and runtime of CBBA-PR. Finally,
we validate the faster convergence and improved solution quality of CBBA-PR in
multi-UAV simulations.
| [
{
"created": "Wed, 13 Jun 2018 03:18:40 GMT",
"version": "v1"
},
{
"created": "Thu, 25 Oct 2018 22:45:04 GMT",
"version": "v2"
}
] | 2023-10-02 | [
[
"Buckman",
"Noam",
""
],
[
"Choi",
"Han-Lim",
""
],
[
"How",
"Jonathan P.",
""
]
] | In time-sensitive and dynamic missions, multi-UAV teams must respond quickly to new information and objectives. This paper presents a dynamic decentralized task allocation algorithm for allocating new tasks that appear online during the solving of the task allocation problem. Our algorithm extends the Consensus-Based Bundle Algorithm (CBBA), a decentralized task allocation algorithm, allowing for the fast allocation of new tasks without a full reallocation of existing tasks. CBBA with Partial Replanning (CBBA-PR) enables the team to trade-off between convergence time and increased coordination by resetting a portion of their previous allocation at every round of bidding on tasks. By resetting the last tasks allocated by each agent, we are able to ensure the convergence of the team to a conflict-free solution. CBBA-PR can be further improved by reducing the team size involved in the replanning, further reducing the communication burden of the team and runtime of CBBA-PR. Finally, we validate the faster convergence and improved solution quality of CBBA-PR in multi-UAV simulations. |
2102.07833 | Aleksei Sorokin | Sou-Cheng T. Choi, Fred J. Hickernell, R. Jagadeeswaran, Michael J.
McCourt, and Aleksei G. Sorokin | Quasi-Monte Carlo Software | 25 pages, 7 figures, to be published in the MCQMC2020 Proceedings | null | null | null | cs.MS cs.NA math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Practitioners wishing to experience the efficiency gains from using low
discrepancy sequences need correct, robust, well-written software. This
article, based on our MCQMC 2020 tutorial, describes some of the better
quasi-Monte Carlo (QMC) software available. We highlight the key software
components required by QMC to approximate multivariate integrals or
expectations of functions of vector random variables. We have combined these
components in QMCPy, a Python open-source library, which we hope will draw the
support of the QMC community. Here we introduce QMCPy.
| [
{
"created": "Mon, 15 Feb 2021 20:21:05 GMT",
"version": "v1"
},
{
"created": "Wed, 29 Sep 2021 14:52:31 GMT",
"version": "v2"
},
{
"created": "Thu, 14 Oct 2021 17:44:05 GMT",
"version": "v3"
}
] | 2021-10-15 | [
[
"Choi",
"Sou-Cheng T.",
""
],
[
"Hickernell",
"Fred J.",
""
],
[
"Jagadeeswaran",
"R.",
""
],
[
"McCourt",
"Michael J.",
""
],
[
"Sorokin",
"Aleksei G.",
""
]
] | Practitioners wishing to experience the efficiency gains from using low discrepancy sequences need correct, robust, well-written software. This article, based on our MCQMC 2020 tutorial, describes some of the better quasi-Monte Carlo (QMC) software available. We highlight the key software components required by QMC to approximate multivariate integrals or expectations of functions of vector random variables. We have combined these components in QMCPy, a Python open-source library, which we hope will draw the support of the QMC community. Here we introduce QMCPy. |
1807.11929 | Mengmi Zhang | Mengmi Zhang, Keng Teck Ma, Shih-Cheng Yen, Joo Hwee Lim, Qi Zhao, and
Jiashi Feng | Egocentric Spatial Memory | 8 pages, 6 figures, accepted in IROS 2018 | null | null | null | cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Egocentric spatial memory (ESM) defines a memory system with encoding,
storing, recognizing and recalling the spatial information about the
environment from an egocentric perspective. We introduce an integrated deep
neural network architecture for modeling ESM. It learns to estimate the
occupancy state of the world and progressively construct top-down 2D global
maps from egocentric views in a spatially extended environment. During the
exploration, our proposed ESM model updates belief of the global map based on
local observations using a recurrent neural network. It also augments the local
mapping with a novel external memory to encode and store latent representations
of the visited places over long-term exploration in large environments which
enables agents to perform place recognition and hence, loop closure. Our
proposed ESM network contributes in the following aspects: (1) without feature
engineering, our model predicts free space based on egocentric views
efficiently in an end-to-end manner; (2) different from other deep
learning-based mapping system, ESMN deals with continuous actions and states
which is vitally important for robotic control in real applications. In the
experiments, we demonstrate its accurate and robust global mapping capacities
in 3D virtual mazes and realistic indoor environments by comparing with several
competitive baselines.
| [
{
"created": "Tue, 31 Jul 2018 17:27:19 GMT",
"version": "v1"
}
] | 2018-08-01 | [
[
"Zhang",
"Mengmi",
""
],
[
"Ma",
"Keng Teck",
""
],
[
"Yen",
"Shih-Cheng",
""
],
[
"Lim",
"Joo Hwee",
""
],
[
"Zhao",
"Qi",
""
],
[
"Feng",
"Jiashi",
""
]
] | Egocentric spatial memory (ESM) defines a memory system with encoding, storing, recognizing and recalling the spatial information about the environment from an egocentric perspective. We introduce an integrated deep neural network architecture for modeling ESM. It learns to estimate the occupancy state of the world and progressively construct top-down 2D global maps from egocentric views in a spatially extended environment. During the exploration, our proposed ESM model updates belief of the global map based on local observations using a recurrent neural network. It also augments the local mapping with a novel external memory to encode and store latent representations of the visited places over long-term exploration in large environments which enables agents to perform place recognition and hence, loop closure. Our proposed ESM network contributes in the following aspects: (1) without feature engineering, our model predicts free space based on egocentric views efficiently in an end-to-end manner; (2) different from other deep learning-based mapping system, ESMN deals with continuous actions and states which is vitally important for robotic control in real applications. In the experiments, we demonstrate its accurate and robust global mapping capacities in 3D virtual mazes and realistic indoor environments by comparing with several competitive baselines. |
2110.14460 | Maciej Drozdowski | Thomas Robertazzi, Maciej Drozdowski | Interaction Maxima in Distributed Systems | 10 pages, 1 figure | null | null | null | cs.DM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper we study the maximum degree of interaction which may emerge in
distributed systems. It is assumed that a distributed system is represented by
a graph of nodes interacting over edges. Each node has some amount of data. The
intensity of interaction over an edge is proportional to the product of the
amounts of data in each node at either end of the edge. The maximum sum of
interactions over the edges is searched for. This model can be extended to
other interacting entities. For bipartite graphs and odd-length cycles we prove
that the greatest degree of interaction emerge when the whole data is
concentrated in an arbitrary pair of neighbors. Equal partitioning of the load
is shown to be optimum for complete graphs. Finally, we show that in general
graphs for maximum interaction the data should be distributed equally between
the nodes of the largest clique in the graph. We also present in this context a
result of Motzkin and Straus from 1965 for the maximal interaction objective.
| [
{
"created": "Wed, 27 Oct 2021 14:28:11 GMT",
"version": "v1"
}
] | 2021-10-28 | [
[
"Robertazzi",
"Thomas",
""
],
[
"Drozdowski",
"Maciej",
""
]
] | In this paper we study the maximum degree of interaction which may emerge in distributed systems. It is assumed that a distributed system is represented by a graph of nodes interacting over edges. Each node has some amount of data. The intensity of interaction over an edge is proportional to the product of the amounts of data in each node at either end of the edge. The maximum sum of interactions over the edges is searched for. This model can be extended to other interacting entities. For bipartite graphs and odd-length cycles we prove that the greatest degree of interaction emerge when the whole data is concentrated in an arbitrary pair of neighbors. Equal partitioning of the load is shown to be optimum for complete graphs. Finally, we show that in general graphs for maximum interaction the data should be distributed equally between the nodes of the largest clique in the graph. We also present in this context a result of Motzkin and Straus from 1965 for the maximal interaction objective. |
2208.07464 | Brendon G. Anderson | Brendon G. Anderson, Tanmay Gautam, Somayeh Sojoudi | An Overview and Prospective Outlook on Robust Training and Certification
of Machine Learning Models | null | null | null | null | cs.LG math.OC stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this discussion paper, we survey recent research surrounding robustness of
machine learning models. As learning algorithms become increasingly more
popular in data-driven control systems, their robustness to data uncertainty
must be ensured in order to maintain reliable safety-critical operations. We
begin by reviewing common formalisms for such robustness, and then move on to
discuss popular and state-of-the-art techniques for training robust machine
learning models as well as methods for provably certifying such robustness.
From this unification of robust machine learning, we identify and discuss
pressing directions for future research in the area.
| [
{
"created": "Mon, 15 Aug 2022 23:09:54 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Sep 2022 16:55:39 GMT",
"version": "v2"
}
] | 2022-09-28 | [
[
"Anderson",
"Brendon G.",
""
],
[
"Gautam",
"Tanmay",
""
],
[
"Sojoudi",
"Somayeh",
""
]
] | In this discussion paper, we survey recent research surrounding robustness of machine learning models. As learning algorithms become increasingly more popular in data-driven control systems, their robustness to data uncertainty must be ensured in order to maintain reliable safety-critical operations. We begin by reviewing common formalisms for such robustness, and then move on to discuss popular and state-of-the-art techniques for training robust machine learning models as well as methods for provably certifying such robustness. From this unification of robust machine learning, we identify and discuss pressing directions for future research in the area. |
2011.05507 | Chun-Na Li | Yan-Ru Guo, Yan-Qin Bai, Chun-Na Li, Lan Bai, Yuan-Hai Shao | Two-dimensional Bhattacharyya bound linear discriminant analysis with
its applications | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently proposed L2-norm linear discriminant analysis criterion via the
Bhattacharyya error bound estimation (L2BLDA) is an effective improvement of
linear discriminant analysis (LDA) for feature extraction. However, L2BLDA is
only proposed to cope with vector input samples. When facing with
two-dimensional (2D) inputs, such as images, it will lose some useful
information, since it does not consider intrinsic structure of images. In this
paper, we extend L2BLDA to a two-dimensional Bhattacharyya bound linear
discriminant analysis (2DBLDA). 2DBLDA maximizes the matrix-based between-class
distance which is measured by the weighted pairwise distances of class means
and meanwhile minimizes the matrix-based within-class distance. The weighting
constant between the between-class and within-class terms is determined by the
involved data that makes the proposed 2DBLDA adaptive. In addition, the
criterion of 2DBLDA is equivalent to optimizing an upper bound of the
Bhattacharyya error. The construction of 2DBLDA makes it avoid the small sample
size problem while also possess robustness, and can be solved through a simple
standard eigenvalue decomposition problem. The experimental results on image
recognition and face image reconstruction demonstrate the effectiveness of the
proposed methods.
| [
{
"created": "Wed, 11 Nov 2020 01:56:42 GMT",
"version": "v1"
}
] | 2020-11-12 | [
[
"Guo",
"Yan-Ru",
""
],
[
"Bai",
"Yan-Qin",
""
],
[
"Li",
"Chun-Na",
""
],
[
"Bai",
"Lan",
""
],
[
"Shao",
"Yuan-Hai",
""
]
] | Recently proposed L2-norm linear discriminant analysis criterion via the Bhattacharyya error bound estimation (L2BLDA) is an effective improvement of linear discriminant analysis (LDA) for feature extraction. However, L2BLDA is only proposed to cope with vector input samples. When facing with two-dimensional (2D) inputs, such as images, it will lose some useful information, since it does not consider intrinsic structure of images. In this paper, we extend L2BLDA to a two-dimensional Bhattacharyya bound linear discriminant analysis (2DBLDA). 2DBLDA maximizes the matrix-based between-class distance which is measured by the weighted pairwise distances of class means and meanwhile minimizes the matrix-based within-class distance. The weighting constant between the between-class and within-class terms is determined by the involved data that makes the proposed 2DBLDA adaptive. In addition, the criterion of 2DBLDA is equivalent to optimizing an upper bound of the Bhattacharyya error. The construction of 2DBLDA makes it avoid the small sample size problem while also possess robustness, and can be solved through a simple standard eigenvalue decomposition problem. The experimental results on image recognition and face image reconstruction demonstrate the effectiveness of the proposed methods. |
2310.20212 | Yuan Wei | Zhiyuan Wei, Jing Sun, Zijian Zhang, Xianhao Zhang, Meng Li, Liehuang
Zhu | A Comparative Evaluation of Automated Analysis Tools for Solidity Smart
Contracts | 24 pages, 6 figure, IEEE Communications Surveys & Tutorials | null | null | null | cs.DC | http://creativecommons.org/licenses/by/4.0/ | Blockchain smart contracts have emerged as a transformative force in the
digital realm, spawning a diverse range of compelling applications. Since
solidity smart contracts across various domains manage trillions of dollars in
virtual coins, they become a prime target for attacks. One of the primary
challenges is keeping abreast of the latest techniques and tools for developing
secure smart contracts and examining those already deployed. In this paper, we
seek to address these challenges from four aspects: (1) We begin by examining
ten automatic tools, specifically focusing on their methodologies and their
ability to identify vulnerabilities in solidity smart contracts. (2) We propose
a novel criterion for evaluating these tools, based on the ISO/IEC 25010
standard. (3) To facilitate the evaluation of the selected tools, we construct
a benchmark that encompasses two distinct datasets: a collection of 389
labelled smart contracts and a scaled set of 20,000 unique cases from
real-world contracts. (4) We provide a comparison of the selected tools,
offering insights into their strengths and weaknesses and highlighting areas
where further improvements are needed. Through this evaluation, we hope to
provide developers and researchers with valuable guidance on selecting and
using smart contract analysis tools and contribute to the ongoing efforts to
improve the security and reliability of smart contracts.
| [
{
"created": "Tue, 31 Oct 2023 06:20:42 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Nov 2023 15:54:52 GMT",
"version": "v2"
},
{
"created": "Thu, 2 Nov 2023 00:33:22 GMT",
"version": "v3"
}
] | 2023-11-03 | [
[
"Wei",
"Zhiyuan",
""
],
[
"Sun",
"Jing",
""
],
[
"Zhang",
"Zijian",
""
],
[
"Zhang",
"Xianhao",
""
],
[
"Li",
"Meng",
""
],
[
"Zhu",
"Liehuang",
""
]
] | Blockchain smart contracts have emerged as a transformative force in the digital realm, spawning a diverse range of compelling applications. Since solidity smart contracts across various domains manage trillions of dollars in virtual coins, they become a prime target for attacks. One of the primary challenges is keeping abreast of the latest techniques and tools for developing secure smart contracts and examining those already deployed. In this paper, we seek to address these challenges from four aspects: (1) We begin by examining ten automatic tools, specifically focusing on their methodologies and their ability to identify vulnerabilities in solidity smart contracts. (2) We propose a novel criterion for evaluating these tools, based on the ISO/IEC 25010 standard. (3) To facilitate the evaluation of the selected tools, we construct a benchmark that encompasses two distinct datasets: a collection of 389 labelled smart contracts and a scaled set of 20,000 unique cases from real-world contracts. (4) We provide a comparison of the selected tools, offering insights into their strengths and weaknesses and highlighting areas where further improvements are needed. Through this evaluation, we hope to provide developers and researchers with valuable guidance on selecting and using smart contract analysis tools and contribute to the ongoing efforts to improve the security and reliability of smart contracts. |
2206.01202 | Chieh Hubert Lin | Chieh Hubert Lin, Hsin-Ying Lee, Hung-Yu Tseng, Maneesh Singh,
Ming-Hsuan Yang | Unveiling The Mask of Position-Information Pattern Through the Mist of
Image Features | null | null | null | null | cs.CV cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent studies show that paddings in convolutional neural networks encode
absolute position information which can negatively affect the model performance
for certain tasks. However, existing metrics for quantifying the strength of
positional information remain unreliable and frequently lead to erroneous
results. To address this issue, we propose novel metrics for measuring (and
visualizing) the encoded positional information. We formally define the encoded
information as PPP (Position-information Pattern from Padding) and conduct a
series of experiments to study its properties as well as its formation. The
proposed metrics measure the presence of positional information more reliably
than the existing metrics based on PosENet and a test in F-Conv. We also
demonstrate that for any extant (and proposed) padding schemes, PPP is
primarily a learning artifact and is less dependent on the characteristics of
the underlying padding schemes.
| [
{
"created": "Thu, 2 Jun 2022 17:59:57 GMT",
"version": "v1"
}
] | 2022-06-03 | [
[
"Lin",
"Chieh Hubert",
""
],
[
"Lee",
"Hsin-Ying",
""
],
[
"Tseng",
"Hung-Yu",
""
],
[
"Singh",
"Maneesh",
""
],
[
"Yang",
"Ming-Hsuan",
""
]
] | Recent studies show that paddings in convolutional neural networks encode absolute position information which can negatively affect the model performance for certain tasks. However, existing metrics for quantifying the strength of positional information remain unreliable and frequently lead to erroneous results. To address this issue, we propose novel metrics for measuring (and visualizing) the encoded positional information. We formally define the encoded information as PPP (Position-information Pattern from Padding) and conduct a series of experiments to study its properties as well as its formation. The proposed metrics measure the presence of positional information more reliably than the existing metrics based on PosENet and a test in F-Conv. We also demonstrate that for any extant (and proposed) padding schemes, PPP is primarily a learning artifact and is less dependent on the characteristics of the underlying padding schemes. |
1011.3571 | Kristina Lerman | Rumi Ghosh and Kristina Lerman | A Framework for Quantitative Analysis of Cascades on Networks | In Proceedings of 4th ACM Conference on Web Search and Data Mining | null | null | null | cs.SI cs.CY physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How does information flow in online social networks? How does the structure
and size of the information cascade evolve in time? How can we efficiently mine
the information contained in cascade dynamics? We approach these questions
empirically and present an efficient and scalable mathematical framework for
quantitative analysis of cascades on networks. We define a cascade generating
function that captures the details of the microscopic dynamics of the cascades.
