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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1906.11278 | Fatemeh Kazemi | Fatemeh Kazemi, Esmaeil Karimi, Anoosheh Heidarzadeh, and Alex
Sprintson | Private Information Retrieval with Private Coded Side Information: The
Multi-Server Case | 11 pages | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we consider the multi-server setting of Private Information
Retrieval with Private Coded Side Information (PIR-PCSI) problem. In this
problem, there is a database of $K$ messages whose copies are replicated across
$N$ servers, and there is a user who knows a random linear combination of a
random subset of $M$ messages in the database as side information. The user
wishes to download one message from the servers, while protecting the
identities of both the demand message and the messages forming the side
information. We assume that the servers know the number of messages forming the
user's side information in advance, whereas the indices of these messages and
their coefficients in the side information are not known to any of the servers
a priori.
Our goal is to characterize (or derive a lower bound on) the capacity, i.e.,
the maximum achievable download rate, for the following two settings. In the
first setting, the set of messages forming the linear combination available to
the user as side information, does not include the user's demanded message. For
this setting, we show that the capacity is equal to
$\left(1+{1}/{N}+\dots+{1}/{N^{K-M-1}}\right)^{-1}$. In the second setting, the
demand message contributes to the linear combination available to the user as
side information, i.e., the demand message is one of the messages that form the
user's side information. For this setting, we show that the capacity is
lower-bounded by $\left(1+{1}/{N}+\dots+{1}/{N^{K-M}}\right)^{-1}$. The
proposed achievability schemes and proof techniques leverage ideas from both
our recent methods proposed for the single-server PIR-PCSI problem as well as
the techniques proposed by Sun and Jafar for multi-server private computation
problem.
| [
{
"created": "Wed, 26 Jun 2019 18:12:01 GMT",
"version": "v1"
}
] | 2019-06-28 | [
[
"Kazemi",
"Fatemeh",
""
],
[
"Karimi",
"Esmaeil",
""
],
[
"Heidarzadeh",
"Anoosheh",
""
],
[
"Sprintson",
"Alex",
""
]
] | In this paper, we consider the multi-server setting of Private Information Retrieval with Private Coded Side Information (PIR-PCSI) problem. In this problem, there is a database of $K$ messages whose copies are replicated across $N$ servers, and there is a user who knows a random linear combination of a random subset of $M$ messages in the database as side information. The user wishes to download one message from the servers, while protecting the identities of both the demand message and the messages forming the side information. We assume that the servers know the number of messages forming the user's side information in advance, whereas the indices of these messages and their coefficients in the side information are not known to any of the servers a priori. Our goal is to characterize (or derive a lower bound on) the capacity, i.e., the maximum achievable download rate, for the following two settings. In the first setting, the set of messages forming the linear combination available to the user as side information, does not include the user's demanded message. For this setting, we show that the capacity is equal to $\left(1+{1}/{N}+\dots+{1}/{N^{K-M-1}}\right)^{-1}$. In the second setting, the demand message contributes to the linear combination available to the user as side information, i.e., the demand message is one of the messages that form the user's side information. For this setting, we show that the capacity is lower-bounded by $\left(1+{1}/{N}+\dots+{1}/{N^{K-M}}\right)^{-1}$. The proposed achievability schemes and proof techniques leverage ideas from both our recent methods proposed for the single-server PIR-PCSI problem as well as the techniques proposed by Sun and Jafar for multi-server private computation problem. |
2309.04025 | Kashyap Todi | Kashyap Todi, Tanya R. Jonker | A Framework for Computational Design and Adaptation of Extended Reality
User Interfaces | 5 pages, CHI 2023 Workshop on The Future of Computational Approaches
for Understanding and Adapting User Interfaces | null | null | null | cs.HC | http://creativecommons.org/licenses/by/4.0/ | To facilitate high quality interaction during the regular use of computing
systems, it is essential that the user interface (UI) deliver content and
components in an appropriate manner. Although extended reality (XR) is emerging
as a new computing platform, we still have a limited understanding of how best
to design and present interactive content to users in such immersive
environments. Adaptive UIs offer a promising approach for optimal presentation
in XR as the user's environment, tasks, capabilities, and preferences vary
under changing context. In this position paper, we present a design framework
for adapting various characteristics of content presented in XR. We frame these
as five considerations that need to be taken into account for adaptive XR UIs:
What?, How Much?, Where?, How?, and When?. With this framework, we review
literature on UI design and adaptation to reflect on approaches that have been
adopted or developed in the past towards identifying current gaps and
challenges, and opportunities for applying such approaches in XR. Using our
framework, future work could identify and develop novel computational
approaches for achieving successful adaptive user interfaces in such immersive
environments.
| [
{
"created": "Thu, 7 Sep 2023 21:37:52 GMT",
"version": "v1"
}
] | 2023-09-11 | [
[
"Todi",
"Kashyap",
""
],
[
"Jonker",
"Tanya R.",
""
]
] | To facilitate high quality interaction during the regular use of computing systems, it is essential that the user interface (UI) deliver content and components in an appropriate manner. Although extended reality (XR) is emerging as a new computing platform, we still have a limited understanding of how best to design and present interactive content to users in such immersive environments. Adaptive UIs offer a promising approach for optimal presentation in XR as the user's environment, tasks, capabilities, and preferences vary under changing context. In this position paper, we present a design framework for adapting various characteristics of content presented in XR. We frame these as five considerations that need to be taken into account for adaptive XR UIs: What?, How Much?, Where?, How?, and When?. With this framework, we review literature on UI design and adaptation to reflect on approaches that have been adopted or developed in the past towards identifying current gaps and challenges, and opportunities for applying such approaches in XR. Using our framework, future work could identify and develop novel computational approaches for achieving successful adaptive user interfaces in such immersive environments. |
2102.04828 | Sebastian U. Stich | Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian
U. Stich | Consensus Control for Decentralized Deep Learning | LK and TL contribute equally - ICML 2021 | Proceedings of the 38th International Conference on Machine
Learning (ICML), PMLR 139, 2021 | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decentralized training of deep learning models enables on-device learning
over networks, as well as efficient scaling to large compute clusters.
Experiments in earlier works reveal that, even in a data-center setup,
decentralized training often suffers from the degradation in the quality of the
model: the training and test performance of models trained in a decentralized
fashion is in general worse than that of models trained in a centralized
fashion, and this performance drop is impacted by parameters such as network
size, communication topology and data partitioning. We identify the changing
consensus distance between devices as a key parameter to explain the gap
between centralized and decentralized training.
We show in theory that when the training consensus distance is lower than a
critical quantity, decentralized training converges as fast as the centralized
counterpart. We empirically validate that the relation between generalization
performance and consensus distance is consistent with this theoretical
observation. Our empirical insights allow the principled design of better
decentralized training schemes that mitigate the performance drop. To this end,
we provide practical training guidelines and exemplify its effectiveness on the
data-center setup as the important first step.
| [
{
"created": "Tue, 9 Feb 2021 13:58:33 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Jun 2021 08:15:00 GMT",
"version": "v2"
}
] | 2021-06-21 | [
[
"Kong",
"Lingjing",
""
],
[
"Lin",
"Tao",
""
],
[
"Koloskova",
"Anastasia",
""
],
[
"Jaggi",
"Martin",
""
],
[
"Stich",
"Sebastian U.",
""
]
] | Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers from the degradation in the quality of the model: the training and test performance of models trained in a decentralized fashion is in general worse than that of models trained in a centralized fashion, and this performance drop is impacted by parameters such as network size, communication topology and data partitioning. We identify the changing consensus distance between devices as a key parameter to explain the gap between centralized and decentralized training. We show in theory that when the training consensus distance is lower than a critical quantity, decentralized training converges as fast as the centralized counterpart. We empirically validate that the relation between generalization performance and consensus distance is consistent with this theoretical observation. Our empirical insights allow the principled design of better decentralized training schemes that mitigate the performance drop. To this end, we provide practical training guidelines and exemplify its effectiveness on the data-center setup as the important first step. |
1507.05283 | Kumar Sankar Ray | Kingshuk Chatterjee, Kumar Sankar Ray | Reversible Watson-Crick Automata | arXiv admin note: text overlap with arXiv:1507.05282 | null | null | null | cs.FL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Watson-Crick automata are finite automata working on double strands.
Extensive research work has already been done on non-deterministic Watson-Crick
automata and on deterministic Watson-Crick automata. In this paper, we
introduce a new model of Watson-Crick automata which is reversible in nature
named reversible Watson-Crick automata and explore its computational power. We
show even though the model is reversible and one way it accepts all regular
languages and also analyze the state complexity of the above stated model with
respect to non-deterministic block automata and non-deterministic finite
automata and establish its superiority. We further explore the relation of the
reversible model with twin-shuffle language and recursively enumerable
languages.
| [
{
"created": "Sun, 19 Jul 2015 13:00:59 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Dec 2015 07:54:05 GMT",
"version": "v2"
}
] | 2015-12-10 | [
[
"Chatterjee",
"Kingshuk",
""
],
[
"Ray",
"Kumar Sankar",
""
]
] | Watson-Crick automata are finite automata working on double strands. Extensive research work has already been done on non-deterministic Watson-Crick automata and on deterministic Watson-Crick automata. In this paper, we introduce a new model of Watson-Crick automata which is reversible in nature named reversible Watson-Crick automata and explore its computational power. We show even though the model is reversible and one way it accepts all regular languages and also analyze the state complexity of the above stated model with respect to non-deterministic block automata and non-deterministic finite automata and establish its superiority. We further explore the relation of the reversible model with twin-shuffle language and recursively enumerable languages. |
2303.10561 | Peng Zou | Peng Zou, Rui Wang, Kehua Wen, Yasi Peng and Xiao Sun | Spatial-temporal Transformer for Affective Behavior Analysis | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The in-the-wild affective behavior analysis has been an important study. In
this paper, we submit our solutions for the 5th Workshop and Competition on
Affective Behavior Analysis in-the-wild (ABAW), which includes V-A Estimation,
Facial Expression Classification and AU Detection Sub-challenges. We propose a
Transformer Encoder with Multi-Head Attention framework to learn the
distribution of both the spatial and temporal features. Besides, there are
virious effective data augmentation strategies employed to alleviate the
problems of sample imbalance during model training. The results fully
demonstrate the effectiveness of our proposed model based on the Aff-Wild2
dataset.
| [
{
"created": "Sun, 19 Mar 2023 04:34:17 GMT",
"version": "v1"
}
] | 2023-03-21 | [
[
"Zou",
"Peng",
""
],
[
"Wang",
"Rui",
""
],
[
"Wen",
"Kehua",
""
],
[
"Peng",
"Yasi",
""
],
[
"Sun",
"Xiao",
""
]
] | The in-the-wild affective behavior analysis has been an important study. In this paper, we submit our solutions for the 5th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW), which includes V-A Estimation, Facial Expression Classification and AU Detection Sub-challenges. We propose a Transformer Encoder with Multi-Head Attention framework to learn the distribution of both the spatial and temporal features. Besides, there are virious effective data augmentation strategies employed to alleviate the problems of sample imbalance during model training. The results fully demonstrate the effectiveness of our proposed model based on the Aff-Wild2 dataset. |
2011.11722 | Deepali Jain | Deepali Jain, Atil Iscen, Ken Caluwaerts | From Pixels to Legs: Hierarchical Learning of Quadruped Locomotion | null | 4th Conference on Robot Learning (CoRL 2020), Cambridge MA, USA | null | null | cs.RO cs.CV cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Legged robots navigating crowded scenes and complex terrains in the real
world are required to execute dynamic leg movements while processing visual
input for obstacle avoidance and path planning. We show that a quadruped robot
can acquire both of these skills by means of hierarchical reinforcement
learning (HRL). By virtue of their hierarchical structure, our policies learn
to implicitly break down this joint problem by concurrently learning High Level
(HL) and Low Level (LL) neural network policies. These two levels are connected
by a low dimensional hidden layer, which we call latent command. HL receives a
first-person camera view, whereas LL receives the latent command from HL and
the robot's on-board sensors to control its actuators. We train policies to
walk in two different environments: a curved cliff and a maze. We show that
hierarchical policies can concurrently learn to locomote and navigate in these
environments, and show they are more efficient than non-hierarchical neural
network policies. This architecture also allows for knowledge reuse across
tasks. LL networks trained on one task can be transferred to a new task in a
new environment. Finally HL, which processes camera images, can be evaluated at
much lower and varying frequencies compared to LL, thus reducing computation
times and bandwidth requirements.
| [
{
"created": "Mon, 23 Nov 2020 20:55:54 GMT",
"version": "v1"
}
] | 2020-12-01 | [
[
"Jain",
"Deepali",
""
],
[
"Iscen",
"Atil",
""
],
[
"Caluwaerts",
"Ken",
""
]
] | Legged robots navigating crowded scenes and complex terrains in the real world are required to execute dynamic leg movements while processing visual input for obstacle avoidance and path planning. We show that a quadruped robot can acquire both of these skills by means of hierarchical reinforcement learning (HRL). By virtue of their hierarchical structure, our policies learn to implicitly break down this joint problem by concurrently learning High Level (HL) and Low Level (LL) neural network policies. These two levels are connected by a low dimensional hidden layer, which we call latent command. HL receives a first-person camera view, whereas LL receives the latent command from HL and the robot's on-board sensors to control its actuators. We train policies to walk in two different environments: a curved cliff and a maze. We show that hierarchical policies can concurrently learn to locomote and navigate in these environments, and show they are more efficient than non-hierarchical neural network policies. This architecture also allows for knowledge reuse across tasks. LL networks trained on one task can be transferred to a new task in a new environment. Finally HL, which processes camera images, can be evaluated at much lower and varying frequencies compared to LL, thus reducing computation times and bandwidth requirements. |
1307.0339 | Cheng-Yuan Liou | Cheng-Yuan Liou, Bo-Shiang Huang, Daw-Ran Liou and Alex A. Simak | Syntactic sensitive complexity for symbol-free sequence | 11 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work uses the L-system to construct a tree structure for the text
sequence and derives its complexity. It serves as a measure of structural
complexity of the text. It is applied to anomaly detection in data
transmission.
| [
{
"created": "Mon, 1 Jul 2013 12:00:59 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Jul 2013 02:08:48 GMT",
"version": "v2"
}
] | 2013-07-03 | [
[
"Liou",
"Cheng-Yuan",
""
],
[
"Huang",
"Bo-Shiang",
""
],
[
"Liou",
"Daw-Ran",
""
],
[
"Simak",
"Alex A.",
""
]
] | This work uses the L-system to construct a tree structure for the text sequence and derives its complexity. It serves as a measure of structural complexity of the text. It is applied to anomaly detection in data transmission. |
1810.03649 | Sainandan Ramakrishnan | Sainandan Ramakrishnan, Aishwarya Agrawal, Stefan Lee | Overcoming Language Priors in Visual Question Answering with Adversarial
Regularization | NIPS 2018. 11 pages ( with references ), 4 figures, 2 tables | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modern Visual Question Answering (VQA) models have been shown to rely heavily
on superficial correlations between question and answer words learned during
training such as overwhelmingly reporting the type of room as kitchen or the
sport being played as tennis, irrespective of the image. Most alarmingly, this
shortcoming is often not well reflected during evaluation because the same
strong priors exist in test distributions; however, a VQA system that fails to
ground questions in image content would likely perform poorly in real-world
settings. In this work, we present a novel regularization scheme for VQA that
reduces this effect. We introduce a question-only model that takes as input the
question encoding from the VQA model and must leverage language biases in order
to succeed. We then pose training as an adversarial game between the VQA model
and this question-only adversary -- discouraging the VQA model from capturing
language biases in its question encoding. Further,we leverage this
question-only model to estimate the increase in model confidence after
considering the image, which we maximize explicitly to encourage visual
grounding. Our approach is a model agnostic training procedure and simple to
implement. We show empirically that it can improve performance significantly on
a bias-sensitive split of the VQA dataset for multiple base models -- achieving
state-of-the-art on this task. Further, on standard VQA tasks, our approach
shows significantly less drop in accuracy compared to existing bias-reducing
VQA models.
| [
{
"created": "Mon, 8 Oct 2018 18:29:05 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Nov 2018 20:51:44 GMT",
"version": "v2"
}
] | 2018-11-12 | [
[
"Ramakrishnan",
"Sainandan",
""
],
[
"Agrawal",
"Aishwarya",
""
],
[
"Lee",
"Stefan",
""
]
] | Modern Visual Question Answering (VQA) models have been shown to rely heavily on superficial correlations between question and answer words learned during training such as overwhelmingly reporting the type of room as kitchen or the sport being played as tennis, irrespective of the image. Most alarmingly, this shortcoming is often not well reflected during evaluation because the same strong priors exist in test distributions; however, a VQA system that fails to ground questions in image content would likely perform poorly in real-world settings. In this work, we present a novel regularization scheme for VQA that reduces this effect. We introduce a question-only model that takes as input the question encoding from the VQA model and must leverage language biases in order to succeed. We then pose training as an adversarial game between the VQA model and this question-only adversary -- discouraging the VQA model from capturing language biases in its question encoding. Further,we leverage this question-only model to estimate the increase in model confidence after considering the image, which we maximize explicitly to encourage visual grounding. Our approach is a model agnostic training procedure and simple to implement. We show empirically that it can improve performance significantly on a bias-sensitive split of the VQA dataset for multiple base models -- achieving state-of-the-art on this task. Further, on standard VQA tasks, our approach shows significantly less drop in accuracy compared to existing bias-reducing VQA models. |
1712.08910 | Simina Br\^anzei | Simina Br\^anzei and Aris Filos-Ratsikas | Walrasian Dynamics in Multi-unit Markets | null | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a multi-unit market, a seller brings multiple units of a good and tries to
sell them to a set of buyers that have monetary endowments. While a Walrasian
equilibrium does not always exist in this model, natural relaxations of the
concept that retain its desirable fairness properties do exist.
We study the dynamics of (Walrasian) envy-free pricing mechanisms in this
environment, showing that for any such pricing mechanism, the best response
dynamic starting from truth-telling converges to a pure Nash equilibrium with
small loss in revenue and welfare. Moreover, we generalize these bounds to
capture all the Nash equilibria for a large class of (monotone) pricing
mechanisms. We also identify a natural mechanism, which selects the minimum
Walrasian envy-free price, in which for $n=2$ buyers the best response dynamic
converges from any starting profile, and for which we conjecture convergence
for any number of buyers.
| [
{
"created": "Sun, 24 Dec 2017 12:13:24 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Feb 2018 01:02:22 GMT",
"version": "v2"
},
{
"created": "Thu, 27 Sep 2018 16:24:04 GMT",
"version": "v3"
}
] | 2018-09-28 | [
[
"Brânzei",
"Simina",
""
],
[
"Filos-Ratsikas",
"Aris",
""
]
] | In a multi-unit market, a seller brings multiple units of a good and tries to sell them to a set of buyers that have monetary endowments. While a Walrasian equilibrium does not always exist in this model, natural relaxations of the concept that retain its desirable fairness properties do exist. We study the dynamics of (Walrasian) envy-free pricing mechanisms in this environment, showing that for any such pricing mechanism, the best response dynamic starting from truth-telling converges to a pure Nash equilibrium with small loss in revenue and welfare. Moreover, we generalize these bounds to capture all the Nash equilibria for a large class of (monotone) pricing mechanisms. We also identify a natural mechanism, which selects the minimum Walrasian envy-free price, in which for $n=2$ buyers the best response dynamic converges from any starting profile, and for which we conjecture convergence for any number of buyers. |
1703.10318 | Sung-Han Lin | Sung-Han Lin, Ranjan Pal, Marco Paolieri, Leana Golubchik | SC-Share: Performance Driven Resource Sharing Markets for the Small
Cloud | To be published in ICDCS 2017 | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Small-scale clouds (SCs) often suffer from resource under-provisioning during
peak demand, leading to inability to satisfy service level agreements (SLAs)
and consequent loss of customers. One approach to address this problem is for a
set of autonomous SCs to share resources among themselves in a cost-induced
cooperative fashion, thereby increasing their individual capacities (when
needed) without having to significantly invest in more resources. A central
problem (in this context) is how to properly share resources (for a price) to
achieve profitable service while maintaining customer SLAs. To address this
problem, in this paper, we propose the SC-Share framework that utilizes two
interacting models: (i) a stochastic performance model that estimates the
achieved performance characteristics under given SLA requirements, and (ii) a
market-based game-theoretic model that (as shown empirically) converges to
efficient resource sharing decisions at market equilibrium. Our results include
extensive evaluations that illustrate the utility of the proposed framework.
| [
{
"created": "Thu, 30 Mar 2017 05:28:36 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Aug 2017 00:04:21 GMT",
"version": "v2"
}
] | 2017-08-08 | [
[
"Lin",
"Sung-Han",
""
],
[
"Pal",
"Ranjan",
""
],
[
"Paolieri",
"Marco",
""
],
[
"Golubchik",
"Leana",
""
]
] | Small-scale clouds (SCs) often suffer from resource under-provisioning during peak demand, leading to inability to satisfy service level agreements (SLAs) and consequent loss of customers. One approach to address this problem is for a set of autonomous SCs to share resources among themselves in a cost-induced cooperative fashion, thereby increasing their individual capacities (when needed) without having to significantly invest in more resources. A central problem (in this context) is how to properly share resources (for a price) to achieve profitable service while maintaining customer SLAs. To address this problem, in this paper, we propose the SC-Share framework that utilizes two interacting models: (i) a stochastic performance model that estimates the achieved performance characteristics under given SLA requirements, and (ii) a market-based game-theoretic model that (as shown empirically) converges to efficient resource sharing decisions at market equilibrium. Our results include extensive evaluations that illustrate the utility of the proposed framework. |
2303.13137 | Liping Yi | Liping Yi, Gang Wang, Xiaoguang Liu, Zhuan Shi, Han Yu | FedGH: Heterogeneous Federated Learning with Generalized Global Header | 11 pages, 5 figures,accepted by Proceedings of the 31st ACM
International Conference on Multimedia (MM 2023) | null | null | null | cs.LG cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Federated learning (FL) is an emerging machine learning paradigm that allows
multiple parties to train a shared model collaboratively in a
privacy-preserving manner. Existing horizontal FL methods generally assume that
the FL server and clients hold the same model structure. However, due to system
heterogeneity and the need for personalization, enabling clients to hold models
with diverse structures has become an important direction. Existing
model-heterogeneous FL approaches often require publicly available datasets and
incur high communication and/or computational costs, which limit their
performances. To address these limitations, we propose a simple but effective
Federated Global prediction Header (FedGH) approach. It is a communication and
computation-efficient model-heterogeneous FL framework which trains a shared
generalized global prediction header with representations extracted by
heterogeneous extractors for clients' models at the FL server. The trained
generalized global prediction header learns from different clients. The
acquired global knowledge is then transferred to clients to substitute each
client's local prediction header. We derive the non-convex convergence rate of
FedGH. Extensive experiments on two real-world datasets demonstrate that FedGH
achieves significantly more advantageous performance in both model-homogeneous
and -heterogeneous FL scenarios compared to seven state-of-the-art personalized
FL models, beating the best-performing baseline by up to 8.87% (for
model-homogeneous FL) and 1.83% (for model-heterogeneous FL) in terms of
average test accuracy, while saving up to 85.53% of communication overhead.
| [
{
"created": "Thu, 23 Mar 2023 09:38:52 GMT",
"version": "v1"
},
{
"created": "Tue, 1 Aug 2023 16:30:48 GMT",
"version": "v2"
}
] | 2023-08-02 | [
[
"Yi",
"Liping",
""
],
[
"Wang",
"Gang",
""
],
[
"Liu",
"Xiaoguang",
""
],
[
"Shi",
"Zhuan",
""
],
[
"Yu",
"Han",
""
]
] | Federated learning (FL) is an emerging machine learning paradigm that allows multiple parties to train a shared model collaboratively in a privacy-preserving manner. Existing horizontal FL methods generally assume that the FL server and clients hold the same model structure. However, due to system heterogeneity and the need for personalization, enabling clients to hold models with diverse structures has become an important direction. Existing model-heterogeneous FL approaches often require publicly available datasets and incur high communication and/or computational costs, which limit their performances. To address these limitations, we propose a simple but effective Federated Global prediction Header (FedGH) approach. It is a communication and computation-efficient model-heterogeneous FL framework which trains a shared generalized global prediction header with representations extracted by heterogeneous extractors for clients' models at the FL server. The trained generalized global prediction header learns from different clients. The acquired global knowledge is then transferred to clients to substitute each client's local prediction header. We derive the non-convex convergence rate of FedGH. Extensive experiments on two real-world datasets demonstrate that FedGH achieves significantly more advantageous performance in both model-homogeneous and -heterogeneous FL scenarios compared to seven state-of-the-art personalized FL models, beating the best-performing baseline by up to 8.87% (for model-homogeneous FL) and 1.83% (for model-heterogeneous FL) in terms of average test accuracy, while saving up to 85.53% of communication overhead. |
2211.08681 | Atsuro Okazawa | Atsuro Okazawa | Interclass Prototype Relation for Few-Shot Segmentation | Accepted to ECCV2022 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditional semantic segmentation requires a large labeled image dataset and
can only be predicted within predefined classes. To solve this problem,
few-shot segmentation, which requires only a handful of annotations for the new
target class, is important. However, with few-shot segmentation, the target
class data distribution in the feature space is sparse and has low coverage
because of the slight variations in the sample data. Setting the classification
boundary that properly separates the target class from other classes is an
impossible task. In particular, it is difficult to classify classes that are
similar to the target class near the boundary. This study proposes the
Interclass Prototype Relation Network (IPRNet), which improves the separation
performance by reducing the similarity between other classes. We conducted
extensive experiments with Pascal-5i and COCO-20i and showed that IPRNet
provides the best segmentation performance compared with previous research.
| [
{
"created": "Wed, 16 Nov 2022 05:27:52 GMT",
"version": "v1"
}
] | 2022-11-17 | [
[
"Okazawa",
"Atsuro",
""
]
] | Traditional semantic segmentation requires a large labeled image dataset and can only be predicted within predefined classes. To solve this problem, few-shot segmentation, which requires only a handful of annotations for the new target class, is important. However, with few-shot segmentation, the target class data distribution in the feature space is sparse and has low coverage because of the slight variations in the sample data. Setting the classification boundary that properly separates the target class from other classes is an impossible task. In particular, it is difficult to classify classes that are similar to the target class near the boundary. This study proposes the Interclass Prototype Relation Network (IPRNet), which improves the separation performance by reducing the similarity between other classes. We conducted extensive experiments with Pascal-5i and COCO-20i and showed that IPRNet provides the best segmentation performance compared with previous research. |
2407.05210 | Rendani Mbuvha | Rendani Mbuvha, Yassine Yaakoubi, John Bagiliko, Santiago Hincapie
Potes, Amal Nammouchi, Sabrina Amrouche | Leveraging AI for Climate Resilience in Africa: Challenges,
Opportunities, and the Need for Collaboration | null | null | null | null | cs.CY cs.AI stat.AP | http://creativecommons.org/licenses/by/4.0/ | As climate change issues become more pressing, their impact in Africa calls
for urgent, innovative solutions tailored to the continent's unique challenges.
While Artificial Intelligence (AI) emerges as a critical and valuable tool for
climate change adaptation and mitigation, its effectiveness and potential are
contingent upon overcoming significant challenges such as data scarcity,
infrastructure gaps, and limited local AI development. This position paper
explores the role of AI in climate change adaptation and mitigation in Africa.
It advocates for a collaborative approach to build capacity, develop
open-source data repositories, and create context-aware, robust AI-driven
climate solutions that are culturally and contextually relevant.
| [
{
"created": "Wed, 24 Apr 2024 14:05:22 GMT",
"version": "v1"
}
] | 2024-07-09 | [
[
"Mbuvha",
"Rendani",
""
],
[
"Yaakoubi",
"Yassine",
""
],
[
"Bagiliko",
"John",
""
],
[
"Potes",
"Santiago Hincapie",
""
],
[
"Nammouchi",
"Amal",
""
],
[
"Amrouche",
"Sabrina",
""
]
] | As climate change issues become more pressing, their impact in Africa calls for urgent, innovative solutions tailored to the continent's unique challenges. While Artificial Intelligence (AI) emerges as a critical and valuable tool for climate change adaptation and mitigation, its effectiveness and potential are contingent upon overcoming significant challenges such as data scarcity, infrastructure gaps, and limited local AI development. This position paper explores the role of AI in climate change adaptation and mitigation in Africa. It advocates for a collaborative approach to build capacity, develop open-source data repositories, and create context-aware, robust AI-driven climate solutions that are culturally and contextually relevant. |
2406.01609 | Akshat Mohan Dasula Mr | Akshat Mohan Dasula, Hrushitha Tigulla, Preethika Bhukya | Judgement Citation Retrieval using Contextual Similarity | 14 pages, 16 images | null | null | null | cs.IR cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Traditionally in the domain of legal research, the retrieval of pertinent
citations from intricate case descriptions has demanded manual effort and
keyword-based search applications that mandate expertise in understanding legal
jargon. Legal case descriptions hold pivotal information for legal
professionals and researchers, necessitating more efficient and automated
approaches. We propose a methodology that combines natural language processing
(NLP) and machine learning techniques to enhance the organization and
utilization of legal case descriptions. This approach revolves around the
creation of textual embeddings with the help of state-of-art embedding models.
