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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2403.06027 | Adam Mahdi | Felix H. Krones, Ben Walker, Guy Parsons, Terry Lyons, Adam Mahdi | Multimodal deep learning approach to predicting neurological recovery
from coma after cardiac arrest | 5 figures, 2 tables | null | null | null | cs.LG eess.SP | http://creativecommons.org/licenses/by/4.0/ | This work showcases our team's (The BEEGees) contributions to the 2023 George
B. Moody PhysioNet Challenge. The aim was to predict neurological recovery from
coma following cardiac arrest using clinical data and time-series such as
multi-channel EEG and ECG signals. Our modelling approach is multimodal, based
on two-dimensional spectrogram representations derived from numerous EEG
channels, alongside the integration of clinical data and features extracted
directly from EEG recordings. Our submitted model achieved a Challenge score of
$0.53$ on the hidden test set for predictions made $72$ hours after return of
spontaneous circulation. Our study shows the efficacy and limitations of
employing transfer learning in medical classification. With regard to
prospective implementation, our analysis reveals that the performance of the
model is strongly linked to the selection of a decision threshold and exhibits
strong variability across data splits.
| [
{
"created": "Sat, 9 Mar 2024 22:29:24 GMT",
"version": "v1"
}
] | 2024-03-12 | [
[
"Krones",
"Felix H.",
""
],
[
"Walker",
"Ben",
""
],
[
"Parsons",
"Guy",
""
],
[
"Lyons",
"Terry",
""
],
[
"Mahdi",
"Adam",
""
]
] | This work showcases our team's (The BEEGees) contributions to the 2023 George B. Moody PhysioNet Challenge. The aim was to predict neurological recovery from coma following cardiac arrest using clinical data and time-series such as multi-channel EEG and ECG signals. Our modelling approach is multimodal, based on two-dimensional spectrogram representations derived from numerous EEG channels, alongside the integration of clinical data and features extracted directly from EEG recordings. Our submitted model achieved a Challenge score of $0.53$ on the hidden test set for predictions made $72$ hours after return of spontaneous circulation. Our study shows the efficacy and limitations of employing transfer learning in medical classification. With regard to prospective implementation, our analysis reveals that the performance of the model is strongly linked to the selection of a decision threshold and exhibits strong variability across data splits. |
2407.02870 | Noam Koren | Noam Koren, Abigail Goldsteen, Ariel Farkash, Guy Amit | Membership Inference Attacks Against Time-Series Models | 16 pages | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Analyzing time-series data that may contain personal information,
particularly in the medical field, presents serious privacy concerns. Sensitive
health data from patients is often used to train machine-learning models for
diagnostics and ongoing care. Assessing the privacy risk of such models is
crucial to making knowledgeable decisions on whether to use a model in
production, share it with third parties, or deploy it in patients homes.
Membership Inference Attacks (MIA) are a key method for this kind of
evaluation, however time-series prediction models have not been thoroughly
studied in this context. We explore existing MIA techniques on time-series
models, and introduce new features, focusing on the seasonality and trend
components of the data. Seasonality is estimated using a multivariate Fourier
transform, and a low-degree polynomial is used to approximate trends. We
applied these techniques to various types of time-series models, using datasets
from the health domain. Our results demonstrate that these new features enhance
the effectiveness of MIAs in identifying membership, improving the
understanding of privacy risks in medical data applications.
| [
{
"created": "Wed, 3 Jul 2024 07:34:49 GMT",
"version": "v1"
}
] | 2024-07-04 | [
[
"Koren",
"Noam",
""
],
[
"Goldsteen",
"Abigail",
""
],
[
"Farkash",
"Ariel",
""
],
[
"Amit",
"Guy",
""
]
] | Analyzing time-series data that may contain personal information, particularly in the medical field, presents serious privacy concerns. Sensitive health data from patients is often used to train machine-learning models for diagnostics and ongoing care. Assessing the privacy risk of such models is crucial to making knowledgeable decisions on whether to use a model in production, share it with third parties, or deploy it in patients homes. Membership Inference Attacks (MIA) are a key method for this kind of evaluation, however time-series prediction models have not been thoroughly studied in this context. We explore existing MIA techniques on time-series models, and introduce new features, focusing on the seasonality and trend components of the data. Seasonality is estimated using a multivariate Fourier transform, and a low-degree polynomial is used to approximate trends. We applied these techniques to various types of time-series models, using datasets from the health domain. Our results demonstrate that these new features enhance the effectiveness of MIAs in identifying membership, improving the understanding of privacy risks in medical data applications. |
2311.12341 | Ziye Qin | Ziye Qin and Ang Ji and Zhanbo Sun and Guoyuan Wu and Peng Hao and
Xishun Liao | Game Theoretic Application to Intersection Management: A Literature
Review | null | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The emergence of vehicle-to-everything (V2X) technology offers new insights
into intersection management. This, however, has also presented new challenges,
such as the need to understand and model the interactions of traffic
participants, including their competition and cooperation behaviors. Game
theory has been widely adopted to study rationally selfish or cooperative
behaviors during interactions and has been applied to advanced intersection
management. In this paper, we review the application of game theory to
intersection management and sort out relevant studies under various levels of
intelligence and connectivity. First, the problem of urban intersection
management and its challenges are briefly introduced. The basic elements of
game theory specifically for intersection applications are then summarized.
Next, we present the game-theoretic models and solutions that have been applied
to intersection management. Finally, the limitations and potential
opportunities for subsequent studies within the game-theoretic application to
intersection management are discussed.
| [
{
"created": "Tue, 21 Nov 2023 04:25:08 GMT",
"version": "v1"
}
] | 2023-11-22 | [
[
"Qin",
"Ziye",
""
],
[
"Ji",
"Ang",
""
],
[
"Sun",
"Zhanbo",
""
],
[
"Wu",
"Guoyuan",
""
],
[
"Hao",
"Peng",
""
],
[
"Liao",
"Xishun",
""
]
] | The emergence of vehicle-to-everything (V2X) technology offers new insights into intersection management. This, however, has also presented new challenges, such as the need to understand and model the interactions of traffic participants, including their competition and cooperation behaviors. Game theory has been widely adopted to study rationally selfish or cooperative behaviors during interactions and has been applied to advanced intersection management. In this paper, we review the application of game theory to intersection management and sort out relevant studies under various levels of intelligence and connectivity. First, the problem of urban intersection management and its challenges are briefly introduced. The basic elements of game theory specifically for intersection applications are then summarized. Next, we present the game-theoretic models and solutions that have been applied to intersection management. Finally, the limitations and potential opportunities for subsequent studies within the game-theoretic application to intersection management are discussed. |
1011.5677 | Sachin Adlakha | Sachin Adlakha and Ramesh Johari | Mean Field Equilibrium in Dynamic Games with Complementarities | 56 pages, 5 figures | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study a class of stochastic dynamic games that exhibit strategic
complementarities between players; formally, in the games we consider, the
payoff of a player has increasing differences between her own state and the
empirical distribution of the states of other players. Such games can be used
to model a diverse set of applications, including network security models,
recommender systems, and dynamic search in markets. Stochastic games are
generally difficult to analyze, and these difficulties are only exacerbated
when the number of players is large (as might be the case in the preceding
examples).
We consider an approximation methodology called mean field equilibrium to
study these games. In such an equilibrium, each player reacts to only the long
run average state of other players. We find necessary conditions for the
existence of a mean field equilibrium in such games. Furthermore, as a simple
consequence of this existence theorem, we obtain several natural monotonicity
properties. We show that there exist a "largest" and a "smallest" equilibrium
among all those where the equilibrium strategy used by a player is
nondecreasing, and we also show that players converge to each of these
equilibria via natural myopic learning dynamics; as we argue, these dynamics
are more reasonable than the standard best response dynamics. We also provide
sensitivity results, where we quantify how the equilibria of such games move in
response to changes in parameters of the game (e.g., the introduction of
incentives to players).
| [
{
"created": "Thu, 25 Nov 2010 19:31:47 GMT",
"version": "v1"
}
] | 2010-12-13 | [
[
"Adlakha",
"Sachin",
""
],
[
"Johari",
"Ramesh",
""
]
] | We study a class of stochastic dynamic games that exhibit strategic complementarities between players; formally, in the games we consider, the payoff of a player has increasing differences between her own state and the empirical distribution of the states of other players. Such games can be used to model a diverse set of applications, including network security models, recommender systems, and dynamic search in markets. Stochastic games are generally difficult to analyze, and these difficulties are only exacerbated when the number of players is large (as might be the case in the preceding examples). We consider an approximation methodology called mean field equilibrium to study these games. In such an equilibrium, each player reacts to only the long run average state of other players. We find necessary conditions for the existence of a mean field equilibrium in such games. Furthermore, as a simple consequence of this existence theorem, we obtain several natural monotonicity properties. We show that there exist a "largest" and a "smallest" equilibrium among all those where the equilibrium strategy used by a player is nondecreasing, and we also show that players converge to each of these equilibria via natural myopic learning dynamics; as we argue, these dynamics are more reasonable than the standard best response dynamics. We also provide sensitivity results, where we quantify how the equilibria of such games move in response to changes in parameters of the game (e.g., the introduction of incentives to players). |
2211.04927 | Hanwei Zhu | Hanwei Zhu, Baoliang Chen, Lingyu Zhu, Shiqi Wang, and Weisi Lin | DeepDC: Deep Distance Correlation as a Perceptual Image Quality
Evaluator | null | null | null | null | cs.CV eess.IV | http://creativecommons.org/licenses/by/4.0/ | ImageNet pre-trained deep neural networks (DNNs) show notable transferability
for building effective image quality assessment (IQA) models. Such a remarkable
byproduct has often been identified as an emergent property in previous
studies. In this work, we attribute such capability to the intrinsic
texture-sensitive characteristic that classifies images using texture features.
We fully exploit this characteristic to develop a novel full-reference IQA
(FR-IQA) model based exclusively on pre-trained DNN features. Specifically, we
compute the distance correlation, a highly promising yet relatively
under-investigated statistic, between reference and distorted images in the
deep feature domain. In addition, the distance correlation quantifies both
linear and nonlinear feature relationships, which is far beyond the widely used
first-order and second-order statistics in the feature space. We conduct
comprehensive experiments to demonstrate the superiority of the proposed
quality model on five standard IQA datasets, one perceptual similarity dataset,
two texture similarity datasets, and one geometric transformation dataset.
Moreover, we optimize the proposed model to generate a broad spectrum of
texture patterns, by treating the model as the style loss function for neural
style transfer (NST). Extensive experiments demonstrate that the proposed
texture synthesis and NST methods achieve the best quantitative and qualitative
results. We release our code at https://github.com/h4nwei/DeepDC.
| [
{
"created": "Wed, 9 Nov 2022 14:57:27 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Nov 2023 12:59:12 GMT",
"version": "v2"
}
] | 2023-11-27 | [
[
"Zhu",
"Hanwei",
""
],
[
"Chen",
"Baoliang",
""
],
[
"Zhu",
"Lingyu",
""
],
[
"Wang",
"Shiqi",
""
],
[
"Lin",
"Weisi",
""
]
] | ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models. Such a remarkable byproduct has often been identified as an emergent property in previous studies. In this work, we attribute such capability to the intrinsic texture-sensitive characteristic that classifies images using texture features. We fully exploit this characteristic to develop a novel full-reference IQA (FR-IQA) model based exclusively on pre-trained DNN features. Specifically, we compute the distance correlation, a highly promising yet relatively under-investigated statistic, between reference and distorted images in the deep feature domain. In addition, the distance correlation quantifies both linear and nonlinear feature relationships, which is far beyond the widely used first-order and second-order statistics in the feature space. We conduct comprehensive experiments to demonstrate the superiority of the proposed quality model on five standard IQA datasets, one perceptual similarity dataset, two texture similarity datasets, and one geometric transformation dataset. Moreover, we optimize the proposed model to generate a broad spectrum of texture patterns, by treating the model as the style loss function for neural style transfer (NST). Extensive experiments demonstrate that the proposed texture synthesis and NST methods achieve the best quantitative and qualitative results. We release our code at https://github.com/h4nwei/DeepDC. |
2004.14257 | Aman Madaan | Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham
Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, Shrimai Prabhumoye | Politeness Transfer: A Tag and Generate Approach | To appear at ACL 2020 | null | null | null | cs.CL | http://creativecommons.org/licenses/by-sa/4.0/ | This paper introduces a new task of politeness transfer which involves
converting non-polite sentences to polite sentences while preserving the
meaning. We also provide a dataset of more than 1.39 instances automatically
labeled for politeness to encourage benchmark evaluations on this new task. We
design a tag and generate pipeline that identifies stylistic attributes and
subsequently generates a sentence in the target style while preserving most of
the source content. For politeness as well as five other transfer tasks, our
model outperforms the state-of-the-art methods on automatic metrics for content
preservation, with a comparable or better performance on style transfer
accuracy. Additionally, our model surpasses existing methods on human
evaluations for grammaticality, meaning preservation and transfer accuracy
across all the six style transfer tasks. The data and code is located at
https://github.com/tag-and-generate.
| [
{
"created": "Wed, 29 Apr 2020 15:08:53 GMT",
"version": "v1"
},
{
"created": "Fri, 1 May 2020 22:33:41 GMT",
"version": "v2"
}
] | 2020-05-05 | [
[
"Madaan",
"Aman",
""
],
[
"Setlur",
"Amrith",
""
],
[
"Parekh",
"Tanmay",
""
],
[
"Poczos",
"Barnabas",
""
],
[
"Neubig",
"Graham",
""
],
[
"Yang",
"Yiming",
""
],
[
"Salakhutdinov",
"Ruslan",
""
],
[
"Black",
"Alan W",
""
],
[
"Prabhumoye",
"Shrimai",
""
]
] | This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset of more than 1.39 instances automatically labeled for politeness to encourage benchmark evaluations on this new task. We design a tag and generate pipeline that identifies stylistic attributes and subsequently generates a sentence in the target style while preserving most of the source content. For politeness as well as five other transfer tasks, our model outperforms the state-of-the-art methods on automatic metrics for content preservation, with a comparable or better performance on style transfer accuracy. Additionally, our model surpasses existing methods on human evaluations for grammaticality, meaning preservation and transfer accuracy across all the six style transfer tasks. The data and code is located at https://github.com/tag-and-generate. |
1612.01492 | Jennifer Iglesias | Jennifer Iglesias and Rajmohan Rajaraman and R Ravi and Ravi Sundaram | Plane Gossip: Approximating rumor spread in planar graphs | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the design of schedules for multi-commodity multicast; we are given
an undirected graph $G$ and a collection of source destination pairs, and the
goal is to schedule a minimum-length sequence of matchings that connects every
source with its respective destination. Multi-commodity multicast models a
classic information dissemination problem in networks where the primary
communication constraint is the number of connections that a node can make, not
link bandwidth.
Multi-commodity multicast is closely related to the problem of finding a
subgraph, $H$, of optimal poise, where the poise is defined as the sum of the
maximum degree of $H$ and the maximum distance between any source-destination
pair in $H$. We first show that the minimum poise subgraph for single-commodity
multicast can be approximated to within a factor of $O(\log k)$ with respect to
the value of a natural LP relaxation in an instance with $k$ terminals. This is
the first upper bound on the integrality gap of the natural LP. Using this
poise result and shortest-path separators in planar graphs, we obtain a
$O(\log^3 k\log n/(\log\log n))$-approximation for multi-commodity multicast
for planar graphs.
We also study the minimum-time radio gossip problem in planar graphs where a
message from each node must be transmitted to all other nodes under a model
where nodes can broadcast to all neighbors in a single step but only nodes with
a single broadcasting neighbor get a message. We give an $O(\log^2
n)$-approximation for radio gossip in planar graphs breaking previous barriers.
This is the first bound for radio gossip that does not rely on the maximum
degree of the graph.
Finally, we show that our techniques for planar graphs extend to graphs with
excluded minors. We establish polylogarithmic-approximation algorithms for both
multi-commodity multicast and radio gossip problems in minor-free graphs.
| [
{
"created": "Mon, 5 Dec 2016 19:41:00 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Jul 2017 20:07:45 GMT",
"version": "v2"
}
] | 2017-07-18 | [
[
"Iglesias",
"Jennifer",
""
],
[
"Rajaraman",
"Rajmohan",
""
],
[
"Ravi",
"R",
""
],
[
"Sundaram",
"Ravi",
""
]
] | We study the design of schedules for multi-commodity multicast; we are given an undirected graph $G$ and a collection of source destination pairs, and the goal is to schedule a minimum-length sequence of matchings that connects every source with its respective destination. Multi-commodity multicast models a classic information dissemination problem in networks where the primary communication constraint is the number of connections that a node can make, not link bandwidth. Multi-commodity multicast is closely related to the problem of finding a subgraph, $H$, of optimal poise, where the poise is defined as the sum of the maximum degree of $H$ and the maximum distance between any source-destination pair in $H$. We first show that the minimum poise subgraph for single-commodity multicast can be approximated to within a factor of $O(\log k)$ with respect to the value of a natural LP relaxation in an instance with $k$ terminals. This is the first upper bound on the integrality gap of the natural LP. Using this poise result and shortest-path separators in planar graphs, we obtain a $O(\log^3 k\log n/(\log\log n))$-approximation for multi-commodity multicast for planar graphs. We also study the minimum-time radio gossip problem in planar graphs where a message from each node must be transmitted to all other nodes under a model where nodes can broadcast to all neighbors in a single step but only nodes with a single broadcasting neighbor get a message. We give an $O(\log^2 n)$-approximation for radio gossip in planar graphs breaking previous barriers. This is the first bound for radio gossip that does not rely on the maximum degree of the graph. Finally, we show that our techniques for planar graphs extend to graphs with excluded minors. We establish polylogarithmic-approximation algorithms for both multi-commodity multicast and radio gossip problems in minor-free graphs. |
2303.14476 | Xiaoru Yuan | Can Liu, Yu Zhang, Cong Wu, Chen Li and Xiaoru Yuan | A Spatial-Constraint Model for Manipulating Static Visualizations | null | null | null | null | cs.HC | http://creativecommons.org/licenses/by/4.0/ | We propose a spatial-constraint approach for modeling spatial-based
interactions and enabling interactive visualizations, which involves the
manipulation of visualizations through selection, filtering, navigation,
arrangement, and aggregation. We proposes a system that activates static
visualizations by adding intelligent interactions, which is achieved by
associating static visual objects with forces. Our force-directed technique
facilitates smooth animated transitions of the visualizations between different
interaction states. We showcase the effectiveness of our technique through
usage scenarios that involve activating visualizations in real-world settings.
| [
{
"created": "Sat, 25 Mar 2023 14:09:18 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Mar 2024 06:49:32 GMT",
"version": "v2"
}
] | 2024-03-21 | [
[
"Liu",
"Can",
""
],
[
"Zhang",
"Yu",
""
],
[
"Wu",
"Cong",
""
],
[
"Li",
"Chen",
""
],
[
"Yuan",
"Xiaoru",
""
]
] | We propose a spatial-constraint approach for modeling spatial-based interactions and enabling interactive visualizations, which involves the manipulation of visualizations through selection, filtering, navigation, arrangement, and aggregation. We proposes a system that activates static visualizations by adding intelligent interactions, which is achieved by associating static visual objects with forces. Our force-directed technique facilitates smooth animated transitions of the visualizations between different interaction states. We showcase the effectiveness of our technique through usage scenarios that involve activating visualizations in real-world settings. |
2406.04356 | Yi Yao | Yi Yao and Jun Wang and Yabai Hu and Lifeng Wang and Yi Zhou and Jack
Chen and Xuming Gai and Zhenming Wang and Wenjun Liu | BugBlitz-AI: An Intelligent QA Assistant | null | null | null | null | cs.SE cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evolution of software testing from manual to automated methods has
significantly influenced quality assurance (QA) practices. However, challenges
persist in post-execution phases, particularly in result analysis and
reporting. Traditional post-execution validation phases require manual
intervention for result analysis and report generation, leading to
inefficiencies and potential development cycle delays. This paper introduces
BugBlitz-AI, an AI-powered validation toolkit designed to enhance end-to-end
test automation by automating result analysis and bug reporting processes.
BugBlitz-AI leverages recent advancements in artificial intelligence to reduce
the time-intensive tasks of manual result analysis and report generation,
allowing QA teams to focus more on crucial aspects of product quality. By
adopting BugBlitz-AI, organizations can advance automated testing practices and
integrate AI into QA processes, ensuring higher product quality and faster
time-to-market. The paper outlines BugBlitz-AI's architecture, discusses
related work, details its quality enhancement strategies, and presents results
demonstrating its effectiveness in real-world scenarios.
| [
{
"created": "Fri, 17 May 2024 11:09:10 GMT",
"version": "v1"
}
] | 2024-06-10 | [
[
"Yao",
"Yi",
""
],
[
"Wang",
"Jun",
""
],
[
"Hu",
"Yabai",
""
],
[
"Wang",
"Lifeng",
""
],
[
"Zhou",
"Yi",
""
],
[
"Chen",
"Jack",
""
],
[
"Gai",
"Xuming",
""
],
[
"Wang",
"Zhenming",
""
],
[
"Liu",
"Wenjun",
""
]
] | The evolution of software testing from manual to automated methods has significantly influenced quality assurance (QA) practices. However, challenges persist in post-execution phases, particularly in result analysis and reporting. Traditional post-execution validation phases require manual intervention for result analysis and report generation, leading to inefficiencies and potential development cycle delays. This paper introduces BugBlitz-AI, an AI-powered validation toolkit designed to enhance end-to-end test automation by automating result analysis and bug reporting processes. BugBlitz-AI leverages recent advancements in artificial intelligence to reduce the time-intensive tasks of manual result analysis and report generation, allowing QA teams to focus more on crucial aspects of product quality. By adopting BugBlitz-AI, organizations can advance automated testing practices and integrate AI into QA processes, ensuring higher product quality and faster time-to-market. The paper outlines BugBlitz-AI's architecture, discusses related work, details its quality enhancement strategies, and presents results demonstrating its effectiveness in real-world scenarios. |
1601.00286 | Christian Lorenz Staudt | Michael Hamann, Gerd Lindner, Henning Meyerhenke, Christian L. Staudt,
Dorothea Wagner | Structure-Preserving Sparsification Methods for Social Networks | null | null | null | null | cs.SI cs.DC physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | Sparsification reduces the size of networks while preserving structural and
statistical properties of interest. Various sparsifying algorithms have been
proposed in different contexts. We contribute the first systematic conceptual
and experimental comparison of \textit{edge sparsification} methods on a
diverse set of network properties. It is shown that they can be understood as
methods for rating edges by importance and then filtering globally or locally
by these scores. We show that applying a local filtering technique improves the
preservation of all kinds of properties. In addition, we propose a new
sparsification method (\textit{Local Degree}) which preserves edges leading to
local hub nodes. All methods are evaluated on a set of social networks from
Facebook, Google+, Twitter and LiveJournal with respect to network properties
including diameter, connected components, community structure, multiple node
centrality measures and the behavior of epidemic simulations. In order to
assess the preservation of the community structure, we also include experiments
on synthetically generated networks with ground truth communities. Experiments
with our implementations of the sparsification methods (included in the
open-source network analysis tool suite NetworKit) show that many network
properties can be preserved down to about 20\% of the original set of edges for
sparse graphs with a reasonable density. The experimental results allow us to
differentiate the behavior of different methods and show which method is
suitable with respect to which property. While our Local Degree method is best
for preserving connectivity and short distances, other newly introduced local
variants are best for preserving the community structure.
| [
{
"created": "Sun, 3 Jan 2016 12:28:37 GMT",
"version": "v1"
}
] | 2016-01-05 | [
[
"Hamann",
"Michael",
""
],
[
"Lindner",
"Gerd",
""
],
[
"Meyerhenke",
"Henning",
""
],
[
"Staudt",
"Christian L.",
""
],
[
"Wagner",
"Dorothea",
""
]
] | Sparsification reduces the size of networks while preserving structural and statistical properties of interest. Various sparsifying algorithms have been proposed in different contexts. We contribute the first systematic conceptual and experimental comparison of \textit{edge sparsification} methods on a diverse set of network properties. It is shown that they can be understood as methods for rating edges by importance and then filtering globally or locally by these scores. We show that applying a local filtering technique improves the preservation of all kinds of properties. In addition, we propose a new sparsification method (\textit{Local Degree}) which preserves edges leading to local hub nodes. All methods are evaluated on a set of social networks from Facebook, Google+, Twitter and LiveJournal with respect to network properties including diameter, connected components, community structure, multiple node centrality measures and the behavior of epidemic simulations. In order to assess the preservation of the community structure, we also include experiments on synthetically generated networks with ground truth communities. Experiments with our implementations of the sparsification methods (included in the open-source network analysis tool suite NetworKit) show that many network properties can be preserved down to about 20\% of the original set of edges for sparse graphs with a reasonable density. The experimental results allow us to differentiate the behavior of different methods and show which method is suitable with respect to which property. While our Local Degree method is best for preserving connectivity and short distances, other newly introduced local variants are best for preserving the community structure. |
2301.01413 | Yuren Cong | Yuren Cong, Martin Renqiang Min, Li Erran Li, Bodo Rosenhahn, Michael
Ying Yang | Attribute-Centric Compositional Text-to-Image Generation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the recent impressive breakthroughs in text-to-image generation,
generative models have difficulty in capturing the data distribution of
underrepresented attribute compositions while over-memorizing overrepresented
attribute compositions, which raises public concerns about their robustness and
fairness. To tackle this challenge, we propose ACTIG, an attribute-centric
compositional text-to-image generation framework. We present an
attribute-centric feature augmentation and a novel image-free training scheme,
which greatly improves model's ability to generate images with underrepresented
attributes. We further propose an attribute-centric contrastive loss to avoid
overfitting to overrepresented attribute compositions. We validate our
framework on the CelebA-HQ and CUB datasets. Extensive experiments show that
the compositional generalization of ACTIG is outstanding, and our framework
outperforms previous works in terms of image quality and text-image
consistency.
| [
{
"created": "Wed, 4 Jan 2023 03:03:08 GMT",
"version": "v1"
}
] | 2023-01-05 | [
[
"Cong",
"Yuren",
""
],
[
"Min",
"Martin Renqiang",
""
],
[
"Li",
"Li Erran",
""
],
[
"Rosenhahn",
"Bodo",
""
],
[
"Yang",
"Michael Ying",
""
]
] | Despite the recent impressive breakthroughs in text-to-image generation, generative models have difficulty in capturing the data distribution of underrepresented attribute compositions while over-memorizing overrepresented attribute compositions, which raises public concerns about their robustness and fairness. To tackle this challenge, we propose ACTIG, an attribute-centric compositional text-to-image generation framework. We present an attribute-centric feature augmentation and a novel image-free training scheme, which greatly improves model's ability to generate images with underrepresented attributes. We further propose an attribute-centric contrastive loss to avoid overfitting to overrepresented attribute compositions. We validate our framework on the CelebA-HQ and CUB datasets. Extensive experiments show that the compositional generalization of ACTIG is outstanding, and our framework outperforms previous works in terms of image quality and text-image consistency. |
1810.01489 | Paul Liu | Paul Liu, Jan Vondrak | Submodular Optimization in the MapReduce Model | 10 pages | null | null | null | cs.DC | http://creativecommons.org/licenses/by/4.0/ | Submodular optimization has received significant attention in both practice
and theory, as a wide array of problems in machine learning, auction theory,
and combinatorial optimization have submodular structure. In practice, these
problems often involve large amounts of data, and must be solved in a
distributed way. One popular framework for running such distributed algorithms
is MapReduce. In this paper, we present two simple algorithms for cardinality
constrained submodular optimization in the MapReduce model: the first is a
$(1/2-o(1))$-approximation in 2 MapReduce rounds, and the second is a
$(1-1/e-\epsilon)$-approximation in $\frac{1+o(1)}{\epsilon}$ MapReduce rounds.
| [
{
"created": "Tue, 2 Oct 2018 20:08:27 GMT",
"version": "v1"
}
] | 2018-10-04 | [
[
"Liu",
"Paul",
""
],
[
"Vondrak",
"Jan",
""
]
] | Submodular optimization has received significant attention in both practice and theory, as a wide array of problems in machine learning, auction theory, and combinatorial optimization have submodular structure. In practice, these problems often involve large amounts of data, and must be solved in a distributed way. One popular framework for running such distributed algorithms is MapReduce. In this paper, we present two simple algorithms for cardinality constrained submodular optimization in the MapReduce model: the first is a $(1/2-o(1))$-approximation in 2 MapReduce rounds, and the second is a $(1-1/e-\epsilon)$-approximation in $\frac{1+o(1)}{\epsilon}$ MapReduce rounds. |
1904.11799 | Mohit Sharma | Mohit Sharma, Jiayu Zhou, Junling Hu, George Karypis | Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n
Item Recommendation | 9 pages, Proceedings of the 2015 SIAM International Conference on
Data Mining | null | 10.1137/1.9781611974010.22 | null | cs.IR cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recommending new items to existing users has remained a challenging problem
due to absence of user's past preferences for these items. The user
personalized non-collaborative methods based on item features can be used to
address this item cold-start problem. These methods rely on similarities
between the target item and user's previous preferred items. While computing
similarities based on item features, these methods overlook the interactions
among the features of the items and consider them independently. Modeling
interactions among features can be helpful as some features, when considered
together, provide a stronger signal on the relevance of an item when compared
to case where features are considered independently. To address this important
issue, in this work we introduce the Feature-based factorized Bilinear
Similarity Model (FBSM), which learns factorized bilinear similarity model for
TOP-n recommendation of new items, given the information about items preferred
by users in past as well as the features of these items. We carry out extensive
empirical evaluations on benchmark datasets, and we find that the proposed FBSM
approach improves upon traditional non-collaborative methods in terms of
recommendation performance. Moreover, the proposed approach also learns
insightful interactions among item features from data, which lead to deep
understanding on how these interactions contribute to personalized
recommendation.
| [
{
"created": "Mon, 22 Apr 2019 05:10:48 GMT",
"version": "v1"
}
] | 2019-04-29 | [
[
"Sharma",
"Mohit",
""
],
[
"Zhou",
"Jiayu",
""
],
[
"Hu",
"Junling",
""
],
[
"Karypis",
"George",
""
]
] | Recommending new items to existing users has remained a challenging problem due to absence of user's past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item and user's previous preferred items. While computing similarities based on item features, these methods overlook the interactions among the features of the items and consider them independently. Modeling interactions among features can be helpful as some features, when considered together, provide a stronger signal on the relevance of an item when compared to case where features are considered independently. To address this important issue, in this work we introduce the Feature-based factorized Bilinear Similarity Model (FBSM), which learns factorized bilinear similarity model for TOP-n recommendation of new items, given the information about items preferred by users in past as well as the features of these items. We carry out extensive empirical evaluations on benchmark datasets, and we find that the proposed FBSM approach improves upon traditional non-collaborative methods in terms of recommendation performance. Moreover, the proposed approach also learns insightful interactions among item features from data, which lead to deep understanding on how these interactions contribute to personalized recommendation. |
1803.01221 | Bhavya Kailkhura | Bhavya Kailkhura, Priyadip Ray, Deepak Rajan, Anton Yen, Peter Barnes,
Ryan Goldhahn | Byzantine-Resilient Locally Optimum Detection Using Collaborative
Autonomous Networks | Proceedings of the 2017 IEEE International Workshop on Computational
Advances in Multi-Sensor Adaptive Processing (CAMSAP 2017), 10.-13. December
2017, Curacao, Dutch Antilles | null | null | null | cs.SY stat.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a locally optimum detection (LOD) scheme for
detecting a weak radioactive source buried in background clutter. We develop a
decentralized algorithm, based on alternating direction method of multipliers
(ADMM), for implementing the proposed scheme in autonomous sensor networks.