We show that this function can also be used to compute the macroscopic
properties of cascades, such as their size, spread, diameter, number of paths,
and average path length. We present an algorithm to efficiently compute cascade
generating function and demonstrate that while significantly compressing
information within a cascade, it nevertheless allows us to accurately
reconstruct its structure. We use this framework to study information dynamics
on the social network of Digg. Digg allows users to post and vote on stories,
and easily see the stories that friends have voted on. As a story spreads on
Digg through voting, it generates cascades. We extract cascades of more than
3,500 Digg stories and calculate their macroscopic and microscopic properties.
We identify several trends in cascade dynamics: spreading via chaining,
branching and community. We discuss how these affect the spread of the story
through the Digg social network. Our computational framework is general and
offers a practical solution to quantitative analysis of the microscopic
structure of even very large cascades.
| [
{
"created": "Tue, 16 Nov 2010 01:54:16 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Nov 2010 20:14:51 GMT",
"version": "v2"
}
] | 2010-11-18 | [
[
"Ghosh",
"Rumi",
""
],
[
"Lerman",
"Kristina",
""
]
] | How does information flow in online social networks? How does the structure and size of the information cascade evolve in time? How can we efficiently mine the information contained in cascade dynamics? We approach these questions empirically and present an efficient and scalable mathematical framework for quantitative analysis of cascades on networks. We define a cascade generating function that captures the details of the microscopic dynamics of the cascades. We show that this function can also be used to compute the macroscopic properties of cascades, such as their size, spread, diameter, number of paths, and average path length. We present an algorithm to efficiently compute cascade generating function and demonstrate that while significantly compressing information within a cascade, it nevertheless allows us to accurately reconstruct its structure. We use this framework to study information dynamics on the social network of Digg. Digg allows users to post and vote on stories, and easily see the stories that friends have voted on. As a story spreads on Digg through voting, it generates cascades. We extract cascades of more than 3,500 Digg stories and calculate their macroscopic and microscopic properties. We identify several trends in cascade dynamics: spreading via chaining, branching and community. We discuss how these affect the spread of the story through the Digg social network. Our computational framework is general and offers a practical solution to quantitative analysis of the microscopic structure of even very large cascades. |
1403.5315 | Emrah Akyol | Mustafa Mehmetoglu, Emrah Akyol, Kenneth Rose | A Deterministic Annealing Optimization Approach for Witsenhausen's and
Related Decentralized Control Settings | submitted to CDC'14 | null | null | null | cs.SY cs.IT math.IT math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies the problem of mapping optimization in decentralized
control problems. A global optimization algorithm is proposed based on the
ideas of ``deterministic annealing" - a powerful non-convex optimization
framework derived from information theoretic principles with analogies to
statistical physics. The key idea is to randomize the mappings and control the
Shannon entropy of the system during optimization. The entropy constraint is
gradually relaxed in a deterministic annealing process while tracking the
minimum, to obtain the ultimate deterministic mappings. Deterministic annealing
has been successfully employed in several problems including clustering, vector
quantization, regression, as well as the Witsenhausen's counterexample in our
recent work[1]. We extend our method to a more involved setting, a variation of
Witsenhausen's counterexample, where there is a side channel between the two
controllers. The problem can be viewed as a two stage cancellation problem. We
demonstrate that there exist complex strategies that can exploit the side
channel efficiently, obtaining significant gains over the best affine and known
non-linear strategies.
| [
{
"created": "Thu, 20 Mar 2014 22:15:24 GMT",
"version": "v1"
}
] | 2014-03-24 | [
[
"Mehmetoglu",
"Mustafa",
""
],
[
"Akyol",
"Emrah",
""
],
[
"Rose",
"Kenneth",
""
]
] | This paper studies the problem of mapping optimization in decentralized control problems. A global optimization algorithm is proposed based on the ideas of ``deterministic annealing" - a powerful non-convex optimization framework derived from information theoretic principles with analogies to statistical physics. The key idea is to randomize the mappings and control the Shannon entropy of the system during optimization. The entropy constraint is gradually relaxed in a deterministic annealing process while tracking the minimum, to obtain the ultimate deterministic mappings. Deterministic annealing has been successfully employed in several problems including clustering, vector quantization, regression, as well as the Witsenhausen's counterexample in our recent work[1]. We extend our method to a more involved setting, a variation of Witsenhausen's counterexample, where there is a side channel between the two controllers. The problem can be viewed as a two stage cancellation problem. We demonstrate that there exist complex strategies that can exploit the side channel efficiently, obtaining significant gains over the best affine and known non-linear strategies. |
cs/0610159 | Vaneet Aggarwal | Vaneet Aggarwal, A. Robert Calderbank | Boolean Functions, Projection Operators and Quantum Error Correcting
Codes | Submitted to IEEE Transactions on Information Theory, October 2006,
to appear in IEEE Transactions on Information Theory, 2008 | IEEE Trans. Inf. Theory, vol. 54, no. 4, pp.1700-1707, Apr. 2008. | 10.1109/TIT.2008.917720 | null | cs.IT math.IT quant-ph | null | This paper describes a fundamental correspondence between Boolean functions
and projection operators in Hilbert space. The correspondence is widely
applicable, and it is used in this paper to provide a common mathematical
framework for the design of both additive and non-additive quantum error
correcting codes. The new framework leads to the construction of a variety of
codes including an infinite class of codes that extend the original ((5,6,2))
code found by Rains [21]. It also extends to operator quantum error correcting
codes.
| [
{
"created": "Fri, 27 Oct 2006 16:50:41 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Mar 2007 20:58:22 GMT",
"version": "v2"
},
{
"created": "Mon, 24 Sep 2007 13:20:15 GMT",
"version": "v3"
}
] | 2009-04-14 | [
[
"Aggarwal",
"Vaneet",
""
],
[
"Calderbank",
"A. Robert",
""
]
] | This paper describes a fundamental correspondence between Boolean functions and projection operators in Hilbert space. The correspondence is widely applicable, and it is used in this paper to provide a common mathematical framework for the design of both additive and non-additive quantum error correcting codes. The new framework leads to the construction of a variety of codes including an infinite class of codes that extend the original ((5,6,2)) code found by Rains [21]. It also extends to operator quantum error correcting codes. |
2303.13126 | Jing Zhao | Jing Zhao, Heliang Zheng, Chaoyue Wang, Long Lan, Wenjing Yang | MagicFusion: Boosting Text-to-Image Generation Performance by Fusing
Diffusion Models | Accepted by ICCV 2023 | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The advent of open-source AI communities has produced a cornucopia of
powerful text-guided diffusion models that are trained on various datasets.
While few explorations have been conducted on ensembling such models to combine
their strengths. In this work, we propose a simple yet effective method called
Saliency-aware Noise Blending (SNB) that can empower the fused text-guided
diffusion models to achieve more controllable generation. Specifically, we
experimentally find that the responses of classifier-free guidance are highly
related to the saliency of generated images. Thus we propose to trust different
models in their areas of expertise by blending the predicted noises of two
diffusion models in a saliency-aware manner. SNB is training-free and can be
completed within a DDIM sampling process. Additionally, it can automatically
align the semantics of two noise spaces without requiring additional
annotations such as masks. Extensive experiments show the impressive
effectiveness of SNB in various applications. Project page is available at
https://magicfusion.github.io/.
| [
{
"created": "Thu, 23 Mar 2023 09:30:39 GMT",
"version": "v1"
},
{
"created": "Sat, 25 Mar 2023 14:38:16 GMT",
"version": "v2"
},
{
"created": "Fri, 14 Jul 2023 09:36:35 GMT",
"version": "v3"
}
] | 2023-07-20 | [
[
"Zhao",
"Jing",
""
],
[
"Zheng",
"Heliang",
""
],
[
"Wang",
"Chaoyue",
""
],
[
"Lan",
"Long",
""
],
[
"Yang",
"Wenjing",
""
]
] | The advent of open-source AI communities has produced a cornucopia of powerful text-guided diffusion models that are trained on various datasets. While few explorations have been conducted on ensembling such models to combine their strengths. In this work, we propose a simple yet effective method called Saliency-aware Noise Blending (SNB) that can empower the fused text-guided diffusion models to achieve more controllable generation. Specifically, we experimentally find that the responses of classifier-free guidance are highly related to the saliency of generated images. Thus we propose to trust different models in their areas of expertise by blending the predicted noises of two diffusion models in a saliency-aware manner. SNB is training-free and can be completed within a DDIM sampling process. Additionally, it can automatically align the semantics of two noise spaces without requiring additional annotations such as masks. Extensive experiments show the impressive effectiveness of SNB in various applications. Project page is available at https://magicfusion.github.io/. |
2212.00881 | Saeed Mohammadzadeh | Saeed Mohammadzadeh, Peerasait Prachaseree, Emma Lejeune | Investigating Deep Learning Model Calibration for Classification
Problems in Mechanics | 21 pages, 9 figures | null | null | null | cs.LG physics.data-an | http://creativecommons.org/licenses/by-sa/4.0/ | Recently, there has been a growing interest in applying machine learning
methods to problems in engineering mechanics. In particular, there has been
significant interest in applying deep learning techniques to predicting the
mechanical behavior of heterogeneous materials and structures. Researchers have
shown that deep learning methods are able to effectively predict mechanical
behavior with low error for systems ranging from engineered composites, to
geometrically complex metamaterials, to heterogeneous biological tissue.
However, there has been comparatively little attention paid to deep learning
model calibration, i.e., the match between predicted probabilities of outcomes
and the true probabilities of outcomes. In this work, we perform a
comprehensive investigation into ML model calibration across seven open access
engineering mechanics datasets that cover three distinct types of mechanical
problems. Specifically, we evaluate both model and model calibration error for
multiple machine learning methods, and investigate the influence of ensemble
averaging and post hoc model calibration via temperature scaling. Overall, we
find that ensemble averaging of deep neural networks is both an effective and
consistent tool for improving model calibration, while temperature scaling has
comparatively limited benefits. Looking forward, we anticipate that this
investigation will lay the foundation for future work in developing mechanics
specific approaches to deep learning model calibration.
| [
{
"created": "Thu, 1 Dec 2022 21:39:48 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Mar 2023 17:22:41 GMT",
"version": "v2"
}
] | 2023-03-15 | [
[
"Mohammadzadeh",
"Saeed",
""
],
[
"Prachaseree",
"Peerasait",
""
],
[
"Lejeune",
"Emma",
""
]
] | Recently, there has been a growing interest in applying machine learning methods to problems in engineering mechanics. In particular, there has been significant interest in applying deep learning techniques to predicting the mechanical behavior of heterogeneous materials and structures. Researchers have shown that deep learning methods are able to effectively predict mechanical behavior with low error for systems ranging from engineered composites, to geometrically complex metamaterials, to heterogeneous biological tissue. However, there has been comparatively little attention paid to deep learning model calibration, i.e., the match between predicted probabilities of outcomes and the true probabilities of outcomes. In this work, we perform a comprehensive investigation into ML model calibration across seven open access engineering mechanics datasets that cover three distinct types of mechanical problems. Specifically, we evaluate both model and model calibration error for multiple machine learning methods, and investigate the influence of ensemble averaging and post hoc model calibration via temperature scaling. Overall, we find that ensemble averaging of deep neural networks is both an effective and consistent tool for improving model calibration, while temperature scaling has comparatively limited benefits. Looking forward, we anticipate that this investigation will lay the foundation for future work in developing mechanics specific approaches to deep learning model calibration. |
2102.09086 | Robi Bhattacharjee | Robi Bhattacharjee and Kamalika Chaudhuri | Consistent Non-Parametric Methods for Maximizing Robustness | accepted to Nuerips 2021 | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Learning classifiers that are robust to adversarial examples has received a
great deal of recent attention. A major drawback of the standard robust
learning framework is there is an artificial robustness radius $r$ that applies
to all inputs. This ignores the fact that data may be highly heterogeneous, in
which case it is plausible that robustness regions should be larger in some
regions of data, and smaller in others. In this paper, we address this
limitation by proposing a new limit classifier, called the neighborhood optimal
classifier, that extends the Bayes optimal classifier outside its support by
using the label of the closest in-support point. We then argue that this
classifier maximizes the size of its robustness regions subject to the
constraint of having accuracy equal to the Bayes optimal. We then present
sufficient conditions under which general non-parametric methods that can be
represented as weight functions converge towards this limit, and show that both
nearest neighbors and kernel classifiers satisfy them under certain conditions.
| [
{
"created": "Thu, 18 Feb 2021 00:44:07 GMT",
"version": "v1"
},
{
"created": "Mon, 8 Nov 2021 04:14:49 GMT",
"version": "v2"
},
{
"created": "Wed, 18 Jan 2023 18:02:20 GMT",
"version": "v3"
}
] | 2023-01-19 | [
[
"Bhattacharjee",
"Robi",
""
],
[
"Chaudhuri",
"Kamalika",
""
]
] | Learning classifiers that are robust to adversarial examples has received a great deal of recent attention. A major drawback of the standard robust learning framework is there is an artificial robustness radius $r$ that applies to all inputs. This ignores the fact that data may be highly heterogeneous, in which case it is plausible that robustness regions should be larger in some regions of data, and smaller in others. In this paper, we address this limitation by proposing a new limit classifier, called the neighborhood optimal classifier, that extends the Bayes optimal classifier outside its support by using the label of the closest in-support point. We then argue that this classifier maximizes the size of its robustness regions subject to the constraint of having accuracy equal to the Bayes optimal. We then present sufficient conditions under which general non-parametric methods that can be represented as weight functions converge towards this limit, and show that both nearest neighbors and kernel classifiers satisfy them under certain conditions. |
2310.13583 | Ofir Arviv | Ofir Arviv, Dmitry Nikolaev, Taelin Karidi and Omri Abend | Improving Cross-Lingual Transfer through Subtree-Aware Word Reordering | Accepted to EMNLP Findings 2023 | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the impressive growth of the abilities of multilingual language
models, such as XLM-R and mT5, it has been shown that they still face
difficulties when tackling typologically-distant languages, particularly in the
low-resource setting. One obstacle for effective cross-lingual transfer is
variability in word-order patterns. It can be potentially mitigated via source-
or target-side word reordering, and numerous approaches to reordering have been
proposed. However, they rely on language-specific rules, work on the level of
POS tags, or only target the main clause, leaving subordinate clauses intact.
To address these limitations, we present a new powerful reordering method,
defined in terms of Universal Dependencies, that is able to learn fine-grained
word-order patterns conditioned on the syntactic context from a small amount of
annotated data and can be applied at all levels of the syntactic tree. We
conduct experiments on a diverse set of tasks and show that our method
consistently outperforms strong baselines over different language pairs and
model architectures. This performance advantage holds true in both zero-shot
and few-shot scenarios.
| [
{
"created": "Fri, 20 Oct 2023 15:25:53 GMT",
"version": "v1"
}
] | 2023-10-23 | [
[
"Arviv",
"Ofir",
""
],
[
"Nikolaev",
"Dmitry",
""
],
[
"Karidi",
"Taelin",
""
],
[
"Abend",
"Omri",
""
]
] | Despite the impressive growth of the abilities of multilingual language models, such as XLM-R and mT5, it has been shown that they still face difficulties when tackling typologically-distant languages, particularly in the low-resource setting. One obstacle for effective cross-lingual transfer is variability in word-order patterns. It can be potentially mitigated via source- or target-side word reordering, and numerous approaches to reordering have been proposed. However, they rely on language-specific rules, work on the level of POS tags, or only target the main clause, leaving subordinate clauses intact. To address these limitations, we present a new powerful reordering method, defined in terms of Universal Dependencies, that is able to learn fine-grained word-order patterns conditioned on the syntactic context from a small amount of annotated data and can be applied at all levels of the syntactic tree. We conduct experiments on a diverse set of tasks and show that our method consistently outperforms strong baselines over different language pairs and model architectures. This performance advantage holds true in both zero-shot and few-shot scenarios. |
2008.08931 | Liyi Guo | Liyi Guo, Rui Lu, Haoqi Zhang, Junqi Jin, Zhenzhe Zheng, Fan Wu, Jin
Li, Haiyang Xu, Han Li, Wenkai Lu, Jian Xu, Kun Gai | A Deep Prediction Network for Understanding Advertiser Intent and
Satisfaction | null | CIKM 2020, Virtual Event, Ireland | 10.1145/3340531.3412681 | null | cs.SI cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For e-commerce platforms such as Taobao and Amazon, advertisers play an
important role in the entire digital ecosystem: their behaviors explicitly
influence users' browsing and shopping experience; more importantly,
advertiser's expenditure on advertising constitutes a primary source of
platform revenue. Therefore, providing better services for advertisers is
essential for the long-term prosperity for e-commerce platforms. To achieve
this goal, the ad platform needs to have an in-depth understanding of
advertisers in terms of both their marketing intents and satisfaction over the
advertising performance, based on which further optimization could be carried
out to service the advertisers in the correct direction. In this paper, we
propose a novel Deep Satisfaction Prediction Network (DSPN), which models
advertiser intent and satisfaction simultaneously. It employs a two-stage
network structure where advertiser intent vector and satisfaction are jointly
learned by considering the features of advertiser's action information and
advertising performance indicators. Experiments on an Alibaba advertisement
dataset and online evaluations show that our proposed DSPN outperforms
state-of-the-art baselines and has stable performance in terms of AUC in the
online environment. Further analyses show that DSPN not only predicts
advertisers' satisfaction accurately but also learns an explainable advertiser
intent, revealing the opportunities to optimize the advertising performance
further.
| [
{
"created": "Thu, 20 Aug 2020 15:08:50 GMT",
"version": "v1"
}
] | 2020-09-01 | [
[
"Guo",
"Liyi",
""
],
[
"Lu",
"Rui",
""
],
[
"Zhang",
"Haoqi",
""
],
[
"Jin",
"Junqi",
""
],
[
"Zheng",
"Zhenzhe",
""
],
[
"Wu",
"Fan",
""
],
[
"Li",
"Jin",
""
],
[
"Xu",
"Haiyang",
""
],
[
"Li",
"Han",
""
],
[
"Lu",
"Wenkai",
""
],
[
"Xu",
"Jian",
""
],
[
"Gai",
"Kun",
""
]
] | For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue. Therefore, providing better services for advertisers is essential for the long-term prosperity for e-commerce platforms. To achieve this goal, the ad platform needs to have an in-depth understanding of advertisers in terms of both their marketing intents and satisfaction over the advertising performance, based on which further optimization could be carried out to service the advertisers in the correct direction. In this paper, we propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. It employs a two-stage network structure where advertiser intent vector and satisfaction are jointly learned by considering the features of advertiser's action information and advertising performance indicators. Experiments on an Alibaba advertisement dataset and online evaluations show that our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment. Further analyses show that DSPN not only predicts advertisers' satisfaction accurately but also learns an explainable advertiser intent, revealing the opportunities to optimize the advertising performance further. |
1304.3249 | Paolo Parisen Toldin | Jean-Yves Moyen, Paolo Parisen Toldin | A polytime complexity analyser for Probabilistic Polynomial Time over
imperative stack programs | null | null | null | null | cs.LO cs.CC | http://creativecommons.org/licenses/by/3.0/ | We present iSAPP (Imperative Static Analyser for Probabilistic Polynomial
Time), a complexity verifier tool that is sound and extensionally complete for
the Probabilistic Polynomial Time (PP) complexity class. iSAPP works on an
imperative programming language for stack machines. The certificate of
polynomiality can be built in polytime, with respect to the number of stacks
used.
| [
{
"created": "Thu, 11 Apr 2013 10:20:24 GMT",
"version": "v1"
}
] | 2013-04-12 | [
[
"Moyen",
"Jean-Yves",
""
],
[
"Toldin",
"Paolo Parisen",
""
]
] | We present iSAPP (Imperative Static Analyser for Probabilistic Polynomial Time), a complexity verifier tool that is sound and extensionally complete for the Probabilistic Polynomial Time (PP) complexity class. iSAPP works on an imperative programming language for stack machines. The certificate of polynomiality can be built in polytime, with respect to the number of stacks used. |
2404.00686 | Srinjoy Roy | Srinjoy Roy, Swagatam Das | Utilizing Maximum Mean Discrepancy Barycenter for Propagating the
Uncertainty of Value Functions in Reinforcement Learning | We found some flaws in our analysis and we are in the process of
rectifying those | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Accounting for the uncertainty of value functions boosts exploration in
Reinforcement Learning (RL). Our work introduces Maximum Mean Discrepancy
Q-Learning (MMD-QL) to improve Wasserstein Q-Learning (WQL) for uncertainty
propagation during Temporal Difference (TD) updates. MMD-QL uses the MMD
barycenter for this purpose, as MMD provides a tighter estimate of closeness
between probability measures than the Wasserstein distance. Firstly, we
establish that MMD-QL is Probably Approximately Correct in MDP (PAC-MDP) under
the average loss metric. Concerning the accumulated rewards, experiments on
tabular environments show that MMD-QL outperforms WQL and other algorithms.