Our methodology addresses two primary objectives: unsupervised clustering and
supervised citation retrieval, both designed to automate the citation
extraction process. Although the proposed methodology can be used for any
dataset, we employed the Supreme Court of The United States (SCOTUS) dataset,
yielding remarkable results. Our methodology achieved an impressive accuracy
rate of 90.9%. By automating labor-intensive processes, we pave the way for a
more efficient, time-saving, and accessible landscape in legal research,
benefiting legal professionals, academics, and researchers.
| [
{
"created": "Tue, 28 May 2024 04:22:28 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Aug 2024 06:11:27 GMT",
"version": "v2"
}
] | 2024-08-16 | [
[
"Dasula",
"Akshat Mohan",
""
],
[
"Tigulla",
"Hrushitha",
""
],
[
"Bhukya",
"Preethika",
""
]
] | Traditionally in the domain of legal research, the retrieval of pertinent citations from intricate case descriptions has demanded manual effort and keyword-based search applications that mandate expertise in understanding legal jargon. Legal case descriptions hold pivotal information for legal professionals and researchers, necessitating more efficient and automated approaches. We propose a methodology that combines natural language processing (NLP) and machine learning techniques to enhance the organization and utilization of legal case descriptions. This approach revolves around the creation of textual embeddings with the help of state-of-art embedding models. Our methodology addresses two primary objectives: unsupervised clustering and supervised citation retrieval, both designed to automate the citation extraction process. Although the proposed methodology can be used for any dataset, we employed the Supreme Court of The United States (SCOTUS) dataset, yielding remarkable results. Our methodology achieved an impressive accuracy rate of 90.9%. By automating labor-intensive processes, we pave the way for a more efficient, time-saving, and accessible landscape in legal research, benefiting legal professionals, academics, and researchers. |
1908.08216 | Sanath Narayan | Sanath Narayan, Hisham Cholakkal, Fahad Shahbaz Khan, Ling Shao | 3C-Net: Category Count and Center Loss for Weakly-Supervised Action
Localization | To appear in ICCV 2019 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporal action localization is a challenging computer vision problem with
numerous real-world applications. Most existing methods require laborious
frame-level supervision to train action localization models. In this work, we
propose a framework, called 3C-Net, which only requires video-level supervision
(weak supervision) in the form of action category labels and the corresponding
count. We introduce a novel formulation to learn discriminative action features
with enhanced localization capabilities. Our joint formulation has three terms:
a classification term to ensure the separability of learned action features, an
adapted multi-label center loss term to enhance the action feature
discriminability and a counting loss term to delineate adjacent action
sequences, leading to improved localization. Comprehensive experiments are
performed on two challenging benchmarks: THUMOS14 and ActivityNet 1.2. Our
approach sets a new state-of-the-art for weakly-supervised temporal action
localization on both datasets. On the THUMOS14 dataset, the proposed method
achieves an absolute gain of 4.6% in terms of mean average precision (mAP),
compared to the state-of-the-art. Source code is available at
https://github.com/naraysa/3c-net.
| [
{
"created": "Thu, 22 Aug 2019 06:20:38 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Nov 2019 12:28:41 GMT",
"version": "v2"
}
] | 2019-11-19 | [
[
"Narayan",
"Sanath",
""
],
[
"Cholakkal",
"Hisham",
""
],
[
"Khan",
"Fahad Shahbaz",
""
],
[
"Shao",
"Ling",
""
]
] | Temporal action localization is a challenging computer vision problem with numerous real-world applications. Most existing methods require laborious frame-level supervision to train action localization models. In this work, we propose a framework, called 3C-Net, which only requires video-level supervision (weak supervision) in the form of action category labels and the corresponding count. We introduce a novel formulation to learn discriminative action features with enhanced localization capabilities. Our joint formulation has three terms: a classification term to ensure the separability of learned action features, an adapted multi-label center loss term to enhance the action feature discriminability and a counting loss term to delineate adjacent action sequences, leading to improved localization. Comprehensive experiments are performed on two challenging benchmarks: THUMOS14 and ActivityNet 1.2. Our approach sets a new state-of-the-art for weakly-supervised temporal action localization on both datasets. On the THUMOS14 dataset, the proposed method achieves an absolute gain of 4.6% in terms of mean average precision (mAP), compared to the state-of-the-art. Source code is available at https://github.com/naraysa/3c-net. |
2309.04381 | Fredrik Hellstr\"om | Fredrik Hellstr\"om, Giuseppe Durisi, Benjamin Guedj, Maxim Raginsky | Generalization Bounds: Perspectives from Information Theory and
PAC-Bayes | 228 pages | null | null | null | cs.LG cs.AI cs.IT math.IT math.ST stat.ML stat.TH | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A fundamental question in theoretical machine learning is generalization.
Over the past decades, the PAC-Bayesian approach has been established as a
flexible framework to address the generalization capabilities of machine
learning algorithms, and design new ones. Recently, it has garnered increased
interest due to its potential applicability for a variety of learning
algorithms, including deep neural networks. In parallel, an
information-theoretic view of generalization has developed, wherein the
relation between generalization and various information measures has been
established. This framework is intimately connected to the PAC-Bayesian
approach, and a number of results have been independently discovered in both
strands. In this monograph, we highlight this strong connection and present a
unified treatment of PAC-Bayesian and information-theoretic generalization
bounds. We present techniques and results that the two perspectives have in
common, and discuss the approaches and interpretations that differ. In
particular, we demonstrate how many proofs in the area share a modular
structure, through which the underlying ideas can be intuited. We pay special
attention to the conditional mutual information (CMI) framework; analytical
studies of the information complexity of learning algorithms; and the
application of the proposed methods to deep learning. This monograph is
intended to provide a comprehensive introduction to information-theoretic
generalization bounds and their connection to PAC-Bayes, serving as a
foundation from which the most recent developments are accessible. It is aimed
broadly towards researchers with an interest in generalization and theoretical
machine learning.
| [
{
"created": "Fri, 8 Sep 2023 15:23:40 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Mar 2024 17:07:47 GMT",
"version": "v2"
}
] | 2024-03-28 | [
[
"Hellström",
"Fredrik",
""
],
[
"Durisi",
"Giuseppe",
""
],
[
"Guedj",
"Benjamin",
""
],
[
"Raginsky",
"Maxim",
""
]
] | A fundamental question in theoretical machine learning is generalization. Over the past decades, the PAC-Bayesian approach has been established as a flexible framework to address the generalization capabilities of machine learning algorithms, and design new ones. Recently, it has garnered increased interest due to its potential applicability for a variety of learning algorithms, including deep neural networks. In parallel, an information-theoretic view of generalization has developed, wherein the relation between generalization and various information measures has been established. This framework is intimately connected to the PAC-Bayesian approach, and a number of results have been independently discovered in both strands. In this monograph, we highlight this strong connection and present a unified treatment of PAC-Bayesian and information-theoretic generalization bounds. We present techniques and results that the two perspectives have in common, and discuss the approaches and interpretations that differ. In particular, we demonstrate how many proofs in the area share a modular structure, through which the underlying ideas can be intuited. We pay special attention to the conditional mutual information (CMI) framework; analytical studies of the information complexity of learning algorithms; and the application of the proposed methods to deep learning. This monograph is intended to provide a comprehensive introduction to information-theoretic generalization bounds and their connection to PAC-Bayes, serving as a foundation from which the most recent developments are accessible. It is aimed broadly towards researchers with an interest in generalization and theoretical machine learning. |
1504.03385 | Chen-Yu Lee | Chen-Yu Lee, Deng-Jyi Chen | A Content Creation and Protection Scheme for Medical Images | 15 pages, submitted | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Medical images contain metadata information on where, when, and how an image
was acquired, and the majority of this information is stored as pixel data.
Image feature descriptions are often captured only as free text stored in the
image file or in the hospital information system. Correlations between the free
text and the location of the feature are often inaccurate, making it difficult
to link image observations to their corresponding image locations. This limits
the interpretation of image data from a clinical, research, and academic
standpoint. An efficient medical image protection design should allow for
compatibility, usability, and privacy. This paper proposes a medical-content
creation and protection scheme that contains a) a DICOM-compatible multimedia
annotation scheme for medical content creation; b) a DICOM-compatible partial
DRM scheme for medical record transmission under this scheme, authorized users
can view only information to which they have been granted to access.
| [
{
"created": "Mon, 13 Apr 2015 22:43:45 GMT",
"version": "v1"
}
] | 2015-04-15 | [
[
"Lee",
"Chen-Yu",
""
],
[
"Chen",
"Deng-Jyi",
""
]
] | Medical images contain metadata information on where, when, and how an image was acquired, and the majority of this information is stored as pixel data. Image feature descriptions are often captured only as free text stored in the image file or in the hospital information system. Correlations between the free text and the location of the feature are often inaccurate, making it difficult to link image observations to their corresponding image locations. This limits the interpretation of image data from a clinical, research, and academic standpoint. An efficient medical image protection design should allow for compatibility, usability, and privacy. This paper proposes a medical-content creation and protection scheme that contains a) a DICOM-compatible multimedia annotation scheme for medical content creation; b) a DICOM-compatible partial DRM scheme for medical record transmission under this scheme, authorized users can view only information to which they have been granted to access. |
2405.19209 | Ziyang Wang | Ziyang Wang, Shoubin Yu, Elias Stengel-Eskin, Jaehong Yoon, Feng
Cheng, Gedas Bertasius, Mohit Bansal | VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on
Long Videos | 20 pages, first three authors contributed equally; Project page:
https://videotree2024.github.io/ | null | null | null | cs.CV cs.AI cs.CL | http://creativecommons.org/licenses/by/4.0/ | Video-language understanding tasks have focused on short video clips, often
struggling with long-form video understanding tasks. Recently, many long
video-language understanding approaches have leveraged the reasoning
capabilities of Large Language Models (LLMs) to perform long video QA,
transforming videos into densely sampled frame captions, and asking LLMs to
respond to text queries over captions. However, the frames used for captioning
are often redundant and contain irrelevant information, making dense sampling
inefficient, and ignoring the fact that video QA requires varying levels of
granularity, with some video segments being highly relevant to the question
(needing more fine-grained detail) while others being less relevant. Thus,
these LLM-based approaches are prone to missing information and operate on
large numbers of irrelevant captions, lowering both performance and efficiency.
To address these issues, we introduce VideoTree, a query-adaptive and
hierarchical framework for long-video understanding with LLMs. VideoTree
dynamically extracts query-related information from a video and builds a
tree-based representation for LLM reasoning. First, VideoTree adaptively
selects frames for captioning by iteratively clustering frames based on their
visual features and scoring clusters using their relevance to the query.
Second, it organizes visual clusters into a query-adaptive and hierarchical
tree structure; the tree encodes varying levels of granularity, with higher
resolution on relevant segments. Finally, VideoTree produces an answer by
traversing the tree's keyframes and passing their captions to an LLM answerer.
Our method improves both reasoning accuracy and efficiency compared to existing
methods: VideoTree achieves a 7.0%, 2.2%, and 2.7% accuracy gain over baselines
on the EgoSchema, NExT-QA, and IntentQA benchmarks, respectively, while
reducing inference time by 40%.
| [
{
"created": "Wed, 29 May 2024 15:49:09 GMT",
"version": "v1"
}
] | 2024-05-30 | [
[
"Wang",
"Ziyang",
""
],
[
"Yu",
"Shoubin",
""
],
[
"Stengel-Eskin",
"Elias",
""
],
[
"Yoon",
"Jaehong",
""
],
[
"Cheng",
"Feng",
""
],
[
"Bertasius",
"Gedas",
""
],
[
"Bansal",
"Mohit",
""
]
] | Video-language understanding tasks have focused on short video clips, often struggling with long-form video understanding tasks. Recently, many long video-language understanding approaches have leveraged the reasoning capabilities of Large Language Models (LLMs) to perform long video QA, transforming videos into densely sampled frame captions, and asking LLMs to respond to text queries over captions. However, the frames used for captioning are often redundant and contain irrelevant information, making dense sampling inefficient, and ignoring the fact that video QA requires varying levels of granularity, with some video segments being highly relevant to the question (needing more fine-grained detail) while others being less relevant. Thus, these LLM-based approaches are prone to missing information and operate on large numbers of irrelevant captions, lowering both performance and efficiency. To address these issues, we introduce VideoTree, a query-adaptive and hierarchical framework for long-video understanding with LLMs. VideoTree dynamically extracts query-related information from a video and builds a tree-based representation for LLM reasoning. First, VideoTree adaptively selects frames for captioning by iteratively clustering frames based on their visual features and scoring clusters using their relevance to the query. Second, it organizes visual clusters into a query-adaptive and hierarchical tree structure; the tree encodes varying levels of granularity, with higher resolution on relevant segments. Finally, VideoTree produces an answer by traversing the tree's keyframes and passing their captions to an LLM answerer. Our method improves both reasoning accuracy and efficiency compared to existing methods: VideoTree achieves a 7.0%, 2.2%, and 2.7% accuracy gain over baselines on the EgoSchema, NExT-QA, and IntentQA benchmarks, respectively, while reducing inference time by 40%. |
1503.04918 | EPTCS | Marcin Benke, Viviana Bono, Aleksy Schubert | Lucretia - intersection type polymorphism for scripting languages | In Proceedings ITRS 2014, arXiv:1503.04377 | EPTCS 177, 2015, pp. 65-78 | 10.4204/EPTCS.177.6 | null | cs.LO cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Scripting code may present maintenance problems in the long run. There is,
then, the call for methodologies that make it possible to control the
properties of programs written in dynamic languages in an automatic fashion. We
introduce Lucretia, a core language with an introspection primitive. Lucretia
is equipped with a (retrofitted) static type system based on local updates of
types that describe the structure of objects being used. In this way, we deal
with one of the most dynamic features of scripting languages, that is, the
runtime modification of object interfaces. Judgements in our systems have a
Hoare-like shape, as they have a precondition and a postcondition part.
Preconditions describe static approximations of the interfaces of visible
objects before a certain expression has been executed and postconditions
describe them after its execution. The field update operation complicates the
issue of aliasing in the system. We cope with it by introducing intersection
types in method signatures.
| [
{
"created": "Tue, 17 Mar 2015 04:03:54 GMT",
"version": "v1"
}
] | 2015-03-18 | [
[
"Benke",
"Marcin",
""
],
[
"Bono",
"Viviana",
""
],
[
"Schubert",
"Aleksy",
""
]
] | Scripting code may present maintenance problems in the long run. There is, then, the call for methodologies that make it possible to control the properties of programs written in dynamic languages in an automatic fashion. We introduce Lucretia, a core language with an introspection primitive. Lucretia is equipped with a (retrofitted) static type system based on local updates of types that describe the structure of objects being used. In this way, we deal with one of the most dynamic features of scripting languages, that is, the runtime modification of object interfaces. Judgements in our systems have a Hoare-like shape, as they have a precondition and a postcondition part. Preconditions describe static approximations of the interfaces of visible objects before a certain expression has been executed and postconditions describe them after its execution. The field update operation complicates the issue of aliasing in the system. We cope with it by introducing intersection types in method signatures. |
1911.12327 | Charalambos Poullis | Yashas Joshi, Charalambos Poullis | Inattentional Blindness for Redirected Walking Using Dynamic Foveated
Rendering | null | null | null | null | cs.GR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Redirected walking is a Virtual Reality(VR) locomotion technique which
enables users to navigate virtual environments (VEs) that are spatially larger
than the available physical tracked space. In this work we present a novel
technique for redirected walking in VR based on the psychological phenomenon of
inattentional blindness. Based on the user's visual fixation points we divide
the user's view into zones. Spatially-varying rotations are applied according
to the zone's importance and are rendered using foveated rendering. Our
technique is real-time and applicable to small and large physical spaces.
Furthermore, the proposed technique does not require the use of stimulated
saccades but rather takes advantage of naturally occurring saccades and blinks
for a complete refresh of the framebuffer. We performed extensive testing and
present the analysis of the results of three user studies conducted for the
evaluation.
| [
{
"created": "Wed, 27 Nov 2019 18:08:21 GMT",
"version": "v1"
}
] | 2019-11-28 | [
[
"Joshi",
"Yashas",
""
],
[
"Poullis",
"Charalambos",
""
]
] | Redirected walking is a Virtual Reality(VR) locomotion technique which enables users to navigate virtual environments (VEs) that are spatially larger than the available physical tracked space. In this work we present a novel technique for redirected walking in VR based on the psychological phenomenon of inattentional blindness. Based on the user's visual fixation points we divide the user's view into zones. Spatially-varying rotations are applied according to the zone's importance and are rendered using foveated rendering. Our technique is real-time and applicable to small and large physical spaces. Furthermore, the proposed technique does not require the use of stimulated saccades but rather takes advantage of naturally occurring saccades and blinks for a complete refresh of the framebuffer. We performed extensive testing and present the analysis of the results of three user studies conducted for the evaluation. |
2407.14086 | Yunfei Zhang | Yunfei Zhang, Chao Liang, Jin Gao, Zhipeng Zhang, Weiming Hu, Stephen
Maybank, Xue Zhou, Liang Li | Temporal Correlation Meets Embedding: Towards a 2nd Generation of
JDE-based Real-Time Multi-Object Tracking | A submission to IJCV | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Joint Detection and Embedding (JDE) trackers have demonstrated excellent
performance in Multi-Object Tracking (MOT) tasks by incorporating the
extraction of appearance features as auxiliary tasks through embedding
Re-Identification task (ReID) into the detector, achieving a balance between
inference speed and tracking performance. However, solving the competition
between the detector and the feature extractor has always been a challenge.
Meanwhile, the issue of directly embedding the ReID task into MOT has remained
unresolved. The lack of high discriminability in appearance features results in
their limited utility. In this paper, a new learning approach using
cross-correlation to capture temporal information of objects is proposed. The
feature extraction network is no longer trained solely on appearance features
from each frame but learns richer motion features by utilizing feature heatmaps
from consecutive frames, which addresses the challenge of inter-class feature
similarity. Furthermore, our learning approach is applied to a more lightweight
feature extraction network, and treat the feature matching scores as strong
cues rather than auxiliary cues, with an appropriate weight calculation to
reflect the compatibility between our obtained features and the MOT task. Our
tracker, named TCBTrack, achieves state-of-the-art performance on multiple
public benchmarks, i.e., MOT17, MOT20, and DanceTrack datasets. Specifically,
on the DanceTrack test set, we achieve 56.8 HOTA, 58.1 IDF1 and 92.5 MOTA,
making it the best online tracker capable of achieving real-time performance.
Comparative evaluations with other trackers prove that our tracker achieves the
best balance between speed, robustness and accuracy. Code is available at
https://github.com/yfzhang1214/TCBTrack.
| [
{
"created": "Fri, 19 Jul 2024 07:48:45 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Aug 2024 09:56:36 GMT",
"version": "v2"
}
] | 2024-08-07 | [
[
"Zhang",
"Yunfei",
""
],
[
"Liang",
"Chao",
""
],
[
"Gao",
"Jin",
""
],
[
"Zhang",
"Zhipeng",
""
],
[
"Hu",
"Weiming",
""
],
[
"Maybank",
"Stephen",
""
],
[
"Zhou",
"Xue",
""
],
[
"Li",
"Liang",
""
]
] | Joint Detection and Embedding (JDE) trackers have demonstrated excellent performance in Multi-Object Tracking (MOT) tasks by incorporating the extraction of appearance features as auxiliary tasks through embedding Re-Identification task (ReID) into the detector, achieving a balance between inference speed and tracking performance. However, solving the competition between the detector and the feature extractor has always been a challenge. Meanwhile, the issue of directly embedding the ReID task into MOT has remained unresolved. The lack of high discriminability in appearance features results in their limited utility. In this paper, a new learning approach using cross-correlation to capture temporal information of objects is proposed. The feature extraction network is no longer trained solely on appearance features from each frame but learns richer motion features by utilizing feature heatmaps from consecutive frames, which addresses the challenge of inter-class feature similarity. Furthermore, our learning approach is applied to a more lightweight feature extraction network, and treat the feature matching scores as strong cues rather than auxiliary cues, with an appropriate weight calculation to reflect the compatibility between our obtained features and the MOT task. Our tracker, named TCBTrack, achieves state-of-the-art performance on multiple public benchmarks, i.e., MOT17, MOT20, and DanceTrack datasets. Specifically, on the DanceTrack test set, we achieve 56.8 HOTA, 58.1 IDF1 and 92.5 MOTA, making it the best online tracker capable of achieving real-time performance. Comparative evaluations with other trackers prove that our tracker achieves the best balance between speed, robustness and accuracy. Code is available at https://github.com/yfzhang1214/TCBTrack. |
1803.05747 | Hongfei Fan | Hongfei Fan, Lin Ding, Xiaodong Xie, Huizhu Jia, Wen Gao | Joint Rate Allocation with Both Look-ahead And Feedback Model For High
Efficiency Video Coding | null | null | null | null | cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The objective of joint rate allocation among multiple coded video streams is
to share the bandwidth to meet the demands of minimum average distortion
(minAVE) or minimum distortion variance (minVAR). In previous works on minVAR
problems, bits are directly assigned in proportion to their complexity measures
and we call it look-ahead allocation model (LAM), which leads to the fact that
the performance will totally depend on the accuracy of the complexity measures.
This paper proposes a look-ahead and feedback allocation model (LFAM) for joint
rate allocation for High Efficiency Video Coding (HEVC) platform which requires
negligible computational cost. We derive the model from the target function of
minVAR theoretically. The bits are assigned according to the complexity
measures, the distortion and bitrate values fed back by the encoder together.
We integrated the proposed allocation model in HEVC reference software HM16.0
and several complexity measures were applied to our allocation model. Results
demonstrate that our proposed LFAM performs better than LAM, and an average of
65.94% variance of mean square error (MSE) is saved with different complexity
measures.
| [
{
"created": "Thu, 15 Mar 2018 13:50:04 GMT",
"version": "v1"
}
] | 2018-03-16 | [
[
"Fan",
"Hongfei",
""
],
[
"Ding",
"Lin",
""
],
[
"Xie",
"Xiaodong",
""
],
[
"Jia",
"Huizhu",
""
],
[
"Gao",
"Wen",
""
]
] | The objective of joint rate allocation among multiple coded video streams is to share the bandwidth to meet the demands of minimum average distortion (minAVE) or minimum distortion variance (minVAR). In previous works on minVAR problems, bits are directly assigned in proportion to their complexity measures and we call it look-ahead allocation model (LAM), which leads to the fact that the performance will totally depend on the accuracy of the complexity measures. This paper proposes a look-ahead and feedback allocation model (LFAM) for joint rate allocation for High Efficiency Video Coding (HEVC) platform which requires negligible computational cost. We derive the model from the target function of minVAR theoretically. The bits are assigned according to the complexity measures, the distortion and bitrate values fed back by the encoder together. We integrated the proposed allocation model in HEVC reference software HM16.0 and several complexity measures were applied to our allocation model. Results demonstrate that our proposed LFAM performs better than LAM, and an average of 65.94% variance of mean square error (MSE) is saved with different complexity measures. |
1908.10717 | Tao Zhuo | Tao Zhuo, Zhiyong Cheng, Mohan Kankanhalli | Fast Video Object Segmentation via Mask Transfer Network | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accuracy and processing speed are two important factors that affect the use
of video object segmentation (VOS) in real applications. With the advanced
techniques of deep neural networks, the accuracy has been significantly
improved, however, the speed is still far below the real-time needs because of
the complicated network design, such as the requirement of the first frame
fine-tuning step. To overcome this limitation, we propose a novel mask transfer
network (MTN), which can greatly boost the processing speed of VOS and also
achieve a reasonable accuracy. The basic idea of MTN is to transfer the
reference mask to the target frame via an efficient global pixel matching
strategy. The global pixel matching between the reference frame and the target
frame is to ensure good matching results. To enhance the matching speed, we
perform the matching on a downsampled feature map with 1/32 of the original
frame size. At the same time, to preserve the detailed mask information in such
a small feature map, a mask network is designed to encode the annotated mask
information with 512 channels. Finally, an efficient feature warping method is
used to transfer the encoded reference mask to the target frame. Based on this
design, our method avoids the fine-tuning step on the first frame and does not
rely on the temporal cues and particular object categories. Therefore, it runs
very fast and can be conveniently trained only with images, as well as being
robust to unseen objects. Experiments on the DAVIS datasets demonstrate that
MTN can achieve a speed of 37 fps, and also shows a competitive accuracy in
comparison to the state-of-the-art methods.
| [
{
"created": "Wed, 28 Aug 2019 13:31:34 GMT",
"version": "v1"
}
] | 2019-08-29 | [
[
"Zhuo",
"Tao",
""
],
[
"Cheng",
"Zhiyong",
""
],
[
"Kankanhalli",
"Mohan",
""
]
] | Accuracy and processing speed are two important factors that affect the use of video object segmentation (VOS) in real applications. With the advanced techniques of deep neural networks, the accuracy has been significantly improved, however, the speed is still far below the real-time needs because of the complicated network design, such as the requirement of the first frame fine-tuning step. To overcome this limitation, we propose a novel mask transfer network (MTN), which can greatly boost the processing speed of VOS and also achieve a reasonable accuracy. The basic idea of MTN is to transfer the reference mask to the target frame via an efficient global pixel matching strategy. The global pixel matching between the reference frame and the target frame is to ensure good matching results. To enhance the matching speed, we perform the matching on a downsampled feature map with 1/32 of the original frame size. At the same time, to preserve the detailed mask information in such a small feature map, a mask network is designed to encode the annotated mask information with 512 channels. Finally, an efficient feature warping method is used to transfer the encoded reference mask to the target frame. Based on this design, our method avoids the fine-tuning step on the first frame and does not rely on the temporal cues and particular object categories. Therefore, it runs very fast and can be conveniently trained only with images, as well as being robust to unseen objects. Experiments on the DAVIS datasets demonstrate that MTN can achieve a speed of 37 fps, and also shows a competitive accuracy in comparison to the state-of-the-art methods. |
2208.10833 | Hongcheng Guo | Hongcheng Guo, Yuhui Guo, Renjie Chen, Jian Yang, Jiaheng Liu, Zhoujun
Li, Tieqiao Zheng, Weichao Hou, Liangfan Zheng, Bo Zhang | LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph
Construction | 12 pages | null | null | null | cs.SE cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fully supervised log anomaly detection methods suffer the heavy burden of
annotating massive unlabeled log data. Recently, many semi-supervised methods
have been proposed to reduce annotation costs with the help of parsed
templates. However, these methods consider each keyword independently, which
disregards the correlation between keywords and the contextual relationships
among log sequences. In this paper, we propose a novel weakly supervised log
anomaly detection framework, named LogLG, to explore the semantic connections
among keywords from sequences. Specifically, we design an end-to-end iterative
process, where the keywords of unlabeled logs are first extracted to construct
a log-event graph. Then, we build a subgraph annotator to generate pseudo
labels for unlabeled log sequences. To ameliorate the annotation quality, we
adopt a self-supervised task to pre-train a subgraph annotator. After that, a
detection model is trained with the generated pseudo labels. Conditioned on the
classification results, we re-extract the keywords from the log sequences and
update the log-event graph for the next iteration. Experiments on five
benchmarks validate the effectiveness of LogLG for detecting anomalies on
unlabeled log data and demonstrate that LogLG, as the state-of-the-art weakly
supervised method, achieves significant performance improvements compared to
existing methods.
| [
{
"created": "Tue, 23 Aug 2022 09:32:19 GMT",
"version": "v1"
},
{
"created": "Thu, 25 Aug 2022 06:42:18 GMT",
"version": "v2"
},
{
"created": "Tue, 6 Sep 2022 02:36:58 GMT",
"version": "v3"
},
{
"created": "Thu, 2 Feb 2023 09:17:52 GMT",
"version": "v4"
},
{
"created": "Tue, 11 Apr 2023 07:46:32 GMT",
"version": "v5"
}
] | 2023-04-12 | [
[
"Guo",
"Hongcheng",
""
],
[
"Guo",
"Yuhui",
""
],
[
"Chen",
"Renjie",
""
],
[
"Yang",
"Jian",
""
],
[
"Liu",
"Jiaheng",
""
],
[
"Li",
"Zhoujun",
""
],
[
"Zheng",
"Tieqiao",
""
],
[
"Hou",
"Weichao",
""
],
[
"Zheng",
"Liangfan",
""
],
[
"Zhang",
"Bo",
""
]
] | Fully supervised log anomaly detection methods suffer the heavy burden of annotating massive unlabeled log data. Recently, many semi-supervised methods have been proposed to reduce annotation costs with the help of parsed templates. However, these methods consider each keyword independently, which disregards the correlation between keywords and the contextual relationships among log sequences. In this paper, we propose a novel weakly supervised log anomaly detection framework, named LogLG, to explore the semantic connections among keywords from sequences. Specifically, we design an end-to-end iterative process, where the keywords of unlabeled logs are first extracted to construct a log-event graph. Then, we build a subgraph annotator to generate pseudo labels for unlabeled log sequences. To ameliorate the annotation quality, we adopt a self-supervised task to pre-train a subgraph annotator. After that, a detection model is trained with the generated pseudo labels. Conditioned on the classification results, we re-extract the keywords from the log sequences and update the log-event graph for the next iteration. Experiments on five benchmarks validate the effectiveness of LogLG for detecting anomalies on unlabeled log data and demonstrate that LogLG, as the state-of-the-art weakly supervised method, achieves significant performance improvements compared to existing methods. |
2204.03082 | Zudi Lin | Leander Lauenburg, Zudi Lin, Ruihan Zhang, M\'arcia dos Santos, Siyu
Huang, Ignacio Arganda-Carreras, Edward S. Boyden, Hanspeter Pfister, Donglai
Wei | Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation
GAN | 13 pages with appendix | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Instance segmentation for unlabeled imaging modalities is a challenging but
essential task as collecting expert annotation can be expensive and
time-consuming. Existing works segment a new modality by either deploying a
pre-trained model optimized on diverse training data or conducting domain
translation and image segmentation as two independent steps. In this work, we
propose a novel Cyclic Segmentation Generative Adversarial Network (CySGAN)
that conducts image translation and instance segmentation jointly using a
unified framework. Besides the CycleGAN losses for image translation and
supervised losses for the annotated source domain, we introduce additional
self-supervised and segmentation-based adversarial objectives to improve the
model performance by leveraging unlabeled target domain images. We benchmark
our approach on the task of 3D neuronal nuclei segmentation with annotated
electron microscopy (EM) images and unlabeled expansion microscopy (ExM) data.
