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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2312.02051 | Shuhuai Ren | Shuhuai Ren, Linli Yao, Shicheng Li, Xu Sun, Lu Hou | TimeChat: A Time-sensitive Multimodal Large Language Model for Long
Video Understanding | CVPR 2024 camera-ready version, code is available at
https://github.com/RenShuhuai-Andy/TimeChat | null | null | null | cs.CV cs.AI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work proposes TimeChat, a time-sensitive multimodal large language model
specifically designed for long video understanding. Our model incorporates two
key architectural contributions: (1) a timestamp-aware frame encoder that binds
visual content with the timestamp of each frame, and (2) a sliding video
Q-Former that produces a video token sequence of varying lengths to accommodate
videos of various durations. Additionally, we construct an instruction-tuning
dataset, encompassing 6 tasks and a total of 125K instances, to further enhance
TimeChat's instruction-following performance. Experiment results across various
video understanding tasks, such as dense captioning, temporal grounding, and
highlight detection, demonstrate TimeChat's strong zero-shot temporal
localization and reasoning capabilities. For example, it achieves +9.2 F1 score
and +2.8 CIDEr on YouCook2, +5.8 HIT@1 on QVHighlights, and +27.5 R@1 (IoU=0.5)
on Charades-STA, compared to state-of-the-art video large language models,
holding the potential to serve as a versatile video assistant for long-form
video comprehension tasks and satisfy realistic user requirements.
| [
{
"created": "Mon, 4 Dec 2023 17:09:52 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Mar 2024 12:41:14 GMT",
"version": "v2"
}
] | 2024-03-29 | [
[
"Ren",
"Shuhuai",
""
],
[
"Yao",
"Linli",
""
],
[
"Li",
"Shicheng",
""
],
[
"Sun",
"Xu",
""
],
[
"Hou",
"Lu",
""
]
] | This work proposes TimeChat, a time-sensitive multimodal large language model specifically designed for long video understanding. Our model incorporates two key architectural contributions: (1) a timestamp-aware frame encoder that binds visual content with the timestamp of each frame, and (2) a sliding video Q-Former that produces a video token sequence of varying lengths to accommodate videos of various durations. Additionally, we construct an instruction-tuning dataset, encompassing 6 tasks and a total of 125K instances, to further enhance TimeChat's instruction-following performance. Experiment results across various video understanding tasks, such as dense captioning, temporal grounding, and highlight detection, demonstrate TimeChat's strong zero-shot temporal localization and reasoning capabilities. For example, it achieves +9.2 F1 score and +2.8 CIDEr on YouCook2, +5.8 HIT@1 on QVHighlights, and +27.5 R@1 (IoU=0.5) on Charades-STA, compared to state-of-the-art video large language models, holding the potential to serve as a versatile video assistant for long-form video comprehension tasks and satisfy realistic user requirements. |
2202.04270 | Kenjiro Tadakuma | Tomoya Takahashi, Masahiro Watanabe, Kenjiro Tadakuma, Naoto Saiki,
Kazuki Abe Masashi Konyo and Satoshi Tadokoro | Inflated Bendable Eversion Cantilever Mechanism with Inner Skeleton for
Increased Payload Holding | This article is consist of 8 pages and 15 figures | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inflatable structures used in soft robotics applications exhibit unique
characteristics. In particular, the tip-extension structure, which grows from
the tip, can grow without friction against the environment. However, these
inflatable structures are inferior to rigid mechanisms in terms of their
load-bearing capacity. The stiffness of the tip-extension structure can be
increased by pressurization, but the structure cannot maintain its curved shape
and compliance. In this study, we proposed a mechanism that combines a skeleton
structure consisting of multi-joint links with functions to increase rigidity
while keeping low pressure and realizing the functions of bending and shape
fixation. We devised a design method for rigid articulated links and combined
it with a membrane structure that utilizes the advantages of the tip-extension
structure. The experimental results show that the payload of the designed
structure increases compared to that of the membrane-only structure. The
findings of this research can be applied to long robots that can be extended in
the air without drooping and to mechanisms that can wrap around the human body.
| [
{
"created": "Wed, 9 Feb 2022 04:35:40 GMT",
"version": "v1"
}
] | 2022-02-10 | [
[
"Takahashi",
"Tomoya",
""
],
[
"Watanabe",
"Masahiro",
""
],
[
"Tadakuma",
"Kenjiro",
""
],
[
"Saiki",
"Naoto",
""
],
[
"Konyo",
"Kazuki Abe Masashi",
""
],
[
"Tadokoro",
"Satoshi",
""
]
] | Inflatable structures used in soft robotics applications exhibit unique characteristics. In particular, the tip-extension structure, which grows from the tip, can grow without friction against the environment. However, these inflatable structures are inferior to rigid mechanisms in terms of their load-bearing capacity. The stiffness of the tip-extension structure can be increased by pressurization, but the structure cannot maintain its curved shape and compliance. In this study, we proposed a mechanism that combines a skeleton structure consisting of multi-joint links with functions to increase rigidity while keeping low pressure and realizing the functions of bending and shape fixation. We devised a design method for rigid articulated links and combined it with a membrane structure that utilizes the advantages of the tip-extension structure. The experimental results show that the payload of the designed structure increases compared to that of the membrane-only structure. The findings of this research can be applied to long robots that can be extended in the air without drooping and to mechanisms that can wrap around the human body. |
2306.09848 | Ege Gursoy | Ege Gursoy, Sonny Tarbouriech, Andrea Cherubini | Can robots mold soft plastic materials by shaping depth images? | Accepted to IEEE Transactions on Robotics (T-RO) | null | null | null | cs.RO | http://creativecommons.org/licenses/by/4.0/ | Can robots mold soft plastic materials by shaping depth images? The short
answer is no: current day robots can't. In this article, we address the problem
of shaping plastic material with an anthropomorphic arm/hand robot, which
observes the material with a fixed depth camera. Robots capable of molding
could assist humans in many tasks, such as cooking, scooping or gardening. Yet,
the problem is complex, due to its high-dimensionality at both perception and
control levels. To address it, we design three alternative data-based methods
for predicting the effect of robot actions on the material. Then, the robot can
plan the sequence of actions and their positions, to mold the material into a
desired shape. To make the prediction problem tractable, we rely on two
original ideas. First, we prove that under reasonable assumptions, the shaping
problem can be mapped from point cloud to depth image space, with many benefits
(simpler processing, no need for registration, lower computation time and
memory requirements). Second, we design a novel, simple metric for quickly
measuring the distance between two depth images. The metric is based on the
inherent point cloud representation of depth images, which enables direct and
consistent comparison of image pairs through a non-uniform scaling approach,
and therefore opens promising perspectives for designing \textit{depth image --
based} robot controllers. We assess our approach in a series of unprecedented
experiments, where a robotic arm/hand molds flour from initial to final shapes,
either with its own dataset, or by transfer learning from a human dataset. We
conclude the article by discussing the limitations of our framework and those
of current day hardware, which make human-like robot molding a challenging open
research problem.
| [
{
"created": "Fri, 16 Jun 2023 13:46:15 GMT",
"version": "v1"
}
] | 2023-06-19 | [
[
"Gursoy",
"Ege",
""
],
[
"Tarbouriech",
"Sonny",
""
],
[
"Cherubini",
"Andrea",
""
]
] | Can robots mold soft plastic materials by shaping depth images? The short answer is no: current day robots can't. In this article, we address the problem of shaping plastic material with an anthropomorphic arm/hand robot, which observes the material with a fixed depth camera. Robots capable of molding could assist humans in many tasks, such as cooking, scooping or gardening. Yet, the problem is complex, due to its high-dimensionality at both perception and control levels. To address it, we design three alternative data-based methods for predicting the effect of robot actions on the material. Then, the robot can plan the sequence of actions and their positions, to mold the material into a desired shape. To make the prediction problem tractable, we rely on two original ideas. First, we prove that under reasonable assumptions, the shaping problem can be mapped from point cloud to depth image space, with many benefits (simpler processing, no need for registration, lower computation time and memory requirements). Second, we design a novel, simple metric for quickly measuring the distance between two depth images. The metric is based on the inherent point cloud representation of depth images, which enables direct and consistent comparison of image pairs through a non-uniform scaling approach, and therefore opens promising perspectives for designing \textit{depth image -- based} robot controllers. We assess our approach in a series of unprecedented experiments, where a robotic arm/hand molds flour from initial to final shapes, either with its own dataset, or by transfer learning from a human dataset. We conclude the article by discussing the limitations of our framework and those of current day hardware, which make human-like robot molding a challenging open research problem. |
1305.3102 | Bart M. P. Jansen | Michael R. Fellows and Bart M. P. Jansen | FPT is Characterized by Useful Obstruction Sets | Extended abstract with appendix, as accepted to WG 2013 | null | null | null | cs.CC cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many graph problems were first shown to be fixed-parameter tractable using
the results of Robertson and Seymour on graph minors. We show that the
combination of finite, computable, obstruction sets and efficient order tests
is not just one way of obtaining strongly uniform FPT algorithms, but that all
of FPT may be captured in this way. Our new characterization of FPT has a
strong connection to the theory of kernelization, as we prove that problems
with polynomial kernels can be characterized by obstruction sets whose elements
have polynomial size. Consequently we investigate the interplay between the
sizes of problem kernels and the sizes of the elements of such obstruction
sets, obtaining several examples of how results in one area yield new insights
in the other. We show how exponential-size minor-minimal obstructions for
pathwidth k form the crucial ingredient in a novel OR-cross-composition for
k-Pathwidth, complementing the trivial AND-composition that is known for this
problem. In the other direction, we show that OR-cross-compositions into a
parameterized problem can be used to rule out the existence of efficiently
generated quasi-orders on its instances that characterize the NO-instances by
polynomial-size obstructions.
| [
{
"created": "Tue, 14 May 2013 10:43:00 GMT",
"version": "v1"
}
] | 2013-05-15 | [
[
"Fellows",
"Michael R.",
""
],
[
"Jansen",
"Bart M. P.",
""
]
] | Many graph problems were first shown to be fixed-parameter tractable using the results of Robertson and Seymour on graph minors. We show that the combination of finite, computable, obstruction sets and efficient order tests is not just one way of obtaining strongly uniform FPT algorithms, but that all of FPT may be captured in this way. Our new characterization of FPT has a strong connection to the theory of kernelization, as we prove that problems with polynomial kernels can be characterized by obstruction sets whose elements have polynomial size. Consequently we investigate the interplay between the sizes of problem kernels and the sizes of the elements of such obstruction sets, obtaining several examples of how results in one area yield new insights in the other. We show how exponential-size minor-minimal obstructions for pathwidth k form the crucial ingredient in a novel OR-cross-composition for k-Pathwidth, complementing the trivial AND-composition that is known for this problem. In the other direction, we show that OR-cross-compositions into a parameterized problem can be used to rule out the existence of efficiently generated quasi-orders on its instances that characterize the NO-instances by polynomial-size obstructions. |
2205.08820 | Antonio Longa | Antonio Longa, Giulia Cencetti, Sune Lehmann, Andrea Passerini and
Bruno Lepri | Generating fine-grained surrogate temporal networks | null | null | null | null | cs.SI cs.CY physics.data-an physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | Temporal networks are essential for modeling and understanding systems whose
behavior varies in time, from social interactions to biological systems. Often,
however, real-world data are prohibitively expensive to collect in a large
scale or unshareable due to privacy concerns. A promising way to bypass the
problem consists in generating arbitrarily large and anonymized synthetic
graphs with the properties of real-world networks, namely `surrogate networks'.
Until now, the generation of realistic surrogate temporal networks has remained
an open problem, due to the difficulty of capturing both the temporal and
topological properties of the input network, as well as their correlations, in
a scalable model. Here, we propose a novel and simple method for generating
surrogate temporal networks. Our method decomposes the input network into
star-like structures evolving in time. Then those structures are used as
building blocks to generate a surrogate temporal network. Our model vastly
outperforms current methods across multiple examples of temporal networks in
terms of both topological and dynamical similarity. We further show that beyond
generating realistic interaction patterns, our method is able to capture
intrinsic temporal periodicity of temporal networks, all with an execution time
lower than competing methods by multiple orders of magnitude. The simplicity of
our algorithm makes it easily interpretable, extendable and algorithmically
scalable.
| [
{
"created": "Wed, 18 May 2022 09:38:22 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Aug 2023 17:35:58 GMT",
"version": "v2"
}
] | 2023-08-23 | [
[
"Longa",
"Antonio",
""
],
[
"Cencetti",
"Giulia",
""
],
[
"Lehmann",
"Sune",
""
],
[
"Passerini",
"Andrea",
""
],
[
"Lepri",
"Bruno",
""
]
] | Temporal networks are essential for modeling and understanding systems whose behavior varies in time, from social interactions to biological systems. Often, however, real-world data are prohibitively expensive to collect in a large scale or unshareable due to privacy concerns. A promising way to bypass the problem consists in generating arbitrarily large and anonymized synthetic graphs with the properties of real-world networks, namely `surrogate networks'. Until now, the generation of realistic surrogate temporal networks has remained an open problem, due to the difficulty of capturing both the temporal and topological properties of the input network, as well as their correlations, in a scalable model. Here, we propose a novel and simple method for generating surrogate temporal networks. Our method decomposes the input network into star-like structures evolving in time. Then those structures are used as building blocks to generate a surrogate temporal network. Our model vastly outperforms current methods across multiple examples of temporal networks in terms of both topological and dynamical similarity. We further show that beyond generating realistic interaction patterns, our method is able to capture intrinsic temporal periodicity of temporal networks, all with an execution time lower than competing methods by multiple orders of magnitude. The simplicity of our algorithm makes it easily interpretable, extendable and algorithmically scalable. |
2309.05352 | Rohan V Kashyap | Pavan Karjol, Rohan Kashyap, Prathosh A P | Neural Discovery of Permutation Subgroups | null | In International Conference on Artificial Intelligence and
Statistics, pp. 4668-4678. Volume 206. PMLR, 2023 | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | We consider the problem of discovering subgroup $H$ of permutation group
$S_{n}$. Unlike the traditional $H$-invariant networks wherein $H$ is assumed
to be known, we present a method to discover the underlying subgroup, given
that it satisfies certain conditions. Our results show that one could discover
any subgroup of type $S_{k} (k \leq n)$ by learning an $S_{n}$-invariant
function and a linear transformation. We also prove similar results for cyclic
and dihedral subgroups. Finally, we provide a general theorem that can be
extended to discover other subgroups of $S_{n}$. We also demonstrate the
applicability of our results through numerical experiments on image-digit sum
and symmetric polynomial regression tasks.
| [
{
"created": "Mon, 11 Sep 2023 09:53:28 GMT",
"version": "v1"
}
] | 2023-09-12 | [
[
"Karjol",
"Pavan",
""
],
[
"Kashyap",
"Rohan",
""
],
[
"P",
"Prathosh A",
""
]
] | We consider the problem of discovering subgroup $H$ of permutation group $S_{n}$. Unlike the traditional $H$-invariant networks wherein $H$ is assumed to be known, we present a method to discover the underlying subgroup, given that it satisfies certain conditions. Our results show that one could discover any subgroup of type $S_{k} (k \leq n)$ by learning an $S_{n}$-invariant function and a linear transformation. We also prove similar results for cyclic and dihedral subgroups. Finally, we provide a general theorem that can be extended to discover other subgroups of $S_{n}$. We also demonstrate the applicability of our results through numerical experiments on image-digit sum and symmetric polynomial regression tasks. |
2303.10991 | Jinyoung Jun | Jinyoung Jun, Jae-Han Lee, and Chang-Su Kim | Versatile Depth Estimator Based on Common Relative Depth Estimation and
Camera-Specific Relative-to-Metric Depth Conversion | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A typical monocular depth estimator is trained for a single camera, so its
performance drops severely on images taken with different cameras. To address
this issue, we propose a versatile depth estimator (VDE), composed of a common
relative depth estimator (CRDE) and multiple relative-to-metric converters
(R2MCs). The CRDE extracts relative depth information, and each R2MC converts
the relative information to predict metric depths for a specific camera. The
proposed VDE can cope with diverse scenes, including both indoor and outdoor
scenes, with only a 1.12\% parameter increase per camera. Experimental results
demonstrate that VDE supports multiple cameras effectively and efficiently and
also achieves state-of-the-art performance in the conventional single-camera
scenario.
| [
{
"created": "Mon, 20 Mar 2023 10:19:50 GMT",
"version": "v1"
}
] | 2023-03-21 | [
[
"Jun",
"Jinyoung",
""
],
[
"Lee",
"Jae-Han",
""
],
[
"Kim",
"Chang-Su",
""
]
] | A typical monocular depth estimator is trained for a single camera, so its performance drops severely on images taken with different cameras. To address this issue, we propose a versatile depth estimator (VDE), composed of a common relative depth estimator (CRDE) and multiple relative-to-metric converters (R2MCs). The CRDE extracts relative depth information, and each R2MC converts the relative information to predict metric depths for a specific camera. The proposed VDE can cope with diverse scenes, including both indoor and outdoor scenes, with only a 1.12\% parameter increase per camera. Experimental results demonstrate that VDE supports multiple cameras effectively and efficiently and also achieves state-of-the-art performance in the conventional single-camera scenario. |
2309.04782 | Feng Zhou | Feng Zhou, Antonio Cicone, Haomin Zhou | RRCNN$^{+}$: An Enhanced Residual Recursive Convolutional Neural Network
for Non-stationary Signal Decomposition | 8 pages, 4 figure | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Time-frequency analysis is an important and challenging task in many
applications. Fourier and wavelet analysis are two classic methods that have
achieved remarkable success in many fields. They also exhibit limitations when
applied to nonlinear and non-stationary signals. To address this challenge, a
series of nonlinear and adaptive methods, pioneered by the empirical mode
decomposition method have been proposed. Their aim is to decompose a
non-stationary signal into quasi-stationary components which reveal better
features in the time-frequency analysis. Recently, inspired by deep learning,
we proposed a novel method called residual recursive convolutional neural
network (RRCNN). Not only RRCNN can achieve more stable decomposition than
existing methods while batch processing large-scale signals with low
computational cost, but also deep learning provides a unique perspective for
non-stationary signal decomposition. In this study, we aim to further improve
RRCNN with the help of several nimble techniques from deep learning and
optimization to ameliorate the method and overcome some of the limitations of
this technique.
| [
{
"created": "Sat, 9 Sep 2023 13:00:30 GMT",
"version": "v1"
}
] | 2023-09-12 | [
[
"Zhou",
"Feng",
""
],
[
"Cicone",
"Antonio",
""
],
[
"Zhou",
"Haomin",
""
]
] | Time-frequency analysis is an important and challenging task in many applications. Fourier and wavelet analysis are two classic methods that have achieved remarkable success in many fields. They also exhibit limitations when applied to nonlinear and non-stationary signals. To address this challenge, a series of nonlinear and adaptive methods, pioneered by the empirical mode decomposition method have been proposed. Their aim is to decompose a non-stationary signal into quasi-stationary components which reveal better features in the time-frequency analysis. Recently, inspired by deep learning, we proposed a novel method called residual recursive convolutional neural network (RRCNN). Not only RRCNN can achieve more stable decomposition than existing methods while batch processing large-scale signals with low computational cost, but also deep learning provides a unique perspective for non-stationary signal decomposition. In this study, we aim to further improve RRCNN with the help of several nimble techniques from deep learning and optimization to ameliorate the method and overcome some of the limitations of this technique. |
2311.18495 | Avery Ma | Avery Ma, Amir-massoud Farahmand, Yangchen Pan, Philip Torr, Jindong
Gu | Improving Adversarial Transferability via Model Alignment | Accepted at the European Conference on Computer Vision (ECCV) 2024.
Code: https://github.com/averyma/model-alignment | null | null | null | cs.LG cs.CV | http://creativecommons.org/licenses/by/4.0/ | Neural networks are susceptible to adversarial perturbations that are
transferable across different models. In this paper, we introduce a novel model
alignment technique aimed at improving a given source model's ability in
generating transferable adversarial perturbations. During the alignment
process, the parameters of the source model are fine-tuned to minimize an
alignment loss. This loss measures the divergence in the predictions between
the source model and another, independently trained model, referred to as the
witness model. To understand the effect of model alignment, we conduct a
geometric analysis of the resulting changes in the loss landscape. Extensive
experiments on the ImageNet dataset, using a variety of model architectures,
demonstrate that perturbations generated from aligned source models exhibit
significantly higher transferability than those from the original source model.
| [
{
"created": "Thu, 30 Nov 2023 12:15:49 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Jul 2024 11:45:09 GMT",
"version": "v2"
}
] | 2024-07-18 | [
[
"Ma",
"Avery",
""
],
[
"Farahmand",
"Amir-massoud",
""
],
[
"Pan",
"Yangchen",
""
],
[
"Torr",
"Philip",
""
],
[
"Gu",
"Jindong",
""
]
] | Neural networks are susceptible to adversarial perturbations that are transferable across different models. In this paper, we introduce a novel model alignment technique aimed at improving a given source model's ability in generating transferable adversarial perturbations. During the alignment process, the parameters of the source model are fine-tuned to minimize an alignment loss. This loss measures the divergence in the predictions between the source model and another, independently trained model, referred to as the witness model. To understand the effect of model alignment, we conduct a geometric analysis of the resulting changes in the loss landscape. Extensive experiments on the ImageNet dataset, using a variety of model architectures, demonstrate that perturbations generated from aligned source models exhibit significantly higher transferability than those from the original source model. |
2102.08628 | Essam Rashed | Essam A. Rashed, Sachiko Kodera, Hidenobu Shirakami, Ryotetsu
Kawaguchi, Kazuhiro Watanabe, Akimasa Hirata | Knowledge discovery from emergency ambulance dispatch during COVID-19: A
case study of Nagoya City, Japan | 15 pages, 12 figures, 2 tables | Journal of Biomedical Informatics, 2021 | 10.1016/j.jbi.2021.103743 | null | cs.AI eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate forecasting of medical service requirements is an important big data
problem that is crucial for resource management in critical times such as
natural disasters and pandemics. With the global spread of coronavirus disease
2019 (COVID-19), several concerns have been raised regarding the ability of
medical systems to handle sudden changes in the daily routines of healthcare
providers. One significant problem is the management of ambulance dispatch and
control during a pandemic. To help address this problem, we first analyze
ambulance dispatch data records from April 2014 to August 2020 for Nagoya City,
Japan. Significant changes were observed in the data during the pandemic,
including the state of emergency (SoE) declared across Japan. In this study, we
propose a deep learning framework based on recurrent neural networks to
estimate the number of emergency ambulance dispatches (EADs) during a SoE. The
fusion of data includes environmental factors, the localization data of mobile
phone users, and the past history of EADs, thereby providing a general
framework for knowledge discovery and better resource management. The results
indicate that the proposed blend of training data can be used efficiently in a
real-world estimation of EAD requirements during periods of high uncertainties
such as pandemics.
| [
{
"created": "Wed, 17 Feb 2021 08:37:05 GMT",
"version": "v1"
}
] | 2021-03-23 | [
[
"Rashed",
"Essam A.",
""
],
[
"Kodera",
"Sachiko",
""
],
[
"Shirakami",
"Hidenobu",
""
],
[
"Kawaguchi",
"Ryotetsu",
""
],
[
"Watanabe",
"Kazuhiro",
""
],
[
"Hirata",
"Akimasa",
""
]
] | Accurate forecasting of medical service requirements is an important big data problem that is crucial for resource management in critical times such as natural disasters and pandemics. With the global spread of coronavirus disease 2019 (COVID-19), several concerns have been raised regarding the ability of medical systems to handle sudden changes in the daily routines of healthcare providers. One significant problem is the management of ambulance dispatch and control during a pandemic. To help address this problem, we first analyze ambulance dispatch data records from April 2014 to August 2020 for Nagoya City, Japan. Significant changes were observed in the data during the pandemic, including the state of emergency (SoE) declared across Japan. In this study, we propose a deep learning framework based on recurrent neural networks to estimate the number of emergency ambulance dispatches (EADs) during a SoE. The fusion of data includes environmental factors, the localization data of mobile phone users, and the past history of EADs, thereby providing a general framework for knowledge discovery and better resource management. The results indicate that the proposed blend of training data can be used efficiently in a real-world estimation of EAD requirements during periods of high uncertainties such as pandemics. |
2312.10371 | Wei Chen | Wei Chen, Gang Zhao, Xiaojin Zhang, Xiang Bai, Xuanjing Huang, Zhongyu
Wei | K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via
Prompt Learning | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatic psychological counseling requires mass of professional knowledge
that can be found in online counseling forums. Motivated by this, we propose
K-ESConv, a novel prompt learning based knowledge injection method for
emotional support dialogue system, transferring forum knowledge to response
generation. We evaluate our model on an emotional support dataset ESConv, where
the model retrieves and incorporates knowledge from external professional
emotional Q\&A forum. Experiment results show that the proposed method
outperforms existing baselines on both automatic evaluation and human
evaluation, which shows that our approach significantly improves the
correlation and diversity of responses and provides more comfort and better
suggestion for the seeker.