Secondly, we incorporate deep networks into MMD-QL to create MMD Q-Network
(MMD-QN). Making reasonable assumptions, we analyze the convergence rates of
MMD-QN using function approximation. Empirical results on challenging Atari
games demonstrate that MMD-QN performs well compared to benchmark deep RL
algorithms, highlighting its effectiveness in handling large state-action
spaces.
| [
{
"created": "Sun, 31 Mar 2024 13:41:56 GMT",
"version": "v1"
},
{
"created": "Wed, 3 Apr 2024 14:32:17 GMT",
"version": "v2"
}
] | 2024-04-04 | [
[
"Roy",
"Srinjoy",
""
],
[
"Das",
"Swagatam",
""
]
] | Accounting for the uncertainty of value functions boosts exploration in Reinforcement Learning (RL). Our work introduces Maximum Mean Discrepancy Q-Learning (MMD-QL) to improve Wasserstein Q-Learning (WQL) for uncertainty propagation during Temporal Difference (TD) updates. MMD-QL uses the MMD barycenter for this purpose, as MMD provides a tighter estimate of closeness between probability measures than the Wasserstein distance. Firstly, we establish that MMD-QL is Probably Approximately Correct in MDP (PAC-MDP) under the average loss metric. Concerning the accumulated rewards, experiments on tabular environments show that MMD-QL outperforms WQL and other algorithms. Secondly, we incorporate deep networks into MMD-QL to create MMD Q-Network (MMD-QN). Making reasonable assumptions, we analyze the convergence rates of MMD-QN using function approximation. Empirical results on challenging Atari games demonstrate that MMD-QN performs well compared to benchmark deep RL algorithms, highlighting its effectiveness in handling large state-action spaces. |
2406.03893 | Anushka Singh | Anushka Singh, Ananya B. Sai, Raj Dabre, Ratish Puduppully, Anoop
Kunchukuttan, Mitesh M Khapra | How Good is Zero-Shot MT Evaluation for Low Resource Indian Languages? | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | While machine translation evaluation has been studied primarily for
high-resource languages, there has been a recent interest in evaluation for
low-resource languages due to the increasing availability of data and models.
In this paper, we focus on a zero-shot evaluation setting focusing on
low-resource Indian languages, namely Assamese, Kannada, Maithili, and Punjabi.
We collect sufficient Multi-Dimensional Quality Metrics (MQM) and Direct
Assessment (DA) annotations to create test sets and meta-evaluate a plethora of
automatic evaluation metrics. We observe that even for learned metrics, which
are known to exhibit zero-shot performance, the Kendall Tau and Pearson
correlations with human annotations are only as high as 0.32 and 0.45.
Synthetic data approaches show mixed results and overall do not help close the
gap by much for these languages. This indicates that there is still a long way
to go for low-resource evaluation.
| [
{
"created": "Thu, 6 Jun 2024 09:28:08 GMT",
"version": "v1"
}
] | 2024-06-07 | [
[
"Singh",
"Anushka",
""
],
[
"Sai",
"Ananya B.",
""
],
[
"Dabre",
"Raj",
""
],
[
"Puduppully",
"Ratish",
""
],
[
"Kunchukuttan",
"Anoop",
""
],
[
"Khapra",
"Mitesh M",
""
]
] | While machine translation evaluation has been studied primarily for high-resource languages, there has been a recent interest in evaluation for low-resource languages due to the increasing availability of data and models. In this paper, we focus on a zero-shot evaluation setting focusing on low-resource Indian languages, namely Assamese, Kannada, Maithili, and Punjabi. We collect sufficient Multi-Dimensional Quality Metrics (MQM) and Direct Assessment (DA) annotations to create test sets and meta-evaluate a plethora of automatic evaluation metrics. We observe that even for learned metrics, which are known to exhibit zero-shot performance, the Kendall Tau and Pearson correlations with human annotations are only as high as 0.32 and 0.45. Synthetic data approaches show mixed results and overall do not help close the gap by much for these languages. This indicates that there is still a long way to go for low-resource evaluation. |
2008.05440 | Jie Yang | Jie Yang, Kaichun Mo, Yu-Kun Lai, Leonidas J. Guibas, Lin Gao | DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape
Generation | Accept to ACM Transaction on Graphics 2022, 26 pages | null | null | null | cs.GR cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | D shape generation is a fundamental operation in computer graphics. While
significant progress has been made, especially with recent deep generative
models, it remains a challenge to synthesize high-quality shapes with rich
geometric details and complex structure, in a controllable manner. To tackle
this, we introduce DSG-Net, a deep neural network that learns a disentangled
structured and geometric mesh representation for 3D shapes, where two key
aspects of shapes, geometry, and structure, are encoded in a synergistic manner
to ensure plausibility of the generated shapes, while also being disentangled
as much as possible. This supports a range of novel shape generation
applications with disentangled control, such as interpolation of structure
(geometry) while keeping geometry (structure) unchanged. To achieve this, we
simultaneously learn structure and geometry through variational autoencoders
(VAEs) in a hierarchical manner for both, with bijective mappings at each
level. In this manner, we effectively encode geometry and structure in separate
latent spaces, while ensuring their compatibility: the structure is used to
guide the geometry and vice versa. At the leaf level, the part geometry is
represented using a conditional part VAE, to encode high-quality geometric
details, guided by the structure context as the condition. Our method not only
supports controllable generation applications but also produces high-quality
synthesized shapes, outperforming state-of-the-art methods. The code has been
released at https://github.com/IGLICT/DSG-Net.
| [
{
"created": "Wed, 12 Aug 2020 17:06:51 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Aug 2020 02:38:45 GMT",
"version": "v2"
},
{
"created": "Mon, 24 May 2021 14:45:26 GMT",
"version": "v3"
},
{
"created": "Sat, 28 May 2022 17:40:15 GMT",
"version": "v4"
}
] | 2022-05-31 | [
[
"Yang",
"Jie",
""
],
[
"Mo",
"Kaichun",
""
],
[
"Lai",
"Yu-Kun",
""
],
[
"Guibas",
"Leonidas J.",
""
],
[
"Gao",
"Lin",
""
]
] | D shape generation is a fundamental operation in computer graphics. While significant progress has been made, especially with recent deep generative models, it remains a challenge to synthesize high-quality shapes with rich geometric details and complex structure, in a controllable manner. To tackle this, we introduce DSG-Net, a deep neural network that learns a disentangled structured and geometric mesh representation for 3D shapes, where two key aspects of shapes, geometry, and structure, are encoded in a synergistic manner to ensure plausibility of the generated shapes, while also being disentangled as much as possible. This supports a range of novel shape generation applications with disentangled control, such as interpolation of structure (geometry) while keeping geometry (structure) unchanged. To achieve this, we simultaneously learn structure and geometry through variational autoencoders (VAEs) in a hierarchical manner for both, with bijective mappings at each level. In this manner, we effectively encode geometry and structure in separate latent spaces, while ensuring their compatibility: the structure is used to guide the geometry and vice versa. At the leaf level, the part geometry is represented using a conditional part VAE, to encode high-quality geometric details, guided by the structure context as the condition. Our method not only supports controllable generation applications but also produces high-quality synthesized shapes, outperforming state-of-the-art methods. The code has been released at https://github.com/IGLICT/DSG-Net. |
1004.2079 | Yashodhan Kanoria | Mohsen Bayati, Christian Borgs, Jennifer Chayes, Yashodhan Kanoria and
Andrea Montanari | Bargaining dynamics in exchange networks | 47 pages, SODA 2011, invited to Journal of Economic Theory | Proc. ACM-SIAM Symp. on Discrete Algorithms (2011) 1518-1537 | null | null | cs.GT cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a one-sided assignment market or exchange network with
transferable utility and propose a model for the dynamics of bargaining in such
a market. Our dynamical model is local, involving iterative updates of 'offers'
based on estimated best alternative matches, in the spirit of pairwise Nash
bargaining. We establish that when a balanced outcome (a generalization of the
pairwise Nash bargaining solution to networks) exists, our dynamics converges
rapidly to such an outcome. We extend our results to the cases of (i) general
agent 'capacity constraints', i.e., an agent may be allowed to participate in
multiple matches, and (ii) 'unequal bargaining powers' (where we also find a
surprising change in rate of convergence).
| [
{
"created": "Mon, 12 Apr 2010 23:11:16 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Dec 2011 19:40:38 GMT",
"version": "v2"
}
] | 2015-03-14 | [
[
"Bayati",
"Mohsen",
""
],
[
"Borgs",
"Christian",
""
],
[
"Chayes",
"Jennifer",
""
],
[
"Kanoria",
"Yashodhan",
""
],
[
"Montanari",
"Andrea",
""
]
] | We consider a one-sided assignment market or exchange network with transferable utility and propose a model for the dynamics of bargaining in such a market. Our dynamical model is local, involving iterative updates of 'offers' based on estimated best alternative matches, in the spirit of pairwise Nash bargaining. We establish that when a balanced outcome (a generalization of the pairwise Nash bargaining solution to networks) exists, our dynamics converges rapidly to such an outcome. We extend our results to the cases of (i) general agent 'capacity constraints', i.e., an agent may be allowed to participate in multiple matches, and (ii) 'unequal bargaining powers' (where we also find a surprising change in rate of convergence). |
2208.04159 | Ningning Wang | Ningning Wang, Guodong Li, Sihuang Hu, Min Ye | Constructing MSR codes with subpacketization $2^{n/3}$ for $k+1$ helper
nodes | null | IEEE Transactions on Information Theory (Volume: 69, Issue: 6,
June 2023) | 10.1109/TIT.2023.3238759 | null | cs.IT math.IT | http://creativecommons.org/licenses/by/4.0/ | Wang et al. (IEEE Transactions on Information Theory, vol. 62, no. 8, 2016)
proposed an explicit construction of an $(n=k+2,k)$ Minimum Storage
Regenerating (MSR) code with $2$ parity nodes and subpacketization $2^{k/3}$.
The number of helper nodes for this code is $d=k+1=n-1$, and this code has the
smallest subpacketization among all the existing explicit constructions of MSR
codes with the same $n,k$ and $d$. In this paper, we present a new construction
of MSR codes for a wider range of parameters. More precisely, we still fix
$d=k+1$, but we allow the code length $n$ to be any integer satisfying $n\ge
k+2$. The field size of our code is linear in $n$, and the subpacketization of
our code is $2^{n/3}$. This value is slightly larger than the subpacketization
of the construction by Wang et al. because their code construction only
guarantees optimal repair for all the systematic nodes while our code
construction guarantees optimal repair for all nodes.
| [
{
"created": "Mon, 8 Aug 2022 13:59:11 GMT",
"version": "v1"
},
{
"created": "Thu, 11 May 2023 14:58:30 GMT",
"version": "v2"
}
] | 2023-05-23 | [
[
"Wang",
"Ningning",
""
],
[
"Li",
"Guodong",
""
],
[
"Hu",
"Sihuang",
""
],
[
"Ye",
"Min",
""
]
] | Wang et al. (IEEE Transactions on Information Theory, vol. 62, no. 8, 2016) proposed an explicit construction of an $(n=k+2,k)$ Minimum Storage Regenerating (MSR) code with $2$ parity nodes and subpacketization $2^{k/3}$. The number of helper nodes for this code is $d=k+1=n-1$, and this code has the smallest subpacketization among all the existing explicit constructions of MSR codes with the same $n,k$ and $d$. In this paper, we present a new construction of MSR codes for a wider range of parameters. More precisely, we still fix $d=k+1$, but we allow the code length $n$ to be any integer satisfying $n\ge k+2$. The field size of our code is linear in $n$, and the subpacketization of our code is $2^{n/3}$. This value is slightly larger than the subpacketization of the construction by Wang et al. because their code construction only guarantees optimal repair for all the systematic nodes while our code construction guarantees optimal repair for all nodes. |
2002.04095 | Juan-Manuel Torres-Moreno | R\'emy Saksik, Alejandro Molina-Villegas, Andr\'ea Carneiro Linhares,
Juan-Manuel Torres-Moreno | Automatic Discourse Segmentation: an evaluation in French | 7 pages, 2 figures, 2 tables | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, we describe some discursive segmentation methods as well as
a preliminary evaluation of the segmentation quality. Although our experiment
were carried for documents in French, we have developed three discursive
segmentation models solely based on resources simultaneously available in
several languages: marker lists and a statistic POS labeling. We have also
carried out automatic evaluations of these systems against the Annodis corpus,
which is a manually annotated reference. The results obtained are very
encouraging.
| [
{
"created": "Mon, 10 Feb 2020 21:35:39 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Jun 2020 20:27:29 GMT",
"version": "v2"
}
] | 2020-06-15 | [
[
"Saksik",
"Rémy",
""
],
[
"Molina-Villegas",
"Alejandro",
""
],
[
"Linhares",
"Andréa Carneiro",
""
],
[
"Torres-Moreno",
"Juan-Manuel",
""
]
] | In this article, we describe some discursive segmentation methods as well as a preliminary evaluation of the segmentation quality. Although our experiment were carried for documents in French, we have developed three discursive segmentation models solely based on resources simultaneously available in several languages: marker lists and a statistic POS labeling. We have also carried out automatic evaluations of these systems against the Annodis corpus, which is a manually annotated reference. The results obtained are very encouraging. |
0907.4547 | EPTCS | Janusz Brzozowski | Quotient Complexity of Regular Languages | null | EPTCS 3, 2009, pp. 17-28 | 10.4204/EPTCS.3.2 | null | cs.FL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The past research on the state complexity of operations on regular languages
is examined, and a new approach based on an old method (derivatives of regular
expressions) is presented. Since state complexity is a property of a language,
it is appropriate to define it in formal-language terms as the number of
distinct quotients of the language, and to call it "quotient complexity". The
problem of finding the quotient complexity of a language f(K,L) is considered,
where K and L are regular languages and f is a regular operation, for example,
union or concatenation. Since quotients can be represented by derivatives, one
can find a formula for the typical quotient of f(K,L) in terms of the quotients
of K and L. To obtain an upper bound on the number of quotients of f(K,L) all
one has to do is count how many such quotients are possible, and this makes
automaton constructions unnecessary. The advantages of this point of view are
illustrated by many examples. Moreover, new general observations are presented
to help in the estimation of the upper bounds on quotient complexity of regular
operations.
| [
{
"created": "Mon, 27 Jul 2009 06:19:09 GMT",
"version": "v1"
}
] | 2009-07-28 | [
[
"Brzozowski",
"Janusz",
""
]
] | The past research on the state complexity of operations on regular languages is examined, and a new approach based on an old method (derivatives of regular expressions) is presented. Since state complexity is a property of a language, it is appropriate to define it in formal-language terms as the number of distinct quotients of the language, and to call it "quotient complexity". The problem of finding the quotient complexity of a language f(K,L) is considered, where K and L are regular languages and f is a regular operation, for example, union or concatenation. Since quotients can be represented by derivatives, one can find a formula for the typical quotient of f(K,L) in terms of the quotients of K and L. To obtain an upper bound on the number of quotients of f(K,L) all one has to do is count how many such quotients are possible, and this makes automaton constructions unnecessary. The advantages of this point of view are illustrated by many examples. Moreover, new general observations are presented to help in the estimation of the upper bounds on quotient complexity of regular operations. |
1912.02858 | Victor Lecomte | Victor Lecomte and Omri Weinstein | Settling the relationship between Wilber's bounds for dynamic optimality | ESA 2020; 25 pages, 18 figures; v3 applies reviewers' comments | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In FOCS 1986, Wilber proposed two combinatorial lower bounds on the
operational cost of any binary search tree (BST) for a given access sequence $X
\in [n]^m$. Both bounds play a central role in the ongoing pursuit of the
dynamic optimality conjecture (Sleator and Tarjan, 1985), but their
relationship remained unknown for more than three decades. We show that
Wilber's Funnel bound dominates his Alternation bound for all $X$, and give a
tight $\Theta(\lg\lg n)$ separation for some $X$, answering Wilber's conjecture
and an open problem of Iacono, Demaine et. al. The main ingredient of the proof
is a new "symmetric" characterization of Wilber's Funnel bound, which proves
that it is invariant under rotations of $X$. We use this characterization to
provide initial indication that the Funnel bound matches the Independent
Rectangle bound (Demaine et al., 2009), by proving that when the Funnel bound
is constant, $\mathsf{IRB}_{\diagup\hspace{-.6em}\square}$ is linear. To the
best of our knowledge, our results provide the first progress on Wilber's
conjecture that the Funnel bound is dynamically optimal (1986).