Our CySGAN outperforms both pretrained generalist models and the baselines that
sequentially conduct image translation and segmentation. Our implementation and
the newly collected, densely annotated ExM nuclei dataset, named NucExM, are
available at https://connectomics-bazaar.github.io/proj/CySGAN/index.html.
| [
{
"created": "Wed, 6 Apr 2022 20:46:39 GMT",
"version": "v1"
}
] | 2022-04-08 | [
[
"Lauenburg",
"Leander",
""
],
[
"Lin",
"Zudi",
""
],
[
"Zhang",
"Ruihan",
""
],
[
"Santos",
"Márcia dos",
""
],
[
"Huang",
"Siyu",
""
],
[
"Arganda-Carreras",
"Ignacio",
""
],
[
"Boyden",
"Edward S.",
""
],
[
"Pfister",
"Hanspeter",
""
],
[
"Wei",
"Donglai",
""
]
] | Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying a pre-trained model optimized on diverse training data or conducting domain translation and image segmentation as two independent steps. In this work, we propose a novel Cyclic Segmentation Generative Adversarial Network (CySGAN) that conducts image translation and instance segmentation jointly using a unified framework. Besides the CycleGAN losses for image translation and supervised losses for the annotated source domain, we introduce additional self-supervised and segmentation-based adversarial objectives to improve the model performance by leveraging unlabeled target domain images. We benchmark our approach on the task of 3D neuronal nuclei segmentation with annotated electron microscopy (EM) images and unlabeled expansion microscopy (ExM) data. Our CySGAN outperforms both pretrained generalist models and the baselines that sequentially conduct image translation and segmentation. Our implementation and the newly collected, densely annotated ExM nuclei dataset, named NucExM, are available at https://connectomics-bazaar.github.io/proj/CySGAN/index.html. |
2308.15726 | Fei Yu | Nan Che and Chenrui Liu and Fei Yu | AGS: An Dataset and Taxonomy for Domestic Scene Sound Event Recognition | null | null | null | null | cs.SD cs.AI eess.AS | http://creativecommons.org/licenses/by/4.0/ | Environmental sound scene and sound event recognition is important for the
recognition of suspicious events in indoor and outdoor environments (such as
nurseries, smart homes, nursing homes, etc.) and is a fundamental task involved
in many audio surveillance applications. In particular, there is no public
common data set for the research field of sound event recognition for the data
set of the indoor environmental sound scene. Therefore, this paper proposes a
data set (called as AGS) for the home environment sound. This data set
considers various types of overlapping audio in the scene, background noise.
Moreover, based on the proposed data set, this paper compares and analyzes the
advanced methods for sound event recognition, and then illustrates the
reliability of the data set proposed in this paper, and studies the challenges
raised by the new data set. Our proposed AGS and the source code of the
corresponding baselines at https://github.com/taolunzu11/AGS .
| [
{
"created": "Wed, 30 Aug 2023 03:03:47 GMT",
"version": "v1"
}
] | 2023-08-31 | [
[
"Che",
"Nan",
""
],
[
"Liu",
"Chenrui",
""
],
[
"Yu",
"Fei",
""
]
] | Environmental sound scene and sound event recognition is important for the recognition of suspicious events in indoor and outdoor environments (such as nurseries, smart homes, nursing homes, etc.) and is a fundamental task involved in many audio surveillance applications. In particular, there is no public common data set for the research field of sound event recognition for the data set of the indoor environmental sound scene. Therefore, this paper proposes a data set (called as AGS) for the home environment sound. This data set considers various types of overlapping audio in the scene, background noise. Moreover, based on the proposed data set, this paper compares and analyzes the advanced methods for sound event recognition, and then illustrates the reliability of the data set proposed in this paper, and studies the challenges raised by the new data set. Our proposed AGS and the source code of the corresponding baselines at https://github.com/taolunzu11/AGS . |
2405.05386 | Andreas Madsen | Andreas Madsen, Himabindu Lakkaraju, Siva Reddy, Sarath Chandar | Interpretability Needs a New Paradigm | null | null | null | null | cs.LG cs.CL cs.CV stat.ML | http://creativecommons.org/licenses/by-sa/4.0/ | Interpretability is the study of explaining models in understandable terms to
humans. At present, interpretability is divided into two paradigms: the
intrinsic paradigm, which believes that only models designed to be explained
can be explained, and the post-hoc paradigm, which believes that black-box
models can be explained. At the core of this debate is how each paradigm
ensures its explanations are faithful, i.e., true to the model's behavior. This
is important, as false but convincing explanations lead to unsupported
confidence in artificial intelligence (AI), which can be dangerous. This
paper's position is that we should think about new paradigms while staying
vigilant regarding faithfulness. First, by examining the history of paradigms
in science, we see that paradigms are constantly evolving. Then, by examining
the current paradigms, we can understand their underlying beliefs, the value
they bring, and their limitations. Finally, this paper presents 3 emerging
paradigms for interpretability. The first paradigm designs models such that
faithfulness can be easily measured. Another optimizes models such that
explanations become faithful. The last paradigm proposes to develop models that
produce both a prediction and an explanation.
| [
{
"created": "Wed, 8 May 2024 19:31:06 GMT",
"version": "v1"
}
] | 2024-05-10 | [
[
"Madsen",
"Andreas",
""
],
[
"Lakkaraju",
"Himabindu",
""
],
[
"Reddy",
"Siva",
""
],
[
"Chandar",
"Sarath",
""
]
] | Interpretability is the study of explaining models in understandable terms to humans. At present, interpretability is divided into two paradigms: the intrinsic paradigm, which believes that only models designed to be explained can be explained, and the post-hoc paradigm, which believes that black-box models can be explained. At the core of this debate is how each paradigm ensures its explanations are faithful, i.e., true to the model's behavior. This is important, as false but convincing explanations lead to unsupported confidence in artificial intelligence (AI), which can be dangerous. This paper's position is that we should think about new paradigms while staying vigilant regarding faithfulness. First, by examining the history of paradigms in science, we see that paradigms are constantly evolving. Then, by examining the current paradigms, we can understand their underlying beliefs, the value they bring, and their limitations. Finally, this paper presents 3 emerging paradigms for interpretability. The first paradigm designs models such that faithfulness can be easily measured. Another optimizes models such that explanations become faithful. The last paradigm proposes to develop models that produce both a prediction and an explanation. |
2312.04362 | Hamed Hematian Hemati | Hamed Hematian Hemati, Atousa Toghyani, Atena Souri, Sayed Hesam
Alavian, Hossein Sameti, Hamid Beigy | PCoQA: Persian Conversational Question Answering Dataset | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Humans seek information regarding a specific topic through performing a
conversation containing a series of questions and answers. In the pursuit of
conversational question answering research, we introduce the PCoQA, the first
\textbf{P}ersian \textbf{Co}nversational \textbf{Q}uestion \textbf{A}nswering
dataset, a resource comprising information-seeking dialogs encompassing a total
of 9,026 contextually-driven questions. Each dialog involves a questioner, a
responder, and a document from the Wikipedia; The questioner asks several
inter-connected questions from the text and the responder provides a span of
the document as the answer for each question. PCoQA is designed to present
novel challenges compared to previous question answering datasets including
having more open-ended non-factual answers, longer answers, and fewer lexical
overlaps. This paper not only presents the comprehensive PCoQA dataset but also
reports the performance of various benchmark models. Our models include
baseline models and pre-trained models, which are leveraged to boost the
performance of the model. The dataset and benchmarks are available at our
Github page.
| [
{
"created": "Thu, 7 Dec 2023 15:29:34 GMT",
"version": "v1"
}
] | 2023-12-08 | [
[
"Hemati",
"Hamed Hematian",
""
],
[
"Toghyani",
"Atousa",
""
],
[
"Souri",
"Atena",
""
],
[
"Alavian",
"Sayed Hesam",
""
],
[
"Sameti",
"Hossein",
""
],
[
"Beigy",
"Hamid",
""
]
] | Humans seek information regarding a specific topic through performing a conversation containing a series of questions and answers. In the pursuit of conversational question answering research, we introduce the PCoQA, the first \textbf{P}ersian \textbf{Co}nversational \textbf{Q}uestion \textbf{A}nswering dataset, a resource comprising information-seeking dialogs encompassing a total of 9,026 contextually-driven questions. Each dialog involves a questioner, a responder, and a document from the Wikipedia; The questioner asks several inter-connected questions from the text and the responder provides a span of the document as the answer for each question. PCoQA is designed to present novel challenges compared to previous question answering datasets including having more open-ended non-factual answers, longer answers, and fewer lexical overlaps. This paper not only presents the comprehensive PCoQA dataset but also reports the performance of various benchmark models. Our models include baseline models and pre-trained models, which are leveraged to boost the performance of the model. The dataset and benchmarks are available at our Github page. |
1807.01544 | Shangbang Long | Shangbang Long, Jiaqiang Ruan, Wenjie Zhang, Xin He, Wenhao Wu, Cong
Yao | TextSnake: A Flexible Representation for Detecting Text of Arbitrary
Shapes | 17 pages, accepted to ECCV2018 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Driven by deep neural networks and large scale datasets, scene text detection
methods have progressed substantially over the past years, continuously
refreshing the performance records on various standard benchmarks. However,
limited by the representations (axis-aligned rectangles, rotated rectangles or
quadrangles) adopted to describe text, existing methods may fall short when
dealing with much more free-form text instances, such as curved text, which are
actually very common in real-world scenarios. To tackle this problem, we
propose a more flexible representation for scene text, termed as TextSnake,
which is able to effectively represent text instances in horizontal, oriented
and curved forms. In TextSnake, a text instance is described as a sequence of
ordered, overlapping disks centered at symmetric axes, each of which is
associated with potentially variable radius and orientation. Such geometry
attributes are estimated via a Fully Convolutional Network (FCN) model. In
experiments, the text detector based on TextSnake achieves state-of-the-art or
comparable performance on Total-Text and SCUT-CTW1500, the two newly published
benchmarks with special emphasis on curved text in natural images, as well as
the widely-used datasets ICDAR 2015 and MSRA-TD500. Specifically, TextSnake
outperforms the baseline on Total-Text by more than 40% in F-measure.
| [
{
"created": "Wed, 4 Jul 2018 12:37:07 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Aug 2020 00:54:35 GMT",
"version": "v2"
}
] | 2020-08-19 | [
[
"Long",
"Shangbang",
""
],
[
"Ruan",
"Jiaqiang",
""
],
[
"Zhang",
"Wenjie",
""
],
[
"He",
"Xin",
""
],
[
"Wu",
"Wenhao",
""
],
[
"Yao",
"Cong",
""
]
] | Driven by deep neural networks and large scale datasets, scene text detection methods have progressed substantially over the past years, continuously refreshing the performance records on various standard benchmarks. However, limited by the representations (axis-aligned rectangles, rotated rectangles or quadrangles) adopted to describe text, existing methods may fall short when dealing with much more free-form text instances, such as curved text, which are actually very common in real-world scenarios. To tackle this problem, we propose a more flexible representation for scene text, termed as TextSnake, which is able to effectively represent text instances in horizontal, oriented and curved forms. In TextSnake, a text instance is described as a sequence of ordered, overlapping disks centered at symmetric axes, each of which is associated with potentially variable radius and orientation. Such geometry attributes are estimated via a Fully Convolutional Network (FCN) model. In experiments, the text detector based on TextSnake achieves state-of-the-art or comparable performance on Total-Text and SCUT-CTW1500, the two newly published benchmarks with special emphasis on curved text in natural images, as well as the widely-used datasets ICDAR 2015 and MSRA-TD500. Specifically, TextSnake outperforms the baseline on Total-Text by more than 40% in F-measure. |
cs/0512105 | Valmir Barbosa | Alexandre O. Stauffer, Valmir C. Barbosa | A study of the edge-switching Markov-chain method for the generation of
random graphs | Minor typos corrected | null | null | null | cs.DM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of generating connected random graphs with no self-loops
or multiple edges and that, in addition, have a given degree sequence. The
generation method we focus on is the edge-switching Markov-chain method, whose
functioning depends on a parameter w related to the method's core operation of
an edge switch. We analyze two existing heuristics for adjusting w during the
generation of a graph and show that they result in a Markov chain whose
stationary distribution is uniform, thus ensuring that generation occurs
uniformly at random. We also introduce a novel w-adjusting heuristic which,
even though it does not always lead to a Markov chain, is still guaranteed to
converge to the uniform distribution under relatively mild conditions. We
report on extensive computer experiments comparing the three heuristics'
performance at generating random graphs whose node degrees are distributed as
power laws.
| [
{
"created": "Thu, 29 Dec 2005 18:37:01 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Jun 2011 18:52:12 GMT",
"version": "v2"
}
] | 2011-07-01 | [
[
"Stauffer",
"Alexandre O.",
""
],
[
"Barbosa",
"Valmir C.",
""
]
] | We study the problem of generating connected random graphs with no self-loops or multiple edges and that, in addition, have a given degree sequence. The generation method we focus on is the edge-switching Markov-chain method, whose functioning depends on a parameter w related to the method's core operation of an edge switch. We analyze two existing heuristics for adjusting w during the generation of a graph and show that they result in a Markov chain whose stationary distribution is uniform, thus ensuring that generation occurs uniformly at random. We also introduce a novel w-adjusting heuristic which, even though it does not always lead to a Markov chain, is still guaranteed to converge to the uniform distribution under relatively mild conditions. We report on extensive computer experiments comparing the three heuristics' performance at generating random graphs whose node degrees are distributed as power laws. |
2207.05643 | Koorosh Aslansefat | Koorosh Aslansefat, Panagiota Nikolaou, Martin Walker, Mohammed Naveed
Akram, Ioannis Sorokos, Jan Reich, Panayiotis Kolios, Maria K. Michael,
Theocharis Theocharides, Georgios Ellinas, Daniel Schneider, Yiannis
Papadopoulos | SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable
Digital Dependable Identities | null | null | null | null | cs.RO cs.SY eess.SY | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The use of Unmanned Arial Vehicles (UAVs) offers many advantages across a
variety of applications. However, safety assurance is a key barrier to
widespread usage, especially given the unpredictable operational and
environmental factors experienced by UAVs, which are hard to capture solely at
design-time. This paper proposes a new reliability modeling approach called
SafeDrones to help address this issue by enabling runtime reliability and risk
assessment of UAVs. It is a prototype instantiation of the Executable Digital
Dependable Identity (EDDI) concept, which aims to create a model-based solution
for real-time, data-driven dependability assurance for multi-robot systems. By
providing real-time reliability estimates, SafeDrones allows UAVs to update
their missions accordingly in an adaptive manner.
| [
{
"created": "Tue, 12 Jul 2022 16:19:03 GMT",
"version": "v1"
}
] | 2022-07-13 | [
[
"Aslansefat",
"Koorosh",
""
],
[
"Nikolaou",
"Panagiota",
""
],
[
"Walker",
"Martin",
""
],
[
"Akram",
"Mohammed Naveed",
""
],
[
"Sorokos",
"Ioannis",
""
],
[
"Reich",
"Jan",
""
],
[
"Kolios",
"Panayiotis",
""
],
[
"Michael",
"Maria K.",
""
],
[
"Theocharides",
"Theocharis",
""
],
[
"Ellinas",
"Georgios",
""
],
[
"Schneider",
"Daniel",
""
],
[
"Papadopoulos",
"Yiannis",
""
]
] | The use of Unmanned Arial Vehicles (UAVs) offers many advantages across a variety of applications. However, safety assurance is a key barrier to widespread usage, especially given the unpredictable operational and environmental factors experienced by UAVs, which are hard to capture solely at design-time. This paper proposes a new reliability modeling approach called SafeDrones to help address this issue by enabling runtime reliability and risk assessment of UAVs. It is a prototype instantiation of the Executable Digital Dependable Identity (EDDI) concept, which aims to create a model-based solution for real-time, data-driven dependability assurance for multi-robot systems. By providing real-time reliability estimates, SafeDrones allows UAVs to update their missions accordingly in an adaptive manner. |
2008.00962 | Lucas Tabelini Torres | Lucas Tabelini, Rodrigo Berriel, Thiago M. Paix\~ao, Alberto F. De
Souza, Claudine Badue, Nicu Sebe and Thiago Oliveira-Santos | Deep Traffic Sign Detection and Recognition Without Target Domain Real
Images | arXiv admin note: text overlap with arXiv:1907.09679 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning has been successfully applied to several problems related to
autonomous driving, often relying on large databases of real target-domain
images for proper training. The acquisition of such real-world data is not
always possible in the self-driving context, and sometimes their annotation is
not feasible. Moreover, in many tasks, there is an intrinsic data imbalance
that most learning-based methods struggle to cope with. Particularly, traffic
sign detection is a challenging problem in which these three issues are seen
altogether. To address these challenges, we propose a novel database generation
method that requires only (i) arbitrary natural images, i.e., requires no real
image from the target-domain, and (ii) templates of the traffic signs. The
method does not aim at overcoming the training with real data, but to be a
compatible alternative when the real data is not available. The effortlessly
generated database is shown to be effective for the training of a deep detector
on traffic signs from multiple countries. On large data sets, training with a
fully synthetic data set almost matches the performance of training with a real
one. When compared to training with a smaller data set of real images, training
with synthetic images increased the accuracy by 12.25%. The proposed method
also improves the performance of the detector when target-domain data are
available.
| [
{
"created": "Thu, 30 Jul 2020 21:06:47 GMT",
"version": "v1"
}
] | 2020-08-04 | [
[
"Tabelini",
"Lucas",
""
],
[
"Berriel",
"Rodrigo",
""
],
[
"Paixão",
"Thiago M.",
""
],
[
"De Souza",
"Alberto F.",
""
],
[
"Badue",
"Claudine",
""
],
[
"Sebe",
"Nicu",
""
],
[
"Oliveira-Santos",
"Thiago",
""
]
] | Deep learning has been successfully applied to several problems related to autonomous driving, often relying on large databases of real target-domain images for proper training. The acquisition of such real-world data is not always possible in the self-driving context, and sometimes their annotation is not feasible. Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. Particularly, traffic sign detection is a challenging problem in which these three issues are seen altogether. To address these challenges, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the target-domain, and (ii) templates of the traffic signs. The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available. The effortlessly generated database is shown to be effective for the training of a deep detector on traffic signs from multiple countries. On large data sets, training with a fully synthetic data set almost matches the performance of training with a real one. When compared to training with a smaller data set of real images, training with synthetic images increased the accuracy by 12.25%. The proposed method also improves the performance of the detector when target-domain data are available. |
2004.08145 | Zhiwei Gao | Zhiwei Gao, Shuntaro Yada, Shoko Wakamiya and Eiji Aramaki | NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset | null | null | null | null | cs.SI cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since the outbreak of coronavirus disease 2019 (COVID-19) in the late 2019,
it has affected over 200 countries and billions of people worldwide. This has
affected the social life of people owing to enforcements, such as "social
distancing" and "stay at home." This has resulted in an increasing interaction
through social media. Given that social media can bring us valuable information
about COVID-19 at a global scale, it is important to share the data and
encourage social media studies against COVID-19 or other infectious diseases.
Therefore, we have released a multilingual dataset of social media posts
related to COVID-19, consisting of microblogs in English and Japanese from
Twitter and those in Chinese from Weibo. The data cover microblogs from January
20, 2020, to March 24, 2020. This paper also provides a quantitative as well as
qualitative analysis of these datasets by creating daily word clouds as an
example of text-mining analysis. The dataset is now available on Github. This
dataset can be analyzed in a multitude of ways and is expected to help in
efficient communication of precautions related to COVID-19.
| [
{
"created": "Fri, 17 Apr 2020 09:48:14 GMT",
"version": "v1"
}
] | 2020-04-20 | [
[
"Gao",
"Zhiwei",
""
],
[
"Yada",
"Shuntaro",
""
],
[
"Wakamiya",
"Shoko",
""
],
[
"Aramaki",
"Eiji",
""
]
] | Since the outbreak of coronavirus disease 2019 (COVID-19) in the late 2019, it has affected over 200 countries and billions of people worldwide. This has affected the social life of people owing to enforcements, such as "social distancing" and "stay at home." This has resulted in an increasing interaction through social media. Given that social media can bring us valuable information about COVID-19 at a global scale, it is important to share the data and encourage social media studies against COVID-19 or other infectious diseases. Therefore, we have released a multilingual dataset of social media posts related to COVID-19, consisting of microblogs in English and Japanese from Twitter and those in Chinese from Weibo. The data cover microblogs from January 20, 2020, to March 24, 2020. This paper also provides a quantitative as well as qualitative analysis of these datasets by creating daily word clouds as an example of text-mining analysis. The dataset is now available on Github. This dataset can be analyzed in a multitude of ways and is expected to help in efficient communication of precautions related to COVID-19. |
2408.04870 | Mulong Luo | Ayush RoyChowdhury, Mulong Luo, Prateek Sahu, Sarbartha Banerjee,
Mohit Tiwari | ConfusedPilot: Confused Deputy Risks in RAG-based LLMs | null | null | null | null | cs.CR cs.AI | http://creativecommons.org/licenses/by/4.0/ | Retrieval augmented generation (RAG) is a process where a large language
model (LLM) retrieves useful information from a database and then generates the
responses. It is becoming popular in enterprise settings for daily business
operations. For example, Copilot for Microsoft 365 has accumulated millions of
businesses. However, the security implications of adopting such RAG-based
systems are unclear.
In this paper, we introduce ConfusedPilot, a class of security
vulnerabilities of RAG systems that confuse Copilot and cause integrity and
confidentiality violations in its responses. First, we investigate a
vulnerability that embeds malicious text in the modified prompt in RAG,
corrupting the responses generated by the LLM. Second, we demonstrate a
vulnerability that leaks secret data, which leverages the caching mechanism
during retrieval. Third, we investigate how both vulnerabilities can be
exploited to propagate misinformation within the enterprise and ultimately
impact its operations, such as sales and manufacturing. We also discuss the
root cause of these attacks by investigating the architecture of a RAG-based
system. This study highlights the security vulnerabilities in today's RAG-based
systems and proposes design guidelines to secure future RAG-based systems.
| [
{
"created": "Fri, 9 Aug 2024 05:20:05 GMT",
"version": "v1"
},
{
"created": "Tue, 13 Aug 2024 22:51:30 GMT",
"version": "v2"
},
{
"created": "Thu, 15 Aug 2024 05:24:19 GMT",
"version": "v3"
}
] | 2024-08-16 | [
[
"RoyChowdhury",
"Ayush",
""
],
[
"Luo",
"Mulong",
""
],
[
"Sahu",
"Prateek",
""
],
[
"Banerjee",
"Sarbartha",
""
],
[
"Tiwari",
"Mohit",
""
]
] | Retrieval augmented generation (RAG) is a process where a large language model (LLM) retrieves useful information from a database and then generates the responses. It is becoming popular in enterprise settings for daily business operations. For example, Copilot for Microsoft 365 has accumulated millions of businesses. However, the security implications of adopting such RAG-based systems are unclear. In this paper, we introduce ConfusedPilot, a class of security vulnerabilities of RAG systems that confuse Copilot and cause integrity and confidentiality violations in its responses. First, we investigate a vulnerability that embeds malicious text in the modified prompt in RAG, corrupting the responses generated by the LLM. Second, we demonstrate a vulnerability that leaks secret data, which leverages the caching mechanism during retrieval. Third, we investigate how both vulnerabilities can be exploited to propagate misinformation within the enterprise and ultimately impact its operations, such as sales and manufacturing. We also discuss the root cause of these attacks by investigating the architecture of a RAG-based system. This study highlights the security vulnerabilities in today's RAG-based systems and proposes design guidelines to secure future RAG-based systems. |
2109.10431 | Hao Wang | Haewon Jeong, Hao Wang, Flavio P. Calmon | Fairness without Imputation: A Decision Tree Approach for Fair
Prediction with Missing Values | null | null | null | null | cs.LG cs.CY cs.IT math.IT stat.ML | http://creativecommons.org/licenses/by/4.0/ | We investigate the fairness concerns of training a machine learning model
using data with missing values. Even though there are a number of fairness
intervention methods in the literature, most of them require a complete
training set as input. In practice, data can have missing values, and data
missing patterns can depend on group attributes (e.g. gender or race). Simply
applying off-the-shelf fair learning algorithms to an imputed dataset may lead
to an unfair model. In this paper, we first theoretically analyze different
sources of discrimination risks when training with an imputed dataset. Then, we
propose an integrated approach based on decision trees that does not require a
separate process of imputation and learning. Instead, we train a tree with
missing incorporated as attribute (MIA), which does not require explicit
imputation, and we optimize a fairness-regularized objective function. We
demonstrate that our approach outperforms existing fairness intervention
methods applied to an imputed dataset, through several experiments on
real-world datasets.
| [
{
"created": "Tue, 21 Sep 2021 20:46:22 GMT",
"version": "v1"
},
{
"created": "Thu, 14 Apr 2022 02:13:34 GMT",
"version": "v2"
}
] | 2022-04-15 | [
[
"Jeong",
"Haewon",
""
],
[
"Wang",
"Hao",
""
],
[
"Calmon",
"Flavio P.",
""
]
] | We investigate the fairness concerns of training a machine learning model using data with missing values. Even though there are a number of fairness intervention methods in the literature, most of them require a complete training set as input. In practice, data can have missing values, and data missing patterns can depend on group attributes (e.g. gender or race). Simply applying off-the-shelf fair learning algorithms to an imputed dataset may lead to an unfair model. In this paper, we first theoretically analyze different sources of discrimination risks when training with an imputed dataset. Then, we propose an integrated approach based on decision trees that does not require a separate process of imputation and learning. Instead, we train a tree with missing incorporated as attribute (MIA), which does not require explicit imputation, and we optimize a fairness-regularized objective function. We demonstrate that our approach outperforms existing fairness intervention methods applied to an imputed dataset, through several experiments on real-world datasets. |
2312.02963 | Zhangyang Xiong | Zhangyang Xiong, Chenghong Li, Kenkun Liu, Hongjie Liao, Jianqiao Hu,
Junyi Zhu, Shuliang Ning, Lingteng Qiu, Chongjie Wang, Shijie Wang, Shuguang
Cui and Xiaoguang Han | MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human
Captures | Project page: https://x-zhangyang.github.io/MVHumanNet/ | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this era, the success of large language models and text-to-image models
can be attributed to the driving force of large-scale datasets. However, in the
realm of 3D vision, while remarkable progress has been made with models trained
on large-scale synthetic and real-captured object data like Objaverse and
MVImgNet, a similar level of progress has not been observed in the domain of
human-centric tasks partially due to the lack of a large-scale human dataset.
Existing datasets of high-fidelity 3D human capture continue to be mid-sized
due to the significant challenges in acquiring large-scale high-quality 3D
human data. To bridge this gap, we present MVHumanNet, a dataset that comprises
multi-view human action sequences of 4,500 human identities. The primary focus
of our work is on collecting human data that features a large number of diverse
identities and everyday clothing using a multi-view human capture system, which
facilitates easily scalable data collection. Our dataset contains 9,000 daily
outfits, 60,000 motion sequences and 645 million frames with extensive
annotations, including human masks, camera parameters, 2D and 3D keypoints,
SMPL/SMPLX parameters, and corresponding textual descriptions. To explore the
potential of MVHumanNet in various 2D and 3D visual tasks, we conducted pilot
studies on view-consistent action recognition, human NeRF reconstruction,
text-driven view-unconstrained human image generation, as well as 2D
view-unconstrained human image and 3D avatar generation. Extensive experiments
demonstrate the performance improvements and effective applications enabled by
the scale provided by MVHumanNet. As the current largest-scale 3D human
dataset, we hope that the release of MVHumanNet data with annotations will
foster further innovations in the domain of 3D human-centric tasks at scale.
| [
{
"created": "Tue, 5 Dec 2023 18:50:12 GMT",
"version": "v1"
}
] | 2023-12-06 | [
[
"Xiong",
"Zhangyang",
""
],
[
"Li",
"Chenghong",
""
],
[
"Liu",
"Kenkun",
""
],
[
"Liao",
"Hongjie",
""
],
[
"Hu",
"Jianqiao",
""
],
[
"Zhu",
"Junyi",
""
],
[
"Ning",
"Shuliang",
""
],
[
"Qiu",
"Lingteng",
""
],
[
"Wang",
"Chongjie",
""
],
[
"Wang",
"Shijie",
""
],
[
"Cui",
"Shuguang",
""
],
[
"Han",
"Xiaoguang",
""
]
] | In this era, the success of large language models and text-to-image models can be attributed to the driving force of large-scale datasets. However, in the realm of 3D vision, while remarkable progress has been made with models trained on large-scale synthetic and real-captured object data like Objaverse and MVImgNet, a similar level of progress has not been observed in the domain of human-centric tasks partially due to the lack of a large-scale human dataset. Existing datasets of high-fidelity 3D human capture continue to be mid-sized due to the significant challenges in acquiring large-scale high-quality 3D human data. To bridge this gap, we present MVHumanNet, a dataset that comprises multi-view human action sequences of 4,500 human identities. The primary focus of our work is on collecting human data that features a large number of diverse identities and everyday clothing using a multi-view human capture system, which facilitates easily scalable data collection. Our dataset contains 9,000 daily outfits, 60,000 motion sequences and 645 million frames with extensive annotations, including human masks, camera parameters, 2D and 3D keypoints, SMPL/SMPLX parameters, and corresponding textual descriptions. To explore the potential of MVHumanNet in various 2D and 3D visual tasks, we conducted pilot studies on view-consistent action recognition, human NeRF reconstruction, text-driven view-unconstrained human image generation, as well as 2D view-unconstrained human image and 3D avatar generation. Extensive experiments demonstrate the performance improvements and effective applications enabled by the scale provided by MVHumanNet. As the current largest-scale 3D human dataset, we hope that the release of MVHumanNet data with annotations will foster further innovations in the domain of 3D human-centric tasks at scale. |
2212.07612 | Kai Huang | Kai Huang, Haibo Hu, Qingqing Ye, Kai Tian, Bolong Zheng, Xiaofang
Zhou | TED: Towards Discovering Top-k Edge-Diversified Patterns in a Graph
Database | This paper is accepted by SIGMOD 2023 | null | null | null | cs.DB | http://creativecommons.org/licenses/by/4.0/ | With an exponentially growing number of graphs from disparate repositories,
there is a strong need to analyze a graph database containing an extensive
collection of small- or medium-sized data graphs (e.g., chemical compounds).