Results show that algorithm performance approaches the centralized clairvoyant
detection algorithm in the low SNR regime, and exhibits excellent convergence
rate and scaling behavior (w.r.t. number of nodes). We also devise a
low-overhead, robust ADMM algorithm for Byzantine-resilient detection, and
demonstrate its robustness to data falsification attacks.
| [
{
"created": "Sat, 3 Mar 2018 19:34:29 GMT",
"version": "v1"
}
] | 2018-03-06 | [
[
"Kailkhura",
"Bhavya",
""
],
[
"Ray",
"Priyadip",
""
],
[
"Rajan",
"Deepak",
""
],
[
"Yen",
"Anton",
""
],
[
"Barnes",
"Peter",
""
],
[
"Goldhahn",
"Ryan",
""
]
] | In this paper, we propose a locally optimum detection (LOD) scheme for detecting a weak radioactive source buried in background clutter. We develop a decentralized algorithm, based on alternating direction method of multipliers (ADMM), for implementing the proposed scheme in autonomous sensor networks. Results show that algorithm performance approaches the centralized clairvoyant detection algorithm in the low SNR regime, and exhibits excellent convergence rate and scaling behavior (w.r.t. number of nodes). We also devise a low-overhead, robust ADMM algorithm for Byzantine-resilient detection, and demonstrate its robustness to data falsification attacks. |
2303.14078 | Jisoo Jeong | Jisoo Jeong, Hong Cai, Risheek Garrepalli, Fatih Porikli | DistractFlow: Improving Optical Flow Estimation via Realistic
Distractions and Pseudo-Labeling | CVPR 2023 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | We propose a novel data augmentation approach, DistractFlow, for training
optical flow estimation models by introducing realistic distractions to the
input frames. Based on a mixing ratio, we combine one of the frames in the pair
with a distractor image depicting a similar domain, which allows for inducing
visual perturbations congruent with natural objects and scenes. We refer to
such pairs as distracted pairs. Our intuition is that using semantically
meaningful distractors enables the model to learn related variations and attain
robustness against challenging deviations, compared to conventional
augmentation schemes focusing only on low-level aspects and modifications. More
specifically, in addition to the supervised loss computed between the estimated
flow for the original pair and its ground-truth flow, we include a second
supervised loss defined between the distracted pair's flow and the original
pair's ground-truth flow, weighted with the same mixing ratio. Furthermore,
when unlabeled data is available, we extend our augmentation approach to
self-supervised settings through pseudo-labeling and cross-consistency
regularization. Given an original pair and its distracted version, we enforce
the estimated flow on the distracted pair to agree with the flow of the
original pair. Our approach allows increasing the number of available training
pairs significantly without requiring additional annotations. It is agnostic to
the model architecture and can be applied to training any optical flow
estimation models. Our extensive evaluations on multiple benchmarks, including
Sintel, KITTI, and SlowFlow, show that DistractFlow improves existing models
consistently, outperforming the latest state of the art.
| [
{
"created": "Fri, 24 Mar 2023 15:42:54 GMT",
"version": "v1"
}
] | 2023-03-27 | [
[
"Jeong",
"Jisoo",
""
],
[
"Cai",
"Hong",
""
],
[
"Garrepalli",
"Risheek",
""
],
[
"Porikli",
"Fatih",
""
]
] | We propose a novel data augmentation approach, DistractFlow, for training optical flow estimation models by introducing realistic distractions to the input frames. Based on a mixing ratio, we combine one of the frames in the pair with a distractor image depicting a similar domain, which allows for inducing visual perturbations congruent with natural objects and scenes. We refer to such pairs as distracted pairs. Our intuition is that using semantically meaningful distractors enables the model to learn related variations and attain robustness against challenging deviations, compared to conventional augmentation schemes focusing only on low-level aspects and modifications. More specifically, in addition to the supervised loss computed between the estimated flow for the original pair and its ground-truth flow, we include a second supervised loss defined between the distracted pair's flow and the original pair's ground-truth flow, weighted with the same mixing ratio. Furthermore, when unlabeled data is available, we extend our augmentation approach to self-supervised settings through pseudo-labeling and cross-consistency regularization. Given an original pair and its distracted version, we enforce the estimated flow on the distracted pair to agree with the flow of the original pair. Our approach allows increasing the number of available training pairs significantly without requiring additional annotations. It is agnostic to the model architecture and can be applied to training any optical flow estimation models. Our extensive evaluations on multiple benchmarks, including Sintel, KITTI, and SlowFlow, show that DistractFlow improves existing models consistently, outperforming the latest state of the art. |
cs/0607095 | Hyundong Shin | Hyundong Shin, Moe Z. Win | Gallager's Exponent for MIMO Channels: A Reliability-Rate Tradeoff | Submitted to the IEEE Transactions on Communications | null | null | null | cs.IT math.IT | null | In this paper, we derive Gallager's random coding error exponent for
multiple-input multiple-output (MIMO) channels, assuming no channel-state
information (CSI) at the transmitter and perfect CSI at the receiver. This
measure gives insight into a fundamental tradeoff between the communication
reliability and information rate of MIMO channels, enabling to determine the
required codeword length to achieve a prescribed error probability at a given
rate below the channel capacity. We quantify the effects of the number of
antennas, channel coherence time, and spatial fading correlation on the MIMO
exponent. In addition, general formulae for the ergodic capacity and the cutoff
rate in the presence of spatial correlation are deduced from the exponent
expressions. These formulae are applicable to arbitrary structures of transmit
and receive correlation, encompassing all the previously known results as
special cases of our expressions.
| [
{
"created": "Thu, 20 Jul 2006 06:56:02 GMT",
"version": "v1"
}
] | 2007-07-13 | [
[
"Shin",
"Hyundong",
""
],
[
"Win",
"Moe Z.",
""
]
] | In this paper, we derive Gallager's random coding error exponent for multiple-input multiple-output (MIMO) channels, assuming no channel-state information (CSI) at the transmitter and perfect CSI at the receiver. This measure gives insight into a fundamental tradeoff between the communication reliability and information rate of MIMO channels, enabling to determine the required codeword length to achieve a prescribed error probability at a given rate below the channel capacity. We quantify the effects of the number of antennas, channel coherence time, and spatial fading correlation on the MIMO exponent. In addition, general formulae for the ergodic capacity and the cutoff rate in the presence of spatial correlation are deduced from the exponent expressions. These formulae are applicable to arbitrary structures of transmit and receive correlation, encompassing all the previously known results as special cases of our expressions. |
cs/0608098 | Arvind Parthasarathy | Arvind Parthasarathy | Improved Content Based Image Watermarking | 24 pages | null | null | null | cs.CR | null | This paper presents a robust and transparent scheme of watermarking that
exploits the human visual systems' sensitivity to frequency, along with local
image characteristics obtained from the spatial domain. The underlying idea is
generating a visual mask based on the visual systems' perception of image
content. This mask is used to embed a decimal sequence while keeping its
amplitude below the distortion sensitivity of the image pixel. We consider
texture, luminance, corner and the edge information in the image to generate a
mask that makes the addition of the watermark imperceptible to the human eye.
| [
{
"created": "Fri, 25 Aug 2006 12:55:42 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Parthasarathy",
"Arvind",
""
]
] | This paper presents a robust and transparent scheme of watermarking that exploits the human visual systems' sensitivity to frequency, along with local image characteristics obtained from the spatial domain. The underlying idea is generating a visual mask based on the visual systems' perception of image content. This mask is used to embed a decimal sequence while keeping its amplitude below the distortion sensitivity of the image pixel. We consider texture, luminance, corner and the edge information in the image to generate a mask that makes the addition of the watermark imperceptible to the human eye. |
1905.02265 | Xusen Yin | Xusen Yin and Jonathan May | Comprehensible Context-driven Text Game Playing | IEEE Conference on Games 2019 Long Paper | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In order to train a computer agent to play a text-based computer game, we
must represent each hidden state of the game. A Long Short-Term Memory (LSTM)
model running over observed texts is a common choice for state construction.
However, a normal Deep Q-learning Network (DQN) for such an agent requires
millions of steps of training or more to converge. As such, an LSTM-based DQN
can take tens of days to finish the training process. Though we can use a
Convolutional Neural Network (CNN) as a text-encoder to construct states much
faster than the LSTM, doing so without an understanding of the syntactic
context of the words being analyzed can slow convergence. In this paper, we use
a fast CNN to encode position- and syntax-oriented structures extracted from
observed texts as states. We additionally augment the reward signal in a
universal and practical manner. Together, we show that our improvements can not
only speed up the process by one order of magnitude but also learn a superior
agent.
| [
{
"created": "Mon, 6 May 2019 21:14:41 GMT",
"version": "v1"
},
{
"created": "Sun, 2 Jun 2019 02:48:31 GMT",
"version": "v2"
},
{
"created": "Thu, 29 Aug 2019 11:50:00 GMT",
"version": "v3"
}
] | 2019-08-30 | [
[
"Yin",
"Xusen",
""
],
[
"May",
"Jonathan",
""
]
] | In order to train a computer agent to play a text-based computer game, we must represent each hidden state of the game. A Long Short-Term Memory (LSTM) model running over observed texts is a common choice for state construction. However, a normal Deep Q-learning Network (DQN) for such an agent requires millions of steps of training or more to converge. As such, an LSTM-based DQN can take tens of days to finish the training process. Though we can use a Convolutional Neural Network (CNN) as a text-encoder to construct states much faster than the LSTM, doing so without an understanding of the syntactic context of the words being analyzed can slow convergence. In this paper, we use a fast CNN to encode position- and syntax-oriented structures extracted from observed texts as states. We additionally augment the reward signal in a universal and practical manner. Together, we show that our improvements can not only speed up the process by one order of magnitude but also learn a superior agent. |
2008.12813 | Sanxing Chen | Sanxing Chen, Xiaodong Liu, Jianfeng Gao, Jian Jiao, Ruofei Zhang and
Yangfeng Ji | HittER: Hierarchical Transformers for Knowledge Graph Embeddings | EMNLP 2021 | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper examines the challenging problem of learning representations of
entities and relations in a complex multi-relational knowledge graph. We
propose HittER, a Hierarchical Transformer model to jointly learn
Entity-relation composition and Relational contextualization based on a source
entity's neighborhood. Our proposed model consists of two different Transformer
blocks: the bottom block extracts features of each entity-relation pair in the
local neighborhood of the source entity and the top block aggregates the
relational information from outputs of the bottom block. We further design a
masked entity prediction task to balance information from the relational
context and the source entity itself. Experimental results show that HittER
achieves new state-of-the-art results on multiple link prediction datasets. We
additionally propose a simple approach to integrate HittER into BERT and
demonstrate its effectiveness on two Freebase factoid question answering
datasets.
| [
{
"created": "Fri, 28 Aug 2020 18:58:15 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Oct 2021 04:52:07 GMT",
"version": "v2"
}
] | 2021-10-07 | [
[
"Chen",
"Sanxing",
""
],
[
"Liu",
"Xiaodong",
""
],
[
"Gao",
"Jianfeng",
""
],
[
"Jiao",
"Jian",
""
],
[
"Zhang",
"Ruofei",
""
],
[
"Ji",
"Yangfeng",
""
]
] | This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph. We propose HittER, a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity's neighborhood. Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block. We further design a masked entity prediction task to balance information from the relational context and the source entity itself. Experimental results show that HittER achieves new state-of-the-art results on multiple link prediction datasets. We additionally propose a simple approach to integrate HittER into BERT and demonstrate its effectiveness on two Freebase factoid question answering datasets. |
2207.12872 | Ishaan Bhat | Ishaan Bhat, Josien P.W. Pluim, Hugo J. Kuijf | Generalized Probabilistic U-Net for medical image segementation | Accepted at Uncertainty for Safe Utilization of Machine Learning in
Medical Imaging (UNSURE) 2022 | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | We propose the Generalized Probabilistic U-Net, which extends the
Probabilistic U-Net by allowing more general forms of the Gaussian distribution
as the latent space distribution that can better approximate the uncertainty in
the reference segmentations. We study the effect the choice of latent space
distribution has on capturing the uncertainty in the reference segmentations
using the LIDC-IDRI dataset. We show that the choice of distribution affects
the sample diversity of the predictions and their overlap with respect to the
reference segmentations. For the LIDC-IDRI dataset, we show that using a
mixture of Gaussians results in a statistically significant improvement in the
generalized energy distance (GED) metric with respect to the standard
Probabilistic U-Net. We have made our implementation available at
https://github.com/ishaanb92/GeneralizedProbabilisticUNet
| [
{
"created": "Tue, 26 Jul 2022 13:03:37 GMT",
"version": "v1"
}
] | 2022-07-27 | [
[
"Bhat",
"Ishaan",
""
],
[
"Pluim",
"Josien P. W.",
""
],
[
"Kuijf",
"Hugo J.",
""
]
] | We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the uncertainty in the reference segmentations using the LIDC-IDRI dataset. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. For the LIDC-IDRI dataset, we show that using a mixture of Gaussians results in a statistically significant improvement in the generalized energy distance (GED) metric with respect to the standard Probabilistic U-Net. We have made our implementation available at https://github.com/ishaanb92/GeneralizedProbabilisticUNet |
2302.13394 | Ahmed Abulila | Ahmed Abulila, Izzat El Hajj, Myoungsoo Jung, Nam Sung Kim | Asynchronous Persistence with ASAP | 2 pages, 2 figures, 14th Annual Non-Volatile Memories Workshop | null | null | null | cs.AR | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Supporting atomic durability of updates for persistent memories is typically
achieved with Write-Ahead Logging (WAL). WAL flushes log entries to persistent
memory before making the actual data persistent to ensure that a consistent
state can be recovered if a crash occurs. Performing WAL in hardware is
attractive because it makes most aspects of log management transparent to
software, and it completes log persist operations (LPs) and data persist
operations (DPs) in the background, overlapping them with the execution of
other instructions.
Prior hardware logging solutions commit atomic regions synchronously. Once
the end of a region is reached, all outstanding persist operations required for
the region to commit must be completed before instruction execution may
proceed. For undo logging, LPs and DPs are both performed synchronously to
ensure that the region commits synchronously. For redo logging, DPs can be
performed asynchronously, but LPs are performed synchronously to ensure that
the region commits synchronously. In both cases, waiting for synchronous
persist operations (LP or DP) at the end of an atomic region causes atomic
regions to incur high latency.
To tackle this limitation, we propose ASAP, a hardware logging solution that
allows atomic regions to commit asynchronously. That is, once the end of an
atomic region is reached, instruction execution may proceed without waiting for
outstanding persist operations to complete. As such, both LPs and DPs can be
performed asynchronously. The challenge with allowing atomic regions to commit
asynchronously is that it can lead to control and data dependence violations in
the commit order of the atomic regions, leaving data in an unrecoverable state
in case of a crash. To address this issue, ASAP tracks and enforces control and
data dependencies between atomic regions in hardware to ensure that the regions
commit in the proper order.
| [
{
"created": "Sun, 26 Feb 2023 19:34:59 GMT",
"version": "v1"
}
] | 2023-02-28 | [
[
"Abulila",
"Ahmed",
""
],
[
"Hajj",
"Izzat El",
""
],
[
"Jung",
"Myoungsoo",
""
],
[
"Kim",
"Nam Sung",
""
]
] | Supporting atomic durability of updates for persistent memories is typically achieved with Write-Ahead Logging (WAL). WAL flushes log entries to persistent memory before making the actual data persistent to ensure that a consistent state can be recovered if a crash occurs. Performing WAL in hardware is attractive because it makes most aspects of log management transparent to software, and it completes log persist operations (LPs) and data persist operations (DPs) in the background, overlapping them with the execution of other instructions. Prior hardware logging solutions commit atomic regions synchronously. Once the end of a region is reached, all outstanding persist operations required for the region to commit must be completed before instruction execution may proceed. For undo logging, LPs and DPs are both performed synchronously to ensure that the region commits synchronously. For redo logging, DPs can be performed asynchronously, but LPs are performed synchronously to ensure that the region commits synchronously. In both cases, waiting for synchronous persist operations (LP or DP) at the end of an atomic region causes atomic regions to incur high latency. To tackle this limitation, we propose ASAP, a hardware logging solution that allows atomic regions to commit asynchronously. That is, once the end of an atomic region is reached, instruction execution may proceed without waiting for outstanding persist operations to complete. As such, both LPs and DPs can be performed asynchronously. The challenge with allowing atomic regions to commit asynchronously is that it can lead to control and data dependence violations in the commit order of the atomic regions, leaving data in an unrecoverable state in case of a crash. To address this issue, ASAP tracks and enforces control and data dependencies between atomic regions in hardware to ensure that the regions commit in the proper order. |
2105.02432 | Taotao Jing | Taotao Jing, Hongfu Liu, Zhengming Ding | Towards Novel Target Discovery Through Open-Set Domain Adaptation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Open-set domain adaptation (OSDA) considers that the target domain contains
samples from novel categories unobserved in external source domain.
Unfortunately, existing OSDA methods always ignore the demand for the
information of unseen categories and simply recognize them as "unknown" set
without further explanation. This motivates us to understand the unknown
categories more specifically by exploring the underlying structures and
recovering their interpretable semantic attributes. In this paper, we propose a
novel framework to accurately identify the seen categories in target domain,
and effectively recover the semantic attributes for unseen categories.
Specifically, structure preserving partial alignment is developed to recognize
the seen categories through domain-invariant feature learning. Attribute
propagation over visual graph is designed to smoothly transit attributes from
seen to unseen categories via visual-semantic mapping. Moreover, two new
cross-main benchmarks are constructed to evaluate the proposed framework in the
novel and practical challenge. Experimental results on open-set recognition and
semantic recovery demonstrate the superiority of the proposed method over other
compared baselines.
| [
{
"created": "Thu, 6 May 2021 04:22:29 GMT",
"version": "v1"
},
{
"created": "Sun, 16 May 2021 22:32:43 GMT",
"version": "v2"
},
{
"created": "Mon, 9 Aug 2021 17:12:45 GMT",
"version": "v3"
},
{
"created": "Wed, 11 Aug 2021 18:32:16 GMT",
"version": "v4"
}
] | 2021-08-13 | [
[
"Jing",
"Taotao",
""
],
[
"Liu",
"Hongfu",
""
],
[
"Ding",
"Zhengming",
""
]
] | Open-set domain adaptation (OSDA) considers that the target domain contains samples from novel categories unobserved in external source domain. Unfortunately, existing OSDA methods always ignore the demand for the information of unseen categories and simply recognize them as "unknown" set without further explanation. This motivates us to understand the unknown categories more specifically by exploring the underlying structures and recovering their interpretable semantic attributes. In this paper, we propose a novel framework to accurately identify the seen categories in target domain, and effectively recover the semantic attributes for unseen categories. Specifically, structure preserving partial alignment is developed to recognize the seen categories through domain-invariant feature learning. Attribute propagation over visual graph is designed to smoothly transit attributes from seen to unseen categories via visual-semantic mapping. Moreover, two new cross-main benchmarks are constructed to evaluate the proposed framework in the novel and practical challenge. Experimental results on open-set recognition and semantic recovery demonstrate the superiority of the proposed method over other compared baselines. |
2302.14533 | Rajiv Kumar V | Rajiv Kumar, G. Sivakumar | DEff-GAN: Diverse Attribute Transfer for Few-Shot Image Synthesis | null | null | 10.5220/0011799600003417 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Requirements of large amounts of data is a difficulty in training many GANs.
Data efficient GANs involve fitting a generators continuous target distribution
with a limited discrete set of data samples, which is a difficult task. Single
image methods have focused on modeling the internal distribution of a single
image and generating its samples. While single image methods can synthesize
image samples with diversity, they do not model multiple images or capture the
inherent relationship possible between two images. Given only a handful of
images, we are interested in generating samples and exploiting the
commonalities in the input images. In this work, we extend the single-image GAN
method to model multiple images for sample synthesis. We modify the
discriminator with an auxiliary classifier branch, which helps to generate a
wide variety of samples and to classify the input labels. Our Data-Efficient
GAN (DEff-GAN) generates excellent results when similarities and
correspondences can be drawn between the input images or classes.
| [
{
"created": "Tue, 28 Feb 2023 12:43:52 GMT",
"version": "v1"
}
] | 2023-03-09 | [
[
"Kumar",
"Rajiv",
""
],
[
"Sivakumar",
"G.",
""
]
] | Requirements of large amounts of data is a difficulty in training many GANs. Data efficient GANs involve fitting a generators continuous target distribution with a limited discrete set of data samples, which is a difficult task. Single image methods have focused on modeling the internal distribution of a single image and generating its samples. While single image methods can synthesize image samples with diversity, they do not model multiple images or capture the inherent relationship possible between two images. Given only a handful of images, we are interested in generating samples and exploiting the commonalities in the input images. In this work, we extend the single-image GAN method to model multiple images for sample synthesis. We modify the discriminator with an auxiliary classifier branch, which helps to generate a wide variety of samples and to classify the input labels. Our Data-Efficient GAN (DEff-GAN) generates excellent results when similarities and correspondences can be drawn between the input images or classes. |
1806.02681 | Wanderson Ten\'orio | Carlos Munuera, Wanderson Ten\'orio, Fernando Torres | Locally Recoverable codes from algebraic curves with separated variables | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A Locally Recoverable code is an error-correcting code such that any erasure
in a single coordinate of a codeword can be recovered from a small subset of
other coordinates. We study Locally Recoverable Algebraic Geometry codes
arising from certain curves defined by equations with separated variables. The
recovery of erasures is obtained by means of Lagrangian interpolation in
general, and simply by one addition in some particular cases.
| [
{
"created": "Thu, 7 Jun 2018 13:48:35 GMT",
"version": "v1"
}
] | 2018-06-08 | [
[
"Munuera",
"Carlos",
""
],
[
"Tenório",
"Wanderson",
""
],
[
"Torres",
"Fernando",
""
]
] | A Locally Recoverable code is an error-correcting code such that any erasure in a single coordinate of a codeword can be recovered from a small subset of other coordinates. We study Locally Recoverable Algebraic Geometry codes arising from certain curves defined by equations with separated variables. The recovery of erasures is obtained by means of Lagrangian interpolation in general, and simply by one addition in some particular cases. |
2104.00675 | Hsin-Ying Lee | Yen-Chi Cheng, Chieh Hubert Lin, Hsin-Ying Lee, Jian Ren, Sergey
Tulyakov, Ming-Hsuan Yang | In&Out : Diverse Image Outpainting via GAN Inversion | Project Page: https://yccyenchicheng.github.io/InOut/ | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Image outpainting seeks for a semantically consistent extension of the input
image beyond its available content. Compared to inpainting -- filling in
missing pixels in a way coherent with the neighboring pixels -- outpainting can
be achieved in more diverse ways since the problem is less constrained by the
surrounding pixels. Existing image outpainting methods pose the problem as a
conditional image-to-image translation task, often generating repetitive
structures and textures by replicating the content available in the input
image. In this work, we formulate the problem from the perspective of inverting
generative adversarial networks. Our generator renders micro-patches
conditioned on their joint latent code as well as their individual positions in
the image. To outpaint an image, we seek for multiple latent codes not only
recovering available patches but also synthesizing diverse outpainting by
patch-based generation. This leads to richer structure and content in the
outpainted regions. Furthermore, our formulation allows for outpainting
conditioned on the categorical input, thereby enabling flexible user controls.