| [
{
"created": "Thu, 5 Dec 2019 20:17:15 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Dec 2019 20:49:57 GMT",
"version": "v2"
},
{
"created": "Sun, 28 Jun 2020 20:51:41 GMT",
"version": "v3"
}
] | 2020-06-30 | [
[
"Lecomte",
"Victor",
""
],
[
"Weinstein",
"Omri",
""
]
] | In FOCS 1986, Wilber proposed two combinatorial lower bounds on the operational cost of any binary search tree (BST) for a given access sequence $X \in [n]^m$. Both bounds play a central role in the ongoing pursuit of the dynamic optimality conjecture (Sleator and Tarjan, 1985), but their relationship remained unknown for more than three decades. We show that Wilber's Funnel bound dominates his Alternation bound for all $X$, and give a tight $\Theta(\lg\lg n)$ separation for some $X$, answering Wilber's conjecture and an open problem of Iacono, Demaine et. al. The main ingredient of the proof is a new "symmetric" characterization of Wilber's Funnel bound, which proves that it is invariant under rotations of $X$. We use this characterization to provide initial indication that the Funnel bound matches the Independent Rectangle bound (Demaine et al., 2009), by proving that when the Funnel bound is constant, $\mathsf{IRB}_{\diagup\hspace{-.6em}\square}$ is linear. To the best of our knowledge, our results provide the first progress on Wilber's conjecture that the Funnel bound is dynamically optimal (1986). |
2110.04946 | Hieu-Thi Luong | Hieu-Thi Luong, Junichi Yamagishi | LaughNet: synthesizing laughter utterances from waveform silhouettes and
a single laughter example | null | null | null | null | cs.SD cs.LG eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Emotional and controllable speech synthesis is a topic that has received much
attention. However, most studies focused on improving the expressiveness and
controllability in the context of linguistic content, even though natural
verbal human communication is inseparable from spontaneous non-speech
expressions such as laughter, crying, or grunting. We propose a model called
LaughNet for synthesizing laughter by using waveform silhouettes as inputs. The
motivation is not simply synthesizing new laughter utterances, but testing a
novel synthesis-control paradigm that uses an abstract representation of the
waveform. We conducted basic listening test experiments, and the results showed
that LaughNet can synthesize laughter utterances with moderate quality and
retain the characteristics of the training example. More importantly, the
generated waveforms have shapes similar to the input silhouettes. For future
work, we will test the same method on other types of human nonverbal
expressions and integrate it into more elaborated synthesis systems.
| [
{
"created": "Mon, 11 Oct 2021 00:45:07 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Jan 2022 01:40:13 GMT",
"version": "v2"
}
] | 2022-01-27 | [
[
"Luong",
"Hieu-Thi",
""
],
[
"Yamagishi",
"Junichi",
""
]
] | Emotional and controllable speech synthesis is a topic that has received much attention. However, most studies focused on improving the expressiveness and controllability in the context of linguistic content, even though natural verbal human communication is inseparable from spontaneous non-speech expressions such as laughter, crying, or grunting. We propose a model called LaughNet for synthesizing laughter by using waveform silhouettes as inputs. The motivation is not simply synthesizing new laughter utterances, but testing a novel synthesis-control paradigm that uses an abstract representation of the waveform. We conducted basic listening test experiments, and the results showed that LaughNet can synthesize laughter utterances with moderate quality and retain the characteristics of the training example. More importantly, the generated waveforms have shapes similar to the input silhouettes. For future work, we will test the same method on other types of human nonverbal expressions and integrate it into more elaborated synthesis systems. |
2006.03179 | Garrett Bingham | Garrett Bingham and Risto Miikkulainen | Discovering Parametric Activation Functions | Published in Neural Networks. 34 pages, 10 figures, 11 tables | Neural Networks, Volume 148, 2022, Pages 48-65, ISSN 0893-6080 | 10.1016/j.neunet.2022.01.001 | null | cs.LG cs.CV cs.NE stat.ML | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recent studies have shown that the choice of activation function can
significantly affect the performance of deep learning networks. However, the
benefits of novel activation functions have been inconsistent and task
dependent, and therefore the rectified linear unit (ReLU) is still the most
commonly used. This paper proposes a technique for customizing activation
functions automatically, resulting in reliable improvements in performance.
Evolutionary search is used to discover the general form of the function, and
gradient descent to optimize its parameters for different parts of the network
and over the learning process. Experiments with four different neural network
architectures on the CIFAR-10 and CIFAR-100 image classification datasets show
that this approach is effective. It discovers both general activation functions
and specialized functions for different architectures, consistently improving
accuracy over ReLU and other activation functions by significant margins. The
approach can therefore be used as an automated optimization step in applying
deep learning to new tasks.
| [
{
"created": "Fri, 5 Jun 2020 00:25:33 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Oct 2020 15:33:14 GMT",
"version": "v2"
},
{
"created": "Tue, 8 Dec 2020 19:28:47 GMT",
"version": "v3"
},
{
"created": "Sat, 30 Jan 2021 02:17:20 GMT",
"version": "v4"
},
{
"created": "Fri, 21 Jan 2022 19:39:36 GMT",
"version": "v5"
}
] | 2022-01-25 | [
[
"Bingham",
"Garrett",
""
],
[
"Miikkulainen",
"Risto",
""
]
] | Recent studies have shown that the choice of activation function can significantly affect the performance of deep learning networks. However, the benefits of novel activation functions have been inconsistent and task dependent, and therefore the rectified linear unit (ReLU) is still the most commonly used. This paper proposes a technique for customizing activation functions automatically, resulting in reliable improvements in performance. Evolutionary search is used to discover the general form of the function, and gradient descent to optimize its parameters for different parts of the network and over the learning process. Experiments with four different neural network architectures on the CIFAR-10 and CIFAR-100 image classification datasets show that this approach is effective. It discovers both general activation functions and specialized functions for different architectures, consistently improving accuracy over ReLU and other activation functions by significant margins. The approach can therefore be used as an automated optimization step in applying deep learning to new tasks. |
1410.0382 | Mircea Andrecut Dr | M. Andrecut | A String-Based Public Key Cryptosystem | In this revised version of the paper we show that the eavesdropper's
problem of the proposed cryptosystem has a solution, and we give the details
of the solution | null | null | null | cs.CR physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditional methods in public key cryptography are based on number theory,
and suffer from problems such as dealing with very large numbers, making key
creation cumbersome. Here, we propose a new public key cryptosystem based on
strings only, which avoids the difficulties of the traditional number theory
approach. The security mechanism for public and secret keys generation is
ensured by a recursive encoding mechanism embedded in a
quasi-commutative-random function, resulted from the composition of a
quasi-commutative function with a pseudo-random function. In this revised
version of the paper we show that the eavesdropper's problem of the proposed
cryptosystem has a solution, and we give the details of the solution.
| [
{
"created": "Fri, 5 Sep 2014 18:44:31 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Jan 2015 18:53:35 GMT",
"version": "v2"
}
] | 2015-01-20 | [
[
"Andrecut",
"M.",
""
]
] | Traditional methods in public key cryptography are based on number theory, and suffer from problems such as dealing with very large numbers, making key creation cumbersome. Here, we propose a new public key cryptosystem based on strings only, which avoids the difficulties of the traditional number theory approach. The security mechanism for public and secret keys generation is ensured by a recursive encoding mechanism embedded in a quasi-commutative-random function, resulted from the composition of a quasi-commutative function with a pseudo-random function. In this revised version of the paper we show that the eavesdropper's problem of the proposed cryptosystem has a solution, and we give the details of the solution. |
2210.10226 | Zillur Rahman | Zillur Rahman, Amit Mazumder Ami, Muhammad Ahsan Ullah | A Real-Time Wrong-Way Vehicle Detection Based on YOLO and Centroid
Tracking | 5 pages | 2020 IEEE Region 10 Symposium (TENSYMP), page:916-920 | 10.1109/TENSYMP50017.2020.9230463 | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Wrong-way driving is one of the main causes of road accidents and traffic jam
all over the world. By detecting wrong-way vehicles, the number of accidents
can be minimized and traffic jam can be reduced. With the increasing popularity
of real-time traffic management systems and due to the availability of cheaper
cameras, the surveillance video has become a big source of data. In this paper,
we propose an automatic wrong-way vehicle detection system from on-road
surveillance camera footage. Our system works in three stages: the detection of
vehicles from the video frame by using the You Only Look Once (YOLO) algorithm,
track each vehicle in a specified region of interest using centroid tracking
algorithm and detect the wrong-way driving vehicles. YOLO is very accurate in
object detection and the centroid tracking algorithm can track any moving
object efficiently. Experiment with some traffic videos shows that our proposed
system can detect and identify any wrong-way vehicle in different light and
weather conditions. The system is very simple and easy to implement.
| [
{
"created": "Wed, 19 Oct 2022 00:53:28 GMT",
"version": "v1"
}
] | 2022-10-20 | [
[
"Rahman",
"Zillur",
""
],
[
"Ami",
"Amit Mazumder",
""
],
[
"Ullah",
"Muhammad Ahsan",
""
]
] | Wrong-way driving is one of the main causes of road accidents and traffic jam all over the world. By detecting wrong-way vehicles, the number of accidents can be minimized and traffic jam can be reduced. With the increasing popularity of real-time traffic management systems and due to the availability of cheaper cameras, the surveillance video has become a big source of data. In this paper, we propose an automatic wrong-way vehicle detection system from on-road surveillance camera footage. Our system works in three stages: the detection of vehicles from the video frame by using the You Only Look Once (YOLO) algorithm, track each vehicle in a specified region of interest using centroid tracking algorithm and detect the wrong-way driving vehicles. YOLO is very accurate in object detection and the centroid tracking algorithm can track any moving object efficiently. Experiment with some traffic videos shows that our proposed system can detect and identify any wrong-way vehicle in different light and weather conditions. The system is very simple and easy to implement. |
2202.13134 | Fu Song | Qi Qin and JulianAndres JiYang and Fu Song and Taolue Chen and Xinyu
Xing | Preventing Timing Side-Channels via Security-Aware Just-In-Time
Compilation | null | null | null | null | cs.PL cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work has shown that Just-In-Time (JIT) compilation can introduce
timing side-channels to constant-time programs, which would otherwise be a
principled and effective means to counter timing attacks. In this paper, we
propose a novel approach to eliminate JIT-induced leaks from these programs.
Specifically, we present an operational semantics and a formal definition of
constant-time programs under JIT compilation, laying the foundation for
reasoning about programs with JIT compilation. We then propose to eliminate
JIT-induced leaks via a fine-grained JIT compilation for which we provide an
automated approach to generate policies and a novel type system to show its
soundness. We develop a tool DeJITLeak for Java based on our approach and
implement the fine-grained JIT compilation in HotSpot. Experimental results
show that DeJITLeak can effectively and efficiently eliminate JIT-induced leaks
on three datasets used in side-channel detection
| [
{
"created": "Sat, 26 Feb 2022 13:06:15 GMT",
"version": "v1"
}
] | 2022-03-01 | [
[
"Qin",
"Qi",
""
],
[
"JiYang",
"JulianAndres",
""
],
[
"Song",
"Fu",
""
],
[
"Chen",
"Taolue",
""
],
[
"Xing",
"Xinyu",
""
]
] | Recent work has shown that Just-In-Time (JIT) compilation can introduce timing side-channels to constant-time programs, which would otherwise be a principled and effective means to counter timing attacks. In this paper, we propose a novel approach to eliminate JIT-induced leaks from these programs. Specifically, we present an operational semantics and a formal definition of constant-time programs under JIT compilation, laying the foundation for reasoning about programs with JIT compilation. We then propose to eliminate JIT-induced leaks via a fine-grained JIT compilation for which we provide an automated approach to generate policies and a novel type system to show its soundness. We develop a tool DeJITLeak for Java based on our approach and implement the fine-grained JIT compilation in HotSpot. Experimental results show that DeJITLeak can effectively and efficiently eliminate JIT-induced leaks on three datasets used in side-channel detection |
1703.09807 | Nhien-An Le-Khac | Lamine M. Aouad, Nhien-An Le-Khac, Tahar Kechadi | Grid-based Approaches for Distributed Data Mining Applications | null | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The data mining field is an important source of large-scale applications and
datasets which are getting more and more common. In this paper, we present
grid-based approaches for two basic data mining applications, and a performance
evaluation on an experimental grid environment that provides interesting
monitoring capabilities and configuration tools. We propose a new distributed
clustering approach and a distributed frequent itemsets generation well-adapted
for grid environments. Performance evaluation is done using the Condor system
and its workflow manager DAGMan. We also compare this performance analysis to a
simple analytical model to evaluate the overheads related to the workflow
engine and the underlying grid system. This will specifically show that
realistic performance expectations are currently difficult to achieve on the
grid.
| [
{
"created": "Tue, 28 Mar 2017 21:19:24 GMT",
"version": "v1"
}
] | 2017-03-30 | [
[
"Aouad",
"Lamine M.",
""
],
[
"Le-Khac",
"Nhien-An",
""
],
[
"Kechadi",
"Tahar",
""
]
] | The data mining field is an important source of large-scale applications and datasets which are getting more and more common. In this paper, we present grid-based approaches for two basic data mining applications, and a performance evaluation on an experimental grid environment that provides interesting monitoring capabilities and configuration tools. We propose a new distributed clustering approach and a distributed frequent itemsets generation well-adapted for grid environments. Performance evaluation is done using the Condor system and its workflow manager DAGMan. We also compare this performance analysis to a simple analytical model to evaluate the overheads related to the workflow engine and the underlying grid system. This will specifically show that realistic performance expectations are currently difficult to achieve on the grid. |
1605.06154 | Michael Nelson | Herbert Van de Sompel, David S. H. Rosenthal, Michael L. Nelson | Web Infrastructure to Support e-Journal Preservation (and More) | 23 pages, 5 figures | null | null | null | cs.DL | http://creativecommons.org/licenses/by/4.0/ | E-journal preservation systems have to ingest millions of articles each year.
Ingest, especially of the "long tail" of journals from small publishers, is the
largest element of their cost. Cost is the major reason that archives contain
less than half the content they should. Automation is essential to minimize
these costs. This paper examines the potential for automation beyond the status
quo based on the API provided by CrossRef, ANSI/NISO Z39.99 ResourceSync, and
the provision of typed links in publishers' HTTP response headers. These
changes would not merely assist e-journal preservation and other cross-venue
scholarly applications, but would help remedy the gap that research has
revealed between DOIs' potential and actual benefits.
| [
{
"created": "Thu, 19 May 2016 21:44:01 GMT",
"version": "v1"
}
] | 2016-05-23 | [
[
"Van de Sompel",
"Herbert",
""
],
[
"Rosenthal",
"David S. H.",
""
],
[
"Nelson",
"Michael L.",
""
]
] | E-journal preservation systems have to ingest millions of articles each year. Ingest, especially of the "long tail" of journals from small publishers, is the largest element of their cost. Cost is the major reason that archives contain less than half the content they should. Automation is essential to minimize these costs. This paper examines the potential for automation beyond the status quo based on the API provided by CrossRef, ANSI/NISO Z39.99 ResourceSync, and the provision of typed links in publishers' HTTP response headers. These changes would not merely assist e-journal preservation and other cross-venue scholarly applications, but would help remedy the gap that research has revealed between DOIs' potential and actual benefits. |
1802.01448 | Deepak Vijaykeerthy | Deepak Vijaykeerthy, Anshuman Suri, Sameep Mehta, Ponnurangam
Kumaraguru | Hardening Deep Neural Networks via Adversarial Model Cascades | null | null | null | null | cs.LG cs.CR stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an
adversary to produce erroneous outputs. Works on securing neural networks
against adversarial examples achieve high empirical robustness on simple
datasets such as MNIST. However, these techniques are inadequate when
empirically tested on complex data sets such as CIFAR-10 and SVHN. Further,
existing techniques are designed to target specific attacks and fail to
generalize across attacks. We propose the Adversarial Model Cascades (AMC) as a
way to tackle the above inadequacies. Our approach trains a cascade of models
sequentially where each model is optimized to be robust towards a mixture of
multiple attacks. Ultimately, it yields a single model which is secure against
a wide range of attacks; namely FGSM, Elastic, Virtual Adversarial
Perturbations and Madry. On an average, AMC increases the model's empirical
robustness against various attacks simultaneously, by a significant margin (of
6.225% for MNIST, 5.075% for SVHN and 2.65% for CIFAR10). At the same time, the
model's performance on non-adversarial inputs is comparable to the
state-of-the-art models.