Although subgraph enumeration and subgraph mining have been proposed to bring
insights into a graph database by a set of subgraph structures, they often end
up with similar or homogenous topologies, which is undesirable in many graph
applications. To address this limitation, we propose the Top-k Edge-Diversified
Patterns Discovery problem to retrieve a set of subgraphs that cover the
maximum number of edges in a database. To efficiently process such query, we
present a generic and extensible framework called Ted which achieves a
guaranteed approximation ratio to the optimal result. Two optimization
strategies are further developed to improve the performance. Experimental
studies on real-world datasets demonstrate the superiority of Ted to
traditional techniques.
| [
{
"created": "Thu, 15 Dec 2022 04:27:29 GMT",
"version": "v1"
}
] | 2022-12-16 | [
[
"Huang",
"Kai",
""
],
[
"Hu",
"Haibo",
""
],
[
"Ye",
"Qingqing",
""
],
[
"Tian",
"Kai",
""
],
[
"Zheng",
"Bolong",
""
],
[
"Zhou",
"Xiaofang",
""
]
] | With an exponentially growing number of graphs from disparate repositories, there is a strong need to analyze a graph database containing an extensive collection of small- or medium-sized data graphs (e.g., chemical compounds). Although subgraph enumeration and subgraph mining have been proposed to bring insights into a graph database by a set of subgraph structures, they often end up with similar or homogenous topologies, which is undesirable in many graph applications. To address this limitation, we propose the Top-k Edge-Diversified Patterns Discovery problem to retrieve a set of subgraphs that cover the maximum number of edges in a database. To efficiently process such query, we present a generic and extensible framework called Ted which achieves a guaranteed approximation ratio to the optimal result. Two optimization strategies are further developed to improve the performance. Experimental studies on real-world datasets demonstrate the superiority of Ted to traditional techniques. |
1511.00148 | Indre Zliobaite | Indre Zliobaite | A survey on measuring indirect discrimination in machine learning | null | null | null | null | cs.CY stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nowadays, many decisions are made using predictive models built on historical
data.Predictive models may systematically discriminate groups of people even if
the computing process is fair and well-intentioned. Discrimination-aware data
mining studies how to make predictive models free from discrimination, when
historical data, on which they are built, may be biased, incomplete, or even
contain past discriminatory decisions. Discrimination refers to disadvantageous
treatment of a person based on belonging to a category rather than on
individual merit. In this survey we review and organize various discrimination
measures that have been used for measuring discrimination in data, as well as
in evaluating performance of discrimination-aware predictive models. We also
discuss related measures from other disciplines, which have not been used for
measuring discrimination, but potentially could be suitable for this purpose.
We computationally analyze properties of selected measures. We also review and
discuss measuring procedures, and present recommendations for practitioners.
The primary target audience is data mining, machine learning, pattern
recognition, statistical modeling researchers developing new methods for
non-discriminatory predictive modeling. In addition, practitioners and policy
makers would use the survey for diagnosing potential discrimination by
predictive models.
| [
{
"created": "Sat, 31 Oct 2015 16:04:12 GMT",
"version": "v1"
}
] | 2015-11-23 | [
[
"Zliobaite",
"Indre",
""
]
] | Nowadays, many decisions are made using predictive models built on historical data.Predictive models may systematically discriminate groups of people even if the computing process is fair and well-intentioned. Discrimination-aware data mining studies how to make predictive models free from discrimination, when historical data, on which they are built, may be biased, incomplete, or even contain past discriminatory decisions. Discrimination refers to disadvantageous treatment of a person based on belonging to a category rather than on individual merit. In this survey we review and organize various discrimination measures that have been used for measuring discrimination in data, as well as in evaluating performance of discrimination-aware predictive models. We also discuss related measures from other disciplines, which have not been used for measuring discrimination, but potentially could be suitable for this purpose. We computationally analyze properties of selected measures. We also review and discuss measuring procedures, and present recommendations for practitioners. The primary target audience is data mining, machine learning, pattern recognition, statistical modeling researchers developing new methods for non-discriminatory predictive modeling. In addition, practitioners and policy makers would use the survey for diagnosing potential discrimination by predictive models. |
2307.06983 | Hu Wei | Wei Hu, Xuhong Wang, Ding Wang, Shengyue Yao, Zuqiu Mao, Li Li,
Fei-Yue Wang, Yilun Lin | IR Design for Application-Specific Natural Language: A Case Study on
Traffic Data | null | null | null | null | cs.SE cs.AI cs.PL | http://creativecommons.org/licenses/by/4.0/ | In the realm of software applications in the transportation industry,
Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their
ease of use and various other benefits. With the ceaseless progress in computer
performance and the rapid development of large-scale models, the possibility of
programming using natural language in specified applications - referred to as
Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits
greater flexibility and freedom, which, in turn, leads to an increase in
computational complexity for parsing and a decrease in processing performance.
To tackle this issue, our paper advances a design for an intermediate
representation (IR) that caters to ASNL and can uniformly process
transportation data into graph data format, improving data processing
performance. Experimental comparisons reveal that in standard data query
operations, our proposed IR design can achieve a speed improvement of over
forty times compared to direct usage of standard XML format data.
| [
{
"created": "Thu, 13 Jul 2023 15:52:05 GMT",
"version": "v1"
}
] | 2023-07-17 | [
[
"Hu",
"Wei",
""
],
[
"Wang",
"Xuhong",
""
],
[
"Wang",
"Ding",
""
],
[
"Yao",
"Shengyue",
""
],
[
"Mao",
"Zuqiu",
""
],
[
"Li",
"Li",
""
],
[
"Wang",
"Fei-Yue",
""
],
[
"Lin",
"Yilun",
""
]
] | In the realm of software applications in the transportation industry, Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their ease of use and various other benefits. With the ceaseless progress in computer performance and the rapid development of large-scale models, the possibility of programming using natural language in specified applications - referred to as Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits greater flexibility and freedom, which, in turn, leads to an increase in computational complexity for parsing and a decrease in processing performance. To tackle this issue, our paper advances a design for an intermediate representation (IR) that caters to ASNL and can uniformly process transportation data into graph data format, improving data processing performance. Experimental comparisons reveal that in standard data query operations, our proposed IR design can achieve a speed improvement of over forty times compared to direct usage of standard XML format data. |
2210.10765 | Fahim Tajwar | Annie Xie, Fahim Tajwar, Archit Sharma, Chelsea Finn | When to Ask for Help: Proactive Interventions in Autonomous
Reinforcement Learning | 36th Conference on Neural Information Processing Systems (NeurIPS
2022) | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A long-term goal of reinforcement learning is to design agents that can
autonomously interact and learn in the world. A critical challenge to such
autonomy is the presence of irreversible states which require external
assistance to recover from, such as when a robot arm has pushed an object off
of a table. While standard agents require constant monitoring to decide when to
intervene, we aim to design proactive agents that can request human
intervention only when needed. To this end, we propose an algorithm that
efficiently learns to detect and avoid states that are irreversible, and
proactively asks for help in case the agent does enter them. On a suite of
continuous control environments with unknown irreversible states, we find that
our algorithm exhibits better sample- and intervention-efficiency compared to
existing methods. Our code is publicly available at
https://sites.google.com/view/proactive-interventions
| [
{
"created": "Wed, 19 Oct 2022 17:57:24 GMT",
"version": "v1"
}
] | 2022-10-20 | [
[
"Xie",
"Annie",
""
],
[
"Tajwar",
"Fahim",
""
],
[
"Sharma",
"Archit",
""
],
[
"Finn",
"Chelsea",
""
]
] | A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world. A critical challenge to such autonomy is the presence of irreversible states which require external assistance to recover from, such as when a robot arm has pushed an object off of a table. While standard agents require constant monitoring to decide when to intervene, we aim to design proactive agents that can request human intervention only when needed. To this end, we propose an algorithm that efficiently learns to detect and avoid states that are irreversible, and proactively asks for help in case the agent does enter them. On a suite of continuous control environments with unknown irreversible states, we find that our algorithm exhibits better sample- and intervention-efficiency compared to existing methods. Our code is publicly available at https://sites.google.com/view/proactive-interventions |
2002.04114 | Guan-An Wang | Guan-An Wang, Tianzhu Zhang. Yang Yang, Jian Cheng, Jianlong Chang, Xu
Liang, Zengguang Hou | Cross-Modality Paired-Images Generation for RGB-Infrared Person
Re-Identification | accepted by AAAI'20 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | RGB-Infrared (IR) person re-identification is very challenging due to the
large cross-modality variations between RGB and IR images. The key solution is
to learn aligned features to the bridge RGB and IR modalities. However, due to
the lack of correspondence labels between every pair of RGB and IR images, most
methods try to alleviate the variations with set-level alignment by reducing
the distance between the entire RGB and IR sets. However, this set-level
alignment may lead to misalignment of some instances, which limits the
performance for RGB-IR Re-ID. Different from existing methods, in this paper,
we propose to generate cross-modality paired-images and perform both global
set-level and fine-grained instance-level alignments. Our proposed method
enjoys several merits. First, our method can perform set-level alignment by
disentangling modality-specific and modality-invariant features. Compared with
conventional methods, ours can explicitly remove the modality-specific features
and the modality variation can be better reduced. Second, given cross-modality
unpaired-images of a person, our method can generate cross-modality paired
images from exchanged images. With them, we can directly perform instance-level
alignment by minimizing distances of every pair of images. Extensive
experimental results on two standard benchmarks demonstrate that the proposed
model favourably against state-of-the-art methods. Especially, on SYSU-MM01
dataset, our model can achieve a gain of 9.2% and 7.7% in terms of Rank-1 and
mAP. Code is available at https://github.com/wangguanan/JSIA-ReID.
| [
{
"created": "Mon, 10 Feb 2020 22:15:19 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Feb 2020 00:03:01 GMT",
"version": "v2"
}
] | 2020-02-19 | [
[
"Wang",
"Guan-An",
""
],
[
"Yang",
"Tianzhu Zhang. Yang",
""
],
[
"Cheng",
"Jian",
""
],
[
"Chang",
"Jianlong",
""
],
[
"Liang",
"Xu",
""
],
[
"Hou",
"Zengguang",
""
]
] | RGB-Infrared (IR) person re-identification is very challenging due to the large cross-modality variations between RGB and IR images. The key solution is to learn aligned features to the bridge RGB and IR modalities. However, due to the lack of correspondence labels between every pair of RGB and IR images, most methods try to alleviate the variations with set-level alignment by reducing the distance between the entire RGB and IR sets. However, this set-level alignment may lead to misalignment of some instances, which limits the performance for RGB-IR Re-ID. Different from existing methods, in this paper, we propose to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments. Our proposed method enjoys several merits. First, our method can perform set-level alignment by disentangling modality-specific and modality-invariant features. Compared with conventional methods, ours can explicitly remove the modality-specific features and the modality variation can be better reduced. Second, given cross-modality unpaired-images of a person, our method can generate cross-modality paired images from exchanged images. With them, we can directly perform instance-level alignment by minimizing distances of every pair of images. Extensive experimental results on two standard benchmarks demonstrate that the proposed model favourably against state-of-the-art methods. Especially, on SYSU-MM01 dataset, our model can achieve a gain of 9.2% and 7.7% in terms of Rank-1 and mAP. Code is available at https://github.com/wangguanan/JSIA-ReID. |
2110.12884 | Boris van Breugel | Boris van Breugel, Trent Kyono, Jeroen Berrevoets, Mihaela van der
Schaar | DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative
Networks | null | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Machine learning models have been criticized for reflecting unfair biases in
the training data. Instead of solving for this by introducing fair learning
algorithms directly, we focus on generating fair synthetic data, such that any
downstream learner is fair. Generating fair synthetic data from unfair data -
while remaining truthful to the underlying data-generating process (DGP) - is
non-trivial. In this paper, we introduce DECAF: a GAN-based fair synthetic data
generator for tabular data. With DECAF we embed the DGP explicitly as a
structural causal model in the input layers of the generator, allowing each
variable to be reconstructed conditioned on its causal parents. This procedure
enables inference time debiasing, where biased edges can be strategically
removed for satisfying user-defined fairness requirements. The DECAF framework
is versatile and compatible with several popular definitions of fairness. In
our experiments, we show that DECAF successfully removes undesired bias and -
in contrast to existing methods - is capable of generating high-quality
synthetic data. Furthermore, we provide theoretical guarantees on the
generator's convergence and the fairness of downstream models.
| [
{
"created": "Mon, 25 Oct 2021 12:39:56 GMT",
"version": "v1"
},
{
"created": "Thu, 4 Nov 2021 21:25:22 GMT",
"version": "v2"
}
] | 2021-11-08 | [
[
"van Breugel",
"Boris",
""
],
[
"Kyono",
"Trent",
""
],
[
"Berrevoets",
"Jeroen",
""
],
[
"van der Schaar",
"Mihaela",
""
]
] | Machine learning models have been criticized for reflecting unfair biases in the training data. Instead of solving for this by introducing fair learning algorithms directly, we focus on generating fair synthetic data, such that any downstream learner is fair. Generating fair synthetic data from unfair data - while remaining truthful to the underlying data-generating process (DGP) - is non-trivial. In this paper, we introduce DECAF: a GAN-based fair synthetic data generator for tabular data. With DECAF we embed the DGP explicitly as a structural causal model in the input layers of the generator, allowing each variable to be reconstructed conditioned on its causal parents. This procedure enables inference time debiasing, where biased edges can be strategically removed for satisfying user-defined fairness requirements. The DECAF framework is versatile and compatible with several popular definitions of fairness. In our experiments, we show that DECAF successfully removes undesired bias and - in contrast to existing methods - is capable of generating high-quality synthetic data. Furthermore, we provide theoretical guarantees on the generator's convergence and the fairness of downstream models. |
1603.01472 | Yinan Wang | Yinan Wang, Hakan Johansson, Hui Xu, and Jietao Diao | Minimax Design and Order Estimation of FIR Filters for Extending the
Bandwidth of ADCs | 4 pages, 3 figures, IEEE Int. Symp. Circuits Syst. (to appear),
Montreal, Canada, 2016 | null | 10.1109/ISCAS.2016.7539015 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The bandwidth of the sampling systems, especially for time-interleaved
analog-to-digital converters, needs to be extended along with the rapid
increase of the sampling rate. A digitally assisted technique becomes a
feasible approach to extend the analog bandwidth, as it is impractical to
implement the extension in analog circuits. This paper derives accurate order
estimation formulas for the bandwidth extension filter, which is designed in
the minimax sense with the ripple constraints as the design criteria. The
derived filter order estimation is significant in evaluating the computational
complexity from the viewpoint of the top-level system design. Moreover, with
the proposed order estimates, one can conveniently obtain the minimal order
that satisfies the given ripple constraints, which contributes to reducing the
design time. Both the performance of the extension filter and its order
estimation are illustrated and demonstrated through simulation examples.
| [
{
"created": "Fri, 4 Mar 2016 14:16:33 GMT",
"version": "v1"
}
] | 2016-11-18 | [
[
"Wang",
"Yinan",
""
],
[
"Johansson",
"Hakan",
""
],
[
"Xu",
"Hui",
""
],
[
"Diao",
"Jietao",
""
]
] | The bandwidth of the sampling systems, especially for time-interleaved analog-to-digital converters, needs to be extended along with the rapid increase of the sampling rate. A digitally assisted technique becomes a feasible approach to extend the analog bandwidth, as it is impractical to implement the extension in analog circuits. This paper derives accurate order estimation formulas for the bandwidth extension filter, which is designed in the minimax sense with the ripple constraints as the design criteria. The derived filter order estimation is significant in evaluating the computational complexity from the viewpoint of the top-level system design. Moreover, with the proposed order estimates, one can conveniently obtain the minimal order that satisfies the given ripple constraints, which contributes to reducing the design time. Both the performance of the extension filter and its order estimation are illustrated and demonstrated through simulation examples. |
2206.11668 | H\'ector Cadavid | H\'ector Cadavid, Vasilios Andrikopoulos, Paris Avgeriou | Documentation-as-code for Interface Control Document Management in
Systems of Systems: a Technical Action Research Study | Preprint of the paper accepted in the 16th European Conference on
Software Architecture (ECSA) | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The architecting of Systems of Systems (SoS), that is, of systems that emerge
from the cooperation of multiple independent constituent systems, is a topic of
increasing interest in both industry and academia. However, recent empirical
studies revealed what seems to be an overlooked aspect of the architecting of
SoS that is linked to major integration and operational issues: the interplay
between the various disciplines involved in such an architecting process. This
aspect becomes particularly relevant for the management of the interfaces
between the SoS constituents, where such disciplines inevitably meet. In this
paper, we present the results of the first cycle of a Technical Action Research
(TAR) study conducted in cooperation between the authors and a group of
practitioners involved in the long-running architecting process of a
large-scale radio astronomy SoS project. This TAR is aimed at exploring
potential improvements of the document-centered interface management approach
currently followed in this project by adopting elements of the
\textit{documentation-as-code} philosophy, which is widely adopted in the
domain of software systems. As a result, a working proof-of-concept of an ICD
(Interface Control Document) management approach was developed by the
researchers and evaluated by the practitioners. The results of the study and
the corresponding lessons learned are reported in this work.
| [
{
"created": "Thu, 23 Jun 2022 12:47:47 GMT",
"version": "v1"
}
] | 2022-06-24 | [
[
"Cadavid",
"Héctor",
""
],
[
"Andrikopoulos",
"Vasilios",
""
],
[
"Avgeriou",
"Paris",
""
]
] | The architecting of Systems of Systems (SoS), that is, of systems that emerge from the cooperation of multiple independent constituent systems, is a topic of increasing interest in both industry and academia. However, recent empirical studies revealed what seems to be an overlooked aspect of the architecting of SoS that is linked to major integration and operational issues: the interplay between the various disciplines involved in such an architecting process. This aspect becomes particularly relevant for the management of the interfaces between the SoS constituents, where such disciplines inevitably meet. In this paper, we present the results of the first cycle of a Technical Action Research (TAR) study conducted in cooperation between the authors and a group of practitioners involved in the long-running architecting process of a large-scale radio astronomy SoS project. This TAR is aimed at exploring potential improvements of the document-centered interface management approach currently followed in this project by adopting elements of the \textit{documentation-as-code} philosophy, which is widely adopted in the domain of software systems. As a result, a working proof-of-concept of an ICD (Interface Control Document) management approach was developed by the researchers and evaluated by the practitioners. The results of the study and the corresponding lessons learned are reported in this work. |
2306.00316 | Jia Li | Jia Li, Shiva Nejati, Mehrdad Sabetzadeh | Using Genetic Programming to Build Self-Adaptivity into Software-Defined
Networks | Accepted for publication by ACM Transactions on Autonomous and
Adaptive Systems (TAAS) (in Aug 2023). arXiv admin note: substantial text
overlap with arXiv:2205.04352 | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Self-adaptation solutions need to periodically monitor, reason about, and
adapt a running system. The adaptation step involves generating an adaptation
strategy and applying it to the running system whenever an anomaly arises. In
this article, we argue that, rather than generating individual adaptation
strategies, the goal should be to adapt the control logic of the running system
in such a way that the system itself would learn how to steer clear of future
anomalies, without triggering self-adaptation too frequently. While the need
for adaptation is never eliminated, especially noting the uncertain and
evolving environment of complex systems, reducing the frequency of adaptation
interventions is advantageous for various reasons, e.g., to increase
performance and to make a running system more robust. We instantiate and
empirically examine the above idea for software-defined networking -- a key
enabling technology for modern data centres and Internet of Things
applications. Using genetic programming,(GP), we propose a self-adaptation
solution that continuously learns and updates the control constructs in the
data-forwarding logic of a software-defined network. Our evaluation, performed
using open-source synthetic and industrial data, indicates that, compared to a
baseline adaptation technique that attempts to generate individual adaptations,
our GP-based approach is more effective in resolving network congestion, and
further, reduces the frequency of adaptation interventions over time. In
addition, we show that, for networks with the same topology, reusing over
larger networks the knowledge that is learned on smaller networks leads to
significant improvements in the performance of our GP-based adaptation
approach. Finally, we compare our approach against a standard data-forwarding
algorithm from the network literature, demonstrating that our approach
significantly reduces packet loss.
| [
{
"created": "Thu, 1 Jun 2023 03:30:33 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Aug 2023 15:38:27 GMT",
"version": "v2"
}
] | 2023-08-16 | [
[
"Li",
"Jia",
""
],
[
"Nejati",
"Shiva",
""
],
[
"Sabetzadeh",
"Mehrdad",
""
]
] | Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that, rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. While the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, e.g., to increase performance and to make a running system more robust. We instantiate and empirically examine the above idea for software-defined networking -- a key enabling technology for modern data centres and Internet of Things applications. Using genetic programming,(GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open-source synthetic and industrial data, indicates that, compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, reduces the frequency of adaptation interventions over time. In addition, we show that, for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss. |
2403.17231 | Saad Abdul Ghani | Saad Abdul Ghani, Zizhao Wang, Peter Stone, Xuesu Xiao | Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from
Learned Hallucination | Submitted to International Conference on Intelligent Robots and
Systems (IROS) 2024 | null | null | null | cs.RO cs.LG | http://creativecommons.org/licenses/by/4.0/ | This paper presents a self-supervised learning method to safely learn a
motion planner for ground robots to navigate environments with dense and
dynamic obstacles. When facing highly-cluttered, fast-moving, hard-to-predict
obstacles, classical motion planners may not be able to keep up with limited
onboard computation. For learning-based planners, high-quality demonstrations
are difficult to acquire for imitation learning while reinforcement learning
becomes inefficient due to the high probability of collision during
exploration. To safely and efficiently provide training data, the Learning from
Hallucination (LfH) approaches synthesize difficult navigation environments
based on past successful navigation experiences in relatively easy or
completely open ones, but unfortunately cannot address dynamic obstacles. In
our new Dynamic Learning from Learned Hallucination (Dyna-LfLH), we design and
learn a novel latent distribution and sample dynamic obstacles from it, so the
generated training data can be used to learn a motion planner to navigate in
dynamic environments. Dyna-LfLH is evaluated on a ground robot in both
simulated and physical environments and achieves up to 25% better success rate
compared to baselines.
| [
{
"created": "Mon, 25 Mar 2024 22:17:51 GMT",
"version": "v1"
}
] | 2024-03-27 | [
[
"Ghani",
"Saad Abdul",
""
],
[
"Wang",
"Zizhao",
""
],
[
"Stone",
"Peter",
""
],
[
"Xiao",
"Xuesu",
""
]
] | This paper presents a self-supervised learning method to safely learn a motion planner for ground robots to navigate environments with dense and dynamic obstacles. When facing highly-cluttered, fast-moving, hard-to-predict obstacles, classical motion planners may not be able to keep up with limited onboard computation. For learning-based planners, high-quality demonstrations are difficult to acquire for imitation learning while reinforcement learning becomes inefficient due to the high probability of collision during exploration. To safely and efficiently provide training data, the Learning from Hallucination (LfH) approaches synthesize difficult navigation environments based on past successful navigation experiences in relatively easy or completely open ones, but unfortunately cannot address dynamic obstacles. In our new Dynamic Learning from Learned Hallucination (Dyna-LfLH), we design and learn a novel latent distribution and sample dynamic obstacles from it, so the generated training data can be used to learn a motion planner to navigate in dynamic environments. Dyna-LfLH is evaluated on a ground robot in both simulated and physical environments and achieves up to 25% better success rate compared to baselines. |
1802.01021 | Jonathan Raiman | Jonathan Raiman and Olivier Raiman | DeepType: Multilingual Entity Linking by Neural Type System Evolution | Presented at AAAI 2018 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The wealth of structured (e.g. Wikidata) and unstructured data about the
world available today presents an incredible opportunity for tomorrow's
Artificial Intelligence. So far, integration of these two different modalities
is a difficult process, involving many decisions concerning how best to
represent the information so that it will be captured or useful, and
hand-labeling large amounts of data. DeepType overcomes this challenge by
explicitly integrating symbolic information into the reasoning process of a
neural network with a type system. First we construct a type system, and
second, we use it to constrain the outputs of a neural network to respect the
symbolic structure. We achieve this by reformulating the design problem into a
mixed integer problem: create a type system and subsequently train a neural
network with it. In this reformulation discrete variables select which
parent-child relations from an ontology are types within the type system, while
continuous variables control a classifier fit to the type system. The original
problem cannot be solved exactly, so we propose a 2-step algorithm: 1)
heuristic search or stochastic optimization over discrete variables that define
a type system informed by an Oracle and a Learnability heuristic, 2) gradient
descent to fit classifier parameters. We apply DeepType to the problem of
Entity Linking on three standard datasets (i.e. WikiDisamb30, CoNLL (YAGO), TAC
KBP 2010) and find that it outperforms all existing solutions by a wide margin,
including approaches that rely on a human-designed type system or recent deep
learning-based entity embeddings, while explicitly using symbolic information
lets it integrate new entities without retraining.
| [
{
"created": "Sat, 3 Feb 2018 20:13:42 GMT",
"version": "v1"
}
] | 2018-02-06 | [
[
"Raiman",
"Jonathan",
""
],
[
"Raiman",
"Olivier",
""
]
] | The wealth of structured (e.g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence. So far, integration of these two different modalities is a difficult process, involving many decisions concerning how best to represent the information so that it will be captured or useful, and hand-labeling large amounts of data. DeepType overcomes this challenge by explicitly integrating symbolic information into the reasoning process of a neural network with a type system. First we construct a type system, and second, we use it to constrain the outputs of a neural network to respect the symbolic structure. We achieve this by reformulating the design problem into a mixed integer problem: create a type system and subsequently train a neural network with it. In this reformulation discrete variables select which parent-child relations from an ontology are types within the type system, while continuous variables control a classifier fit to the type system. The original problem cannot be solved exactly, so we propose a 2-step algorithm: 1) heuristic search or stochastic optimization over discrete variables that define a type system informed by an Oracle and a Learnability heuristic, 2) gradient descent to fit classifier parameters. We apply DeepType to the problem of Entity Linking on three standard datasets (i.e. WikiDisamb30, CoNLL (YAGO), TAC KBP 2010) and find that it outperforms all existing solutions by a wide margin, including approaches that rely on a human-designed type system or recent deep learning-based entity embeddings, while explicitly using symbolic information lets it integrate new entities without retraining. |
2102.11395 | Ghani Lawal Mr. | Ghani O. Lawal and Michael Greenspan | Procam Calibration from a Single Pose of a Planar Target | 11 pages, 9 figures, 10 tables. Submitted to the VISAPP Conference.
Stored in the SciTepress Digital Library:
https://www.scitepress.org/PublicationsDetail.aspx?ID=rGG70YCQyOs=&t=1 | In Proceedings of the 16th International Joint Conference on
Computer Vision, Imaging and Computer Graphics Theory and Applications
(VISIGRAPP 2021) - Volume 5: VISAPP, pages 817-827 | 10.5220/0010327708170827 | null | cs.CV eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A novel user friendly method is proposed for calibrating a procam system from
a single pose of a planar chessboard target. The user simply needs to orient
the chessboard in a single appropriate pose. A sequence of Gray Code patterns
are projected onto the chessboard, which allows correspondences between the
camera, projector and the chessboard to be automatically extracted. These
correspondences are fed as input to a nonlinear optimization method that models
the projector of the principle points onto the chessboard, and accurately
calculates the intrinsic and extrinsic parameters of both the camera and the
projector, as well as the camera's distortion coefficients. The method is
experimentally validated on the procam system, which is shown to be comparable
in accuracy with existing multi-pose approaches. The impact of the orientation
of the chessboard with respect to the procam imaging places is also explored
through extensive simulation.
| [
{
"created": "Mon, 22 Feb 2021 22:53:29 GMT",
"version": "v1"
}
] | 2021-02-24 | [
[
"Lawal",
"Ghani O.",
""
],
[
"Greenspan",
"Michael",
""
]
] | A novel user friendly method is proposed for calibrating a procam system from a single pose of a planar chessboard target. The user simply needs to orient the chessboard in a single appropriate pose. A sequence of Gray Code patterns are projected onto the chessboard, which allows correspondences between the camera, projector and the chessboard to be automatically extracted. These correspondences are fed as input to a nonlinear optimization method that models the projector of the principle points onto the chessboard, and accurately calculates the intrinsic and extrinsic parameters of both the camera and the projector, as well as the camera's distortion coefficients. The method is experimentally validated on the procam system, which is shown to be comparable in accuracy with existing multi-pose approaches. The impact of the orientation of the chessboard with respect to the procam imaging places is also explored through extensive simulation. |
1807.09380 | Jue Wang | Jue Wang and Anoop Cherian | Contrastive Video Representation Learning via Adversarial Perturbations | Revised version of ECCV 2018 Paper: Learning Discriminative Video
Representations Using Adversarial Perturbations | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Adversarial perturbations are noise-like patterns that can subtly change the
data, while failing an otherwise accurate classifier. In this paper, we propose
to use such perturbations within a novel contrastive learning setup to build
negative samples, which are then used to produce improved video
representations. To this end, given a well-trained deep model for per-frame
video recognition, we first generate adversarial noise adapted to this model.