Extensive experimental results demonstrate the proposed method performs
favorably against existing in- and outpainting methods, featuring higher visual
quality and diversity.
| [
{
"created": "Thu, 1 Apr 2021 17:59:10 GMT",
"version": "v1"
}
] | 2021-04-02 | [
[
"Cheng",
"Yen-Chi",
""
],
[
"Lin",
"Chieh Hubert",
""
],
[
"Lee",
"Hsin-Ying",
""
],
[
"Ren",
"Jian",
""
],
[
"Tulyakov",
"Sergey",
""
],
[
"Yang",
"Ming-Hsuan",
""
]
] | Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. Compared to inpainting -- filling in missing pixels in a way coherent with the neighboring pixels -- outpainting can be achieved in more diverse ways since the problem is less constrained by the surrounding pixels. Existing image outpainting methods pose the problem as a conditional image-to-image translation task, often generating repetitive structures and textures by replicating the content available in the input image. In this work, we formulate the problem from the perspective of inverting generative adversarial networks. Our generator renders micro-patches conditioned on their joint latent code as well as their individual positions in the image. To outpaint an image, we seek for multiple latent codes not only recovering available patches but also synthesizing diverse outpainting by patch-based generation. This leads to richer structure and content in the outpainted regions. Furthermore, our formulation allows for outpainting conditioned on the categorical input, thereby enabling flexible user controls. Extensive experimental results demonstrate the proposed method performs favorably against existing in- and outpainting methods, featuring higher visual quality and diversity. |
2112.09647 | Matteo Dunnhofer | Matteo Dunnhofer, Alberto Zurini, Maurizio Dunnhofer, Christian
Micheloni | Video-Based Reconstruction of the Trajectories Performed by Skiers | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Trajectories are fundamental in different skiing disciplines. Tools enabling
the analysis of such curves can enhance the training activity and enrich the
broadcasting contents. However, the solutions currently available are based on
geo-localized sensors and surface models. In this short paper, we propose a
video-based approach to reconstruct the sequence of points traversed by an
athlete during its performance. Our prototype is constituted by a pipeline of
deep learning-based algorithms to reconstruct the athlete's motion and to
visualize it according to the camera perspective. This is achieved for
different skiing disciplines in the wild without any camera calibration. We
tested our solution on broadcast and smartphone-captured videos of alpine
skiing and ski jumping professional competitions. The qualitative results
achieved show the potential of our solution.
| [
{
"created": "Fri, 17 Dec 2021 17:40:06 GMT",
"version": "v1"
}
] | 2021-12-20 | [
[
"Dunnhofer",
"Matteo",
""
],
[
"Zurini",
"Alberto",
""
],
[
"Dunnhofer",
"Maurizio",
""
],
[
"Micheloni",
"Christian",
""
]
] | Trajectories are fundamental in different skiing disciplines. Tools enabling the analysis of such curves can enhance the training activity and enrich the broadcasting contents. However, the solutions currently available are based on geo-localized sensors and surface models. In this short paper, we propose a video-based approach to reconstruct the sequence of points traversed by an athlete during its performance. Our prototype is constituted by a pipeline of deep learning-based algorithms to reconstruct the athlete's motion and to visualize it according to the camera perspective. This is achieved for different skiing disciplines in the wild without any camera calibration. We tested our solution on broadcast and smartphone-captured videos of alpine skiing and ski jumping professional competitions. The qualitative results achieved show the potential of our solution. |
2109.00958 | Juan David Guerrero Balaguera | Juan-David Guerrero-Balaguera, Josie E. Rodriguez Condia, Matteo Sonza
Reorda | A Novel Compaction Approach for SBST Test Programs | Paper accepted to be presented in The 30th IEEE Asian Test Symposium
(ATS 2021) November 22 - 25, 2021, Japan. to be published in the IEEE xplorer
after the presentation in the event | null | null | null | cs.AR | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In-field test of processor-based devices is a must when considering
safety-critical systems (e.g., in robotics, aerospace, and automotive
applications). During in-field testing, different solutions can be adopted,
depending on the specific constraints of each scenario. In the last years,
Self-Test Libraries (STLs) developed by IP or semiconductor companies became
widely adopted. Given the strict constraints of in-field test, the size and
time duration of a STL is a crucial parameter. This work introduces a novel
approach to compress functional test programs belonging to an STL. The proposed
approach is based on analyzing (via logic simulation) the interaction between
the micro-architectural operation performed by each instruction and its
capacity to propagate fault effects on any observable output, reducing the
required fault simulations to only one. The proposed compaction strategy was
validated by resorting to a RISC-V processor and several test programs stemming
from diverse generation strategies. Results showed that the proposed compaction
approach can reduce the length of test programs by up to 93.9% and their
duration by up to 95%, with minimal effect on fault coverage.
| [
{
"created": "Thu, 2 Sep 2021 13:58:02 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Sep 2021 12:07:03 GMT",
"version": "v2"
}
] | 2021-09-09 | [
[
"Guerrero-Balaguera",
"Juan-David",
""
],
[
"Condia",
"Josie E. Rodriguez",
""
],
[
"Reorda",
"Matteo Sonza",
""
]
] | In-field test of processor-based devices is a must when considering safety-critical systems (e.g., in robotics, aerospace, and automotive applications). During in-field testing, different solutions can be adopted, depending on the specific constraints of each scenario. In the last years, Self-Test Libraries (STLs) developed by IP or semiconductor companies became widely adopted. Given the strict constraints of in-field test, the size and time duration of a STL is a crucial parameter. This work introduces a novel approach to compress functional test programs belonging to an STL. The proposed approach is based on analyzing (via logic simulation) the interaction between the micro-architectural operation performed by each instruction and its capacity to propagate fault effects on any observable output, reducing the required fault simulations to only one. The proposed compaction strategy was validated by resorting to a RISC-V processor and several test programs stemming from diverse generation strategies. Results showed that the proposed compaction approach can reduce the length of test programs by up to 93.9% and their duration by up to 95%, with minimal effect on fault coverage. |
2309.00504 | Manuel Sorge | Alexander Firbas and Alexander Dobler and Fabian Holzer and Jakob
Schafellner and Manuel Sorge and Ana\"is Villedieu and Monika Wi{\ss}mann | The Complexity of Cluster Vertex Splitting and Company | 30 pages, 9 figures. Appears in SOFSEM 2024 | null | null | null | cs.DS cs.CC cs.DM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Clustering a graph when the clusters can overlap can be seen from three
different angles: We may look for cliques that cover the edges of the graph
with bounded overlap, we may look to add or delete few edges to uncover the
cluster structure, or we may split vertices to separate the clusters from each
other. Splitting a vertex $v$ means to remove it and to add two new copies of
$v$ and to make each previous neighbor of $v$ adjacent with at least one of the
copies. In this work, we study underlying computational problems regarding the
three angles to overlapping clusterings, in particular when the overlap is
small. We show that the above-mentioned covering problem is NP-complete. We
then make structural observations that show that the covering viewpoint and the
vertex-splitting viewpoint are equivalent, yielding NP-hardness for the
vertex-splitting problem. On the positive side, we show that splitting at most
$k$ vertices to obtain a cluster graph has a problem kernel with $O(k)$
vertices. Finally, we observe that combining our hardness results with the
so-called critical-clique lemma yields NP-hardness for Cluster Editing with
Vertex Splitting, which was previously open (Abu-Khzam et al. [ISCO 2018]) and
independently shown to be NP-hard by Arrighi et al. [IPEC 2023]. We observe
that a previous version of the critical-clique lemma was flawed; a corrected
version has appeared in the meantime on which our hardness result is based.
| [
{
"created": "Fri, 1 Sep 2023 14:51:28 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Sep 2023 12:24:07 GMT",
"version": "v2"
},
{
"created": "Wed, 3 Apr 2024 13:20:13 GMT",
"version": "v3"
}
] | 2024-04-04 | [
[
"Firbas",
"Alexander",
""
],
[
"Dobler",
"Alexander",
""
],
[
"Holzer",
"Fabian",
""
],
[
"Schafellner",
"Jakob",
""
],
[
"Sorge",
"Manuel",
""
],
[
"Villedieu",
"Anaïs",
""
],
[
"Wißmann",
"Monika",
""
]
] | Clustering a graph when the clusters can overlap can be seen from three different angles: We may look for cliques that cover the edges of the graph with bounded overlap, we may look to add or delete few edges to uncover the cluster structure, or we may split vertices to separate the clusters from each other. Splitting a vertex $v$ means to remove it and to add two new copies of $v$ and to make each previous neighbor of $v$ adjacent with at least one of the copies. In this work, we study underlying computational problems regarding the three angles to overlapping clusterings, in particular when the overlap is small. We show that the above-mentioned covering problem is NP-complete. We then make structural observations that show that the covering viewpoint and the vertex-splitting viewpoint are equivalent, yielding NP-hardness for the vertex-splitting problem. On the positive side, we show that splitting at most $k$ vertices to obtain a cluster graph has a problem kernel with $O(k)$ vertices. Finally, we observe that combining our hardness results with the so-called critical-clique lemma yields NP-hardness for Cluster Editing with Vertex Splitting, which was previously open (Abu-Khzam et al. [ISCO 2018]) and independently shown to be NP-hard by Arrighi et al. [IPEC 2023]. We observe that a previous version of the critical-clique lemma was flawed; a corrected version has appeared in the meantime on which our hardness result is based. |
1605.01880 | Kittipong Kittichokechai | Kittipong Kittichokechai and Giuseppe Caire | Privacy-Constrained Remote Source Coding | 10 pages, 1 figure, to be presented at ISIT 2016 | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of revealing/sharing data in an efficient and secure
way via a compact representation. The representation should ensure reliable
reconstruction of the desired features/attributes while still preserve privacy
of the secret parts of the data. The problem is formulated as a remote lossy
source coding with a privacy constraint where the remote source consists of
public and secret parts. Inner and outer bounds for the optimal tradeoff region
of compression rate, distortion, and privacy leakage rate are given and shown
to coincide for some special cases. When specializing the distortion measure to
a logarithmic loss function, the resulting rate-distortion-leakage tradeoff for
the case of identical side information forms an optimization problem which
corresponds to the "secure" version of the so-called information bottleneck.
| [
{
"created": "Fri, 6 May 2016 10:15:57 GMT",
"version": "v1"
}
] | 2016-05-09 | [
[
"Kittichokechai",
"Kittipong",
""
],
[
"Caire",
"Giuseppe",
""
]
] | We consider the problem of revealing/sharing data in an efficient and secure way via a compact representation. The representation should ensure reliable reconstruction of the desired features/attributes while still preserve privacy of the secret parts of the data. The problem is formulated as a remote lossy source coding with a privacy constraint where the remote source consists of public and secret parts. Inner and outer bounds for the optimal tradeoff region of compression rate, distortion, and privacy leakage rate are given and shown to coincide for some special cases. When specializing the distortion measure to a logarithmic loss function, the resulting rate-distortion-leakage tradeoff for the case of identical side information forms an optimization problem which corresponds to the "secure" version of the so-called information bottleneck. |
1804.00755 | Mahmoud Mohammadi | Mahmoud Mohammadi, Bill Chu, Heather Richter Lipford | Detecting Cross-Site Scripting Vulnerabilities through Automated Unit
Testing | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The best practice to prevent Cross Site Scripting (XSS) attacks is to apply
encoders to sanitize untrusted data. To balance security and functionality,
encoders should be applied to match the web page context, such as HTML body,
JavaScript, and style sheets. A common programming error is the use of a wrong
encoder to sanitize untrusted data, leaving the application vulnerable. We
present a security unit testing approach to detect XSS vulnerabilities caused
by improper encoding of untrusted data. Unit tests for the XSS vulnerability
are automatically constructed out of each web page and then evaluated by a unit
test execution framework. A grammar-based attack generator is used to
automatically generate test inputs. We evaluate our approach on a large open
source medical records application, demonstrating that we can detect many 0-day
XSS vulnerabilities with very low false positives, and that the grammar-based
attack generator has better test coverage than industry best practices.
| [
{
"created": "Mon, 2 Apr 2018 22:59:18 GMT",
"version": "v1"
}
] | 2018-04-04 | [
[
"Mohammadi",
"Mahmoud",
""
],
[
"Chu",
"Bill",
""
],
[
"Lipford",
"Heather Richter",
""
]
] | The best practice to prevent Cross Site Scripting (XSS) attacks is to apply encoders to sanitize untrusted data. To balance security and functionality, encoders should be applied to match the web page context, such as HTML body, JavaScript, and style sheets. A common programming error is the use of a wrong encoder to sanitize untrusted data, leaving the application vulnerable. We present a security unit testing approach to detect XSS vulnerabilities caused by improper encoding of untrusted data. Unit tests for the XSS vulnerability are automatically constructed out of each web page and then evaluated by a unit test execution framework. A grammar-based attack generator is used to automatically generate test inputs. We evaluate our approach on a large open source medical records application, demonstrating that we can detect many 0-day XSS vulnerabilities with very low false positives, and that the grammar-based attack generator has better test coverage than industry best practices. |
2008.13300 | Michael Luby | Michael Luby | SOPI design and analysis for LDN | This is a companion paper to the LDN paper that appears in ACM ICN
2020 | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Liquid Data Networking (LDN) is an ICN architecture that is designed to
enable the benefits of erasure-code enabled object delivery. A primary
contribution of LDN is the introduction of SOPIs, which enables client s to
concurrently download encoded data for the same object from multiple edge
nodes, optimizes caching efficiency, and enables seamless mobility. This paper
provides an enhanced design and analysis of SOPI s.
| [
{
"created": "Mon, 31 Aug 2020 00:16:20 GMT",
"version": "v1"
},
{
"created": "Sat, 5 Sep 2020 00:04:23 GMT",
"version": "v2"
}
] | 2020-09-08 | [
[
"Luby",
"Michael",
""
]
] | Liquid Data Networking (LDN) is an ICN architecture that is designed to enable the benefits of erasure-code enabled object delivery. A primary contribution of LDN is the introduction of SOPIs, which enables client s to concurrently download encoded data for the same object from multiple edge nodes, optimizes caching efficiency, and enables seamless mobility. This paper provides an enhanced design and analysis of SOPI s. |
2109.02941 | Chandni Saxena | Mudit Chaudhary, Chandni Saxena, Helen Meng | Countering Online Hate Speech: An NLP Perspective | 12 pages | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Online hate speech has caught everyone's attention from the news related to
the COVID-19 pandemic, US elections, and worldwide protests. Online toxicity -
an umbrella term for online hateful behavior, manifests itself in forms such as
online hate speech. Hate speech is a deliberate attack directed towards an
individual or a group motivated by the targeted entity's identity or opinions.
The rising mass communication through social media further exacerbates the
harmful consequences of online hate speech. While there has been significant
research on hate-speech identification using Natural Language Processing (NLP),
the work on utilizing NLP for prevention and intervention of online hate speech
lacks relatively. This paper presents a holistic conceptual framework on
hate-speech NLP countering methods along with a thorough survey on the current
progress of NLP for countering online hate speech. It classifies the countering
techniques based on their time of action, and identifies potential future
research areas on this topic.
| [
{
"created": "Tue, 7 Sep 2021 08:48:13 GMT",
"version": "v1"
}
] | 2021-09-08 | [
[
"Chaudhary",
"Mudit",
""
],
[
"Saxena",
"Chandni",
""
],
[
"Meng",
"Helen",
""
]
] | Online hate speech has caught everyone's attention from the news related to the COVID-19 pandemic, US elections, and worldwide protests. Online toxicity - an umbrella term for online hateful behavior, manifests itself in forms such as online hate speech. Hate speech is a deliberate attack directed towards an individual or a group motivated by the targeted entity's identity or opinions. The rising mass communication through social media further exacerbates the harmful consequences of online hate speech. While there has been significant research on hate-speech identification using Natural Language Processing (NLP), the work on utilizing NLP for prevention and intervention of online hate speech lacks relatively. This paper presents a holistic conceptual framework on hate-speech NLP countering methods along with a thorough survey on the current progress of NLP for countering online hate speech. It classifies the countering techniques based on their time of action, and identifies potential future research areas on this topic. |
2405.02580 | Ye Liu | Ye Liu, Yue Xue, Daoyuan Wu, Yuqiang Sun, Yi Li, Miaolei Shi, Yang Liu | PropertyGPT: LLM-driven Formal Verification of Smart Contracts through
Retrieval-Augmented Property Generation | null | null | null | null | cs.SE cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With recent advances in large language models (LLMs), this paper explores the
potential of leveraging state-of-the-art LLMs, such as GPT-4, to transfer
existing human-written properties (e.g., those from Certora auditing reports)
and automatically generate customized properties for unknown code. To this end,
we embed existing properties into a vector database and retrieve a reference
property for LLM-based in-context learning to generate a new prop- erty for a
given code. While this basic process is relatively straight- forward, ensuring
that the generated properties are (i) compilable, (ii) appropriate, and (iii)
runtime-verifiable presents challenges. To address (i), we use the compilation
and static analysis feedback as an external oracle to guide LLMs in iteratively
revising the generated properties. For (ii), we consider multiple dimensions of
similarity to rank the properties and employ a weighted algorithm to identify
the top-K properties as the final result. For (iii), we design a dedicated
prover to formally verify the correctness of the generated prop- erties. We
have implemented these strategies into a novel system called PropertyGPT, with
623 human-written properties collected from 23 Certora projects. Our
experiments show that PropertyGPT can generate comprehensive and high-quality
properties, achieving an 80% recall compared to the ground truth. It
successfully detected 26 CVEs/attack incidents out of 37 tested and also
uncovered 12 zero-day vulnerabilities, resulting in $8,256 bug bounty rewards.
| [
{
"created": "Sat, 4 May 2024 06:28:27 GMT",
"version": "v1"
}
] | 2024-05-07 | [
[
"Liu",
"Ye",
""
],
[
"Xue",
"Yue",
""
],
[
"Wu",
"Daoyuan",
""
],
[
"Sun",
"Yuqiang",
""
],
[
"Li",
"Yi",
""
],
[
"Shi",
"Miaolei",
""
],
[
"Liu",
"Yang",
""
]
] | With recent advances in large language models (LLMs), this paper explores the potential of leveraging state-of-the-art LLMs, such as GPT-4, to transfer existing human-written properties (e.g., those from Certora auditing reports) and automatically generate customized properties for unknown code. To this end, we embed existing properties into a vector database and retrieve a reference property for LLM-based in-context learning to generate a new prop- erty for a given code. While this basic process is relatively straight- forward, ensuring that the generated properties are (i) compilable, (ii) appropriate, and (iii) runtime-verifiable presents challenges. To address (i), we use the compilation and static analysis feedback as an external oracle to guide LLMs in iteratively revising the generated properties. For (ii), we consider multiple dimensions of similarity to rank the properties and employ a weighted algorithm to identify the top-K properties as the final result. For (iii), we design a dedicated prover to formally verify the correctness of the generated prop- erties. We have implemented these strategies into a novel system called PropertyGPT, with 623 human-written properties collected from 23 Certora projects. Our experiments show that PropertyGPT can generate comprehensive and high-quality properties, achieving an 80% recall compared to the ground truth. It successfully detected 26 CVEs/attack incidents out of 37 tested and also uncovered 12 zero-day vulnerabilities, resulting in $8,256 bug bounty rewards. |
1708.00980 | Yudong Guo | Yudong Guo, Juyong Zhang, Jianfei Cai, Boyi Jiang and Jianmin Zheng | CNN-based Real-time Dense Face Reconstruction with Inverse-rendered
Photo-realistic Face Images | Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2018 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the powerfulness of convolution neural networks (CNN), CNN based face
reconstruction has recently shown promising performance in reconstructing
detailed face shape from 2D face images. The success of CNN-based methods
relies on a large number of labeled data. The state-of-the-art synthesizes such
data using a coarse morphable face model, which however has difficulty to
generate detailed photo-realistic images of faces (with wrinkles). This paper
presents a novel face data generation method. Specifically, we render a large
number of photo-realistic face images with different attributes based on
inverse rendering. Furthermore, we construct a fine-detailed face image dataset
by transferring different scales of details from one image to another. We also
construct a large number of video-type adjacent frame pairs by simulating the
distribution of real video data. With these nicely constructed datasets, we
propose a coarse-to-fine learning framework consisting of three convolutional
networks. The networks are trained for real-time detailed 3D face
reconstruction from monocular video as well as from a single image. Extensive
experimental results demonstrate that our framework can produce high-quality
reconstruction but with much less computation time compared to the
state-of-the-art. Moreover, our method is robust to pose, expression and
lighting due to the diversity of data.
| [
{
"created": "Thu, 3 Aug 2017 03:18:34 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Sep 2017 11:32:01 GMT",
"version": "v2"
},
{
"created": "Tue, 15 May 2018 07:02:35 GMT",
"version": "v3"
}
] | 2018-05-16 | [
[
"Guo",
"Yudong",
""
],
[
"Zhang",
"Juyong",
""
],
[
"Cai",
"Jianfei",
""
],
[
"Jiang",
"Boyi",
""
],
[
"Zheng",
"Jianmin",
""
]
] | With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data. |
2309.04175 | Haochun Wang | Haochun Wang, Sendong Zhao, Zewen Qiang, Zijian Li, Nuwa Xi, Yanrui
Du, MuZhen Cai, Haoqiang Guo, Yuhan Chen, Haoming Xu, Bing Qin, Ting Liu | Knowledge-tuning Large Language Models with Structured Medical Knowledge
Bases for Reliable Response Generation in Chinese | 11 pages, 5 figures | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) have demonstrated remarkable success in diverse
natural language processing (NLP) tasks in general domains. However, LLMs
sometimes generate responses with the hallucination about medical facts due to
limited domain knowledge. Such shortcomings pose potential risks in the
utilization of LLMs within medical contexts. To address this challenge, we
propose knowledge-tuning, which leverages structured medical knowledge bases
for the LLMs to grasp domain knowledge efficiently and facilitate reliable
response generation. We also release cMedKnowQA, a Chinese medical knowledge
question-answering dataset constructed from medical knowledge bases to assess
the medical knowledge proficiency of LLMs. Experimental results show that the
LLMs which are knowledge-tuned with cMedKnowQA, can exhibit higher levels of
accuracy in response generation compared with vanilla instruction-tuning and
offer a new reliable way for the domain adaptation of LLMs.
| [
{
"created": "Fri, 8 Sep 2023 07:42:57 GMT",
"version": "v1"
}
] | 2023-09-11 | [
[
"Wang",
"Haochun",
""
],
[
"Zhao",
"Sendong",
""
],
[
"Qiang",
"Zewen",
""
],
[
"Li",
"Zijian",
""
],
[
"Xi",
"Nuwa",
""
],
[
"Du",
"Yanrui",
""
],
[
"Cai",
"MuZhen",
""
],
[
"Guo",
"Haoqiang",
""
],
[
"Chen",
"Yuhan",
""
],
[
"Xu",
"Haoming",
""
],
[
"Qin",
"Bing",
""
],
[
"Liu",
"Ting",
""
]
] | Large Language Models (LLMs) have demonstrated remarkable success in diverse natural language processing (NLP) tasks in general domains. However, LLMs sometimes generate responses with the hallucination about medical facts due to limited domain knowledge. Such shortcomings pose potential risks in the utilization of LLMs within medical contexts. To address this challenge, we propose knowledge-tuning, which leverages structured medical knowledge bases for the LLMs to grasp domain knowledge efficiently and facilitate reliable response generation. We also release cMedKnowQA, a Chinese medical knowledge question-answering dataset constructed from medical knowledge bases to assess the medical knowledge proficiency of LLMs. Experimental results show that the LLMs which are knowledge-tuned with cMedKnowQA, can exhibit higher levels of accuracy in response generation compared with vanilla instruction-tuning and offer a new reliable way for the domain adaptation of LLMs. |
2303.16611 | Sebastien Valette Dr | Kaifeng Zou, Sylvain Faisan, Boyang Yu, S\'ebastien Valette, Hyewon
Seo | 4D Facial Expression Diffusion Model | null | null | 10.1145/3653455 | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Facial expression generation is one of the most challenging and long-sought
aspects of character animation, with many interesting applications. The
challenging task, traditionally having relied heavily on digital craftspersons,
remains yet to be explored. In this paper, we introduce a generative framework
for generating 3D facial expression sequences (i.e. 4D faces) that can be
conditioned on different inputs to animate an arbitrary 3D face mesh. It is
composed of two tasks: (1) Learning the generative model that is trained over a
set of 3D landmark sequences, and (2) Generating 3D mesh sequences of an input
facial mesh driven by the generated landmark sequences. The generative model is
based on a Denoising Diffusion Probabilistic Model (DDPM), which has achieved
remarkable success in generative tasks of other domains. While it can be
trained unconditionally, its reverse process can still be conditioned by
various condition signals. This allows us to efficiently develop several
downstream tasks involving various conditional generation, by using expression
labels, text, partial sequences, or simply a facial geometry. To obtain the
full mesh deformation, we then develop a landmark-guided encoder-decoder to
apply the geometrical deformation embedded in landmarks on a given facial mesh.
Experiments show that our model has learned to generate realistic, quality
expressions solely from the dataset of relatively small size, improving over
the state-of-the-art methods. Videos and qualitative comparisons with other
methods can be found at \url{https://github.com/ZOUKaifeng/4DFM}.
| [
{
"created": "Wed, 29 Mar 2023 11:50:21 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Apr 2024 13:29:47 GMT",
"version": "v2"
}
] | 2024-04-16 | [
[
"Zou",
"Kaifeng",
""
],
[
"Faisan",
"Sylvain",
""
],
[
"Yu",
"Boyang",
""
],
[
"Valette",
"Sébastien",
""
],
[
"Seo",
"Hyewon",
""
]
] | Facial expression generation is one of the most challenging and long-sought aspects of character animation, with many interesting applications. The challenging task, traditionally having relied heavily on digital craftspersons, remains yet to be explored. In this paper, we introduce a generative framework for generating 3D facial expression sequences (i.e. 4D faces) that can be conditioned on different inputs to animate an arbitrary 3D face mesh. It is composed of two tasks: (1) Learning the generative model that is trained over a set of 3D landmark sequences, and (2) Generating 3D mesh sequences of an input facial mesh driven by the generated landmark sequences. The generative model is based on a Denoising Diffusion Probabilistic Model (DDPM), which has achieved remarkable success in generative tasks of other domains. While it can be trained unconditionally, its reverse process can still be conditioned by various condition signals. This allows us to efficiently develop several downstream tasks involving various conditional generation, by using expression labels, text, partial sequences, or simply a facial geometry. To obtain the full mesh deformation, we then develop a landmark-guided encoder-decoder to apply the geometrical deformation embedded in landmarks on a given facial mesh. Experiments show that our model has learned to generate realistic, quality expressions solely from the dataset of relatively small size, improving over the state-of-the-art methods. Videos and qualitative comparisons with other methods can be found at \url{https://github.com/ZOUKaifeng/4DFM}. |
2405.03734 | Xiaoning Wang | Silan Hu, Xiaoning Wang | FOKE: A Personalized and Explainable Education Framework Integrating
Foundation Models, Knowledge Graphs, and Prompt Engineering | null | null | null | null | cs.HC cs.AI stat.AP | http://creativecommons.org/licenses/by/4.0/ | Integrating large language models (LLMs) and knowledge graphs (KGs) holds
great promise for revolutionizing intelligent education, but challenges remain
in achieving personalization, interactivity, and explainability. We propose
FOKE, a Forest Of Knowledge and Education framework that synergizes foundation
models, knowledge graphs, and prompt engineering to address these challenges.
FOKE introduces three key innovations: (1) a hierarchical knowledge forest for
structured domain knowledge representation; (2) a multi-dimensional user
profiling mechanism for comprehensive learner modeling; and (3) an interactive
prompt engineering scheme for generating precise and tailored learning
guidance.
We showcase FOKE's application in programming education, homework assessment,
and learning path planning, demonstrating its effectiveness and practicality.
Additionally, we implement Scholar Hero, a real-world instantiation of FOKE.
Our research highlights the potential of integrating foundation models,
knowledge graphs, and prompt engineering to revolutionize intelligent education
practices, ultimately benefiting learners worldwide. FOKE provides a principled
and unified approach to harnessing cutting-edge AI technologies for
personalized, interactive, and explainable educational services, paving the way
for further research and development in this critical direction.
| [
{
"created": "Mon, 6 May 2024 15:11:05 GMT",
"version": "v1"
}
] | 2024-05-08 | [
[
"Hu",
"Silan",
""
],
[
"Wang",
"Xiaoning",
""
]
] | Integrating large language models (LLMs) and knowledge graphs (KGs) holds great promise for revolutionizing intelligent education, but challenges remain in achieving personalization, interactivity, and explainability. We propose FOKE, a Forest Of Knowledge and Education framework that synergizes foundation models, knowledge graphs, and prompt engineering to address these challenges. FOKE introduces three key innovations: (1) a hierarchical knowledge forest for structured domain knowledge representation; (2) a multi-dimensional user profiling mechanism for comprehensive learner modeling; and (3) an interactive prompt engineering scheme for generating precise and tailored learning guidance. We showcase FOKE's application in programming education, homework assessment, and learning path planning, demonstrating its effectiveness and practicality. Additionally, we implement Scholar Hero, a real-world instantiation of FOKE. Our research highlights the potential of integrating foundation models, knowledge graphs, and prompt engineering to revolutionize intelligent education practices, ultimately benefiting learners worldwide. FOKE provides a principled and unified approach to harnessing cutting-edge AI technologies for personalized, interactive, and explainable educational services, paving the way for further research and development in this critical direction. |
1612.03052 | Joe Yue-Hei Ng | Joe Yue-Hei Ng, Jonghyun Choi, Jan Neumann, Larry S. Davis | ActionFlowNet: Learning Motion Representation for Action Recognition | WACV 2018 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Even with the recent advances in convolutional neural networks (CNN) in
various visual recognition tasks, the state-of-the-art action recognition
system still relies on hand crafted motion feature such as optical flow to
achieve the best performance. We propose a multitask learning model
ActionFlowNet to train a single stream network directly from raw pixels to
jointly estimate optical flow while recognizing actions with convolutional
neural networks, capturing both appearance and motion in a single model. We
additionally provide insights to how the quality of the learned optical flow
affects the action recognition. Our model significantly improves action
recognition accuracy by a large margin 31% compared to state-of-the-art
CNN-based action recognition models trained without external large scale data
and additional optical flow input. Without pretraining on large external
labeled datasets, our model, by well exploiting the motion information,
achieves competitive recognition accuracy to the models trained with large
labeled datasets such as ImageNet and Sport-1M.
| [
{
"created": "Fri, 9 Dec 2016 15:20:23 GMT",
"version": "v1"
},
{
"created": "Fri, 21 Apr 2017 01:45:42 GMT",
"version": "v2"
},
{
"created": "Fri, 16 Feb 2018 22:15:25 GMT",
"version": "v3"
}
] | 2018-02-20 | [
[
"Ng",
"Joe Yue-Hei",
""
],
[
"Choi",
"Jonghyun",
""
],
[
"Neumann",
"Jan",
""
],
[
"Davis",
"Larry S.",
""
]
] | Even with the recent advances in convolutional neural networks (CNN) in various visual recognition tasks, the state-of-the-art action recognition system still relies on hand crafted motion feature such as optical flow to achieve the best performance. We propose a multitask learning model ActionFlowNet to train a single stream network directly from raw pixels to jointly estimate optical flow while recognizing actions with convolutional neural networks, capturing both appearance and motion in a single model. We additionally provide insights to how the quality of the learned optical flow affects the action recognition. Our model significantly improves action recognition accuracy by a large margin 31% compared to state-of-the-art CNN-based action recognition models trained without external large scale data and additional optical flow input. Without pretraining on large external labeled datasets, our model, by well exploiting the motion information, achieves competitive recognition accuracy to the models trained with large labeled datasets such as ImageNet and Sport-1M. |
1904.10386 | Mario Gleirscher | Mario Gleirscher | Risk Structures: Towards Engineering Risk-aware Autonomous Systems | null | null | 10.1007/s00165-021-00545-4 | null | cs.SE cs.AI cs.RO | http://creativecommons.org/licenses/by/4.0/ | Inspired by widely-used techniques of causal modelling in risk, failure, and
accident analysis, this work discusses a compositional framework for risk
modelling. Risk models capture fragments of the space of risky events likely to
occur when operating a machine in a given environment. Moreover, one can build
such models into machines such as autonomous robots, to equip them with the
ability of risk-aware perception, monitoring, decision making, and control.
With the notion of a risk factor as the modelling primitive, the framework
provides several means to construct and shape risk models. Relational and
algebraic properties are investigated and proofs support the validity and
consistency of these properties over the corresponding models. Several examples
throughout the discussion illustrate the applicability of the concepts.