| [
{
"created": "Fri, 2 Feb 2018 09:02:38 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Feb 2018 16:38:56 GMT",
"version": "v2"
},
{
"created": "Mon, 12 Feb 2018 06:28:25 GMT",
"version": "v3"
},
{
"created": "Sun, 4 Nov 2018 11:16:23 GMT",
"version": "v4"
}
] | 2018-11-06 | [
[
"Vijaykeerthy",
"Deepak",
""
],
[
"Suri",
"Anshuman",
""
],
[
"Mehta",
"Sameep",
""
],
[
"Kumaraguru",
"Ponnurangam",
""
]
] | Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an adversary to produce erroneous outputs. Works on securing neural networks against adversarial examples achieve high empirical robustness on simple datasets such as MNIST. However, these techniques are inadequate when empirically tested on complex data sets such as CIFAR-10 and SVHN. Further, existing techniques are designed to target specific attacks and fail to generalize across attacks. We propose the Adversarial Model Cascades (AMC) as a way to tackle the above inadequacies. Our approach trains a cascade of models sequentially where each model is optimized to be robust towards a mixture of multiple attacks. Ultimately, it yields a single model which is secure against a wide range of attacks; namely FGSM, Elastic, Virtual Adversarial Perturbations and Madry. On an average, AMC increases the model's empirical robustness against various attacks simultaneously, by a significant margin (of 6.225% for MNIST, 5.075% for SVHN and 2.65% for CIFAR10). At the same time, the model's performance on non-adversarial inputs is comparable to the state-of-the-art models. |
1908.02999 | Fabian Schilling | Fabian Schilling and Julien Lecoeur and Fabrizio Schiano and Dario
Floreano | Learning Vision-based Flight in Drone Swarms by Imitation | 8 pages, 8 figures, accepted for publication in the IEEE Robotics and
Automation Letters (RA-L) on July 28, 2019. arXiv admin note: substantial
text overlap with arXiv:1809.00543 | null | null | null | cs.RO cs.CV cs.LG cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decentralized drone swarms deployed today either rely on sharing of positions
among agents or detecting swarm members with the help of visual markers. This
work proposes an entirely visual approach to coordinate markerless drone swarms
based on imitation learning. Each agent is controlled by a small and efficient
convolutional neural network that takes raw omnidirectional images as inputs
and predicts 3D velocity commands that match those computed by a flocking
algorithm. We start training in simulation and propose a simple yet effective
unsupervised domain adaptation approach to transfer the learned controller to
the real world. We further train the controller with data collected in our
motion capture hall. We show that the convolutional neural network trained on
the visual inputs of the drone can learn not only robust inter-agent collision
avoidance but also cohesion of the swarm in a sample-efficient manner. The
neural controller effectively learns to localize other agents in the visual
input, which we show by visualizing the regions with the most influence on the
motion of an agent. We remove the dependence on sharing positions among swarm
members by taking only local visual information into account for control. Our
work can therefore be seen as the first step towards a fully decentralized,
vision-based swarm without the need for communication or visual markers.
| [
{
"created": "Thu, 8 Aug 2019 10:19:48 GMT",
"version": "v1"
}
] | 2019-08-09 | [
[
"Schilling",
"Fabian",
""
],
[
"Lecoeur",
"Julien",
""
],
[
"Schiano",
"Fabrizio",
""
],
[
"Floreano",
"Dario",
""
]
] | Decentralized drone swarms deployed today either rely on sharing of positions among agents or detecting swarm members with the help of visual markers. This work proposes an entirely visual approach to coordinate markerless drone swarms based on imitation learning. Each agent is controlled by a small and efficient convolutional neural network that takes raw omnidirectional images as inputs and predicts 3D velocity commands that match those computed by a flocking algorithm. We start training in simulation and propose a simple yet effective unsupervised domain adaptation approach to transfer the learned controller to the real world. We further train the controller with data collected in our motion capture hall. We show that the convolutional neural network trained on the visual inputs of the drone can learn not only robust inter-agent collision avoidance but also cohesion of the swarm in a sample-efficient manner. The neural controller effectively learns to localize other agents in the visual input, which we show by visualizing the regions with the most influence on the motion of an agent. We remove the dependence on sharing positions among swarm members by taking only local visual information into account for control. Our work can therefore be seen as the first step towards a fully decentralized, vision-based swarm without the need for communication or visual markers. |
1911.11547 | Dat Quoc Nguyen | Dai Quoc Nguyen, Dat Quoc Nguyen, Son Bao Pham | A Vietnamese Text-Based Conversational Agent | In Proceedings of the 25th International Conference on Industrial,
Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE
2012) | null | 10.1007/978-3-642-31087-4_71 | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a Vietnamese text-based conversational agent
architecture on specific knowledge domain which is integrated in a question
answering system. When the question answering system fails to provide answers
to users' input, our conversational agent can step in to interact with users to
provide answers to users. Experimental results are promising where our
Vietnamese text-based conversational agent achieves positive feedback in a
study conducted in the university academic regulation domain.
| [
{
"created": "Tue, 26 Nov 2019 14:11:50 GMT",
"version": "v1"
}
] | 2019-11-27 | [
[
"Nguyen",
"Dai Quoc",
""
],
[
"Nguyen",
"Dat Quoc",
""
],
[
"Pham",
"Son Bao",
""
]
] | This paper introduces a Vietnamese text-based conversational agent architecture on specific knowledge domain which is integrated in a question answering system. When the question answering system fails to provide answers to users' input, our conversational agent can step in to interact with users to provide answers to users. Experimental results are promising where our Vietnamese text-based conversational agent achieves positive feedback in a study conducted in the university academic regulation domain. |
1312.0525 | Kiryung Lee | Kiryung Lee, Yihong Wu, and Yoram Bresler | Near Optimal Compressed Sensing of a Class of Sparse Low-Rank Matrices
via Sparse Power Factorization | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Compressed sensing of simultaneously sparse and low-rank matrices enables
recovery of sparse signals from a few linear measurements of their bilinear
form. One important question is how many measurements are needed for a stable
reconstruction in the presence of measurement noise. Unlike conventional
compressed sensing for sparse vectors, where convex relaxation via the
$\ell_1$-norm achieves near optimal performance, for compressed sensing of
sparse low-rank matrices, it has been shown recently Oymak et al. that convex
programmings using the nuclear norm and the mixed norm are highly suboptimal
even in the noise-free scenario.
We propose an alternating minimization algorithm called sparse power
factorization (SPF) for compressed sensing of sparse rank-one matrices. For a
class of signals whose sparse representation coefficients are fast-decaying,
SPF achieves stable recovery of the rank-1 matrix formed by their outer product
and requires number of measurements within a logarithmic factor of the
information-theoretic fundamental limit. For the recovery of general sparse
low-rank matrices, we propose subspace-concatenated SPF (SCSPF), which has
analogous near optimal performance guarantees to SPF in the rank-1 case.
Numerical results show that SPF and SCSPF empirically outperform convex
programmings using the best known combinations of mixed norm and nuclear norm.
| [
{
"created": "Mon, 2 Dec 2013 17:37:00 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Jun 2016 02:43:34 GMT",
"version": "v2"
}
] | 2016-07-01 | [
[
"Lee",
"Kiryung",
""
],
[
"Wu",
"Yihong",
""
],
[
"Bresler",
"Yoram",
""
]
] | Compressed sensing of simultaneously sparse and low-rank matrices enables recovery of sparse signals from a few linear measurements of their bilinear form. One important question is how many measurements are needed for a stable reconstruction in the presence of measurement noise. Unlike conventional compressed sensing for sparse vectors, where convex relaxation via the $\ell_1$-norm achieves near optimal performance, for compressed sensing of sparse low-rank matrices, it has been shown recently Oymak et al. that convex programmings using the nuclear norm and the mixed norm are highly suboptimal even in the noise-free scenario. We propose an alternating minimization algorithm called sparse power factorization (SPF) for compressed sensing of sparse rank-one matrices. For a class of signals whose sparse representation coefficients are fast-decaying, SPF achieves stable recovery of the rank-1 matrix formed by their outer product and requires number of measurements within a logarithmic factor of the information-theoretic fundamental limit. For the recovery of general sparse low-rank matrices, we propose subspace-concatenated SPF (SCSPF), which has analogous near optimal performance guarantees to SPF in the rank-1 case. Numerical results show that SPF and SCSPF empirically outperform convex programmings using the best known combinations of mixed norm and nuclear norm. |
2011.07200 | Yingtao Luo | Ziyang Zhang and Yingtao Luo | Deep Spatial Learning with Molecular Vibration | NeurIPS 2020 Machine Learning for Molecules Workshop, Vancouver,
Canada | null | null | null | cs.LG physics.chem-ph | http://creativecommons.org/licenses/by/4.0/ | Machine learning over-fitting caused by data scarcity greatly limits the
application of machine learning for molecules. Due to manufacturing processes
difference, big data is not always rendered available through computational
chemistry methods for some tasks, causing data scarcity problem for machine
learning algorithms. Here we propose to extract the natural features of
molecular structures and rationally distort them to augment the data
availability. This method allows a machine learning project to leverage the
powerful fit of physics-informed augmentation for providing significant boost
to predictive accuracy. Successfully verified by the prediction of rejection
rate and flux of thin film polyamide nanofiltration membranes, with the
relative error dropping from 16.34% to 6.71% and the coefficient of
determination rising from 0.16 to 0.75, the proposed deep spatial learning with
molecular vibration is widely instructive for molecular science. Experimental
comparison unequivocally demonstrates its superiority over common learning
algorithms.
| [
{
"created": "Sat, 14 Nov 2020 02:46:43 GMT",
"version": "v1"
}
] | 2020-11-20 | [
[
"Zhang",
"Ziyang",
""
],
[
"Luo",
"Yingtao",
""
]
] | Machine learning over-fitting caused by data scarcity greatly limits the application of machine learning for molecules. Due to manufacturing processes difference, big data is not always rendered available through computational chemistry methods for some tasks, causing data scarcity problem for machine learning algorithms. Here we propose to extract the natural features of molecular structures and rationally distort them to augment the data availability. This method allows a machine learning project to leverage the powerful fit of physics-informed augmentation for providing significant boost to predictive accuracy. Successfully verified by the prediction of rejection rate and flux of thin film polyamide nanofiltration membranes, with the relative error dropping from 16.34% to 6.71% and the coefficient of determination rising from 0.16 to 0.75, the proposed deep spatial learning with molecular vibration is widely instructive for molecular science. Experimental comparison unequivocally demonstrates its superiority over common learning algorithms. |
2001.00522 | Na-Young Ahn | Na-Young Ahn, Dong Hoon Lee | Schemes for Privacy Data Destruction in a NAND Flash Memory | Pages 181305 - 181313 | null | 10.1109/ACCESS.2019.2958628 | null | cs.CR cs.SY eess.SP eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose schemes for efficiently destroying privacy data in a NAND flash
memory. Generally, even if privcy data is discarded from NAND flash memories,
there is a high probability that the data will remain in an invalid block. This
is a management problem that arises from the specificity of a program operation
and an erase operation of NAND flash memories. When updating pages or
performing a garbage collection, there is a problem that valid data remains in
at least one unmapped memory block. Is it possible to apply the obligation to
delete privacy data from existing NAND flash memory? This paper is the answer
to this question. We propose a partial overwriting scheme, a SLC programming
scheme, and a deletion duty pulse application scheme for invalid pages to
effectively solve privacy data destruction issues due to the remaining data.
Such privacy data destruction schemes basically utilize at least one state in
which data can be written to the programmed cells based on a multi-level cell
program operation. Our privacy data destruction schemes have advantages in
terms of block management as compared with conventional erase schemes, and are
very economical in terms of time and cost. The proposed privacy data
destruction schemes can be easily applied to many storage devices and data
centers using NAND flash memories.
| [
{
"created": "Sat, 28 Dec 2019 03:52:02 GMT",
"version": "v1"
}
] | 2020-11-20 | [
[
"Ahn",
"Na-Young",
""
],
[
"Lee",
"Dong Hoon",
""
]
] | We propose schemes for efficiently destroying privacy data in a NAND flash memory. Generally, even if privcy data is discarded from NAND flash memories, there is a high probability that the data will remain in an invalid block. This is a management problem that arises from the specificity of a program operation and an erase operation of NAND flash memories. When updating pages or performing a garbage collection, there is a problem that valid data remains in at least one unmapped memory block. Is it possible to apply the obligation to delete privacy data from existing NAND flash memory? This paper is the answer to this question. We propose a partial overwriting scheme, a SLC programming scheme, and a deletion duty pulse application scheme for invalid pages to effectively solve privacy data destruction issues due to the remaining data. Such privacy data destruction schemes basically utilize at least one state in which data can be written to the programmed cells based on a multi-level cell program operation. Our privacy data destruction schemes have advantages in terms of block management as compared with conventional erase schemes, and are very economical in terms of time and cost. The proposed privacy data destruction schemes can be easily applied to many storage devices and data centers using NAND flash memories. |
2301.04696 | Joberto Martins Prof. Dr. | Eduardo S. Xavier and Nazim Agoulmine and Joberto S. B. Martins | On Modeling Network Slicing Communication Resources with SARSA
Optimization | 8 pages, 9 figures, ADVANCE conference paper | null | 10.5281/zenodo.7513695 | null | cs.NI cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Network slicing is a crucial enabler to support the composition and
deployment of virtual network infrastructures required by the dynamic behavior
of networks like 5G/6G mobile networks, IoT-aware networks, e-health systems,
and industry verticals like the internet of vehicles (IoV) and industry 4.0.
The communication slices and their allocated communication resources are
essential in slicing architectures for resource orchestration and allocation,
virtual network function (VNF) deployment, and slice operation functionalities.
The communication slices provide the communications capabilities required to
support slice operation, SLA guarantees, and QoS/ QoE application requirements.
Therefore, this contribution proposes a networking slicing conceptual model to
formulate the optimization problem related to the sharing of communication
resources among communication slices. First, we present a conceptual model of
network slicing, we then formulate analytically some aspects of the model and
the optimization problem to address. Next, we proposed to use a SARSA agent to
solve the problem and implement a proof of concept prototype. Finally, we
present the obtained results and discuss them.
| [
{
"created": "Wed, 11 Jan 2023 20:00:42 GMT",
"version": "v1"
}
] | 2023-01-13 | [
[
"Xavier",
"Eduardo S.",
""
],
[
"Agoulmine",
"Nazim",
""
],
[
"Martins",
"Joberto S. B.",
""
]
] | Network slicing is a crucial enabler to support the composition and deployment of virtual network infrastructures required by the dynamic behavior of networks like 5G/6G mobile networks, IoT-aware networks, e-health systems, and industry verticals like the internet of vehicles (IoV) and industry 4.0. The communication slices and their allocated communication resources are essential in slicing architectures for resource orchestration and allocation, virtual network function (VNF) deployment, and slice operation functionalities. The communication slices provide the communications capabilities required to support slice operation, SLA guarantees, and QoS/ QoE application requirements. Therefore, this contribution proposes a networking slicing conceptual model to formulate the optimization problem related to the sharing of communication resources among communication slices. First, we present a conceptual model of network slicing, we then formulate analytically some aspects of the model and the optimization problem to address. Next, we proposed to use a SARSA agent to solve the problem and implement a proof of concept prototype. Finally, we present the obtained results and discuss them. |
2107.02378 | Jun Shu | Jun Shu, Deyu Meng, Zongben Xu | Learning an Explicit Hyperparameter Prediction Function Conditioned on
Tasks | 74 pages | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Meta learning has attracted much attention recently in machine learning
community. Contrary to conventional machine learning aiming to learn inherent
prediction rules to predict labels for new query data, meta learning aims to
learn the learning methodology for machine learning from observed tasks, so as
to generalize to new query tasks by leveraging the meta-learned learning
methodology. In this study, we interpret such learning methodology as learning
an explicit hyper-parameter prediction function shared by all training tasks.
Specifically, this function is represented as a parameterized function called
meta-learner, mapping from a training/test task to its suitable hyper-parameter
setting, extracted from a pre-specified function set called meta learning
machine. Such setting guarantees that the meta-learned learning methodology is
able to flexibly fit diverse query tasks, instead of only obtaining fixed
hyper-parameters by many current meta learning methods, with less adaptability
to query task's variations. Such understanding of meta learning also makes it
easily succeed from traditional learning theory for analyzing its
generalization bounds with general losses/tasks/models. The theory naturally
leads to some feasible controlling strategies for ameliorating the quality of
the extracted meta-learner, verified to be able to finely ameliorate its
generalization capability in some typical meta learning applications, including
few-shot regression, few-shot classification and domain generalization.
| [
{
"created": "Tue, 6 Jul 2021 04:05:08 GMT",
"version": "v1"
},
{
"created": "Sat, 13 May 2023 09:41:42 GMT",
"version": "v2"
},
{
"created": "Sat, 1 Jul 2023 09:27:29 GMT",
"version": "v3"
}
] | 2023-07-04 | [
[
"Shu",
"Jun",
""
],
[
"Meng",
"Deyu",
""
],
[
"Xu",
"Zongben",
""
]
] | Meta learning has attracted much attention recently in machine learning community. Contrary to conventional machine learning aiming to learn inherent prediction rules to predict labels for new query data, meta learning aims to learn the learning methodology for machine learning from observed tasks, so as to generalize to new query tasks by leveraging the meta-learned learning methodology. In this study, we interpret such learning methodology as learning an explicit hyper-parameter prediction function shared by all training tasks. Specifically, this function is represented as a parameterized function called meta-learner, mapping from a training/test task to its suitable hyper-parameter setting, extracted from a pre-specified function set called meta learning machine. Such setting guarantees that the meta-learned learning methodology is able to flexibly fit diverse query tasks, instead of only obtaining fixed hyper-parameters by many current meta learning methods, with less adaptability to query task's variations. Such understanding of meta learning also makes it easily succeed from traditional learning theory for analyzing its generalization bounds with general losses/tasks/models. The theory naturally leads to some feasible controlling strategies for ameliorating the quality of the extracted meta-learner, verified to be able to finely ameliorate its generalization capability in some typical meta learning applications, including few-shot regression, few-shot classification and domain generalization. |
1709.08360 | Jiaqi Zhang | Jiaqi Zhang, Keyou You, and Tamer Ba\c{s}ar | Distributed Discrete-time Optimization in Multi-agent Networks Using
only Sign of Relative State | Part of this work has been presented in American Control Conference
(ACC) 2018, first version posted on arxiv on Sep. 2017, IEEE Transactions on
Automatic Control, 2018 | null | 10.1109/TAC.2018.2884998 | null | cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes distributed discrete-time algorithms to cooperatively
solve an additive cost optimization problem in multi-agent networks. The
striking feature lies in the use of only the sign of relative state information
between neighbors, which substantially differentiates our algorithms from
others in the existing literature. We first interpret the proposed algorithms
in terms of the penalty method in optimization theory and then perform
non-asymptotic analysis to study convergence for static network graphs.