Positive and negative bags are produced using the original data features from
the full video sequence and their perturbed counterparts, respectively. Unlike
the classic contrastive learning methods, we develop a binary classification
problem that learns a set of discriminative hyperplanes -- as a subspace --
that will separate the two bags from each other. This subspace is then used as
a descriptor for the video, dubbed \emph{discriminative subspace pooling}. As
the perturbed features belong to data classes that are likely to be confused
with the original features, the discriminative subspace will characterize parts
of the feature space that are more representative of the original data, and
thus may provide robust video representations. To learn such descriptors, we
formulate a subspace learning objective on the Stiefel manifold and resort to
Riemannian optimization methods for solving it efficiently. We provide
experiments on several video datasets and demonstrate state-of-the-art results.
| [
{
"created": "Tue, 24 Jul 2018 22:46:42 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Jul 2018 13:46:24 GMT",
"version": "v2"
},
{
"created": "Thu, 16 Apr 2020 00:03:53 GMT",
"version": "v3"
}
] | 2020-04-17 | [
[
"Wang",
"Jue",
""
],
[
"Cherian",
"Anoop",
""
]
] | Adversarial perturbations are noise-like patterns that can subtly change the data, while failing an otherwise accurate classifier. In this paper, we propose to use such perturbations within a novel contrastive learning setup to build negative samples, which are then used to produce improved video representations. To this end, given a well-trained deep model for per-frame video recognition, we first generate adversarial noise adapted to this model. Positive and negative bags are produced using the original data features from the full video sequence and their perturbed counterparts, respectively. Unlike the classic contrastive learning methods, we develop a binary classification problem that learns a set of discriminative hyperplanes -- as a subspace -- that will separate the two bags from each other. This subspace is then used as a descriptor for the video, dubbed \emph{discriminative subspace pooling}. As the perturbed features belong to data classes that are likely to be confused with the original features, the discriminative subspace will characterize parts of the feature space that are more representative of the original data, and thus may provide robust video representations. To learn such descriptors, we formulate a subspace learning objective on the Stiefel manifold and resort to Riemannian optimization methods for solving it efficiently. We provide experiments on several video datasets and demonstrate state-of-the-art results. |
2211.01812 | Hadi Hajieghrary | Sevag Tafnakaji and Hadi Hajieghrary and Quentin Teixeira and Yasemin
Bekiroglu | Benchmarking local motion planners for navigation of mobile manipulators | Accepted to be presented at 2023 IEEE/SICE International Symposium on
System Integration | null | null | null | cs.RO cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | There are various trajectory planners for mobile manipulators. It is often
challenging to compare their performance under similar circumstances due to
differences in hardware, dissimilarity of tasks and objectives, as well as
uncertainties in measurements and operating environments. In this paper, we
propose a simulation framework to evaluate the performance of the local
trajectory planners to generate smooth, and dynamically and kinematically
feasible trajectories for mobile manipulators in the same environment. We focus
on local planners as they are key components that provide smooth trajectories
while carrying a load, react to dynamic obstacles, and avoid collisions. We
evaluate two prominent local trajectory planners, Dynamic-Window Approach (DWA)
and Time Elastic Band (TEB) using the metrics that we introduce. Moreover, our
software solution is applicable to any other local planners used in the Robot
Operating System (ROS) framework, without additional programming effort.
| [
{
"created": "Thu, 3 Nov 2022 13:45:55 GMT",
"version": "v1"
}
] | 2022-11-04 | [
[
"Tafnakaji",
"Sevag",
""
],
[
"Hajieghrary",
"Hadi",
""
],
[
"Teixeira",
"Quentin",
""
],
[
"Bekiroglu",
"Yasemin",
""
]
] | There are various trajectory planners for mobile manipulators. It is often challenging to compare their performance under similar circumstances due to differences in hardware, dissimilarity of tasks and objectives, as well as uncertainties in measurements and operating environments. In this paper, we propose a simulation framework to evaluate the performance of the local trajectory planners to generate smooth, and dynamically and kinematically feasible trajectories for mobile manipulators in the same environment. We focus on local planners as they are key components that provide smooth trajectories while carrying a load, react to dynamic obstacles, and avoid collisions. We evaluate two prominent local trajectory planners, Dynamic-Window Approach (DWA) and Time Elastic Band (TEB) using the metrics that we introduce. Moreover, our software solution is applicable to any other local planners used in the Robot Operating System (ROS) framework, without additional programming effort. |
2205.09837 | Keming Lu | Keming Lu, I-Hung Hsu, Wenxuan Zhou, Mingyu Derek Ma, Muhao Chen | Summarization as Indirect Supervision for Relation Extraction | Accepted by EMNLP 2022 | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relation extraction (RE) models have been challenged by their reliance on
training data with expensive annotations. Considering that summarization tasks
aim at acquiring concise expressions of synoptical information from the longer
context, these tasks naturally align with the objective of RE, i.e., extracting
a kind of synoptical information that describes the relation of entity
mentions. We present SuRE, which converts RE into a summarization formulation.
SuRE leads to more precise and resource-efficient RE based on indirect
supervision from summarization tasks. To achieve this goal, we develop sentence
and relation conversion techniques that essentially bridge the formulation of
summarization and RE tasks. We also incorporate constraint decoding techniques
with Trie scoring to further enhance summarization-based RE with robust
inference. Experiments on three RE datasets demonstrate the effectiveness of
SuRE in both full-dataset and low-resource settings, showing that summarization
is a promising source of indirect supervision to improve RE models.
| [
{
"created": "Thu, 19 May 2022 20:25:29 GMT",
"version": "v1"
},
{
"created": "Fri, 21 Oct 2022 04:31:52 GMT",
"version": "v2"
}
] | 2022-10-24 | [
[
"Lu",
"Keming",
""
],
[
"Hsu",
"I-Hung",
""
],
[
"Zhou",
"Wenxuan",
""
],
[
"Ma",
"Mingyu Derek",
""
],
[
"Chen",
"Muhao",
""
]
] | Relation extraction (RE) models have been challenged by their reliance on training data with expensive annotations. Considering that summarization tasks aim at acquiring concise expressions of synoptical information from the longer context, these tasks naturally align with the objective of RE, i.e., extracting a kind of synoptical information that describes the relation of entity mentions. We present SuRE, which converts RE into a summarization formulation. SuRE leads to more precise and resource-efficient RE based on indirect supervision from summarization tasks. To achieve this goal, we develop sentence and relation conversion techniques that essentially bridge the formulation of summarization and RE tasks. We also incorporate constraint decoding techniques with Trie scoring to further enhance summarization-based RE with robust inference. Experiments on three RE datasets demonstrate the effectiveness of SuRE in both full-dataset and low-resource settings, showing that summarization is a promising source of indirect supervision to improve RE models. |
1201.1650 | Matthew Patitz | Sarah Cannon, Erik D. Demaine, Martin L. Demaine, Sarah Eisenstat,
Matthew J. Patitz, Robert Schweller, Scott M. Summers, Andrew Winslow | Two Hands Are Better Than One (up to constant factors) | null | null | null | null | cs.CC cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the difference between the standard seeded model of tile
self-assembly, and the "seedless" two-handed model of tile self-assembly. Most
of our results suggest that the two-handed model is more powerful. In
particular, we show how to simulate any seeded system with a two-handed system
that is essentially just a constant factor larger. We exhibit finite shapes
with a busy-beaver separation in the number of distinct tiles required by
seeded versus two-handed, and exhibit an infinite shape that can be constructed
two-handed but not seeded. Finally, we show that verifying whether a given
system uniquely assembles a desired supertile is co-NP-complete in the
two-handed model, while it was known to be polynomially solvable in the seeded
model.
| [
{
"created": "Sun, 8 Jan 2012 18:51:32 GMT",
"version": "v1"
}
] | 2015-03-20 | [
[
"Cannon",
"Sarah",
""
],
[
"Demaine",
"Erik D.",
""
],
[
"Demaine",
"Martin L.",
""
],
[
"Eisenstat",
"Sarah",
""
],
[
"Patitz",
"Matthew J.",
""
],
[
"Schweller",
"Robert",
""
],
[
"Summers",
"Scott M.",
""
],
[
"Winslow",
"Andrew",
""
]
] | We study the difference between the standard seeded model of tile self-assembly, and the "seedless" two-handed model of tile self-assembly. Most of our results suggest that the two-handed model is more powerful. In particular, we show how to simulate any seeded system with a two-handed system that is essentially just a constant factor larger. We exhibit finite shapes with a busy-beaver separation in the number of distinct tiles required by seeded versus two-handed, and exhibit an infinite shape that can be constructed two-handed but not seeded. Finally, we show that verifying whether a given system uniquely assembles a desired supertile is co-NP-complete in the two-handed model, while it was known to be polynomially solvable in the seeded model. |
2011.10776 | Suping Wu | Lei Li, Suping Wu | DmifNet:3D Shape Reconstruction Based on Dynamic Multi-Branch
Information Fusion | ICPR 2020 (Oral) | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | 3D object reconstruction from a single-view image is a long-standing
challenging problem. Previous work was difficult to accurately reconstruct 3D
shapes with a complex topology which has rich details at the edges and corners.
Moreover, previous works used synthetic data to train their network, but domain
adaptation problems occurred when tested on real data. In this paper, we
propose a Dynamic Multi-branch Information Fusion Network (DmifNet) which can
recover a high-fidelity 3D shape of arbitrary topology from a 2D image.
Specifically, we design several side branches from the intermediate layers to
make the network produce more diverse representations to improve the
generalization ability of network. In addition, we utilize DoG (Difference of
Gaussians) to extract edge geometry and corners information from input images.
Then, we use a separate side branch network to process the extracted data to
better capture edge geometry and corners feature information. Finally, we
dynamically fuse the information of all branches to gain final predicted
probability. Extensive qualitative and quantitative experiments on a
large-scale publicly available dataset demonstrate the validity and efficiency
of our method. Code and models are publicly available at
https://github.com/leilimaster/DmifNet.
| [
{
"created": "Sat, 21 Nov 2020 11:31:27 GMT",
"version": "v1"
}
] | 2020-11-24 | [
[
"Li",
"Lei",
""
],
[
"Wu",
"Suping",
""
]
] | 3D object reconstruction from a single-view image is a long-standing challenging problem. Previous work was difficult to accurately reconstruct 3D shapes with a complex topology which has rich details at the edges and corners. Moreover, previous works used synthetic data to train their network, but domain adaptation problems occurred when tested on real data. In this paper, we propose a Dynamic Multi-branch Information Fusion Network (DmifNet) which can recover a high-fidelity 3D shape of arbitrary topology from a 2D image. Specifically, we design several side branches from the intermediate layers to make the network produce more diverse representations to improve the generalization ability of network. In addition, we utilize DoG (Difference of Gaussians) to extract edge geometry and corners information from input images. Then, we use a separate side branch network to process the extracted data to better capture edge geometry and corners feature information. Finally, we dynamically fuse the information of all branches to gain final predicted probability. Extensive qualitative and quantitative experiments on a large-scale publicly available dataset demonstrate the validity and efficiency of our method. Code and models are publicly available at https://github.com/leilimaster/DmifNet. |
2210.16160 | Melissa Antonelli | Melissa Antonelli | Some Remarks on Counting Propositional Logic | joint work with Ugo Dal Lago and Paolo Pistone | null | null | null | cs.LO | http://creativecommons.org/licenses/by/4.0/ | Counting propositional logic was recently introduced in relation to
randomized computation and shown able to logically characterize the full
counting hierarchy. In this paper we aim to clarify the intuitive meaning and
expressive power of its univariate fragment. On the one hand, we provide an
effective procedure to measure the probability of counting formulas. On the
other, we make the connection between this logic and stochastic experiments
explicit, proving that the counting language can simulate any (and only) event
associated with dyadic distributions.
| [
{
"created": "Fri, 28 Oct 2022 14:34:22 GMT",
"version": "v1"
},
{
"created": "Wed, 16 Nov 2022 17:14:33 GMT",
"version": "v2"
}
] | 2022-11-17 | [
[
"Antonelli",
"Melissa",
""
]
] | Counting propositional logic was recently introduced in relation to randomized computation and shown able to logically characterize the full counting hierarchy. In this paper we aim to clarify the intuitive meaning and expressive power of its univariate fragment. On the one hand, we provide an effective procedure to measure the probability of counting formulas. On the other, we make the connection between this logic and stochastic experiments explicit, proving that the counting language can simulate any (and only) event associated with dyadic distributions. |
1808.02941 | Xiangyi Chen | Xiangyi Chen, Sijia Liu, Ruoyu Sun, Mingyi Hong | On the Convergence of A Class of Adam-Type Algorithms for Non-Convex
Optimization | null | null | null | null | cs.LG math.OC stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies a class of adaptive gradient based momentum algorithms
that update the search directions and learning rates simultaneously using past
gradients. This class, which we refer to as the "Adam-type", includes the
popular algorithms such as the Adam, AMSGrad and AdaGrad. Despite their
popularity in training deep neural networks, the convergence of these
algorithms for solving nonconvex problems remains an open question. This paper
provides a set of mild sufficient conditions that guarantee the convergence for
the Adam-type methods. We prove that under our derived conditions, these
methods can achieve the convergence rate of order $O(\log{T}/\sqrt{T})$ for
nonconvex stochastic optimization. We show the conditions are essential in the
sense that violating them may make the algorithm diverge. Moreover, we propose
and analyze a class of (deterministic) incremental adaptive gradient
algorithms, which has the same $O(\log{T}/\sqrt{T})$ convergence rate. Our
study could also be extended to a broader class of adaptive gradient methods in
machine learning and optimization.
| [
{
"created": "Wed, 8 Aug 2018 21:14:07 GMT",
"version": "v1"
},
{
"created": "Sun, 10 Mar 2019 00:48:35 GMT",
"version": "v2"
}
] | 2019-03-12 | [
[
"Chen",
"Xiangyi",
""
],
[
"Liu",
"Sijia",
""
],
[
"Sun",
"Ruoyu",
""
],
[
"Hong",
"Mingyi",
""
]
] | This paper studies a class of adaptive gradient based momentum algorithms that update the search directions and learning rates simultaneously using past gradients. This class, which we refer to as the "Adam-type", includes the popular algorithms such as the Adam, AMSGrad and AdaGrad. Despite their popularity in training deep neural networks, the convergence of these algorithms for solving nonconvex problems remains an open question. This paper provides a set of mild sufficient conditions that guarantee the convergence for the Adam-type methods. We prove that under our derived conditions, these methods can achieve the convergence rate of order $O(\log{T}/\sqrt{T})$ for nonconvex stochastic optimization. We show the conditions are essential in the sense that violating them may make the algorithm diverge. Moreover, we propose and analyze a class of (deterministic) incremental adaptive gradient algorithms, which has the same $O(\log{T}/\sqrt{T})$ convergence rate. Our study could also be extended to a broader class of adaptive gradient methods in machine learning and optimization. |
2405.15556 | Chong Xiang | Chong Xiang, Tong Wu, Zexuan Zhong, David Wagner, Danqi Chen, Prateek
Mittal | Certifiably Robust RAG against Retrieval Corruption | null | null | null | null | cs.LG cs.CL cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Retrieval-augmented generation (RAG) has been shown vulnerable to retrieval
corruption attacks: an attacker can inject malicious passages into retrieval
results to induce inaccurate responses. In this paper, we propose RobustRAG as
the first defense framework against retrieval corruption attacks. The key
insight of RobustRAG is an isolate-then-aggregate strategy: we get LLM
responses from each passage in isolation and then securely aggregate these
isolated responses. To instantiate RobustRAG, we design keyword-based and
decoding-based algorithms for securely aggregating unstructured text responses.
Notably, RobustRAG can achieve certifiable robustness: we can formally prove
and certify that, for certain queries, RobustRAG can always return accurate
responses, even when the attacker has full knowledge of our defense and can
arbitrarily inject a small number of malicious passages. We evaluate RobustRAG
on open-domain QA and long-form text generation datasets and demonstrate its
effectiveness and generalizability across various tasks and datasets.
| [
{
"created": "Fri, 24 May 2024 13:44:25 GMT",
"version": "v1"
}
] | 2024-05-27 | [
[
"Xiang",
"Chong",
""
],
[
"Wu",
"Tong",
""
],
[
"Zhong",
"Zexuan",
""
],
[
"Wagner",
"David",
""
],
[
"Chen",
"Danqi",
""
],
[
"Mittal",
"Prateek",
""
]
] | Retrieval-augmented generation (RAG) has been shown vulnerable to retrieval corruption attacks: an attacker can inject malicious passages into retrieval results to induce inaccurate responses. In this paper, we propose RobustRAG as the first defense framework against retrieval corruption attacks. The key insight of RobustRAG is an isolate-then-aggregate strategy: we get LLM responses from each passage in isolation and then securely aggregate these isolated responses. To instantiate RobustRAG, we design keyword-based and decoding-based algorithms for securely aggregating unstructured text responses. Notably, RobustRAG can achieve certifiable robustness: we can formally prove and certify that, for certain queries, RobustRAG can always return accurate responses, even when the attacker has full knowledge of our defense and can arbitrarily inject a small number of malicious passages. We evaluate RobustRAG on open-domain QA and long-form text generation datasets and demonstrate its effectiveness and generalizability across various tasks and datasets. |
2308.03299 | Aimen Gaba | Aimen Gaba, Zhanna Kaufman, Jason Chueng, Marie Shvakel, Kyle Wm.
Hall, Yuriy Brun, and Cindy Xiong Bearfield | My Model is Unfair, Do People Even Care? Visual Design Affects Trust and
Perceived Bias in Machine Learning | 11 pages, 6 figures, to appear in IEEE Transactions of Visualization
and Computer Graphics (Also in proceedings of IEEE VIS 2023) | IEEE TVCG 30(1):327-337 | 10.1109/TVCG.2023.3327192 | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine learning technology has become ubiquitous, but, unfortunately, often
exhibits bias. As a consequence, disparate stakeholders need to interact with
and make informed decisions about using machine learning models in everyday
systems. Visualization technology can support stakeholders in understanding and
evaluating trade-offs between, for example, accuracy and fairness of models.
This paper aims to empirically answer "Can visualization design choices affect
a stakeholder's perception of model bias, trust in a model, and willingness to
adopt a model?" Through a series of controlled, crowd-sourced experiments with
more than 1,500 participants, we identify a set of strategies people follow in
deciding which models to trust. Our results show that men and women prioritize
fairness and performance differently and that visual design choices
significantly affect that prioritization. For example, women trust fairer
models more often than men do, participants value fairness more when it is
explained using text than as a bar chart, and being explicitly told a model is
biased has a bigger impact than showing past biased performance. We test the
generalizability of our results by comparing the effect of multiple textual and
visual design choices and offer potential explanations of the cognitive
mechanisms behind the difference in fairness perception and trust. Our research
guides design considerations to support future work developing visualization
systems for machine learning.
| [
{
"created": "Mon, 7 Aug 2023 05:01:39 GMT",
"version": "v1"
}
] | 2024-01-12 | [
[
"Gaba",
"Aimen",
""
],
[
"Kaufman",
"Zhanna",
""
],
[
"Chueng",
"Jason",
""
],
[
"Shvakel",
"Marie",
""
],
[
"Hall",
"Kyle Wm.",
""
],
[
"Brun",
"Yuriy",
""
],
[
"Bearfield",
"Cindy Xiong",
""
]
] | Machine learning technology has become ubiquitous, but, unfortunately, often exhibits bias. As a consequence, disparate stakeholders need to interact with and make informed decisions about using machine learning models in everyday systems. Visualization technology can support stakeholders in understanding and evaluating trade-offs between, for example, accuracy and fairness of models. This paper aims to empirically answer "Can visualization design choices affect a stakeholder's perception of model bias, trust in a model, and willingness to adopt a model?" Through a series of controlled, crowd-sourced experiments with more than 1,500 participants, we identify a set of strategies people follow in deciding which models to trust. Our results show that men and women prioritize fairness and performance differently and that visual design choices significantly affect that prioritization. For example, women trust fairer models more often than men do, participants value fairness more when it is explained using text than as a bar chart, and being explicitly told a model is biased has a bigger impact than showing past biased performance. We test the generalizability of our results by comparing the effect of multiple textual and visual design choices and offer potential explanations of the cognitive mechanisms behind the difference in fairness perception and trust. Our research guides design considerations to support future work developing visualization systems for machine learning. |
1002.4057 | Julien Langou | Emmanuel Agullo, Henricus Bouwmeester, Jack Dongarra, Jakub Kurzak,
Julien Langou, and Lee Rosenberg | Towards an Efficient Tile Matrix Inversion of Symmetric Positive
Definite Matrices on Multicore Architectures | 8 pages, extended abstract submitted to VecPar10 on 12/11/09,
notification of acceptance received on 02/05/10. See:
http://vecpar.fe.up.pt/2010/ | null | null | null | cs.MS cs.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The algorithms in the current sequential numerical linear algebra libraries
(e.g. LAPACK) do not parallelize well on multicore architectures. A new family
of algorithms, the tile algorithms, has recently been introduced. Previous
research has shown that it is possible to write efficient and scalable tile
algorithms for performing a Cholesky factorization, a (pseudo) LU
factorization, and a QR factorization. In this extended abstract, we attack the
problem of the computation of the inverse of a symmetric positive definite
matrix. We observe that, using a dynamic task scheduler, it is relatively
painless to translate existing LAPACK code to obtain a ready-to-be-executed
tile algorithm. However we demonstrate that non trivial compiler techniques
(array renaming, loop reversal and pipelining) need then to be applied to
further increase the parallelism of our application. We present preliminary
experimental results.
| [
{
"created": "Mon, 22 Feb 2010 06:11:41 GMT",
"version": "v1"
}
] | 2010-02-23 | [
[
"Agullo",
"Emmanuel",
""
],
[
"Bouwmeester",
"Henricus",
""
],
[
"Dongarra",
"Jack",
""
],
[
"Kurzak",
"Jakub",
""
],
[
"Langou",
"Julien",
""
],
[
"Rosenberg",
"Lee",
""
]
] | The algorithms in the current sequential numerical linear algebra libraries (e.g. LAPACK) do not parallelize well on multicore architectures. A new family of algorithms, the tile algorithms, has recently been introduced. Previous research has shown that it is possible to write efficient and scalable tile algorithms for performing a Cholesky factorization, a (pseudo) LU factorization, and a QR factorization. In this extended abstract, we attack the problem of the computation of the inverse of a symmetric positive definite matrix. We observe that, using a dynamic task scheduler, it is relatively painless to translate existing LAPACK code to obtain a ready-to-be-executed tile algorithm. However we demonstrate that non trivial compiler techniques (array renaming, loop reversal and pipelining) need then to be applied to further increase the parallelism of our application. We present preliminary experimental results. |
2206.01441 | Paula Delgado-Santos | Paula Delgado-Santos, Ruben Tolosana, Richard Guest, Farzin Deravi,
Ruben Vera-Rodriguez | Exploring Transformers for Behavioural Biometrics: A Case Study in Gait
Recognition | null | null | null | null | cs.CV cs.HC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Biometrics on mobile devices has attracted a lot of attention in recent years
as it is considered a user-friendly authentication method. This interest has
also been motivated by the success of Deep Learning (DL). Architectures based
on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
have been established to be convenient for the task, improving the performance
and robustness in comparison to traditional machine learning techniques.
However, some aspects must still be revisited and improved. To the best of our
knowledge, this is the first article that intends to explore and propose novel
gait biometric recognition systems based on Transformers, which currently
obtain state-of-the-art performance in many applications. Several
state-of-the-art architectures (Vanilla, Informer, Autoformer, Block-Recurrent
Transformer, and THAT) are considered in the experimental framework. In
addition, new configurations of the Transformers are proposed to further
increase the performance. Experiments are carried out using the two popular
public databases whuGAIT and OU-ISIR. The results achieved prove the high
ability of the proposed Transformer, outperforming state-of-the-art CNN and RNN
architectures.
| [
{
"created": "Fri, 3 Jun 2022 08:08:40 GMT",
"version": "v1"
}
] | 2022-06-06 | [
[
"Delgado-Santos",
"Paula",
""
],
[
"Tolosana",
"Ruben",
""
],
[
"Guest",
"Richard",
""
],
[
"Deravi",
"Farzin",
""
],
[
"Vera-Rodriguez",
"Ruben",
""
]
] | Biometrics on mobile devices has attracted a lot of attention in recent years as it is considered a user-friendly authentication method. This interest has also been motivated by the success of Deep Learning (DL). Architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been established to be convenient for the task, improving the performance and robustness in comparison to traditional machine learning techniques. However, some aspects must still be revisited and improved. To the best of our knowledge, this is the first article that intends to explore and propose novel gait biometric recognition systems based on Transformers, which currently obtain state-of-the-art performance in many applications. Several state-of-the-art architectures (Vanilla, Informer, Autoformer, Block-Recurrent Transformer, and THAT) are considered in the experimental framework. In addition, new configurations of the Transformers are proposed to further increase the performance. Experiments are carried out using the two popular public databases whuGAIT and OU-ISIR. The results achieved prove the high ability of the proposed Transformer, outperforming state-of-the-art CNN and RNN architectures. |
2403.15472 | Boxuan Ma Dr. | Boxaun Ma, Li Chen and Shin'ichi Konomi | Enhancing Programming Education with ChatGPT: A Case Study on Student
Perceptions and Interactions in a Python Course | null | null | null | null | cs.CY cs.AI cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The integration of ChatGPT as a supportive tool in education, notably in
programming courses, addresses the unique challenges of programming education
by providing assistance with debugging, code generation, and explanations.
Despite existing research validating ChatGPT's effectiveness, its application
in university-level programming education and a detailed understanding of
student interactions and perspectives remain limited. This paper explores
ChatGPT's impact on learning in a Python programming course tailored for
first-year students over eight weeks. By analyzing responses from surveys,
open-ended questions, and student-ChatGPT dialog data, we aim to provide a
comprehensive view of ChatGPT's utility and identify both its advantages and
limitations as perceived by students. Our study uncovers a generally positive
reception toward ChatGPT and offers insights into its role in enhancing the
programming education experience. These findings contribute to the broader
discourse on AI's potential in education, suggesting paths for future research
and application.
| [
{
"created": "Wed, 20 Mar 2024 15:47:28 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Mar 2024 06:22:41 GMT",
"version": "v2"
},
{
"created": "Fri, 5 Apr 2024 11:32:24 GMT",
"version": "v3"
}
] | 2024-04-08 | [
[
"Ma",
"Boxaun",
""
],
[
"Chen",
"Li",
""
],
[
"Konomi",
"Shin'ichi",
""
]
] | The integration of ChatGPT as a supportive tool in education, notably in programming courses, addresses the unique challenges of programming education by providing assistance with debugging, code generation, and explanations. Despite existing research validating ChatGPT's effectiveness, its application in university-level programming education and a detailed understanding of student interactions and perspectives remain limited. This paper explores ChatGPT's impact on learning in a Python programming course tailored for first-year students over eight weeks. By analyzing responses from surveys, open-ended questions, and student-ChatGPT dialog data, we aim to provide a comprehensive view of ChatGPT's utility and identify both its advantages and limitations as perceived by students. Our study uncovers a generally positive reception toward ChatGPT and offers insights into its role in enhancing the programming education experience. These findings contribute to the broader discourse on AI's potential in education, suggesting paths for future research and application. |
1107.2781 | Rami C. | Rami Cohen | Face Recognition using Curvelet Transform | 24 pages | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Face recognition has been studied extensively for more than 20 years now.
Since the beginning of 90s the subject has became a major issue. This
technology is used in many important real-world applications, such as video
surveillance, smart cards, database security, internet and intranet access.