| [
{
"created": "Sat, 16 Dec 2023 08:10:10 GMT",
"version": "v1"
}
] | 2023-12-19 | [
[
"Chen",
"Wei",
""
],
[
"Zhao",
"Gang",
""
],
[
"Zhang",
"Xiaojin",
""
],
[
"Bai",
"Xiang",
""
],
[
"Huang",
"Xuanjing",
""
],
[
"Wei",
"Zhongyu",
""
]
] | Automatic psychological counseling requires mass of professional knowledge that can be found in online counseling forums. Motivated by this, we propose K-ESConv, a novel prompt learning based knowledge injection method for emotional support dialogue system, transferring forum knowledge to response generation. We evaluate our model on an emotional support dataset ESConv, where the model retrieves and incorporates knowledge from external professional emotional Q\&A forum. Experiment results show that the proposed method outperforms existing baselines on both automatic evaluation and human evaluation, which shows that our approach significantly improves the correlation and diversity of responses and provides more comfort and better suggestion for the seeker. |
2405.00154 | Aleksandr Katrutsa | Alexander Demin, Yuriy Dorn, Aleksandr Katrutsa, Daniil Kazantsev,
Ilgam Latypov, Yulia Maximlyuk, Denis Ponomaryov | EEvA: Fast Expert-Based Algorithms for Buffer Page Replacement | null | null | null | null | cs.DB | http://creativecommons.org/licenses/by/4.0/ | Optimal page replacement is an important problem in efficient buffer
management. The range of replacement strategies known in the literature varies
from simple but efficient FIFO-based algorithms to more accurate but
potentially costly methods tailored to specific data access patterns. The
principal issue in adopting a pattern-specific replacement logic in a DB buffer
manager is to guarantee non-degradation in general high-load regimes. In this
paper, we propose a new family of page replacement algorithms for DB buffer
manager which demonstrate a superior performance wrt competitors on custom data
access patterns and imply a low computational overhead on TPC-C. We provide
theoretical foundations and an extensive experimental study on the proposed
algorithms which covers synthetic benchmarks and an implementation in an
open-source DB kernel evaluated on TPC-C.
| [
{
"created": "Tue, 30 Apr 2024 19:04:53 GMT",
"version": "v1"
}
] | 2024-05-02 | [
[
"Demin",
"Alexander",
""
],
[
"Dorn",
"Yuriy",
""
],
[
"Katrutsa",
"Aleksandr",
""
],
[
"Kazantsev",
"Daniil",
""
],
[
"Latypov",
"Ilgam",
""
],
[
"Maximlyuk",
"Yulia",
""
],
[
"Ponomaryov",
"Denis",
""
]
] | Optimal page replacement is an important problem in efficient buffer management. The range of replacement strategies known in the literature varies from simple but efficient FIFO-based algorithms to more accurate but potentially costly methods tailored to specific data access patterns. The principal issue in adopting a pattern-specific replacement logic in a DB buffer manager is to guarantee non-degradation in general high-load regimes. In this paper, we propose a new family of page replacement algorithms for DB buffer manager which demonstrate a superior performance wrt competitors on custom data access patterns and imply a low computational overhead on TPC-C. We provide theoretical foundations and an extensive experimental study on the proposed algorithms which covers synthetic benchmarks and an implementation in an open-source DB kernel evaluated on TPC-C. |
2403.10338 | Priyanka Sukumaran | Priyanka Sukumaran, Conor Houghton, Nina Kazanina | Investigating grammatical abstraction in language models using few-shot
learning of novel noun gender | EACL 2024; Findings of the Association for Computational Linguistics | null | null | null | cs.CL | http://creativecommons.org/licenses/by-sa/4.0/ | Humans can learn a new word and infer its grammatical properties from very
few examples. They have an abstract notion of linguistic properties like
grammatical gender and agreement rules that can be applied to novel syntactic
contexts and words. Drawing inspiration from psycholinguistics, we conduct a
noun learning experiment to assess whether an LSTM and a decoder-only
transformer can achieve human-like abstraction of grammatical gender in French.
Language models were tasked with learning the gender of a novel noun embedding
from a few examples in one grammatical agreement context and predicting
agreement in another, unseen context. We find that both language models
effectively generalise novel noun gender from one to two learning examples and
apply the learnt gender across agreement contexts, albeit with a bias for the
masculine gender category. Importantly, the few-shot updates were only applied
to the embedding layers, demonstrating that models encode sufficient gender
information within the word embedding space. While the generalisation behaviour
of models suggests that they represent grammatical gender as an abstract
category, like humans, further work is needed to explore the details of how
exactly this is implemented. For a comparative perspective with human
behaviour, we conducted an analogous one-shot novel noun gender learning
experiment, which revealed that native French speakers, like language models,
also exhibited a masculine gender bias and are not excellent one-shot learners
either.
| [
{
"created": "Fri, 15 Mar 2024 14:25:59 GMT",
"version": "v1"
}
] | 2024-03-18 | [
[
"Sukumaran",
"Priyanka",
""
],
[
"Houghton",
"Conor",
""
],
[
"Kazanina",
"Nina",
""
]
] | Humans can learn a new word and infer its grammatical properties from very few examples. They have an abstract notion of linguistic properties like grammatical gender and agreement rules that can be applied to novel syntactic contexts and words. Drawing inspiration from psycholinguistics, we conduct a noun learning experiment to assess whether an LSTM and a decoder-only transformer can achieve human-like abstraction of grammatical gender in French. Language models were tasked with learning the gender of a novel noun embedding from a few examples in one grammatical agreement context and predicting agreement in another, unseen context. We find that both language models effectively generalise novel noun gender from one to two learning examples and apply the learnt gender across agreement contexts, albeit with a bias for the masculine gender category. Importantly, the few-shot updates were only applied to the embedding layers, demonstrating that models encode sufficient gender information within the word embedding space. While the generalisation behaviour of models suggests that they represent grammatical gender as an abstract category, like humans, further work is needed to explore the details of how exactly this is implemented. For a comparative perspective with human behaviour, we conducted an analogous one-shot novel noun gender learning experiment, which revealed that native French speakers, like language models, also exhibited a masculine gender bias and are not excellent one-shot learners either. |
2003.10670 | Xuesong Li | Xuesong Li, Jose Guivant, Subhan Khan | Real-time 3D object proposal generation and classification under limited
processing resources | null | Robotics and Autonomous Systems, 130 (2020) 103557 | 10.1016/j.robot.2020.103557 | 2-s2.0-85084829367 | cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The task of detecting 3D objects is important to various robotic
applications. The existing deep learning-based detection techniques have
achieved impressive performance. However, these techniques are limited to run
with a graphics processing unit (GPU) in a real-time environment. To achieve
real-time 3D object detection with limited computational resources for robots,
we propose an efficient detection method consisting of 3D proposal generation
and classification. The proposal generation is mainly based on point
segmentation, while the proposal classification is performed by a lightweight
convolution neural network (CNN) model. To validate our method, KITTI datasets
are utilized. The experimental results demonstrate the capability of proposed
real-time 3D object detection method from the point cloud with a competitive
performance of object recall and classification.
| [
{
"created": "Tue, 24 Mar 2020 05:36:53 GMT",
"version": "v1"
}
] | 2020-08-14 | [
[
"Li",
"Xuesong",
""
],
[
"Guivant",
"Jose",
""
],
[
"Khan",
"Subhan",
""
]
] | The task of detecting 3D objects is important to various robotic applications. The existing deep learning-based detection techniques have achieved impressive performance. However, these techniques are limited to run with a graphics processing unit (GPU) in a real-time environment. To achieve real-time 3D object detection with limited computational resources for robots, we propose an efficient detection method consisting of 3D proposal generation and classification. The proposal generation is mainly based on point segmentation, while the proposal classification is performed by a lightweight convolution neural network (CNN) model. To validate our method, KITTI datasets are utilized. The experimental results demonstrate the capability of proposed real-time 3D object detection method from the point cloud with a competitive performance of object recall and classification. |
2310.15642 | Andr\'e Silva | Nuno Saavedra, Andr\'e Silva, Martin Monperrus | GitBug-Actions: Building Reproducible Bug-Fix Benchmarks with GitHub
Actions | Accepted to ICSE 2024 Demo | Proceedings of ICSE Tool, 2024 | 10.1145/3639478.3640023 | null | cs.SE | http://creativecommons.org/licenses/by-sa/4.0/ | Bug-fix benchmarks are fundamental in advancing various sub-fields of
software engineering such as automatic program repair (APR) and fault
localization (FL). A good benchmark must include recent examples that
accurately reflect technologies and development practices of today. To be
executable in the long term, a benchmark must feature test suites that do not
degrade overtime due to, for example, dependencies that are no longer
available. Existing benchmarks fail in meeting both criteria. For instance,
Defects4J, one of the foremost Java benchmarks, last received an update in
2020. Moreover, full-reproducibility has been neglected by the majority of
existing benchmarks. In this paper, we present GitBug-Actions: a novel tool for
building bug-fix benchmarks with modern and fully-reproducible bug-fixes.
GitBug-Actions relies on the most popular CI platform, GitHub Actions, to
detect bug-fixes and smartly locally execute the CI pipeline in a controlled
and reproducible environment. To the best of our knowledge, we are the first to
rely on GitHub Actions to collect bug-fixes. To demonstrate our toolchain, we
deploy GitBug-Actions to build a proof-of-concept Go bug-fix benchmark
containing executable, fully-reproducible bug-fixes from different
repositories. A video demonstrating GitBug-Actions is available at:
https://youtu.be/aBWwa1sJYBs.
| [
{
"created": "Tue, 24 Oct 2023 09:04:14 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Nov 2023 13:25:08 GMT",
"version": "v2"
},
{
"created": "Sun, 21 Jan 2024 12:01:33 GMT",
"version": "v3"
}
] | 2024-03-15 | [
[
"Saavedra",
"Nuno",
""
],
[
"Silva",
"André",
""
],
[
"Monperrus",
"Martin",
""
]
] | Bug-fix benchmarks are fundamental in advancing various sub-fields of software engineering such as automatic program repair (APR) and fault localization (FL). A good benchmark must include recent examples that accurately reflect technologies and development practices of today. To be executable in the long term, a benchmark must feature test suites that do not degrade overtime due to, for example, dependencies that are no longer available. Existing benchmarks fail in meeting both criteria. For instance, Defects4J, one of the foremost Java benchmarks, last received an update in 2020. Moreover, full-reproducibility has been neglected by the majority of existing benchmarks. In this paper, we present GitBug-Actions: a novel tool for building bug-fix benchmarks with modern and fully-reproducible bug-fixes. GitBug-Actions relies on the most popular CI platform, GitHub Actions, to detect bug-fixes and smartly locally execute the CI pipeline in a controlled and reproducible environment. To the best of our knowledge, we are the first to rely on GitHub Actions to collect bug-fixes. To demonstrate our toolchain, we deploy GitBug-Actions to build a proof-of-concept Go bug-fix benchmark containing executable, fully-reproducible bug-fixes from different repositories. A video demonstrating GitBug-Actions is available at: https://youtu.be/aBWwa1sJYBs. |
1507.02531 | Robert Koenighofer | Roderick Bloem and Ruediger Ehlers and Robert Koenighofer | Cooperative Reactive Synthesis | 18 pages, 3 figures. This is an extended version of [7], featuring an
additional appendix | null | null | null | cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A modern approach to engineering correct-by-construction systems is to
synthesize them automatically from formal specifications. Oftentimes, a system
can only satisfy its guarantees if certain environment assumptions hold, which
motivates their inclusion in the system specification. Experience with modern
synthesis approaches shows that synthesized systems tend to satisfy their
specifications by actively working towards the violation of the assumptions
rather than satisfying assumptions and guarantees together. Such uncooperative
behavior is undesirable because it violates the aim of synthesis: the system
should try to satisfy its guarantees and use the assumptions only when needed.
Also, the assumptions often describe the valid behavior of other components in
a bigger system, which should not be obstructed unnecessarily.
In this paper, we present a hierarchy of cooperation levels between system
and environment. Each level describes how well the system enforces both the
assumptions and guarantees. We show how to synthesize systems that achieve the
highest possible cooperation level for a given specification in Linear Temporal
Logic (LTL). The synthesized systems can also exploit cooperative environment
behavior during operation to reach a higher cooperation level that is not
enforceable by the system initially. The worst-case time complexity of our
synthesis procedure is doubly-exponential, which matches the complexity of
standard LTL synthesis.
This is an extended version of [7] that features an additional appendix.
| [
{
"created": "Thu, 9 Jul 2015 14:39:25 GMT",
"version": "v1"
}
] | 2015-07-10 | [
[
"Bloem",
"Roderick",
""
],
[
"Ehlers",
"Ruediger",
""
],
[
"Koenighofer",
"Robert",
""
]
] | A modern approach to engineering correct-by-construction systems is to synthesize them automatically from formal specifications. Oftentimes, a system can only satisfy its guarantees if certain environment assumptions hold, which motivates their inclusion in the system specification. Experience with modern synthesis approaches shows that synthesized systems tend to satisfy their specifications by actively working towards the violation of the assumptions rather than satisfying assumptions and guarantees together. Such uncooperative behavior is undesirable because it violates the aim of synthesis: the system should try to satisfy its guarantees and use the assumptions only when needed. Also, the assumptions often describe the valid behavior of other components in a bigger system, which should not be obstructed unnecessarily. In this paper, we present a hierarchy of cooperation levels between system and environment. Each level describes how well the system enforces both the assumptions and guarantees. We show how to synthesize systems that achieve the highest possible cooperation level for a given specification in Linear Temporal Logic (LTL). The synthesized systems can also exploit cooperative environment behavior during operation to reach a higher cooperation level that is not enforceable by the system initially. The worst-case time complexity of our synthesis procedure is doubly-exponential, which matches the complexity of standard LTL synthesis. This is an extended version of [7] that features an additional appendix. |
2404.17094 | Yufeng Li | Yufeng Li, Yiwei Ci, Qiusong Yang | TIUP: Effective Processor Verification with Tautology-Induced Universal
Properties | Accepted by ASP-DAC 2024, please note that this is not the final
camera-ready version | null | 10.1109/ASP-DAC58780.2024.10473912. | null | cs.LO cs.AR cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Design verification is a complex and costly task, especially for large and
intricate processor projects. Formal verification techniques provide advantages
by thoroughly examining design behaviors, but they require extensive labor and
expertise in property formulation. Recent research focuses on verifying designs
using the self-consistency universal property, reducing verification difficulty
as it is design-independent. However, the single self-consistency property
faces false positives and scalability issues due to exponential state space
growth. To tackle these challenges, this paper introduces TIUP, a technique
using tautologies as universal properties. We show how TIUP effectively uses
tautologies as abstract specifications, covering processor data and control
paths. TIUP simplifies and streamlines verification for engineers, enabling
efficient formal processor verification.
| [
{
"created": "Fri, 26 Apr 2024 01:05:36 GMT",
"version": "v1"
}
] | 2024-04-29 | [
[
"Li",
"Yufeng",
""
],
[
"Ci",
"Yiwei",
""
],
[
"Yang",
"Qiusong",
""
]
] | Design verification is a complex and costly task, especially for large and intricate processor projects. Formal verification techniques provide advantages by thoroughly examining design behaviors, but they require extensive labor and expertise in property formulation. Recent research focuses on verifying designs using the self-consistency universal property, reducing verification difficulty as it is design-independent. However, the single self-consistency property faces false positives and scalability issues due to exponential state space growth. To tackle these challenges, this paper introduces TIUP, a technique using tautologies as universal properties. We show how TIUP effectively uses tautologies as abstract specifications, covering processor data and control paths. TIUP simplifies and streamlines verification for engineers, enabling efficient formal processor verification. |
2007.04118 | Xiao Yang | Xiao Yang, Dingcheng Yang, Yinpeng Dong, Hang Su, Wenjian Yu, Jun Zhu | RobFR: Benchmarking Adversarial Robustness on Face Recognition | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Face recognition (FR) has recently made substantial progress and achieved
high accuracy on standard benchmarks. However, it has raised security concerns
in enormous FR applications because deep CNNs are unusually vulnerable to
adversarial examples, and it is still lack of a comprehensive robustness
evaluation before a FR model is deployed in safety-critical scenarios. To
facilitate a better understanding of the adversarial vulnerability on FR, we
develop an adversarial robustness evaluation library on FR named
\textbf{RobFR}, which serves as a reference for evaluating the robustness of
downstream tasks. Specifically, RobFR involves 15 popular naturally trained FR
models, 9 models with representative defense mechanisms and 2 commercial FR API
services, to perform the robustness evaluation by using various adversarial
attacks as an important surrogate. The evaluations are conducted under diverse
adversarial settings in terms of dodging and impersonation, $\ell_2$ and
$\ell_\infty$, as well as white-box and black-box attacks. We further propose a
landmark-guided cutout (LGC) attack method to improve the transferability of
adversarial examples for black-box attacks by considering the special
characteristics of FR. Based on large-scale evaluations, the commercial FR API
services fail to exhibit acceptable performance on robustness evaluation, and
we also draw several important conclusions for understanding the adversarial
robustness of FR models and providing insights for the design of robust FR
models. RobFR is open-source and maintains all extendable modules, i.e.,
\emph{Datasets}, \emph{FR Models}, \emph{Attacks\&Defenses}, and
\emph{Evaluations} at
\url{https://github.com/ShawnXYang/Face-Robustness-Benchmark}, which will be
continuously updated to promote future research on robust FR.
| [
{
"created": "Wed, 8 Jul 2020 13:39:22 GMT",
"version": "v1"
},
{
"created": "Wed, 29 Sep 2021 08:01:13 GMT",
"version": "v2"
}
] | 2021-09-30 | [
[
"Yang",
"Xiao",
""
],
[
"Yang",
"Dingcheng",
""
],
[
"Dong",
"Yinpeng",
""
],
[
"Su",
"Hang",
""
],
[
"Yu",
"Wenjian",
""
],
[
"Zhu",
"Jun",
""
]
] | Face recognition (FR) has recently made substantial progress and achieved high accuracy on standard benchmarks. However, it has raised security concerns in enormous FR applications because deep CNNs are unusually vulnerable to adversarial examples, and it is still lack of a comprehensive robustness evaluation before a FR model is deployed in safety-critical scenarios. To facilitate a better understanding of the adversarial vulnerability on FR, we develop an adversarial robustness evaluation library on FR named \textbf{RobFR}, which serves as a reference for evaluating the robustness of downstream tasks. Specifically, RobFR involves 15 popular naturally trained FR models, 9 models with representative defense mechanisms and 2 commercial FR API services, to perform the robustness evaluation by using various adversarial attacks as an important surrogate. The evaluations are conducted under diverse adversarial settings in terms of dodging and impersonation, $\ell_2$ and $\ell_\infty$, as well as white-box and black-box attacks. We further propose a landmark-guided cutout (LGC) attack method to improve the transferability of adversarial examples for black-box attacks by considering the special characteristics of FR. Based on large-scale evaluations, the commercial FR API services fail to exhibit acceptable performance on robustness evaluation, and we also draw several important conclusions for understanding the adversarial robustness of FR models and providing insights for the design of robust FR models. RobFR is open-source and maintains all extendable modules, i.e., \emph{Datasets}, \emph{FR Models}, \emph{Attacks\&Defenses}, and \emph{Evaluations} at \url{https://github.com/ShawnXYang/Face-Robustness-Benchmark}, which will be continuously updated to promote future research on robust FR. |
1703.07387 | Tamal Dey | Tamal K. Dey, Facundo Memoli, Yusu Wang | Topological Analysis of Nerves, Reeb Spaces, Mappers, and Multiscale
Mappers | Full version of the paper appearing in International Symposium on
Computational Geometry, 2017 | null | null | null | cs.CG math.AT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data analysis often concerns not only the space where data come from, but
also various types of maps attached to data. In recent years, several related
structures have been used to study maps on data, including Reeb spaces, mappers
and multiscale mappers. The construction of these structures also relies on the
so-called \emph{nerve} of a cover of the domain.
In this paper, we aim to analyze the topological information encoded in these
structures in order to provide better understanding of these structures and
facilitate their practical usage.
More specifically, we show that the one-dimensional homology of the nerve
complex $N(\mathcal{U})$ of a path-connected cover $\mathcal{U}$ of a domain
$X$ cannot be richer than that of the domain $X$ itself. Intuitively, this
result means that no new $H_1$-homology class can be "created" under a natural
map from $X$ to the nerve complex $N(\mathcal{U})$. Equipping $X$ with a
pseudometric $d$, we further refine this result and characterize the classes of
$H_1(X)$ that may survive in the nerve complex using the notion of \emph{size}
of the covering elements in $\mathcal{U}$. These fundamental results about
nerve complexes then lead to an analysis of the $H_1$-homology of Reeb spaces,
mappers and multiscale mappers.
The analysis of $H_1$-homology groups unfortunately does not extend to higher
dimensions. Nevertheless, by using a map-induced metric, establishing a
Gromov-Hausdorff convergence result between mappers and the domain, and
interleaving relevant modules, we can still analyze the persistent homology
groups of (multiscale) mappers to establish a connection to Reeb spaces.
| [
{
"created": "Tue, 21 Mar 2017 18:50:24 GMT",
"version": "v1"
}
] | 2017-03-23 | [
[
"Dey",
"Tamal K.",
""
],
[
"Memoli",
"Facundo",
""
],
[
"Wang",
"Yusu",
""
]
] | Data analysis often concerns not only the space where data come from, but also various types of maps attached to data. In recent years, several related structures have been used to study maps on data, including Reeb spaces, mappers and multiscale mappers. The construction of these structures also relies on the so-called \emph{nerve} of a cover of the domain. In this paper, we aim to analyze the topological information encoded in these structures in order to provide better understanding of these structures and facilitate their practical usage. More specifically, we show that the one-dimensional homology of the nerve complex $N(\mathcal{U})$ of a path-connected cover $\mathcal{U}$ of a domain $X$ cannot be richer than that of the domain $X$ itself. Intuitively, this result means that no new $H_1$-homology class can be "created" under a natural map from $X$ to the nerve complex $N(\mathcal{U})$. Equipping $X$ with a pseudometric $d$, we further refine this result and characterize the classes of $H_1(X)$ that may survive in the nerve complex using the notion of \emph{size} of the covering elements in $\mathcal{U}$. These fundamental results about nerve complexes then lead to an analysis of the $H_1$-homology of Reeb spaces, mappers and multiscale mappers. The analysis of $H_1$-homology groups unfortunately does not extend to higher dimensions. Nevertheless, by using a map-induced metric, establishing a Gromov-Hausdorff convergence result between mappers and the domain, and interleaving relevant modules, we can still analyze the persistent homology groups of (multiscale) mappers to establish a connection to Reeb spaces. |
1707.02000 | Kamesh Madduri | Humayun Kabir, Kamesh Madduri | Shared-memory Graph Truss Decomposition | 10 pages, conference submission | null | null | null | cs.DC cs.DS cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present PKT, a new shared-memory parallel algorithm and OpenMP
implementation for the truss decomposition of large sparse graphs. A k-truss is
a dense subgraph definition that can be considered a relaxation of a clique.