Compared with the celebrated distributed subgradient algorithms, which however
use the exact relative state information, the convergence speed is essentially
not affected by the loss of information. We also study how introducing noise
into the relative state information and randomly activated graphs affect the
performance of our algorithms. Finally, we validate the theoretical results on
a class of distributed quantile regression problems.
| [
{
"created": "Mon, 25 Sep 2017 08:05:04 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Nov 2017 16:17:55 GMT",
"version": "v2"
},
{
"created": "Mon, 10 Dec 2018 07:01:54 GMT",
"version": "v3"
}
] | 2018-12-11 | [
[
"Zhang",
"Jiaqi",
""
],
[
"You",
"Keyou",
""
],
[
"Başar",
"Tamer",
""
]
] | This paper proposes distributed discrete-time algorithms to cooperatively solve an additive cost optimization problem in multi-agent networks. The striking feature lies in the use of only the sign of relative state information between neighbors, which substantially differentiates our algorithms from others in the existing literature. We first interpret the proposed algorithms in terms of the penalty method in optimization theory and then perform non-asymptotic analysis to study convergence for static network graphs. Compared with the celebrated distributed subgradient algorithms, which however use the exact relative state information, the convergence speed is essentially not affected by the loss of information. We also study how introducing noise into the relative state information and randomly activated graphs affect the performance of our algorithms. Finally, we validate the theoretical results on a class of distributed quantile regression problems. |
cs/0611058 | Marie Cottrell | Marie Cottrell (CES, SAMOS), Michel Verleysen (DICE) | Advances in Self Organising Maps | Special Issue of the Neural Networks Journal after WSOM 05 in Paris | Neural Networks Volume 19, Issues 6-7 (2006) 721-722 | 10.1016/j.neunet.2006.05.011 | null | cs.NE math.ST nlin.AO stat.TH | null | The Self-Organizing Map (SOM) with its related extensions is the most popular
artificial neural algorithm for use in unsupervised learning, clustering,
classification and data visualization. Over 5,000 publications have been
reported in the open literature, and many commercial projects employ the SOM as
a tool for solving hard real-world problems. Each two years, the "Workshop on
Self-Organizing Maps" (WSOM) covers the new developments in the field. The WSOM
series of conferences was initiated in 1997 by Prof. Teuvo Kohonen, and has
been successfully organized in 1997 and 1999 by the Helsinki University of
Technology, in 2001 by the University of Lincolnshire and Humberside, and in
2003 by the Kyushu Institute of Technology. The Universit\'{e} Paris I
Panth\'{e}on Sorbonne (SAMOS-MATISSE research centre) organized WSOM 2005 in
Paris on September 5-8, 2005.
| [
{
"created": "Tue, 14 Nov 2006 13:19:46 GMT",
"version": "v1"
}
] | 2011-11-09 | [
[
"Cottrell",
"Marie",
"",
"CES, SAMOS"
],
[
"Verleysen",
"Michel",
"",
"DICE"
]
] | The Self-Organizing Map (SOM) with its related extensions is the most popular artificial neural algorithm for use in unsupervised learning, clustering, classification and data visualization. Over 5,000 publications have been reported in the open literature, and many commercial projects employ the SOM as a tool for solving hard real-world problems. Each two years, the "Workshop on Self-Organizing Maps" (WSOM) covers the new developments in the field. The WSOM series of conferences was initiated in 1997 by Prof. Teuvo Kohonen, and has been successfully organized in 1997 and 1999 by the Helsinki University of Technology, in 2001 by the University of Lincolnshire and Humberside, and in 2003 by the Kyushu Institute of Technology. The Universit\'{e} Paris I Panth\'{e}on Sorbonne (SAMOS-MATISSE research centre) organized WSOM 2005 in Paris on September 5-8, 2005. |
2005.00198 | EPTCS | Artjoms {\v{S}}inkarovs (Heriot-Watt University) | Multi-dimensional Arrays with Levels | In Proceedings MSFP 2020, arXiv:2004.14735 | EPTCS 317, 2020, pp. 57-71 | 10.4204/EPTCS.317.4 | null | cs.DS cs.LO cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore a data structure that generalises rectangular multi-dimensional
arrays. The shape of an n-dimensional array is typically given by a tuple of n
natural numbers. Each element in that tuple defines the length of the
corresponding axis. If we treat this tuple as an array, the shape of that array
is described by the single natural number n. A natural number itself can be
also treated as an array with the shape described by the natural number 1 (or
the element of any singleton set). This observation gives rise to the hierarchy
of array types where the shape of an array of level l+1 is a level-l array of
natural numbers. Such a hierarchy occurs naturally when treating arrays as
containers, which makes it possible to define both rank- and level-polymorphic
operations. The former can be found in most array languages, whereas the latter
gives rise to partial selections on a large set of hyperplanes, which is often
useful in practice. In this paper we present an Agda formalisation of arrays
with levels. We show that the proposed formalism supports standard
rank-polymorphic array operations, while type system gives static guarantees
that indexing is within bounds. We generalise the notion of ranked operator so
that it becomes applicable on arrays of arbitrary levels and we show why this
may be useful in practice.
| [
{
"created": "Fri, 1 May 2020 03:42:41 GMT",
"version": "v1"
}
] | 2020-05-04 | [
[
"{Š}inkarovs",
"Artjoms",
"",
"Heriot-Watt University"
]
] | We explore a data structure that generalises rectangular multi-dimensional arrays. The shape of an n-dimensional array is typically given by a tuple of n natural numbers. Each element in that tuple defines the length of the corresponding axis. If we treat this tuple as an array, the shape of that array is described by the single natural number n. A natural number itself can be also treated as an array with the shape described by the natural number 1 (or the element of any singleton set). This observation gives rise to the hierarchy of array types where the shape of an array of level l+1 is a level-l array of natural numbers. Such a hierarchy occurs naturally when treating arrays as containers, which makes it possible to define both rank- and level-polymorphic operations. The former can be found in most array languages, whereas the latter gives rise to partial selections on a large set of hyperplanes, which is often useful in practice. In this paper we present an Agda formalisation of arrays with levels. We show that the proposed formalism supports standard rank-polymorphic array operations, while type system gives static guarantees that indexing is within bounds. We generalise the notion of ranked operator so that it becomes applicable on arrays of arbitrary levels and we show why this may be useful in practice. |
2310.02029 | Gianluca Bontempi | Gianluca Bontempi | Between accurate prediction and poor decision making: the AI/ML gap | Position paper presented in the BENELEARN 2022 conference | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Intelligent agents rely on AI/ML functionalities to predict the consequence
of possible actions and optimise the policy. However, the effort of the
research community in addressing prediction accuracy has been so intense (and
successful) that it created the illusion that the more accurate the learner
prediction (or classification) the better would have been the final decision.
Now, such an assumption is valid only if the (human or artificial) decision
maker has complete knowledge of the utility of the possible actions. This paper
argues that AI/ML community has taken so far a too unbalanced approach by
devoting excessive attention to the estimation of the state (or target)
probability to the detriment of accurate and reliable estimations of the
utility. In particular, few evidence exists about the impact of a wrong utility
assessment on the resulting expected utility of the decision strategy. This
situation is creating a substantial gap between the expectations and the
effective impact of AI solutions, as witnessed by recent criticisms and
emphasised by the regulatory legislative efforts. This paper aims to study this
gap by quantifying the sensitivity of the expected utility to the utility
uncertainty and comparing it to the one due to probability estimation.
Theoretical and simulated results show that an inaccurate utility assessment
may as (and sometimes) more harmful than a poor probability estimation. The
final recommendation to the community is then to undertake a focus shift from a
pure accuracy-driven (or obsessed) approach to a more utility-aware
methodology.
| [
{
"created": "Tue, 3 Oct 2023 13:15:02 GMT",
"version": "v1"
}
] | 2023-10-04 | [
[
"Bontempi",
"Gianluca",
""
]
] | Intelligent agents rely on AI/ML functionalities to predict the consequence of possible actions and optimise the policy. However, the effort of the research community in addressing prediction accuracy has been so intense (and successful) that it created the illusion that the more accurate the learner prediction (or classification) the better would have been the final decision. Now, such an assumption is valid only if the (human or artificial) decision maker has complete knowledge of the utility of the possible actions. This paper argues that AI/ML community has taken so far a too unbalanced approach by devoting excessive attention to the estimation of the state (or target) probability to the detriment of accurate and reliable estimations of the utility. In particular, few evidence exists about the impact of a wrong utility assessment on the resulting expected utility of the decision strategy. This situation is creating a substantial gap between the expectations and the effective impact of AI solutions, as witnessed by recent criticisms and emphasised by the regulatory legislative efforts. This paper aims to study this gap by quantifying the sensitivity of the expected utility to the utility uncertainty and comparing it to the one due to probability estimation. Theoretical and simulated results show that an inaccurate utility assessment may as (and sometimes) more harmful than a poor probability estimation. The final recommendation to the community is then to undertake a focus shift from a pure accuracy-driven (or obsessed) approach to a more utility-aware methodology. |
2108.11717 | Soroush Seifi | Soroush Seifi, Abhishek Jha, Tinne Tuytelaars | Glimpse-Attend-and-Explore: Self-Attention for Active Visual Exploration | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Active visual exploration aims to assist an agent with a limited field of
view to understand its environment based on partial observations made by
choosing the best viewing directions in the scene. Recent methods have tried to
address this problem either by using reinforcement learning, which is difficult
to train, or by uncertainty maps, which are task-specific and can only be
implemented for dense prediction tasks. In this paper, we propose the
Glimpse-Attend-and-Explore model which: (a) employs self-attention to guide the
visual exploration instead of task-specific uncertainty maps; (b) can be used
for both dense and sparse prediction tasks; and (c) uses a contrastive stream
to further improve the representations learned. Unlike previous works, we show
the application of our model on multiple tasks like reconstruction,
segmentation and classification. Our model provides encouraging results while
being less dependent on dataset bias in driving the exploration. We further
perform an ablation study to investigate the features and attention learned by
our model. Finally, we show that our self-attention module learns to attend
different regions of the scene by minimizing the loss on the downstream task.
Code: https://github.com/soroushseifi/glimpse-attend-explore.
| [
{
"created": "Thu, 26 Aug 2021 11:41:03 GMT",
"version": "v1"
}
] | 2021-08-27 | [
[
"Seifi",
"Soroush",
""
],
[
"Jha",
"Abhishek",
""
],
[
"Tuytelaars",
"Tinne",
""
]
] | Active visual exploration aims to assist an agent with a limited field of view to understand its environment based on partial observations made by choosing the best viewing directions in the scene. Recent methods have tried to address this problem either by using reinforcement learning, which is difficult to train, or by uncertainty maps, which are task-specific and can only be implemented for dense prediction tasks. In this paper, we propose the Glimpse-Attend-and-Explore model which: (a) employs self-attention to guide the visual exploration instead of task-specific uncertainty maps; (b) can be used for both dense and sparse prediction tasks; and (c) uses a contrastive stream to further improve the representations learned. Unlike previous works, we show the application of our model on multiple tasks like reconstruction, segmentation and classification. Our model provides encouraging results while being less dependent on dataset bias in driving the exploration. We further perform an ablation study to investigate the features and attention learned by our model. Finally, we show that our self-attention module learns to attend different regions of the scene by minimizing the loss on the downstream task. Code: https://github.com/soroushseifi/glimpse-attend-explore. |
1401.0092 | Shraddha Shinde | Shraddha S. Shinde and Prof. Anagha P. Khedkar | A Novel Approach For Generating Face Template Using Bda | 11 pages, ITCSE 2013 conference | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In identity management system, commonly used biometric recognition system
needs attention towards issue of biometric template protection as far as more
reliable solution is concerned. In view of this biometric template protection
algorithm should satisfy security, discriminability and cancelability. As no
single template protection method is capable of satisfying the basic
requirements, a novel technique for face template generation and protection is
proposed. The novel approach is proposed to provide security and accuracy in
new user enrollment as well as authentication process. This novel technique
takes advantage of both the hybrid approach and the binary discriminant
analysis algorithm. This algorithm is designed on the basis of random
projection, binary discriminant analysis and fuzzy commitment scheme. Three
publicly available benchmark face databases are used for evaluation. The
proposed novel technique enhances the discriminability and recognition accuracy
by 80% in terms of matching score of the face images and provides high
security.
| [
{
"created": "Tue, 31 Dec 2013 04:48:43 GMT",
"version": "v1"
}
] | 2014-01-03 | [
[
"Shinde",
"Shraddha S.",
""
],
[
"Khedkar",
"Prof. Anagha P.",
""
]
] | In identity management system, commonly used biometric recognition system needs attention towards issue of biometric template protection as far as more reliable solution is concerned. In view of this biometric template protection algorithm should satisfy security, discriminability and cancelability. As no single template protection method is capable of satisfying the basic requirements, a novel technique for face template generation and protection is proposed. The novel approach is proposed to provide security and accuracy in new user enrollment as well as authentication process. This novel technique takes advantage of both the hybrid approach and the binary discriminant analysis algorithm. This algorithm is designed on the basis of random projection, binary discriminant analysis and fuzzy commitment scheme. Three publicly available benchmark face databases are used for evaluation. The proposed novel technique enhances the discriminability and recognition accuracy by 80% in terms of matching score of the face images and provides high security. |
1709.06668 | Daniel Seita | Daniel Seita, Sanjay Krishnan, Roy Fox, Stephen McKinley, John Canny,
Ken Goldberg | Fast and Reliable Autonomous Surgical Debridement with Cable-Driven
Robots Using a Two-Phase Calibration Procedure | Code, data, and videos are available at
https://sites.google.com/view/calib-icra/. Final version for ICRA 2018 | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automating precision subtasks such as debridement (removing dead or diseased
tissue fragments) with Robotic Surgical Assistants (RSAs) such as the da Vinci
Research Kit (dVRK) is challenging due to inherent non-linearities in
cable-driven systems. We propose and evaluate a novel two-phase coarse-to-fine
calibration method. In Phase I (coarse), we place a red calibration marker on
the end effector and let it randomly move through a set of open-loop
trajectories to obtain a large sample set of camera pixels and internal robot
end-effector configurations. This coarse data is then used to train a Deep
Neural Network (DNN) to learn the coarse transformation bias. In Phase II
(fine), the bias from Phase I is applied to move the end-effector toward a
small set of specific target points on a printed sheet. For each target, a
human operator manually adjusts the end-effector position by direct contact
(not through teleoperation) and the residual compensation bias is recorded.
This fine data is then used to train a Random Forest (RF) to learn the fine
transformation bias. Subsequent experiments suggest that without calibration,
position errors average 4.55mm. Phase I can reduce average error to 2.14mm and
the combination of Phase I and Phase II can reduces average error to 1.08mm. We
apply these results to debridement of raisins and pumpkin seeds as fragment
phantoms. Using an endoscopic stereo camera with standard edge detection,
experiments with 120 trials achieved average success rates of 94.5%, exceeding
prior results with much larger fragments (89.4%) and achieving a speedup of
2.1x, decreasing time per fragment from 15.8 seconds to 7.3 seconds. Source
code, data, and videos are available at
https://sites.google.com/view/calib-icra/.
| [
{
"created": "Tue, 19 Sep 2017 22:51:36 GMT",
"version": "v1"
},
{
"created": "Sat, 24 Feb 2018 08:34:58 GMT",
"version": "v2"
}
] | 2018-02-27 | [
[
"Seita",
"Daniel",
""
],
[
"Krishnan",
"Sanjay",
""
],
[
"Fox",
"Roy",
""
],
[
"McKinley",
"Stephen",
""
],
[
"Canny",
"John",
""
],
[
"Goldberg",
"Ken",
""
]
] | Automating precision subtasks such as debridement (removing dead or diseased tissue fragments) with Robotic Surgical Assistants (RSAs) such as the da Vinci Research Kit (dVRK) is challenging due to inherent non-linearities in cable-driven systems. We propose and evaluate a novel two-phase coarse-to-fine calibration method. In Phase I (coarse), we place a red calibration marker on the end effector and let it randomly move through a set of open-loop trajectories to obtain a large sample set of camera pixels and internal robot end-effector configurations. This coarse data is then used to train a Deep Neural Network (DNN) to learn the coarse transformation bias. In Phase II (fine), the bias from Phase I is applied to move the end-effector toward a small set of specific target points on a printed sheet. For each target, a human operator manually adjusts the end-effector position by direct contact (not through teleoperation) and the residual compensation bias is recorded. This fine data is then used to train a Random Forest (RF) to learn the fine transformation bias. Subsequent experiments suggest that without calibration, position errors average 4.55mm. Phase I can reduce average error to 2.14mm and the combination of Phase I and Phase II can reduces average error to 1.08mm. We apply these results to debridement of raisins and pumpkin seeds as fragment phantoms. Using an endoscopic stereo camera with standard edge detection, experiments with 120 trials achieved average success rates of 94.5%, exceeding prior results with much larger fragments (89.4%) and achieving a speedup of 2.1x, decreasing time per fragment from 15.8 seconds to 7.3 seconds. Source code, data, and videos are available at https://sites.google.com/view/calib-icra/. |
2312.15608 | Yupei Zhang | Yupei Zhang, Yuxin Li, Yifei Wang, Shuangshuang Wei, Yunan Xu, and
Xuequn Shang | Federated learning-outcome prediction with multi-layer privacy
protection | 10 pages, 9 figures, 3 tables. This preprint will be published in
Frontiers of Computer Science on Dec 15, 2024 | Frontiers of Computer Science, 2024,18(6):186604 | 10.1007/s11704-023-2791-8 | null | cs.LG cs.CR cs.DC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Learning-outcome prediction (LOP) is a long-standing and critical problem in
educational routes. Many studies have contributed to developing effective
models while often suffering from data shortage and low generalization to
various institutions due to the privacy-protection issue. To this end, this
study proposes a distributed grade prediction model, dubbed FecMap, by
exploiting the federated learning (FL) framework that preserves the private
data of local clients and communicates with others through a global generalized
model. FecMap considers local subspace learning (LSL), which explicitly learns
the local features against the global features, and multi-layer privacy
protection (MPP), which hierarchically protects the private features, including
model-shareable features and not-allowably shared features, to achieve
client-specific classifiers of high performance on LOP per institution. FecMap
is then achieved in an iteration manner with all datasets distributed on
clients by training a local neural network composed of a global part, a local
part, and a classification head in clients and averaging the global parts from
clients on the server. To evaluate the FecMap model, we collected three
higher-educational datasets of student academic records from engineering
majors. Experiment results manifest that FecMap benefits from the proposed LSL
and MPP and achieves steady performance on the task of LOP, compared with the
state-of-the-art models. This study makes a fresh attempt at the use of
federated learning in the learning-analytical task, potentially paving the way
to facilitating personalized education with privacy protection.