Overall, this work focuses on the qualitative treatment of risk with the
outlook of transferring these results to probabilistic refinements of the
discussed framework.
| [
{
"created": "Tue, 23 Apr 2019 15:29:00 GMT",
"version": "v1"
}
] | 2021-12-22 | [
[
"Gleirscher",
"Mario",
""
]
] | Inspired by widely-used techniques of causal modelling in risk, failure, and accident analysis, this work discusses a compositional framework for risk modelling. Risk models capture fragments of the space of risky events likely to occur when operating a machine in a given environment. Moreover, one can build such models into machines such as autonomous robots, to equip them with the ability of risk-aware perception, monitoring, decision making, and control. With the notion of a risk factor as the modelling primitive, the framework provides several means to construct and shape risk models. Relational and algebraic properties are investigated and proofs support the validity and consistency of these properties over the corresponding models. Several examples throughout the discussion illustrate the applicability of the concepts. Overall, this work focuses on the qualitative treatment of risk with the outlook of transferring these results to probabilistic refinements of the discussed framework. |
2301.07634 | Panagiotis Meletis | Panagiotis Meletis, Gijs Dubbelman | Training Semantic Segmentation on Heterogeneous Datasets | Submitted 2021 (under review) | null | null | null | cs.CV cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | We explore semantic segmentation beyond the conventional, single-dataset
homogeneous training and bring forward the problem of Heterogeneous Training of
Semantic Segmentation (HTSS). HTSS involves simultaneous training on multiple
heterogeneous datasets, i.e. datasets with conflicting label spaces and
different (weak) annotation types from the perspective of semantic
segmentation. The HTSS formulation exposes deep networks to a larger and
previously unexplored aggregation of information that can potentially enhance
semantic segmentation in three directions: i) performance: increased
segmentation metrics on seen datasets, ii) generalization: improved
segmentation metrics on unseen datasets, and iii) knowledgeability: increased
number of recognizable semantic concepts. To research these benefits of HTSS,
we propose a unified framework, that incorporates heterogeneous datasets in a
single-network training pipeline following the established FCN standard. Our
framework first curates heterogeneous datasets to bring them into a common
format and then trains a single-backbone FCN on all of them simultaneously. To
achieve this, it transforms weak annotations, which are incompatible with
semantic segmentation, to per-pixel labels, and hierarchizes their label spaces
into a universal taxonomy. The trained HTSS models demonstrate performance and
generalization gains over a wide range of datasets and extend the inference
label space entailing hundreds of semantic classes.
| [
{
"created": "Wed, 18 Jan 2023 16:22:40 GMT",
"version": "v1"
}
] | 2023-01-19 | [
[
"Meletis",
"Panagiotis",
""
],
[
"Dubbelman",
"Gijs",
""
]
] | We explore semantic segmentation beyond the conventional, single-dataset homogeneous training and bring forward the problem of Heterogeneous Training of Semantic Segmentation (HTSS). HTSS involves simultaneous training on multiple heterogeneous datasets, i.e. datasets with conflicting label spaces and different (weak) annotation types from the perspective of semantic segmentation. The HTSS formulation exposes deep networks to a larger and previously unexplored aggregation of information that can potentially enhance semantic segmentation in three directions: i) performance: increased segmentation metrics on seen datasets, ii) generalization: improved segmentation metrics on unseen datasets, and iii) knowledgeability: increased number of recognizable semantic concepts. To research these benefits of HTSS, we propose a unified framework, that incorporates heterogeneous datasets in a single-network training pipeline following the established FCN standard. Our framework first curates heterogeneous datasets to bring them into a common format and then trains a single-backbone FCN on all of them simultaneously. To achieve this, it transforms weak annotations, which are incompatible with semantic segmentation, to per-pixel labels, and hierarchizes their label spaces into a universal taxonomy. The trained HTSS models demonstrate performance and generalization gains over a wide range of datasets and extend the inference label space entailing hundreds of semantic classes. |
1906.10104 | Weilian Song | Weilian Song, Tawfiq Salem, Hunter Blanton, Nathan Jacobs | Remote Estimation of Free-Flow Speeds | 4 pages, 4 figures, IGARSS 2019 camera-ready submission | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an automated method to estimate a road segment's free-flow speed
from overhead imagery and road metadata. The free-flow speed of a road segment
is the average observed vehicle speed in ideal conditions, without congestion
or adverse weather. Standard practice for estimating free-flow speeds depends
on several road attributes, including grade, curve, and width of the right of
way. Unfortunately, many of these fine-grained labels are not always readily
available and are costly to manually annotate. To compensate, our model uses a
small, easy to obtain subset of road features along with aerial imagery to
directly estimate free-flow speed with a deep convolutional neural network
(CNN). We evaluate our approach on a large dataset, and demonstrate that using
imagery alone performs nearly as well as the road features and that the
combination of imagery with road features leads to the highest accuracy.
| [
{
"created": "Mon, 24 Jun 2019 17:41:46 GMT",
"version": "v1"
}
] | 2019-06-25 | [
[
"Song",
"Weilian",
""
],
[
"Salem",
"Tawfiq",
""
],
[
"Blanton",
"Hunter",
""
],
[
"Jacobs",
"Nathan",
""
]
] | We propose an automated method to estimate a road segment's free-flow speed from overhead imagery and road metadata. The free-flow speed of a road segment is the average observed vehicle speed in ideal conditions, without congestion or adverse weather. Standard practice for estimating free-flow speeds depends on several road attributes, including grade, curve, and width of the right of way. Unfortunately, many of these fine-grained labels are not always readily available and are costly to manually annotate. To compensate, our model uses a small, easy to obtain subset of road features along with aerial imagery to directly estimate free-flow speed with a deep convolutional neural network (CNN). We evaluate our approach on a large dataset, and demonstrate that using imagery alone performs nearly as well as the road features and that the combination of imagery with road features leads to the highest accuracy. |
1508.06710 | EPTCS | Pedro R. D'Argenio (FaMAF, Universidad Nacional de C\'ordoba -
CONICET), Matias David Lee (FaMAF, Universidad Nacional de C\'ordoba -
CONICET), Daniel Gebler (Department of Computer Science, VU University
Amsterdam) | SOS rule formats for convex and abstract probabilistic bisimulations | In Proceedings EXPRESS/SOS 2015, arXiv:1508.06347 | EPTCS 190, 2015, pp. 31-45 | 10.4204/EPTCS.190.3 | null | cs.LO cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic transition system specifications (PTSSs) in the $nt \mu f\theta
/ nt\mu x\theta$ format provide structural operational semantics for
Segala-type systems that exhibit both probabilistic and nondeterministic
behavior and guarantee that bisimilarity is a congruence for all operator
defined in such format. Starting from the $nt \mu f\theta / nt\mu x\theta$
format, we obtain restricted formats that guarantee that three coarser
bisimulation equivalences are congruences. We focus on (i) Segala's variant of
bisimulation that considers combined transitions, which we call here "convex
bisimulation"; (ii) the bisimulation equivalence resulting from considering
Park & Milner's bisimulation on the usual stripped probabilistic transition
system (translated into a labelled transition system), which we call here
"probability obliterated bisimulation"; and (iii) a "probability abstracted
bisimulation", which, like bisimulation, preserves the structure of the
distributions but instead, it ignores the probability values. In addition, we
compare these bisimulation equivalences and provide a logic characterization
for each of them.
| [
{
"created": "Thu, 27 Aug 2015 03:21:26 GMT",
"version": "v1"
}
] | 2015-08-28 | [
[
"D'Argenio",
"Pedro R.",
"",
"FaMAF, Universidad Nacional de Córdoba -\n CONICET"
],
[
"Lee",
"Matias David",
"",
"FaMAF, Universidad Nacional de Córdoba -\n CONICET"
],
[
"Gebler",
"Daniel",
"",
"Department of Computer Science, VU University\n Amsterdam"
]
] | Probabilistic transition system specifications (PTSSs) in the $nt \mu f\theta / nt\mu x\theta$ format provide structural operational semantics for Segala-type systems that exhibit both probabilistic and nondeterministic behavior and guarantee that bisimilarity is a congruence for all operator defined in such format. Starting from the $nt \mu f\theta / nt\mu x\theta$ format, we obtain restricted formats that guarantee that three coarser bisimulation equivalences are congruences. We focus on (i) Segala's variant of bisimulation that considers combined transitions, which we call here "convex bisimulation"; (ii) the bisimulation equivalence resulting from considering Park & Milner's bisimulation on the usual stripped probabilistic transition system (translated into a labelled transition system), which we call here "probability obliterated bisimulation"; and (iii) a "probability abstracted bisimulation", which, like bisimulation, preserves the structure of the distributions but instead, it ignores the probability values. In addition, we compare these bisimulation equivalences and provide a logic characterization for each of them. |
2205.00772 | Jonas Falkner | Jonas K. Falkner, Daniela Thyssens, Lars Schmidt-Thieme | Large Neighborhood Search based on Neural Construction Heuristics | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | We propose a Large Neighborhood Search (LNS) approach utilizing a learned
construction heuristic based on neural networks as repair operator to solve the
vehicle routing problem with time windows (VRPTW). Our method uses graph neural
networks to encode the problem and auto-regressively decodes a solution and is
trained with reinforcement learning on the construction task without requiring
any labels for supervision. The neural repair operator is combined with a local
search routine, heuristic destruction operators and a selection procedure
applied to a small population to arrive at a sophisticated solution approach.
The key idea is to use the learned model to re-construct the partially
destructed solution and to introduce randomness via the destruction heuristics
(or the stochastic policy itself) to effectively explore a large neighborhood.
| [
{
"created": "Mon, 2 May 2022 09:38:19 GMT",
"version": "v1"
},
{
"created": "Tue, 10 May 2022 12:02:44 GMT",
"version": "v2"
}
] | 2022-05-11 | [
[
"Falkner",
"Jonas K.",
""
],
[
"Thyssens",
"Daniela",
""
],
[
"Schmidt-Thieme",
"Lars",
""
]
] | We propose a Large Neighborhood Search (LNS) approach utilizing a learned construction heuristic based on neural networks as repair operator to solve the vehicle routing problem with time windows (VRPTW). Our method uses graph neural networks to encode the problem and auto-regressively decodes a solution and is trained with reinforcement learning on the construction task without requiring any labels for supervision. The neural repair operator is combined with a local search routine, heuristic destruction operators and a selection procedure applied to a small population to arrive at a sophisticated solution approach. The key idea is to use the learned model to re-construct the partially destructed solution and to introduce randomness via the destruction heuristics (or the stochastic policy itself) to effectively explore a large neighborhood. |
2406.07393 | Peng Hu | Peng Hu, Changjiang Gao, Ruiqi Gao, Jiajun Chen, and Shujian Huang | Limited Out-of-Context Knowledge Reasoning in Large Language Models | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) have demonstrated strong capabilities as
knowledge bases and significant in-context reasoning capabilities. However,
previous work challenges their out-of-context reasoning ability, i.e., the
ability to infer information from their training data, instead of from the
context or prompt. This paper focuses on a significant facet of out-of-context
reasoning: Out-of-Context Knowledge Reasoning (OCKR), which is to combine
multiple knowledge to infer new knowledge. We designed a synthetic dataset with
seven representative OCKR tasks to systematically assess the OCKR capabilities
of LLMs. Using this dataset, we evaluated the LLaMA2-13B-chat model and
discovered that its proficiency in this aspect is limited, regardless of
whether the knowledge is trained in a separate or adjacent training settings.
Moreover, training the model to reason with complete reasoning data did not
result in significant improvement. Training the model to perform explicit
knowledge retrieval helps in only one of the tasks, indicating that the model's
limited OCKR capabilities are due to difficulties in retrieving relevant
knowledge. Furthermore, we treat cross-lingual knowledge transfer as a distinct
form of OCKR, and evaluate this ability. Our results show that the evaluated
model also exhibits limited ability in transferring knowledge across languages.
The dataset used in this study is available at
https://github.com/NJUNLP/ID-OCKR.
| [
{
"created": "Tue, 11 Jun 2024 15:58:59 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Jun 2024 14:59:54 GMT",
"version": "v2"
}
] | 2024-06-25 | [
[
"Hu",
"Peng",
""
],
[
"Gao",
"Changjiang",
""
],
[
"Gao",
"Ruiqi",
""
],
[
"Chen",
"Jiajun",
""
],
[
"Huang",
"Shujian",
""
]
] | Large Language Models (LLMs) have demonstrated strong capabilities as knowledge bases and significant in-context reasoning capabilities. However, previous work challenges their out-of-context reasoning ability, i.e., the ability to infer information from their training data, instead of from the context or prompt. This paper focuses on a significant facet of out-of-context reasoning: Out-of-Context Knowledge Reasoning (OCKR), which is to combine multiple knowledge to infer new knowledge. We designed a synthetic dataset with seven representative OCKR tasks to systematically assess the OCKR capabilities of LLMs. Using this dataset, we evaluated the LLaMA2-13B-chat model and discovered that its proficiency in this aspect is limited, regardless of whether the knowledge is trained in a separate or adjacent training settings. Moreover, training the model to reason with complete reasoning data did not result in significant improvement. Training the model to perform explicit knowledge retrieval helps in only one of the tasks, indicating that the model's limited OCKR capabilities are due to difficulties in retrieving relevant knowledge. Furthermore, we treat cross-lingual knowledge transfer as a distinct form of OCKR, and evaluate this ability. Our results show that the evaluated model also exhibits limited ability in transferring knowledge across languages. The dataset used in this study is available at https://github.com/NJUNLP/ID-OCKR. |
2203.08737 | Giorgos Armeniakos | Giorgos Armeniakos, Georgios Zervakis, Dimitrios Soudris, J\"org
Henkel | Hardware Approximate Techniques for Deep Neural Network Accelerators: A
Survey | This paper has been accepted by ACM Computing Surveys (CSUR), 2022 | ACM Computing Surveys 2022 | 10.1145/3527156 | null | cs.AR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep Neural Networks (DNNs) are very popular because of their high
performance in various cognitive tasks in Machine Learning (ML). Recent
advancements in DNNs have brought beyond human accuracy in many tasks, but at
the cost of high computational complexity. To enable efficient execution of DNN
inference, more and more research works, therefore, exploit the inherent error
resilience of DNNs and employ Approximate Computing (AC) principles to address
the elevated energy demands of DNN accelerators. This article provides a
comprehensive survey and analysis of hardware approximation techniques for DNN
accelerators. First, we analyze the state of the art and by identifying
approximation families, we cluster the respective works with respect to the
approximation type. Next, we analyze the complexity of the performed
evaluations (with respect to the dataset and DNN size) to assess the
efficiency, the potential, and limitations of approximate DNN accelerators.
Moreover, a broad discussion is provided, regarding error metrics that are more
suitable for designing approximate units for DNN accelerators as well as
accuracy recovery approaches that are tailored to DNN inference. Finally, we
present how Approximate Computing for DNN accelerators can go beyond energy
efficiency and address reliability and security issues, as well.
| [
{
"created": "Wed, 16 Mar 2022 16:33:13 GMT",
"version": "v1"
}
] | 2022-03-18 | [
[
"Armeniakos",
"Giorgos",
""
],
[
"Zervakis",
"Georgios",
""
],
[
"Soudris",
"Dimitrios",
""
],
[
"Henkel",
"Jörg",
""
]
] | Deep Neural Networks (DNNs) are very popular because of their high performance in various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have brought beyond human accuracy in many tasks, but at the cost of high computational complexity. To enable efficient execution of DNN inference, more and more research works, therefore, exploit the inherent error resilience of DNNs and employ Approximate Computing (AC) principles to address the elevated energy demands of DNN accelerators. This article provides a comprehensive survey and analysis of hardware approximation techniques for DNN accelerators. First, we analyze the state of the art and by identifying approximation families, we cluster the respective works with respect to the approximation type. Next, we analyze the complexity of the performed evaluations (with respect to the dataset and DNN size) to assess the efficiency, the potential, and limitations of approximate DNN accelerators. Moreover, a broad discussion is provided, regarding error metrics that are more suitable for designing approximate units for DNN accelerators as well as accuracy recovery approaches that are tailored to DNN inference. Finally, we present how Approximate Computing for DNN accelerators can go beyond energy efficiency and address reliability and security issues, as well. |
2401.15966 | Kenta Izumi | Kenta Izumi, Hiroki Tanaka, Kazuhiro Shidara, Hiroyoshi Adachi,
Daisuke Kanayama, Takashi Kudo, and Satoshi Nakamura | Response Generation for Cognitive Behavioral Therapy with Large Language
Models: Comparative Study with Socratic Questioning | Accepted by IWSDS2024 | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Dialogue systems controlled by predefined or rule-based scenarios derived
from counseling techniques, such as cognitive behavioral therapy (CBT), play an
important role in mental health apps. Despite the need for responsible
responses, it is conceivable that using the newly emerging LLMs to generate
contextually relevant utterances will enhance these apps. In this study, we
construct dialogue modules based on a CBT scenario focused on conventional
Socratic questioning using two kinds of LLMs: a Transformer-based dialogue
model further trained with a social media empathetic counseling dataset,
provided by Osaka Prefecture (OsakaED), and GPT-4, a state-of-the art LLM
created by OpenAI. By comparing systems that use LLM-generated responses with
those that do not, we investigate the impact of generated responses on
subjective evaluations such as mood change, cognitive change, and dialogue
quality (e.g., empathy). As a result, no notable improvements are observed when
using the OsakaED model. When using GPT-4, the amount of mood change, empathy,
and other dialogue qualities improve significantly. Results suggest that GPT-4
possesses a high counseling ability. However, they also indicate that even when
using a dialogue model trained with a human counseling dataset, it does not
necessarily yield better outcomes compared to scenario-based dialogues. While
presenting LLM-generated responses, including GPT-4, and having them interact
directly with users in real-life mental health care services may raise ethical
issues, it is still possible for human professionals to produce example
responses or response templates using LLMs in advance in systems that use
rules, scenarios, or example responses.
| [
{
"created": "Mon, 29 Jan 2024 08:53:41 GMT",
"version": "v1"
}
] | 2024-01-30 | [
[
"Izumi",
"Kenta",
""
],
[
"Tanaka",
"Hiroki",
""
],
[
"Shidara",
"Kazuhiro",
""
],
[
"Adachi",
"Hiroyoshi",
""
],
[
"Kanayama",
"Daisuke",
""
],
[
"Kudo",
"Takashi",
""
],
[
"Nakamura",
"Satoshi",
""
]
] | Dialogue systems controlled by predefined or rule-based scenarios derived from counseling techniques, such as cognitive behavioral therapy (CBT), play an important role in mental health apps. Despite the need for responsible responses, it is conceivable that using the newly emerging LLMs to generate contextually relevant utterances will enhance these apps. In this study, we construct dialogue modules based on a CBT scenario focused on conventional Socratic questioning using two kinds of LLMs: a Transformer-based dialogue model further trained with a social media empathetic counseling dataset, provided by Osaka Prefecture (OsakaED), and GPT-4, a state-of-the art LLM created by OpenAI. By comparing systems that use LLM-generated responses with those that do not, we investigate the impact of generated responses on subjective evaluations such as mood change, cognitive change, and dialogue quality (e.g., empathy). As a result, no notable improvements are observed when using the OsakaED model. When using GPT-4, the amount of mood change, empathy, and other dialogue qualities improve significantly. Results suggest that GPT-4 possesses a high counseling ability. However, they also indicate that even when using a dialogue model trained with a human counseling dataset, it does not necessarily yield better outcomes compared to scenario-based dialogues. While presenting LLM-generated responses, including GPT-4, and having them interact directly with users in real-life mental health care services may raise ethical issues, it is still possible for human professionals to produce example responses or response templates using LLMs in advance in systems that use rules, scenarios, or example responses. |
0708.1150 | Marko Antonio Rodriguez | Marko A. Rodriguez, Johah Bollen, Herbert Van de Sompel | A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts
and their Usage | null | Proceedings of the IEEE/ACM Joint Conference on Digital Libraries
(JCDL'07), pp. 278-287, 2007 | 10.1145/1255175.1255229 | null | cs.DL cs.AI | null | The large-scale analysis of scholarly artifact usage is constrained primarily
by current practices in usage data archiving, privacy issues concerned with the
dissemination of usage data, and the lack of a practical ontology for modeling
the usage domain. As a remedy to the third constraint, this article presents a
scholarly ontology that was engineered to represent those classes for which
large-scale bibliographic and usage data exists, supports usage research, and
whose instantiation is scalable to the order of 50 million articles along with
their associated artifacts (e.g. authors and journals) and an accompanying 1
billion usage events. The real world instantiation of the presented abstract
ontology is a semantic network model of the scholarly community which lends the
scholarly process to statistical analysis and computational support. We present
the ontology, discuss its instantiation, and provide some example inference
rules for calculating various scholarly artifact metrics.
| [
{
"created": "Wed, 8 Aug 2007 17:06:55 GMT",
"version": "v1"
}
] | 2007-08-09 | [
[
"Rodriguez",
"Marko A.",
""
],
[
"Bollen",
"Johah",
""
],
[
"Van de Sompel",
"Herbert",
""
]
] | The large-scale analysis of scholarly artifact usage is constrained primarily by current practices in usage data archiving, privacy issues concerned with the dissemination of usage data, and the lack of a practical ontology for modeling the usage domain. As a remedy to the third constraint, this article presents a scholarly ontology that was engineered to represent those classes for which large-scale bibliographic and usage data exists, supports usage research, and whose instantiation is scalable to the order of 50 million articles along with their associated artifacts (e.g. authors and journals) and an accompanying 1 billion usage events. The real world instantiation of the presented abstract ontology is a semantic network model of the scholarly community which lends the scholarly process to statistical analysis and computational support. We present the ontology, discuss its instantiation, and provide some example inference rules for calculating various scholarly artifact metrics. |
1905.09514 | Min Qiu | Min Qiu, Yu-Chih Huang, Jinhong Yuan | Downlink Non-Orthogonal Multiple Access without SIC for Block Fading
Channels: An Algebraic Rotation Approach | 15 pages, 8 figures, accepted by IEEE Transactions on Wireless
Communications | null | 10.1109/TWC.2019.2919292 | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we investigate the problem of downlink non-orthogonal multiple
access (NOMA) over block fading channels. For the single antenna case, we
propose a class of NOMA schemes where all the users' signals are mapped into
$n$-dimensional constellations corresponding to the same algebraic lattices
from a number field, allowing every user attains full diversity gain with
single-user decoding, i.e., no successive interference cancellation (SIC). The
minimum product distances of the proposed scheme with arbitrary power
allocation factor are analyzed and their upper bounds are derived. Within the
proposed class of schemes, we also identify a special family of NOMA schemes
based on lattice partitions of the underlying ideal lattices, whose minimum
product distances can be easily controlled. Our analysis shows that among the
proposed schemes, the lattice-partition-based schemes achieve the largest
minimum product distances of the superimposed constellations, which are closely
related to the symbol error rates for receivers with single-user decoding.
Simulation results are presented to verify our analysis and to show the
effectiveness of the proposed schemes as compared to benchmark NOMA schemes.
Extensions of our design to the multi-antenna case are also considered where
similar analysis and results are presented.
| [
{
"created": "Thu, 23 May 2019 07:45:07 GMT",
"version": "v1"
}
] | 2019-06-18 | [
[
"Qiu",
"Min",
""
],
[
"Huang",
"Yu-Chih",
""
],
[
"Yuan",
"Jinhong",
""
]
] | In this paper, we investigate the problem of downlink non-orthogonal multiple access (NOMA) over block fading channels. For the single antenna case, we propose a class of NOMA schemes where all the users' signals are mapped into $n$-dimensional constellations corresponding to the same algebraic lattices from a number field, allowing every user attains full diversity gain with single-user decoding, i.e., no successive interference cancellation (SIC). The minimum product distances of the proposed scheme with arbitrary power allocation factor are analyzed and their upper bounds are derived. Within the proposed class of schemes, we also identify a special family of NOMA schemes based on lattice partitions of the underlying ideal lattices, whose minimum product distances can be easily controlled. Our analysis shows that among the proposed schemes, the lattice-partition-based schemes achieve the largest minimum product distances of the superimposed constellations, which are closely related to the symbol error rates for receivers with single-user decoding. Simulation results are presented to verify our analysis and to show the effectiveness of the proposed schemes as compared to benchmark NOMA schemes. Extensions of our design to the multi-antenna case are also considered where similar analysis and results are presented. |
2002.07418 | Peng Zhang | Peng Zhang, Jianye Hao, Weixun Wang, Hongyao Tang, Yi Ma, Yihai Duan,
Yan Zheng | KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human
Suboptimal Knowledge | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning agents usually learn from scratch, which requires a
large number of interactions with the environment. This is quite different from
the learning process of human. When faced with a new task, human naturally have
the common sense and use the prior knowledge to derive an initial policy and
guide the learning process afterwards. Although the prior knowledge may be not
fully applicable to the new task, the learning process is significantly sped up
since the initial policy ensures a quick-start of learning and intermediate
guidance allows to avoid unnecessary exploration. Taking this inspiration, we
propose knowledge guided policy network (KoGuN), a novel framework that
combines human prior suboptimal knowledge with reinforcement learning. Our
framework consists of a fuzzy rule controller to represent human knowledge and
a refine module to fine-tune suboptimal prior knowledge. The proposed framework
is end-to-end and can be combined with existing policy-based reinforcement
learning algorithm. We conduct experiments on both discrete and continuous
control tasks. The empirical results show that our approach, which combines
human suboptimal knowledge and RL, achieves significant improvement on learning
efficiency of flat RL algorithms, even with very low-performance human prior
knowledge.
| [
{
"created": "Tue, 18 Feb 2020 07:58:27 GMT",
"version": "v1"
},
{
"created": "Thu, 21 May 2020 07:02:41 GMT",
"version": "v2"
}
] | 2020-05-22 | [
[
"Zhang",
"Peng",
""
],
[
"Hao",
"Jianye",
""
],
[
"Wang",
"Weixun",
""
],
[
"Tang",
"Hongyao",
""
],
[
"Ma",
"Yi",
""
],
[
"Duan",
"Yihai",
""
],
[
"Zheng",
"Yan",
""
]
] | Reinforcement learning agents usually learn from scratch, which requires a large number of interactions with the environment. This is quite different from the learning process of human. When faced with a new task, human naturally have the common sense and use the prior knowledge to derive an initial policy and guide the learning process afterwards. Although the prior knowledge may be not fully applicable to the new task, the learning process is significantly sped up since the initial policy ensures a quick-start of learning and intermediate guidance allows to avoid unnecessary exploration. Taking this inspiration, we propose knowledge guided policy network (KoGuN), a novel framework that combines human prior suboptimal knowledge with reinforcement learning. Our framework consists of a fuzzy rule controller to represent human knowledge and a refine module to fine-tune suboptimal prior knowledge. The proposed framework is end-to-end and can be combined with existing policy-based reinforcement learning algorithm. We conduct experiments on both discrete and continuous control tasks. The empirical results show that our approach, which combines human suboptimal knowledge and RL, achieves significant improvement on learning efficiency of flat RL algorithms, even with very low-performance human prior knowledge. |
2108.01154 | Ali Raheem Mandeel | Ali Raheem Mandeel, Mohammed Salah Al-Radhi, Tam\'as G\'abor Csap\'o | Speaker Adaptation with Continuous Vocoder-based DNN-TTS | 10 pages, 3 figures, 23RD INTERNATIONAL CONFERENCE ON SPEECH AND
COMPUTER SPECOM 2021 | null | null | null | cs.SD eess.AS | http://creativecommons.org/licenses/by/4.0/ | Traditional vocoder-based statistical parametric speech synthesis can be
advantageous in applications that require low computational complexity. Recent
neural vocoders, which can produce high naturalness, still cannot fulfill the
requirement of being real-time during synthesis. In this paper, we experiment
with our earlier continuous vocoder, in which the excitation is modeled with
two one-dimensional parameters: continuous F0 and Maximum Voiced Frequency. We
show on the data of 9 speakers that an average voice can be trained for
DNN-TTS, and speaker adaptation is feasible 400 utterances (about 14 minutes).
Objective experiments support that the quality of speaker adaptation with
Continuous Vocoder-based DNN-TTS is similar to the quality of the speaker
adaptation with a WORLD Vocoder-based baseline.
| [
{
"created": "Mon, 2 Aug 2021 20:08:07 GMT",
"version": "v1"
}
] | 2021-08-04 | [
[
"Mandeel",
"Ali Raheem",
""
],
[
"Al-Radhi",
"Mohammed Salah",
""
],
[
"Csapó",
"Tamás Gábor",
""
]
] | Traditional vocoder-based statistical parametric speech synthesis can be advantageous in applications that require low computational complexity. Recent neural vocoders, which can produce high naturalness, still cannot fulfill the requirement of being real-time during synthesis. In this paper, we experiment with our earlier continuous vocoder, in which the excitation is modeled with two one-dimensional parameters: continuous F0 and Maximum Voiced Frequency. We show on the data of 9 speakers that an average voice can be trained for DNN-TTS, and speaker adaptation is feasible 400 utterances (about 14 minutes). Objective experiments support that the quality of speaker adaptation with Continuous Vocoder-based DNN-TTS is similar to the quality of the speaker adaptation with a WORLD Vocoder-based baseline. |
1609.08154 | Zhiyong Shan | Zhiyong Shan, Yu-fang Sun | Implementing RBAC model in An Operating System Kernel | in Chinese | null | null | null | cs.OS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, the implementation of an operating system oriented RBAC model
is discussed. Firstly, on the basis of RBAC96 model, a new RBAC model named OSR
is presented. Secondly, the OSR model is enforced in RFSOS kernel by the way of
integrating GFAC method and Capability mechanism together. All parts of the OSR
implementation are described in detail.
| [
{
"created": "Mon, 26 Sep 2016 17:36:18 GMT",
"version": "v1"
}
] | 2016-09-28 | [
[
"Shan",
"Zhiyong",
""
],
[
"Sun",
"Yu-fang",
""
]
] | In this paper, the implementation of an operating system oriented RBAC model is discussed. Firstly, on the basis of RBAC96 model, a new RBAC model named OSR is presented. Secondly, the OSR model is enforced in RFSOS kernel by the way of integrating GFAC method and Capability mechanism together. All parts of the OSR implementation are described in detail. |
1903.00172 | Yoshihiko Suhara | Nikita Bhutani, Yoshihiko Suhara, Wang-Chiew Tan, Alon Halevy, H. V.