Truss decomposition refers to a partitioning of all the edges in the graph
based on their k-truss membership. The truss decomposition of a graph has many
applications. We show that our new approach PKT consistently outperforms other
truss decomposition approaches for a collection of large sparse graphs and on a
24-core shared-memory server. PKT is based on a recently proposed algorithm for
k-core decomposition.
| [
{
"created": "Fri, 7 Jul 2017 00:09:09 GMT",
"version": "v1"
}
] | 2017-07-10 | [
[
"Kabir",
"Humayun",
""
],
[
"Madduri",
"Kamesh",
""
]
] | We present PKT, a new shared-memory parallel algorithm and OpenMP implementation for the truss decomposition of large sparse graphs. A k-truss is a dense subgraph definition that can be considered a relaxation of a clique. Truss decomposition refers to a partitioning of all the edges in the graph based on their k-truss membership. The truss decomposition of a graph has many applications. We show that our new approach PKT consistently outperforms other truss decomposition approaches for a collection of large sparse graphs and on a 24-core shared-memory server. PKT is based on a recently proposed algorithm for k-core decomposition. |
1907.07388 | Samarth Manoj Brahmbhatt | Samarth Brahmbhatt, Charles C. Kemp and James Hays | Towards Markerless Grasp Capture | Third Workshop on Computer Vision for AR/VR, CVPR 2019 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans excel at grasping objects and manipulating them. Capturing human
grasps is important for understanding grasping behavior and reconstructing it
realistically in Virtual Reality (VR). However, grasp capture - capturing the
pose of a hand grasping an object, and orienting it w.r.t. the object - is
difficult because of the complexity and diversity of the human hand, and
occlusion. Reflective markers and magnetic trackers traditionally used to
mitigate this difficulty introduce undesirable artifacts in images and can
interfere with natural grasping behavior. We present preliminary work on a
completely marker-less algorithm for grasp capture from a video depicting a
grasp. We show how recent advances in 2D hand pose estimation can be used with
well-established optimization techniques. Uniquely, our algorithm can also
capture hand-object contact in detail and integrate it in the grasp capture
process. This is work in progress, find more details at https://contactdb.
cc.gatech.edu/grasp_capture.html.
| [
{
"created": "Wed, 17 Jul 2019 08:41:21 GMT",
"version": "v1"
}
] | 2019-07-18 | [
[
"Brahmbhatt",
"Samarth",
""
],
[
"Kemp",
"Charles C.",
""
],
[
"Hays",
"James",
""
]
] | Humans excel at grasping objects and manipulating them. Capturing human grasps is important for understanding grasping behavior and reconstructing it realistically in Virtual Reality (VR). However, grasp capture - capturing the pose of a hand grasping an object, and orienting it w.r.t. the object - is difficult because of the complexity and diversity of the human hand, and occlusion. Reflective markers and magnetic trackers traditionally used to mitigate this difficulty introduce undesirable artifacts in images and can interfere with natural grasping behavior. We present preliminary work on a completely marker-less algorithm for grasp capture from a video depicting a grasp. We show how recent advances in 2D hand pose estimation can be used with well-established optimization techniques. Uniquely, our algorithm can also capture hand-object contact in detail and integrate it in the grasp capture process. This is work in progress, find more details at https://contactdb. cc.gatech.edu/grasp_capture.html. |
2201.09536 | Thang X. Vu | Thang X. Vu, Nicola Maturo, Symeon Chatzinotas, Joel Grotz, Tom
Christophory, Bj\"orn Ottersten | Dynamic Bandwidth Allocation and Edge Caching Optimization for Nonlinear
Content Delivery through Flexible Multibeam Satellites | Accepted to IEEE ICC 2022 | null | null | null | cs.IT math.IT | http://creativecommons.org/licenses/by/4.0/ | The next generation multibeam satellites open up a new way to design
satellite communication channels with the full flexibility in bandwidth,
transmit power and beam coverage management. In this paper, we exploit the
flexible multibeam satellite capabilities and the geographical distribution of
users to improve the performance of satellite-assisted edge caching systems.
Our aim is to jointly optimize the bandwidth allocation in multibeam and
caching decisions at the edge nodes to address two important problems: i) cache
feeding time minimization and ii) cache hits maximization. To tackle the
non-convexity of the joint optimization problem, we transform the original
problem into a difference-of-convex (DC) form, which is then solved by the
proposed iterative algorithm whose convergence to at least a local optimum is
theoretically guaranteed. Furthermore, the effectiveness of the proposed design
is evaluated under the realistic beams coverage of the satellite SES-14 and
Movielens data set. Numerical results show that our proposed joint design can
reduce the caching feeding time by 50\% and increase the cache hit ratio (CHR)
by 10\% to 20\% compared to existing solutions. Furthermore, we examine the
impact of multispot beam and multicarrier wide-beam on the joint design and
discuss potential research directions.
| [
{
"created": "Mon, 24 Jan 2022 09:07:41 GMT",
"version": "v1"
}
] | 2022-01-25 | [
[
"Vu",
"Thang X.",
""
],
[
"Maturo",
"Nicola",
""
],
[
"Chatzinotas",
"Symeon",
""
],
[
"Grotz",
"Joel",
""
],
[
"Christophory",
"Tom",
""
],
[
"Ottersten",
"Björn",
""
]
] | The next generation multibeam satellites open up a new way to design satellite communication channels with the full flexibility in bandwidth, transmit power and beam coverage management. In this paper, we exploit the flexible multibeam satellite capabilities and the geographical distribution of users to improve the performance of satellite-assisted edge caching systems. Our aim is to jointly optimize the bandwidth allocation in multibeam and caching decisions at the edge nodes to address two important problems: i) cache feeding time minimization and ii) cache hits maximization. To tackle the non-convexity of the joint optimization problem, we transform the original problem into a difference-of-convex (DC) form, which is then solved by the proposed iterative algorithm whose convergence to at least a local optimum is theoretically guaranteed. Furthermore, the effectiveness of the proposed design is evaluated under the realistic beams coverage of the satellite SES-14 and Movielens data set. Numerical results show that our proposed joint design can reduce the caching feeding time by 50\% and increase the cache hit ratio (CHR) by 10\% to 20\% compared to existing solutions. Furthermore, we examine the impact of multispot beam and multicarrier wide-beam on the joint design and discuss potential research directions. |
2206.13773 | Max Koster | Max Koster | On Relaxation of Dominant Sets | null | null | null | null | cs.DS cs.DM math.CO | http://creativecommons.org/licenses/by/4.0/ | In a graph $G = (V,E)$, a k-ruling set $S$ is one in which all vertices $V$ \
$S$ are at most $k$ distance from $S$. Finding a minimum k-ruling set is
intrinsically linked to the minimum dominating set problem and maximal
independent set problem, which have been extensively studied in graph theory.
This paper presents the first known algorithm for solving all k-ruling set
problems in conjunction with known minimum dominating set algorithms at only
additional polynomial time cost compared to a minimum dominating set. The
algorithm further succeeds for $(\alpha, \alpha - 1)$ ruling sets in which
$\alpha > 1$, for which constraints exist on the proximity of vertices v $\in
S$. This secondary application instead works in conjunction with maximal
independent set algorithms.
| [
{
"created": "Tue, 28 Jun 2022 05:59:51 GMT",
"version": "v1"
}
] | 2022-06-29 | [
[
"Koster",
"Max",
""
]
] | In a graph $G = (V,E)$, a k-ruling set $S$ is one in which all vertices $V$ \ $S$ are at most $k$ distance from $S$. Finding a minimum k-ruling set is intrinsically linked to the minimum dominating set problem and maximal independent set problem, which have been extensively studied in graph theory. This paper presents the first known algorithm for solving all k-ruling set problems in conjunction with known minimum dominating set algorithms at only additional polynomial time cost compared to a minimum dominating set. The algorithm further succeeds for $(\alpha, \alpha - 1)$ ruling sets in which $\alpha > 1$, for which constraints exist on the proximity of vertices v $\in S$. This secondary application instead works in conjunction with maximal independent set algorithms. |
2004.11992 | Bram Wallace | Bram Wallace, Bharath Hariharan | Extending and Analyzing Self-Supervised Learning Across Domains | null | null | null | null | cs.CV cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Self-supervised representation learning has achieved impressive results in
recent years, with experiments primarily coming on ImageNet or other similarly
large internet imagery datasets. There has been little to no work with these
methods on other smaller domains, such as satellite, textural, or biological
imagery. We experiment with several popular methods on an unprecedented variety
of domains. We discover, among other findings, that Rotation is by far the most
semantically meaningful task, with much of the performance of Jigsaw and
Instance Discrimination being attributable to the nature of their induced
distribution rather than semantic understanding. Additionally, there are
several areas, such as fine-grain classification, where all tasks underperform.
We quantitatively and qualitatively diagnose the reasons for these failures and
successes via novel experiments studying pretext generalization, random
labelings, and implicit dimensionality. Code and models are available at
https://github.com/BramSW/Extending_SSRL_Across_Domains/.
| [
{
"created": "Fri, 24 Apr 2020 21:18:02 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Aug 2020 16:13:46 GMT",
"version": "v2"
}
] | 2020-08-18 | [
[
"Wallace",
"Bram",
""
],
[
"Hariharan",
"Bharath",
""
]
] | Self-supervised representation learning has achieved impressive results in recent years, with experiments primarily coming on ImageNet or other similarly large internet imagery datasets. There has been little to no work with these methods on other smaller domains, such as satellite, textural, or biological imagery. We experiment with several popular methods on an unprecedented variety of domains. We discover, among other findings, that Rotation is by far the most semantically meaningful task, with much of the performance of Jigsaw and Instance Discrimination being attributable to the nature of their induced distribution rather than semantic understanding. Additionally, there are several areas, such as fine-grain classification, where all tasks underperform. We quantitatively and qualitatively diagnose the reasons for these failures and successes via novel experiments studying pretext generalization, random labelings, and implicit dimensionality. Code and models are available at https://github.com/BramSW/Extending_SSRL_Across_Domains/. |
2110.00821 | Elisa Claire Alem\'an Carre\'on | Elisa Claire Alem\'an Carre\'on and Hirofumi Nonaka and Toru Hiraoka | Relation Analysis between Hotel Review Rating Scores and Sentiment
Analysis of Reviews by Chinese Tourists Visiting Japan | Translation of the original in Japanese | The Japanese Journal of the Institute of Industrial Applications
Engineers (JJIIAE), 2018, Vol. 6, No. 2. pp. 95-99 | 10.12792/jjiiae.6.2.95 | null | cs.IR | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In current times, the importance of online hotel review sites has become more
and more apparent. Users of these sites reference of reviews strongly
influences their purchase behavior and as such, reviews are important to
companies and researchers alike. The majority of review sites offer both text
reviews and numerical hotel ratings, and both information sources are widely
used by researchers as a representation of a customer's sentiment and opinion.
However, an opinion is a difficult concept to measure, and as such, depending
on the relation these two sources have, it would be apparent whether or not it
is safe to consider them equally in research. In this study we utilize an
entropy-based Support Vector Machine to classify positive and negative
sentiments in hotel reviews from the site Ctrip, then calculating the ratio of
positive and negative sentiment in each review and examine their correlation
with said review's rating score using Spearman and Kendall Correlation
coefficients and Maximal Information Coefficient (MIC).
| [
{
"created": "Sat, 2 Oct 2021 15:07:46 GMT",
"version": "v1"
}
] | 2021-10-05 | [
[
"Carreón",
"Elisa Claire Alemán",
""
],
[
"Nonaka",
"Hirofumi",
""
],
[
"Hiraoka",
"Toru",
""
]
] | In current times, the importance of online hotel review sites has become more and more apparent. Users of these sites reference of reviews strongly influences their purchase behavior and as such, reviews are important to companies and researchers alike. The majority of review sites offer both text reviews and numerical hotel ratings, and both information sources are widely used by researchers as a representation of a customer's sentiment and opinion. However, an opinion is a difficult concept to measure, and as such, depending on the relation these two sources have, it would be apparent whether or not it is safe to consider them equally in research. In this study we utilize an entropy-based Support Vector Machine to classify positive and negative sentiments in hotel reviews from the site Ctrip, then calculating the ratio of positive and negative sentiment in each review and examine their correlation with said review's rating score using Spearman and Kendall Correlation coefficients and Maximal Information Coefficient (MIC). |
2107.07771 | Yajing Sun | Yajing Sun, Yue Hu, Luxi Xing, Yuqiang Xie, Xiangpeng Wei | Know Deeper: Knowledge-Conversation Cyclic Utilization Mechanism for
Open-domain Dialogue Generation | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | End-to-End intelligent neural dialogue systems suffer from the problems of
generating inconsistent and repetitive responses. Existing dialogue models pay
attention to unilaterally incorporating personal knowledge into the dialog
while ignoring the fact that incorporating the personality-related conversation
information into personal knowledge taken as the bilateral information flow
boosts the quality of the subsequent conversation. Besides, it is indispensable
to control personal knowledge utilization over the conversation level. In this
paper, we propose a conversation-adaption multi-view persona aware response
generation model that aims at enhancing conversation consistency and
alleviating the repetition from two folds. First, we consider conversation
consistency from multiple views. From the view of the persona profile, we
design a novel interaction module that not only iteratively incorporates
personalized knowledge into each turn conversation but also captures the
personality-related information from conversation to enhance personalized
knowledge semantic representation. From the view of speaking style, we
introduce the speaking style vector and feed it into the decoder to keep the
speaking style consistency. To avoid conversation repetition, we devise a
coverage mechanism to keep track of the activation of personal knowledge
utilization. Experiments on both automatic and human evaluation verify the
superiority of our model over previous models.
| [
{
"created": "Fri, 16 Jul 2021 08:59:06 GMT",
"version": "v1"
}
] | 2021-07-19 | [
[
"Sun",
"Yajing",
""
],
[
"Hu",
"Yue",
""
],
[
"Xing",
"Luxi",
""
],
[
"Xie",
"Yuqiang",
""
],
[
"Wei",
"Xiangpeng",
""
]
] | End-to-End intelligent neural dialogue systems suffer from the problems of generating inconsistent and repetitive responses. Existing dialogue models pay attention to unilaterally incorporating personal knowledge into the dialog while ignoring the fact that incorporating the personality-related conversation information into personal knowledge taken as the bilateral information flow boosts the quality of the subsequent conversation. Besides, it is indispensable to control personal knowledge utilization over the conversation level. In this paper, we propose a conversation-adaption multi-view persona aware response generation model that aims at enhancing conversation consistency and alleviating the repetition from two folds. First, we consider conversation consistency from multiple views. From the view of the persona profile, we design a novel interaction module that not only iteratively incorporates personalized knowledge into each turn conversation but also captures the personality-related information from conversation to enhance personalized knowledge semantic representation. From the view of speaking style, we introduce the speaking style vector and feed it into the decoder to keep the speaking style consistency. To avoid conversation repetition, we devise a coverage mechanism to keep track of the activation of personal knowledge utilization. Experiments on both automatic and human evaluation verify the superiority of our model over previous models. |
1708.05125 | Feiyun Zhu | Feiyun Zhu | Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark
Performances and Survey | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hyperspectral unmixing (HU) is a very useful and increasingly popular
preprocessing step for a wide range of hyperspectral applications. However, the
HU research has been constrained a lot by three factors: (a) the number of
hyperspectral images (especially the ones with ground truths) are very limited;
(b) the ground truths of most hyperspectral images are not shared on the web,
which may cause lots of unnecessary troubles for researchers to evaluate their
algorithms; (c) the codes of most state-of-the-art methods are not shared,
which may also delay the testing of new methods.
Accordingly, this paper deals with the above issues from the following three
perspectives: (1) as a profound contribution, we provide a general labeling
method for the HU. With it, we labeled up to 15 hyperspectral images, providing
18 versions of ground truths. To the best of our knowledge, this is the first
paper to summarize and share up to 15 hyperspectral images and their 18
versions of ground truths for the HU. Observing that the hyperspectral
classification (HyC) has much more standard datasets (whose ground truths are
generally publicly shared) than the HU, we propose an interesting method to
transform the HyC datasets for the HU research. (2) To further facilitate the
evaluation of HU methods under different conditions, we reviewed and
implemented the algorithm to generate a complex synthetic hyperspectral image.
By tuning the hyper-parameters in the code, we may verify the HU methods from
four perspectives. The code would also be shared on the web. (3) To provide a
standard comparison, we reviewed up to 10 state-of-the-art HU algorithms, then
selected the 5 most benchmark HU algorithms, and compared them on the 15 real
hyperspectral datasets. The experiment results are surely reproducible; the
implemented codes would be shared on the web.
| [
{
"created": "Thu, 17 Aug 2017 03:35:02 GMT",
"version": "v1"
},
{
"created": "Wed, 11 Oct 2017 16:22:06 GMT",
"version": "v2"
}
] | 2017-10-12 | [
[
"Zhu",
"Feiyun",
""
]
] | Hyperspectral unmixing (HU) is a very useful and increasingly popular preprocessing step for a wide range of hyperspectral applications. However, the HU research has been constrained a lot by three factors: (a) the number of hyperspectral images (especially the ones with ground truths) are very limited; (b) the ground truths of most hyperspectral images are not shared on the web, which may cause lots of unnecessary troubles for researchers to evaluate their algorithms; (c) the codes of most state-of-the-art methods are not shared, which may also delay the testing of new methods. Accordingly, this paper deals with the above issues from the following three perspectives: (1) as a profound contribution, we provide a general labeling method for the HU. With it, we labeled up to 15 hyperspectral images, providing 18 versions of ground truths. To the best of our knowledge, this is the first paper to summarize and share up to 15 hyperspectral images and their 18 versions of ground truths for the HU. Observing that the hyperspectral classification (HyC) has much more standard datasets (whose ground truths are generally publicly shared) than the HU, we propose an interesting method to transform the HyC datasets for the HU research. (2) To further facilitate the evaluation of HU methods under different conditions, we reviewed and implemented the algorithm to generate a complex synthetic hyperspectral image. By tuning the hyper-parameters in the code, we may verify the HU methods from four perspectives. The code would also be shared on the web. (3) To provide a standard comparison, we reviewed up to 10 state-of-the-art HU algorithms, then selected the 5 most benchmark HU algorithms, and compared them on the 15 real hyperspectral datasets. The experiment results are surely reproducible; the implemented codes would be shared on the web. |
2308.14404 | Forouzan Farzinnejad | Forouzan Farzinnejad, Javad Rasti, Navid Khezrian, Jens Grubert | The Effect of an Exergame on the Shadow Play Skill Based on Muscle
Memory for Young Female Participants: The Case of Forehand Drive in Table
Tennis | 9 pages, 6 figures, The 22nd IEEE International Symposium on Mixed
and Augmented Reality (ISMAR) | null | null | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning and practicing table tennis with traditional methods is a long,
tedious process and may even lead to the internalization of incorrect
techniques if not supervised by a coach. To overcome these issues, the
presented study proposes an exergame with the aim of enhancing young female
novice players' performance by boosting muscle memory, making practice more
interesting, and decreasing the probability of faulty training. Specifically,
we propose an exergame based on skeleton tracking and a virtual avatar to
support correct shadow practice to learn forehand drive technique without the
presence of a coach. We recruited 44 schoolgirls aged between 8 and 12 years
without a background in playing table tennis and divided them into control and
experimental groups. We examined their stroke skills (via the Mott-Lockhart
test) and the error coefficient of their forehand drives (using a ball machine)
in the pretest, post-test, and follow-up tests (10 days after the post-test).
Our results showed that the experimental group had progress in the short and
long term, while the control group had an improvement only in the short term.
Further, the scale of improvement in the experimental group was significantly
higher than in the control group. Given that the early stages of learning,
particularly in girls children, are important in the internalization of
individual skills in would-be athletes, this method could support promoting
correct training for young females.
| [
{
"created": "Mon, 28 Aug 2023 08:39:26 GMT",
"version": "v1"
}
] | 2023-08-29 | [
[
"Farzinnejad",
"Forouzan",
""
],
[
"Rasti",
"Javad",
""
],
[
"Khezrian",
"Navid",
""
],
[
"Grubert",
"Jens",
""
]
] | Learning and practicing table tennis with traditional methods is a long, tedious process and may even lead to the internalization of incorrect techniques if not supervised by a coach. To overcome these issues, the presented study proposes an exergame with the aim of enhancing young female novice players' performance by boosting muscle memory, making practice more interesting, and decreasing the probability of faulty training. Specifically, we propose an exergame based on skeleton tracking and a virtual avatar to support correct shadow practice to learn forehand drive technique without the presence of a coach. We recruited 44 schoolgirls aged between 8 and 12 years without a background in playing table tennis and divided them into control and experimental groups. We examined their stroke skills (via the Mott-Lockhart test) and the error coefficient of their forehand drives (using a ball machine) in the pretest, post-test, and follow-up tests (10 days after the post-test). Our results showed that the experimental group had progress in the short and long term, while the control group had an improvement only in the short term. Further, the scale of improvement in the experimental group was significantly higher than in the control group. Given that the early stages of learning, particularly in girls children, are important in the internalization of individual skills in would-be athletes, this method could support promoting correct training for young females. |
2204.03503 | Nicola Strisciuglio | Stefan Haller, Adina Aldea, Christin Seifert, Nicola Strisciuglio | Survey on Automated Short Answer Grading with Deep Learning: from Word
Embeddings to Transformers | Under review | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Automated short answer grading (ASAG) has gained attention in education as a
means to scale educational tasks to the growing number of students. Recent
progress in Natural Language Processing and Machine Learning has largely
influenced the field of ASAG, of which we survey the recent research
advancements. We complement previous surveys by providing a comprehensive
analysis of recently published methods that deploy deep learning approaches. In
particular, we focus our analysis on the transition from hand engineered
features to representation learning approaches, which learn representative
features for the task at hand automatically from large corpora of data. We
structure our analysis of deep learning methods along three categories: word
embeddings, sequential models, and attention-based methods. Deep learning
impacted ASAG differently than other fields of NLP, as we noticed that the
learned representations alone do not contribute to achieve the best results,
but they rather show to work in a complementary way with hand-engineered
features. The best performance are indeed achieved by methods that combine the
carefully hand-engineered features with the power of the semantic descriptions
provided by the latest models, like transformers architectures. We identify
challenges and provide an outlook on research direction that can be addressed
in the future
| [
{
"created": "Fri, 11 Mar 2022 13:47:08 GMT",
"version": "v1"
}
] | 2022-04-08 | [
[
"Haller",
"Stefan",
""
],
[
"Aldea",
"Adina",
""
],
[
"Seifert",
"Christin",
""
],
[
"Strisciuglio",
"Nicola",
""
]
] | Automated short answer grading (ASAG) has gained attention in education as a means to scale educational tasks to the growing number of students. Recent progress in Natural Language Processing and Machine Learning has largely influenced the field of ASAG, of which we survey the recent research advancements. We complement previous surveys by providing a comprehensive analysis of recently published methods that deploy deep learning approaches. In particular, we focus our analysis on the transition from hand engineered features to representation learning approaches, which learn representative features for the task at hand automatically from large corpora of data. We structure our analysis of deep learning methods along three categories: word embeddings, sequential models, and attention-based methods. Deep learning impacted ASAG differently than other fields of NLP, as we noticed that the learned representations alone do not contribute to achieve the best results, but they rather show to work in a complementary way with hand-engineered features. The best performance are indeed achieved by methods that combine the carefully hand-engineered features with the power of the semantic descriptions provided by the latest models, like transformers architectures. We identify challenges and provide an outlook on research direction that can be addressed in the future |
2202.05433 | Jieyu Zhang | Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, Alexander Ratner | A Survey on Programmatic Weak Supervision | 8 pages | null | null | null | cs.LG cs.AI stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Labeling training data has become one of the major roadblocks to using
machine learning. Among various weak supervision paradigms, programmatic weak
supervision (PWS) has achieved remarkable success in easing the manual labeling
bottleneck by programmatically synthesizing training labels from multiple
potentially noisy supervision sources. This paper presents a comprehensive
survey of recent advances in PWS. In particular, we give a brief introduction
of the PWS learning paradigm, and review representative approaches for each
component within PWS's learning workflow. In addition, we discuss complementary
learning paradigms for tackling limited labeled data scenarios and how these
related approaches can be used in conjunction with PWS. Finally, we identify
several critical challenges that remain under-explored in the area to hopefully
inspire future research directions in the field.
| [
{
"created": "Fri, 11 Feb 2022 04:05:38 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Feb 2022 05:45:58 GMT",
"version": "v2"
}
] | 2022-02-15 | [
[
"Zhang",
"Jieyu",
""
],
[
"Hsieh",
"Cheng-Yu",
""
],
[
"Yu",
"Yue",
""
],
[
"Zhang",
"Chao",
""
],
[
"Ratner",
"Alexander",
""
]
] | Labeling training data has become one of the major roadblocks to using machine learning. Among various weak supervision paradigms, programmatic weak supervision (PWS) has achieved remarkable success in easing the manual labeling bottleneck by programmatically synthesizing training labels from multiple potentially noisy supervision sources. This paper presents a comprehensive survey of recent advances in PWS. In particular, we give a brief introduction of the PWS learning paradigm, and review representative approaches for each component within PWS's learning workflow. In addition, we discuss complementary learning paradigms for tackling limited labeled data scenarios and how these related approaches can be used in conjunction with PWS. Finally, we identify several critical challenges that remain under-explored in the area to hopefully inspire future research directions in the field. |
1607.03408 | Gabriel Martins Dias | Gabriel Martins Dias | Performance Optimization of WSNs using External Information | Published in: IEEE 14th International Symposium and Workshops on a
World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2013 (copyright
has been transferred to IEEE) | null | 10.1109/WoWMoM.2013.6583430 | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of this work is to describe a self-management system that correlates
data sensed by different Wireless Sensor Networks (WSNs) and adjusts the number
of active nodes in each network to provide an appropriate amount of
measurements. The architecture considers the factors that make the external
data relevant to the local network, such as the distance between covered areas,
the relation between the types of sensed data and the reliability of the
measurements. As a result, the operation of each network will be tuned to
trade-off the accuracy of the measurements and the power consumption.
| [
{
"created": "Tue, 12 Jul 2016 15:35:42 GMT",
"version": "v1"
}
] | 2016-07-13 | [
[
"Dias",
"Gabriel Martins",
""
]
] | The goal of this work is to describe a self-management system that correlates data sensed by different Wireless Sensor Networks (WSNs) and adjusts the number of active nodes in each network to provide an appropriate amount of measurements. The architecture considers the factors that make the external data relevant to the local network, such as the distance between covered areas, the relation between the types of sensed data and the reliability of the measurements. As a result, the operation of each network will be tuned to trade-off the accuracy of the measurements and the power consumption. |
1907.04592 | Alexey Potapov | Alexey Potapov, Anatoly Belikov, Vitaly Bogdanov, Alexander Scherbatiy | Differentiable Probabilistic Logic Networks | null | null | null | null | cs.AI cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic logic reasoning is a central component of such cognitive
architectures as OpenCog. However, as an integrative architecture, OpenCog
facilitates cognitive synergy via hybridization of different inference methods.