This report reviews recent two algorithms for face recognition which take
advantage of a relatively new multiscale geometric analysis tool - Curvelet
transform, for facial processing and feature extraction. This transform proves
to be efficient especially due to its good ability to detect curves and lines,
which characterize the human's face. An algorithm which is based on the two
algorithms mentioned above is proposed, and its performance is evaluated on
three data bases of faces: AT&T (ORL), Essex Grimace and Georgia-Tech.
k-nearest neighbour (k-NN) and Support vector machine (SVM) classifiers are
used, along with Principal Component Analysis (PCA) for dimensionality
reduction. This algorithm shows good results, and it even outperforms other
algorithms in some cases.
| [
{
"created": "Thu, 14 Jul 2011 10:44:01 GMT",
"version": "v1"
}
] | 2011-07-15 | [
[
"Cohen",
"Rami",
""
]
] | Face recognition has been studied extensively for more than 20 years now. Since the beginning of 90s the subject has became a major issue. This technology is used in many important real-world applications, such as video surveillance, smart cards, database security, internet and intranet access. This report reviews recent two algorithms for face recognition which take advantage of a relatively new multiscale geometric analysis tool - Curvelet transform, for facial processing and feature extraction. This transform proves to be efficient especially due to its good ability to detect curves and lines, which characterize the human's face. An algorithm which is based on the two algorithms mentioned above is proposed, and its performance is evaluated on three data bases of faces: AT&T (ORL), Essex Grimace and Georgia-Tech. k-nearest neighbour (k-NN) and Support vector machine (SVM) classifiers are used, along with Principal Component Analysis (PCA) for dimensionality reduction. This algorithm shows good results, and it even outperforms other algorithms in some cases. |
2109.12052 | Ajith Anil Meera | Fred Bos, Ajith Anil Meera, Dennis Benders and Martijn Wisse | Free Energy Principle for State and Input Estimation of a Quadcopter
Flying in Wind | Submitted manuscript under review | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The free energy principle from neuroscience provides a brain-inspired
perception scheme through a data-driven model learning algorithm called Dynamic
Expectation Maximization (DEM). This paper aims at introducing an experimental
design to provide the first experimental confirmation of the usefulness of DEM
as a state and input estimator for real robots. Through a series of quadcopter
flight experiments under unmodelled wind dynamics, we prove that DEM can
leverage the information from colored noise for accurate state and input
estimation through the use of generalized coordinates. We demonstrate the
superior performance of DEM for state estimation under colored noise with
respect to other benchmarks like State Augmentation, SMIKF and Kalman Filtering
through its minimal estimation error. We demonstrate the similarities in the
performance of DEM and Unknown Input Observer (UIO) for input estimation. The
paper concludes by showing the influence of prior beliefs in shaping the
accuracy-complexity trade-off during DEM's estimation.
| [
{
"created": "Fri, 24 Sep 2021 16:18:04 GMT",
"version": "v1"
}
] | 2021-09-27 | [
[
"Bos",
"Fred",
""
],
[
"Meera",
"Ajith Anil",
""
],
[
"Benders",
"Dennis",
""
],
[
"Wisse",
"Martijn",
""
]
] | The free energy principle from neuroscience provides a brain-inspired perception scheme through a data-driven model learning algorithm called Dynamic Expectation Maximization (DEM). This paper aims at introducing an experimental design to provide the first experimental confirmation of the usefulness of DEM as a state and input estimator for real robots. Through a series of quadcopter flight experiments under unmodelled wind dynamics, we prove that DEM can leverage the information from colored noise for accurate state and input estimation through the use of generalized coordinates. We demonstrate the superior performance of DEM for state estimation under colored noise with respect to other benchmarks like State Augmentation, SMIKF and Kalman Filtering through its minimal estimation error. We demonstrate the similarities in the performance of DEM and Unknown Input Observer (UIO) for input estimation. The paper concludes by showing the influence of prior beliefs in shaping the accuracy-complexity trade-off during DEM's estimation. |
1511.03383 | Weimin Wang | Xue Dong, Kun Wang, Chong Xu, Weimin Wang | Information Rate Decomposition for Feedback Systems with Output
Disturbance | 5 pages, technical note | null | null | null | cs.IT cs.SY math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This technical note considers the problem of resource allocation in linear
feedback control system with output disturbance. By decomposing the information
rate in the feedback communication channel, the channel resource allocation is
thoroughly analyzed. The results show that certain amount of resource is used
to transmit the output disturbance and this resource allocation is independent
from feedback controller design.
| [
{
"created": "Wed, 11 Nov 2015 04:22:21 GMT",
"version": "v1"
}
] | 2015-11-12 | [
[
"Dong",
"Xue",
""
],
[
"Wang",
"Kun",
""
],
[
"Xu",
"Chong",
""
],
[
"Wang",
"Weimin",
""
]
] | This technical note considers the problem of resource allocation in linear feedback control system with output disturbance. By decomposing the information rate in the feedback communication channel, the channel resource allocation is thoroughly analyzed. The results show that certain amount of resource is used to transmit the output disturbance and this resource allocation is independent from feedback controller design. |
2304.06287 | Chen Yang | Chen Yang, Peihao Li, Zanwei Zhou, Shanxin Yuan, Bingbing Liu,
Xiaokang Yang, Weichao Qiu, Wei Shen | NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry
Scaffolds | 10 pages, 7 figures | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present NeRFVS, a novel neural radiance fields (NeRF) based method to
enable free navigation in a room. NeRF achieves impressive performance in
rendering images for novel views similar to the input views while suffering for
novel views that are significantly different from the training views. To
address this issue, we utilize the holistic priors, including pseudo depth maps
and view coverage information, from neural reconstruction to guide the learning
of implicit neural representations of 3D indoor scenes. Concretely, an
off-the-shelf neural reconstruction method is leveraged to generate a geometry
scaffold. Then, two loss functions based on the holistic priors are proposed to
improve the learning of NeRF: 1) A robust depth loss that can tolerate the
error of the pseudo depth map to guide the geometry learning of NeRF; 2) A
variance loss to regularize the variance of implicit neural representations to
reduce the geometry and color ambiguity in the learning procedure. These two
loss functions are modulated during NeRF optimization according to the view
coverage information to reduce the negative influence brought by the view
coverage imbalance. Extensive results demonstrate that our NeRFVS outperforms
state-of-the-art view synthesis methods quantitatively and qualitatively on
indoor scenes, achieving high-fidelity free navigation results.
| [
{
"created": "Thu, 13 Apr 2023 06:40:08 GMT",
"version": "v1"
},
{
"created": "Tue, 23 May 2023 12:49:17 GMT",
"version": "v2"
}
] | 2023-05-24 | [
[
"Yang",
"Chen",
""
],
[
"Li",
"Peihao",
""
],
[
"Zhou",
"Zanwei",
""
],
[
"Yuan",
"Shanxin",
""
],
[
"Liu",
"Bingbing",
""
],
[
"Yang",
"Xiaokang",
""
],
[
"Qiu",
"Weichao",
""
],
[
"Shen",
"Wei",
""
]
] | We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room. NeRF achieves impressive performance in rendering images for novel views similar to the input views while suffering for novel views that are significantly different from the training views. To address this issue, we utilize the holistic priors, including pseudo depth maps and view coverage information, from neural reconstruction to guide the learning of implicit neural representations of 3D indoor scenes. Concretely, an off-the-shelf neural reconstruction method is leveraged to generate a geometry scaffold. Then, two loss functions based on the holistic priors are proposed to improve the learning of NeRF: 1) A robust depth loss that can tolerate the error of the pseudo depth map to guide the geometry learning of NeRF; 2) A variance loss to regularize the variance of implicit neural representations to reduce the geometry and color ambiguity in the learning procedure. These two loss functions are modulated during NeRF optimization according to the view coverage information to reduce the negative influence brought by the view coverage imbalance. Extensive results demonstrate that our NeRFVS outperforms state-of-the-art view synthesis methods quantitatively and qualitatively on indoor scenes, achieving high-fidelity free navigation results. |
2105.12374 | Khadija Shaheen | Khadija Shaheen, Muhammad Abdullah Hanif, Osman Hasan, Muhammad
Shafique | Continual Learning for Real-World Autonomous Systems: Algorithms,
Challenges and Frameworks | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Continual learning is essential for all real-world applications, as frozen
pre-trained models cannot effectively deal with non-stationary data
distributions. The purpose of this study is to review the state-of-the-art
methods that allow continuous learning of computational models over time. We
primarily focus on the learning algorithms that perform continuous learning in
an online fashion from considerably large (or infinite) sequential data and
require substantially low computational and memory resources. We critically
analyze the key challenges associated with continual learning for autonomous
real-world systems and compare current methods in terms of computations,
memory, and network/model complexity. We also briefly describe the
implementations of continuous learning algorithms under three main autonomous
systems, i.e., self-driving vehicles, unmanned aerial vehicles, and urban
robots. The learning methods of these autonomous systems and their strengths
and limitations are extensively explored in this article.
| [
{
"created": "Wed, 26 May 2021 07:38:20 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Feb 2022 00:08:20 GMT",
"version": "v2"
}
] | 2022-02-28 | [
[
"Shaheen",
"Khadija",
""
],
[
"Hanif",
"Muhammad Abdullah",
""
],
[
"Hasan",
"Osman",
""
],
[
"Shafique",
"Muhammad",
""
]
] | Continual learning is essential for all real-world applications, as frozen pre-trained models cannot effectively deal with non-stationary data distributions. The purpose of this study is to review the state-of-the-art methods that allow continuous learning of computational models over time. We primarily focus on the learning algorithms that perform continuous learning in an online fashion from considerably large (or infinite) sequential data and require substantially low computational and memory resources. We critically analyze the key challenges associated with continual learning for autonomous real-world systems and compare current methods in terms of computations, memory, and network/model complexity. We also briefly describe the implementations of continuous learning algorithms under three main autonomous systems, i.e., self-driving vehicles, unmanned aerial vehicles, and urban robots. The learning methods of these autonomous systems and their strengths and limitations are extensively explored in this article. |
1806.07586 | Andreas Bjorklund | Andreas Bj\"orklund and Thore Husfeldt | Counting Shortest Two Disjoint Paths in Cubic Planar Graphs with an NC
Algorithm | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given an undirected graph and two disjoint vertex pairs $s_1,t_1$ and
$s_2,t_2$, the Shortest two disjoint paths problem (S2DP) asks for the minimum
total length of two vertex disjoint paths connecting $s_1$ with $t_1$, and
$s_2$ with $t_2$, respectively.
We show that for cubic planar graphs there are NC algorithms, uniform
circuits of polynomial size and polylogarithmic depth, that compute the S2DP
and moreover also output the number of such minimum length path pairs.
Previously, to the best of our knowledge, no deterministic polynomial time
algorithm was known for S2DP in cubic planar graphs with arbitrary placement of
the terminals. In contrast, the randomized polynomial time algorithm by
Bj\"orklund and Husfeldt, ICALP 2014, for general graphs is much slower, is
serial in nature, and cannot count the solutions.
Our results are built on an approach by Hirai and Namba, Algorithmica 2017,
for a generalisation of S2DP, and fast algorithms for counting perfect
matchings in planar graphs.
| [
{
"created": "Wed, 20 Jun 2018 07:26:37 GMT",
"version": "v1"
}
] | 2018-06-21 | [
[
"Björklund",
"Andreas",
""
],
[
"Husfeldt",
"Thore",
""
]
] | Given an undirected graph and two disjoint vertex pairs $s_1,t_1$ and $s_2,t_2$, the Shortest two disjoint paths problem (S2DP) asks for the minimum total length of two vertex disjoint paths connecting $s_1$ with $t_1$, and $s_2$ with $t_2$, respectively. We show that for cubic planar graphs there are NC algorithms, uniform circuits of polynomial size and polylogarithmic depth, that compute the S2DP and moreover also output the number of such minimum length path pairs. Previously, to the best of our knowledge, no deterministic polynomial time algorithm was known for S2DP in cubic planar graphs with arbitrary placement of the terminals. In contrast, the randomized polynomial time algorithm by Bj\"orklund and Husfeldt, ICALP 2014, for general graphs is much slower, is serial in nature, and cannot count the solutions. Our results are built on an approach by Hirai and Namba, Algorithmica 2017, for a generalisation of S2DP, and fast algorithms for counting perfect matchings in planar graphs. |
2204.01731 | Shuang Liang | Shuang Liang, Yinan Zou, and Yong Zhou | Gan-Based Joint Activity Detection and Channel Estimation For Grant-free
Random Access | 5 pages, 5 figures IEEE ICASSP2022 | null | null | null | cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Joint activity detection and channel estimation (JADCE) for grant-free random
access is a critical issue that needs to be addressed to support massive
connectivity in IoT networks. However, the existing model-free learning method
can only achieve either activity detection or channel estimation, but not both.
In this paper, we propose a novel model-free learning method based on
generative adversarial network (GAN) to tackle the JADCE problem. We adopt the
U-net architecture to build the generator rather than the standard GAN
architecture, where a pre-estimated value that contains the activity
information is adopted as input to the generator. By leveraging the properties
of the pseudoinverse, the generator is refined by using an affine projection
and a skip connection to ensure the output of the generator is consistent with
the measurement. Moreover, we build a two-layer fully-connected neural network
to design pilot matrix for reducing the impact of receiver noise. Simulation
results show that the proposed method outperforms the existing methods in high
SNR regimes, as both data consistency projection and pilot matrix optimization
improve the learning ability.
| [
{
"created": "Mon, 4 Apr 2022 12:35:37 GMT",
"version": "v1"
}
] | 2022-04-06 | [
[
"Liang",
"Shuang",
""
],
[
"Zou",
"Yinan",
""
],
[
"Zhou",
"Yong",
""
]
] | Joint activity detection and channel estimation (JADCE) for grant-free random access is a critical issue that needs to be addressed to support massive connectivity in IoT networks. However, the existing model-free learning method can only achieve either activity detection or channel estimation, but not both. In this paper, we propose a novel model-free learning method based on generative adversarial network (GAN) to tackle the JADCE problem. We adopt the U-net architecture to build the generator rather than the standard GAN architecture, where a pre-estimated value that contains the activity information is adopted as input to the generator. By leveraging the properties of the pseudoinverse, the generator is refined by using an affine projection and a skip connection to ensure the output of the generator is consistent with the measurement. Moreover, we build a two-layer fully-connected neural network to design pilot matrix for reducing the impact of receiver noise. Simulation results show that the proposed method outperforms the existing methods in high SNR regimes, as both data consistency projection and pilot matrix optimization improve the learning ability. |
2106.14139 | Zhongyun Hua | Zhongyun Hua and Yanxiang Wang and Shuang Yi and Yicong Zhou and
Xiaohua Jia | Secure Reversible Data Hiding in Encrypted Images Using Cipher-Feedback
Secret Sharing | 14 pages | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reversible data hiding in encrypted images (RDH-EI) has attracted increasing
attention, since it can protect the privacy of original images while the
embedded data can be exactly extracted. Recently, some RDH-EI schemes with
multiple data hiders have been proposed using secret sharing technique.
However, these schemes protect the contents of the original images with
lightweight security level. In this paper, we propose a high-security RDH-EI
scheme with multiple data hiders. First, we introduce a cipher-feedback secret
sharing (CFSS) technique. It follows the cryptography standards by introducing
the cipher-feedback strategy of AES. Then, using the CFSS technique, we devise
a new (r,n)-threshold (r<=n) RDH-EI scheme with multiple data hiders called
CFSS-RDHEI. It can encrypt an original image into n encrypted images with
reduced size using an encryption key and sends each encrypted image to one data
hider. Each data hider can independently embed secret data into the encrypted
image to obtain the corresponding marked encrypted image. The original image
can be completely recovered from r marked encrypted images and the encryption
key. Performance evaluations show that our CFSS-RDHEI scheme has high embedding
rate and its generated encrypted images are much smaller, compared to existing
secret sharing-based RDH-EI schemes. Security analysis demonstrates that it can
achieve high security to defense some commonly used security attacks.
| [
{
"created": "Sun, 27 Jun 2021 04:03:56 GMT",
"version": "v1"
}
] | 2021-06-29 | [
[
"Hua",
"Zhongyun",
""
],
[
"Wang",
"Yanxiang",
""
],
[
"Yi",
"Shuang",
""
],
[
"Zhou",
"Yicong",
""
],
[
"Jia",
"Xiaohua",
""
]
] | Reversible data hiding in encrypted images (RDH-EI) has attracted increasing attention, since it can protect the privacy of original images while the embedded data can be exactly extracted. Recently, some RDH-EI schemes with multiple data hiders have been proposed using secret sharing technique. However, these schemes protect the contents of the original images with lightweight security level. In this paper, we propose a high-security RDH-EI scheme with multiple data hiders. First, we introduce a cipher-feedback secret sharing (CFSS) technique. It follows the cryptography standards by introducing the cipher-feedback strategy of AES. Then, using the CFSS technique, we devise a new (r,n)-threshold (r<=n) RDH-EI scheme with multiple data hiders called CFSS-RDHEI. It can encrypt an original image into n encrypted images with reduced size using an encryption key and sends each encrypted image to one data hider. Each data hider can independently embed secret data into the encrypted image to obtain the corresponding marked encrypted image. The original image can be completely recovered from r marked encrypted images and the encryption key. Performance evaluations show that our CFSS-RDHEI scheme has high embedding rate and its generated encrypted images are much smaller, compared to existing secret sharing-based RDH-EI schemes. Security analysis demonstrates that it can achieve high security to defense some commonly used security attacks. |
2403.07118 | Ameeta Agrawal | Atharva Phatak, Vijay K. Mago, Ameeta Agrawal, Aravind Inbasekaran,
Philippe J. Giabbanelli | Narrating Causal Graphs with Large Language Models | HICSS '24 | Proceedings of the 57th Hawaii International Conference on System
Sciences 2024 | null | https://hdl.handle.net/10125/107290 | cs.CL | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The use of generative AI to create text descriptions from graphs has mostly
focused on knowledge graphs, which connect concepts using facts. In this work
we explore the capability of large pretrained language models to generate text
from causal graphs, where salient concepts are represented as nodes and
causality is represented via directed, typed edges. The causal reasoning
encoded in these graphs can support applications as diverse as healthcare or
marketing. Using two publicly available causal graph datasets, we empirically
investigate the performance of four GPT-3 models under various settings. Our
results indicate that while causal text descriptions improve with training
data, compared to fact-based graphs, they are harder to generate under
zero-shot settings. Results further suggest that users of generative AI can
deploy future applications faster since similar performances are obtained when
training a model with only a few examples as compared to fine-tuning via a
large curated dataset.
| [
{
"created": "Mon, 11 Mar 2024 19:19:59 GMT",
"version": "v1"
}
] | 2024-04-09 | [
[
"Phatak",
"Atharva",
""
],
[
"Mago",
"Vijay K.",
""
],
[
"Agrawal",
"Ameeta",
""
],
[
"Inbasekaran",
"Aravind",
""
],
[
"Giabbanelli",
"Philippe J.",
""
]
] | The use of generative AI to create text descriptions from graphs has mostly focused on knowledge graphs, which connect concepts using facts. In this work we explore the capability of large pretrained language models to generate text from causal graphs, where salient concepts are represented as nodes and causality is represented via directed, typed edges. The causal reasoning encoded in these graphs can support applications as diverse as healthcare or marketing. Using two publicly available causal graph datasets, we empirically investigate the performance of four GPT-3 models under various settings. Our results indicate that while causal text descriptions improve with training data, compared to fact-based graphs, they are harder to generate under zero-shot settings. Results further suggest that users of generative AI can deploy future applications faster since similar performances are obtained when training a model with only a few examples as compared to fine-tuning via a large curated dataset. |
2111.14018 | Raja Karmakar | Priyanka Bothra, Raja Karmakar, Sanjukta Bhattacharya, Sayantani De | How Can Applications of Blockchain and Artificial Intelligence Improve
Performance of Internet of Things? -- A Survey | null | null | null | null | cs.NI | http://creativecommons.org/licenses/by/4.0/ | In the era of the Internet of Things (IoT), massive computing devices
surrounding us operate and interact with each other to provide several
significant services in industries, medical as well as in daily life activities
at home, office, education sectors, and so on. The participating devices in an
IoT network usually have resource constraints and the devices are prone to
different cyber attacks, leading to the loopholes in the security and
authentication. As a revolutionized and innovated technology, blockchain, that
is applied in cryptocurrency, market prediction, etc., uses a distributed
ledger that records transactions securely and efficiently. To utilize the great
potential of blockchain, both industries and academia have paid a significant
attention to integrate it with the IoT, as reported by several existing
literature. On the other hand, Artificial Intelligence (AI) is able to embed
intelligence in a system, and thus the AI can be integrated with IoT devices in
order to automatically cope with different environments according to the
demands. Furthermore, both blockchain and AI can be integrated with the IoT to
design an automated secure and robust IoT model, as mentioned by numerous
existing works. In this survey, we present a discussion on the IoT, blockchain,
and AI, along with the descriptions of several research works that apply
blockchain and AI in the IoT. In this direction, we point out strengths and
limitations of the related existing researches. We also discuss different open
challenges to exploit the full capacities of blockchain and AI in designing an
IoT-based model. Therefore, the highlighted challenging issues can open the
door for the development of future IoT models which will be intelligent and
secure based on the integration of blockchain and AI with the IoT.
| [
{
"created": "Sun, 28 Nov 2021 01:45:15 GMT",
"version": "v1"
}
] | 2021-11-30 | [
[
"Bothra",
"Priyanka",
""
],
[
"Karmakar",
"Raja",
""
],
[
"Bhattacharya",
"Sanjukta",
""
],
[
"De",
"Sayantani",
""
]
] | In the era of the Internet of Things (IoT), massive computing devices surrounding us operate and interact with each other to provide several significant services in industries, medical as well as in daily life activities at home, office, education sectors, and so on. The participating devices in an IoT network usually have resource constraints and the devices are prone to different cyber attacks, leading to the loopholes in the security and authentication. As a revolutionized and innovated technology, blockchain, that is applied in cryptocurrency, market prediction, etc., uses a distributed ledger that records transactions securely and efficiently. To utilize the great potential of blockchain, both industries and academia have paid a significant attention to integrate it with the IoT, as reported by several existing literature. On the other hand, Artificial Intelligence (AI) is able to embed intelligence in a system, and thus the AI can be integrated with IoT devices in order to automatically cope with different environments according to the demands. Furthermore, both blockchain and AI can be integrated with the IoT to design an automated secure and robust IoT model, as mentioned by numerous existing works. In this survey, we present a discussion on the IoT, blockchain, and AI, along with the descriptions of several research works that apply blockchain and AI in the IoT. In this direction, we point out strengths and limitations of the related existing researches. We also discuss different open challenges to exploit the full capacities of blockchain and AI in designing an IoT-based model. Therefore, the highlighted challenging issues can open the door for the development of future IoT models which will be intelligent and secure based on the integration of blockchain and AI with the IoT. |
2309.01949 | Berthy Feng | Berthy T. Feng, Katherine L. Bouman | Efficient Bayesian Computational Imaging with a Surrogate Score-Based
Prior | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a surrogate function for efficient use of score-based priors for
Bayesian inverse imaging. Recent work turned score-based diffusion models into
probabilistic priors for solving ill-posed imaging problems by appealing to an
ODE-based log-probability function. However, evaluating this function is
computationally inefficient and inhibits posterior estimation of
high-dimensional images. Our proposed surrogate prior is based on the evidence
lower-bound of a score-based diffusion model. We demonstrate the surrogate
prior on variational inference for efficient approximate posterior sampling of
large images. Compared to the exact prior in previous work, our surrogate prior
accelerates optimization of the variational image distribution by at least two
orders of magnitude. We also find that our principled approach achieves
higher-fidelity images than non-Bayesian baselines that involve
hyperparameter-tuning at inference. Our work establishes a practical path
forward for using score-based diffusion models as general-purpose priors for
imaging.
| [
{
"created": "Tue, 5 Sep 2023 04:55:10 GMT",
"version": "v1"
}
] | 2023-09-06 | [
[
"Feng",
"Berthy T.",
""
],
[
"Bouman",
"Katherine L.",
""
]
] | We propose a surrogate function for efficient use of score-based priors for Bayesian inverse imaging. Recent work turned score-based diffusion models into probabilistic priors for solving ill-posed imaging problems by appealing to an ODE-based log-probability function. However, evaluating this function is computationally inefficient and inhibits posterior estimation of high-dimensional images. Our proposed surrogate prior is based on the evidence lower-bound of a score-based diffusion model. We demonstrate the surrogate prior on variational inference for efficient approximate posterior sampling of large images. Compared to the exact prior in previous work, our surrogate prior accelerates optimization of the variational image distribution by at least two orders of magnitude. We also find that our principled approach achieves higher-fidelity images than non-Bayesian baselines that involve hyperparameter-tuning at inference. Our work establishes a practical path forward for using score-based diffusion models as general-purpose priors for imaging. |
2402.04168 | Daniel Bogdoll | Daniel Bogdoll, Jing Qin, Moritz Nekolla, Ahmed Abouelazm, Tim Joseph,
J. Marius Z\"ollner | Informed Reinforcement Learning for Situation-Aware Traffic Rule
Exceptions | Daniel Bogdoll and Jing Qin contributed equally. Accepted for
publication at ICRA 2024 | null | null | null | cs.LG cs.CV cs.RO | http://creativecommons.org/licenses/by/4.0/ | Reinforcement Learning is a highly active research field with promising
advancements. In the field of autonomous driving, however, often very simple
scenarios are being examined. Common approaches use non-interpretable control
commands as the action space and unstructured reward designs which lack
structure. In this work, we introduce Informed Reinforcement Learning, where a
structured rulebook is integrated as a knowledge source. We learn trajectories
and asses them with a situation-aware reward design, leading to a dynamic
reward which allows the agent to learn situations which require controlled
traffic rule exceptions. Our method is applicable to arbitrary RL models. We
successfully demonstrate high completion rates of complex scenarios with recent
model-based agents.
| [
{
"created": "Tue, 6 Feb 2024 17:24:06 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Jun 2024 11:34:30 GMT",
"version": "v2"
}
] | 2024-06-13 | [
[
"Bogdoll",
"Daniel",
""
],
[
"Qin",
"Jing",
""
],
[
"Nekolla",
"Moritz",
""
],
[
"Abouelazm",
"Ahmed",
""
],
[
"Joseph",
"Tim",
""
],
[
"Zöllner",
"J. Marius",
""
]
] | Reinforcement Learning is a highly active research field with promising advancements. In the field of autonomous driving, however, often very simple scenarios are being examined. Common approaches use non-interpretable control commands as the action space and unstructured reward designs which lack structure. In this work, we introduce Informed Reinforcement Learning, where a structured rulebook is integrated as a knowledge source. We learn trajectories and asses them with a situation-aware reward design, leading to a dynamic reward which allows the agent to learn situations which require controlled traffic rule exceptions. Our method is applicable to arbitrary RL models. We successfully demonstrate high completion rates of complex scenarios with recent model-based agents. |
2310.05471 | Siddharth Gupta | Siddharth Gupta, Guy Sa'ar, Meirav Zehavi | Drawn Tree Decomposition: New Approach for Graph Drawing Problems | A preliminary version of this paper will appear in the Proceedings of
IPEC 2023 | null | null | null | cs.DS cs.CG | http://creativecommons.org/licenses/by/4.0/ | Over the past decade, we witness an increasing amount of interest in the
design of exact exponential-time and parameterized algorithms for problems in
Graph Drawing. Unfortunately, we still lack knowledge of general methods to
develop such algorithms. An even more serious issue is that, here, "standard"
parameters very often yield intractability. In particular, for the most common
structural parameter, namely, treewidth, we frequently observe NP-hardness
already when the input graphs are restricted to have constant (often, being
just $1$ or $2$) treewidth.
Our work deals with both drawbacks simultaneously. We introduce a novel form
of tree decomposition that, roughly speaking, does not decompose (only) a
graph, but an entire drawing. As such, its bags and separators are of geometric
(rather than only combinatorial) nature. While the corresponding parameter --
like treewidth -- can be arbitrarily smaller than the height (and width) of the
drawing, we show that -- unlike treewidth -- it gives rise to efficient
algorithms. Specifically, we get slice-wise polynomial (XP) time algorithms
parameterized by our parameter. We present a general scheme for the design of
such algorithms, and apply it to several central problems in Graph Drawing,
including the recognition of grid graphs, minimization of crossings and bends,
and compaction. Other than for the class of problems we discussed in the paper,
we believe that our decomposition and scheme are of independent interest and
can be further extended or generalized to suit even a wider class of problems.
Additionally, we discuss classes of drawings where our parameter is bounded by
$O(\sqrt{n})$ (where $n$ is the number of vertices of the graph), yielding
subexponential-time algorithms. Lastly, we prove which relations exist between
drawn treewidth and other width measures, including treewidth, pathwidth,
(dual) carving-width and embedded-width.
| [
{
"created": "Mon, 9 Oct 2023 07:27:17 GMT",
"version": "v1"
}
] | 2023-10-10 | [
[
"Gupta",
"Siddharth",
""
],
[
"Sa'ar",
"Guy",
""
],
[
"Zehavi",
"Meirav",
""
]
] | Over the past decade, we witness an increasing amount of interest in the design of exact exponential-time and parameterized algorithms for problems in Graph Drawing. Unfortunately, we still lack knowledge of general methods to develop such algorithms. An even more serious issue is that, here, "standard" parameters very often yield intractability. In particular, for the most common structural parameter, namely, treewidth, we frequently observe NP-hardness already when the input graphs are restricted to have constant (often, being just $1$ or $2$) treewidth. Our work deals with both drawbacks simultaneously. We introduce a novel form of tree decomposition that, roughly speaking, does not decompose (only) a graph, but an entire drawing. As such, its bags and separators are of geometric (rather than only combinatorial) nature. While the corresponding parameter -- like treewidth -- can be arbitrarily smaller than the height (and width) of the drawing, we show that -- unlike treewidth -- it gives rise to efficient algorithms. Specifically, we get slice-wise polynomial (XP) time algorithms parameterized by our parameter. We present a general scheme for the design of such algorithms, and apply it to several central problems in Graph Drawing, including the recognition of grid graphs, minimization of crossings and bends, and compaction. Other than for the class of problems we discussed in the paper, we believe that our decomposition and scheme are of independent interest and can be further extended or generalized to suit even a wider class of problems. Additionally, we discuss classes of drawings where our parameter is bounded by $O(\sqrt{n})$ (where $n$ is the number of vertices of the graph), yielding subexponential-time algorithms. Lastly, we prove which relations exist between drawn treewidth and other width measures, including treewidth, pathwidth, (dual) carving-width and embedded-width. |
1810.01172 | Yousef Alnagar | Yousef AlNagar, Sameh Hosny and Amr A. El-Sherif | Towards Mobility-Aware Proactive Caching for Vehicular Ad hoc Networks | null | null | null | null | cs.NI | http://creativecommons.org/licenses/by/4.0/ | Harnessing information about the user mobility pattern and daily demand can
enhance the network capability to improve the quality of experience (QoE) at
Vehicular Ad-Hoc Networks (VANETs). Proactive caching, as one of the key
features offered by 5G networks, has lately received much interest. However,
more research is still needed to convey large-sized multimedia content
including video, audio and pictures to the high speed moving vehicles. In this
paper, we study the gains achieved by proactive caching in Roadside Units
(RSUs) where we take into consideration the effect of the vehicle velocity on
the optimal caching decision. Information about the user demand and mobility is
harnessed to cache some files in RSUs, which will communicate with vehicles
traversing along the visited roads before the actual demand. Our main objective
is to minimize the total network latency. Towards this objective, we formulate
two optimization problems for non-cooperative and cooperative caching schemes
to find the optimal caching policy to decide which files to be cached by the
RSUs. Due to the complexity of these problems, we propose a sub-optimal caching
policy for each scheme. We compare the performance of the optimal caching
policy to that of the sub-optimal caching policy. Numerical results show that
proactive caching has a significant performance gain when compared to the
baseline reactive scenario. Moreover, results reveal that the cooperative
caching scheme is more efficient than the non-cooperative scheme.
| [
{
"created": "Tue, 2 Oct 2018 11:19:30 GMT",
"version": "v1"
}
] | 2018-10-03 | [
[
"AlNagar",
"Yousef",
""
],
[
"Hosny",
"Sameh",
""
],
[
"El-Sherif",
"Amr A.",
""
]
] | Harnessing information about the user mobility pattern and daily demand can enhance the network capability to improve the quality of experience (QoE) at Vehicular Ad-Hoc Networks (VANETs). Proactive caching, as one of the key features offered by 5G networks, has lately received much interest. However, more research is still needed to convey large-sized multimedia content including video, audio and pictures to the high speed moving vehicles. In this paper, we study the gains achieved by proactive caching in Roadside Units (RSUs) where we take into consideration the effect of the vehicle velocity on the optimal caching decision. Information about the user demand and mobility is harnessed to cache some files in RSUs, which will communicate with vehicles traversing along the visited roads before the actual demand. Our main objective is to minimize the total network latency. Towards this objective, we formulate two optimization problems for non-cooperative and cooperative caching schemes to find the optimal caching policy to decide which files to be cached by the RSUs. Due to the complexity of these problems, we propose a sub-optimal caching policy for each scheme. We compare the performance of the optimal caching policy to that of the sub-optimal caching policy. Numerical results show that proactive caching has a significant performance gain when compared to the baseline reactive scenario. Moreover, results reveal that the cooperative caching scheme is more efficient than the non-cooperative scheme. |
1607.04629 | Aronee Dasgupta Mr | Aronee Dasgupta, Sahil Chakraborty, Astha Nachrani and Pritam Gajkumar
Shah | Lightweight Security Protocol for WiSense based Wireless Sensor Network | 5 pages, 4 figures, 2 tables. Published with International Journal of
Computer Applications (IJCA) | International Journal of Computer Applications 145(3):6-10, July
2016 | 10.5120/ijca2016910172 | null | cs.NI cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wireless Sensor Networks have emerged as one of the leading technologies.