Jagadish | Open Information Extraction from Question-Answer Pairs | NAACL 2019 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Open Information Extraction (OpenIE) extracts meaningful structured tuples
from free-form text. Most previous work on OpenIE considers extracting data
from one sentence at a time. We describe NeurON, a system for extracting tuples
from question-answer pairs. Since real questions and answers often contain
precisely the information that users care about, such information is
particularly desirable to extend a knowledge base with.
NeurON addresses several challenges. First, an answer text is often hard to
understand without knowing the question, and second, relevant information can
span multiple sentences. To address these, NeurON formulates extraction as a
multi-source sequence-to-sequence learning task, wherein it combines
distributed representations of a question and an answer to generate knowledge
facts. We describe experiments on two real-world datasets that demonstrate that
NeurON can find a significant number of new and interesting facts to extend a
knowledge base compared to state-of-the-art OpenIE methods.
| [
{
"created": "Fri, 1 Mar 2019 06:26:50 GMT",
"version": "v1"
},
{
"created": "Sat, 6 Apr 2019 08:56:40 GMT",
"version": "v2"
}
] | 2019-04-09 | [
[
"Bhutani",
"Nikita",
""
],
[
"Suhara",
"Yoshihiko",
""
],
[
"Tan",
"Wang-Chiew",
""
],
[
"Halevy",
"Alon",
""
],
[
"Jagadish",
"H. V.",
""
]
] | Open Information Extraction (OpenIE) extracts meaningful structured tuples from free-form text. Most previous work on OpenIE considers extracting data from one sentence at a time. We describe NeurON, a system for extracting tuples from question-answer pairs. Since real questions and answers often contain precisely the information that users care about, such information is particularly desirable to extend a knowledge base with. NeurON addresses several challenges. First, an answer text is often hard to understand without knowing the question, and second, relevant information can span multiple sentences. To address these, NeurON formulates extraction as a multi-source sequence-to-sequence learning task, wherein it combines distributed representations of a question and an answer to generate knowledge facts. We describe experiments on two real-world datasets that demonstrate that NeurON can find a significant number of new and interesting facts to extend a knowledge base compared to state-of-the-art OpenIE methods. |
1910.09579 | Steven W.T. Cheung | Steven W.T. Cheung, Dan R. Ghica, Koko Muroya | Transparent Synchronous Dataflow | null | The Art, Science, and Engineering of Programming, 2021, Vol. 5,
Issue 3, Article 12 | 10.22152/programming-journal.org/2021/5/12 | null | cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dataflow programming is a popular and convenient programming paradigm in
systems modelling, optimisation, and machine learning. It has a number of
advantages, for instance the lacks of control flow allows computation to be
carried out in parallel as well as in distributed machines. More recently the
idea of dataflow graphs has also been brought into the design of various deep
learning frameworks. They facilitate an easy and efficient implementation of
automatic differentiation, which is the heart of modern deep learning paradigm.
[abstract abridged]
| [
{
"created": "Mon, 21 Oct 2019 18:12:46 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Mar 2021 21:12:23 GMT",
"version": "v2"
}
] | 2021-03-03 | [
[
"Cheung",
"Steven W. T.",
""
],
[
"Ghica",
"Dan R.",
""
],
[
"Muroya",
"Koko",
""
]
] | Dataflow programming is a popular and convenient programming paradigm in systems modelling, optimisation, and machine learning. It has a number of advantages, for instance the lacks of control flow allows computation to be carried out in parallel as well as in distributed machines. More recently the idea of dataflow graphs has also been brought into the design of various deep learning frameworks. They facilitate an easy and efficient implementation of automatic differentiation, which is the heart of modern deep learning paradigm. [abstract abridged] |
2004.03424 | Joon Sik Kim | Joon Sik Kim, Jiahao Chen, Ameet Talwalkar | FACT: A Diagnostic for Group Fairness Trade-offs | Accepted to International Conference on Machine Learning (ICML 2020) | null | null | null | cs.LG cs.CY stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Group fairness, a class of fairness notions that measure how different groups
of individuals are treated differently according to their protected attributes,
has been shown to conflict with one another, often with a necessary cost in
loss of model's predictive performance. We propose a general diagnostic that
enables systematic characterization of these trade-offs in group fairness. We
observe that the majority of group fairness notions can be expressed via the
fairness-confusion tensor, which is the confusion matrix split according to the
protected attribute values. We frame several optimization problems that
directly optimize both accuracy and fairness objectives over the elements of
this tensor, which yield a general perspective for understanding multiple
trade-offs including group fairness incompatibilities. It also suggests an
alternate post-processing method for designing fair classifiers. On synthetic
and real datasets, we demonstrate the use cases of our diagnostic, particularly
on understanding the trade-off landscape between accuracy and fairness.
| [
{
"created": "Tue, 7 Apr 2020 14:15:51 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Apr 2020 17:55:32 GMT",
"version": "v2"
},
{
"created": "Tue, 7 Jul 2020 17:34:11 GMT",
"version": "v3"
}
] | 2020-07-08 | [
[
"Kim",
"Joon Sik",
""
],
[
"Chen",
"Jiahao",
""
],
[
"Talwalkar",
"Ameet",
""
]
] | Group fairness, a class of fairness notions that measure how different groups of individuals are treated differently according to their protected attributes, has been shown to conflict with one another, often with a necessary cost in loss of model's predictive performance. We propose a general diagnostic that enables systematic characterization of these trade-offs in group fairness. We observe that the majority of group fairness notions can be expressed via the fairness-confusion tensor, which is the confusion matrix split according to the protected attribute values. We frame several optimization problems that directly optimize both accuracy and fairness objectives over the elements of this tensor, which yield a general perspective for understanding multiple trade-offs including group fairness incompatibilities. It also suggests an alternate post-processing method for designing fair classifiers. On synthetic and real datasets, we demonstrate the use cases of our diagnostic, particularly on understanding the trade-off landscape between accuracy and fairness. |
2307.08233 | Liu Liu | Liu Liu, Shuaifeng Zhi, Zhenhua Du, Li Liu, Xinyu Zhang, Kai Huo, and
Weidong Jiang | ROFusion: Efficient Object Detection using Hybrid Point-wise
Radar-Optical Fusion | null | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Radars, due to their robustness to adverse weather conditions and ability to
measure object motions, have served in autonomous driving and intelligent
agents for years. However, Radar-based perception suffers from its unintuitive
sensing data, which lack of semantic and structural information of scenes. To
tackle this problem, camera and Radar sensor fusion has been investigated as a
trending strategy with low cost, high reliability and strong maintenance. While
most recent works explore how to explore Radar point clouds and images, rich
contextual information within Radar observation are discarded. In this paper,
we propose a hybrid point-wise Radar-Optical fusion approach for object
detection in autonomous driving scenarios. The framework benefits from dense
contextual information from both the range-doppler spectrum and images which
are integrated to learn a multi-modal feature representation. Furthermore, we
propose a novel local coordinate formulation, tackling the object detection
task in an object-centric coordinate. Extensive results show that with the
information gained from optical images, we could achieve leading performance in
object detection (97.69\% recall) compared to recent state-of-the-art methods
FFT-RadNet (82.86\% recall). Ablation studies verify the key design choices and
practicability of our approach given machine generated imperfect detections.
The code will be available at https://github.com/LiuLiu-55/ROFusion.
| [
{
"created": "Mon, 17 Jul 2023 04:25:46 GMT",
"version": "v1"
}
] | 2023-07-18 | [
[
"Liu",
"Liu",
""
],
[
"Zhi",
"Shuaifeng",
""
],
[
"Du",
"Zhenhua",
""
],
[
"Liu",
"Li",
""
],
[
"Zhang",
"Xinyu",
""
],
[
"Huo",
"Kai",
""
],
[
"Jiang",
"Weidong",
""
]
] | Radars, due to their robustness to adverse weather conditions and ability to measure object motions, have served in autonomous driving and intelligent agents for years. However, Radar-based perception suffers from its unintuitive sensing data, which lack of semantic and structural information of scenes. To tackle this problem, camera and Radar sensor fusion has been investigated as a trending strategy with low cost, high reliability and strong maintenance. While most recent works explore how to explore Radar point clouds and images, rich contextual information within Radar observation are discarded. In this paper, we propose a hybrid point-wise Radar-Optical fusion approach for object detection in autonomous driving scenarios. The framework benefits from dense contextual information from both the range-doppler spectrum and images which are integrated to learn a multi-modal feature representation. Furthermore, we propose a novel local coordinate formulation, tackling the object detection task in an object-centric coordinate. Extensive results show that with the information gained from optical images, we could achieve leading performance in object detection (97.69\% recall) compared to recent state-of-the-art methods FFT-RadNet (82.86\% recall). Ablation studies verify the key design choices and practicability of our approach given machine generated imperfect detections. The code will be available at https://github.com/LiuLiu-55/ROFusion. |
cs/0106006 | Aspassia Daskalopulu | Aspassia Daskalopulu, Marek Sergot | A Constraint-Driven System for Contract Assembly | null | Proc. 5th International Conference on Artificial Intelligence and
Law, ACM Press, pp. 62-69, 1995 | null | null | cs.AI | null | We present an approach for modelling the structure and coarse content of
legal documents with a view to providing automated support for the drafting of
contracts and contract database retrieval. The approach is designed to be
applicable where contract drafting is based on model-form contracts or on
existing examples of a similar type. The main features of the approach are: (1)
the representation addresses the structure and the interrelationships between
the constituent parts of contracts, but not the text of the document itself;
(2) the representation of documents is separated from the mechanisms that
manipulate it; and (3) the drafting process is subject to a collection of
explicitly stated constraints that govern the structure of the documents. We
describe the representation of document instances and of 'generic documents',
which are data structures used to drive the creation of new document instances,
and we show extracts from a sample session to illustrate the features of a
prototype system implemented in MacProlog.
| [
{
"created": "Thu, 7 Jun 2001 14:27:30 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Daskalopulu",
"Aspassia",
""
],
[
"Sergot",
"Marek",
""
]
] | We present an approach for modelling the structure and coarse content of legal documents with a view to providing automated support for the drafting of contracts and contract database retrieval. The approach is designed to be applicable where contract drafting is based on model-form contracts or on existing examples of a similar type. The main features of the approach are: (1) the representation addresses the structure and the interrelationships between the constituent parts of contracts, but not the text of the document itself; (2) the representation of documents is separated from the mechanisms that manipulate it; and (3) the drafting process is subject to a collection of explicitly stated constraints that govern the structure of the documents. We describe the representation of document instances and of 'generic documents', which are data structures used to drive the creation of new document instances, and we show extracts from a sample session to illustrate the features of a prototype system implemented in MacProlog. |
2210.03154 | Loukas Ilias | Konstantinos Psychogyios, Loukas Ilias, Dimitris Askounis | Comparison of Missing Data Imputation Methods using the Framingham Heart
study dataset | 2022 IEEE EMBS International Conference on Biomedical & Health
Informatics (BHI) | null | 10.1109/BHI56158.2022.9926882 | null | cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Cardiovascular disease (CVD) is a class of diseases that involve the heart or
blood vessels and according to World Health Organization is the leading cause
of death worldwide. EHR data regarding this case, as well as medical cases in
general, contain missing values very frequently. The percentage of missingness
may vary and is linked with instrument errors, manual data entry procedures,
etc. Even though the missing rate is usually significant, in many cases the
missing value imputation part is handled poorly either with case-deletion or
with simple statistical approaches such as mode and median imputation. These
methods are known to introduce significant bias, since they do not account for
the relationships between the dataset's variables. Within the medical
framework, many datasets consist of lab tests or patient medical tests, where
these relationships are present and strong. To address these limitations, in
this paper we test and modify state-of-the-art missing value imputation methods
based on Generative Adversarial Networks (GANs) and Autoencoders. The
evaluation is accomplished for both the tasks of data imputation and
post-imputation prediction. Regarding the imputation task, we achieve
improvements of 0.20, 7.00% in normalised Root Mean Squared Error (RMSE) and
Area Under the Receiver Operating Characteristic Curve (AUROC) respectively. In
terms of the post-imputation prediction task, our models outperform the
standard approaches by 2.50% in F1-score.
| [
{
"created": "Thu, 6 Oct 2022 18:35:08 GMT",
"version": "v1"
},
{
"created": "Mon, 10 Oct 2022 07:22:00 GMT",
"version": "v2"
}
] | 2022-11-08 | [
[
"Psychogyios",
"Konstantinos",
""
],
[
"Ilias",
"Loukas",
""
],
[
"Askounis",
"Dimitris",
""
]
] | Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels and according to World Health Organization is the leading cause of death worldwide. EHR data regarding this case, as well as medical cases in general, contain missing values very frequently. The percentage of missingness may vary and is linked with instrument errors, manual data entry procedures, etc. Even though the missing rate is usually significant, in many cases the missing value imputation part is handled poorly either with case-deletion or with simple statistical approaches such as mode and median imputation. These methods are known to introduce significant bias, since they do not account for the relationships between the dataset's variables. Within the medical framework, many datasets consist of lab tests or patient medical tests, where these relationships are present and strong. To address these limitations, in this paper we test and modify state-of-the-art missing value imputation methods based on Generative Adversarial Networks (GANs) and Autoencoders. The evaluation is accomplished for both the tasks of data imputation and post-imputation prediction. Regarding the imputation task, we achieve improvements of 0.20, 7.00% in normalised Root Mean Squared Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUROC) respectively. In terms of the post-imputation prediction task, our models outperform the standard approaches by 2.50% in F1-score. |
2405.17914 | Xiumei Deng | Xiumei Deng, Jun Li, Long Shi, Kang Wei, Ming Ding, Yumeng Shao, Wen
Chen, Shi Jin | Trustworthy DNN Partition for Blockchain-enabled Digital Twin in
Wireless IIoT Networks | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Digital twin (DT) has emerged as a promising solution to enhance
manufacturing efficiency in industrial Internet of Things (IIoT) networks. To
promote the efficiency and trustworthiness of DT for wireless IIoT networks, we
propose a blockchain-enabled DT (B-DT) framework that employs deep neural
network (DNN) partitioning technique and reputation-based consensus mechanism,
wherein the DTs maintained at the gateway side execute DNN inference tasks
using the data collected from their associated IIoT devices. First, we employ
DNN partitioning technique to offload the top-layer DNN inference tasks to the
access point (AP) side, which alleviates the computation burden at the gateway
side and thereby improves the efficiency of DNN inference. Second, we propose a
reputation-based consensus mechanism that integrates Proof of Work (PoW) and
Proof of Stake (PoS). Specifically, the proposed consensus mechanism evaluates
the off-chain reputation of each AP according to its computation resource
contributions to the DNN inference tasks, and utilizes the off-chain reputation
as a stake to adjust the block generation difficulty. Third, we formulate a
stochastic optimization problem of communication resource (i.e., partition
point) and computation resource allocation (i.e., computation frequency of APs
for top-layer DNN inference and block generation) to minimize system latency
under the time-varying channel state and long-term constraints of off-chain
reputation, and solve the problem using Lyapunov optimization method.
Experimental results show that the proposed dynamic DNN partitioning and
resource allocation (DPRA) algorithm outperforms the baselines in terms of
reducing the overall latency while guaranteeing the trustworthiness of the B-DT
system.
| [
{
"created": "Tue, 28 May 2024 07:34:12 GMT",
"version": "v1"
}
] | 2024-05-29 | [
[
"Deng",
"Xiumei",
""
],
[
"Li",
"Jun",
""
],
[
"Shi",
"Long",
""
],
[
"Wei",
"Kang",
""
],
[
"Ding",
"Ming",
""
],
[
"Shao",
"Yumeng",
""
],
[
"Chen",
"Wen",
""
],
[
"Jin",
"Shi",
""
]
] | Digital twin (DT) has emerged as a promising solution to enhance manufacturing efficiency in industrial Internet of Things (IIoT) networks. To promote the efficiency and trustworthiness of DT for wireless IIoT networks, we propose a blockchain-enabled DT (B-DT) framework that employs deep neural network (DNN) partitioning technique and reputation-based consensus mechanism, wherein the DTs maintained at the gateway side execute DNN inference tasks using the data collected from their associated IIoT devices. First, we employ DNN partitioning technique to offload the top-layer DNN inference tasks to the access point (AP) side, which alleviates the computation burden at the gateway side and thereby improves the efficiency of DNN inference. Second, we propose a reputation-based consensus mechanism that integrates Proof of Work (PoW) and Proof of Stake (PoS). Specifically, the proposed consensus mechanism evaluates the off-chain reputation of each AP according to its computation resource contributions to the DNN inference tasks, and utilizes the off-chain reputation as a stake to adjust the block generation difficulty. Third, we formulate a stochastic optimization problem of communication resource (i.e., partition point) and computation resource allocation (i.e., computation frequency of APs for top-layer DNN inference and block generation) to minimize system latency under the time-varying channel state and long-term constraints of off-chain reputation, and solve the problem using Lyapunov optimization method. Experimental results show that the proposed dynamic DNN partitioning and resource allocation (DPRA) algorithm outperforms the baselines in terms of reducing the overall latency while guaranteeing the trustworthiness of the B-DT system. |
1906.12140 | Swanand Kadhe | Swanand Kadhe, Jichan Chung, and Kannan Ramchandran | SeF: A Secure Fountain Architecture for Slashing Storage Costs in
Blockchains | null | null | null | null | cs.CR cs.DC cs.IT math.IT | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Full nodes, which synchronize the entire blockchain history and independently
validate all the blocks, form the backbone of any blockchain network by playing
a vital role in ensuring security properties. On the other hand, a user running
a full node needs to pay a heavy price in terms of storage costs. E.g., the
Bitcoin blockchain size has grown over 215GB, in spite of its low throughput.
The ledger size for a high throughput blockchain Ripple has already reached
9TB, and it is growing at an astonishing rate of 12GB per day!
In this paper, we propose an architecture based on 'fountain codes', a class
of erasure codes, that enables any full node to 'encode' validated blocks into
a small number of 'coded blocks', thereby reducing its storage costs by orders
of magnitude. In particular, our proposed "Secure Fountain (SeF)" architecture
can achieve a near-optimal trade-off between the storage savings per node and
the 'bootstrap cost' in terms of the number of (honest) storage-constrained
nodes a new node needs to contact to recover the blockchain. A key technical
innovation in SeF codes is to make fountain codes secure against adversarial
nodes that can provide maliciously formed coded blocks. Our idea is to use the
header-chain as a 'side-information' to check whether a coded block is
maliciously formed while it is getting decoded. Further, the 'rateless
property' of fountain codes helps in achieving high decentralization and
scalability. Our experiments demonstrate that SeF codes tuned to achieve 1000x
storage savings enable full nodes to encode the 191GB Bitcoin blockchain into
195MB on average. A new node can recover the blockchain from an arbitrary set
of storage-constrained nodes as long as the set contains ~1100 honest nodes on
average. Note that for a 1000x storage savings, the fundamental bound on the
number of honest nodes to contact is 1000: we need about 10% more in practice.
| [
{
"created": "Fri, 28 Jun 2019 11:32:33 GMT",
"version": "v1"
}
] | 2019-07-01 | [
[
"Kadhe",
"Swanand",
""
],
[
"Chung",
"Jichan",
""
],
[
"Ramchandran",
"Kannan",
""
]
] | Full nodes, which synchronize the entire blockchain history and independently validate all the blocks, form the backbone of any blockchain network by playing a vital role in ensuring security properties. On the other hand, a user running a full node needs to pay a heavy price in terms of storage costs. E.g., the Bitcoin blockchain size has grown over 215GB, in spite of its low throughput. The ledger size for a high throughput blockchain Ripple has already reached 9TB, and it is growing at an astonishing rate of 12GB per day! In this paper, we propose an architecture based on 'fountain codes', a class of erasure codes, that enables any full node to 'encode' validated blocks into a small number of 'coded blocks', thereby reducing its storage costs by orders of magnitude. In particular, our proposed "Secure Fountain (SeF)" architecture can achieve a near-optimal trade-off between the storage savings per node and the 'bootstrap cost' in terms of the number of (honest) storage-constrained nodes a new node needs to contact to recover the blockchain. A key technical innovation in SeF codes is to make fountain codes secure against adversarial nodes that can provide maliciously formed coded blocks. Our idea is to use the header-chain as a 'side-information' to check whether a coded block is maliciously formed while it is getting decoded. Further, the 'rateless property' of fountain codes helps in achieving high decentralization and scalability. Our experiments demonstrate that SeF codes tuned to achieve 1000x storage savings enable full nodes to encode the 191GB Bitcoin blockchain into 195MB on average. A new node can recover the blockchain from an arbitrary set of storage-constrained nodes as long as the set contains ~1100 honest nodes on average. Note that for a 1000x storage savings, the fundamental bound on the number of honest nodes to contact is 1000: we need about 10% more in practice. |
2407.12024 | Jordan Rey-Jouanchicot | Jordan Rey-Jouanchicot (IRIT-ELIPSE, LAAS), Andr\'e Bottaro, Eric
Campo (LAAS-S4M), Jean-L\'eon Bouraoui, Nadine Vigouroux (IRIT-ELIPSE),
Fr\'ed\'eric Vella (IRIT-ELIPSE) | Leveraging Large Language Models for enhanced personalised user
experience in Smart Homes | null | null | null | null | cs.HC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Smart home automation systems aim to improve the comfort and convenience of
users in their living environment. However, adapting automation to user needs
remains a challenge. Indeed, many systems still rely on hand-crafted routines
for each smart object.This paper presents an original smart home architecture
leveraging Large Language Models (LLMs) and user preferences to push the
boundaries of personalisation and intuitiveness in the home environment.This
article explores a human-centred approach that uses the general knowledge
provided by LLMs to learn and facilitate interactions with the environment.The
advantages of the proposed model are demonstrated on a set of scenarios, as
well as a comparative analysis with various LLM implementations. Some metrics
are assessed to determine the system's ability to maintain comfort, safety, and
user preferences. The paper details the approach to real-world implementation
and evaluation.The proposed approach of using preferences shows up to 52.3%
increase in average grade, and with an average processing time reduced by 35.6%
on Starling 7B Alpha LLM. In addition, performance is 26.4% better than the
results of the larger models without preferences, with processing time almost
20 times faster.
| [
{
"created": "Fri, 28 Jun 2024 07:08:20 GMT",
"version": "v1"
}
] | 2024-07-18 | [
[
"Rey-Jouanchicot",
"Jordan",
"",
"IRIT-ELIPSE, LAAS"
],
[
"Bottaro",
"André",
"",
"LAAS-S4M"
],
[
"Campo",
"Eric",
"",
"LAAS-S4M"
],
[
"Bouraoui",
"Jean-Léon",
"",
"IRIT-ELIPSE"
],
[
"Vigouroux",
"Nadine",
"",
"IRIT-ELIPSE"
],
[
"Vella",
"Frédéric",
"",
"IRIT-ELIPSE"
]
] | Smart home automation systems aim to improve the comfort and convenience of users in their living environment. However, adapting automation to user needs remains a challenge. Indeed, many systems still rely on hand-crafted routines for each smart object.This paper presents an original smart home architecture leveraging Large Language Models (LLMs) and user preferences to push the boundaries of personalisation and intuitiveness in the home environment.This article explores a human-centred approach that uses the general knowledge provided by LLMs to learn and facilitate interactions with the environment.The advantages of the proposed model are demonstrated on a set of scenarios, as well as a comparative analysis with various LLM implementations. Some metrics are assessed to determine the system's ability to maintain comfort, safety, and user preferences. The paper details the approach to real-world implementation and evaluation.The proposed approach of using preferences shows up to 52.3% increase in average grade, and with an average processing time reduced by 35.6% on Starling 7B Alpha LLM. In addition, performance is 26.4% better than the results of the larger models without preferences, with processing time almost 20 times faster. |
2401.11257 | Tianyi Hu | Tianyi Hu, Zhiqiang Pu, Xiaolin Ai, Tenghai Qiu, Jianqiang Yi | Measuring Policy Distance for Multi-Agent Reinforcement Learning | 9 pages, 6 figures | null | null | null | cs.MA cs.AI | http://creativecommons.org/licenses/by/4.0/ | Diversity plays a crucial role in improving the performance of multi-agent
reinforcement learning (MARL). Currently, many diversity-based methods have
been developed to overcome the drawbacks of excessive parameter sharing in
traditional MARL. However, there remains a lack of a general metric to quantify
policy differences among agents. Such a metric would not only facilitate the
evaluation of the diversity evolution in multi-agent systems, but also provide
guidance for the design of diversity-based MARL algorithms. In this paper, we
propose the multi-agent policy distance (MAPD), a general tool for measuring
policy differences in MARL. By learning the conditional representations of
agents' decisions, MAPD can computes the policy distance between any pair of
agents. Furthermore, we extend MAPD to a customizable version, which can
quantify differences among agent policies on specified aspects. Based on the
online deployment of MAPD, we design a multi-agent dynamic parameter sharing
(MADPS) algorithm as an example of the MAPD's applications. Extensive
experiments demonstrate that our method is effective in measuring differences
in agent policies and specific behavioral tendencies. Moreover, in comparison
to other methods of parameter sharing, MADPS exhibits superior performance.
| [
{
"created": "Sat, 20 Jan 2024 15:34:51 GMT",
"version": "v1"
},
{
"created": "Sun, 28 Jan 2024 15:37:54 GMT",
"version": "v2"
}
] | 2024-01-30 | [
[
"Hu",
"Tianyi",
""
],
[
"Pu",
"Zhiqiang",
""
],
[
"Ai",
"Xiaolin",
""
],
[
"Qiu",
"Tenghai",
""
],
[
"Yi",
"Jianqiang",
""
]
] | Diversity plays a crucial role in improving the performance of multi-agent reinforcement learning (MARL). Currently, many diversity-based methods have been developed to overcome the drawbacks of excessive parameter sharing in traditional MARL. However, there remains a lack of a general metric to quantify policy differences among agents. Such a metric would not only facilitate the evaluation of the diversity evolution in multi-agent systems, but also provide guidance for the design of diversity-based MARL algorithms. In this paper, we propose the multi-agent policy distance (MAPD), a general tool for measuring policy differences in MARL. By learning the conditional representations of agents' decisions, MAPD can computes the policy distance between any pair of agents. Furthermore, we extend MAPD to a customizable version, which can quantify differences among agent policies on specified aspects. Based on the online deployment of MAPD, we design a multi-agent dynamic parameter sharing (MADPS) algorithm as an example of the MAPD's applications. Extensive experiments demonstrate that our method is effective in measuring differences in agent policies and specific behavioral tendencies. Moreover, in comparison to other methods of parameter sharing, MADPS exhibits superior performance. |
1210.6112 | EPTCS | James Smith | The Jasper Framework: Towards a Platform Independent, Formal Treatment
of Web Programming | In Proceedings WWV 2012, arXiv:1210.5783. Added doi references where
possible | EPTCS 98, 2012, pp. 31-45 | 10.4204/EPTCS.98.5 | null | cs.SE cs.LO cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces Jasper, a web programming framework which allows web
applications to be developed in an essentially platform indepedent manner and
which is also suited to a formal treatment. It outlines Jasper conceptually and
shows how Jasper is implemented on several commonplace platforms. It also
introduces the Jasper Music Store, a web application powered by Jasper and
implemented on each of these platforms. And it briefly describes a formal
treatment and outlines the tools and languages planned that will allow this
treatment to be automated.
| [
{
"created": "Tue, 23 Oct 2012 02:54:58 GMT",
"version": "v1"
}
] | 2012-12-07 | [
[
"Smith",
"James",
""
]
] | This paper introduces Jasper, a web programming framework which allows web applications to be developed in an essentially platform indepedent manner and which is also suited to a formal treatment. It outlines Jasper conceptually and shows how Jasper is implemented on several commonplace platforms. It also introduces the Jasper Music Store, a web application powered by Jasper and implemented on each of these platforms. And it briefly describes a formal treatment and outlines the tools and languages planned that will allow this treatment to be automated. |
2105.04749 | Aron Laszka | Shanto Roy, Nazia Sharmin, Jaime C. Acosta, Christopher Kiekintveld,
Aron Laszka | Survey and Taxonomy of Adversarial Reconnaissance Techniques | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Adversaries are often able to penetrate networks and compromise systems by
exploiting vulnerabilities in people and systems. The key to the success of
these attacks is information that adversaries collect throughout the phases of
the cyber kill chain. We summarize and analyze the methods, tactics, and tools
that adversaries use to conduct reconnaissance activities throughout the attack
process. First, we discuss what types of information adversaries seek, and how
and when they can obtain this information. Then, we provide a taxonomy and
detailed overview of adversarial reconnaissance techniques. The taxonomy
introduces a categorization of reconnaissance techniques based on the source as
third-party, human-, and system-based information gathering. This paper
provides a comprehensive view of adversarial reconnaissance that can help in
understanding and modeling this complex but vital aspect of cyber attacks as
well as insights that can improve defensive strategies, such as cyber
deception.
| [
{
"created": "Tue, 11 May 2021 02:09:12 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Apr 2022 00:11:54 GMT",
"version": "v2"
}
] | 2022-05-02 | [
[
"Roy",
"Shanto",
""
],
[
"Sharmin",
"Nazia",
""
],
[
"Acosta",
"Jaime C.",
""
],
[
"Kiekintveld",
"Christopher",
""
],
[
"Laszka",
"Aron",
""
]
] | Adversaries are often able to penetrate networks and compromise systems by exploiting vulnerabilities in people and systems. The key to the success of these attacks is information that adversaries collect throughout the phases of the cyber kill chain. We summarize and analyze the methods, tactics, and tools that adversaries use to conduct reconnaissance activities throughout the attack process. First, we discuss what types of information adversaries seek, and how and when they can obtain this information. Then, we provide a taxonomy and detailed overview of adversarial reconnaissance techniques. The taxonomy introduces a categorization of reconnaissance techniques based on the source as third-party, human-, and system-based information gathering. This paper provides a comprehensive view of adversarial reconnaissance that can help in understanding and modeling this complex but vital aspect of cyber attacks as well as insights that can improve defensive strategies, such as cyber deception. |
1501.06802 | Gamal Abd El-Nasser A. Said | Gamal Abd El-Nasser A. Said, El-Sayed M. El-Horbaty | A Simulation Modeling Approach for Optimization of Storage Space
Allocation in Container Terminal | International Journal of Computer, Information, Systems and Control
Engineering Vol:9 No:1, 2015 | Information, Systems and Control Engineering Vol. 9, No. 1, 2015,
pp. 168-173 | null | null | cs.OH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Container handling problems at container terminals are NP-hard problems. This
paper presents an approach using discrete-event simulation modeling to optimize
solution for storage space allocation problem, taking into account all various
interrelated container terminal handling activities. The proposed approach is
applied on a real case study data of container terminal at Alexandria port. The
computational results show the effectiveness of the proposed model for
optimization of storage space allocation in container terminal where 54%
reduction in containers handling time in port is achieved.
| [
{
"created": "Tue, 27 Jan 2015 16:10:23 GMT",
"version": "v1"
}
] | 2015-04-14 | [
[
"Said",
"Gamal Abd El-Nasser A.",
""
],
[
"El-Horbaty",
"El-Sayed M.",
""
]
] | Container handling problems at container terminals are NP-hard problems. This paper presents an approach using discrete-event simulation modeling to optimize solution for storage space allocation problem, taking into account all various interrelated container terminal handling activities. The proposed approach is applied on a real case study data of container terminal at Alexandria port. The computational results show the effectiveness of the proposed model for optimization of storage space allocation in container terminal where 54% reduction in containers handling time in port is achieved. |
1011.5065 | Woohyuk Chang | Woohyuk Chang, Sae-Young Chung, Yong H. Lee | Gaussian Relay Channel Capacity to Within a Fixed Number of Bits | 6 pages, 7 figures | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we show that the capacity of the three-node Gaussian relay
channel can be achieved to within 1 and 2 bit/sec/Hz using compress-and-forward
and amplify-and-forward relaying, respectively.
| [
{
"created": "Tue, 23 Nov 2010 11:27:46 GMT",
"version": "v1"
}
] | 2010-11-24 | [
[
"Chang",
"Woohyuk",
""
],
[
"Chung",
"Sae-Young",
""
],
[
"Lee",
"Yong H.",
""
]
] | In this paper, we show that the capacity of the three-node Gaussian relay channel can be achieved to within 1 and 2 bit/sec/Hz using compress-and-forward and amplify-and-forward relaying, respectively. |
2104.13869 | Francisco Romero | Francisco Romero, Gohar Irfan Chaudhry, \'I\~nigo Goiri, Pragna Gopa,
Paul Batum, Neeraja J. Yadwadkar, Rodrigo Fonseca, Christos Kozyrakis,
Ricardo Bianchini | Faa$T: A Transparent Auto-Scaling Cache for Serverless Applications | 18 pages, 15 figures | null | null | null | cs.DC | http://creativecommons.org/licenses/by/4.0/ | Function-as-a-Service (FaaS) has become an increasingly popular way for users
to deploy their applications without the burden of managing the underlying
infrastructure. However, existing FaaS platforms rely on remote storage to
maintain state, limiting the set of applications that can be run efficiently.