In this paper, we introduce a differentiable version of Probabilistic Logic
networks, which rules operate over tensor truth values in such a way that a
chain of reasoning steps constructs a computation graph over tensors that
accepts truth values of premises from the knowledge base as input and produces
truth values of conclusions as output. This allows for both learning truth
values of premises and formulas for rules (specified in a form with trainable
weights) by backpropagation combining subsymbolic optimization and symbolic
reasoning.
| [
{
"created": "Wed, 10 Jul 2019 09:44:10 GMT",
"version": "v1"
}
] | 2019-07-11 | [
[
"Potapov",
"Alexey",
""
],
[
"Belikov",
"Anatoly",
""
],
[
"Bogdanov",
"Vitaly",
""
],
[
"Scherbatiy",
"Alexander",
""
]
] | Probabilistic logic reasoning is a central component of such cognitive architectures as OpenCog. However, as an integrative architecture, OpenCog facilitates cognitive synergy via hybridization of different inference methods. In this paper, we introduce a differentiable version of Probabilistic Logic networks, which rules operate over tensor truth values in such a way that a chain of reasoning steps constructs a computation graph over tensors that accepts truth values of premises from the knowledge base as input and produces truth values of conclusions as output. This allows for both learning truth values of premises and formulas for rules (specified in a form with trainable weights) by backpropagation combining subsymbolic optimization and symbolic reasoning. |
2104.04996 | Ran Tamir (Averbuch) | Ran Tamir (Averbuch), Ariel Livshits, and Yonatan Shadmi | Simple Majority Consensus in Networks with Unreliable Communication | null | null | 10.3390/e24030333 | null | cs.IT cs.DC math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we analyze the performance of a simple majority-rule protocol
solving a fundamental coordination problem in distributed systems -
\emph{binary majority consensus}, in the presence of probabilistic message
loss. Using probabilistic analysis for a large scale, fully-connected, network
of $2n$ agents, we prove that the Simple Majority Protocol (SMP) reaches
consensus in only three communication rounds with probability approaching $1$
as $n$ grows to infinity. Moreover, if the difference between the numbers of
agents that hold different opinions grows at a rate of $\sqrt{n}$, then the SMP
with only two communication rounds attains consensus on the majority opinion of
the network, and if this difference grows faster than $\sqrt{n}$, then the SMP
reaches consensus on the majority opinion of the network in a single round,
with probability converging to $1$ exponentially fast as $n \rightarrow
\infty$. We also provide some converse results, showing that these requirements
are not only sufficient, but also necessary.
| [
{
"created": "Sun, 11 Apr 2021 11:36:21 GMT",
"version": "v1"
}
] | 2022-03-09 | [
[
"Tamir",
"Ran",
"",
"Averbuch"
],
[
"Livshits",
"Ariel",
""
],
[
"Shadmi",
"Yonatan",
""
]
] | In this work, we analyze the performance of a simple majority-rule protocol solving a fundamental coordination problem in distributed systems - \emph{binary majority consensus}, in the presence of probabilistic message loss. Using probabilistic analysis for a large scale, fully-connected, network of $2n$ agents, we prove that the Simple Majority Protocol (SMP) reaches consensus in only three communication rounds with probability approaching $1$ as $n$ grows to infinity. Moreover, if the difference between the numbers of agents that hold different opinions grows at a rate of $\sqrt{n}$, then the SMP with only two communication rounds attains consensus on the majority opinion of the network, and if this difference grows faster than $\sqrt{n}$, then the SMP reaches consensus on the majority opinion of the network in a single round, with probability converging to $1$ exponentially fast as $n \rightarrow \infty$. We also provide some converse results, showing that these requirements are not only sufficient, but also necessary. |
2011.00616 | Alexander Wolpert | Evgeny Dantsin and Alexander Wolpert | Similarity Between Points in Metric Measure Spaces | 10 pages, 2 figures. In: Proceedings of the 13th International
Conference on Similarity Search and Applications, SISAP 2020. Vol. 12440.
Lecture Notes in Computer Science. Springer, 2020, pp. 177-184 | null | null | null | cs.DM | http://creativecommons.org/licenses/by/4.0/ | This paper is about similarity between objects that can be represented as
points in metric measure spaces. A metric measure space is a metric space that
is also equipped with a measure. For example, a network with distances between
its nodes and weights assigned to its nodes is a metric measure space. Given
points x and y in different metric measure spaces or in the same space, how
similar are they? A well known approach is to consider x and y similar if their
neighborhoods are similar. For metric measure spaces, similarity between
neighborhoods is well captured by the Gromov-Hausdorff-Prokhorov distance, but
it is NP-hard to compute this distance even in quite simple cases. We propose a
tractable alternative: the radial distribution distance between the
neighborhoods of x and y. The similarity measure based on the radial
distribution distance is coarser than the similarity based on the
Gromov-Hausdorff-Prokhorov distance but much easier to compute.
| [
{
"created": "Sun, 1 Nov 2020 19:52:54 GMT",
"version": "v1"
}
] | 2020-11-03 | [
[
"Dantsin",
"Evgeny",
""
],
[
"Wolpert",
"Alexander",
""
]
] | This paper is about similarity between objects that can be represented as points in metric measure spaces. A metric measure space is a metric space that is also equipped with a measure. For example, a network with distances between its nodes and weights assigned to its nodes is a metric measure space. Given points x and y in different metric measure spaces or in the same space, how similar are they? A well known approach is to consider x and y similar if their neighborhoods are similar. For metric measure spaces, similarity between neighborhoods is well captured by the Gromov-Hausdorff-Prokhorov distance, but it is NP-hard to compute this distance even in quite simple cases. We propose a tractable alternative: the radial distribution distance between the neighborhoods of x and y. The similarity measure based on the radial distribution distance is coarser than the similarity based on the Gromov-Hausdorff-Prokhorov distance but much easier to compute. |
2007.15652 | Peyman Moghadam | Thomas Lowe, Peyman Moghadam, Everard Edwards, Jason Williams | Canopy Density Estimation in Perennial Horticulture Crops Using 3D
Spinning Lidar SLAM | Accepted to Journal of Field Robotics. More information at
https://github.com/csiro-robotics/agscan3d | null | 10.1002/rob.22006 | null | cs.RO eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel, canopy density estimation solution using a 3D ray cloud
representation for perennial horticultural crops at the field scale. To attain
high spatial and temporal fidelity in field conditions, we propose the
application of continuous-time 3D SLAM (Simultaneous Localisation and Mapping)
to a spinning lidar payload (AgScan3D) mounted on a moving farm vehicle. The
AgScan3D data is processed through a Continuous-Time SLAM algorithm into a
globally registered 3D ray cloud. The global ray cloud is a canonical data
format (a digital twin) from which we can compare vineyard snapshots over
multiple times within a season and across seasons. Then, the vineyard rows are
automatically extracted from the ray cloud and a novel density calculation is
performed to estimate the maximum likelihood canopy densities of the vineyard.
This combination of digital twinning, together with the accurate extraction of
canopy structure information, allows entire vineyards to be analysed and
compared, across the growing season and from year to year. The proposed method
is evaluated both in simulation and field experiments. Field experiments were
performed at four sites, which varied in vineyard structure and vine
management, over two growing seasons and 64 data collection campaigns,
resulting in a total traversal of 160 kilometres, 42.4 scanned hectares of
vines with a combined total of approximately 93,000 scanned vines. Our
experiments show canopy density repeatability of 3.8% (Relative RMSE) per
vineyard panel, for acquisition speeds of 5-6 km/h, and under half the standard
deviation in estimated densities when compared to an industry standard
gap-fraction based solution. The code and field datasets are available at
https://github.com/csiro-robotics/agscan3d.
| [
{
"created": "Thu, 30 Jul 2020 05:51:38 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Dec 2020 00:56:20 GMT",
"version": "v2"
}
] | 2020-12-16 | [
[
"Lowe",
"Thomas",
""
],
[
"Moghadam",
"Peyman",
""
],
[
"Edwards",
"Everard",
""
],
[
"Williams",
"Jason",
""
]
] | We propose a novel, canopy density estimation solution using a 3D ray cloud representation for perennial horticultural crops at the field scale. To attain high spatial and temporal fidelity in field conditions, we propose the application of continuous-time 3D SLAM (Simultaneous Localisation and Mapping) to a spinning lidar payload (AgScan3D) mounted on a moving farm vehicle. The AgScan3D data is processed through a Continuous-Time SLAM algorithm into a globally registered 3D ray cloud. The global ray cloud is a canonical data format (a digital twin) from which we can compare vineyard snapshots over multiple times within a season and across seasons. Then, the vineyard rows are automatically extracted from the ray cloud and a novel density calculation is performed to estimate the maximum likelihood canopy densities of the vineyard. This combination of digital twinning, together with the accurate extraction of canopy structure information, allows entire vineyards to be analysed and compared, across the growing season and from year to year. The proposed method is evaluated both in simulation and field experiments. Field experiments were performed at four sites, which varied in vineyard structure and vine management, over two growing seasons and 64 data collection campaigns, resulting in a total traversal of 160 kilometres, 42.4 scanned hectares of vines with a combined total of approximately 93,000 scanned vines. Our experiments show canopy density repeatability of 3.8% (Relative RMSE) per vineyard panel, for acquisition speeds of 5-6 km/h, and under half the standard deviation in estimated densities when compared to an industry standard gap-fraction based solution. The code and field datasets are available at https://github.com/csiro-robotics/agscan3d. |
1803.07724 | Jasdeep Singh | Jasdeep Singh, Vincent Ying, Alex Nutkiewicz | Attention on Attention: Architectures for Visual Question Answering
(VQA) | Visual Question Answering Project | null | null | null | cs.CL cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual Question Answering (VQA) is an increasingly popular topic in deep
learning research, requiring coordination of natural language processing and
computer vision modules into a single architecture. We build upon the model
which placed first in the VQA Challenge by developing thirteen new attention
mechanisms and introducing a simplified classifier. We performed 300 GPU hours
of extensive hyperparameter and architecture searches and were able to achieve
an evaluation score of 64.78%, outperforming the existing state-of-the-art
single model's validation score of 63.15%.
| [
{
"created": "Wed, 21 Mar 2018 03:05:58 GMT",
"version": "v1"
}
] | 2018-03-22 | [
[
"Singh",
"Jasdeep",
""
],
[
"Ying",
"Vincent",
""
],
[
"Nutkiewicz",
"Alex",
""
]
] | Visual Question Answering (VQA) is an increasingly popular topic in deep learning research, requiring coordination of natural language processing and computer vision modules into a single architecture. We build upon the model which placed first in the VQA Challenge by developing thirteen new attention mechanisms and introducing a simplified classifier. We performed 300 GPU hours of extensive hyperparameter and architecture searches and were able to achieve an evaluation score of 64.78%, outperforming the existing state-of-the-art single model's validation score of 63.15%. |
2408.04232 | Wei Zhang | Wei Zhang, Peng Tang | Enhanced Traffic Flow Prediction with Multi-Segment Fusion Tensor Graph
Convolutional Networks | null | null | null | null | cs.LG cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate traffic Flow Prediction can assist in traffic management, route
planning, and congestion mitigation, which holds significant importance in
enhancing the efficiency and reliability of intelligent transportation systems
(ITS). However, existing traffic flow prediction models suffer from limitations
in capturing the complex spatial-temporal dependencies within traffic networks.
In order to address this issue, this study proposes a multi-segment fusion
tensor graph convolutional network (MS-FTGCN) for traffic flow prediction with
the following three-fold ideas: a) building a unified spatial-temporal graph
convolutional framework based on Tensor M-product, which capture the
spatial-temporal patterns simultaneously; b) incorporating hourly, daily, and
weekly components to model multi temporal properties of traffic flows,
respectively; c) fusing the outputs of the three components by attention
mechanism to obtain the final traffic flow prediction results. The results of
experiments conducted on two traffic flow datasets demonstrate that the
proposed MS-FTGCN outperforms the state-of-the-art models.
| [
{
"created": "Thu, 8 Aug 2024 05:37:17 GMT",
"version": "v1"
}
] | 2024-08-09 | [
[
"Zhang",
"Wei",
""
],
[
"Tang",
"Peng",
""
]
] | Accurate traffic Flow Prediction can assist in traffic management, route planning, and congestion mitigation, which holds significant importance in enhancing the efficiency and reliability of intelligent transportation systems (ITS). However, existing traffic flow prediction models suffer from limitations in capturing the complex spatial-temporal dependencies within traffic networks. In order to address this issue, this study proposes a multi-segment fusion tensor graph convolutional network (MS-FTGCN) for traffic flow prediction with the following three-fold ideas: a) building a unified spatial-temporal graph convolutional framework based on Tensor M-product, which capture the spatial-temporal patterns simultaneously; b) incorporating hourly, daily, and weekly components to model multi temporal properties of traffic flows, respectively; c) fusing the outputs of the three components by attention mechanism to obtain the final traffic flow prediction results. The results of experiments conducted on two traffic flow datasets demonstrate that the proposed MS-FTGCN outperforms the state-of-the-art models. |
2211.03128 | Giuseppe Vietri | Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth,
Giuseppe Vietri, Zhiwei Steven Wu | Confidence-Ranked Reconstruction of Census Microdata from Published
Statistics | null | null | 10.1073/pnas.2218605120 | null | cs.CY cs.CR cs.LG | http://creativecommons.org/licenses/by/4.0/ | A reconstruction attack on a private dataset $D$ takes as input some publicly
accessible information about the dataset and produces a list of candidate
elements of $D$. We introduce a new class of data reconstruction attacks based
on randomized methods for non-convex optimization. We empirically demonstrate
that our attacks can not only reconstruct full rows of $D$ from aggregate query
statistics $Q(D)\in \mathbb{R}^m$, but can do so in a way that reliably ranks
reconstructed rows by their odds of appearing in the private data, providing a
signature that could be used for prioritizing reconstructed rows for further
actions such as identify theft or hate crime. We also design a sequence of
baselines for evaluating reconstruction attacks. Our attacks significantly
outperform those that are based only on access to a public distribution or
population from which the private dataset $D$ was sampled, demonstrating that
they are exploiting information in the aggregate statistics $Q(D)$, and not
simply the overall structure of the distribution. In other words, the queries
$Q(D)$ are permitting reconstruction of elements of this dataset, not the
distribution from which $D$ was drawn. These findings are established both on
2010 U.S. decennial Census data and queries and Census-derived American
Community Survey datasets. Taken together, our methods and experiments
illustrate the risks in releasing numerically precise aggregate statistics of a
large dataset, and provide further motivation for the careful application of
provably private techniques such as differential privacy.
| [
{
"created": "Sun, 6 Nov 2022 14:08:43 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Feb 2023 17:32:02 GMT",
"version": "v2"
}
] | 2023-03-29 | [
[
"Dick",
"Travis",
""
],
[
"Dwork",
"Cynthia",
""
],
[
"Kearns",
"Michael",
""
],
[
"Liu",
"Terrance",
""
],
[
"Roth",
"Aaron",
""
],
[
"Vietri",
"Giuseppe",
""
],
[
"Wu",
"Zhiwei Steven",
""
]
] | A reconstruction attack on a private dataset $D$ takes as input some publicly accessible information about the dataset and produces a list of candidate elements of $D$. We introduce a new class of data reconstruction attacks based on randomized methods for non-convex optimization. We empirically demonstrate that our attacks can not only reconstruct full rows of $D$ from aggregate query statistics $Q(D)\in \mathbb{R}^m$, but can do so in a way that reliably ranks reconstructed rows by their odds of appearing in the private data, providing a signature that could be used for prioritizing reconstructed rows for further actions such as identify theft or hate crime. We also design a sequence of baselines for evaluating reconstruction attacks. Our attacks significantly outperform those that are based only on access to a public distribution or population from which the private dataset $D$ was sampled, demonstrating that they are exploiting information in the aggregate statistics $Q(D)$, and not simply the overall structure of the distribution. In other words, the queries $Q(D)$ are permitting reconstruction of elements of this dataset, not the distribution from which $D$ was drawn. These findings are established both on 2010 U.S. decennial Census data and queries and Census-derived American Community Survey datasets. Taken together, our methods and experiments illustrate the risks in releasing numerically precise aggregate statistics of a large dataset, and provide further motivation for the careful application of provably private techniques such as differential privacy. |
2302.14543 | Himanshu . | Himanshu, Jinraj V Pushpangathan and Harikumar Kandath | RRT and Velocity Obstacles-based motion planning for Unmanned Aircraft
Systems Traffic Management (UTM) | Currently under review in The 2023 International Conference On
Unmanned Aircraft Systems | null | null | null | cs.RO | http://creativecommons.org/licenses/by/4.0/ | In this paper, an algorithm for Unmanned Aircraft Systems Traffic Management
(UTM) for a finite number of unmanned aerial vehicles (UAVs) is proposed. This
algorithm is developed by combining the Rapidly-Exploring Random Trees (RRT)
and Velocity Obstacle (VO) algorithms and is referred to as the RRT-VO UTM
algorithm. Here, the RRT algorithm works offline to generate obstacle-free
waypoints in a given environment with known static obstacles. The VO algorithm,
on the other hand, operates online to avoid collisions with other UAVS and
known static obstacles. The boundary of the static obstacles are approximated
by small circles to facilitate the formulation of VO algorithm. The proposed
algorithm's performance is evaluated using numerical simulation and then
compared to the well-known artificial potential field (APF) algorithm for
collision avoidance. The advantages of the proposed method are clearly shown in
terms of lower path length and collision avoidance capabilities for a
challenging scenario.
| [
{
"created": "Tue, 28 Feb 2023 13:08:11 GMT",
"version": "v1"
}
] | 2023-03-01 | [
[
"Himanshu",
"",
""
],
[
"Pushpangathan",
"Jinraj V",
""
],
[
"Kandath",
"Harikumar",
""
]
] | In this paper, an algorithm for Unmanned Aircraft Systems Traffic Management (UTM) for a finite number of unmanned aerial vehicles (UAVs) is proposed. This algorithm is developed by combining the Rapidly-Exploring Random Trees (RRT) and Velocity Obstacle (VO) algorithms and is referred to as the RRT-VO UTM algorithm. Here, the RRT algorithm works offline to generate obstacle-free waypoints in a given environment with known static obstacles. The VO algorithm, on the other hand, operates online to avoid collisions with other UAVS and known static obstacles. The boundary of the static obstacles are approximated by small circles to facilitate the formulation of VO algorithm. The proposed algorithm's performance is evaluated using numerical simulation and then compared to the well-known artificial potential field (APF) algorithm for collision avoidance. The advantages of the proposed method are clearly shown in terms of lower path length and collision avoidance capabilities for a challenging scenario. |
1710.10800 | Bharath Ramesh | Bharath Ramesh, Hong Yang, Garrick Orchard, Ngoc Anh Le Thi, Shihao
Zhang and Cheng Xiang | DART: Distribution Aware Retinal Transform for Event-based Cameras | 12 pages, revision submitted to TPAMI in Nov 2018 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a generic visual descriptor, termed as distribution aware
retinal transform (DART), that encodes the structural context using log-polar
grids for event cameras. The DART descriptor is applied to four different
problems, namely object classification, tracking, detection and feature
matching: (1) The DART features are directly employed as local descriptors in a
bag-of-features classification framework and testing is carried out on four
standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS,
NCaltech-101). (2) Extending the classification system, tracking is
demonstrated using two key novelties: (i) For overcoming the low-sample problem
for the one-shot learning of a binary classifier, statistical bootstrapping is
leveraged with online learning; (ii) To achieve tracker robustness, the scale
and rotation equivariance property of the DART descriptors is exploited for the
one-shot learning. (3) To solve the long-term object tracking problem, an
object detector is designed using the principle of cluster majority voting. The
detection scheme is then combined with the tracker to result in a high
intersection-over-union score with augmented ground truth annotations on the
publicly available event camera dataset. (4) Finally, the event context encoded
by DART greatly simplifies the feature correspondence problem, especially for
spatio-temporal slices far apart in time, which has not been explicitly tackled
in the event-based vision domain.
| [
{
"created": "Mon, 30 Oct 2017 08:08:57 GMT",
"version": "v1"
},
{
"created": "Tue, 13 Nov 2018 02:37:41 GMT",
"version": "v2"
},
{
"created": "Wed, 14 Nov 2018 07:40:55 GMT",
"version": "v3"
}
] | 2018-11-15 | [
[
"Ramesh",
"Bharath",
""
],
[
"Yang",
"Hong",
""
],
[
"Orchard",
"Garrick",
""
],
[
"Thi",
"Ngoc Anh Le",
""
],
[
"Zhang",
"Shihao",
""
],
[
"Xiang",
"Cheng",
""
]
] | We introduce a generic visual descriptor, termed as distribution aware retinal transform (DART), that encodes the structural context using log-polar grids for event cameras. The DART descriptor is applied to four different problems, namely object classification, tracking, detection and feature matching: (1) The DART features are directly employed as local descriptors in a bag-of-features classification framework and testing is carried out on four standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS, NCaltech-101). (2) Extending the classification system, tracking is demonstrated using two key novelties: (i) For overcoming the low-sample problem for the one-shot learning of a binary classifier, statistical bootstrapping is leveraged with online learning; (ii) To achieve tracker robustness, the scale and rotation equivariance property of the DART descriptors is exploited for the one-shot learning. (3) To solve the long-term object tracking problem, an object detector is designed using the principle of cluster majority voting. The detection scheme is then combined with the tracker to result in a high intersection-over-union score with augmented ground truth annotations on the publicly available event camera dataset. (4) Finally, the event context encoded by DART greatly simplifies the feature correspondence problem, especially for spatio-temporal slices far apart in time, which has not been explicitly tackled in the event-based vision domain. |
1008.5189 | Anastasia Paparrizou Ms | Thanasis Balafoutis, Anastasia Paparrizou, Kostas Stergiou and Toby
Walsh | Improving the Performance of maxRPC | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Max Restricted Path Consistency (maxRPC) is a local consistency for binary
constraints that can achieve considerably stronger pruning than arc
consistency. However, existing maxRRC algorithms suffer from overheads and
redundancies as they can repeatedly perform many constraint checks without
triggering any value deletions. In this paper we propose techniques that can
boost the performance of maxRPC algorithms. These include the combined use of
two data structures to avoid many redundant constraint checks, and heuristics
for the efficient ordering and execution of certain operations. Based on these,
we propose two closely related algorithms. The first one which is a maxRPC
algorithm with optimal O(end^3) time complexity, displays good performance when
used stand-alone, but is expensive to apply during search. The second one
approximates maxRPC and has O(en^2d^4) time complexity, but a restricted
version with O(end^4) complexity can be very efficient when used during search.