| [
{
"created": "Mon, 25 Dec 2023 04:29:05 GMT",
"version": "v1"
}
] | 2023-12-27 | [
[
"Zhang",
"Yupei",
""
],
[
"Li",
"Yuxin",
""
],
[
"Wang",
"Yifei",
""
],
[
"Wei",
"Shuangshuang",
""
],
[
"Xu",
"Yunan",
""
],
[
"Shang",
"Xuequn",
""
]
] | Learning-outcome prediction (LOP) is a long-standing and critical problem in educational routes. Many studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection issue. To this end, this study proposes a distributed grade prediction model, dubbed FecMap, by exploiting the federated learning (FL) framework that preserves the private data of local clients and communicates with others through a global generalized model. FecMap considers local subspace learning (LSL), which explicitly learns the local features against the global features, and multi-layer privacy protection (MPP), which hierarchically protects the private features, including model-shareable features and not-allowably shared features, to achieve client-specific classifiers of high performance on LOP per institution. FecMap is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part, a local part, and a classification head in clients and averaging the global parts from clients on the server. To evaluate the FecMap model, we collected three higher-educational datasets of student academic records from engineering majors. Experiment results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP, compared with the state-of-the-art models. This study makes a fresh attempt at the use of federated learning in the learning-analytical task, potentially paving the way to facilitating personalized education with privacy protection. |
2110.04683 | Alexander Lin | Alexander Lin, Andrew H. Song, Demba Ba | Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning | 5 pages, 3 figures | IEEE ICASSP 2022 | null | null | cs.LG eess.SP | http://creativecommons.org/licenses/by/4.0/ | State-of-the-art approaches for clustering high-dimensional data utilize deep
auto-encoder architectures. Many of these networks require a large number of
parameters and suffer from a lack of interpretability, due to the black-box
nature of the auto-encoders. We introduce Mixture Model Auto-Encoders
(MixMate), a novel architecture that clusters data by performing inference on a
generative model. Derived from the perspective of sparse dictionary learning
and mixture models, MixMate comprises several auto-encoders, each tasked with
reconstructing data in a distinct cluster, while enforcing sparsity in the
latent space. Through experiments on various image datasets, we show that
MixMate achieves competitive performance compared to state-of-the-art deep
clustering algorithms, while using orders of magnitude fewer parameters.
| [
{
"created": "Sun, 10 Oct 2021 02:30:31 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Feb 2022 16:35:10 GMT",
"version": "v2"
}
] | 2022-02-28 | [
[
"Lin",
"Alexander",
""
],
[
"Song",
"Andrew H.",
""
],
[
"Ba",
"Demba",
""
]
] | State-of-the-art approaches for clustering high-dimensional data utilize deep auto-encoder architectures. Many of these networks require a large number of parameters and suffer from a lack of interpretability, due to the black-box nature of the auto-encoders. We introduce Mixture Model Auto-Encoders (MixMate), a novel architecture that clusters data by performing inference on a generative model. Derived from the perspective of sparse dictionary learning and mixture models, MixMate comprises several auto-encoders, each tasked with reconstructing data in a distinct cluster, while enforcing sparsity in the latent space. Through experiments on various image datasets, we show that MixMate achieves competitive performance compared to state-of-the-art deep clustering algorithms, while using orders of magnitude fewer parameters. |
1511.07275 | Wojciech Zaremba | Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus | Learning Simple Algorithms from Examples | null | null | null | null | cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an approach for learning simple algorithms such as copying,
multi-digit addition and single digit multiplication directly from examples.
Our framework consists of a set of interfaces, accessed by a controller.
Typical interfaces are 1-D tapes or 2-D grids that hold the input and output
data. For the controller, we explore a range of neural network-based models
which vary in their ability to abstract the underlying algorithm from training
instances and generalize to test examples with many thousands of digits. The
controller is trained using $Q$-learning with several enhancements and we show
that the bottleneck is in the capabilities of the controller rather than in the
search incurred by $Q$-learning.
| [
{
"created": "Mon, 23 Nov 2015 15:31:54 GMT",
"version": "v1"
},
{
"created": "Tue, 24 Nov 2015 03:28:35 GMT",
"version": "v2"
}
] | 2015-11-25 | [
[
"Zaremba",
"Wojciech",
""
],
[
"Mikolov",
"Tomas",
""
],
[
"Joulin",
"Armand",
""
],
[
"Fergus",
"Rob",
""
]
] | We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples. Our framework consists of a set of interfaces, accessed by a controller. Typical interfaces are 1-D tapes or 2-D grids that hold the input and output data. For the controller, we explore a range of neural network-based models which vary in their ability to abstract the underlying algorithm from training instances and generalize to test examples with many thousands of digits. The controller is trained using $Q$-learning with several enhancements and we show that the bottleneck is in the capabilities of the controller rather than in the search incurred by $Q$-learning. |
2211.13724 | Ali Harakeh | Ali Harakeh, Jordan Hu, Naiqing Guan, Steven L. Waslander, and Liam
Paull | Estimating Regression Predictive Distributions with Sample Networks | Accepted for publication in AAAI 2023. Example code at:
https://samplenet.github.io/ | null | null | null | cs.LG cs.CV | http://creativecommons.org/licenses/by/4.0/ | Estimating the uncertainty in deep neural network predictions is crucial for
many real-world applications. A common approach to model uncertainty is to
choose a parametric distribution and fit the data to it using maximum
likelihood estimation. The chosen parametric form can be a poor fit to the
data-generating distribution, resulting in unreliable uncertainty estimates. In
this work, we propose SampleNet, a flexible and scalable architecture for
modeling uncertainty that avoids specifying a parametric form on the output
distribution. SampleNets do so by defining an empirical distribution using
samples that are learned with the Energy Score and regularized with the
Sinkhorn Divergence. SampleNets are shown to be able to well-fit a wide range
of distributions and to outperform baselines on large-scale real-world
regression tasks.
| [
{
"created": "Thu, 24 Nov 2022 17:23:29 GMT",
"version": "v1"
}
] | 2022-11-28 | [
[
"Harakeh",
"Ali",
""
],
[
"Hu",
"Jordan",
""
],
[
"Guan",
"Naiqing",
""
],
[
"Waslander",
"Steven L.",
""
],
[
"Paull",
"Liam",
""
]
] | Estimating the uncertainty in deep neural network predictions is crucial for many real-world applications. A common approach to model uncertainty is to choose a parametric distribution and fit the data to it using maximum likelihood estimation. The chosen parametric form can be a poor fit to the data-generating distribution, resulting in unreliable uncertainty estimates. In this work, we propose SampleNet, a flexible and scalable architecture for modeling uncertainty that avoids specifying a parametric form on the output distribution. SampleNets do so by defining an empirical distribution using samples that are learned with the Energy Score and regularized with the Sinkhorn Divergence. SampleNets are shown to be able to well-fit a wide range of distributions and to outperform baselines on large-scale real-world regression tasks. |
2201.01219 | Sansit Patnaik | Wei Ding and Sansit Patnaik and Fabio Semperlotti | Multiscale Nonlocal Elasticity: A Distributed Order Fractional
Formulation | 31 pages, 9 images, 3 Tables | null | null | null | cs.CE cs.NA math.NA physics.app-ph | http://creativecommons.org/licenses/by/4.0/ | This study presents a generalized multiscale nonlocal elasticity theory that
leverages distributed order fractional calculus to accurately capture
coexisting multiscale and nonlocal effects within a macroscopic continuum. The
nonlocal multiscale behavior is captured via distributed order fractional
constitutive relations derived from a nonlocal thermodynamic formulation. The
governing equations of the inhomogeneous continuum are obtained via the
Hamilton principle. As a generalization of the constant order fractional
continuum theory, the distributed order theory can model complex media
characterized by inhomogeneous nonlocality and multiscale effects. In order to
understand the correspondence between microscopic effects and the properties of
the continuum, an equivalent mass-spring lattice model is also developed by
direct discretization of the distributed order elastic continuum. Detailed
theoretical arguments are provided to show the equivalence between the discrete
and the continuum distributed order models in terms of internal nonlocal
forces, potential energy distribution, and boundary conditions. These
theoretical arguments facilitate the physical interpretation of the role played
by the distributed order framework within nonlocal elasticity theories. They
also highlight the outstanding potential and opportunities offered by this
methodology to account for multiscale nonlocal effects. The capabilities of the
methodology are also illustrated via a numerical study that highlights the
excellent agreement between the displacement profiles and the total potential
energy predicted by the two models under various order distributions.
Remarkably, multiscale effects such as displacement distortion, material
softening, and energy concentration are well captured at continuum level by the
distributed order theory.
| [
{
"created": "Fri, 24 Dec 2021 23:38:07 GMT",
"version": "v1"
}
] | 2022-01-05 | [
[
"Ding",
"Wei",
""
],
[
"Patnaik",
"Sansit",
""
],
[
"Semperlotti",
"Fabio",
""
]
] | This study presents a generalized multiscale nonlocal elasticity theory that leverages distributed order fractional calculus to accurately capture coexisting multiscale and nonlocal effects within a macroscopic continuum. The nonlocal multiscale behavior is captured via distributed order fractional constitutive relations derived from a nonlocal thermodynamic formulation. The governing equations of the inhomogeneous continuum are obtained via the Hamilton principle. As a generalization of the constant order fractional continuum theory, the distributed order theory can model complex media characterized by inhomogeneous nonlocality and multiscale effects. In order to understand the correspondence between microscopic effects and the properties of the continuum, an equivalent mass-spring lattice model is also developed by direct discretization of the distributed order elastic continuum. Detailed theoretical arguments are provided to show the equivalence between the discrete and the continuum distributed order models in terms of internal nonlocal forces, potential energy distribution, and boundary conditions. These theoretical arguments facilitate the physical interpretation of the role played by the distributed order framework within nonlocal elasticity theories. They also highlight the outstanding potential and opportunities offered by this methodology to account for multiscale nonlocal effects. The capabilities of the methodology are also illustrated via a numerical study that highlights the excellent agreement between the displacement profiles and the total potential energy predicted by the two models under various order distributions. Remarkably, multiscale effects such as displacement distortion, material softening, and energy concentration are well captured at continuum level by the distributed order theory. |
1704.07078 | Yunior Ram\'irez-Cruz | Sjouke Mauw, Yunior Ram\'irez-Cruz, Rolando Trujillo-Rasua | Rethinking $(k,\ell)$-anonymity in social graphs: $(k,\ell)$-adjacency
anonymity and $(k,\ell)$-(adjacency) anonymous transformations | null | "Conditional adjacency anonymity in social graphs under active
attacks", Knowledge and Information Systems 61(1):485-511, 2019 | 10.1007/s10115-018-1283-x | null | cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper treats the privacy-preserving publication of social graphs in the
presence of active adversaries, that is, adversaries with the ability to
introduce sybil nodes in the graph prior to publication and leverage them to
create unique fingerprints for a set of victim nodes and re-identify them after
publication. Stemming from the notion of $(k,\ell)$-anonymity, we introduce
$(k,\ell)$-anonymous transformations, characterising graph perturbation methods
that ensure protection from active adversaries levaraging up to $\ell$ sybil
nodes. Additionally, we introduce a new privacy property: $(k,\ell)$-adjacency
anonymity, which relaxes the assumption made by $(k,\ell)$-anonymity that
adversaries can control all distances between sybil nodes and the rest of the
nodes in the graph. The new privacy property is in turn the basis for a new
type of graph perturbation: $(k,\ell)$-adjacency anonymous transformations. We
propose algorithms for obtaining $(k,1)$-adjacency anonymous transformations
for arbitrary values of $k$, as well as $(2,\ell)$-adjacency anonymous
transformations for small values of $\ell$.
| [
{
"created": "Mon, 24 Apr 2017 08:14:03 GMT",
"version": "v1"
}
] | 2019-09-04 | [
[
"Mauw",
"Sjouke",
""
],
[
"Ramírez-Cruz",
"Yunior",
""
],
[
"Trujillo-Rasua",
"Rolando",
""
]
] | This paper treats the privacy-preserving publication of social graphs in the presence of active adversaries, that is, adversaries with the ability to introduce sybil nodes in the graph prior to publication and leverage them to create unique fingerprints for a set of victim nodes and re-identify them after publication. Stemming from the notion of $(k,\ell)$-anonymity, we introduce $(k,\ell)$-anonymous transformations, characterising graph perturbation methods that ensure protection from active adversaries levaraging up to $\ell$ sybil nodes. Additionally, we introduce a new privacy property: $(k,\ell)$-adjacency anonymity, which relaxes the assumption made by $(k,\ell)$-anonymity that adversaries can control all distances between sybil nodes and the rest of the nodes in the graph. The new privacy property is in turn the basis for a new type of graph perturbation: $(k,\ell)$-adjacency anonymous transformations. We propose algorithms for obtaining $(k,1)$-adjacency anonymous transformations for arbitrary values of $k$, as well as $(2,\ell)$-adjacency anonymous transformations for small values of $\ell$. |
1205.3181 | Sebastien Bubeck | S\'ebastien Bubeck, Tengyao Wang, Nitin Viswanathan | Multiple Identifications in Multi-Armed Bandits | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of identifying the top $m$ arms in a multi-armed bandit
game. Our proposed solution relies on a new algorithm based on successive
rejects of the seemingly bad arms, and successive accepts of the good ones.
This algorithmic contribution allows to tackle other multiple identifications
settings that were previously out of reach. In particular we show that this
idea of successive accepts and rejects applies to the multi-bandit best arm
identification problem.
| [
{
"created": "Mon, 14 May 2012 20:10:04 GMT",
"version": "v1"
}
] | 2012-05-16 | [
[
"Bubeck",
"Sébastien",
""
],
[
"Wang",
"Tengyao",
""
],
[
"Viswanathan",
"Nitin",
""
]
] | We study the problem of identifying the top $m$ arms in a multi-armed bandit game. Our proposed solution relies on a new algorithm based on successive rejects of the seemingly bad arms, and successive accepts of the good ones. This algorithmic contribution allows to tackle other multiple identifications settings that were previously out of reach. In particular we show that this idea of successive accepts and rejects applies to the multi-bandit best arm identification problem. |
1509.02975 | Steve Huntsman | Steve Huntsman and Arman Rezaee | De Bruijn entropy and string similarity | Extended version of a paper presented at WORDS 2015; MATLAB source
code and scripts for reproducing results are included | null | null | https://nbn-resolving.org/urn:nbn:de:gbv:8:1-zs-00000305-a5 | cs.DM cs.IT math.CO math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the notion of de Bruijn entropy of an Eulerian quiver and show
how the corresponding relative entropy can be applied to practical string
similarity problems. This approach explicitly links the combinatorial and
information-theoretical properties of words and its performance is superior to
edit distances in many respects and competitive in most others. The
computational complexity of our current implementation is parametrically
tunable between linear and cubic, and we outline how an optimized linear
algebra subroutine can reduce the cubic complexity to approximately linear.
Numerous examples are provided, including a realistic application to molecular
phylogenetics.
| [
{
"created": "Wed, 9 Sep 2015 23:27:04 GMT",
"version": "v1"
}
] | 2022-01-24 | [
[
"Huntsman",
"Steve",
""
],
[
"Rezaee",
"Arman",
""
]
] | We introduce the notion of de Bruijn entropy of an Eulerian quiver and show how the corresponding relative entropy can be applied to practical string similarity problems. This approach explicitly links the combinatorial and information-theoretical properties of words and its performance is superior to edit distances in many respects and competitive in most others. The computational complexity of our current implementation is parametrically tunable between linear and cubic, and we outline how an optimized linear algebra subroutine can reduce the cubic complexity to approximately linear. Numerous examples are provided, including a realistic application to molecular phylogenetics. |
1701.01491 | Amina Piemontese Ph.D | Amina Piemontese, and Alexandre Graell i Amat | MDS-Coded Distributed Caching for Low Delay Wireless Content Delivery | submitted to IEEE Transactions on Communications. arXiv admin note:
text overlap with arXiv:1607.00880 | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the use of maximum distance separable (MDS) codes to cache
popular content to reduce the download delay of wireless content delivery. In
particular, we consider a cellular system where devices roam in an out of a
cell according to a Poisson random process. Popular content is cached in a
limited number of the mobile devices using an MDS code and can be downloaded
from the mobile devices using device-to-device communication. We derive an
analytical expression for the delay incurred in downloading content from the
wireless network and show that distributed caching using MDS codes can
dramatically reduce the download delay with respect to the scenario where
content is always downloaded from the base station and to the case of uncoded
distributed caching.
| [
{
"created": "Thu, 5 Jan 2017 21:59:20 GMT",
"version": "v1"
}
] | 2017-01-09 | [
[
"Piemontese",
"Amina",
""
],
[
"Amat",
"Alexandre Graell i",
""
]
] | We investigate the use of maximum distance separable (MDS) codes to cache popular content to reduce the download delay of wireless content delivery. In particular, we consider a cellular system where devices roam in an out of a cell according to a Poisson random process. Popular content is cached in a limited number of the mobile devices using an MDS code and can be downloaded from the mobile devices using device-to-device communication. We derive an analytical expression for the delay incurred in downloading content from the wireless network and show that distributed caching using MDS codes can dramatically reduce the download delay with respect to the scenario where content is always downloaded from the base station and to the case of uncoded distributed caching. |
1011.0298 | Nayyar Mehmood | Nayyar Mehmood, Imran Haider Qureshi | Intuitionistic Fuzzy Ideal Extensions of {\Gamma}-Semigroups | Accepted, 11 pages | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper the concept of the extensions of intuitionistic fuzzy ideals in
a semigroup has been extended to a {\Gamma}-Semigroups. Among other results
characterization of prime ideals in a {\Gamma}-Semigroups in terms of
intuitionistic fuzzy ideal extension has been obtained.
| [
{
"created": "Mon, 1 Nov 2010 11:54:14 GMT",
"version": "v1"
}
] | 2010-11-13 | [
[
"Mehmood",
"Nayyar",
""
],
[
"Qureshi",
"Imran Haider",
""
]
] | In this paper the concept of the extensions of intuitionistic fuzzy ideals in a semigroup has been extended to a {\Gamma}-Semigroups. Among other results characterization of prime ideals in a {\Gamma}-Semigroups in terms of intuitionistic fuzzy ideal extension has been obtained. |
2111.03362 | Moran Baruch | Moran Baruch, Nir Drucker, Lev Greenberg and Guy Moshkowich | A methodology for training homomorphicencryption friendly neural
networks | null | null | 10.1007/978-3-031-16815-4_29 | null | cs.CR cs.LG | http://creativecommons.org/licenses/by/4.0/ | Privacy-preserving deep neural network (DNN) inference is a necessity in
different regulated industries such as healthcare, finance and retail.