Recent caching work for FaaS platforms has tried to address this problem, but
has fallen short: it disregards the widely different characteristics of FaaS
applications, does not scale the cache based on data access patterns, or
requires changes to applications. To address these limitations, we present
Faa\$T, a transparent auto-scaling distributed cache for serverless
applications. Each application gets its own Faa\$T cache. After a function
executes and the application becomes inactive, the cache is unloaded from
memory with the application. Upon reloading for the next invocation, Faa\$T
pre-warms the cache with objects likely to be accessed. In addition to
traditional compute-based scaling, Faa\$T scales based on working set and
object sizes to manage cache space and I/O bandwidth. We motivate our design
with a comprehensive study of data access patterns in a large-scale commercial
FaaS provider. We implement Faa\$T for the provider's production FaaS platform.
Our experiments show that Faa\$T can improve performance by up to 92% (57% on
average) for challenging applications, and reduce cost for most users compared
to state-of-the-art caching systems, i.e. the cost of having to stand up
additional serverful resources.
| [
{
"created": "Wed, 28 Apr 2021 16:31:19 GMT",
"version": "v1"
}
] | 2021-04-29 | [
[
"Romero",
"Francisco",
""
],
[
"Chaudhry",
"Gohar Irfan",
""
],
[
"Goiri",
"Íñigo",
""
],
[
"Gopa",
"Pragna",
""
],
[
"Batum",
"Paul",
""
],
[
"Yadwadkar",
"Neeraja J.",
""
],
[
"Fonseca",
"Rodrigo",
""
],
[
"Kozyrakis",
"Christos",
""
],
[
"Bianchini",
"Ricardo",
""
]
] | Function-as-a-Service (FaaS) has become an increasingly popular way for users to deploy their applications without the burden of managing the underlying infrastructure. However, existing FaaS platforms rely on remote storage to maintain state, limiting the set of applications that can be run efficiently. Recent caching work for FaaS platforms has tried to address this problem, but has fallen short: it disregards the widely different characteristics of FaaS applications, does not scale the cache based on data access patterns, or requires changes to applications. To address these limitations, we present Faa\$T, a transparent auto-scaling distributed cache for serverless applications. Each application gets its own Faa\$T cache. After a function executes and the application becomes inactive, the cache is unloaded from memory with the application. Upon reloading for the next invocation, Faa\$T pre-warms the cache with objects likely to be accessed. In addition to traditional compute-based scaling, Faa\$T scales based on working set and object sizes to manage cache space and I/O bandwidth. We motivate our design with a comprehensive study of data access patterns in a large-scale commercial FaaS provider. We implement Faa\$T for the provider's production FaaS platform. Our experiments show that Faa\$T can improve performance by up to 92% (57% on average) for challenging applications, and reduce cost for most users compared to state-of-the-art caching systems, i.e. the cost of having to stand up additional serverful resources. |
2001.03102 | Roy Miles | Roy Miles, Krystian Mikolajczyk | Compression of descriptor models for mobile applications | ICASSP 2021 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks have demonstrated state-of-the-art performance for
feature-based image matching through the advent of new large and diverse
datasets. However, there has been little work on evaluating the computational
cost, model size, and matching accuracy tradeoffs for these models. This paper
explicitly addresses these practical metrics by considering the
state-of-the-art HardNet model. We observe a significant redundancy in the
learned weights, which we exploit through the use of depthwise separable layers
and an efficient Tucker decomposition. We demonstrate that a combination of
these methods is very effective, but still sacrifices the top-end accuracy. To
resolve this, we propose the Convolution-Depthwise-Pointwise(CDP) layer, which
provides a means of interpolating between the standard and depthwise separable
convolutions. With this proposed layer, we can achieve an 8 times reduction in
the number of parameters on the HardNet model, 13 times reduction in the
computational complexity, while sacrificing less than 1% on the overall
accuracy across theHPatchesbenchmarks. To further demonstrate the
generalisation of this approach, we apply it to the state-of-the-art SuperPoint
model, where we can significantly reduce the number of parameters and
floating-point operations, with minimal degradation in the matching accuracy.
| [
{
"created": "Thu, 9 Jan 2020 17:00:21 GMT",
"version": "v1"
},
{
"created": "Sun, 29 Mar 2020 20:37:33 GMT",
"version": "v2"
},
{
"created": "Fri, 5 Feb 2021 10:41:09 GMT",
"version": "v3"
}
] | 2021-02-08 | [
[
"Miles",
"Roy",
""
],
[
"Mikolajczyk",
"Krystian",
""
]
] | Deep neural networks have demonstrated state-of-the-art performance for feature-based image matching through the advent of new large and diverse datasets. However, there has been little work on evaluating the computational cost, model size, and matching accuracy tradeoffs for these models. This paper explicitly addresses these practical metrics by considering the state-of-the-art HardNet model. We observe a significant redundancy in the learned weights, which we exploit through the use of depthwise separable layers and an efficient Tucker decomposition. We demonstrate that a combination of these methods is very effective, but still sacrifices the top-end accuracy. To resolve this, we propose the Convolution-Depthwise-Pointwise(CDP) layer, which provides a means of interpolating between the standard and depthwise separable convolutions. With this proposed layer, we can achieve an 8 times reduction in the number of parameters on the HardNet model, 13 times reduction in the computational complexity, while sacrificing less than 1% on the overall accuracy across theHPatchesbenchmarks. To further demonstrate the generalisation of this approach, we apply it to the state-of-the-art SuperPoint model, where we can significantly reduce the number of parameters and floating-point operations, with minimal degradation in the matching accuracy. |
2009.04336 | Gabriele Farina | Gabriele Farina and Tuomas Sandholm | Polynomial-Time Computation of Optimal Correlated Equilibria in
Two-Player Extensive-Form Games with Public Chance Moves and Beyond | null | null | null | null | cs.GT cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Unlike normal-form games, where correlated equilibria have been studied for
more than 45 years, extensive-form correlation is still generally not well
understood. Part of the reason for this gap is that the sequential nature of
extensive-form games allows for a richness of behaviors and incentives that are
not possible in normal-form settings. This richness translates to a
significantly different complexity landscape surrounding extensive-form
correlated equilibria. As of today, it is known that finding an optimal
extensive-form correlated equilibrium (EFCE), extensive-form coarse correlated
equilibrium (EFCCE), or normal-form coarse correlated equilibrium (NFCCE) in a
two-player extensive-form game is computationally tractable when the game does
not include chance moves, and intractable when the game involves chance moves.
In this paper we significantly refine this complexity threshold by showing
that, in two-player games, an optimal correlated equilibrium can be computed in
polynomial time, provided that a certain condition is satisfied. We show that
the condition holds, for example, when all chance moves are public, that is,
both players observe all chance moves. This implies that an optimal EFCE, EFCCE
and NFCCE can be computed in polynomial time in the game size in two-player
games with public chance moves, providing the biggest positive complexity
result surrounding extensive-form correlation in more than a decade.
| [
{
"created": "Wed, 9 Sep 2020 14:51:58 GMT",
"version": "v1"
}
] | 2020-09-10 | [
[
"Farina",
"Gabriele",
""
],
[
"Sandholm",
"Tuomas",
""
]
] | Unlike normal-form games, where correlated equilibria have been studied for more than 45 years, extensive-form correlation is still generally not well understood. Part of the reason for this gap is that the sequential nature of extensive-form games allows for a richness of behaviors and incentives that are not possible in normal-form settings. This richness translates to a significantly different complexity landscape surrounding extensive-form correlated equilibria. As of today, it is known that finding an optimal extensive-form correlated equilibrium (EFCE), extensive-form coarse correlated equilibrium (EFCCE), or normal-form coarse correlated equilibrium (NFCCE) in a two-player extensive-form game is computationally tractable when the game does not include chance moves, and intractable when the game involves chance moves. In this paper we significantly refine this complexity threshold by showing that, in two-player games, an optimal correlated equilibrium can be computed in polynomial time, provided that a certain condition is satisfied. We show that the condition holds, for example, when all chance moves are public, that is, both players observe all chance moves. This implies that an optimal EFCE, EFCCE and NFCCE can be computed in polynomial time in the game size in two-player games with public chance moves, providing the biggest positive complexity result surrounding extensive-form correlation in more than a decade. |
1308.6505 | Anna Huber | Anna Huber and Andrei Krokhin | Oracle Tractability of Skew Bisubmodular Functions | null | null | null | null | cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we consider skew bisubmodular functions as introduced in [9].
We construct a convex extension of a skew bisubmodular function which we call
Lov\'asz extension in correspondence to the submodular case. We use this
extension to show that skew bisubmodular functions given by an oracle can be
minimised in polynomial time.
| [
{
"created": "Thu, 29 Aug 2013 16:03:49 GMT",
"version": "v1"
}
] | 2013-08-30 | [
[
"Huber",
"Anna",
""
],
[
"Krokhin",
"Andrei",
""
]
] | In this paper we consider skew bisubmodular functions as introduced in [9]. We construct a convex extension of a skew bisubmodular function which we call Lov\'asz extension in correspondence to the submodular case. We use this extension to show that skew bisubmodular functions given by an oracle can be minimised in polynomial time. |
2007.03204 | Pashootan Vaezipoor | Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger
Grosse, Sanjit A. Seshia, Fahiem Bacchus | Learning Branching Heuristics for Propositional Model Counting | null | 35(14), 2021, 12427-12435 | 10.1609/aaai.v35i14.17474 | null | cs.LG cs.AI cs.LO stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Propositional model counting, or #SAT, is the problem of computing the number
of satisfying assignments of a Boolean formula. Many problems from different
application areas, including many discrete probabilistic inference problems,
can be translated into model counting problems to be solved by #SAT solvers.
Exact #SAT solvers, however, are often not scalable to industrial size
instances. In this paper, we present Neuro#, an approach for learning branching
heuristics to improve the performance of exact #SAT solvers on instances from a
given family of problems. We experimentally show that our method reduces the
step count on similarly distributed held-out instances and generalizes to much
larger instances from the same problem family. It is able to achieve these
results on a number of different problem families having very different
structures. In addition to step count improvements, Neuro# can also achieve
orders of magnitude wall-clock speedups over the vanilla solver on larger
instances in some problem families, despite the runtime overhead of querying
the model.
| [
{
"created": "Tue, 7 Jul 2020 05:20:29 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Sep 2022 21:47:20 GMT",
"version": "v2"
}
] | 2022-09-12 | [
[
"Vaezipoor",
"Pashootan",
""
],
[
"Lederman",
"Gil",
""
],
[
"Wu",
"Yuhuai",
""
],
[
"Maddison",
"Chris J.",
""
],
[
"Grosse",
"Roger",
""
],
[
"Seshia",
"Sanjit A.",
""
],
[
"Bacchus",
"Fahiem",
""
]
] | Propositional model counting, or #SAT, is the problem of computing the number of satisfying assignments of a Boolean formula. Many problems from different application areas, including many discrete probabilistic inference problems, can be translated into model counting problems to be solved by #SAT solvers. Exact #SAT solvers, however, are often not scalable to industrial size instances. In this paper, we present Neuro#, an approach for learning branching heuristics to improve the performance of exact #SAT solvers on instances from a given family of problems. We experimentally show that our method reduces the step count on similarly distributed held-out instances and generalizes to much larger instances from the same problem family. It is able to achieve these results on a number of different problem families having very different structures. In addition to step count improvements, Neuro# can also achieve orders of magnitude wall-clock speedups over the vanilla solver on larger instances in some problem families, despite the runtime overhead of querying the model. |
cs/0601116 | Laurent Noe | Gregory Kucherov (LIFL), Laurent No\'e (LIFL), Mihkail Roytberg (LIFL) | A unifying framework for seed sensitivity and its application to subset
seeds | null | Journal of Bioinformatics and Computational Biology 4 (2006) 2, pp
553--569 | 10.1142/S0219720006001977 | null | cs.DS q-bio.QM | null | We propose a general approach to compute the seed sensitivity, that can be
applied to different definitions of seeds. It treats separately three
components of the seed sensitivity problem -- a set of target alignments, an
associated probability distribution, and a seed model -- that are specified by
distinct finite automata. The approach is then applied to a new concept of
subset seeds for which we propose an efficient automaton construction.
Experimental results confirm that sensitive subset seeds can be efficiently
designed using our approach, and can then be used in similarity search
producing better results than ordinary spaced seeds.
| [
{
"created": "Fri, 27 Jan 2006 18:53:01 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Sep 2006 07:05:58 GMT",
"version": "v2"
}
] | 2010-01-19 | [
[
"Kucherov",
"Gregory",
"",
"LIFL"
],
[
"Noé",
"Laurent",
"",
"LIFL"
],
[
"Roytberg",
"Mihkail",
"",
"LIFL"
]
] | We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem -- a set of target alignments, an associated probability distribution, and a seed model -- that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which we propose an efficient automaton construction. Experimental results confirm that sensitive subset seeds can be efficiently designed using our approach, and can then be used in similarity search producing better results than ordinary spaced seeds. |
1903.07377 | Johannes Michael | Johannes Michael, Roger Labahn, Tobias Gr\"uning, Jochen Z\"ollner | Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition | 8 pages, 1 figure, 8 tables | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Encoder-decoder models have become an effective approach for sequence
learning tasks like machine translation, image captioning and speech
recognition, but have yet to show competitive results for handwritten text
recognition. To this end, we propose an attention-based sequence-to-sequence
model. It combines a convolutional neural network as a generic feature
extractor with a recurrent neural network to encode both the visual
information, as well as the temporal context between characters in the input
image, and uses a separate recurrent neural network to decode the actual
character sequence. We make experimental comparisons between various attention
mechanisms and positional encodings, in order to find an appropriate alignment
between the input and output sequence. The model can be trained end-to-end and
the optional integration of a hybrid loss allows the encoder to retain an
interpretable and usable output, if desired. We achieve competitive results on
the IAM and ICFHR2016 READ data sets compared to the state-of-the-art without
the use of a language model, and we significantly improve over any recent
sequence-to-sequence approaches.
| [
{
"created": "Mon, 18 Mar 2019 11:51:33 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Jul 2019 11:40:53 GMT",
"version": "v2"
}
] | 2019-07-16 | [
[
"Michael",
"Johannes",
""
],
[
"Labahn",
"Roger",
""
],
[
"Grüning",
"Tobias",
""
],
[
"Zöllner",
"Jochen",
""
]
] | Encoder-decoder models have become an effective approach for sequence learning tasks like machine translation, image captioning and speech recognition, but have yet to show competitive results for handwritten text recognition. To this end, we propose an attention-based sequence-to-sequence model. It combines a convolutional neural network as a generic feature extractor with a recurrent neural network to encode both the visual information, as well as the temporal context between characters in the input image, and uses a separate recurrent neural network to decode the actual character sequence. We make experimental comparisons between various attention mechanisms and positional encodings, in order to find an appropriate alignment between the input and output sequence. The model can be trained end-to-end and the optional integration of a hybrid loss allows the encoder to retain an interpretable and usable output, if desired. We achieve competitive results on the IAM and ICFHR2016 READ data sets compared to the state-of-the-art without the use of a language model, and we significantly improve over any recent sequence-to-sequence approaches. |
2305.02484 | Shilun Li | Venkatesan Guruswami and Shilun Li | A Deterministic Construction of a Large Distance Code from the
Wozencraft Ensemble | null | null | null | null | cs.IT math.CO math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an explicit construction of a sequence of rate $1/2$ Wozencraft
ensemble codes (over any fixed finite field $\mathbb{F}_q$) that achieve
minimum distance $\Omega(\sqrt{k})$ where $k$ is the message length. The
coefficients of the Wozencraft ensemble codes are constructed using Sidon Sets
and the cyclic structure of $\mathbb{F}_{q^{k}}$ where $k+1$ is prime with $q$
a primitive root modulo $k+1$. Assuming Artin's conjecture, there are
infinitely many such $k$ for any prime power $q$.
| [
{
"created": "Thu, 4 May 2023 01:29:34 GMT",
"version": "v1"
},
{
"created": "Tue, 11 Jul 2023 08:03:43 GMT",
"version": "v2"
}
] | 2023-07-12 | [
[
"Guruswami",
"Venkatesan",
""
],
[
"Li",
"Shilun",
""
]
] | We present an explicit construction of a sequence of rate $1/2$ Wozencraft ensemble codes (over any fixed finite field $\mathbb{F}_q$) that achieve minimum distance $\Omega(\sqrt{k})$ where $k$ is the message length. The coefficients of the Wozencraft ensemble codes are constructed using Sidon Sets and the cyclic structure of $\mathbb{F}_{q^{k}}$ where $k+1$ is prime with $q$ a primitive root modulo $k+1$. Assuming Artin's conjecture, there are infinitely many such $k$ for any prime power $q$. |
2307.13018 | Artur Tarassow | Artur Tarassow | The potential of LLMs for coding with low-resource and domain-specific
programming languages | null | null | null | null | cs.CL cs.SE | http://creativecommons.org/licenses/by/4.0/ | This paper presents a study on the feasibility of using large language models
(LLM) for coding with low-resource and domain-specific programming languages
that typically lack the amount of data required for effective LLM processing
techniques. This study focuses on the econometric scripting language named
hansl of the open-source software gretl and employs a proprietary LLM based on
GPT-3.5. Our findings suggest that LLMs can be a useful tool for writing,
understanding, improving, and documenting gretl code, which includes generating
descriptive docstrings for functions and providing precise explanations for
abstract and poorly documented econometric code. While the LLM showcased
promoting docstring-to-code translation capability, we also identify some
limitations, such as its inability to improve certain sections of code and to
write accurate unit tests. This study is a step towards leveraging the power of
LLMs to facilitate software development in low-resource programming languages
and ultimately to lower barriers to entry for their adoption.
| [
{
"created": "Mon, 24 Jul 2023 17:17:13 GMT",
"version": "v1"
}
] | 2023-07-26 | [
[
"Tarassow",
"Artur",
""
]
] | This paper presents a study on the feasibility of using large language models (LLM) for coding with low-resource and domain-specific programming languages that typically lack the amount of data required for effective LLM processing techniques. This study focuses on the econometric scripting language named hansl of the open-source software gretl and employs a proprietary LLM based on GPT-3.5. Our findings suggest that LLMs can be a useful tool for writing, understanding, improving, and documenting gretl code, which includes generating descriptive docstrings for functions and providing precise explanations for abstract and poorly documented econometric code. While the LLM showcased promoting docstring-to-code translation capability, we also identify some limitations, such as its inability to improve certain sections of code and to write accurate unit tests. This study is a step towards leveraging the power of LLMs to facilitate software development in low-resource programming languages and ultimately to lower barriers to entry for their adoption. |
1803.05120 | Yufan He | Yufan He, Aaron Carass, Bruno M. Jedynak, Sharon D. Solomon, Shiv
Saidha, Peter A. Calabresi, Jerry L. Prince | Topology guaranteed segmentation of the human retina from OCT using
convolutional neural networks | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Optical coherence tomography (OCT) is a noninvasive imaging modality which
can be used to obtain depth images of the retina. The changing layer
thicknesses can thus be quantified by analyzing these OCT images, moreover
these changes have been shown to correlate with disease progression in multiple
sclerosis. Recent automated retinal layer segmentation tools use machine
learning methods to perform pixel-wise labeling and graph methods to guarantee
the layer hierarchy or topology. However, graph parameters like distance and
smoothness constraints must be experimentally assigned by retinal region and
pathology, thus degrading the flexibility and time efficiency of the whole
framework. In this paper, we develop cascaded deep networks to provide a
topologically correct segmentation of the retinal layers in a single feed
forward propagation. The first network (S-Net) performs pixel-wise labeling and
the second regression network (R-Net) takes the topologically unconstrained
S-Net results and outputs layer thicknesses for each layer and each position.
Relu activation is used as the final operation of the R-Net which guarantees
non-negativity of the output layer thickness. Since the segmentation boundary
position is acquired by summing up the corresponding non-negative layer
thicknesses, the layer ordering (i.e., topology) of the reconstructed
boundaries is guaranteed even at the fovea where the distances between
boundaries can be zero. The R-Net is trained using simulated masks and thus can
be generalized to provide topology guaranteed segmentation for other layered
structures. This deep network has achieved comparable mean absolute boundary
error (2.82 {\mu}m) to state-of-the-art graph methods (2.83 {\mu}m).
| [
{
"created": "Wed, 14 Mar 2018 03:21:01 GMT",
"version": "v1"
}
] | 2018-03-15 | [
[
"He",
"Yufan",
""
],
[
"Carass",
"Aaron",
""
],
[
"Jedynak",
"Bruno M.",
""
],
[
"Solomon",
"Sharon D.",
""
],
[
"Saidha",
"Shiv",
""
],
[
"Calabresi",
"Peter A.",
""
],
[
"Prince",
"Jerry L.",
""
]
] | Optical coherence tomography (OCT) is a noninvasive imaging modality which can be used to obtain depth images of the retina. The changing layer thicknesses can thus be quantified by analyzing these OCT images, moreover these changes have been shown to correlate with disease progression in multiple sclerosis. Recent automated retinal layer segmentation tools use machine learning methods to perform pixel-wise labeling and graph methods to guarantee the layer hierarchy or topology. However, graph parameters like distance and smoothness constraints must be experimentally assigned by retinal region and pathology, thus degrading the flexibility and time efficiency of the whole framework. In this paper, we develop cascaded deep networks to provide a topologically correct segmentation of the retinal layers in a single feed forward propagation. The first network (S-Net) performs pixel-wise labeling and the second regression network (R-Net) takes the topologically unconstrained S-Net results and outputs layer thicknesses for each layer and each position. Relu activation is used as the final operation of the R-Net which guarantees non-negativity of the output layer thickness. Since the segmentation boundary position is acquired by summing up the corresponding non-negative layer thicknesses, the layer ordering (i.e., topology) of the reconstructed boundaries is guaranteed even at the fovea where the distances between boundaries can be zero. The R-Net is trained using simulated masks and thus can be generalized to provide topology guaranteed segmentation for other layered structures. This deep network has achieved comparable mean absolute boundary error (2.82 {\mu}m) to state-of-the-art graph methods (2.83 {\mu}m). |
2011.09577 | Sadra Naddaf | Sadra Naddaf-Sh, M-Mahdi Naddaf-Sh, Amir R. Kashani and Hassan
Zargarzadeh | An Efficient and Scalable Deep Learning Approach for Road Damage
Detection | removed redundant postscripts | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Pavement condition evaluation is essential to time the preventative or
rehabilitative actions and control distress propagation. Failing to conduct
timely evaluations can lead to severe structural and financial loss of the
infrastructure and complete reconstructions. Automated computer-aided surveying
measures can provide a database of road damage patterns and their locations.