Both algorithms have O(ed) space complexity. Experimental results demonstrate
that the resulting methods constantly outperform previous algorithms for
maxRPC, often by large margins, and constitute a more than viable alternative
to arc consistency on many problems.
| [
{
"created": "Mon, 30 Aug 2010 23:50:33 GMT",
"version": "v1"
}
] | 2010-09-01 | [
[
"Balafoutis",
"Thanasis",
""
],
[
"Paparrizou",
"Anastasia",
""
],
[
"Stergiou",
"Kostas",
""
],
[
"Walsh",
"Toby",
""
]
] | Max Restricted Path Consistency (maxRPC) is a local consistency for binary constraints that can achieve considerably stronger pruning than arc consistency. However, existing maxRRC algorithms suffer from overheads and redundancies as they can repeatedly perform many constraint checks without triggering any value deletions. In this paper we propose techniques that can boost the performance of maxRPC algorithms. These include the combined use of two data structures to avoid many redundant constraint checks, and heuristics for the efficient ordering and execution of certain operations. Based on these, we propose two closely related algorithms. The first one which is a maxRPC algorithm with optimal O(end^3) time complexity, displays good performance when used stand-alone, but is expensive to apply during search. The second one approximates maxRPC and has O(en^2d^4) time complexity, but a restricted version with O(end^4) complexity can be very efficient when used during search. Both algorithms have O(ed) space complexity. Experimental results demonstrate that the resulting methods constantly outperform previous algorithms for maxRPC, often by large margins, and constitute a more than viable alternative to arc consistency on many problems. |
2110.11155 | Luca Traini PhD | Luca Traini, Vittorio Cortellessa | DeLag: Using Multi-Objective Optimization to Enhance the Detection of
Latency Degradation Patterns in Service-based Systems | Accepted for publication in IEEE Transactions on Software Engineering
(TSE) | null | 10.1109/TSE.2023.3266041 | null | cs.SE cs.LG cs.PF | http://creativecommons.org/licenses/by/4.0/ | Performance debugging in production is a fundamental activity in modern
service-based systems. The diagnosis of performance issues is often
time-consuming, since it requires thorough inspection of large volumes of
traces and performance indices. In this paper we present DeLag, a novel
automated search-based approach for diagnosing performance issues in
service-based systems. DeLag identifies subsets of requests that show, in the
combination of their Remote Procedure Call execution times, symptoms of
potentially relevant performance issues. We call such symptoms Latency
Degradation Patterns. DeLag simultaneously searches for multiple latency
degradation patterns while optimizing precision, recall and latency
dissimilarity. Experimentation on 700 datasets of requests generated from two
microservice-based systems shows that our approach provides better and more
stable effectiveness than three state-of-the-art approaches and general purpose
machine learning clustering algorithms. DeLag is more effective than all
baseline techniques in at least one case study (with p $\leq$ 0.05 and
non-negligible effect size). Moreover, DeLag outperforms in terms of efficiency
the second and the third most effective baseline techniques on the largest
datasets used in our evaluation (up to 22%).
| [
{
"created": "Thu, 21 Oct 2021 13:59:32 GMT",
"version": "v1"
},
{
"created": "Fri, 30 Sep 2022 10:58:53 GMT",
"version": "v2"
},
{
"created": "Thu, 29 Dec 2022 18:53:22 GMT",
"version": "v3"
},
{
"created": "Fri, 7 Apr 2023 14:09:42 GMT",
"version": "v4"
}
] | 2023-04-10 | [
[
"Traini",
"Luca",
""
],
[
"Cortellessa",
"Vittorio",
""
]
] | Performance debugging in production is a fundamental activity in modern service-based systems. The diagnosis of performance issues is often time-consuming, since it requires thorough inspection of large volumes of traces and performance indices. In this paper we present DeLag, a novel automated search-based approach for diagnosing performance issues in service-based systems. DeLag identifies subsets of requests that show, in the combination of their Remote Procedure Call execution times, symptoms of potentially relevant performance issues. We call such symptoms Latency Degradation Patterns. DeLag simultaneously searches for multiple latency degradation patterns while optimizing precision, recall and latency dissimilarity. Experimentation on 700 datasets of requests generated from two microservice-based systems shows that our approach provides better and more stable effectiveness than three state-of-the-art approaches and general purpose machine learning clustering algorithms. DeLag is more effective than all baseline techniques in at least one case study (with p $\leq$ 0.05 and non-negligible effect size). Moreover, DeLag outperforms in terms of efficiency the second and the third most effective baseline techniques on the largest datasets used in our evaluation (up to 22%). |
2209.03496 | Allen Chang | Allen Chang, Lauren Klein, Marcelo R. Rosales, Weiyang Deng, Beth A.
Smith, Maja J. Matari\'c | Evaluating Temporal Patterns in Applied Infant Affect Recognition | 8 pages, 6 figures, 10th International Conference on Affective
Computing and Intelligent Interaction (ACII 2022) | null | null | null | cs.HC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Agents must monitor their partners' affective states continuously in order to
understand and engage in social interactions. However, methods for evaluating
affect recognition do not account for changes in classification performance
that may occur during occlusions or transitions between affective states. This
paper addresses temporal patterns in affect classification performance in the
context of an infant-robot interaction, where infants' affective states
contribute to their ability to participate in a therapeutic leg movement
activity. To support robustness to facial occlusions in video recordings, we
trained infant affect recognition classifiers using both facial and body
features. Next, we conducted an in-depth analysis of our best-performing models
to evaluate how performance changed over time as the models encountered missing
data and changing infant affect. During time windows when features were
extracted with high confidence, a unimodal model trained on facial features
achieved the same optimal performance as multimodal models trained on both
facial and body features. However, multimodal models outperformed unimodal
models when evaluated on the entire dataset. Additionally, model performance
was weakest when predicting an affective state transition and improved after
multiple predictions of the same affective state. These findings emphasize the
benefits of incorporating body features in continuous affect recognition for
infants. Our work highlights the importance of evaluating variability in model
performance both over time and in the presence of missing data when applying
affect recognition to social interactions.
| [
{
"created": "Wed, 7 Sep 2022 23:29:15 GMT",
"version": "v1"
}
] | 2022-09-09 | [
[
"Chang",
"Allen",
""
],
[
"Klein",
"Lauren",
""
],
[
"Rosales",
"Marcelo R.",
""
],
[
"Deng",
"Weiyang",
""
],
[
"Smith",
"Beth A.",
""
],
[
"Matarić",
"Maja J.",
""
]
] | Agents must monitor their partners' affective states continuously in order to understand and engage in social interactions. However, methods for evaluating affect recognition do not account for changes in classification performance that may occur during occlusions or transitions between affective states. This paper addresses temporal patterns in affect classification performance in the context of an infant-robot interaction, where infants' affective states contribute to their ability to participate in a therapeutic leg movement activity. To support robustness to facial occlusions in video recordings, we trained infant affect recognition classifiers using both facial and body features. Next, we conducted an in-depth analysis of our best-performing models to evaluate how performance changed over time as the models encountered missing data and changing infant affect. During time windows when features were extracted with high confidence, a unimodal model trained on facial features achieved the same optimal performance as multimodal models trained on both facial and body features. However, multimodal models outperformed unimodal models when evaluated on the entire dataset. Additionally, model performance was weakest when predicting an affective state transition and improved after multiple predictions of the same affective state. These findings emphasize the benefits of incorporating body features in continuous affect recognition for infants. Our work highlights the importance of evaluating variability in model performance both over time and in the presence of missing data when applying affect recognition to social interactions. |
2007.15415 | Luca Reggio | Mai Gehrke, Tomas Jakl, Luca Reggio | A Cook's tour of duality in logic: from quantifiers, through Vietoris,
to measures | 29 pages | null | null | null | cs.LO math.CT math.GN math.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We identify and highlight certain landmark results in Samson Abramsky's work
which we believe are fundamental to current developments and future trends. In
particular, we focus on the use of (i) topological duality methods to solve
problems in logic and computer science; (ii) category theory and, more
particularly, free (and co-free) constructions; (iii) these tools to unify the
`power' and `structure' strands in computer science.
| [
{
"created": "Thu, 30 Jul 2020 12:22:10 GMT",
"version": "v1"
}
] | 2020-07-31 | [
[
"Gehrke",
"Mai",
""
],
[
"Jakl",
"Tomas",
""
],
[
"Reggio",
"Luca",
""
]
] | We identify and highlight certain landmark results in Samson Abramsky's work which we believe are fundamental to current developments and future trends. In particular, we focus on the use of (i) topological duality methods to solve problems in logic and computer science; (ii) category theory and, more particularly, free (and co-free) constructions; (iii) these tools to unify the `power' and `structure' strands in computer science. |
1809.05515 | Marko Angjelichinoski | Marko Angjelichinoski, Kasper Fl{\o}e Trillingsgaard and Petar
Popovski | A Statistical Learning Approach to Ultra-Reliable Low Latency
Communication | Submitted for publication | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mission-critical applications require Ultra-Reliable Low Latency (URLLC)
wireless connections, where the packet error rate (PER) goes down to $10^{-9}$.
Fulfillment of the bold reliability figures becomes meaningful only if it can
be related to a statistical model in which the URLLC system operates. However,
this model is generally not known and needs to be learned by sampling the
wireless environment. In this paper we treat this fundamental problem in the
simplest possible communication-theoretic setting: selecting a transmission
rate over a dynamic wireless channel in order to guarantee high transmission
reliability. We introduce a novel statistical framework for design and
assessment of URLLC systems, consisting of three key components: (i) channel
model selection; (ii) learning the model using training; (3) selecting the
transmission rate to satisfy the required reliability. As it is insufficient to
specify the URLLC requirements only through PER, two types of statistical
constraints are introduced, Averaged Reliability (AR) and Probably Correct
Reliability (PCR). The analysis and the evaluations show that adequate model
selection and learning are indispensable for designing consistent physical
layer that asymptotically behaves as if the channel was known perfectly, while
maintaining the reliability requirements in URLLC systems.
| [
{
"created": "Fri, 14 Sep 2018 17:30:58 GMT",
"version": "v1"
}
] | 2018-09-17 | [
[
"Angjelichinoski",
"Marko",
""
],
[
"Trillingsgaard",
"Kasper Fløe",
""
],
[
"Popovski",
"Petar",
""
]
] | Mission-critical applications require Ultra-Reliable Low Latency (URLLC) wireless connections, where the packet error rate (PER) goes down to $10^{-9}$. Fulfillment of the bold reliability figures becomes meaningful only if it can be related to a statistical model in which the URLLC system operates. However, this model is generally not known and needs to be learned by sampling the wireless environment. In this paper we treat this fundamental problem in the simplest possible communication-theoretic setting: selecting a transmission rate over a dynamic wireless channel in order to guarantee high transmission reliability. We introduce a novel statistical framework for design and assessment of URLLC systems, consisting of three key components: (i) channel model selection; (ii) learning the model using training; (3) selecting the transmission rate to satisfy the required reliability. As it is insufficient to specify the URLLC requirements only through PER, two types of statistical constraints are introduced, Averaged Reliability (AR) and Probably Correct Reliability (PCR). The analysis and the evaluations show that adequate model selection and learning are indispensable for designing consistent physical layer that asymptotically behaves as if the channel was known perfectly, while maintaining the reliability requirements in URLLC systems. |
2406.11081 | Sara Ahmadi | Sara Ahmadi, Peter Desain, Jordy Thielen | A Bayesian dynamic stopping method for evoked response brain-computer
interfacing | null | null | null | null | cs.HC | http://creativecommons.org/licenses/by/4.0/ | As brain-computer interfacing (BCI) systems transition from assistive
technology to more diverse applications, their speed, reliability, and user
experience become increasingly important. Dynamic stopping methods enhance BCI
system speed by deciding at any moment whether to output a result or wait for
more information. Such approach leverages trial variance, allowing good trials
to be detected earlier, thereby speeding up the process without significantly
compromising accuracy. Existing dynamic stopping algorithms typically optimize
measures such as symbols per minute (SPM) and information transfer rate (ITR).
However, these metrics may not accurately reflect system performance for
specific applications or user types. Moreover, many methods depend on arbitrary
thresholds or parameters that require extensive training data. We propose a
model-based approach that takes advantage of the analytical knowledge that we
have about the underlying classification model. By using a risk minimisation
approach, our model allows precise control over the types of errors and the
balance between precision and speed. This adaptability makes it ideal for
customizing BCI systems to meet the diverse needs of various applications. We
validate our proposed method on a publicly available dataset, comparing it with
established static and dynamic stopping methods. Our results demonstrate that
our approach offers a broad range of accuracy-speed trade-offs and achieves
higher precision than baseline stopping methods.
| [
{
"created": "Sun, 16 Jun 2024 21:41:48 GMT",
"version": "v1"
}
] | 2024-06-18 | [
[
"Ahmadi",
"Sara",
""
],
[
"Desain",
"Peter",
""
],
[
"Thielen",
"Jordy",
""
]
] | As brain-computer interfacing (BCI) systems transition from assistive technology to more diverse applications, their speed, reliability, and user experience become increasingly important. Dynamic stopping methods enhance BCI system speed by deciding at any moment whether to output a result or wait for more information. Such approach leverages trial variance, allowing good trials to be detected earlier, thereby speeding up the process without significantly compromising accuracy. Existing dynamic stopping algorithms typically optimize measures such as symbols per minute (SPM) and information transfer rate (ITR). However, these metrics may not accurately reflect system performance for specific applications or user types. Moreover, many methods depend on arbitrary thresholds or parameters that require extensive training data. We propose a model-based approach that takes advantage of the analytical knowledge that we have about the underlying classification model. By using a risk minimisation approach, our model allows precise control over the types of errors and the balance between precision and speed. This adaptability makes it ideal for customizing BCI systems to meet the diverse needs of various applications. We validate our proposed method on a publicly available dataset, comparing it with established static and dynamic stopping methods. Our results demonstrate that our approach offers a broad range of accuracy-speed trade-offs and achieves higher precision than baseline stopping methods. |
1506.09075 | Byeongkeun Kang | Yuanyuan Wu, Xiaohai He, Byeongkeun Kang, Haiying Song, and Truong Q.
Nguyen | Long-Range Motion Trajectories Extraction of Articulated Human Using
Mesh Evolution | IEEE Signal Processing Letters | null | 10.1109/LSP.2016.2536647 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This letter presents a novel approach to extract reliable dense and
long-range motion trajectories of articulated human in a video sequence.
Compared with existing approaches that emphasize temporal consistency of each
tracked point, we also consider the spatial structure of tracked points on the
articulated human. We treat points as a set of vertices, and build a triangle
mesh to join them in image space. The problem of extracting long-range motion
trajectories is changed to the issue of consistency of mesh evolution over
time. First, self-occlusion is detected by a novel mesh-based method and an
adaptive motion estimation method is proposed to initialize mesh between
successive frames. Furthermore, we propose an iterative algorithm to
efficiently adjust vertices of mesh for a physically plausible deformation,
which can meet the local rigidity of mesh and silhouette constraints. Finally,
we compare the proposed method with the state-of-the-art methods on a set of
challenging sequences. Evaluations demonstrate that our method achieves
favorable performance in terms of both accuracy and integrity of extracted
trajectories.
| [
{
"created": "Tue, 30 Jun 2015 13:18:18 GMT",
"version": "v1"
},
{
"created": "Mon, 29 Feb 2016 17:10:11 GMT",
"version": "v2"
},
{
"created": "Tue, 29 Mar 2016 00:21:40 GMT",
"version": "v3"
}
] | 2016-03-30 | [
[
"Wu",
"Yuanyuan",
""
],
[
"He",
"Xiaohai",
""
],
[
"Kang",
"Byeongkeun",
""
],
[
"Song",
"Haiying",
""
],
[
"Nguyen",
"Truong Q.",
""
]
] | This letter presents a novel approach to extract reliable dense and long-range motion trajectories of articulated human in a video sequence. Compared with existing approaches that emphasize temporal consistency of each tracked point, we also consider the spatial structure of tracked points on the articulated human. We treat points as a set of vertices, and build a triangle mesh to join them in image space. The problem of extracting long-range motion trajectories is changed to the issue of consistency of mesh evolution over time. First, self-occlusion is detected by a novel mesh-based method and an adaptive motion estimation method is proposed to initialize mesh between successive frames. Furthermore, we propose an iterative algorithm to efficiently adjust vertices of mesh for a physically plausible deformation, which can meet the local rigidity of mesh and silhouette constraints. Finally, we compare the proposed method with the state-of-the-art methods on a set of challenging sequences. Evaluations demonstrate that our method achieves favorable performance in terms of both accuracy and integrity of extracted trajectories. |
2305.19860 | Likang Wu | Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia
Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, Enhong Chen | A Survey on Large Language Models for Recommendation | 34 pages, 7 figures, 2 tables | null | null | null | cs.IR cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) have emerged as powerful tools in the field of
Natural Language Processing (NLP) and have recently gained significant
attention in the domain of Recommendation Systems (RS). These models, trained
on massive amounts of data using self-supervised learning, have demonstrated
remarkable success in learning universal representations and have the potential
to enhance various aspects of recommendation systems by some effective transfer
techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect
of harnessing the power of language models in enhancing recommendation quality
is the utilization of their high-quality representations of textual features
and their extensive coverage of external knowledge to establish correlations
between items and users. To provide a comprehensive understanding of the
existing LLM-based recommendation systems, this survey presents a taxonomy that
categorizes these models into two major paradigms, respectively Discriminative
LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation
(GLLM4Rec), with the latter being systematically sorted out for the first time.
Furthermore, we systematically review and analyze existing LLM-based
recommendation systems within each paradigm, providing insights into their
methodologies, techniques, and performance. Additionally, we identify key
challenges and several valuable findings to provide researchers and
practitioners with inspiration. We have also created a GitHub repository to
index relevant papers on LLMs for recommendation,
https://github.com/WLiK/LLM4Rec.
| [
{
"created": "Wed, 31 May 2023 13:51:26 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Jun 2023 03:22:17 GMT",
"version": "v2"
},
{
"created": "Fri, 4 Aug 2023 02:58:15 GMT",
"version": "v3"
},
{
"created": "Fri, 18 Aug 2023 05:56:05 GMT",
"version": "v4"
},
{
"created": "Tue, 18 Jun 2024 08:07:01 GMT",
"version": "v5"
}
] | 2024-06-19 | [
[
"Wu",
"Likang",
""
],
[
"Zheng",
"Zhi",
""
],
[
"Qiu",
"Zhaopeng",
""
],
[
"Wang",
"Hao",
""
],
[
"Gu",
"Hongchao",
""
],
[
"Shen",
"Tingjia",
""
],
[
"Qin",
"Chuan",
""
],
[
"Zhu",
"Chen",
""
],
[
"Zhu",
"Hengshu",
""
],
[
"Liu",
"Qi",
""
],
[
"Xiong",
"Hui",
""
],
[
"Chen",
"Enhong",
""
]
] | Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems by some effective transfer techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect of harnessing the power of language models in enhancing recommendation quality is the utilization of their high-quality representations of textual features and their extensive coverage of external knowledge to establish correlations between items and users. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), with the latter being systematically sorted out for the first time. Furthermore, we systematically review and analyze existing LLM-based recommendation systems within each paradigm, providing insights into their methodologies, techniques, and performance. Additionally, we identify key challenges and several valuable findings to provide researchers and practitioners with inspiration. We have also created a GitHub repository to index relevant papers on LLMs for recommendation, https://github.com/WLiK/LLM4Rec. |
2408.02814 | Shaopeng Fu | Shaopeng Fu, Xuexue Sun, Ke Qing, Tianhang Zheng, Di Wang | Pre-trained Encoder Inference: Revealing Upstream Encoders In Downstream
Machine Learning Services | null | null | null | null | cs.LG cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Though pre-trained encoders can be easily accessed online to build downstream
machine learning (ML) services quickly, various attacks have been designed to
compromise the security and privacy of these encoders. While most attacks
target encoders on the upstream side, it remains unknown how an encoder could
be threatened when deployed in a downstream ML service. This paper unveils a
new vulnerability: the Pre-trained Encoder Inference (PEI) attack, which posts
privacy threats toward encoders hidden behind downstream ML services. By only
providing API accesses to a targeted downstream service and a set of candidate
encoders, the PEI attack can infer which encoder is secretly used by the
targeted service based on candidate ones. We evaluate the attack performance of
PEI against real-world encoders on three downstream tasks: image
classification, text classification, and text-to-image generation. Experiments
show that the PEI attack succeeds in revealing the hidden encoder in most cases
and seldom makes mistakes even when the hidden encoder is not in the candidate
set. We also conducted a case study on one of the most recent vision-language
models, LLaVA, to illustrate that the PEI attack is useful in assisting other
ML attacks such as adversarial attacks. The code is available at
https://github.com/fshp971/encoder-inference.
| [
{
"created": "Mon, 5 Aug 2024 20:27:54 GMT",
"version": "v1"
}
] | 2024-08-07 | [
[
"Fu",
"Shaopeng",
""
],
[
"Sun",
"Xuexue",
""
],
[
"Qing",
"Ke",
""
],
[
"Zheng",
"Tianhang",
""
],
[
"Wang",
"Di",
""
]
] | Though pre-trained encoders can be easily accessed online to build downstream machine learning (ML) services quickly, various attacks have been designed to compromise the security and privacy of these encoders. While most attacks target encoders on the upstream side, it remains unknown how an encoder could be threatened when deployed in a downstream ML service. This paper unveils a new vulnerability: the Pre-trained Encoder Inference (PEI) attack, which posts privacy threats toward encoders hidden behind downstream ML services. By only providing API accesses to a targeted downstream service and a set of candidate encoders, the PEI attack can infer which encoder is secretly used by the targeted service based on candidate ones. We evaluate the attack performance of PEI against real-world encoders on three downstream tasks: image classification, text classification, and text-to-image generation. Experiments show that the PEI attack succeeds in revealing the hidden encoder in most cases and seldom makes mistakes even when the hidden encoder is not in the candidate set. We also conducted a case study on one of the most recent vision-language models, LLaVA, to illustrate that the PEI attack is useful in assisting other ML attacks such as adversarial attacks. The code is available at https://github.com/fshp971/encoder-inference. |
2305.00077 | Binnur Gorer | Binnur G\"orer and Fatma Ba\c{s}ak Aydemir | Exploring Emerging Technologies for Requirements Elicitation Interview
Training: Empirical Assessment of Robotic and Virtual Tutors | Author submitted manuscript | null | null | null | cs.SE cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Requirements elicitation interviews are a widely adopted technique, where the
interview success heavily depends on the interviewer's preparedness and
communication skills. Students can enhance these skills through practice
interviews. However, organizing practice interviews for many students presents
scalability challenges, given the time and effort required to involve
stakeholders in each session. To address this, we propose REIT, an extensible
architecture for Requirements Elicitation Interview Training system based on
emerging educational technologies. REIT has components to support both the
interview phase, wherein students act as interviewers while the system assumes
the role of an interviewee, and the feedback phase, during which the system
assesses students' performance and offers contextual and behavioral feedback to
enhance their interviewing skills. We demonstrate the applicability of REIT
through two implementations: RoREIT with a physical robotic agent and VoREIT
with a virtual voice-only agent. We empirically evaluated both instances with a
group of graduate students. The participants appreciated both systems. They
demonstrated higher learning gain when trained with RoREIT, but they found
VoREIT more engaging and easier to use. These findings indicate that each
system has distinct benefits and drawbacks, suggesting that REIT can be
realized for various educational settings based on preferences and available
resources.
| [
{
"created": "Fri, 28 Apr 2023 20:03:48 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Aug 2023 23:21:20 GMT",
"version": "v2"
},
{
"created": "Wed, 30 Aug 2023 14:39:22 GMT",
"version": "v3"
}
] | 2023-08-31 | [
[
"Görer",
"Binnur",
""
],
[
"Aydemir",
"Fatma Başak",
""
]
] | Requirements elicitation interviews are a widely adopted technique, where the interview success heavily depends on the interviewer's preparedness and communication skills. Students can enhance these skills through practice interviews. However, organizing practice interviews for many students presents scalability challenges, given the time and effort required to involve stakeholders in each session. To address this, we propose REIT, an extensible architecture for Requirements Elicitation Interview Training system based on emerging educational technologies. REIT has components to support both the interview phase, wherein students act as interviewers while the system assumes the role of an interviewee, and the feedback phase, during which the system assesses students' performance and offers contextual and behavioral feedback to enhance their interviewing skills. We demonstrate the applicability of REIT through two implementations: RoREIT with a physical robotic agent and VoREIT with a virtual voice-only agent. We empirically evaluated both instances with a group of graduate students. The participants appreciated both systems. They demonstrated higher learning gain when trained with RoREIT, but they found VoREIT more engaging and easier to use. These findings indicate that each system has distinct benefits and drawbacks, suggesting that REIT can be realized for various educational settings based on preferences and available resources. |
2103.02270 | Dian Fan | Dian Fan, Xiaojun Yuan, Ying-Jun Angela Zhang | Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air
Federated Edge Learning | null | null | null | null | cs.IT cs.LG math.IT | http://creativecommons.org/licenses/by/4.0/ | In this paper, we investigate over-the-air model aggregation in a federated
edge learning (FEEL) system. We introduce a Markovian probability model to
characterize the intrinsic temporal structure of the model aggregation series.