These networks are designed to monitor crucial environmental parameters of
humidity, temperature, wind speed, soil moisture content, UV index, sound, etc.
and then transfer the required information to the base station. However,
security remains the key challenge of such networks as critical data is being
transferred. Most sensor nodes currently deployed have constraints on memory
and processing power and hence operate without an efficient security protocol.
Hereby a protocol which is lightweight and is secure for wireless sensor
applications is proposed.
| [
{
"created": "Fri, 15 Jul 2016 19:46:36 GMT",
"version": "v1"
}
] | 2016-07-18 | [
[
"Dasgupta",
"Aronee",
""
],
[
"Chakraborty",
"Sahil",
""
],
[
"Nachrani",
"Astha",
""
],
[
"Shah",
"Pritam Gajkumar",
""
]
] | Wireless Sensor Networks have emerged as one of the leading technologies. These networks are designed to monitor crucial environmental parameters of humidity, temperature, wind speed, soil moisture content, UV index, sound, etc. and then transfer the required information to the base station. However, security remains the key challenge of such networks as critical data is being transferred. Most sensor nodes currently deployed have constraints on memory and processing power and hence operate without an efficient security protocol. Hereby a protocol which is lightweight and is secure for wireless sensor applications is proposed. |
2011.04702 | Zhiqian Qiao | Josiah Coad, Zhiqian Qiao, John M. Dolan | Safe Trajectory Planning Using Reinforcement Learning for Self Driving | 7 pages, 5 figures | null | null | null | cs.RO cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Self-driving vehicles must be able to act intelligently in diverse and
difficult environments, marked by high-dimensional state spaces, a myriad of
optimization objectives and complex behaviors. Traditionally, classical
optimization and search techniques have been applied to the problem of
self-driving; but they do not fully address operations in environments with
high-dimensional states and complex behaviors. Recently, imitation learning has
been proposed for the task of self-driving; but it is labor-intensive to obtain
enough training data. Reinforcement learning has been proposed as a way to
directly control the car, but this has safety and comfort concerns. We propose
using model-free reinforcement learning for the trajectory planning stage of
self-driving and show that this approach allows us to operate the car in a more
safe, general and comfortable manner, required for the task of self driving.
| [
{
"created": "Mon, 9 Nov 2020 19:29:14 GMT",
"version": "v1"
}
] | 2020-11-11 | [
[
"Coad",
"Josiah",
""
],
[
"Qiao",
"Zhiqian",
""
],
[
"Dolan",
"John M.",
""
]
] | Self-driving vehicles must be able to act intelligently in diverse and difficult environments, marked by high-dimensional state spaces, a myriad of optimization objectives and complex behaviors. Traditionally, classical optimization and search techniques have been applied to the problem of self-driving; but they do not fully address operations in environments with high-dimensional states and complex behaviors. Recently, imitation learning has been proposed for the task of self-driving; but it is labor-intensive to obtain enough training data. Reinforcement learning has been proposed as a way to directly control the car, but this has safety and comfort concerns. We propose using model-free reinforcement learning for the trajectory planning stage of self-driving and show that this approach allows us to operate the car in a more safe, general and comfortable manner, required for the task of self driving. |
2006.12063 | Perttu H\"am\"al\"ainen | Perttu H\"am\"al\"ainen and Martin Trapp and Tuure Saloheimo and Arno
Solin | Deep Residual Mixture Models | Code and examples can be found at
https://github.com/PerttuHamalainen/DRMM | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose Deep Residual Mixture Models (DRMMs), a novel deep generative
model architecture. Compared to other deep models, DRMMs allow more flexible
conditional sampling: The model can be trained once with all variables, and
then used for sampling with arbitrary combinations of conditioning variables,
Gaussian priors, and (in)equality constraints. This provides new opportunities
for interactive and exploratory machine learning, where one should minimize the
user waiting for retraining a model. We demonstrate DRMMs in constrained
multi-limb inverse kinematics and controllable generation of animations.
| [
{
"created": "Mon, 22 Jun 2020 08:25:41 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Nov 2020 14:35:15 GMT",
"version": "v2"
},
{
"created": "Wed, 21 Jul 2021 06:26:14 GMT",
"version": "v3"
}
] | 2021-07-22 | [
[
"Hämäläinen",
"Perttu",
""
],
[
"Trapp",
"Martin",
""
],
[
"Saloheimo",
"Tuure",
""
],
[
"Solin",
"Arno",
""
]
] | We propose Deep Residual Mixture Models (DRMMs), a novel deep generative model architecture. Compared to other deep models, DRMMs allow more flexible conditional sampling: The model can be trained once with all variables, and then used for sampling with arbitrary combinations of conditioning variables, Gaussian priors, and (in)equality constraints. This provides new opportunities for interactive and exploratory machine learning, where one should minimize the user waiting for retraining a model. We demonstrate DRMMs in constrained multi-limb inverse kinematics and controllable generation of animations. |
1512.00659 | Sanjay Sahay | Aruna Govada, Bhavul Gauri and S.K.Sahay | Centroid Based Binary Tree Structured SVM for Multi Classification | Presented in ICACCI, Kochi, India, 2015 | IEEE Xplore, Advances in Computing, Communications and Informatics
(ICACCI), p.258 - 262, 2015 | 10.1109/ICACCI.2015.7275618 | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Support Vector Machines (SVMs) were primarily designed for 2-class
classification. But they have been extended for N-class classification also
based on the requirement of multiclasses in the practical applications.
Although N-class classification using SVM has considerable research attention,
getting minimum number of classifiers at the time of training and testing is
still a continuing research. We propose a new algorithm CBTS-SVM (Centroid
based Binary Tree Structured SVM) which addresses this issue. In this we build
a binary tree of SVM models based on the similarity of the class labels by
finding their distance from the corresponding centroids at the root level. The
experimental results demonstrates the comparable accuracy for CBTS with OVO
with reasonable gamma and cost values. On the other hand when CBTS is compared
with OVA, it gives the better accuracy with reduced training time and testing
time. Furthermore CBTS is also scalable as it is able to handle the large data
sets.
| [
{
"created": "Wed, 2 Dec 2015 11:48:38 GMT",
"version": "v1"
}
] | 2015-12-03 | [
[
"Govada",
"Aruna",
""
],
[
"Gauri",
"Bhavul",
""
],
[
"Sahay",
"S. K.",
""
]
] | Support Vector Machines (SVMs) were primarily designed for 2-class classification. But they have been extended for N-class classification also based on the requirement of multiclasses in the practical applications. Although N-class classification using SVM has considerable research attention, getting minimum number of classifiers at the time of training and testing is still a continuing research. We propose a new algorithm CBTS-SVM (Centroid based Binary Tree Structured SVM) which addresses this issue. In this we build a binary tree of SVM models based on the similarity of the class labels by finding their distance from the corresponding centroids at the root level. The experimental results demonstrates the comparable accuracy for CBTS with OVO with reasonable gamma and cost values. On the other hand when CBTS is compared with OVA, it gives the better accuracy with reduced training time and testing time. Furthermore CBTS is also scalable as it is able to handle the large data sets. |
1911.03904 | Deli Chen | Deli Chen, Xiaoqian Liu, Yankai Lin, Peng Li, Jie Zhou, Qi Su, Xu Sun | HighwayGraph: Modelling Long-distance Node Relations for Improving
General Graph Neural Network | 8 pages | null | null | null | cs.LG cs.CL cs.SI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graph Neural Networks (GNNs) are efficient approaches to process
graph-structured data. Modelling long-distance node relations is essential for
GNN training and applications. However, conventional GNNs suffer from bad
performance in modelling long-distance node relations due to limited-layer
information propagation. Existing studies focus on building deep GNN
architectures, which face the over-smoothing issue and cannot model node
relations in particularly long distance. To address this issue, we propose to
model long-distance node relations by simply relying on shallow GNN
architectures with two solutions: (1) Implicitly modelling by learning to
predict node pair relations (2) Explicitly modelling by adding edges between
nodes that potentially have the same label. To combine our two solutions, we
propose a model-agnostic training framework named HighwayGraph, which overcomes
the challenge of insufficient labeled nodes by sampling node pairs from the
training set and adopting the self-training method. Extensive experimental
results show that our HighwayGraph achieves consistent and significant
improvements over four representative GNNs on three benchmark datasets.
| [
{
"created": "Sun, 10 Nov 2019 11:23:37 GMT",
"version": "v1"
},
{
"created": "Sun, 17 May 2020 05:18:55 GMT",
"version": "v2"
}
] | 2020-05-19 | [
[
"Chen",
"Deli",
""
],
[
"Liu",
"Xiaoqian",
""
],
[
"Lin",
"Yankai",
""
],
[
"Li",
"Peng",
""
],
[
"Zhou",
"Jie",
""
],
[
"Su",
"Qi",
""
],
[
"Sun",
"Xu",
""
]
] | Graph Neural Networks (GNNs) are efficient approaches to process graph-structured data. Modelling long-distance node relations is essential for GNN training and applications. However, conventional GNNs suffer from bad performance in modelling long-distance node relations due to limited-layer information propagation. Existing studies focus on building deep GNN architectures, which face the over-smoothing issue and cannot model node relations in particularly long distance. To address this issue, we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions: (1) Implicitly modelling by learning to predict node pair relations (2) Explicitly modelling by adding edges between nodes that potentially have the same label. To combine our two solutions, we propose a model-agnostic training framework named HighwayGraph, which overcomes the challenge of insufficient labeled nodes by sampling node pairs from the training set and adopting the self-training method. Extensive experimental results show that our HighwayGraph achieves consistent and significant improvements over four representative GNNs on three benchmark datasets. |
1401.0734 | Megasthenis Asteris | Megasthenis Asteris, Alexandros G. Dimakis | Repairable Fountain Codes | To appear in IEEE Journal on Selected Areas in Communications, Issue
on Communication Methodologies for Next-Generation Storage Systems 2013, 11
pages, 2 figures | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new family of Fountain codes that are systematic and also have
sparse parities. Given an input of $k$ symbols, our codes produce an unbounded
number of output symbols, generating each parity independently by linearly
combining a logarithmic number of randomly selected input symbols. The
construction guarantees that for any $\epsilon>0$ accessing a random subset of
$(1+\epsilon)k$ encoded symbols, asymptotically suffices to recover the $k$
input symbols with high probability.
Our codes have the additional benefit of logarithmic locality: a single lost
symbol can be repaired by accessing a subset of $O(\log k)$ of the remaining
encoded symbols. This is a desired property for distributed storage systems
where symbols are spread over a network of storage nodes. Beyond recovery upon
loss, local reconstruction provides an efficient alternative for reading
symbols that cannot be accessed directly. In our code, a logarithmic number of
disjoint local groups is associated with each systematic symbol, allowing
multiple parallel reads.
Our main mathematical contribution involves analyzing the rank of sparse
random matrices with specific structure over finite fields. We rely on
establishing that a new family of sparse random bipartite graphs have perfect
matchings with high probability.
| [
{
"created": "Fri, 3 Jan 2014 21:18:12 GMT",
"version": "v1"
}
] | 2014-01-07 | [
[
"Asteris",
"Megasthenis",
""
],
[
"Dimakis",
"Alexandros G.",
""
]
] | We introduce a new family of Fountain codes that are systematic and also have sparse parities. Given an input of $k$ symbols, our codes produce an unbounded number of output symbols, generating each parity independently by linearly combining a logarithmic number of randomly selected input symbols. The construction guarantees that for any $\epsilon>0$ accessing a random subset of $(1+\epsilon)k$ encoded symbols, asymptotically suffices to recover the $k$ input symbols with high probability. Our codes have the additional benefit of logarithmic locality: a single lost symbol can be repaired by accessing a subset of $O(\log k)$ of the remaining encoded symbols. This is a desired property for distributed storage systems where symbols are spread over a network of storage nodes. Beyond recovery upon loss, local reconstruction provides an efficient alternative for reading symbols that cannot be accessed directly. In our code, a logarithmic number of disjoint local groups is associated with each systematic symbol, allowing multiple parallel reads. Our main mathematical contribution involves analyzing the rank of sparse random matrices with specific structure over finite fields. We rely on establishing that a new family of sparse random bipartite graphs have perfect matchings with high probability. |
2306.07618 | Lingfeng Wen | Lingfeng Wen, Xuan Tang, Mingjie Ouyang, Xiangxiang Shen, Jian Yang,
Daxin Zhu, Mingsong Chen, Xian Wei | Hyperbolic Graph Diffusion Model | accepted by AAAI 2024 | null | null | null | cs.LG cs.AI q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Diffusion generative models (DMs) have achieved promising results in image
and graph generation. However, real-world graphs, such as social networks,
molecular graphs, and traffic graphs, generally share non-Euclidean topologies
and hidden hierarchies. For example, the degree distributions of graphs are
mostly power-law distributions. The current latent diffusion model embeds the
hierarchical data in a Euclidean space, which leads to distortions and
interferes with modeling the distribution. Instead, hyperbolic space has been
found to be more suitable for capturing complex hierarchical structures due to
its exponential growth property. In order to simultaneously utilize the data
generation capabilities of diffusion models and the ability of hyperbolic
embeddings to extract latent hierarchical distributions, we propose a novel
graph generation method called, Hyperbolic Graph Diffusion Model (HGDM), which
consists of an auto-encoder to encode nodes into successive hyperbolic
embeddings, and a DM that operates in the hyperbolic latent space. HGDM
captures the crucial graph structure distributions by constructing a hyperbolic
potential node space that incorporates edge information. Extensive experiments
show that HGDM achieves better performance in generic graph and molecule
generation benchmarks, with a $48\%$ improvement in the quality of graph
generation with highly hierarchical structures.
| [
{
"created": "Tue, 13 Jun 2023 08:22:18 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Jun 2023 06:25:24 GMT",
"version": "v2"
},
{
"created": "Wed, 3 Jan 2024 11:22:21 GMT",
"version": "v3"
}
] | 2024-01-04 | [
[
"Wen",
"Lingfeng",
""
],
[
"Tang",
"Xuan",
""
],
[
"Ouyang",
"Mingjie",
""
],
[
"Shen",
"Xiangxiang",
""
],
[
"Yang",
"Jian",
""
],
[
"Zhu",
"Daxin",
""
],
[
"Chen",
"Mingsong",
""
],
[
"Wei",
"Xian",
""
]
] | Diffusion generative models (DMs) have achieved promising results in image and graph generation. However, real-world graphs, such as social networks, molecular graphs, and traffic graphs, generally share non-Euclidean topologies and hidden hierarchies. For example, the degree distributions of graphs are mostly power-law distributions. The current latent diffusion model embeds the hierarchical data in a Euclidean space, which leads to distortions and interferes with modeling the distribution. Instead, hyperbolic space has been found to be more suitable for capturing complex hierarchical structures due to its exponential growth property. In order to simultaneously utilize the data generation capabilities of diffusion models and the ability of hyperbolic embeddings to extract latent hierarchical distributions, we propose a novel graph generation method called, Hyperbolic Graph Diffusion Model (HGDM), which consists of an auto-encoder to encode nodes into successive hyperbolic embeddings, and a DM that operates in the hyperbolic latent space. HGDM captures the crucial graph structure distributions by constructing a hyperbolic potential node space that incorporates edge information. Extensive experiments show that HGDM achieves better performance in generic graph and molecule generation benchmarks, with a $48\%$ improvement in the quality of graph generation with highly hierarchical structures. |
2312.16210 | James Davenport | James H. Davenport and Matthew England and Scott McCallum and Ali K.
Uncu | Iterated Resultants and Rational Functions in Real Quantifier
Elimination | To be submitted to Mathematics in Computer Science | null | null | null | cs.SC math.AG | http://creativecommons.org/licenses/by/4.0/ | This paper builds and extends on the authors previous work related to the
algorithmic tool, Cylindrical Algebraic Decomposition (CAD), and one of its
core applications, Real Quantifier Elimination (QE). These topics are at the
heart of symbolic computation and were first implemented in computer algebra
systems decades ago, but have recently received renewed interest as part of the
ongoing development of SMT solvers for non-linear real arithmetic.
First, we consider the use of iterated univariate resultants in traditional
CAD, and how this leads to inefficiencies, especially in the case of an input
with multiple equational constraints. We reproduce the workshop paper
[Davenport \& England, 2023], adding important clarifications to our
suggestions first made there to make use of multivariate resultants in the
projection phase of CAD. We then consider an alternative approach to this
problem first documented in [McCallum \& Brown, 2009] which redefines the
actual object under construction, albeit only in the case of two equational
constraints. We correct an important typo and provide a missing proof in that
paper.
We finish by revising the topic of how to deal with SMT or Real QE problems
expressed using rational functions (as opposed to the usual polynomial ones)
noting that these are often found in industrial applications. We revisit a
proposal made in [Uncu, Davenport and England, 2023] for doing this in the case
of satisfiability, explaining why such an approach does not trivially extend to
more complicated quantification structure and giving a suitable alternative.
| [
{
"created": "Sat, 23 Dec 2023 17:32:45 GMT",
"version": "v1"
}
] | 2023-12-29 | [
[
"Davenport",
"James H.",
""
],
[
"England",
"Matthew",
""
],
[
"McCallum",
"Scott",
""
],
[
"Uncu",
"Ali K.",
""
]
] | This paper builds and extends on the authors previous work related to the algorithmic tool, Cylindrical Algebraic Decomposition (CAD), and one of its core applications, Real Quantifier Elimination (QE). These topics are at the heart of symbolic computation and were first implemented in computer algebra systems decades ago, but have recently received renewed interest as part of the ongoing development of SMT solvers for non-linear real arithmetic. First, we consider the use of iterated univariate resultants in traditional CAD, and how this leads to inefficiencies, especially in the case of an input with multiple equational constraints. We reproduce the workshop paper [Davenport \& England, 2023], adding important clarifications to our suggestions first made there to make use of multivariate resultants in the projection phase of CAD. We then consider an alternative approach to this problem first documented in [McCallum \& Brown, 2009] which redefines the actual object under construction, albeit only in the case of two equational constraints. We correct an important typo and provide a missing proof in that paper. We finish by revising the topic of how to deal with SMT or Real QE problems expressed using rational functions (as opposed to the usual polynomial ones) noting that these are often found in industrial applications. We revisit a proposal made in [Uncu, Davenport and England, 2023] for doing this in the case of satisfiability, explaining why such an approach does not trivially extend to more complicated quantification structure and giving a suitable alternative. |
2302.09079 | Biplav Srivastava | Biplav Srivastava, Kausik Lakkaraju, Mariana Bernagozzi, Marco
Valtorta | Advances in Automatically Rating the Trustworthiness of Text Processing
Services | 9 pages, Accepted at 2023 Spring Symposium on AI Trustworthiness
Assessment | null | null | null | cs.HC cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | AI services are known to have unstable behavior when subjected to changes in
data, models or users. Such behaviors, whether triggered by omission or
commission, lead to trust issues when AI works with humans. The current
approach of assessing AI services in a black box setting, where the consumer
does not have access to the AI's source code or training data, is limited. The
consumer has to rely on the AI developer's documentation and trust that the
system has been built as stated. Further, if the AI consumer reuses the service
to build other services which they sell to their customers, the consumer is at
the risk of the service providers (both data and model providers). Our
approach, in this context, is inspired by the success of nutritional labeling
in food industry to promote health and seeks to assess and rate AI services for
trust from the perspective of an independent stakeholder. The ratings become a
means to communicate the behavior of AI systems so that the consumer is
informed about the risks and can make an informed decision. In this paper, we
will first describe recent progress in developing rating methods for text-based
machine translator AI services that have been found promising with user
studies. Then, we will outline challenges and vision for a principled,
multi-modal, causality-based rating methodologies and its implication for
decision-support in real-world scenarios like health and food recommendation.
| [
{
"created": "Sat, 4 Feb 2023 14:27:46 GMT",
"version": "v1"
}
] | 2023-02-21 | [
[
"Srivastava",
"Biplav",
""
],
[
"Lakkaraju",
"Kausik",
""
],
[
"Bernagozzi",
"Mariana",
""
],
[
"Valtorta",
"Marco",
""
]
] | AI services are known to have unstable behavior when subjected to changes in data, models or users. Such behaviors, whether triggered by omission or commission, lead to trust issues when AI works with humans. The current approach of assessing AI services in a black box setting, where the consumer does not have access to the AI's source code or training data, is limited. The consumer has to rely on the AI developer's documentation and trust that the system has been built as stated. Further, if the AI consumer reuses the service to build other services which they sell to their customers, the consumer is at the risk of the service providers (both data and model providers). Our approach, in this context, is inspired by the success of nutritional labeling in food industry to promote health and seeks to assess and rate AI services for trust from the perspective of an independent stakeholder. The ratings become a means to communicate the behavior of AI systems so that the consumer is informed about the risks and can make an informed decision. In this paper, we will first describe recent progress in developing rating methods for text-based machine translator AI services that have been found promising with user studies. Then, we will outline challenges and vision for a principled, multi-modal, causality-based rating methodologies and its implication for decision-support in real-world scenarios like health and food recommendation. |
2204.07724 | Dongxiao Zhang | Hao Xu, Yuntian Chen, Dongxiao Zhang | Semantic interpretation for convolutional neural networks: What makes a
cat a cat? | 33 pages, 11 figures | Advanced Science, 2022 | 10.1002/advs.202204723 | null | cs.LG cs.AI cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The interpretability of deep neural networks has attracted increasing
attention in recent years, and several methods have been created to interpret
the "black box" model. Fundamental limitations remain, however, that impede the
pace of understanding the networks, especially the extraction of understandable
semantic space. In this work, we introduce the framework of semantic
explainable AI (S-XAI), which utilizes row-centered principal component
analysis to obtain the common traits from the best combination of superpixels
discovered by a genetic algorithm, and extracts understandable semantic spaces
on the basis of discovered semantically sensitive neurons and visualization
techniques. Statistical interpretation of the semantic space is also provided,
and the concept of semantic probability is proposed for the first time. Our
experimental results demonstrate that S-XAI is effective in providing a
semantic interpretation for the CNN, and offers broad usage, including
trustworthiness assessment and semantic sample searching.
| [
{
"created": "Sat, 16 Apr 2022 05:25:17 GMT",
"version": "v1"
}
] | 2023-12-05 | [
[
"Xu",
"Hao",
""
],
[
"Chen",
"Yuntian",
""
],
[
"Zhang",
"Dongxiao",
""
]
] | The interpretability of deep neural networks has attracted increasing attention in recent years, and several methods have been created to interpret the "black box" model. Fundamental limitations remain, however, that impede the pace of understanding the networks, especially the extraction of understandable semantic space. In this work, we introduce the framework of semantic explainable AI (S-XAI), which utilizes row-centered principal component analysis to obtain the common traits from the best combination of superpixels discovered by a genetic algorithm, and extracts understandable semantic spaces on the basis of discovered semantically sensitive neurons and visualization techniques. Statistical interpretation of the semantic space is also provided, and the concept of semantic probability is proposed for the first time. Our experimental results demonstrate that S-XAI is effective in providing a semantic interpretation for the CNN, and offers broad usage, including trustworthiness assessment and semantic sample searching. |
2301.10761 | Tanvi Dinkar | Tanvi Dinkar, Chlo\'e Clavel, Ioana Vasilescu | Fillers in Spoken Language Understanding: Computational and
Psycholinguistic Perspectives | \footnote{This article has been published in the journal "Traitement
Automatique des Langues" 63(3): 37-62, 2022,@ATALA. The original manuscript
is available on the web site www.atala.org} | null | null | null | cs.CL cs.HC | http://creativecommons.org/licenses/by/4.0/ | Disfluencies (i.e. interruptions in the regular flow of speech), are
ubiquitous to spoken discourse. Fillers ("uh", "um") are disfluencies that
occur the most frequently compared to other kinds of disfluencies. Yet, to the
best of our knowledge, there isn't a resource that brings together the research
perspectives influencing Spoken Language Understanding (SLU) on these speech
events. This aim of this article is to survey a breadth of perspectives in a
holistic way; i.e. from considering underlying (psycho)linguistic theory, to
their annotation and consideration in Automatic Speech Recognition (ASR) and
SLU systems, to lastly, their study from a generation standpoint. This article
aims to present the perspectives in an approachable way to the SLU and
Conversational AI community, and discuss moving forward, what we believe are
the trends and challenges in each area.
| [
{
"created": "Wed, 25 Jan 2023 18:55:05 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Mar 2023 19:10:39 GMT",
"version": "v2"
},
{
"created": "Fri, 10 Mar 2023 11:04:26 GMT",
"version": "v3"
},
{
"created": "Fri, 24 Mar 2023 15:35:49 GMT",
"version": "v4"
}
] | 2023-03-27 | [
[
"Dinkar",
"Tanvi",
""
],
[
"Clavel",
"Chloé",
""
],
[
"Vasilescu",
"Ioana",
""
]
] | Disfluencies (i.e. interruptions in the regular flow of speech), are ubiquitous to spoken discourse. Fillers ("uh", "um") are disfluencies that occur the most frequently compared to other kinds of disfluencies. Yet, to the best of our knowledge, there isn't a resource that brings together the research perspectives influencing Spoken Language Understanding (SLU) on these speech events. This aim of this article is to survey a breadth of perspectives in a holistic way; i.e. from considering underlying (psycho)linguistic theory, to their annotation and consideration in Automatic Speech Recognition (ASR) and SLU systems, to lastly, their study from a generation standpoint. This article aims to present the perspectives in an approachable way to the SLU and Conversational AI community, and discuss moving forward, what we believe are the trends and challenges in each area. |
2110.09443 | Benjamin Chamberlain Dr | Benjamin Paul Chamberlain, James Rowbottom, Davide Eynard, Francesco
Di Giovanni, Xiaowen Dong, Michael M Bronstein | Beltrami Flow and Neural Diffusion on Graphs | 21 pages, 5 figures. Proceedings of the Thirty-fifth Conference on
Neural Information Processing Systems (NeurIPS) 2021 | null | null | null | cs.LG cs.AI stat.ML | http://creativecommons.org/licenses/by/4.0/ | We propose a novel class of graph neural networks based on the discretised
Beltrami flow, a non-Euclidean diffusion PDE. In our model, node features are
supplemented with positional encodings derived from the graph topology and
jointly evolved by the Beltrami flow, producing simultaneously continuous
feature learning and topology evolution. The resulting model generalises many
popular graph neural networks and achieves state-of-the-art results on several
benchmarks.
| [
{
"created": "Mon, 18 Oct 2021 16:23:38 GMT",
"version": "v1"
}
] | 2021-10-19 | [
[
"Chamberlain",
"Benjamin Paul",
""
],
[
"Rowbottom",
"James",
""
],
[
"Eynard",
"Davide",
""
],
[
"Di Giovanni",
"Francesco",
""
],
[
"Dong",
"Xiaowen",
""
],
[
"Bronstein",
"Michael M",
""
]
] | We propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE. In our model, node features are supplemented with positional encodings derived from the graph topology and jointly evolved by the Beltrami flow, producing simultaneously continuous feature learning and topology evolution. The resulting model generalises many popular graph neural networks and achieves state-of-the-art results on several benchmarks. |
2309.08871 | Yanhao Yang | Yanhao Yang, Capprin Bass, Ross L. Hatton | Towards Geometric Motion Planning for High-Dimensional Systems:
Gait-Based Coordinate Optimization and Local Metrics | 7 pages, 6 figures, accepted to the 2024 IEEE International
Conference on Robotics and Automation (ICRA 2024) | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Geometric motion planning offers effective and interpretable gait analysis
and optimization tools for locomoting systems. However, due to the curse of
dimensionality in coordinate optimization, a key component of geometric motion
planning, it is almost infeasible to apply current geometric motion planning to
high-dimensional systems. In this paper, we propose a gait-based coordinate
optimization method that overcomes the curse of dimensionality. We also
identify a unified geometric representation of locomotion by generalizing
various nonholonomic constraints into local metrics. By combining these two
approaches, we take a step towards geometric motion planning for
high-dimensional systems. We test our method in two classes of high-dimensional
systems - low Reynolds number swimmers and free-falling Cassie - with up to
11-dimensional shape variables. The resulting optimal gait in the
high-dimensional system shows better efficiency compared to that of the
reduced-order model. Furthermore, we provide a geometric optimality
interpretation of the optimal gait.
| [
{
"created": "Sat, 16 Sep 2023 04:28:12 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Mar 2024 04:51:34 GMT",
"version": "v2"
}
] | 2024-03-08 | [
[
"Yang",
"Yanhao",
""
],
[
"Bass",
"Capprin",
""
],
[
"Hatton",
"Ross L.",
""
]
] | Geometric motion planning offers effective and interpretable gait analysis and optimization tools for locomoting systems. However, due to the curse of dimensionality in coordinate optimization, a key component of geometric motion planning, it is almost infeasible to apply current geometric motion planning to high-dimensional systems. In this paper, we propose a gait-based coordinate optimization method that overcomes the curse of dimensionality. We also identify a unified geometric representation of locomotion by generalizing various nonholonomic constraints into local metrics. By combining these two approaches, we take a step towards geometric motion planning for high-dimensional systems. We test our method in two classes of high-dimensional systems - low Reynolds number swimmers and free-falling Cassie - with up to 11-dimensional shape variables. The resulting optimal gait in the high-dimensional system shows better efficiency compared to that of the reduced-order model. Furthermore, we provide a geometric optimality interpretation of the optimal gait. |
2403.15458 | Daniel Fesalbon | Daniel Fesalbon, Arvin De La Cruz, Marvin Mallari, and Nelson Rodelas | Fine-Tuning Pre-trained Language Models to Detect In-Game Trash Talks | null | IJFMR Volume 6, Issue 2, March-April 2024 | 10.36948/ijfmr.2024.v06i02.14927 | null | cs.CL cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Common problems in playing online mobile and computer games were related to
toxic behavior and abusive communication among players. Based on different
reports and studies, the study also discusses the impact of online hate speech
and toxicity on players' in-game performance and overall well-being. This study
investigates the capability of pre-trained language models to classify or
detect trash talk or toxic in-game messages The study employs and evaluates the
performance of pre-trained BERT and GPT language models in detecting toxicity
within in-game chats. Using publicly available APIs, in-game chat data from
DOTA 2 game matches were collected, processed, reviewed, and labeled as
non-toxic, mild (toxicity), and toxic. The study was able to collect around two
thousand in-game chats to train and test BERT (Base-uncased), BERT
(Large-uncased), and GPT-3 models. Based on the three models' state-of-the-art
performance, this study concludes pre-trained language models' promising
potential for addressing online hate speech and in-game insulting trash talk.
| [
{
"created": "Tue, 19 Mar 2024 11:36:53 GMT",
"version": "v1"
}
] | 2024-03-28 | [
[
"Fesalbon",
"Daniel",
""
],
[
"De La Cruz",
"Arvin",
""
],
[
"Mallari",
"Marvin",
""
],
[
"Rodelas",
"Nelson",
""
]
] | Common problems in playing online mobile and computer games were related to toxic behavior and abusive communication among players. Based on different reports and studies, the study also discusses the impact of online hate speech and toxicity on players' in-game performance and overall well-being. This study investigates the capability of pre-trained language models to classify or detect trash talk or toxic in-game messages The study employs and evaluates the performance of pre-trained BERT and GPT language models in detecting toxicity within in-game chats. Using publicly available APIs, in-game chat data from DOTA 2 game matches were collected, processed, reviewed, and labeled as non-toxic, mild (toxicity), and toxic. The study was able to collect around two thousand in-game chats to train and test BERT (Base-uncased), BERT (Large-uncased), and GPT-3 models. Based on the three models' state-of-the-art performance, this study concludes pre-trained language models' promising potential for addressing online hate speech and in-game insulting trash talk. |
2303.08225 | Hao Tang | Hao Tang, Zhenyu Zhang, Humphrey Shi, Bo Li, Ling Shao, Nicu Sebe,
Radu Timofte, Luc Van Gool | Graph Transformer GANs for Graph-Constrained House Generation | CVPR 2023 | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel graph Transformer generative adversarial network (GTGAN)
to learn effective graph node relations in an end-to-end fashion for the
challenging graph-constrained house generation task. The proposed
graph-Transformer-based generator includes a novel graph Transformer encoder
that combines graph convolutions and self-attentions in a Transformer to model
both local and global interactions across connected and non-connected graph
nodes. Specifically, the proposed connected node attention (CNA) and
non-connected node attention (NNA) aim to capture the global relations across
connected nodes and non-connected nodes in the input graph, respectively. The
proposed graph modeling block (GMB) aims to exploit local vertex interactions
based on a house layout topology. Moreover, we propose a new node
classification-based discriminator to preserve the high-level semantic and
discriminative node features for different house components. Finally, we
propose a novel graph-based cycle-consistency loss that aims at maintaining the
relative spatial relationships between ground truth and predicted graphs.