This database can be utilized for timely road repairs to gain the minimum cost
of maintenance and the asphalt's maximum durability. This paper introduces a
deep learning-based surveying scheme to analyze the image-based distress data
in real-time. A database consisting of a diverse population of crack distress
types such as longitudinal, transverse, and alligator cracks, photographed
using mobile-device is used. Then, a family of efficient and scalable models
that are tuned for pavement crack detection is trained, and various
augmentation policies are explored. Proposed models, resulted in F1-scores,
ranging from 52% to 56%, and average inference time from 178-10 images per
second. Finally, the performance of the object detectors are examined, and
error analysis is reported against various images. The source code is available
at https://github.com/mahdi65/roadDamageDetection2020.
| [
{
"created": "Wed, 18 Nov 2020 23:05:41 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Nov 2020 18:58:18 GMT",
"version": "v2"
},
{
"created": "Thu, 17 Dec 2020 17:58:08 GMT",
"version": "v3"
}
] | 2020-12-18 | [
[
"Naddaf-Sh",
"Sadra",
""
],
[
"Naddaf-Sh",
"M-Mahdi",
""
],
[
"Kashani",
"Amir R.",
""
],
[
"Zargarzadeh",
"Hassan",
""
]
] | Pavement condition evaluation is essential to time the preventative or rehabilitative actions and control distress propagation. Failing to conduct timely evaluations can lead to severe structural and financial loss of the infrastructure and complete reconstructions. Automated computer-aided surveying measures can provide a database of road damage patterns and their locations. This database can be utilized for timely road repairs to gain the minimum cost of maintenance and the asphalt's maximum durability. This paper introduces a deep learning-based surveying scheme to analyze the image-based distress data in real-time. A database consisting of a diverse population of crack distress types such as longitudinal, transverse, and alligator cracks, photographed using mobile-device is used. Then, a family of efficient and scalable models that are tuned for pavement crack detection is trained, and various augmentation policies are explored. Proposed models, resulted in F1-scores, ranging from 52% to 56%, and average inference time from 178-10 images per second. Finally, the performance of the object detectors are examined, and error analysis is reported against various images. The source code is available at https://github.com/mahdi65/roadDamageDetection2020. |
2010.03108 | Pan Ji | Pengfei Fang, Pan Ji, Jieming Zhou, Lars Petersson, Mehrtash Harandi | Channel Recurrent Attention Networks for Video Pedestrian Retrieval | To appear in ACCV 2020 | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Full attention, which generates an attention value per element of the input
feature maps, has been successfully demonstrated to be beneficial in visual
tasks. In this work, we propose a fully attentional network, termed {\it
channel recurrent attention network}, for the task of video pedestrian
retrieval. The main attention unit, \textit{channel recurrent attention},
identifies attention maps at the frame level by jointly leveraging spatial and
channel patterns via a recurrent neural network. This channel recurrent
attention is designed to build a global receptive field by recurrently
receiving and learning the spatial vectors. Then, a \textit{set aggregation}
cell is employed to generate a compact video representation. Empirical
experimental results demonstrate the superior performance of the proposed deep
network, outperforming current state-of-the-art results across standard video
person retrieval benchmarks, and a thorough ablation study shows the
effectiveness of the proposed units.
| [
{
"created": "Wed, 7 Oct 2020 02:01:13 GMT",
"version": "v1"
}
] | 2020-10-08 | [
[
"Fang",
"Pengfei",
""
],
[
"Ji",
"Pan",
""
],
[
"Zhou",
"Jieming",
""
],
[
"Petersson",
"Lars",
""
],
[
"Harandi",
"Mehrtash",
""
]
] | Full attention, which generates an attention value per element of the input feature maps, has been successfully demonstrated to be beneficial in visual tasks. In this work, we propose a fully attentional network, termed {\it channel recurrent attention network}, for the task of video pedestrian retrieval. The main attention unit, \textit{channel recurrent attention}, identifies attention maps at the frame level by jointly leveraging spatial and channel patterns via a recurrent neural network. This channel recurrent attention is designed to build a global receptive field by recurrently receiving and learning the spatial vectors. Then, a \textit{set aggregation} cell is employed to generate a compact video representation. Empirical experimental results demonstrate the superior performance of the proposed deep network, outperforming current state-of-the-art results across standard video person retrieval benchmarks, and a thorough ablation study shows the effectiveness of the proposed units. |
1710.06785 | Ramviyas Parasuraman | Ramviyas Parasuraman, Sergio Caccamo, Fredrik B{\aa}berg, Petter
\"Ogren and Mark Neerincx | A New UGV Teleoperation Interface for Improved Awareness of Network
Connectivity and Physical Surroundings | Accepted for publication in the Journal of Human-Robot Interaction
(JHRI) | null | null | null | cs.RO cs.HC cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A reliable wireless connection between the operator and the teleoperated
Unmanned Ground Vehicle (UGV) is critical in many Urban Search and Rescue
(USAR) missions. Unfortunately, as was seen in e.g. the Fukushima disaster, the
networks available in areas where USAR missions take place are often severely
limited in range and coverage. Therefore, during mission execution, the
operator needs to keep track of not only the physical parts of the mission,
such as navigating through an area or searching for victims, but also the
variations in network connectivity across the environment. In this paper, we
propose and evaluate a new teleoperation User Interface (UI) that includes a
way of estimating the Direction of Arrival (DoA) of the Radio Signal Strength
(RSS) and integrating the DoA information in the interface. The evaluation
shows that using the interface results in more objects found, and less aborted
missions due to connectivity problems, as compared to a standard interface. The
proposed interface is an extension to an existing interface centered around the
video stream captured by the UGV. But instead of just showing the network
signal strength in terms of percent and a set of bars, the additional
information of DoA is added in terms of a color bar surrounding the video feed.
With this information, the operator knows what movement directions are safe,
even when moving in regions close to the connectivity threshold.
| [
{
"created": "Wed, 4 Oct 2017 21:39:53 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Oct 2017 17:50:45 GMT",
"version": "v2"
},
{
"created": "Sun, 5 Nov 2017 08:57:19 GMT",
"version": "v3"
},
{
"created": "Tue, 7 Nov 2017 19:34:45 GMT",
"version": "v4"
}
] | 2017-11-09 | [
[
"Parasuraman",
"Ramviyas",
""
],
[
"Caccamo",
"Sergio",
""
],
[
"Båberg",
"Fredrik",
""
],
[
"Ögren",
"Petter",
""
],
[
"Neerincx",
"Mark",
""
]
] | A reliable wireless connection between the operator and the teleoperated Unmanned Ground Vehicle (UGV) is critical in many Urban Search and Rescue (USAR) missions. Unfortunately, as was seen in e.g. the Fukushima disaster, the networks available in areas where USAR missions take place are often severely limited in range and coverage. Therefore, during mission execution, the operator needs to keep track of not only the physical parts of the mission, such as navigating through an area or searching for victims, but also the variations in network connectivity across the environment. In this paper, we propose and evaluate a new teleoperation User Interface (UI) that includes a way of estimating the Direction of Arrival (DoA) of the Radio Signal Strength (RSS) and integrating the DoA information in the interface. The evaluation shows that using the interface results in more objects found, and less aborted missions due to connectivity problems, as compared to a standard interface. The proposed interface is an extension to an existing interface centered around the video stream captured by the UGV. But instead of just showing the network signal strength in terms of percent and a set of bars, the additional information of DoA is added in terms of a color bar surrounding the video feed. With this information, the operator knows what movement directions are safe, even when moving in regions close to the connectivity threshold. |
2305.19894 | Zhongwei Wan | Zhongwei Wan, Che Liu, Mi Zhang, Jie Fu, Benyou Wang, Sibo Cheng, Lei
Ma, C\'esar Quilodr\'an-Casas, Rossella Arcucci | Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by
Diminishing Bias | NeurIPS 2023 Main track | null | null | null | cs.CL cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The scarcity of data presents a critical obstacle to the efficacy of medical
visionlanguage pre-training (VLP). A potential solution lies in the combination
of datasets from various language communities. Nevertheless, the main challenge
stems from the complexity of integrating diverse syntax and semantics,
language-specific medical terminology, and culture-specific implicit knowledge.
Therefore, one crucial aspect to consider is the presence of community bias
caused by different languages. This paper presents a novel framework named
Unifying Cross-Lingual Medical Vision-Language Pre-Training (Med-UniC),
designed to integrate multimodal medical data from the two most prevalent
languages, English and Spanish. Specifically, we propose Cross-lingual Text
Alignment Regularization (CTR) to explicitly unify cross-lingual semantic
representations of medical reports originating from diverse language
communities. CTR is optimized through latent language disentanglement,
rendering our optimization objective to not depend on negative samples, thereby
significantly mitigating the bias from determining positive-negative sample
pairs within analogous medical reports. Furthermore, it ensures that the
cross-lingual representation is not biased toward any specific language
community. Med-UniC reaches superior performance across 5 medical image tasks
and 10 datasets encompassing over 30 diseases, offering a versatile framework
for unifying multi-modal medical data within diverse linguistic communities.
The experimental outcomes highlight the presence of community bias in
cross-lingual VLP. Reducing this bias enhances the performance not only in
vision-language tasks but also in uni-modal visual tasks.
| [
{
"created": "Wed, 31 May 2023 14:28:19 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Sep 2023 18:58:36 GMT",
"version": "v2"
},
{
"created": "Sat, 17 Feb 2024 19:49:54 GMT",
"version": "v3"
}
] | 2024-02-20 | [
[
"Wan",
"Zhongwei",
""
],
[
"Liu",
"Che",
""
],
[
"Zhang",
"Mi",
""
],
[
"Fu",
"Jie",
""
],
[
"Wang",
"Benyou",
""
],
[
"Cheng",
"Sibo",
""
],
[
"Ma",
"Lei",
""
],
[
"Quilodrán-Casas",
"César",
""
],
[
"Arcucci",
"Rossella",
""
]
] | The scarcity of data presents a critical obstacle to the efficacy of medical visionlanguage pre-training (VLP). A potential solution lies in the combination of datasets from various language communities. Nevertheless, the main challenge stems from the complexity of integrating diverse syntax and semantics, language-specific medical terminology, and culture-specific implicit knowledge. Therefore, one crucial aspect to consider is the presence of community bias caused by different languages. This paper presents a novel framework named Unifying Cross-Lingual Medical Vision-Language Pre-Training (Med-UniC), designed to integrate multimodal medical data from the two most prevalent languages, English and Spanish. Specifically, we propose Cross-lingual Text Alignment Regularization (CTR) to explicitly unify cross-lingual semantic representations of medical reports originating from diverse language communities. CTR is optimized through latent language disentanglement, rendering our optimization objective to not depend on negative samples, thereby significantly mitigating the bias from determining positive-negative sample pairs within analogous medical reports. Furthermore, it ensures that the cross-lingual representation is not biased toward any specific language community. Med-UniC reaches superior performance across 5 medical image tasks and 10 datasets encompassing over 30 diseases, offering a versatile framework for unifying multi-modal medical data within diverse linguistic communities. The experimental outcomes highlight the presence of community bias in cross-lingual VLP. Reducing this bias enhances the performance not only in vision-language tasks but also in uni-modal visual tasks. |
1902.02139 | Anton Pirogov | Christof L\"oding, Anton Pirogov | Determinization of B\"uchi Automata: Unifying the Approaches of Safra
and Muller-Schupp | Full version of ICALP 2019 paper | null | 10.4230/LIPIcs.ICALP.2019.120 | null | cs.FL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Determinization of B\"uchi automata is a long-known difficult problem and
after the seminal result of Safra, who developed the first asymptotically
optimal construction from B\"uchi into Rabin automata, much work went into
improving, simplifying or avoiding Safra's construction. A different, less
known determinization construction was derived by Muller and Schupp and appears
to be unrelated to Safra's construction on the first sight. In this paper we
propose a new meta-construction from nondeterministic B\"uchi to deterministic
parity automata which strictly subsumes both the construction of Safra and the
construction of Muller and Schupp. It is based on a correspondence between
structures that are encoded in the macrostates of the determinization
procedures - Safra trees on one hand, and levels of the split-tree, which
underlies the Muller and Schupp construction, on the other. Our construction
allows for combining the mentioned constructions and opens up new directions
for the development of heuristics.
| [
{
"created": "Wed, 6 Feb 2019 12:31:09 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Apr 2019 07:47:34 GMT",
"version": "v2"
}
] | 2020-04-30 | [
[
"Löding",
"Christof",
""
],
[
"Pirogov",
"Anton",
""
]
] | Determinization of B\"uchi automata is a long-known difficult problem and after the seminal result of Safra, who developed the first asymptotically optimal construction from B\"uchi into Rabin automata, much work went into improving, simplifying or avoiding Safra's construction. A different, less known determinization construction was derived by Muller and Schupp and appears to be unrelated to Safra's construction on the first sight. In this paper we propose a new meta-construction from nondeterministic B\"uchi to deterministic parity automata which strictly subsumes both the construction of Safra and the construction of Muller and Schupp. It is based on a correspondence between structures that are encoded in the macrostates of the determinization procedures - Safra trees on one hand, and levels of the split-tree, which underlies the Muller and Schupp construction, on the other. Our construction allows for combining the mentioned constructions and opens up new directions for the development of heuristics. |
2006.01358 | Sandra Ramirez | Sandra L. Ram\'irez-Mora, Hanna Oktaba, Helena G\'omez-Adorno | Descriptions of issues and comments for predicting issue success in
software projects | 65 pages; 15 figures | Journal of Systems and Software, Vol. 168, 2020, 110663, ISSN
0164-1212 | 10.1016/j.jss.2020.110663 | null | cs.SE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Software development tasks must be performed successfully to achieve software
quality and customer satisfaction. Knowing whether software tasks are likely to
fail is essential to ensure the success of software projects. Issue Tracking
Systems store information of software tasks (issues) and comments, which can be
useful to predict issue success; however; almost no research on this topic
exists. This work studies the usefulness of textual descriptions of issues and
comments for predicting whether issues will be resolved successfully or not.
Issues and comments of 588 software projects were extracted from four popular
Issue Tracking Systems. Seven machine learning classifiers were trained on 30k
issues and more than 120k comments, and more than 6000 experiments were
performed to predict the success of three types of issues: bugs, improvements
and new features. The results provided evidence that descriptions of issues and
comments are useful for predicting issue success with more than 85% of accuracy
and precision, and that the predictions of issue success vary over time. Words
related to software development were particularly relevant for predicting issue
success. Other communication aspects and their relationship to the success of
software projects must be researched in detail using data from software tools.
| [
{
"created": "Tue, 2 Jun 2020 02:49:22 GMT",
"version": "v1"
}
] | 2020-06-03 | [
[
"Ramírez-Mora",
"Sandra L.",
""
],
[
"Oktaba",
"Hanna",
""
],
[
"Gómez-Adorno",
"Helena",
""
]
] | Software development tasks must be performed successfully to achieve software quality and customer satisfaction. Knowing whether software tasks are likely to fail is essential to ensure the success of software projects. Issue Tracking Systems store information of software tasks (issues) and comments, which can be useful to predict issue success; however; almost no research on this topic exists. This work studies the usefulness of textual descriptions of issues and comments for predicting whether issues will be resolved successfully or not. Issues and comments of 588 software projects were extracted from four popular Issue Tracking Systems. Seven machine learning classifiers were trained on 30k issues and more than 120k comments, and more than 6000 experiments were performed to predict the success of three types of issues: bugs, improvements and new features. The results provided evidence that descriptions of issues and comments are useful for predicting issue success with more than 85% of accuracy and precision, and that the predictions of issue success vary over time. Words related to software development were particularly relevant for predicting issue success. Other communication aspects and their relationship to the success of software projects must be researched in detail using data from software tools. |
2406.16250 | Prerana Khatiwada | Prerana Khatiwada, Pranjal Dhakal | Evaluating Serverless Machine Learning Performance on Google Cloud Run | 5 pages, 12 figures | null | null | null | cs.DC cs.OS | http://creativecommons.org/licenses/by/4.0/ | End-users can get functions-as-a-service from serverless platforms, which
promise lower hosting costs, high availability, fault tolerance, and dynamic
flexibility for hosting individual functions known as microservices. Machine
learning tools are seen to be reliably useful, and the services created using
these tools are in increasing demand on a large scale. The serverless platforms
are uniquely suited for hosting these machine learning services to be used for
large-scale applications. These platforms are well known for their cost
efficiency, fault tolerance, resource scaling, robust APIs for communication,
and global reach. However, machine learning services are different from the
web-services in that these serverless platforms were originally designed to
host web services. We aimed to understand how these serverless platforms handle
machine learning workloads with our study. We examine machine learning
performance on one of the serverless platforms - Google Cloud Run, which is a
GPU-less infrastructure that is not designed for machine learning application
deployment.
| [
{
"created": "Mon, 24 Jun 2024 01:10:20 GMT",
"version": "v1"
}
] | 2024-06-25 | [
[
"Khatiwada",
"Prerana",
""
],
[
"Dhakal",
"Pranjal",
""
]
] | End-users can get functions-as-a-service from serverless platforms, which promise lower hosting costs, high availability, fault tolerance, and dynamic flexibility for hosting individual functions known as microservices. Machine learning tools are seen to be reliably useful, and the services created using these tools are in increasing demand on a large scale. The serverless platforms are uniquely suited for hosting these machine learning services to be used for large-scale applications. These platforms are well known for their cost efficiency, fault tolerance, resource scaling, robust APIs for communication, and global reach. However, machine learning services are different from the web-services in that these serverless platforms were originally designed to host web services. We aimed to understand how these serverless platforms handle machine learning workloads with our study. We examine machine learning performance on one of the serverless platforms - Google Cloud Run, which is a GPU-less infrastructure that is not designed for machine learning application deployment. |
2407.10725 | Xiaoyuan Yi | Jing Yao, Xiaoyuan Yi, Xing Xie | CLAVE: An Adaptive Framework for Evaluating Values of LLM Generated
Responses | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The rapid progress in Large Language Models (LLMs) poses potential risks such
as generating unethical content. Assessing LLMs' values can help expose their
misalignment, but relies on reference-free evaluators, e.g., fine-tuned LLMs or
close-source ones like GPT-4, to identify values reflected in generated
responses. Nevertheless, these evaluators face two challenges in open-ended
value evaluation: they should align with changing human value definitions with
minimal annotation, against their own bias (adaptability), and detect varying
value expressions and scenarios robustly (generalizability). To handle these
challenges, we introduce CLAVE, a novel framework which integrates two
complementary LLMs, a large one to extract high-level value concepts from a few
human labels, leveraging its extensive knowledge and generalizability, and a
smaller one fine-tuned on such concepts to better align with human value
understanding. This dual-model approach enables calibration with any value
systems using <100 human-labeled samples per value type. Then we present
ValEval, a comprehensive dataset comprising 13k+ (text,value,label) tuples
across diverse domains, covering three major value systems. We benchmark the
capabilities of 12+ popular LLM evaluators and analyze their strengths and
weaknesses. Our findings reveal that combining fine-tuned small models and
prompt-based large ones serves as a superior balance in value evaluation.
| [
{
"created": "Mon, 15 Jul 2024 13:51:37 GMT",
"version": "v1"
}
] | 2024-07-16 | [
[
"Yao",
"Jing",
""
],
[
"Yi",
"Xiaoyuan",
""
],
[
"Xie",
"Xing",
""
]
] | The rapid progress in Large Language Models (LLMs) poses potential risks such as generating unethical content. Assessing LLMs' values can help expose their misalignment, but relies on reference-free evaluators, e.g., fine-tuned LLMs or close-source ones like GPT-4, to identify values reflected in generated responses. Nevertheless, these evaluators face two challenges in open-ended value evaluation: they should align with changing human value definitions with minimal annotation, against their own bias (adaptability), and detect varying value expressions and scenarios robustly (generalizability). To handle these challenges, we introduce CLAVE, a novel framework which integrates two complementary LLMs, a large one to extract high-level value concepts from a few human labels, leveraging its extensive knowledge and generalizability, and a smaller one fine-tuned on such concepts to better align with human value understanding. This dual-model approach enables calibration with any value systems using <100 human-labeled samples per value type. Then we present ValEval, a comprehensive dataset comprising 13k+ (text,value,label) tuples across diverse domains, covering three major value systems. We benchmark the capabilities of 12+ popular LLM evaluators and analyze their strengths and weaknesses. Our findings reveal that combining fine-tuned small models and prompt-based large ones serves as a superior balance in value evaluation. |
1807.11618 | Kamal Al-Sabahi Ph.D. | Kamal Al-Sabahi, Zuping Zhang, Jun Long, Khaled Alwesabi | An Enhanced Latent Semantic Analysis Approach for Arabic Document
Summarization | This is a pre-print of an article published in Arabian Journal for
Science and Engineering. The final authenticated version is available online
at: https://doi.org/10.1007/s13369-018-3286-z | K. Al-Sabahi, Z. Zhang, J. Long, and K. Alwesabi, "An Enhanced
Latent Semantic Analysis Approach for Arabic Document Summarization," Arabian
Journal for Science and Engineering, journal article May 05 2018 | 10.1007/s13369-018-3286-z | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The fast-growing amount of information on the Internet makes the research in
automatic document summarization very urgent. It is an effective solution for
information overload. Many approaches have been proposed based on different
strategies, such as latent semantic analysis (LSA). However, LSA, when applied
to document summarization, has some limitations which diminish its performance.
In this work, we try to overcome these limitations by applying statistic and
linear algebraic approaches combined with syntactic and semantic processing of
text. First, the part of speech tagger is utilized to reduce the dimension of
LSA. Then, the weight of the term in four adjacent sentences is added to the
weighting schemes while calculating the input matrix to take into account the
word order and the syntactic relations. In addition, a new LSA-based sentence
selection algorithm is proposed, in which the term description is combined with
sentence description for each topic which in turn makes the generated summary
more informative and diverse. To ensure the effectiveness of the proposed
LSA-based sentence selection algorithm, extensive experiment on Arabic and
English are done. Four datasets are used to evaluate the new model, Linguistic
Data Consortium (LDC) Arabic Newswire-a corpus, Essex Arabic Summaries Corpus
(EASC), DUC2002, and Multilingual MSS 2015 dataset. Experimental results on the
four datasets show the effectiveness of the proposed model on Arabic and
English datasets. It performs comprehensively better compared to the
state-of-the-art methods.
| [
{
"created": "Tue, 31 Jul 2018 00:50:15 GMT",
"version": "v1"
}
] | 2018-08-01 | [
[
"Al-Sabahi",
"Kamal",
""
],
[
"Zhang",
"Zuping",
""
],
[
"Long",
"Jun",
""
],
[
"Alwesabi",
"Khaled",
""
]
] | The fast-growing amount of information on the Internet makes the research in automatic document summarization very urgent. It is an effective solution for information overload. Many approaches have been proposed based on different strategies, such as latent semantic analysis (LSA). However, LSA, when applied to document summarization, has some limitations which diminish its performance. In this work, we try to overcome these limitations by applying statistic and linear algebraic approaches combined with syntactic and semantic processing of text. First, the part of speech tagger is utilized to reduce the dimension of LSA. Then, the weight of the term in four adjacent sentences is added to the weighting schemes while calculating the input matrix to take into account the word order and the syntactic relations. In addition, a new LSA-based sentence selection algorithm is proposed, in which the term description is combined with sentence description for each topic which in turn makes the generated summary more informative and diverse. To ensure the effectiveness of the proposed LSA-based sentence selection algorithm, extensive experiment on Arabic and English are done. Four datasets are used to evaluate the new model, Linguistic Data Consortium (LDC) Arabic Newswire-a corpus, Essex Arabic Summaries Corpus (EASC), DUC2002, and Multilingual MSS 2015 dataset. Experimental results on the four datasets show the effectiveness of the proposed model on Arabic and English datasets. It performs comprehensively better compared to the state-of-the-art methods. |
2204.08935 | Xinyue Shen | Xinyue Shen, Xinlei He, Michael Backes, Jeremy Blackburn, Savvas
Zannettou, Yang Zhang | On Xing Tian and the Perseverance of Anti-China Sentiment Online | To Appear in the 16th International Conference on Web and Social
Media (ICWSM), 2022 | null | null | null | cs.SI cs.CY | http://creativecommons.org/licenses/by/4.0/ | Sinophobia, anti-Chinese sentiment, has existed on the Web for a long time.
The outbreak of COVID-19 and the extended quarantine has further amplified it.
However, we lack a quantitative understanding of the cause of Sinophobia as
well as how it evolves over time. In this paper, we conduct a large-scale
longitudinal measurement of Sinophobia, between 2016 and 2021, on two
mainstream and fringe Web communities. By analyzing 8B posts from Reddit and
206M posts from 4chan's /pol/, we investigate the origins, evolution, and
content of Sinophobia. We find that, anti-Chinese content may be evoked by
political events not directly related to China, e.g., the U.S. withdrawal from
the Paris Agreement. And during the COVID-19 pandemic, daily usage of
Sinophobic slurs has significantly increased even with the hate-speech ban
policy. We also show that the semantic meaning of the words "China" and
"Chinese" are shifting towards Sinophobic slurs with the rise of COVID-19 and
remain the same in the pandemic period. We further use topic modeling to show
the topics of Sinophobic discussion are pretty diverse and broad. We find that
both Web communities share some common Sinophobic topics like ethnics,
economics and commerce, weapons and military, foreign relations, etc. However,
compared to 4chan's /pol/, more daily life-related topics including food, game,
and stock are found in Reddit. Our finding also reveals that the topics related
to COVID-19 and blaming the Chinese government are more prevalent in the
pandemic period. To the best of our knowledge, this paper is the longest
quantitative measurement of Sinophobia.
| [
{
"created": "Tue, 19 Apr 2022 15:17:28 GMT",
"version": "v1"
}
] | 2022-04-20 | [
[
"Shen",
"Xinyue",
""
],
[
"He",
"Xinlei",
""
],
[
"Backes",
"Michael",
""
],
[
"Blackburn",
"Jeremy",
""
],
[
"Zannettou",
"Savvas",
""
],
[
"Zhang",
"Yang",
""
]
] | Sinophobia, anti-Chinese sentiment, has existed on the Web for a long time. The outbreak of COVID-19 and the extended quarantine has further amplified it. However, we lack a quantitative understanding of the cause of Sinophobia as well as how it evolves over time. In this paper, we conduct a large-scale longitudinal measurement of Sinophobia, between 2016 and 2021, on two mainstream and fringe Web communities. By analyzing 8B posts from Reddit and 206M posts from 4chan's /pol/, we investigate the origins, evolution, and content of Sinophobia. We find that, anti-Chinese content may be evoked by political events not directly related to China, e.g., the U.S. withdrawal from the Paris Agreement. And during the COVID-19 pandemic, daily usage of Sinophobic slurs has significantly increased even with the hate-speech ban policy. We also show that the semantic meaning of the words "China" and "Chinese" are shifting towards Sinophobic slurs with the rise of COVID-19 and remain the same in the pandemic period. We further use topic modeling to show the topics of Sinophobic discussion are pretty diverse and broad. We find that both Web communities share some common Sinophobic topics like ethnics, economics and commerce, weapons and military, foreign relations, etc. However, compared to 4chan's /pol/, more daily life-related topics including food, game, and stock are found in Reddit. Our finding also reveals that the topics related to COVID-19 and blaming the Chinese government are more prevalent in the pandemic period. To the best of our knowledge, this paper is the longest quantitative measurement of Sinophobia. |
1601.03295 | Gabriela Csurka | Gabriela Csurka | Document image classification, with a specific view on applications of
patent images | Paper submitted in 2014 as book chapter of Current Challenges in
Patent Information Retrieval, Second edition by M. Lupu et al (eds.). To
appear in 2016 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The main focus of this paper is document image classification and retrieval,
where we analyze and compare different parameters for the RunLeght Histogram
(RL) and Fisher Vector (FV) based image representations. We do an exhaustive
experimental study using different document image datasets, including the MARG
benchmarks, two datasets built on customer data and the images from the Patent
Image Classification task of the Clef-IP 2011. The aim of the study is to give
guidelines on how to best choose the parameters such that the same features
perform well on different tasks. As an example of such need, we describe the
Image-based Patent Retrieval task's of Clef-IP 2011, where we used the same
image representation to predict the image type and retrieve relevant patents.
| [
{
"created": "Wed, 13 Jan 2016 16:02:13 GMT",
"version": "v1"
}
] | 2016-01-14 | [
[
"Csurka",
"Gabriela",
""
]
] | The main focus of this paper is document image classification and retrieval, where we analyze and compare different parameters for the RunLeght Histogram (RL) and Fisher Vector (FV) based image representations. We do an exhaustive experimental study using different document image datasets, including the MARG benchmarks, two datasets built on customer data and the images from the Patent Image Classification task of the Clef-IP 2011. The aim of the study is to give guidelines on how to best choose the parameters such that the same features perform well on different tasks. As an example of such need, we describe the Image-based Patent Retrieval task's of Clef-IP 2011, where we used the same image representation to predict the image type and retrieve relevant patents. |
2105.11134 | Jiacheng Ye | Jiacheng Ye, Tao Gui, Yichao Luo, Yige Xu, Qi Zhang | One2Set: Generating Diverse Keyphrases as a Set | Accepted by ACL 2021 | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recently, the sequence-to-sequence models have made remarkable progress on
the task of keyphrase generation (KG) by concatenating multiple keyphrases in a
predefined order as a target sequence during training. However, the keyphrases
are inherently an unordered set rather than an ordered sequence. Imposing a
predefined order will introduce wrong bias during training, which can highly
penalize shifts in the order between keyphrases. In this work, we propose a new
training paradigm One2Set without predefining an order to concatenate the
keyphrases. To fit this paradigm, we propose a novel model that utilizes a
fixed set of learned control codes as conditions to generate a set of
keyphrases in parallel. To solve the problem that there is no correspondence
between each prediction and target during training, we propose a $K$-step
target assignment mechanism via bipartite matching, which greatly increases the
diversity and reduces the duplication ratio of generated keyphrases. The
experimental results on multiple benchmarks demonstrate that our approach
significantly outperforms the state-of-the-art methods.
| [
{
"created": "Mon, 24 May 2021 07:29:47 GMT",
"version": "v1"
}
] | 2021-05-25 | [
[
"Ye",
"Jiacheng",
""
],
[
"Gui",
"Tao",
""
],
[
"Luo",
"Yichao",
""
],
[
"Xu",
"Yige",
""
],
[
"Zhang",
"Qi",
""
]
] | Recently, the sequence-to-sequence models have made remarkable progress on the task of keyphrase generation (KG) by concatenating multiple keyphrases in a predefined order as a target sequence during training. However, the keyphrases are inherently an unordered set rather than an ordered sequence. Imposing a predefined order will introduce wrong bias during training, which can highly penalize shifts in the order between keyphrases. In this work, we propose a new training paradigm One2Set without predefining an order to concatenate the keyphrases. To fit this paradigm, we propose a novel model that utilizes a fixed set of learned control codes as conditions to generate a set of keyphrases in parallel. To solve the problem that there is no correspondence between each prediction and target during training, we propose a $K$-step target assignment mechanism via bipartite matching, which greatly increases the diversity and reduces the duplication ratio of generated keyphrases. The experimental results on multiple benchmarks demonstrate that our approach significantly outperforms the state-of-the-art methods. |
2201.09377 | Ali Emami Mr. | Darren Abramson and Ali Emami | An Application of Pseudo-Log-Likelihoods to Natural Language Scoring | null | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Language models built using semi-supervised machine learning on large corpora
of natural language have very quickly enveloped the fields of natural language
generation and understanding. In this paper we apply a zero-shot approach
independently developed by a number of researchers now gaining recognition as a
significant alternative to fine-tuning for evaluation on common sense tasks. A
language model with relatively few parameters and training steps compared to a
more recent language model (T5) can outperform it on a recent large data set
(TimeDial), while displaying robustness in its performance across a similar
class of language tasks. Surprisingly, this result is achieved by using a
hyperparameter-free zero-shot method with the smaller model, compared to
fine-tuning to the larger model. We argue that robustness of the smaller model
ought to be understood in terms of compositionality, in a sense that we draw
from recent literature on a class of similar models. We identify a practical
cost for our method and model: high GPU-time for natural language evaluation.