With this temporal probability model, we formulate the model aggregation
problem as to infer the desired aggregated update given all the past
observations from a Bayesian perspective. We develop a message passing based
algorithm, termed temporal-structure-assisted gradient aggregation (TSA-GA), to
fulfil this estimation task with low complexity and near-optimal performance.
We further establish the state evolution (SE) analysis to characterize the
behaviour of the proposed TSA-GA algorithm, and derive an explicit bound of the
expected loss reduction of the FEEL system under certain standard regularity
conditions. In addition, we develop an expectation maximization (EM) strategy
to learn the unknown parameters in the Markovian model. We show that the
proposed TSAGA algorithm significantly outperforms the state-of-the-art, and is
able to achieve comparable learning performance as the error-free benchmark in
terms of both convergence rate and final test accuracy.
| [
{
"created": "Wed, 3 Mar 2021 09:13:27 GMT",
"version": "v1"
}
] | 2021-03-04 | [
[
"Fan",
"Dian",
""
],
[
"Yuan",
"Xiaojun",
""
],
[
"Zhang",
"Ying-Jun Angela",
""
]
] | In this paper, we investigate over-the-air model aggregation in a federated edge learning (FEEL) system. We introduce a Markovian probability model to characterize the intrinsic temporal structure of the model aggregation series. With this temporal probability model, we formulate the model aggregation problem as to infer the desired aggregated update given all the past observations from a Bayesian perspective. We develop a message passing based algorithm, termed temporal-structure-assisted gradient aggregation (TSA-GA), to fulfil this estimation task with low complexity and near-optimal performance. We further establish the state evolution (SE) analysis to characterize the behaviour of the proposed TSA-GA algorithm, and derive an explicit bound of the expected loss reduction of the FEEL system under certain standard regularity conditions. In addition, we develop an expectation maximization (EM) strategy to learn the unknown parameters in the Markovian model. We show that the proposed TSAGA algorithm significantly outperforms the state-of-the-art, and is able to achieve comparable learning performance as the error-free benchmark in terms of both convergence rate and final test accuracy. |
2406.08164 | Muhammad Jehanzeb Mirza | Irene Huang, Wei Lin, M. Jehanzeb Mirza, Jacob A. Hansen, Sivan Doveh,
Victor Ion Butoi, Roei Herzig, Assaf Arbelle, Hilde Kuhene, Trevor Darrel,
Chuang Gan, Aude Oliva, Rogerio Feris, Leonid Karlinsky | ConMe: Rethinking Evaluation of Compositional Reasoning for Modern VLMs | The first three authors contributed equally | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Compositional Reasoning (CR) entails grasping the significance of attributes,
relations, and word order. Recent Vision-Language Models (VLMs), comprising a
visual encoder and a Large Language Model (LLM) decoder, have demonstrated
remarkable proficiency in such reasoning tasks. This prompts a crucial
question: have VLMs effectively tackled the CR challenge? We conjecture that
existing CR benchmarks may not adequately push the boundaries of modern VLMs
due to the reliance on an LLM-only negative text generation pipeline.
Consequently, the negatives produced either appear as outliers from the natural
language distribution learned by VLMs' LLM decoders or as improbable within the
corresponding image context. To address these limitations, we introduce ConMe
-- a compositional reasoning benchmark and a novel data generation pipeline
leveraging VLMs to produce `hard CR Q&A'. Through a new concept of VLMs
conversing with each other to collaboratively expose their weaknesses, our
pipeline autonomously generates, evaluates, and selects challenging
compositional reasoning questions, establishing a robust CR benchmark, also
subsequently validated manually. Our benchmark provokes a noteworthy, up to
33%, decrease in CR performance compared to preceding benchmarks, reinstating
the CR challenge even for state-of-the-art VLMs.
| [
{
"created": "Wed, 12 Jun 2024 12:54:27 GMT",
"version": "v1"
}
] | 2024-06-13 | [
[
"Huang",
"Irene",
""
],
[
"Lin",
"Wei",
""
],
[
"Mirza",
"M. Jehanzeb",
""
],
[
"Hansen",
"Jacob A.",
""
],
[
"Doveh",
"Sivan",
""
],
[
"Butoi",
"Victor Ion",
""
],
[
"Herzig",
"Roei",
""
],
[
"Arbelle",
"Assaf",
""
],
[
"Kuhene",
"Hilde",
""
],
[
"Darrel",
"Trevor",
""
],
[
"Gan",
"Chuang",
""
],
[
"Oliva",
"Aude",
""
],
[
"Feris",
"Rogerio",
""
],
[
"Karlinsky",
"Leonid",
""
]
] | Compositional Reasoning (CR) entails grasping the significance of attributes, relations, and word order. Recent Vision-Language Models (VLMs), comprising a visual encoder and a Large Language Model (LLM) decoder, have demonstrated remarkable proficiency in such reasoning tasks. This prompts a crucial question: have VLMs effectively tackled the CR challenge? We conjecture that existing CR benchmarks may not adequately push the boundaries of modern VLMs due to the reliance on an LLM-only negative text generation pipeline. Consequently, the negatives produced either appear as outliers from the natural language distribution learned by VLMs' LLM decoders or as improbable within the corresponding image context. To address these limitations, we introduce ConMe -- a compositional reasoning benchmark and a novel data generation pipeline leveraging VLMs to produce `hard CR Q&A'. Through a new concept of VLMs conversing with each other to collaboratively expose their weaknesses, our pipeline autonomously generates, evaluates, and selects challenging compositional reasoning questions, establishing a robust CR benchmark, also subsequently validated manually. Our benchmark provokes a noteworthy, up to 33%, decrease in CR performance compared to preceding benchmarks, reinstating the CR challenge even for state-of-the-art VLMs. |
2111.00610 | Anurag Katakkar | Anurag Katakkar, Alan W Black | Towards Language Modelling in the Speech Domain Using Sub-word
Linguistic Units | null | null | null | null | cs.CL cs.LG cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Language models (LMs) for text data have been studied extensively for their
usefulness in language generation and other downstream tasks. However, language
modelling purely in the speech domain is still a relatively unexplored topic,
with traditional speech LMs often depending on auxiliary text LMs for learning
distributional aspects of the language. For the English language, these LMs
treat words as atomic units, which presents inherent challenges to language
modelling in the speech domain. In this paper, we propose a novel LSTM-based
generative speech LM that is inspired by the CBOW model and built on linguistic
units including syllables and phonemes. This offers better acoustic consistency
across utterances in the dataset, as opposed to single melspectrogram frames,
or whole words. With a limited dataset, orders of magnitude smaller than that
required by contemporary generative models, our model closely approximates
babbling speech. We show the effect of training with auxiliary text LMs,
multitask learning objectives, and auxiliary articulatory features. Through our
experiments, we also highlight some well known, but poorly documented
challenges in training generative speech LMs, including the mismatch between
the supervised learning objective with which these models are trained such as
Mean Squared Error (MSE), and the true objective, which is speech quality. Our
experiments provide an early indication that while validation loss and Mel
Cepstral Distortion (MCD) are not strongly correlated with generated speech
quality, traditional text language modelling metrics like perplexity and
next-token-prediction accuracy might be.
| [
{
"created": "Sun, 31 Oct 2021 22:48:30 GMT",
"version": "v1"
}
] | 2021-11-02 | [
[
"Katakkar",
"Anurag",
""
],
[
"Black",
"Alan W",
""
]
] | Language models (LMs) for text data have been studied extensively for their usefulness in language generation and other downstream tasks. However, language modelling purely in the speech domain is still a relatively unexplored topic, with traditional speech LMs often depending on auxiliary text LMs for learning distributional aspects of the language. For the English language, these LMs treat words as atomic units, which presents inherent challenges to language modelling in the speech domain. In this paper, we propose a novel LSTM-based generative speech LM that is inspired by the CBOW model and built on linguistic units including syllables and phonemes. This offers better acoustic consistency across utterances in the dataset, as opposed to single melspectrogram frames, or whole words. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features. Through our experiments, we also highlight some well known, but poorly documented challenges in training generative speech LMs, including the mismatch between the supervised learning objective with which these models are trained such as Mean Squared Error (MSE), and the true objective, which is speech quality. Our experiments provide an early indication that while validation loss and Mel Cepstral Distortion (MCD) are not strongly correlated with generated speech quality, traditional text language modelling metrics like perplexity and next-token-prediction accuracy might be. |
2009.01229 | Marialejandra Garcia-Corretjer | Marialejandra Garcia Corretjer, David Miralles, and Raquel Ros | A Theoretical Approach for a Novel Model to Realizing Empathy | 47 pages, 11 figures | null | null | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The first objective of this paper are to introduce a strong theoretical
concept as a proposed model that visualizes the process of realizing empathy,
based on the ample analysis of the collected work in the survey. Secondly, the
intended purpose of this proposed model, is to create an initial blueprint that
may be applicable to a range of disciplines with clear must-have concepts
important to consider for the realization of empathy between people and their
technology.For this reason, after the model is explained, this paper
exemplifies tools for its application and a couple of encouraging case study
projects that begin to integrate this model into their interactive experiments.
| [
{
"created": "Thu, 3 Sep 2020 17:21:49 GMT",
"version": "v1"
}
] | 2020-09-04 | [
[
"Corretjer",
"Marialejandra Garcia",
""
],
[
"Miralles",
"David",
""
],
[
"Ros",
"Raquel",
""
]
] | The first objective of this paper are to introduce a strong theoretical concept as a proposed model that visualizes the process of realizing empathy, based on the ample analysis of the collected work in the survey. Secondly, the intended purpose of this proposed model, is to create an initial blueprint that may be applicable to a range of disciplines with clear must-have concepts important to consider for the realization of empathy between people and their technology.For this reason, after the model is explained, this paper exemplifies tools for its application and a couple of encouraging case study projects that begin to integrate this model into their interactive experiments. |
1712.00811 | Hamoon Mousavi | Hamoon Mousavi | Lower Bounds on Regular Expression Size | 29 pages | null | null | null | cs.FL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce linear programs encoding regular expressions of finite
languages. We show that, given a language, the optimum value of the associated
linear program is a lower bound on the size of any regular expression of the
language. Moreover we show that any regular expression can be turned into a
dual feasible solution with an objective value that is equal to the size of the
regular expression. For binomial languages we can relax the associated linear
program using duality theorem. We use this relaxation to prove lower bounds on
the size of regular expressions of binomial and threshold languages.
| [
{
"created": "Sun, 3 Dec 2017 18:35:48 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Dec 2017 22:05:17 GMT",
"version": "v2"
}
] | 2017-12-08 | [
[
"Mousavi",
"Hamoon",
""
]
] | We introduce linear programs encoding regular expressions of finite languages. We show that, given a language, the optimum value of the associated linear program is a lower bound on the size of any regular expression of the language. Moreover we show that any regular expression can be turned into a dual feasible solution with an objective value that is equal to the size of the regular expression. For binomial languages we can relax the associated linear program using duality theorem. We use this relaxation to prove lower bounds on the size of regular expressions of binomial and threshold languages. |
1808.07712 | Gurkirt Singh | Gurkirt Singh and Suman Saha and Fabio Cuzzolin | Predicting Action Tubes | ECCV workshop; Anticipating Human Behaviour 2018; 16 page 7 figures | null | null | null | cs.CV cs.AI cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we present a method to predict an entire `action tube' (a set
of temporally linked bounding boxes) in a trimmed video just by observing a
smaller subset of it. Predicting where an action is going to take place in the
near future is essential to many computer vision based applications such as
autonomous driving or surgical robotics. Importantly, it has to be done in
real-time and in an online fashion. We propose a Tube Prediction network
(TPnet) which jointly predicts the past, present and future bounding boxes
along with their action classification scores. At test time TPnet is used in a
(temporal) sliding window setting, and its predictions are put into a tube
estimation framework to construct/predict the video long action tubes not only
for the observed part of the video but also for the unobserved part.
Additionally, the proposed action tube predictor helps in completing action
tubes for unobserved segments of the video. We quantitatively demonstrate the
latter ability, and the fact that TPnet improves state-of-the-art detection
performance, on one of the standard action detection benchmarks - J-HMDB-21
dataset.
| [
{
"created": "Thu, 23 Aug 2018 12:11:06 GMT",
"version": "v1"
}
] | 2018-08-24 | [
[
"Singh",
"Gurkirt",
""
],
[
"Saha",
"Suman",
""
],
[
"Cuzzolin",
"Fabio",
""
]
] | In this work, we present a method to predict an entire `action tube' (a set of temporally linked bounding boxes) in a trimmed video just by observing a smaller subset of it. Predicting where an action is going to take place in the near future is essential to many computer vision based applications such as autonomous driving or surgical robotics. Importantly, it has to be done in real-time and in an online fashion. We propose a Tube Prediction network (TPnet) which jointly predicts the past, present and future bounding boxes along with their action classification scores. At test time TPnet is used in a (temporal) sliding window setting, and its predictions are put into a tube estimation framework to construct/predict the video long action tubes not only for the observed part of the video but also for the unobserved part. Additionally, the proposed action tube predictor helps in completing action tubes for unobserved segments of the video. We quantitatively demonstrate the latter ability, and the fact that TPnet improves state-of-the-art detection performance, on one of the standard action detection benchmarks - J-HMDB-21 dataset. |
2303.12696 | Zhiyuan Hu | Zhiyuan Hu, Yunsheng Li, Jiancheng Lyu, Dashan Gao, Nuno Vasconcelos | Dense Network Expansion for Class Incremental Learning | Accepted by CVPR2023 | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The problem of class incremental learning (CIL) is considered.
State-of-the-art approaches use a dynamic architecture based on network
expansion (NE), in which a task expert is added per task. While effective from
a computational standpoint, these methods lead to models that grow quickly with
the number of tasks. A new NE method, dense network expansion (DNE), is
proposed to achieve a better trade-off between accuracy and model complexity.
This is accomplished by the introduction of dense connections between the
intermediate layers of the task expert networks, that enable the transfer of
knowledge from old to new tasks via feature sharing and reusing. This sharing
is implemented with a cross-task attention mechanism, based on a new task
attention block (TAB), that fuses information across tasks. Unlike traditional
attention mechanisms, TAB operates at the level of the feature mixing and is
decoupled with spatial attentions. This is shown more effective than a joint
spatial-and-task attention for CIL. The proposed DNE approach can strictly
maintain the feature space of old classes while growing the network and feature
scale at a much slower rate than previous methods. In result, it outperforms
the previous SOTA methods by a margin of 4\% in terms of accuracy, with similar
or even smaller model scale.
| [
{
"created": "Wed, 22 Mar 2023 16:42:26 GMT",
"version": "v1"
}
] | 2023-03-23 | [
[
"Hu",
"Zhiyuan",
""
],
[
"Li",
"Yunsheng",
""
],
[
"Lyu",
"Jiancheng",
""
],
[
"Gao",
"Dashan",
""
],
[
"Vasconcelos",
"Nuno",
""
]
] | The problem of class incremental learning (CIL) is considered. State-of-the-art approaches use a dynamic architecture based on network expansion (NE), in which a task expert is added per task. While effective from a computational standpoint, these methods lead to models that grow quickly with the number of tasks. A new NE method, dense network expansion (DNE), is proposed to achieve a better trade-off between accuracy and model complexity. This is accomplished by the introduction of dense connections between the intermediate layers of the task expert networks, that enable the transfer of knowledge from old to new tasks via feature sharing and reusing. This sharing is implemented with a cross-task attention mechanism, based on a new task attention block (TAB), that fuses information across tasks. Unlike traditional attention mechanisms, TAB operates at the level of the feature mixing and is decoupled with spatial attentions. This is shown more effective than a joint spatial-and-task attention for CIL. The proposed DNE approach can strictly maintain the feature space of old classes while growing the network and feature scale at a much slower rate than previous methods. In result, it outperforms the previous SOTA methods by a margin of 4\% in terms of accuracy, with similar or even smaller model scale. |
2401.10338 | Jingchao Ni | Jingchao Ni, Gauthier Guinet, Peihong Jiang, Laurent Callot, Andrey
Kan | MELODY: Robust Semi-Supervised Hybrid Model for Entity-Level Online
Anomaly Detection with Multivariate Time Series | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | In large IT systems, software deployment is a crucial process in online
services as their code is regularly updated. However, a faulty code change may
degrade the target service's performance and cause cascading outages in
downstream services. Thus, software deployments should be comprehensively
monitored, and their anomalies should be detected timely. In this paper, we
study the problem of anomaly detection for deployments. We begin by identifying
the challenges unique to this anomaly detection problem, which is at
entity-level (e.g., deployments), relative to the more typical problem of
anomaly detection in multivariate time series (MTS). The unique challenges
include the heterogeneity of deployments, the low latency tolerance, the
ambiguous anomaly definition, and the limited supervision. To address them, we
propose a novel framework, semi-supervised hybrid Model for Entity-Level Online
Detection of anomalY (MELODY). MELODY first transforms the MTS of different
entities to the same feature space by an online feature extractor, then uses a
newly proposed semi-supervised deep one-class model for detecting anomalous
entities. We evaluated MELODY on real data of cloud services with 1.2M+ time
series. The relative F1 score improvement of MELODY over the state-of-the-art
methods ranges from 7.6% to 56.5%. The user evaluation suggests MELODY is
suitable for monitoring deployments in large online systems.
| [
{
"created": "Thu, 18 Jan 2024 19:02:41 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Jun 2024 04:35:00 GMT",
"version": "v2"
}
] | 2024-06-07 | [
[
"Ni",
"Jingchao",
""
],
[
"Guinet",
"Gauthier",
""
],
[
"Jiang",
"Peihong",
""
],
[
"Callot",
"Laurent",
""
],
[
"Kan",
"Andrey",
""
]
] | In large IT systems, software deployment is a crucial process in online services as their code is regularly updated. However, a faulty code change may degrade the target service's performance and cause cascading outages in downstream services. Thus, software deployments should be comprehensively monitored, and their anomalies should be detected timely. In this paper, we study the problem of anomaly detection for deployments. We begin by identifying the challenges unique to this anomaly detection problem, which is at entity-level (e.g., deployments), relative to the more typical problem of anomaly detection in multivariate time series (MTS). The unique challenges include the heterogeneity of deployments, the low latency tolerance, the ambiguous anomaly definition, and the limited supervision. To address them, we propose a novel framework, semi-supervised hybrid Model for Entity-Level Online Detection of anomalY (MELODY). MELODY first transforms the MTS of different entities to the same feature space by an online feature extractor, then uses a newly proposed semi-supervised deep one-class model for detecting anomalous entities. We evaluated MELODY on real data of cloud services with 1.2M+ time series. The relative F1 score improvement of MELODY over the state-of-the-art methods ranges from 7.6% to 56.5%. The user evaluation suggests MELODY is suitable for monitoring deployments in large online systems. |
1705.01143 | Shih-Chieh Su | Shih-Chieh Su | Summarized Network Behavior Prediction | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work studies the entity-wise topical behavior from massive network logs.
Both the temporal and the spatial relationships of the behavior are explored
with the learning architectures combing the recurrent neural network (RNN) and
the convolutional neural network (CNN). To make the behavioral data appropriate
for the spatial learning in CNN, several reduction steps are taken to form the
topical metrics and place them homogeneously like pixels in the images. The
experimental result shows both the temporal- and the spatial- gains when
compared to a multilayer perceptron (MLP) network. A new learning framework
called spatially connected convolutional networks (SCCN) is introduced to more
efficiently predict the behavior.
| [
{
"created": "Tue, 2 May 2017 19:12:23 GMT",
"version": "v1"
}
] | 2017-05-04 | [
[
"Su",
"Shih-Chieh",
""
]
] | This work studies the entity-wise topical behavior from massive network logs. Both the temporal and the spatial relationships of the behavior are explored with the learning architectures combing the recurrent neural network (RNN) and the convolutional neural network (CNN). To make the behavioral data appropriate for the spatial learning in CNN, several reduction steps are taken to form the topical metrics and place them homogeneously like pixels in the images. The experimental result shows both the temporal- and the spatial- gains when compared to a multilayer perceptron (MLP) network. A new learning framework called spatially connected convolutional networks (SCCN) is introduced to more efficiently predict the behavior. |
2010.03110 | Sumedh Sontakke | Sumedh A. Sontakke, Arash Mehrjou, Laurent Itti, Bernhard Sch\"olkopf | Causal Curiosity: RL Agents Discovering Self-supervised Experiments for
Causal Representation Learning | International Conference on Machine Learning, PMLR 139, 2021 | null | null | null | cs.LG cs.AI cs.RO | http://creativecommons.org/licenses/by/4.0/ | Animals exhibit an innate ability to learn regularities of the world through
interaction. By performing experiments in their environment, they are able to
discern the causal factors of variation and infer how they affect the world's
dynamics. Inspired by this, we attempt to equip reinforcement learning agents
with the ability to perform experiments that facilitate a categorization of the
rolled-out trajectories, and to subsequently infer the causal factors of the
environment in a hierarchical manner. We introduce {\em causal curiosity}, a
novel intrinsic reward, and show that it allows our agents to learn optimal
sequences of actions and discover causal factors in the dynamics of the
environment. The learned behavior allows the agents to infer a binary quantized
representation for the ground-truth causal factors in every environment.