Recently, homomorphic encryption (HE) has been used as a method to enable
analytics while addressing privacy concerns. HE enables secure predictions over
encrypted data. However, there are several challenges related to the use of HE,
including DNN size limitations and the lack of support for some operation
types. Most notably, the commonly used ReLU activation is not supported under
some HE schemes. We propose a structured methodology to replace ReLU with a
quadratic polynomial activation. To address the accuracy degradation issue, we
use a pre-trained model that trains another HE-friendly model, using techniques
such as trainable activation functions and knowledge distillation. We
demonstrate our methodology on the AlexNet architecture, using the chest X-Ray
and CT datasets for COVID-19 detection. Experiments using our approach reduced
the gap between the F1 score and accuracy of the models trained with ReLU and
the HE-friendly model to within a mere 0.32-5.3 percent degradation. We also
demonstrate our methodology using the SqueezeNet architecture, for which we
observed 7 percent accuracy and F1 improvements over training similar networks
with other HE-friendly training methods.
| [
{
"created": "Fri, 5 Nov 2021 10:04:15 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Nov 2021 08:22:48 GMT",
"version": "v2"
},
{
"created": "Thu, 7 Jul 2022 19:22:02 GMT",
"version": "v3"
}
] | 2023-06-13 | [
[
"Baruch",
"Moran",
""
],
[
"Drucker",
"Nir",
""
],
[
"Greenberg",
"Lev",
""
],
[
"Moshkowich",
"Guy",
""
]
] | Privacy-preserving deep neural network (DNN) inference is a necessity in different regulated industries such as healthcare, finance and retail. Recently, homomorphic encryption (HE) has been used as a method to enable analytics while addressing privacy concerns. HE enables secure predictions over encrypted data. However, there are several challenges related to the use of HE, including DNN size limitations and the lack of support for some operation types. Most notably, the commonly used ReLU activation is not supported under some HE schemes. We propose a structured methodology to replace ReLU with a quadratic polynomial activation. To address the accuracy degradation issue, we use a pre-trained model that trains another HE-friendly model, using techniques such as trainable activation functions and knowledge distillation. We demonstrate our methodology on the AlexNet architecture, using the chest X-Ray and CT datasets for COVID-19 detection. Experiments using our approach reduced the gap between the F1 score and accuracy of the models trained with ReLU and the HE-friendly model to within a mere 0.32-5.3 percent degradation. We also demonstrate our methodology using the SqueezeNet architecture, for which we observed 7 percent accuracy and F1 improvements over training similar networks with other HE-friendly training methods. |
1402.3613 | Pavel Janovsk\'y | Pavel Janovsk\'y and Michal \v{C}\'ap and Ji\v{r}\'i Vok\v{r}\'inek | Finding Coordinated Paths for Multiple Holonomic Agents in 2-d Polygonal
Environment | Proceedings of the 13th International Conference on Autonomous Agents
and Multiagent Systems (AAMAS 2014) | null | null | null | cs.AI cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Avoiding collisions is one of the vital tasks for systems of autonomous
mobile agents. We focus on the problem of finding continuous coordinated paths
for multiple mobile disc agents in a 2-d environment with polygonal obstacles.
The problem is PSPACE-hard, with the state space growing exponentially in the
number of agents. Therefore, the state of the art methods include mainly
reactive techniques and sampling-based iterative algorithms.
We compare the performance of a widely-used reactive method ORCA with three
variants of a popular planning algorithm RRT* applied to multi-agent path
planning and find that an algorithm combining reactive collision avoidance and
RRT* planning, which we call ORCA-RRT* can be used to solve instances that are
out of the reach of either of the techniques. We experimentally show that: 1)
the reactive part of the algorithm can efficiently solve many multi-agent path
finding problems involving large number of agents, for which RRT* algorithm is
often unable to find a solution in limited time and 2) the planning component
of the algorithm is able to solve many instances containing local minima, where
reactive techniques typically fail.
| [
{
"created": "Fri, 14 Feb 2014 22:09:44 GMT",
"version": "v1"
}
] | 2014-02-18 | [
[
"Janovský",
"Pavel",
""
],
[
"Čáp",
"Michal",
""
],
[
"Vokřínek",
"Jiří",
""
]
] | Avoiding collisions is one of the vital tasks for systems of autonomous mobile agents. We focus on the problem of finding continuous coordinated paths for multiple mobile disc agents in a 2-d environment with polygonal obstacles. The problem is PSPACE-hard, with the state space growing exponentially in the number of agents. Therefore, the state of the art methods include mainly reactive techniques and sampling-based iterative algorithms. We compare the performance of a widely-used reactive method ORCA with three variants of a popular planning algorithm RRT* applied to multi-agent path planning and find that an algorithm combining reactive collision avoidance and RRT* planning, which we call ORCA-RRT* can be used to solve instances that are out of the reach of either of the techniques. We experimentally show that: 1) the reactive part of the algorithm can efficiently solve many multi-agent path finding problems involving large number of agents, for which RRT* algorithm is often unable to find a solution in limited time and 2) the planning component of the algorithm is able to solve many instances containing local minima, where reactive techniques typically fail. |
1704.06700 | Ahmad Nauman Ghazi | Shoaib Bakhtyar and Ahmad Nauman Ghazi | On Improving Research Methodology Course at Blekinge Institute of
Technology | Conference on Higher Education, L\"ararl\"ardom2016 Kristianstad
University Press Sweden. 2016 | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Research Methodology in Software Engineering and Computer Science (RM) is
a compulsory course that must be studied by graduate students at Blekinge
Institute of Technology (BTH) prior to undertaking their theses work. The
course is focused on teaching research methods and techniques for data
collection and analysis in the fields of Computer Science and Software
Engineering. It is intended that the course should help students in practically
applying appropriate research methods in different courses (in addition to the
RM course) including their Master's theses. However, it is believed that there
exist deficiencies in the course due to which the course implementation
(learning and assessment activities) as well as the performance of different
participants (students, teachers, and evaluators) are affected negatively. In
this article our aim is to investigate potential deficiencies in the RM course
at BTH in order to provide a concrete evidence on the deficiencies faced by
students, evaluators, and teachers in the course. Additionally, we suggest
recommendations for resolving the identified deficiencies. Our findings
gathered through semi-structured interviews with students, teachers, and
evaluators in the course are presented in this article. By identifying a total
of twenty one deficiencies from different perspectives, we found that there
exist critical deficiencies at different levels within the course. Furthermore,
in order to overcome the identified deficiencies, we suggest seven
recommendations that may be implemented at different levels within the course
and the study program. Our suggested recommendations, if implemented, will help
in resolving deficiencies in the course, which may lead to achieving an
improved teaching and learning in the RM course at BTH.
| [
{
"created": "Fri, 21 Apr 2017 20:11:57 GMT",
"version": "v1"
}
] | 2017-04-25 | [
[
"Bakhtyar",
"Shoaib",
""
],
[
"Ghazi",
"Ahmad Nauman",
""
]
] | The Research Methodology in Software Engineering and Computer Science (RM) is a compulsory course that must be studied by graduate students at Blekinge Institute of Technology (BTH) prior to undertaking their theses work. The course is focused on teaching research methods and techniques for data collection and analysis in the fields of Computer Science and Software Engineering. It is intended that the course should help students in practically applying appropriate research methods in different courses (in addition to the RM course) including their Master's theses. However, it is believed that there exist deficiencies in the course due to which the course implementation (learning and assessment activities) as well as the performance of different participants (students, teachers, and evaluators) are affected negatively. In this article our aim is to investigate potential deficiencies in the RM course at BTH in order to provide a concrete evidence on the deficiencies faced by students, evaluators, and teachers in the course. Additionally, we suggest recommendations for resolving the identified deficiencies. Our findings gathered through semi-structured interviews with students, teachers, and evaluators in the course are presented in this article. By identifying a total of twenty one deficiencies from different perspectives, we found that there exist critical deficiencies at different levels within the course. Furthermore, in order to overcome the identified deficiencies, we suggest seven recommendations that may be implemented at different levels within the course and the study program. Our suggested recommendations, if implemented, will help in resolving deficiencies in the course, which may lead to achieving an improved teaching and learning in the RM course at BTH. |
2305.20056 | Arvind Pillai | Arvind Pillai, Subigya Nepal and Andrew Campbell | Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning | 15 pages, 4 figures, CHIL 2023 (Accepted) | null | null | null | cs.LG cs.HC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Rare life events significantly impact mental health, and their detection in
behavioral studies is a crucial step towards health-based interventions. We
envision that mobile sensing data can be used to detect these anomalies.
However, the human-centered nature of the problem, combined with the
infrequency and uniqueness of these events makes it challenging for
unsupervised machine learning methods. In this paper, we first investigate
granger-causality between life events and human behavior using sensing data.
Next, we propose a multi-task framework with an unsupervised autoencoder to
capture irregular behavior, and an auxiliary sequence predictor that identifies
transitions in workplace performance to contextualize events. We perform
experiments using data from a mobile sensing study comprising N=126 information
workers from multiple industries, spanning 10106 days with 198 rare events
(<2%). Through personalized inference, we detect the exact day of a rare event
with an F1 of 0.34, demonstrating that our method outperforms several
baselines. Finally, we discuss the implications of our work from the context of
real-world deployment.
| [
{
"created": "Wed, 31 May 2023 17:29:24 GMT",
"version": "v1"
}
] | 2023-06-01 | [
[
"Pillai",
"Arvind",
""
],
[
"Nepal",
"Subigya",
""
],
[
"Campbell",
"Andrew",
""
]
] | Rare life events significantly impact mental health, and their detection in behavioral studies is a crucial step towards health-based interventions. We envision that mobile sensing data can be used to detect these anomalies. However, the human-centered nature of the problem, combined with the infrequency and uniqueness of these events makes it challenging for unsupervised machine learning methods. In this paper, we first investigate granger-causality between life events and human behavior using sensing data. Next, we propose a multi-task framework with an unsupervised autoencoder to capture irregular behavior, and an auxiliary sequence predictor that identifies transitions in workplace performance to contextualize events. We perform experiments using data from a mobile sensing study comprising N=126 information workers from multiple industries, spanning 10106 days with 198 rare events (<2%). Through personalized inference, we detect the exact day of a rare event with an F1 of 0.34, demonstrating that our method outperforms several baselines. Finally, we discuss the implications of our work from the context of real-world deployment. |
2310.10687 | Md. Imtiaz Habib | Md. Imtiaz Habib, Abdullah Al Maruf, Md. Jobair Ahmed Nabil | An Exploration Into Web Session Security- A Systematic Literature Review | 9 pages, 8 sections, survey article | null | null | null | cs.SE cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The most common attacks against web sessions are reviewed in this paper, for
example, some attacks against web browsers' honest users attempting to create
session with trusted web browser application legally. We have assessed with
four different ways to judge the viability of a certain solution by reviewing
existing security solutions which prevent or halt the different attacks. Then
we have pointed out some guidelines that have been taken into account by the
designers of the proposals we reviewed. The guidelines we have identified will
be helpful for the creative solutions proceeding web security in a more
structured and holistic way.
| [
{
"created": "Sat, 14 Oct 2023 16:22:07 GMT",
"version": "v1"
}
] | 2023-10-18 | [
[
"Habib",
"Md. Imtiaz",
""
],
[
"Maruf",
"Abdullah Al",
""
],
[
"Nabil",
"Md. Jobair Ahmed",
""
]
] | The most common attacks against web sessions are reviewed in this paper, for example, some attacks against web browsers' honest users attempting to create session with trusted web browser application legally. We have assessed with four different ways to judge the viability of a certain solution by reviewing existing security solutions which prevent or halt the different attacks. Then we have pointed out some guidelines that have been taken into account by the designers of the proposals we reviewed. The guidelines we have identified will be helpful for the creative solutions proceeding web security in a more structured and holistic way. |
2005.13811 | Thomas Studer | Thomas Studer | No-Go Theorems for Data Privacy | null | null | null | null | cs.LO cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Controlled query evaluation (CQE) is an approach to guarantee data privacy
for database and knowledge base systems. CQE-systems feature a censor function
that may distort the answer to a query in order to hide sensitive information.
We introduce a high-level formalization of controlled query evaluation and
define several desirable properties of CQE-systems. Finally we establish two
no-go theorems, which show that certain combinations of these properties cannot
be obtained.
| [
{
"created": "Thu, 28 May 2020 07:10:37 GMT",
"version": "v1"
}
] | 2020-05-29 | [
[
"Studer",
"Thomas",
""
]
] | Controlled query evaluation (CQE) is an approach to guarantee data privacy for database and knowledge base systems. CQE-systems feature a censor function that may distort the answer to a query in order to hide sensitive information. We introduce a high-level formalization of controlled query evaluation and define several desirable properties of CQE-systems. Finally we establish two no-go theorems, which show that certain combinations of these properties cannot be obtained. |
2302.05520 | Eli Gafni professor | Eli Gafni and Vasileios Zikas | Synchrony/Asynchrony vs. Stationary/Mobile? The Latter is Superior...in
Theory | null | null | null | null | cs.DS | http://creativecommons.org/publicdomain/zero/1.0/ | Like Asynchrony, Mobility of faults precludes consensus. Yet, a model M in
which Consensus is solvable, has an analogue relaxed model in which Consensus
is not solvable and for which we can ask, whether Consensus is solvable if the
system initially behaves like the relaxed analogue model, but eventually morphs
into M. We consider two relaxed analogues of M. The first is the traditional
Asynchronous model, and the second to be defined, the Mobile analogue. While
for some M we show that Consensus is not solvable in the Asynchronous analogue,
it is solvable in all the Mobile analogues. Hence, from this perspective
Mobility is superior to Asynchrony.
The pie in the sky relationship we envision is: Consensus is solvable in M,
if and only if binary Commit-Adopt is solvable in the mobile analogue.
The ``only if'' is easy. Here we show case by case that the ``if'' holds for
all the common faults types.
| [
{
"created": "Fri, 10 Feb 2023 21:49:55 GMT",
"version": "v1"
}
] | 2023-02-14 | [
[
"Gafni",
"Eli",
""
],
[
"Zikas",
"Vasileios",
""
]
] | Like Asynchrony, Mobility of faults precludes consensus. Yet, a model M in which Consensus is solvable, has an analogue relaxed model in which Consensus is not solvable and for which we can ask, whether Consensus is solvable if the system initially behaves like the relaxed analogue model, but eventually morphs into M. We consider two relaxed analogues of M. The first is the traditional Asynchronous model, and the second to be defined, the Mobile analogue. While for some M we show that Consensus is not solvable in the Asynchronous analogue, it is solvable in all the Mobile analogues. Hence, from this perspective Mobility is superior to Asynchrony. The pie in the sky relationship we envision is: Consensus is solvable in M, if and only if binary Commit-Adopt is solvable in the mobile analogue. The ``only if'' is easy. Here we show case by case that the ``if'' holds for all the common faults types. |
2101.08100 | Weixuan Zhang | Weixuan Zhang, Marco Tognon, Lionel Ott, Roland Siegwart, and Juan
Nieto | Active Model Learning using Informative Trajectories for Improved
Closed-Loop Control on Real Robots | null | null | null | null | cs.RO cs.SY eess.SY | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Model-based controllers on real robots require accurate knowledge of the
system dynamics to perform optimally. For complex dynamics, first-principles
modeling is not sufficiently precise, and data-driven approaches can be
leveraged to learn a statistical model from real experiments. However, the
efficient and effective data collection for such a data-driven system on real
robots is still an open challenge. This paper introduces an optimization
problem formulation to find an informative trajectory that allows for efficient
data collection and model learning. We present a sampling-based method that
computes an approximation of the trajectory that minimizes the prediction
uncertainty of the dynamics model. This trajectory is then executed, collecting
the data to update the learned model. In experiments we demonstrate the
capabilities of our proposed framework when applied to a complex
omnidirectional flying vehicle with tiltable rotors. Using our informative
trajectories results in models which outperform models obtained from
non-informative trajectory by 13.3\% with the same amount of training data.
Furthermore, we show that the model learned from informative trajectories
generalizes better than the one learned from non-informative trajectories,
achieving better tracking performance on different tasks.
| [
{
"created": "Wed, 20 Jan 2021 12:54:26 GMT",
"version": "v1"
},
{
"created": "Fri, 14 May 2021 13:34:39 GMT",
"version": "v2"
}
] | 2021-05-17 | [
[
"Zhang",
"Weixuan",
""
],
[
"Tognon",
"Marco",
""
],
[
"Ott",
"Lionel",
""
],
[
"Siegwart",
"Roland",
""
],
[
"Nieto",
"Juan",
""
]
] | Model-based controllers on real robots require accurate knowledge of the system dynamics to perform optimally. For complex dynamics, first-principles modeling is not sufficiently precise, and data-driven approaches can be leveraged to learn a statistical model from real experiments. However, the efficient and effective data collection for such a data-driven system on real robots is still an open challenge. This paper introduces an optimization problem formulation to find an informative trajectory that allows for efficient data collection and model learning. We present a sampling-based method that computes an approximation of the trajectory that minimizes the prediction uncertainty of the dynamics model. This trajectory is then executed, collecting the data to update the learned model. In experiments we demonstrate the capabilities of our proposed framework when applied to a complex omnidirectional flying vehicle with tiltable rotors. Using our informative trajectories results in models which outperform models obtained from non-informative trajectory by 13.3\% with the same amount of training data. Furthermore, we show that the model learned from informative trajectories generalizes better than the one learned from non-informative trajectories, achieving better tracking performance on different tasks. |
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