The zero-shot measurement technique that produces remarkable stability, both
for ALBERT and other BERT variants, is an application of pseudo-log-likelihoods
to masked language models for the relative measurement of probability for
substitution alternatives in forced choice language tasks such as the Winograd
Schema Challenge, Winogrande, and others. One contribution of this paper is to
bring together a number of similar, but independent strands of research. We
produce some absolute state-of-the-art results for common sense reasoning in
binary choice tasks, performing better than any published result in the
literature, including fine-tuned efforts. We show a remarkable consistency of
the model's performance under adversarial settings, which we argue is best
explained by the model's compositionality of representations.
| [
{
"created": "Sun, 23 Jan 2022 22:00:54 GMT",
"version": "v1"
}
] | 2022-01-25 | [
[
"Abramson",
"Darren",
""
],
[
"Emami",
"Ali",
""
]
] | Language models built using semi-supervised machine learning on large corpora of natural language have very quickly enveloped the fields of natural language generation and understanding. In this paper we apply a zero-shot approach independently developed by a number of researchers now gaining recognition as a significant alternative to fine-tuning for evaluation on common sense tasks. A language model with relatively few parameters and training steps compared to a more recent language model (T5) can outperform it on a recent large data set (TimeDial), while displaying robustness in its performance across a similar class of language tasks. Surprisingly, this result is achieved by using a hyperparameter-free zero-shot method with the smaller model, compared to fine-tuning to the larger model. We argue that robustness of the smaller model ought to be understood in terms of compositionality, in a sense that we draw from recent literature on a class of similar models. We identify a practical cost for our method and model: high GPU-time for natural language evaluation. The zero-shot measurement technique that produces remarkable stability, both for ALBERT and other BERT variants, is an application of pseudo-log-likelihoods to masked language models for the relative measurement of probability for substitution alternatives in forced choice language tasks such as the Winograd Schema Challenge, Winogrande, and others. One contribution of this paper is to bring together a number of similar, but independent strands of research. We produce some absolute state-of-the-art results for common sense reasoning in binary choice tasks, performing better than any published result in the literature, including fine-tuned efforts. We show a remarkable consistency of the model's performance under adversarial settings, which we argue is best explained by the model's compositionality of representations. |
2402.02211 | Guangmo Tong | Guangmo Tong, Peng Zhao, Mina Samizadeh | Query-decision Regression between Shortest Path and Minimum Steiner Tree | PAKDD 2024 | null | null | null | cs.LG cs.DS | http://creativecommons.org/licenses/by/4.0/ | Considering a graph with unknown weights, can we find the shortest path for a
pair of nodes if we know the minimal Steiner trees associated with some subset
of nodes? That is, with respect to a fixed latent decision-making system (e.g.,
a weighted graph), we seek to solve one optimization problem (e.g., the
shortest path problem) by leveraging information associated with another
optimization problem (e.g., the minimal Steiner tree problem). In this paper,
we study such a prototype problem called \textit{query-decision regression with
task shifts}, focusing on the shortest path problem and the minimum Steiner
tree problem. We provide theoretical insights regarding the design of
realizable hypothesis spaces for building scoring models, and present two
principled learning frameworks. Our experimental studies show that such
problems can be solved to a decent extent with statistical significance.
| [
{
"created": "Sat, 3 Feb 2024 17:05:01 GMT",
"version": "v1"
}
] | 2024-02-06 | [
[
"Tong",
"Guangmo",
""
],
[
"Zhao",
"Peng",
""
],
[
"Samizadeh",
"Mina",
""
]
] | Considering a graph with unknown weights, can we find the shortest path for a pair of nodes if we know the minimal Steiner trees associated with some subset of nodes? That is, with respect to a fixed latent decision-making system (e.g., a weighted graph), we seek to solve one optimization problem (e.g., the shortest path problem) by leveraging information associated with another optimization problem (e.g., the minimal Steiner tree problem). In this paper, we study such a prototype problem called \textit{query-decision regression with task shifts}, focusing on the shortest path problem and the minimum Steiner tree problem. We provide theoretical insights regarding the design of realizable hypothesis spaces for building scoring models, and present two principled learning frameworks. Our experimental studies show that such problems can be solved to a decent extent with statistical significance. |
2003.06773 | Jinnan Piao | Jinnan Piao, Kai Niu, Jincheng Dai, and Chao Dong | Sphere Constraint based Enumeration Methods to Analyze the Minimum
Weight Distribution of Polar Codes | 11 pages, 6 figures. Submitted to IEEE Transactions on Vehicular
Technology | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, the minimum weight distributions (MWDs) of polar codes and
concatenated polar codes are exactly enumerated according to the distance
property of codewords. We first propose a sphere constraint based enumeration
method (SCEM) to analyze the MWD of polar codes with moderate complexity. The
SCEM exploits the distance property that all the codewords with the identical
Hamming weight are distributed on a spherical shell. Then, based on the SCEM
and the Plotkin's construction of polar codes, a sphere constraint based
recursive enumeration method (SCREM) is proposed to recursively calculate the
MWD with a lower complexity. Finally, we propose a parity-check SCEM (PC-SCEM)
to analyze the MWD of concatenated polar codes by introducing the parity-check
equations of outer codes. Moreover, due to the distance property of codewords,
the proposed three methods can exactly enumerate all the codewords belonging to
the MWD. The enumeration results show that the SCREM can enumerate the MWD of
polar codes with code length up to $2^{14}$ and the PC-SCEM can be used to
optimize CRC-polar concatenated codes.
| [
{
"created": "Sun, 15 Mar 2020 07:34:29 GMT",
"version": "v1"
}
] | 2020-03-17 | [
[
"Piao",
"Jinnan",
""
],
[
"Niu",
"Kai",
""
],
[
"Dai",
"Jincheng",
""
],
[
"Dong",
"Chao",
""
]
] | In this paper, the minimum weight distributions (MWDs) of polar codes and concatenated polar codes are exactly enumerated according to the distance property of codewords. We first propose a sphere constraint based enumeration method (SCEM) to analyze the MWD of polar codes with moderate complexity. The SCEM exploits the distance property that all the codewords with the identical Hamming weight are distributed on a spherical shell. Then, based on the SCEM and the Plotkin's construction of polar codes, a sphere constraint based recursive enumeration method (SCREM) is proposed to recursively calculate the MWD with a lower complexity. Finally, we propose a parity-check SCEM (PC-SCEM) to analyze the MWD of concatenated polar codes by introducing the parity-check equations of outer codes. Moreover, due to the distance property of codewords, the proposed three methods can exactly enumerate all the codewords belonging to the MWD. The enumeration results show that the SCREM can enumerate the MWD of polar codes with code length up to $2^{14}$ and the PC-SCEM can be used to optimize CRC-polar concatenated codes. |
2006.09205 | Andrew Dowsey | William Andrew, Jing Gao, Siobhan Mullan, Neill Campbell, Andrew W
Dowsey, Tilo Burghardt | Visual Identification of Individual Holstein-Friesian Cattle via Deep
Metric Learning | 41 pages, 18 figures, 2 tables; Submitted to Computers and
Electronics in Agriculture ; Source code and network weights available at
https://github.com/CWOA/MetricLearningIdentification ; OpenCows2020 dataset
available at https://doi.org/10.5523/bris.10m32xl88x2b61zlkkgz3fml17 | Computers and Electronics in Agriculture 185, 106133 (2021) | 10.1016/j.compag.2021.106133 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Holstein-Friesian cattle exhibit individually-characteristic black and white
coat patterns visually akin to those arising from Turing's reaction-diffusion
systems. This work takes advantage of these natural markings in order to
automate visual detection and biometric identification of individual
Holstein-Friesians via convolutional neural networks and deep metric learning
techniques. Existing approaches rely on markings, tags or wearables with a
variety of maintenance requirements, whereas we present a totally hands-off
method for the automated detection, localisation, and identification of
individual animals from overhead imaging in an open herd setting, i.e. where
new additions to the herd are identified without re-training. We propose the
use of SoftMax-based reciprocal triplet loss to address the identification
problem and evaluate the techniques in detail against fixed herd paradigms. We
find that deep metric learning systems show strong performance even when many
cattle unseen during system training are to be identified and re-identified --
achieving 93.8% accuracy when trained on just half of the population. This work
paves the way for facilitating the non-intrusive monitoring of cattle
applicable to precision farming and surveillance for automated productivity,
health and welfare monitoring, and to veterinary research such as behavioural
analysis, disease outbreak tracing, and more. Key parts of the source code,
network weights and datasets are available publicly.
| [
{
"created": "Tue, 16 Jun 2020 14:41:55 GMT",
"version": "v1"
},
{
"created": "Sat, 4 Jul 2020 11:38:09 GMT",
"version": "v2"
},
{
"created": "Wed, 14 Oct 2020 10:58:30 GMT",
"version": "v3"
}
] | 2021-05-04 | [
[
"Andrew",
"William",
""
],
[
"Gao",
"Jing",
""
],
[
"Mullan",
"Siobhan",
""
],
[
"Campbell",
"Neill",
""
],
[
"Dowsey",
"Andrew W",
""
],
[
"Burghardt",
"Tilo",
""
]
] | Holstein-Friesian cattle exhibit individually-characteristic black and white coat patterns visually akin to those arising from Turing's reaction-diffusion systems. This work takes advantage of these natural markings in order to automate visual detection and biometric identification of individual Holstein-Friesians via convolutional neural networks and deep metric learning techniques. Existing approaches rely on markings, tags or wearables with a variety of maintenance requirements, whereas we present a totally hands-off method for the automated detection, localisation, and identification of individual animals from overhead imaging in an open herd setting, i.e. where new additions to the herd are identified without re-training. We propose the use of SoftMax-based reciprocal triplet loss to address the identification problem and evaluate the techniques in detail against fixed herd paradigms. We find that deep metric learning systems show strong performance even when many cattle unseen during system training are to be identified and re-identified -- achieving 93.8% accuracy when trained on just half of the population. This work paves the way for facilitating the non-intrusive monitoring of cattle applicable to precision farming and surveillance for automated productivity, health and welfare monitoring, and to veterinary research such as behavioural analysis, disease outbreak tracing, and more. Key parts of the source code, network weights and datasets are available publicly. |
2104.11057 | Lie Ju | Lie Ju, Xin Wang, Lin Wang, Tongliang Liu, Xin Zhao, Tom Drummond,
Dwarikanath Mahapatra, Zongyuan Ge | Relational Subsets Knowledge Distillation for Long-tailed Retinal
Diseases Recognition | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the real world, medical datasets often exhibit a long-tailed data
distribution (i.e., a few classes occupy most of the data, while most classes
have rarely few samples), which results in a challenging imbalance learning
scenario. For example, there are estimated more than 40 different kinds of
retinal diseases with variable morbidity, however with more than 30+ conditions
are very rare from the global patient cohorts, which results in a typical
long-tailed learning problem for deep learning-based screening models. In this
study, we propose class subset learning by dividing the long-tailed data into
multiple class subsets according to prior knowledge, such as regions and
phenotype information. It enforces the model to focus on learning the
subset-specific knowledge. More specifically, there are some relational classes
that reside in the fixed retinal regions, or some common pathological features
are observed in both the majority and minority conditions. With those subsets
learnt teacher models, then we are able to distill the multiple teacher models
into a unified model with weighted knowledge distillation loss. The proposed
framework proved to be effective for the long-tailed retinal diseases
recognition task. The experimental results on two different datasets
demonstrate that our method is flexible and can be easily plugged into many
other state-of-the-art techniques with significant improvements.
| [
{
"created": "Thu, 22 Apr 2021 13:39:33 GMT",
"version": "v1"
}
] | 2021-04-23 | [
[
"Ju",
"Lie",
""
],
[
"Wang",
"Xin",
""
],
[
"Wang",
"Lin",
""
],
[
"Liu",
"Tongliang",
""
],
[
"Zhao",
"Xin",
""
],
[
"Drummond",
"Tom",
""
],
[
"Mahapatra",
"Dwarikanath",
""
],
[
"Ge",
"Zongyuan",
""
]
] | In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy most of the data, while most classes have rarely few samples), which results in a challenging imbalance learning scenario. For example, there are estimated more than 40 different kinds of retinal diseases with variable morbidity, however with more than 30+ conditions are very rare from the global patient cohorts, which results in a typical long-tailed learning problem for deep learning-based screening models. In this study, we propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge, such as regions and phenotype information. It enforces the model to focus on learning the subset-specific knowledge. More specifically, there are some relational classes that reside in the fixed retinal regions, or some common pathological features are observed in both the majority and minority conditions. With those subsets learnt teacher models, then we are able to distill the multiple teacher models into a unified model with weighted knowledge distillation loss. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task. The experimental results on two different datasets demonstrate that our method is flexible and can be easily plugged into many other state-of-the-art techniques with significant improvements. |
1605.00313 | Konstantin Kobylkin S. | Konstantin Kobylkin | Stabbing line segments with disks: complexity and approximation
algorithms | 12 pages, 1 appendix, 15 bibliography items, 6th International
Conference on Analysis of Images, Social Networks and Texts (AIST-2017) | Kobylkin K.Stabbing Line Segments with Disks: Complexity and
Approximation Algorithms. // Lecture Notes in Computer Science, 2018. vol
10716. pp 356-367 Springer | 10.1007/978-3-319-73013-4_33 | Eng21 | cs.CG cs.CC cs.DM | http://creativecommons.org/licenses/by/4.0/ | Computational complexity and approximation algorithms are reported for a
problem of stabbing a set of straight line segments with the least cardinality
set of disks of fixed radii $r>0$ where the set of segments forms a straight
line drawing $G=(V,E)$ of a planar graph without edge crossings. Close
geometric problems arise in network security applications. We give strong
NP-hardness of the problem for edge sets of Delaunay triangulations, Gabriel
graphs and other subgraphs (which are often used in network design) for $r\in
[d_{\min},\eta d_{\max}]$ and some constant $\eta$ where $d_{\max}$ and
$d_{\min}$ are Euclidean lengths of the longest and shortest graph edges
respectively. Fast $O(|E|\log|E|)$-time $O(1)$-approximation algorithm is
proposed within the class of straight line drawings of planar graphs for which
the inequality $r\geq \eta d_{\max}$ holds uniformly for some constant
$\eta>0,$ i.e. when lengths of edges of $G$ are uniformly bounded from above by
some linear function of $r.$
| [
{
"created": "Sun, 1 May 2016 21:54:15 GMT",
"version": "v1"
},
{
"created": "Wed, 4 May 2016 14:06:50 GMT",
"version": "v2"
},
{
"created": "Tue, 26 Jul 2016 09:32:56 GMT",
"version": "v3"
},
{
"created": "Thu, 20 Jul 2017 08:56:24 GMT",
"version": "v4"
}
] | 2018-03-23 | [
[
"Kobylkin",
"Konstantin",
""
]
] | Computational complexity and approximation algorithms are reported for a problem of stabbing a set of straight line segments with the least cardinality set of disks of fixed radii $r>0$ where the set of segments forms a straight line drawing $G=(V,E)$ of a planar graph without edge crossings. Close geometric problems arise in network security applications. We give strong NP-hardness of the problem for edge sets of Delaunay triangulations, Gabriel graphs and other subgraphs (which are often used in network design) for $r\in [d_{\min},\eta d_{\max}]$ and some constant $\eta$ where $d_{\max}$ and $d_{\min}$ are Euclidean lengths of the longest and shortest graph edges respectively. Fast $O(|E|\log|E|)$-time $O(1)$-approximation algorithm is proposed within the class of straight line drawings of planar graphs for which the inequality $r\geq \eta d_{\max}$ holds uniformly for some constant $\eta>0,$ i.e. when lengths of edges of $G$ are uniformly bounded from above by some linear function of $r.$ |
2307.11412 | Weiyu Zhang | Weiyu Zhang | Hybrid deliberation: Citizen dialogues in a post-pandemic era | null | null | null | null | cs.CY | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This report first provides a brief review of various forms of dialogue-based
participation, e.g., Citizen Assembly, Citizen Lottery, Citizen Jury,
Deliberative Polling, and Participatory Budgeting. Challenges associated with
these long-lasting practices are identified and hybrid deliberation is proposed
as a concept to address the challenges. The report then analyzes six leading
examples of digital or hybrid formats of citizen dialogues. Through the
comparison of the cases, the report concludes about the hurdles/risks, success
factors/opportunities, and best practices for a complementary use of digital
and analogue participation formats. Hybrid deliberation is proposed to be the
future direction for dialogue-based participation that involves masses and
generates high-quality outcomes.
| [
{
"created": "Fri, 21 Jul 2023 08:13:53 GMT",
"version": "v1"
}
] | 2023-07-24 | [
[
"Zhang",
"Weiyu",
""
]
] | This report first provides a brief review of various forms of dialogue-based participation, e.g., Citizen Assembly, Citizen Lottery, Citizen Jury, Deliberative Polling, and Participatory Budgeting. Challenges associated with these long-lasting practices are identified and hybrid deliberation is proposed as a concept to address the challenges. The report then analyzes six leading examples of digital or hybrid formats of citizen dialogues. Through the comparison of the cases, the report concludes about the hurdles/risks, success factors/opportunities, and best practices for a complementary use of digital and analogue participation formats. Hybrid deliberation is proposed to be the future direction for dialogue-based participation that involves masses and generates high-quality outcomes. |
1306.4037 | Travis Gagie | H. Ferrada, T. Gagie, T. Hirvola and S. J. Puglisi | Hybrid Indexes for Repetitive Datasets | null | null | 10.1098/rsta.2013.0137 | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Advances in DNA sequencing mean databases of thousands of human genomes will
soon be commonplace. In this paper we introduce a simple technique for reducing
the size of conventional indexes on such highly repetitive texts. Given upper
bounds on pattern lengths and edit distances, we preprocess the text with LZ77
to obtain a filtered text, for which we store a conventional index. Later,
given a query, we find all matches in the filtered text, then use their
positions and the structure of the LZ77 parse to find all matches in the
original text. Our experiments show this also significantly reduces query
times.
| [
{
"created": "Mon, 17 Jun 2013 22:48:15 GMT",
"version": "v1"
}
] | 2015-06-16 | [
[
"Ferrada",
"H.",
""
],
[
"Gagie",
"T.",
""
],
[
"Hirvola",
"T.",
""
],
[
"Puglisi",
"S. J.",
""
]
] | Advances in DNA sequencing mean databases of thousands of human genomes will soon be commonplace. In this paper we introduce a simple technique for reducing the size of conventional indexes on such highly repetitive texts. Given upper bounds on pattern lengths and edit distances, we preprocess the text with LZ77 to obtain a filtered text, for which we store a conventional index. Later, given a query, we find all matches in the filtered text, then use their positions and the structure of the LZ77 parse to find all matches in the original text. Our experiments show this also significantly reduces query times. |
2405.14106 | Emiliano De Cristofaro | Meenatchi Sundaram Muthu Selva Annamalai and Emiliano De Cristofaro | Nearly Tight Black-Box Auditing of Differentially Private Machine
Learning | null | null | null | null | cs.CR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a nearly tight audit of the Differentially Private
Stochastic Gradient Descent (DP-SGD) algorithm in the black-box model. Our
auditing procedure empirically estimates the privacy leakage from DP-SGD using
membership inference attacks; unlike prior work, the estimates are appreciably
close to the theoretical DP bounds. The main intuition is to craft worst-case
initial model parameters, as DP-SGD's privacy analysis is agnostic to the
choice of the initial model parameters. For models trained with theoretical
$\varepsilon=10.0$ on MNIST and CIFAR-10, our auditing procedure yields
empirical estimates of $7.21$ and $6.95$, respectively, on 1,000-record samples
and $6.48$ and $4.96$ on the full datasets. By contrast, previous work achieved
tight audits only in stronger (i.e., less realistic) white-box models that
allow the adversary to access the model's inner parameters and insert arbitrary
gradients. Our auditing procedure can be used to detect bugs and DP violations
more easily and offers valuable insight into how the privacy analysis of DP-SGD
can be further improved.
| [
{
"created": "Thu, 23 May 2024 02:24:52 GMT",
"version": "v1"
}
] | 2024-05-24 | [
[
"Annamalai",
"Meenatchi Sundaram Muthu Selva",
""
],
[
"De Cristofaro",
"Emiliano",
""
]
] | This paper presents a nearly tight audit of the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box model. Our auditing procedure empirically estimates the privacy leakage from DP-SGD using membership inference attacks; unlike prior work, the estimates are appreciably close to the theoretical DP bounds. The main intuition is to craft worst-case initial model parameters, as DP-SGD's privacy analysis is agnostic to the choice of the initial model parameters. For models trained with theoretical $\varepsilon=10.0$ on MNIST and CIFAR-10, our auditing procedure yields empirical estimates of $7.21$ and $6.95$, respectively, on 1,000-record samples and $6.48$ and $4.96$ on the full datasets. By contrast, previous work achieved tight audits only in stronger (i.e., less realistic) white-box models that allow the adversary to access the model's inner parameters and insert arbitrary gradients. Our auditing procedure can be used to detect bugs and DP violations more easily and offers valuable insight into how the privacy analysis of DP-SGD can be further improved. |
1907.05560 | Vahid Noormofidi | Vahid Noormofidi | Simulating Nonlinear Neutrino Oscillations on Next-Generation Many-Core
Architectures | null | null | null | null | cs.DC cs.CE cs.PF | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work an astrophysical simulation code, XFLAT, is developed to study
neutrino oscillations in supernovae. XFLAT is designed to utilize multiple
levels of parallelism through MPI, OpenMP, and SIMD instructions
(vectorization). It can run on both the CPU and the Xeon Phi co-processor, the
latter of which is based on the Intel Many Integrated Core Architecture (MIC).
The performance of XFLAT on configurations and scenarios has been analyzed. In
addition, the impact of I/O and the multi-node configuration on the Xeon
Phi-equipped heterogeneous supercomputers such as Stampede at the Texas
Advanced Computing Center (TACC) was investigated.
| [
{
"created": "Fri, 12 Jul 2019 03:21:54 GMT",
"version": "v1"
}
] | 2019-07-15 | [
[
"Noormofidi",
"Vahid",
""
]
] | In this work an astrophysical simulation code, XFLAT, is developed to study neutrino oscillations in supernovae. XFLAT is designed to utilize multiple levels of parallelism through MPI, OpenMP, and SIMD instructions (vectorization). It can run on both the CPU and the Xeon Phi co-processor, the latter of which is based on the Intel Many Integrated Core Architecture (MIC). The performance of XFLAT on configurations and scenarios has been analyzed. In addition, the impact of I/O and the multi-node configuration on the Xeon Phi-equipped heterogeneous supercomputers such as Stampede at the Texas Advanced Computing Center (TACC) was investigated. |
2404.14465 | Ilias Siniosoglou | Dimitris Asimopoulos, Ilias Siniosoglou, Vasileios Argyriou, Thomai
Karamitsou, Eleftherios Fountoukidis, Sotirios K. Goudos, Ioannis D.
Moscholios, Konstantinos E. Psannis, Panagiotis Sarigiannidis | Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on
Novel and Traditional Approaches | null | null | null | null | cs.CL cs.AI cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the realm of data privacy, the ability to effectively anonymise text is
paramount. With the proliferation of deep learning and, in particular,
transformer architectures, there is a burgeoning interest in leveraging these
advanced models for text anonymisation tasks. This paper presents a
comprehensive benchmarking study comparing the performance of transformer-based
models and Large Language Models(LLM) against traditional architectures for
text anonymisation. Utilising the CoNLL-2003 dataset, known for its robustness
and diversity, we evaluate several models. Our results showcase the strengths
and weaknesses of each approach, offering a clear perspective on the efficacy
of modern versus traditional methods. Notably, while modern models exhibit
advanced capabilities in capturing con textual nuances, certain traditional
architectures still keep high performance. This work aims to guide researchers
in selecting the most suitable model for their anonymisation needs, while also
shedding light on potential paths for future advancements in the field.
| [
{
"created": "Mon, 22 Apr 2024 12:06:54 GMT",
"version": "v1"
}
] | 2024-04-24 | [
[
"Asimopoulos",
"Dimitris",
""
],
[
"Siniosoglou",
"Ilias",
""
],
[
"Argyriou",
"Vasileios",
""
],
[
"Karamitsou",
"Thomai",
""
],
[
"Fountoukidis",
"Eleftherios",
""
],
[
"Goudos",
"Sotirios K.",
""
],
[
"Moscholios",
"Ioannis D.",
""
],
[
"Psannis",
"Konstantinos E.",
""
],
[
"Sarigiannidis",
"Panagiotis",
""
]
] | In the realm of data privacy, the ability to effectively anonymise text is paramount. With the proliferation of deep learning and, in particular, transformer architectures, there is a burgeoning interest in leveraging these advanced models for text anonymisation tasks. This paper presents a comprehensive benchmarking study comparing the performance of transformer-based models and Large Language Models(LLM) against traditional architectures for text anonymisation. Utilising the CoNLL-2003 dataset, known for its robustness and diversity, we evaluate several models. Our results showcase the strengths and weaknesses of each approach, offering a clear perspective on the efficacy of modern versus traditional methods. Notably, while modern models exhibit advanced capabilities in capturing con textual nuances, certain traditional architectures still keep high performance. This work aims to guide researchers in selecting the most suitable model for their anonymisation needs, while also shedding light on potential paths for future advancements in the field. |
2010.12723 | Yuning Mao | Yuning Mao, Xiang Ren, Heng Ji, Jiawei Han | Constrained Abstractive Summarization: Preserving Factual Consistency
with Constrained Generation | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite significant progress, state-of-the-art abstractive summarization
methods are still prone to hallucinate content inconsistent with the source
document. In this paper, we propose Constrained Abstractive Summarization
(CAS), a general setup that preserves the factual consistency of abstractive
summarization by specifying tokens as constraints that must be present in the
summary. We adopt lexically constrained decoding, a technique generally
applicable to autoregressive generative models, to fulfill CAS and conduct
experiments in two scenarios: (1) automatic summarization without human
involvement, where keyphrases are extracted from the source document and used
as constraints; (2) human-guided interactive summarization, where human
feedback in the form of manual constraints are used to guide summary
generation. Automatic and human evaluations on two benchmark datasets
demonstrate that CAS improves both lexical overlap (ROUGE) and factual
consistency of abstractive summarization. In particular, we observe up to 13.8
ROUGE-2 gains when only one manual constraint is used in interactive
summarization.
| [
{
"created": "Sat, 24 Oct 2020 00:27:44 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Dec 2021 05:20:15 GMT",
"version": "v2"
}
] | 2021-12-17 | [
[
"Mao",
"Yuning",
""
],
[
"Ren",
"Xiang",
""
],
[
"Ji",
"Heng",
""
],
[
"Han",
"Jiawei",
""
]
] | Despite significant progress, state-of-the-art abstractive summarization methods are still prone to hallucinate content inconsistent with the source document. In this paper, we propose Constrained Abstractive Summarization (CAS), a general setup that preserves the factual consistency of abstractive summarization by specifying tokens as constraints that must be present in the summary. We adopt lexically constrained decoding, a technique generally applicable to autoregressive generative models, to fulfill CAS and conduct experiments in two scenarios: (1) automatic summarization without human involvement, where keyphrases are extracted from the source document and used as constraints; (2) human-guided interactive summarization, where human feedback in the form of manual constraints are used to guide summary generation. Automatic and human evaluations on two benchmark datasets demonstrate that CAS improves both lexical overlap (ROUGE) and factual consistency of abstractive summarization. In particular, we observe up to 13.8 ROUGE-2 gains when only one manual constraint is used in interactive summarization. |
1210.1630 | Herbert Tanner | Jie Fu, Herbert G. Tanner, Jeffrey Heinz, Jane Chandlee, Konstantinos
Karydis, and Cesar Koirala | Symbolic Planning and Control Using Game Theory and Grammatical
Inference | null | null | null | null | cs.RO cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an approach that brings together game theory with
grammatical inference and discrete abstractions in order to synthesize control
strategies for hybrid dynamical systems performing tasks in partially unknown
but rule-governed adversarial environments. The combined formulation guarantees
that a system specification is met if (a) the true model of the environment is
in the class of models inferable from a positive presentation, (b) a
characteristic sample is observed, and (c) the task specification is
satisfiable given the capabilities of the system (agent) and the environment.
| [
{
"created": "Fri, 5 Oct 2012 02:40:39 GMT",
"version": "v1"
}
] | 2012-10-08 | [
[
"Fu",
"Jie",
""
],
[
"Tanner",
"Herbert G.",
""
],
[
"Heinz",
"Jeffrey",
""
],
[
"Chandlee",
"Jane",
""
],
[
"Karydis",
"Konstantinos",
""
],
[
"Koirala",
"Cesar",
""
]
] | This paper presents an approach that brings together game theory with grammatical inference and discrete abstractions in order to synthesize control strategies for hybrid dynamical systems performing tasks in partially unknown but rule-governed adversarial environments. The combined formulation guarantees that a system specification is met if (a) the true model of the environment is in the class of models inferable from a positive presentation, (b) a characteristic sample is observed, and (c) the task specification is satisfiable given the capabilities of the system (agent) and the environment. |
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