Experiments on two challenging graph-constrained house generation tasks (i.e.,
house layout and roof generation) with two public datasets demonstrate the
effectiveness of GTGAN in terms of objective quantitative scores and subjective
visual realism. New state-of-the-art results are established by large margins
on both tasks.
| [
{
"created": "Tue, 14 Mar 2023 20:35:45 GMT",
"version": "v1"
}
] | 2023-03-16 | [
[
"Tang",
"Hao",
""
],
[
"Zhang",
"Zhenyu",
""
],
[
"Shi",
"Humphrey",
""
],
[
"Li",
"Bo",
""
],
[
"Shao",
"Ling",
""
],
[
"Sebe",
"Nicu",
""
],
[
"Timofte",
"Radu",
""
],
[
"Van Gool",
"Luc",
""
]
] | We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolutions and self-attentions in a Transformer to model both local and global interactions across connected and non-connected graph nodes. Specifically, the proposed connected node attention (CNA) and non-connected node attention (NNA) aim to capture the global relations across connected nodes and non-connected nodes in the input graph, respectively. The proposed graph modeling block (GMB) aims to exploit local vertex interactions based on a house layout topology. Moreover, we propose a new node classification-based discriminator to preserve the high-level semantic and discriminative node features for different house components. Finally, we propose a novel graph-based cycle-consistency loss that aims at maintaining the relative spatial relationships between ground truth and predicted graphs. Experiments on two challenging graph-constrained house generation tasks (i.e., house layout and roof generation) with two public datasets demonstrate the effectiveness of GTGAN in terms of objective quantitative scores and subjective visual realism. New state-of-the-art results are established by large margins on both tasks. |
2110.03246 | Jannik Vierling | Stefan Hetzl, Jannik Vierling | Unprovability results for clause set cycles | Revised version | null | null | null | cs.LO | http://creativecommons.org/licenses/by/4.0/ | The notion of clause set cycle abstracts a family of methods for automated
inductive theorem proving based on the detection of cyclic dependencies between
clause sets. By discerning the underlying logical features of clause set
cycles, we are able to characterize clause set cycles by a logical theory. We
make use of this characterization to provide practically relevant unprovability
results for clause set cycles that exploit different logical features.
| [
{
"created": "Thu, 7 Oct 2021 08:00:11 GMT",
"version": "v1"
},
{
"created": "Thu, 4 Aug 2022 15:12:47 GMT",
"version": "v2"
}
] | 2022-08-05 | [
[
"Hetzl",
"Stefan",
""
],
[
"Vierling",
"Jannik",
""
]
] | The notion of clause set cycle abstracts a family of methods for automated inductive theorem proving based on the detection of cyclic dependencies between clause sets. By discerning the underlying logical features of clause set cycles, we are able to characterize clause set cycles by a logical theory. We make use of this characterization to provide practically relevant unprovability results for clause set cycles that exploit different logical features. |
0808.0247 | Danilo Gligoroski | Danilo Gligoroski and Smile Markovski and Svein Johan Knapskog | A Public Key Block Cipher Based on Multivariate Quadratic Quasigroups | This is an extended and updated version of a paper "Multivariate
Quadratic Trapdoor Functions Based on Multivariate Quadratic Quasigroups",
Proceedings of the American Conference On Applied Mathematics (MATH '08),
Cambridge, Massachusetts, USA, March 24-26, 2008 | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have designed a new class of public key algorithms based on quasigroup
string transformations using a specific class of quasigroups called
multivariate quadratic quasigroups (MQQ). Our public key algorithm is a
bijective mapping, it does not perform message expansions and can be used both
for encryption and signatures. The public key consist of n quadratic
polynomials with n variables where n=140, 160, ... . A particular
characteristic of our public key algorithm is that it is very fast and highly
parallelizable. More concretely, it has the speed of a typical modern symmetric
block cipher - the reason for the phrase "A Public Key Block Cipher" in the
title of this paper. Namely the reference C code for the 160-bit variant of the
algorithm performs decryption in less than 11,000 cycles (on Intel Core 2 Duo
-- using only one processor core), and around 6,000 cycles using two CPU cores
and OpenMP 2.0 library. However, implemented in Xilinx Virtex-5 FPGA that is
running on 249.4 MHz it achieves decryption throughput of 399 Mbps, and
implemented on four Xilinx Virtex-5 chips that are running on 276.7 MHz it
achieves encryption throughput of 44.27 Gbps. Compared to fastest RSA
implementations on similar FPGA platforms, MQQ algorithm is more than 10,000
times faster.
| [
{
"created": "Sat, 2 Aug 2008 09:48:16 GMT",
"version": "v1"
}
] | 2008-08-05 | [
[
"Gligoroski",
"Danilo",
""
],
[
"Markovski",
"Smile",
""
],
[
"Knapskog",
"Svein Johan",
""
]
] | We have designed a new class of public key algorithms based on quasigroup string transformations using a specific class of quasigroups called multivariate quadratic quasigroups (MQQ). Our public key algorithm is a bijective mapping, it does not perform message expansions and can be used both for encryption and signatures. The public key consist of n quadratic polynomials with n variables where n=140, 160, ... . A particular characteristic of our public key algorithm is that it is very fast and highly parallelizable. More concretely, it has the speed of a typical modern symmetric block cipher - the reason for the phrase "A Public Key Block Cipher" in the title of this paper. Namely the reference C code for the 160-bit variant of the algorithm performs decryption in less than 11,000 cycles (on Intel Core 2 Duo -- using only one processor core), and around 6,000 cycles using two CPU cores and OpenMP 2.0 library. However, implemented in Xilinx Virtex-5 FPGA that is running on 249.4 MHz it achieves decryption throughput of 399 Mbps, and implemented on four Xilinx Virtex-5 chips that are running on 276.7 MHz it achieves encryption throughput of 44.27 Gbps. Compared to fastest RSA implementations on similar FPGA platforms, MQQ algorithm is more than 10,000 times faster. |
2305.14936 | Ivan Habernal | Cleo Matzken, Steffen Eger, Ivan Habernal | Trade-Offs Between Fairness and Privacy in Language Modeling | Findings of ACL 2023 | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Protecting privacy in contemporary NLP models is gaining in importance. So
does the need to mitigate social biases of such models. But can we have both at
the same time? Existing research suggests that privacy preservation comes at
the price of worsening biases in classification tasks. In this paper, we
explore the extent to which this tradeoff really holds when we incorporate both
privacy preservation and de-biasing techniques into training text generation
models. How does improving the model along one dimension affect the other
dimension as well as the utility of the model? We conduct an extensive set of
experiments that include bias detection, privacy attacks, language modeling,
and performance on downstream tasks.
| [
{
"created": "Wed, 24 May 2023 09:18:28 GMT",
"version": "v1"
}
] | 2023-05-25 | [
[
"Matzken",
"Cleo",
""
],
[
"Eger",
"Steffen",
""
],
[
"Habernal",
"Ivan",
""
]
] | Protecting privacy in contemporary NLP models is gaining in importance. So does the need to mitigate social biases of such models. But can we have both at the same time? Existing research suggests that privacy preservation comes at the price of worsening biases in classification tasks. In this paper, we explore the extent to which this tradeoff really holds when we incorporate both privacy preservation and de-biasing techniques into training text generation models. How does improving the model along one dimension affect the other dimension as well as the utility of the model? We conduct an extensive set of experiments that include bias detection, privacy attacks, language modeling, and performance on downstream tasks. |
2403.10698 | Minh-Hao Van | Minh-Hao Van, Alycia N. Carey, Xintao Wu | Robust Influence-based Training Methods for Noisy Brain MRI | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Correctly classifying brain tumors is imperative to the prompt and accurate
treatment of a patient. While several classification algorithms based on
classical image processing or deep learning methods have been proposed to
rapidly classify tumors in MR images, most assume the unrealistic setting of
noise-free training data. In this work, we study a difficult but realistic
setting of training a deep learning model on noisy MR images to classify brain
tumors. We propose two training methods that are robust to noisy MRI training
data, Influence-based Sample Reweighing (ISR) and Influence-based Sample
Perturbation (ISP), which are based on influence functions from robust
statistics. Using the influence functions, in ISR, we adaptively reweigh
training examples according to how helpful/harmful they are to the training
process, while in ISP, we craft and inject helpful perturbation proportional to
the influence score. Both ISR and ISP harden the classification model against
noisy training data without significantly affecting the generalization ability
of the model on test data. We conduct empirical evaluations over a common brain
tumor dataset and compare ISR and ISP to three baselines. Our empirical results
show that ISR and ISP can efficiently train deep learning models robust against
noisy training data.
| [
{
"created": "Fri, 15 Mar 2024 21:30:25 GMT",
"version": "v1"
},
{
"created": "Thu, 9 May 2024 22:38:25 GMT",
"version": "v2"
}
] | 2024-05-13 | [
[
"Van",
"Minh-Hao",
""
],
[
"Carey",
"Alycia N.",
""
],
[
"Wu",
"Xintao",
""
]
] | Correctly classifying brain tumors is imperative to the prompt and accurate treatment of a patient. While several classification algorithms based on classical image processing or deep learning methods have been proposed to rapidly classify tumors in MR images, most assume the unrealistic setting of noise-free training data. In this work, we study a difficult but realistic setting of training a deep learning model on noisy MR images to classify brain tumors. We propose two training methods that are robust to noisy MRI training data, Influence-based Sample Reweighing (ISR) and Influence-based Sample Perturbation (ISP), which are based on influence functions from robust statistics. Using the influence functions, in ISR, we adaptively reweigh training examples according to how helpful/harmful they are to the training process, while in ISP, we craft and inject helpful perturbation proportional to the influence score. Both ISR and ISP harden the classification model against noisy training data without significantly affecting the generalization ability of the model on test data. We conduct empirical evaluations over a common brain tumor dataset and compare ISR and ISP to three baselines. Our empirical results show that ISR and ISP can efficiently train deep learning models robust against noisy training data. |
2002.06765 | Teppei Suzuki | Teppei Suzuki | Superpixel Segmentation via Convolutional Neural Networks with
Regularized Information Maximization | To appear in ICASSP 2020 | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an unsupervised superpixel segmentation method by optimizing a
randomly-initialized convolutional neural network (CNN) in inference time. Our
method generates superpixels via CNN from a single image without any labels by
minimizing a proposed objective function for superpixel segmentation in
inference time. There are three advantages to our method compared with many of
existing methods: (i) leverages an image prior of CNN for superpixel
segmentation, (ii) adaptively changes the number of superpixels according to
the given images, and (iii) controls the property of superpixels by adding an
auxiliary cost to the objective function. We verify the advantages of our
method quantitatively and qualitatively on BSDS500 and SBD datasets.
| [
{
"created": "Mon, 17 Feb 2020 04:32:03 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Apr 2020 02:12:09 GMT",
"version": "v2"
},
{
"created": "Fri, 26 Jun 2020 14:02:13 GMT",
"version": "v3"
}
] | 2020-06-29 | [
[
"Suzuki",
"Teppei",
""
]
] | We propose an unsupervised superpixel segmentation method by optimizing a randomly-initialized convolutional neural network (CNN) in inference time. Our method generates superpixels via CNN from a single image without any labels by minimizing a proposed objective function for superpixel segmentation in inference time. There are three advantages to our method compared with many of existing methods: (i) leverages an image prior of CNN for superpixel segmentation, (ii) adaptively changes the number of superpixels according to the given images, and (iii) controls the property of superpixels by adding an auxiliary cost to the objective function. We verify the advantages of our method quantitatively and qualitatively on BSDS500 and SBD datasets. |
1603.08853 | Jeffrey Liu | Jeffrey Liu, Saurabh Amin, Galina Schwartz | Effects of Information Heterogeneity in Bayesian Routing Games | null | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article studies the value of information in route choice decisions when
a fraction of players have access to high accuracy information about traffic
incidents relative to others. To model such environments, we introduce a
Bayesian congestion game, in which players have private information about
incidents, and each player chooses her route on a network of parallel links.
The links are prone to incidents that occur with an ex-ante known probability.
The demand is comprised of two player populations: one with access to high
accuracy incident information and another with low accuracy information, i.e.
the populations differ only by their access to information. The common
knowledge includes: (i) the demand and route cost functions, (ii) the fraction
of highly-informed players, (iii) the incident probability, and (iv) the
marginal type distributions induced by the information structure of the game.
We present a full characterization of the Bayesian Wardrop Equilibrium of this
game under the assumption that low information players receive no additional
information beyond common knowledge. We also compute the cost to individual
players and the social cost as a function of the fraction of highly-informed
players when they receive perfectly accurate information. Our first result
suggests that below a certain threshold of highly-informed players, both
populations experience a reduction in individual cost, with the highly-informed
players receiving a greater reduction. However, above this threshold, both
populations realize the same equilibrium cost. Secondly, there exists another
(lower or equal) threshold above which a further increase in the fraction of
highly-informed players does not reduce the expected social costs. Thus, once a
sufficiently large number of players are highly informed, wider distribution of
more accurate information is ineffective at best, and otherwise socially
harmful.
| [
{
"created": "Tue, 29 Mar 2016 17:22:31 GMT",
"version": "v1"
}
] | 2016-03-30 | [
[
"Liu",
"Jeffrey",
""
],
[
"Amin",
"Saurabh",
""
],
[
"Schwartz",
"Galina",
""
]
] | This article studies the value of information in route choice decisions when a fraction of players have access to high accuracy information about traffic incidents relative to others. To model such environments, we introduce a Bayesian congestion game, in which players have private information about incidents, and each player chooses her route on a network of parallel links. The links are prone to incidents that occur with an ex-ante known probability. The demand is comprised of two player populations: one with access to high accuracy incident information and another with low accuracy information, i.e. the populations differ only by their access to information. The common knowledge includes: (i) the demand and route cost functions, (ii) the fraction of highly-informed players, (iii) the incident probability, and (iv) the marginal type distributions induced by the information structure of the game. We present a full characterization of the Bayesian Wardrop Equilibrium of this game under the assumption that low information players receive no additional information beyond common knowledge. We also compute the cost to individual players and the social cost as a function of the fraction of highly-informed players when they receive perfectly accurate information. Our first result suggests that below a certain threshold of highly-informed players, both populations experience a reduction in individual cost, with the highly-informed players receiving a greater reduction. However, above this threshold, both populations realize the same equilibrium cost. Secondly, there exists another (lower or equal) threshold above which a further increase in the fraction of highly-informed players does not reduce the expected social costs. Thus, once a sufficiently large number of players are highly informed, wider distribution of more accurate information is ineffective at best, and otherwise socially harmful. |
2306.09048 | Shubhada Agrawal | Shubhada Agrawal, Sandeep Juneja, Karthikeyan Shanmugam, Arun Sai
Suggala | Optimal Best-Arm Identification in Bandits with Access to Offline Data | 45 pages, 5 figures | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning paradigms based purely on offline data as well as those based solely
on sequential online learning have been well-studied in the literature. In this
paper, we consider combining offline data with online learning, an area less
studied but of obvious practical importance. We consider the stochastic
$K$-armed bandit problem, where our goal is to identify the arm with the
highest mean in the presence of relevant offline data, with confidence
$1-\delta$. We conduct a lower bound analysis on policies that provide such
$1-\delta$ probabilistic correctness guarantees. We develop algorithms that
match the lower bound on sample complexity when $\delta$ is small. Our
algorithms are computationally efficient with an average per-sample acquisition
cost of $\tilde{O}(K)$, and rely on a careful characterization of the
optimality conditions of the lower bound problem.
| [
{
"created": "Thu, 15 Jun 2023 11:12:35 GMT",
"version": "v1"
}
] | 2023-06-16 | [
[
"Agrawal",
"Shubhada",
""
],
[
"Juneja",
"Sandeep",
""
],
[
"Shanmugam",
"Karthikeyan",
""
],
[
"Suggala",
"Arun Sai",
""
]
] | Learning paradigms based purely on offline data as well as those based solely on sequential online learning have been well-studied in the literature. In this paper, we consider combining offline data with online learning, an area less studied but of obvious practical importance. We consider the stochastic $K$-armed bandit problem, where our goal is to identify the arm with the highest mean in the presence of relevant offline data, with confidence $1-\delta$. We conduct a lower bound analysis on policies that provide such $1-\delta$ probabilistic correctness guarantees. We develop algorithms that match the lower bound on sample complexity when $\delta$ is small. Our algorithms are computationally efficient with an average per-sample acquisition cost of $\tilde{O}(K)$, and rely on a careful characterization of the optimality conditions of the lower bound problem. |
1212.2865 | Martin Kasparick | Gerhard Wunder, Robert F. H. Fischer, Holger Boche, Simon Litsyn,
Jong-Seon No | The PAPR Problem in OFDM Transmission: New Directions for a Long-Lasting
Problem | Accepted for publication in IEEE Signal Processing Magazine | null | 10.1109/MSP.2012.2218138 | null | cs.IT math.IT math.MG math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Peak power control for multicarrier communications has been a long-lasting
problem in signal processing and communications. However, industry and academia
are confronted with new challenges regarding energy efficient system design.
Particularly, the envisioned boost in network energy efficiency (e.g. at least
by a factor of 1000 in the Green Touch consortium) will tighten the
requirements on component level so that the efficiency gap with respect to
single-carrier transmission must considerably diminish. This paper reflects
these challenges together with a unified framework and new directions in this
field. The combination of large deviation theory, de-randomization and selected
elements of Banach space geometry will offer a novel approach and will provide
ideas and concepts for researchers with a background in industry as well as
those from academia.
| [
{
"created": "Wed, 12 Dec 2012 16:25:56 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Dec 2012 16:48:50 GMT",
"version": "v2"
}
] | 2016-11-18 | [
[
"Wunder",
"Gerhard",
""
],
[
"Fischer",
"Robert F. H.",
""
],
[
"Boche",
"Holger",
""
],
[
"Litsyn",
"Simon",
""
],
[
"No",
"Jong-Seon",
""
]
] | Peak power control for multicarrier communications has been a long-lasting problem in signal processing and communications. However, industry and academia are confronted with new challenges regarding energy efficient system design. Particularly, the envisioned boost in network energy efficiency (e.g. at least by a factor of 1000 in the Green Touch consortium) will tighten the requirements on component level so that the efficiency gap with respect to single-carrier transmission must considerably diminish. This paper reflects these challenges together with a unified framework and new directions in this field. The combination of large deviation theory, de-randomization and selected elements of Banach space geometry will offer a novel approach and will provide ideas and concepts for researchers with a background in industry as well as those from academia. |
2108.09108 | Hyeongseok Son | Hyeongseok Son, Junyong Lee, Sunghyun Cho, Seungyong Lee | Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous
Convolutions | Accepted to ICCV 2021 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a novel deep learning approach for single image defocus
deblurring based on inverse kernels. In a defocused image, the blur shapes are
similar among pixels although the blur sizes can spatially vary. To utilize the
property with inverse kernels, we exploit the observation that when only the
size of a defocus blur changes while keeping the shape, the shape of the
corresponding inverse kernel remains the same and only the scale changes. Based
on the observation, we propose a kernel-sharing parallel atrous convolutional
(KPAC) block specifically designed by incorporating the property of inverse
kernels for single image defocus deblurring. To effectively simulate the
invariant shapes of inverse kernels with different scales, KPAC shares the same
convolutional weights among multiple atrous convolution layers. To efficiently
simulate the varying scales of inverse kernels, KPAC consists of only a few
atrous convolution layers with different dilations and learns per-pixel scale
attentions to aggregate the outputs of the layers. KPAC also utilizes the shape
attention to combine the outputs of multiple convolution filters in each atrous
convolution layer, to deal with defocus blur with a slightly varying shape. We
demonstrate that our approach achieves state-of-the-art performance with a much
smaller number of parameters than previous methods.
| [
{
"created": "Fri, 20 Aug 2021 11:06:19 GMT",
"version": "v1"
}
] | 2021-08-23 | [
[
"Son",
"Hyeongseok",
""
],
[
"Lee",
"Junyong",
""
],
[
"Cho",
"Sunghyun",
""
],
[
"Lee",
"Seungyong",
""
]
] | This paper proposes a novel deep learning approach for single image defocus deblurring based on inverse kernels. In a defocused image, the blur shapes are similar among pixels although the blur sizes can spatially vary. To utilize the property with inverse kernels, we exploit the observation that when only the size of a defocus blur changes while keeping the shape, the shape of the corresponding inverse kernel remains the same and only the scale changes. Based on the observation, we propose a kernel-sharing parallel atrous convolutional (KPAC) block specifically designed by incorporating the property of inverse kernels for single image defocus deblurring. To effectively simulate the invariant shapes of inverse kernels with different scales, KPAC shares the same convolutional weights among multiple atrous convolution layers. To efficiently simulate the varying scales of inverse kernels, KPAC consists of only a few atrous convolution layers with different dilations and learns per-pixel scale attentions to aggregate the outputs of the layers. KPAC also utilizes the shape attention to combine the outputs of multiple convolution filters in each atrous convolution layer, to deal with defocus blur with a slightly varying shape. We demonstrate that our approach achieves state-of-the-art performance with a much smaller number of parameters than previous methods. |
1208.2261 | Sudarshan Nandy | Sudarshan Nandy, Partha Pratim Sarkar and Achintya Das | Analysis of Statistical Hypothesis based Learning Mechanism for Faster
Crawling | 14 Pages, 7 Figures This paper has been withdrawn by the author due
to a crucial sign error in page no. 3,4,7 and 11. The error is also observed
with equation no in page 10 | International Journal of Artificial Intelligence & Applications
(IJAIA), Vol.3, No.4, July 2012, 117-130 | 10.5121/ijaia.2012.3409 | null | cs.IR cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The growth of world-wide-web (WWW) spreads its wings from an intangible
quantities of web-pages to a gigantic hub of web information which gradually
increases the complexity of crawling process in a search engine. A search
engine handles a lot of queries from various parts of this world, and the
answers of it solely depend on the knowledge that it gathers by means of
crawling. The information sharing becomes a most common habit of the society,
and it is done by means of publishing structured, semi-structured and
unstructured resources on the web. This social practice leads to an exponential
growth of web-resource, and hence it became essential to crawl for continuous
updating of web-knowledge and modification of several existing resources in any
situation. In this paper one statistical hypothesis based learning mechanism is
incorporated for learning the behavior of crawling speed in different
environment of network, and for intelligently control of the speed of crawler.
The scaling technique is used to compare the performance proposed method with
the standard crawler. The high speed performance is observed after scaling, and
the retrieval of relevant web-resource in such a high speed is analyzed.
| [
{
"created": "Fri, 10 Aug 2012 19:43:43 GMT",
"version": "v1"
},
{
"created": "Mon, 13 Aug 2012 05:40:17 GMT",
"version": "v2"
}
] | 2012-08-14 | [
[
"Nandy",
"Sudarshan",
""
],
[
"Sarkar",
"Partha Pratim",
""
],
[
"Das",
"Achintya",
""
]
] | The growth of world-wide-web (WWW) spreads its wings from an intangible quantities of web-pages to a gigantic hub of web information which gradually increases the complexity of crawling process in a search engine. A search engine handles a lot of queries from various parts of this world, and the answers of it solely depend on the knowledge that it gathers by means of crawling. The information sharing becomes a most common habit of the society, and it is done by means of publishing structured, semi-structured and unstructured resources on the web. This social practice leads to an exponential growth of web-resource, and hence it became essential to crawl for continuous updating of web-knowledge and modification of several existing resources in any situation. In this paper one statistical hypothesis based learning mechanism is incorporated for learning the behavior of crawling speed in different environment of network, and for intelligently control of the speed of crawler. The scaling technique is used to compare the performance proposed method with the standard crawler. The high speed performance is observed after scaling, and the retrieval of relevant web-resource in such a high speed is analyzed. |
2208.04125 | Haoye Tian | Haoye Tian, Xunzhu Tang, Andrew Habib, Shangwen Wang, Kui Liu, Xin
Xia, Jacques Klein, Tegawend\'e F. Bissyand\'e | Is this Change the Answer to that Problem? Correlating Descriptions of
Bug and Code Changes for Evaluating Patch Correctness | null | null | 10.1145/3551349.3556914 | null | cs.SE cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we propose a novel perspective to the problem of patch
correctness assessment: a correct patch implements changes that "answer" to a
problem posed by buggy behaviour. Concretely, we turn the patch correctness
assessment into a Question Answering problem. To tackle this problem, our
intuition is that natural language processing can provide the necessary
representations and models for assessing the semantic correlation between a bug
(question) and a patch (answer). Specifically, we consider as inputs the bug
reports as well as the natural language description of the generated patches.
Our approach, Quatrain, first considers state of the art commit message
generation models to produce the relevant inputs associated to each generated
patch. Then we leverage a neural network architecture to learn the semantic
correlation between bug reports and commit messages. Experiments on a large
dataset of 9135 patches generated for three bug datasets (Defects4j, Bugs.jar
and Bears) show that Quatrain can achieve an AUC of 0.886 on predicting patch
correctness, and recalling 93% correct patches while filtering out 62%
incorrect patches. Our experimental results further demonstrate the influence
of inputs quality on prediction performance. We further perform experiments to
highlight that the model indeed learns the relationship between bug reports and
code change descriptions for the prediction. Finally, we compare against prior
work and discuss the benefits of our approach.
| [
{
"created": "Mon, 8 Aug 2022 13:32:58 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Sep 2022 08:37:38 GMT",
"version": "v2"
}
] | 2022-09-02 | [
[
"Tian",
"Haoye",
""
],
[
"Tang",
"Xunzhu",
""
],
[
"Habib",
"Andrew",
""
],
[
"Wang",
"Shangwen",
""
],
[
"Liu",
"Kui",
""
],
[
"Xia",
"Xin",
""
],
[
"Klein",
"Jacques",
""
],
[
"Bissyandé",
"Tegawendé F.",
""
]
] | In this work, we propose a novel perspective to the problem of patch correctness assessment: a correct patch implements changes that "answer" to a problem posed by buggy behaviour. Concretely, we turn the patch correctness assessment into a Question Answering problem. To tackle this problem, our intuition is that natural language processing can provide the necessary representations and models for assessing the semantic correlation between a bug (question) and a patch (answer). Specifically, we consider as inputs the bug reports as well as the natural language description of the generated patches. Our approach, Quatrain, first considers state of the art commit message generation models to produce the relevant inputs associated to each generated patch. Then we leverage a neural network architecture to learn the semantic correlation between bug reports and commit messages. Experiments on a large dataset of 9135 patches generated for three bug datasets (Defects4j, Bugs.jar and Bears) show that Quatrain can achieve an AUC of 0.886 on predicting patch correctness, and recalling 93% correct patches while filtering out 62% incorrect patches. Our experimental results further demonstrate the influence of inputs quality on prediction performance. We further perform experiments to highlight that the model indeed learns the relationship between bug reports and code change descriptions for the prediction. Finally, we compare against prior work and discuss the benefits of our approach. |
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