Additionally, we find that these experimental behaviors are semantically
meaningful (e.g., our agents learn to lift blocks to categorize them by
weight), and are learnt in a self-supervised manner with approximately 2.5
times less data than conventional supervised planners. We show that these
behaviors can be re-purposed and fine-tuned (e.g., from lifting to pushing or
other downstream tasks). Finally, we show that the knowledge of causal factor
representations aids zero-shot learning for more complex tasks. Visit
https://sites.google.com/usc.edu/causal-curiosity/home for website.
| [
{
"created": "Wed, 7 Oct 2020 02:07:51 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Apr 2021 23:59:04 GMT",
"version": "v2"
},
{
"created": "Wed, 9 Jun 2021 01:19:39 GMT",
"version": "v3"
},
{
"created": "Fri, 6 Aug 2021 21:53:05 GMT",
"version": "v4"
}
] | 2021-08-10 | [
[
"Sontakke",
"Sumedh A.",
""
],
[
"Mehrjou",
"Arash",
""
],
[
"Itti",
"Laurent",
""
],
[
"Schölkopf",
"Bernhard",
""
]
] | Animals exhibit an innate ability to learn regularities of the world through interaction. By performing experiments in their environment, they are able to discern the causal factors of variation and infer how they affect the world's dynamics. Inspired by this, we attempt to equip reinforcement learning agents with the ability to perform experiments that facilitate a categorization of the rolled-out trajectories, and to subsequently infer the causal factors of the environment in a hierarchical manner. We introduce {\em causal curiosity}, a novel intrinsic reward, and show that it allows our agents to learn optimal sequences of actions and discover causal factors in the dynamics of the environment. The learned behavior allows the agents to infer a binary quantized representation for the ground-truth causal factors in every environment. Additionally, we find that these experimental behaviors are semantically meaningful (e.g., our agents learn to lift blocks to categorize them by weight), and are learnt in a self-supervised manner with approximately 2.5 times less data than conventional supervised planners. We show that these behaviors can be re-purposed and fine-tuned (e.g., from lifting to pushing or other downstream tasks). Finally, we show that the knowledge of causal factor representations aids zero-shot learning for more complex tasks. Visit https://sites.google.com/usc.edu/causal-curiosity/home for website. |
1602.00251 | Kaveh Bakhtiyari | Kaveh Bakhtiyari | Do we have privacy in the digital world? | null | null | 10.13140/RG.2.1.2492.5203/2 | null | cs.CR cs.HC cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Not really.
| [
{
"created": "Sun, 31 Jan 2016 14:22:47 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Jan 2017 15:53:55 GMT",
"version": "v2"
}
] | 2017-01-27 | [
[
"Bakhtiyari",
"Kaveh",
""
]
] | Not really. |
2312.00220 | Linzi Xing | Linzi Xing, Quan Tran, Fabian Caba, Franck Dernoncourt, Seunghyun
Yoon, Zhaowen Wang, Trung Bui, Giuseppe Carenini | Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain
Adaptation | Accepted at the 30th International Conference on Multimedia Modeling
(MMM 2024) | null | null | null | cs.MM cs.CL cs.CV | http://creativecommons.org/licenses/by/4.0/ | Video topic segmentation unveils the coarse-grained semantic structure
underlying videos and is essential for other video understanding tasks. Given
the recent surge in multi-modal, relying solely on a single modality is
arguably insufficient. On the other hand, prior solutions for similar tasks
like video scene/shot segmentation cater to short videos with clear visual
shifts but falter for long videos with subtle changes, such as livestreams. In
this paper, we introduce a multi-modal video topic segmenter that utilizes both
video transcripts and frames, bolstered by a cross-modal attention mechanism.
Furthermore, we propose a dual-contrastive learning framework adhering to the
unsupervised domain adaptation paradigm, enhancing our model's adaptability to
longer, more semantically complex videos. Experiments on short and long video
corpora demonstrate that our proposed solution, significantly surpasses
baseline methods in terms of both accuracy and transferability, in both intra-
and cross-domain settings.
| [
{
"created": "Thu, 30 Nov 2023 21:59:05 GMT",
"version": "v1"
}
] | 2023-12-04 | [
[
"Xing",
"Linzi",
""
],
[
"Tran",
"Quan",
""
],
[
"Caba",
"Fabian",
""
],
[
"Dernoncourt",
"Franck",
""
],
[
"Yoon",
"Seunghyun",
""
],
[
"Wang",
"Zhaowen",
""
],
[
"Bui",
"Trung",
""
],
[
"Carenini",
"Giuseppe",
""
]
] | Video topic segmentation unveils the coarse-grained semantic structure underlying videos and is essential for other video understanding tasks. Given the recent surge in multi-modal, relying solely on a single modality is arguably insufficient. On the other hand, prior solutions for similar tasks like video scene/shot segmentation cater to short videos with clear visual shifts but falter for long videos with subtle changes, such as livestreams. In this paper, we introduce a multi-modal video topic segmenter that utilizes both video transcripts and frames, bolstered by a cross-modal attention mechanism. Furthermore, we propose a dual-contrastive learning framework adhering to the unsupervised domain adaptation paradigm, enhancing our model's adaptability to longer, more semantically complex videos. Experiments on short and long video corpora demonstrate that our proposed solution, significantly surpasses baseline methods in terms of both accuracy and transferability, in both intra- and cross-domain settings. |
2004.14793 | Gal Mendelson | Gal Mendelson | A Lower Bound on the stability region of Redundancy-d with FIFO service
discipline | null | null | null | null | cs.PF cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Redundancy-d (R(d)) is a load balancing method used to route incoming jobs to
K servers, each with its own queue. Every arriving job is replicated into
2<=d<=K tasks, which are then routed to d servers chosen uniformly at random.
When the first task finishes service, the remaining d-1 tasks are cancelled and
the job departs the system.
Despite the fact that R(d) is known, under certain conditions, to
substantially improve job completion times compared to not using redundancy at
all, little is known on a more fundamental performance criterion: what is the
set of arrival rates under which the R(d) queueing system with FIFO service
discipline is stable? In this context, due to the complex dynamics of systems
with redundancy and cancellations, existing results are scarce and are limited
to very special cases with respect to the joint service time distribution of
tasks.
In this paper we provide a non-trivial, closed form lower bound on the
stability region of R(d) for a general joint service time distribution of tasks
with finite first and second moments. We consider a discrete time system with
Bernoulli arrivals and assume that jobs are processed by their order of
arrival. We use the workload processes and a quadratic Lyapunov function to
characterize the set of arrival rates for which the system is stable. While
simulation results indicate our bound is not tight, it provides an
easy-to-check performance guarantee.
| [
{
"created": "Thu, 30 Apr 2020 14:07:25 GMT",
"version": "v1"
},
{
"created": "Thu, 21 May 2020 18:15:06 GMT",
"version": "v2"
}
] | 2020-05-25 | [
[
"Mendelson",
"Gal",
""
]
] | Redundancy-d (R(d)) is a load balancing method used to route incoming jobs to K servers, each with its own queue. Every arriving job is replicated into 2<=d<=K tasks, which are then routed to d servers chosen uniformly at random. When the first task finishes service, the remaining d-1 tasks are cancelled and the job departs the system. Despite the fact that R(d) is known, under certain conditions, to substantially improve job completion times compared to not using redundancy at all, little is known on a more fundamental performance criterion: what is the set of arrival rates under which the R(d) queueing system with FIFO service discipline is stable? In this context, due to the complex dynamics of systems with redundancy and cancellations, existing results are scarce and are limited to very special cases with respect to the joint service time distribution of tasks. In this paper we provide a non-trivial, closed form lower bound on the stability region of R(d) for a general joint service time distribution of tasks with finite first and second moments. We consider a discrete time system with Bernoulli arrivals and assume that jobs are processed by their order of arrival. We use the workload processes and a quadratic Lyapunov function to characterize the set of arrival rates for which the system is stable. While simulation results indicate our bound is not tight, it provides an easy-to-check performance guarantee. |
2404.00492 | Lijie Hu | Keyuan Cheng, Gang Lin, Haoyang Fei, Yuxuan zhai, Lu Yu, Muhammad Asif
Ali, Lijie Hu, and Di Wang | Multi-hop Question Answering under Temporal Knowledge Editing | 23 pages | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Multi-hop question answering (MQA) under knowledge editing (KE) has garnered
significant attention in the era of large language models. However, existing
models for MQA under KE exhibit poor performance when dealing with questions
containing explicit temporal contexts. To address this limitation, we propose a
novel framework, namely TEMPoral knowLEdge augmented Multi-hop Question
Answering (TEMPLE-MQA). Unlike previous methods, TEMPLE-MQA first constructs a
time-aware graph (TAG) to store edit knowledge in a structured manner. Then,
through our proposed inference path, structural retrieval, and joint reasoning
stages, TEMPLE-MQA effectively discerns temporal contexts within the question
query. Experiments on benchmark datasets demonstrate that TEMPLE-MQA
significantly outperforms baseline models. Additionally, we contribute a new
dataset, namely TKEMQA, which serves as the inaugural benchmark tailored
specifically for MQA with temporal scopes.
| [
{
"created": "Sat, 30 Mar 2024 23:22:51 GMT",
"version": "v1"
}
] | 2024-04-02 | [
[
"Cheng",
"Keyuan",
""
],
[
"Lin",
"Gang",
""
],
[
"Fei",
"Haoyang",
""
],
[
"zhai",
"Yuxuan",
""
],
[
"Yu",
"Lu",
""
],
[
"Ali",
"Muhammad Asif",
""
],
[
"Hu",
"Lijie",
""
],
[
"Wang",
"Di",
""
]
] | Multi-hop question answering (MQA) under knowledge editing (KE) has garnered significant attention in the era of large language models. However, existing models for MQA under KE exhibit poor performance when dealing with questions containing explicit temporal contexts. To address this limitation, we propose a novel framework, namely TEMPoral knowLEdge augmented Multi-hop Question Answering (TEMPLE-MQA). Unlike previous methods, TEMPLE-MQA first constructs a time-aware graph (TAG) to store edit knowledge in a structured manner. Then, through our proposed inference path, structural retrieval, and joint reasoning stages, TEMPLE-MQA effectively discerns temporal contexts within the question query. Experiments on benchmark datasets demonstrate that TEMPLE-MQA significantly outperforms baseline models. Additionally, we contribute a new dataset, namely TKEMQA, which serves as the inaugural benchmark tailored specifically for MQA with temporal scopes. |
1706.02499 | Burak Benligiray | Burak Benligiray, Cihan Topal, Cuneyt Akinlar | SliceType: Fast Gaze Typing with a Merging Keyboard | null | Journal on Multimodal User Interfaces, 2018 | 10.1007/s12193-018-0285-z | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Jitter is an inevitable by-product of gaze detection. Because of this, gaze
typing tends to be a slow and frustrating process. In this paper, we propose
SliceType, a soft keyboard that is optimized for gaze input. Our main design
objective is to use the screen area more efficiently by allocating a larger
area to the target keys. We achieve this by determining the keys that will not
be used for the next input, and allocating their space to the adjacent keys
with a merging animation. Larger keys are faster to navigate towards, and easy
to dwell on in the presence of eye tracking jitter. As a result, the user types
faster and more comfortably. In addition, we employ a word completion scheme
that complements gaze typing mechanics. A character and a related prediction is
displayed at each key. Dwelling at a key enters the character, and
double-dwelling enters the prediction. While dwelling on a key to enter a
character, the user reads the related prediction effortlessly. The improvements
provided by these features are quantified using the Fitts' law. The performance
of the proposed keyboard is compared with two other soft keyboards designed for
gaze typing, Dasher and GazeTalk. 37 novice users gaze-typed a piece of text
using all three keyboards. The results of the experiment show that the proposed
keyboard allows faster typing, and is more preferred by the users.
| [
{
"created": "Thu, 8 Jun 2017 10:06:52 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Mar 2018 13:39:05 GMT",
"version": "v2"
},
{
"created": "Sun, 18 Mar 2018 19:14:36 GMT",
"version": "v3"
},
{
"created": "Thu, 27 Dec 2018 13:59:19 GMT",
"version": "v4"
}
] | 2018-12-31 | [
[
"Benligiray",
"Burak",
""
],
[
"Topal",
"Cihan",
""
],
[
"Akinlar",
"Cuneyt",
""
]
] | Jitter is an inevitable by-product of gaze detection. Because of this, gaze typing tends to be a slow and frustrating process. In this paper, we propose SliceType, a soft keyboard that is optimized for gaze input. Our main design objective is to use the screen area more efficiently by allocating a larger area to the target keys. We achieve this by determining the keys that will not be used for the next input, and allocating their space to the adjacent keys with a merging animation. Larger keys are faster to navigate towards, and easy to dwell on in the presence of eye tracking jitter. As a result, the user types faster and more comfortably. In addition, we employ a word completion scheme that complements gaze typing mechanics. A character and a related prediction is displayed at each key. Dwelling at a key enters the character, and double-dwelling enters the prediction. While dwelling on a key to enter a character, the user reads the related prediction effortlessly. The improvements provided by these features are quantified using the Fitts' law. The performance of the proposed keyboard is compared with two other soft keyboards designed for gaze typing, Dasher and GazeTalk. 37 novice users gaze-typed a piece of text using all three keyboards. The results of the experiment show that the proposed keyboard allows faster typing, and is more preferred by the users. |
2405.07456 | ABDELLAH Zakaria Sellam | Zakaria Abdellah Sellam, Cosimo Distante, Abdelmalik Taleb-Ahmed, Pier
Luigi Mazzeo | Boosting House Price Estimations with Multi-Head Gated Attention | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evaluating house prices is crucial for various stakeholders, including
homeowners, investors, and policymakers. However, traditional spatial
interpolation methods have limitations in capturing the complex spatial
relationships that affect property values. To address these challenges, we have
developed a new method called Multi-Head Gated Attention for spatial
interpolation. Our approach builds upon attention-based interpolation models
and incorporates multiple attention heads and gating mechanisms to capture
spatial dependencies and contextual information better. Importantly, our model
produces embeddings that reduce the dimensionality of the data, enabling
simpler models like linear regression to outperform complex ensembling models.
We conducted extensive experiments to compare our model with baseline methods
and the original attention-based interpolation model. The results show a
significant improvement in the accuracy of house price predictions, validating
the effectiveness of our approach. This research advances the field of spatial
interpolation and provides a robust tool for more precise house price
evaluation. Our GitHub repository.contains the data and code for all datasets,
which are available for researchers and practitioners interested in replicating
or building upon our work.
| [
{
"created": "Mon, 13 May 2024 04:12:03 GMT",
"version": "v1"
}
] | 2024-05-14 | [
[
"Sellam",
"Zakaria Abdellah",
""
],
[
"Distante",
"Cosimo",
""
],
[
"Taleb-Ahmed",
"Abdelmalik",
""
],
[
"Mazzeo",
"Pier Luigi",
""
]
] | Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relationships that affect property values. To address these challenges, we have developed a new method called Multi-Head Gated Attention for spatial interpolation. Our approach builds upon attention-based interpolation models and incorporates multiple attention heads and gating mechanisms to capture spatial dependencies and contextual information better. Importantly, our model produces embeddings that reduce the dimensionality of the data, enabling simpler models like linear regression to outperform complex ensembling models. We conducted extensive experiments to compare our model with baseline methods and the original attention-based interpolation model. The results show a significant improvement in the accuracy of house price predictions, validating the effectiveness of our approach. This research advances the field of spatial interpolation and provides a robust tool for more precise house price evaluation. Our GitHub repository.contains the data and code for all datasets, which are available for researchers and practitioners interested in replicating or building upon our work. |
2204.07333 | Prabhat Kumar | Prabhat Kumar, Eduardo Fern\'andez | Topology optimization for additive manufacturing with length scale,
overhang, and building orientation constraints | null | null | null | null | cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a density-based topology optimization approach
considering additive manufacturing limitations. The presented method considers
the minimum size of parts, the minimum size of cavities, the inability of
printing overhanging parts without the use of sacrificial supporting
structures, and the printing directions. These constraints are geometrically
addressed and implemented. The minimum size on solid and void zones is imposed
through a well-known filtering technique. The sacrificial support material is
reduced using a constraint that limits the maximum overhang angle of parts by
comparing the structural gradient with a critical reference slope. Due to the
local nature of the gradient, the chosen restriction is prone to introduce
parts that meet the structural slope but that may not be self-supporting. The
restriction limits the maximum overhang angle for a user-defined printing
direction, which could reduce structural performance if the orientation is not
properly selected. To ease these challenges, a new approach to reduce the
introduction of such non-self-supporting parts and a novel method that includes
different printing directions in the maximum overhang angle constraint are
presented. The proposed strategy for considering the minimum size of solid and
void phases, maximum overhang angle, and printing direction, is illustrated by
solving a set of 2D benchmark design problems including stiff structures and
compliant mechanisms. We also provide MATLAB codes in the appendix for
educational purposes and for replication of the results.
| [
{
"created": "Fri, 15 Apr 2022 05:16:58 GMT",
"version": "v1"
}
] | 2022-04-18 | [
[
"Kumar",
"Prabhat",
""
],
[
"Fernández",
"Eduardo",
""
]
] | This paper presents a density-based topology optimization approach considering additive manufacturing limitations. The presented method considers the minimum size of parts, the minimum size of cavities, the inability of printing overhanging parts without the use of sacrificial supporting structures, and the printing directions. These constraints are geometrically addressed and implemented. The minimum size on solid and void zones is imposed through a well-known filtering technique. The sacrificial support material is reduced using a constraint that limits the maximum overhang angle of parts by comparing the structural gradient with a critical reference slope. Due to the local nature of the gradient, the chosen restriction is prone to introduce parts that meet the structural slope but that may not be self-supporting. The restriction limits the maximum overhang angle for a user-defined printing direction, which could reduce structural performance if the orientation is not properly selected. To ease these challenges, a new approach to reduce the introduction of such non-self-supporting parts and a novel method that includes different printing directions in the maximum overhang angle constraint are presented. The proposed strategy for considering the minimum size of solid and void phases, maximum overhang angle, and printing direction, is illustrated by solving a set of 2D benchmark design problems including stiff structures and compliant mechanisms. We also provide MATLAB codes in the appendix for educational purposes and for replication of the results. |
2304.10512 | Orchid Chetia Phukan | Usha Lokala, Orchid Chetia Phukan, Triyasha Ghosh Dastidar, Francois
Lamy, Raminta Daniulaityte, Amit Sheth | "Can We Detect Substance Use Disorder?": Knowledge and Time Aware
Classification on Social Media from Darkweb | null | null | null | null | cs.LG cs.CL cs.SI | http://creativecommons.org/licenses/by/4.0/ | Opioid and substance misuse is rampant in the United States today, with the
phenomenon known as the "opioid crisis". The relationship between substance use
and mental health has been extensively studied, with one possible relationship
being: substance misuse causes poor mental health. However, the lack of
evidence on the relationship has resulted in opioids being largely inaccessible
through legal means. This study analyzes the substance use posts on social
media with opioids being sold through crypto market listings. We use the Drug
Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based
models to generate sentiment and emotion for the social media posts to
understand users' perceptions on social media by investigating questions such
as: which synthetic opioids people are optimistic, neutral, or negative about?
or what kind of drugs induced fear and sorrow? or what kind of drugs people
love or are thankful about? or which drugs people think negatively about? or
which opioids cause little to no sentimental reaction. We discuss how we
crawled crypto market data and its use in extracting posts for fentanyl,
fentanyl analogs, and other novel synthetic opioids. We also perform topic
analysis associated with the generated sentiments and emotions to understand
which topics correlate with people's responses to various drugs. Additionally,
we analyze time-aware neural models built on these features while considering
historical sentiment and emotional activity of posts related to a drug. The
most effective model performs well (statistically significant) with
(macroF1=82.12, recall =83.58) to identify substance use disorder.
| [
{
"created": "Thu, 20 Apr 2023 17:47:13 GMT",
"version": "v1"
}
] | 2023-04-21 | [
[
"Lokala",
"Usha",
""
],
[
"Phukan",
"Orchid Chetia",
""
],
[
"Dastidar",
"Triyasha Ghosh",
""
],
[
"Lamy",
"Francois",
""
],
[
"Daniulaityte",
"Raminta",
""
],
[
"Sheth",
"Amit",
""
]
] | Opioid and substance misuse is rampant in the United States today, with the phenomenon known as the "opioid crisis". The relationship between substance use and mental health has been extensively studied, with one possible relationship being: substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means. This study analyzes the substance use posts on social media with opioids being sold through crypto market listings. We use the Drug Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based models to generate sentiment and emotion for the social media posts to understand users' perceptions on social media by investigating questions such as: which synthetic opioids people are optimistic, neutral, or negative about? or what kind of drugs induced fear and sorrow? or what kind of drugs people love or are thankful about? or which drugs people think negatively about? or which opioids cause little to no sentimental reaction. We discuss how we crawled crypto market data and its use in extracting posts for fentanyl, fentanyl analogs, and other novel synthetic opioids. We also perform topic analysis associated with the generated sentiments and emotions to understand which topics correlate with people's responses to various drugs. Additionally, we analyze time-aware neural models built on these features while considering historical sentiment and emotional activity of posts related to a drug. The most effective model performs well (statistically significant) with (macroF1=82.12, recall =83.58) to identify substance use disorder. |
1604.03178 | Luca de Alfaro | Luca de Alfaro, Michael Shavlovsky, Vassilis Polychronopoulos | Incentives for Truthful Peer Grading | 26 pages | null | null | UCSC-SOE-15-19 | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Peer grading systems work well only if users have incentives to grade
truthfully. An example of non-truthful grading, that we observed in classrooms,
consists in students assigning the maximum grade to all submissions. With a
naive grading scheme, such as averaging the assigned grades, all students would
receive the maximum grade. In this paper, we develop three grading schemes that
provide incentives for truthful peer grading. In the first scheme, the
instructor grades a fraction p of the submissions, and penalizes students whose
grade deviates from the instructor grade. We provide lower bounds on p to
ensure truthfulness, and conclude that these schemes work only for moderate
class sizes, up to a few hundred students. To overcome this limitation, we
propose a hierarchical extension of this supervised scheme, and we show that it
can handle classes of any size with bounded (and little) instructor work, and
is therefore applicable to Massive Open Online Courses (MOOCs). Finally, we
propose unsupervised incentive schemes, in which the student incentive is based
on statistical properties of the grade distribution, without any grading
required by the instructor. We show that the proposed unsupervised schemes
provide incentives to truthful grading, at the price of being possibly unfair
to individual students.
| [
{
"created": "Mon, 11 Apr 2016 23:56:21 GMT",
"version": "v1"
}
] | 2016-04-13 | [
[
"de Alfaro",
"Luca",
""
],
[
"Shavlovsky",
"Michael",
""
],
[
"Polychronopoulos",
"Vassilis",
""
]
] | Peer grading systems work well only if users have incentives to grade truthfully. An example of non-truthful grading, that we observed in classrooms, consists in students assigning the maximum grade to all submissions. With a naive grading scheme, such as averaging the assigned grades, all students would receive the maximum grade. In this paper, we develop three grading schemes that provide incentives for truthful peer grading. In the first scheme, the instructor grades a fraction p of the submissions, and penalizes students whose grade deviates from the instructor grade. We provide lower bounds on p to ensure truthfulness, and conclude that these schemes work only for moderate class sizes, up to a few hundred students. To overcome this limitation, we propose a hierarchical extension of this supervised scheme, and we show that it can handle classes of any size with bounded (and little) instructor work, and is therefore applicable to Massive Open Online Courses (MOOCs). Finally, we propose unsupervised incentive schemes, in which the student incentive is based on statistical properties of the grade distribution, without any grading required by the instructor. We show that the proposed unsupervised schemes provide incentives to truthful grading, at the price of being possibly unfair to individual students. |
2303.00871 | Lorenzo Mur-Labadia | Lorenzo Mur-Labadia, Ruben Martinez-Cantin and Jose J. Guerrero | Bayesian Deep Learning for Affordance Segmentation in images | 2023 IEEE International Conference on Robotics and Automation (ICRA) | null | null | null | cs.CV cs.AI cs.LG cs.RO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Affordances are a fundamental concept in robotics since they relate available
actions for an agent depending on its sensory-motor capabilities and the
environment. We present a novel Bayesian deep network to detect affordances in
images, at the same time that we quantify the distribution of the aleatoric and
epistemic variance at the spatial level. We adapt the Mask-RCNN architecture to
learn a probabilistic representation using Monte Carlo dropout. Our results
outperform the state-of-the-art of deterministic networks. We attribute this
improvement to a better probabilistic feature space representation on the
encoder and the Bayesian variability induced at the mask generation, which
adapts better to the object contours. We also introduce the new
Probability-based Mask Quality measure that reveals the semantic and spatial
differences on a probabilistic instance segmentation model. We modify the
existing Probabilistic Detection Quality metric by comparing the binary masks
rather than the predicted bounding boxes, achieving a finer-grained evaluation
of the probabilistic segmentation. We find aleatoric variance in the contours
of the objects due to the camera noise, while epistemic variance appears in
visual challenging pixels.
| [
{
"created": "Thu, 2 Mar 2023 00:01:13 GMT",
"version": "v1"
}
] | 2023-03-03 | [
[
"Mur-Labadia",
"Lorenzo",
""
],
[
"Martinez-Cantin",
"Ruben",
""
],
[
"Guerrero",
"Jose J.",
""
]
] | Affordances are a fundamental concept in robotics since they relate available actions for an agent depending on its sensory-motor capabilities and the environment. We present a novel Bayesian deep network to detect affordances in images, at the same time that we quantify the distribution of the aleatoric and epistemic variance at the spatial level. We adapt the Mask-RCNN architecture to learn a probabilistic representation using Monte Carlo dropout. Our results outperform the state-of-the-art of deterministic networks. We attribute this improvement to a better probabilistic feature space representation on the encoder and the Bayesian variability induced at the mask generation, which adapts better to the object contours. We also introduce the new Probability-based Mask Quality measure that reveals the semantic and spatial differences on a probabilistic instance segmentation model. We modify the existing Probabilistic Detection Quality metric by comparing the binary masks rather than the predicted bounding boxes, achieving a finer-grained evaluation of the probabilistic segmentation. We find aleatoric variance in the contours of the objects due to the camera noise, while epistemic variance appears in visual challenging pixels. |
2211.13720 | Sachit Rao | Wayne Paul Martis and Sachit Rao | Cooperative Collision Avoidance in Mobile Robots using Dynamic Vortex
Potential Fields | null | null | null | null | cs.RO | http://creativecommons.org/licenses/by/4.0/ | In this paper, the collision avoidance problem for non-holonomic robots
moving at constant linear speeds in the 2-D plane is considered. The maneuvers
to avoid collisions are designed using dynamic vortex potential fields (PFs)
and their negative gradients; this formulation leads to a reciprocal behaviour
between the robots, denoted as being cooperative. The repulsive field is
selected as a function of the velocity and position of a robot relative to
another and introducing vorticity in its definition guarantees the absence of
local minima. Such a repulsive field is activated by a robot only when it is on
a collision path with other mobile robots or stationary obstacles. By analysing
the kinematics-based engagement dynamics in polar coordinates, it is shown that
a cooperative robot is able to avoid collisions with non-cooperating robots,
such as stationary and constant velocity robots, as well as those actively
seeking to collide with it. Conditions on the PF parameters are identified that
ensure collision avoidance for all cases. Experimental results acquired using a
mobile robot platform support the theoretical contributions.
| [
{
"created": "Thu, 24 Nov 2022 17:16:01 GMT",
"version": "v1"
}
] | 2022-11-28 | [
[
"Martis",
"Wayne Paul",
""
],
[
"Rao",
"Sachit",
""
]
] | In this paper, the collision avoidance problem for non-holonomic robots moving at constant linear speeds in the 2-D plane is considered. The maneuvers to avoid collisions are designed using dynamic vortex potential fields (PFs) and their negative gradients; this formulation leads to a reciprocal behaviour between the robots, denoted as being cooperative. The repulsive field is selected as a function of the velocity and position of a robot relative to another and introducing vorticity in its definition guarantees the absence of local minima. Such a repulsive field is activated by a robot only when it is on a collision path with other mobile robots or stationary obstacles. By analysing the kinematics-based engagement dynamics in polar coordinates, it is shown that a cooperative robot is able to avoid collisions with non-cooperating robots, such as stationary and constant velocity robots, as well as those actively seeking to collide with it. Conditions on the PF parameters are identified that ensure collision avoidance for all cases. Experimental results acquired using a mobile robot platform support the theoretical contributions. |
2101.10488 | EPTCS | Paul Wilson (University of Southampton), Fabio Zanasi (University
College London) | Reverse Derivative Ascent: A Categorical Approach to Learning Boolean
Circuits | In Proceedings ACT 2020, arXiv:2101.07888 | EPTCS 333, 2021, pp. 247-260 | 10.4204/EPTCS.333.17 | null | cs.LO cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce Reverse Derivative Ascent: a categorical analogue of gradient
based methods for machine learning. Our algorithm is defined at the level of
so-called reverse differential categories. It can be used to learn the
parameters of models which are expressed as morphisms of such categories. Our
motivating example is boolean circuits: we show how our algorithm can be
applied to such circuits by using the theory of reverse differential
categories. Note our methodology allows us to learn the parameters of boolean
circuits directly, in contrast to existing binarised neural network approaches.
Moreover, we demonstrate its empirical value by giving experimental results on
benchmark machine learning datasets.
| [
{
"created": "Tue, 26 Jan 2021 00:07:20 GMT",
"version": "v1"
}
] | 2021-01-27 | [
[
"Wilson",
"Paul",
"",
"University of Southampton"
],
[
"Zanasi",
"Fabio",
"",
"University\n College London"
]
] | We introduce Reverse Derivative Ascent: a categorical analogue of gradient based methods for machine learning. Our algorithm is defined at the level of so-called reverse differential categories. It can be used to learn the parameters of models which are expressed as morphisms of such categories. Our motivating example is boolean circuits: we show how our algorithm can be applied to such circuits by using the theory of reverse differential categories. Note our methodology allows us to learn the parameters of boolean circuits directly, in contrast to existing binarised neural network approaches. Moreover, we demonstrate its empirical value by giving experimental results on benchmark machine learning datasets. |
1511.03532 | Ali Keles | Ali Keles, Ayturk Keles | IBMMS Decision Support Tool For Management of Bank Telemarketing
Campaigns | 15 pages, 4 figures, 4 tables, journal in International Journal of
Database Management Systems, Vol.7, No.5, October 2015 | null | 10.5121/ijdms.2015.7501 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although direct marketing is a good method for banks to utilize in the face
of global competition and the financial crisis, it has been shown to exhibit
poor performance. However, there are some drawbacks to direct campaigns, such
as those related to improving the negative attributes that customers ascribe to
banks. To overcome these problems, attractive long-term deposit campaigns
should be organized and managed more effectively. The aim of this study is to
develop an Intelligent Bank Market Management System (IBMMS) for bank managers
who want to manage efficient marketing campaigns. IBMMS is the first system
developed by combining the power of data mining with the capabilities of expert
systems in this area. Moreover, IBMMS includes important features that enable
it to be intelligent: a knowledge base, an inference engine and an advisor.
Using this system, a manager can successfully direct marketing campaigns and
follow the decision schemas of customers both as individuals and as a group;
moreover, a manager can make decisions that lead to the desired response by
customers.
| [
{
"created": "Wed, 11 Nov 2015 15:26:08 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Nov 2015 14:14:01 GMT",
"version": "v2"
}
] | 2015-11-13 | [
[
"Keles",
"Ali",
""
],
[
"Keles",
"Ayturk",
""
]
] | Although direct marketing is a good method for banks to utilize in the face of global competition and the financial crisis, it has been shown to exhibit poor performance. However, there are some drawbacks to direct campaigns, such as those related to improving the negative attributes that customers ascribe to banks. To overcome these problems, attractive long-term deposit campaigns should be organized and managed more effectively. The aim of this study is to develop an Intelligent Bank Market Management System (IBMMS) for bank managers who want to manage efficient marketing campaigns. IBMMS is the first system developed by combining the power of data mining with the capabilities of expert systems in this area. Moreover, IBMMS includes important features that enable it to be intelligent: a knowledge base, an inference engine and an advisor. Using this system, a manager can successfully direct marketing campaigns and follow the decision schemas of customers both as individuals and as a group; moreover, a manager can make decisions that lead to the desired response by customers. |
1808.08106 | Joseph Schuchart | Joseph Schuchart, Daniel Hackenberg, Robert Sch\"one, Thomas Ilsche,
Ramkumar Nagappan, Michael K. Patterson | The Shift from Processor Power Consumption to Performance Variations:
Fundamental Implications at Scale | null | Computer Science - Research and Development, Vol. 31, pp.
197--205, Nov 2016 | 10.1007/s00450-016-0327-2 | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Intel Haswell-EP processor generation introduces several major
advancements of power control and energy-efficiency features. For
computationally intense applications using advanced vector extension (AVX)
instructions, the processor cannot continuously operate at full speed but
instead reduces its frequency below the nominal frequency to maintain
operations within thermal design power (TDP) limitations. Moreover, the running
average power limitation (RAPL) mechanism to enforce the TDP limitation has
changed from a modeling to a measurement approach. The combination of these two
novelties have significant implications. Through measurements on an Intel Sandy
Bridge-EP cluster, we show that previous generations have sustained homogeneous
performance across multiple CPUs and compensated for hardware manufacturing
variability through varying power consumption. In contrast, our measurements on
a Petaflop Haswell system show that this generation exhibits rather homogeneous
power consumption limited by the TDP and capped by the improved RAPL while
providing inhomogeneous performance under full load. Since all of these
controls are transparent to the user, this behavior is likely to complicate
performance analysis tasks and impact tightly coupled parallel applications.
| [
{
"created": "Fri, 24 Aug 2018 12:40:03 GMT",
"version": "v1"
}
] | 2018-08-27 | [
[
"Schuchart",
"Joseph",
""
],
[
"Hackenberg",
"Daniel",
""
],
[
"Schöne",
"Robert",
""
],
[
"Ilsche",
"Thomas",
""
],
[
"Nagappan",
"Ramkumar",
""
],
[
"Patterson",
"Michael K.",
""
]
] | The Intel Haswell-EP processor generation introduces several major advancements of power control and energy-efficiency features. For computationally intense applications using advanced vector extension (AVX) instructions, the processor cannot continuously operate at full speed but instead reduces its frequency below the nominal frequency to maintain operations within thermal design power (TDP) limitations. Moreover, the running average power limitation (RAPL) mechanism to enforce the TDP limitation has changed from a modeling to a measurement approach. The combination of these two novelties have significant implications. Through measurements on an Intel Sandy Bridge-EP cluster, we show that previous generations have sustained homogeneous performance across multiple CPUs and compensated for hardware manufacturing variability through varying power consumption. In contrast, our measurements on a Petaflop Haswell system show that this generation exhibits rather homogeneous power consumption limited by the TDP and capped by the improved RAPL while providing inhomogeneous performance under full load. Since all of these controls are transparent to the user, this behavior is likely to complicate performance analysis tasks and impact tightly coupled parallel applications. |
2012.08483 | Valerio Perrone | Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar Karnin,
Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut
Lienart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela
Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav
Miladinovic, Giovanni Zappella, C\'edric Archambeau, Matthias Seeger, Bhaskar
Dutt, Laurence Rouesnel | Amazon SageMaker Autopilot: a white box AutoML solution at scale | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | AutoML systems provide a black-box solution to machine learning problems by
selecting the right way of processing features, choosing an algorithm and
tuning the hyperparameters of the entire pipeline. Although these systems
perform well on many datasets, there is still a non-negligible number of
datasets for which the one-shot solution produced by each particular system
would provide sub-par performance. In this paper, we present Amazon SageMaker
Autopilot: a fully managed system providing an automated ML solution that can
be modified when needed. Given a tabular dataset and the target column name,
Autopilot identifies the problem type, analyzes the data and produces a diverse
set of complete ML pipelines including feature preprocessing and ML algorithms,
which are tuned to generate a leaderboard of candidate models. In the scenario
where the performance is not satisfactory, a data scientist is able to view and
edit the proposed ML pipelines in order to infuse their expertise and business
knowledge without having to revert to a fully manual solution. This paper
describes the different components of Autopilot, emphasizing the infrastructure
choices that allow scalability, high quality models, editable ML pipelines,
consumption of artifacts of offline meta-learning, and a convenient integration
with the entire SageMaker suite allowing these trained models to be used in a
production setting.
| [
{
"created": "Tue, 15 Dec 2020 18:29:04 GMT",
"version": "v1"
},
{
"created": "Wed, 16 Dec 2020 18:51:27 GMT",
"version": "v2"
}
] | 2020-12-17 | [
[
"Das",
"Piali",
""
],
[
"Perrone",
"Valerio",
""
],
[
"Ivkin",
"Nikita",
""
],
[
"Bansal",
"Tanya",
""
],
[
"Karnin",
"Zohar",
""
],
[
"Shen",
"Huibin",
""
],
[
"Shcherbatyi",
"Iaroslav",
""
],
[
"Elor",
"Yotam",
""
],
[
"Wu",
"Wilton",
""
],
[
"Zolic",
"Aida",
""
],
[
"Lienart",
"Thibaut",
""
],
[
"Tang",
"Alex",
""
],
[
"Ahmed",
"Amr",
""
],
[
"Faddoul",
"Jean Baptiste",
""
],
[
"Jenatton",
"Rodolphe",
""
],
[
"Winkelmolen",
"Fela",
""
],
[
"Gautier",
"Philip",
""
],
[
"Dirac",
"Leo",
""
],
[
"Perunicic",
"Andre",
""
],
[
"Miladinovic",
"Miroslav",
""
],
[
"Zappella",
"Giovanni",
""
],
[
"Archambeau",
"Cédric",
""
],
[
"Seeger",
"Matthias",
""
],
[
"Dutt",
"Bhaskar",
""
],
[
"Rouesnel",
"Laurence",
""
]
] | AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline. Although these systems perform well on many datasets, there is still a non-negligible number of datasets for which the one-shot solution produced by each particular system would provide sub-par performance. In this paper, we present Amazon SageMaker Autopilot: a fully managed system providing an automated ML solution that can be modified when needed. Given a tabular dataset and the target column name, Autopilot identifies the problem type, analyzes the data and produces a diverse set of complete ML pipelines including feature preprocessing and ML algorithms, which are tuned to generate a leaderboard of candidate models. In the scenario where the performance is not satisfactory, a data scientist is able to view and edit the proposed ML pipelines in order to infuse their expertise and business knowledge without having to revert to a fully manual solution. This paper describes the different components of Autopilot, emphasizing the infrastructure choices that allow scalability, high quality models, editable ML pipelines, consumption of artifacts of offline meta-learning, and a convenient integration with the entire SageMaker suite allowing these trained models to be used in a production setting. |
1905.06641 | Lumin Liu | Lumin Liu, Jun Zhang, S. H. Song, Khaled B. Letaief | Client-Edge-Cloud Hierarchical Federated Learning | 6 pages, 4 figures | null | null | null | cs.NI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Federated Learning is a collaborative machine learning framework to train a
deep learning model without accessing clients' private data. Previous works
assume one central parameter server either at the cloud or at the edge. The
cloud server can access more data but with excessive communication overhead and
long latency, while the edge server enjoys more efficient communications with
the clients. To combine their advantages, we propose a client-edge-cloud
hierarchical Federated Learning system, supported with a HierFAVG algorithm
that allows multiple edge servers to perform partial model aggregation. In this
way, the model can be trained faster and better communication-computation
trade-offs can be achieved. Convergence analysis is provided for HierFAVG and
the effects of key parameters are also investigated, which lead to qualitative
design guidelines. Empirical experiments verify the analysis and demonstrate
the benefits of this hierarchical architecture in different data distribution
scenarios. Particularly, it is shown that by introducing the intermediate edge
servers, the model training time and the energy consumption of the end devices
can be simultaneously reduced compared to cloud-based Federated Learning.
| [
{
"created": "Thu, 16 May 2019 10:23:36 GMT",
"version": "v1"
},
{
"created": "Thu, 31 Oct 2019 14:45:01 GMT",
"version": "v2"
}
] | 2019-11-01 | [
[
"Liu",
"Lumin",
""
],
[
"Zhang",
"Jun",
""
],
[
"Song",
"S. H.",
""
],
[
"Letaief",
"Khaled B.",
""
]
] | Federated Learning is a collaborative machine learning framework to train a deep learning model without accessing clients' private data. Previous works assume one central parameter server either at the cloud or at the edge. The cloud server can access more data but with excessive communication overhead and long latency, while the edge server enjoys more efficient communications with the clients. To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation. In this way, the model can be trained faster and better communication-computation trade-offs can be achieved. Convergence analysis is provided for HierFAVG and the effects of key parameters are also investigated, which lead to qualitative design guidelines. Empirical experiments verify the analysis and demonstrate the benefits of this hierarchical architecture in different data distribution scenarios. Particularly, it is shown that by introducing the intermediate edge servers, the model training time and the energy consumption of the end devices can be simultaneously reduced compared to cloud-based Federated Learning. |
1810.08313 | Jianyu Wang | Jianyu Wang, Gauri Joshi | Adaptive Communication Strategies to Achieve the Best Error-Runtime
Trade-off in Local-Update SGD | Accepted to SysML 2019 | null | null | null | cs.LG cs.DC stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large-scale machine learning training, in particular distributed stochastic
gradient descent, needs to be robust to inherent system variability such as
node straggling and random communication delays. This work considers a
distributed training framework where each worker node is allowed to perform
local model updates and the resulting models are averaged periodically. We
analyze the true speed of error convergence with respect to wall-clock time
(instead of the number of iterations), and analyze how it is affected by the
frequency of averaging. The main contribution is the design of AdaComm, an
adaptive communication strategy that starts with infrequent averaging to save
communication delay and improve convergence speed, and then increases the
communication frequency in order to achieve a low error floor. Rigorous
experiments on training deep neural networks show that AdaComm can take $3
\times$ less time than fully synchronous SGD, and still reach the same final
training loss.
| [
{
"created": "Fri, 19 Oct 2018 00:04:05 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Mar 2019 16:45:02 GMT",
"version": "v2"
}
] | 2019-03-08 | [
[
"Wang",
"Jianyu",
""
],
[
"Joshi",
"Gauri",
""
]
] | Large-scale machine learning training, in particular distributed stochastic gradient descent, needs to be robust to inherent system variability such as node straggling and random communication delays. This work considers a distributed training framework where each worker node is allowed to perform local model updates and the resulting models are averaged periodically. We analyze the true speed of error convergence with respect to wall-clock time (instead of the number of iterations), and analyze how it is affected by the frequency of averaging. The main contribution is the design of AdaComm, an adaptive communication strategy that starts with infrequent averaging to save communication delay and improve convergence speed, and then increases the communication frequency in order to achieve a low error floor. Rigorous experiments on training deep neural networks show that AdaComm can take $3 \times$ less time than fully synchronous SGD, and still reach the same final training loss. |
1311.5058 | EPTCS | Sebastian Maneth (University of Edinburgh) | Proceedings Second International Workshop on Trends in Tree Automata and
Tree Transducers | null | EPTCS 134, 2013 | 10.4204/EPTCS.134 | null | cs.FL cs.LO cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This volume contains the papers that were presented at the second
international workshop on Trends in Tree Automata and Transducers (TTATT 2013)
which took place on October 19th, 2013 in Hanoi/Vietnam. The workshop was
colocated with the verification conference ATVA. The first edition of the
workshop was colocated with RTA and took place in Nagoya/Japan. The interest of
the workshop lies at the intersection of programming languages, verification,
and database theory, which are areas to which tree automata and transducers are
applied recently.
| [
{
"created": "Wed, 20 Nov 2013 14:11:27 GMT",
"version": "v1"
}
] | 2013-11-21 | [
[
"Maneth",
"Sebastian",
"",
"University of Edinburgh"
]
] | This volume contains the papers that were presented at the second international workshop on Trends in Tree Automata and Transducers (TTATT 2013) which took place on October 19th, 2013 in Hanoi/Vietnam. The workshop was colocated with the verification conference ATVA. The first edition of the workshop was colocated with RTA and took place in Nagoya/Japan. The interest of the workshop lies at the intersection of programming languages, verification, and database theory, which are areas to which tree automata and transducers are applied recently. |
1711.03588 | Carroll Morgan | Annabelle McIver, Carroll Morgan, Benjamin Lucien Kaminski,
Joost-Pieter Katoen | A New Proof Rule for Almost-Sure Termination | V1 to appear in PoPL18. This version collects some existing text into
new example subsection 5.5 and adds a new example 5.6 and makes further
remarks about uncountable branching. The new example 5.6 relates to work on
lexicographic termination methods, also to appear in PoPL18 [Agrawal et al,
2018] | null | null | null | cs.PL cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An important question for a probabilistic program is whether the probability
mass of all its diverging runs is zero, that is that it terminates "almost
surely". Proving that can be hard, and this paper presents a new method for
doing so; it is expressed in a program logic, and so applies directly to source
code. The programs may contain both probabilistic- and demonic choice, and the
probabilistic choices may depend on the current state.
As do other researchers, we use variant functions (a.k.a.
"super-martingales") that are real-valued and probabilistically might decrease
on each loop iteration; but our key innovation is that the amount as well as
the probability of the decrease are parametric.
We prove the soundness of the new rule, indicate where its applicability goes
beyond existing rules, and explain its connection to classical results on
denumerable (non-demonic) Markov chains.
| [
{
"created": "Thu, 9 Nov 2017 20:29:00 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Dec 2017 01:09:43 GMT",
"version": "v2"
}
] | 2017-12-27 | [
[
"McIver",
"Annabelle",
""
],
[
"Morgan",
"Carroll",
""
],
[
"Kaminski",
"Benjamin Lucien",
""
],
[
"Katoen",
"Joost-Pieter",
""
]
] | An important question for a probabilistic program is whether the probability mass of all its diverging runs is zero, that is that it terminates "almost surely". Proving that can be hard, and this paper presents a new method for doing so; it is expressed in a program logic, and so applies directly to source code. The programs may contain both probabilistic- and demonic choice, and the probabilistic choices may depend on the current state. As do other researchers, we use variant functions (a.k.a. "super-martingales") that are real-valued and probabilistically might decrease on each loop iteration; but our key innovation is that the amount as well as the probability of the decrease are parametric. We prove the soundness of the new rule, indicate where its applicability goes beyond existing rules, and explain its connection to classical results on denumerable (non-demonic) Markov chains. |
2209.15149 | Alexandros Hollender | Argyrios Deligkas, John Fearnley, Alexandros Hollender, Themistoklis
Melissourgos | Pure-Circuit: Strong Inapproximability for PPAD | Improved inapproximability result for approximate NE in polymatrix
games | null | null | null | cs.CC cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The current state-of-the-art methods for showing inapproximability in PPAD
arise from the $\varepsilon$-Generalized-Circuit ($\varepsilon$-GCircuit)
problem. Rubinstein (2018) showed that there exists a small unknown constant
$\varepsilon$ for which $\varepsilon$-GCircuit is PPAD-hard, and subsequent
work has shown hardness results for other problems in PPAD by using
$\varepsilon$-GCircuit as an intermediate problem.
We introduce Pure-Circuit, a new intermediate problem for PPAD, which can be
thought of as $\varepsilon$-GCircuit pushed to the limit as $\varepsilon
\rightarrow 1$, and we show that the problem is PPAD-complete. We then prove
that $\varepsilon$-GCircuit is PPAD-hard for all $\varepsilon < 0.1$ by a
reduction from Pure-Circuit, and thus strengthen all prior work that has used
GCircuit as an intermediate problem from the existential-constant regime to the
large-constant regime.
We show that stronger inapproximability results can be derived by reducing
directly from Pure-Circuit. In particular, we prove tight inapproximability
results for computing $\varepsilon$-well-supported Nash equilibria in
two-action polymatrix games, as well as for finding approximate equilibria in
threshold games.
| [
{
"created": "Fri, 30 Sep 2022 00:25:04 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Mar 2023 15:41:21 GMT",
"version": "v2"
}
] | 2023-03-06 | [
[
"Deligkas",
"Argyrios",
""
],
[
"Fearnley",
"John",
""
],
[
"Hollender",
"Alexandros",
""
],
[
"Melissourgos",
"Themistoklis",
""
]
] | The current state-of-the-art methods for showing inapproximability in PPAD arise from the $\varepsilon$-Generalized-Circuit ($\varepsilon$-GCircuit) problem. Rubinstein (2018) showed that there exists a small unknown constant $\varepsilon$ for which $\varepsilon$-GCircuit is PPAD-hard, and subsequent work has shown hardness results for other problems in PPAD by using $\varepsilon$-GCircuit as an intermediate problem. We introduce Pure-Circuit, a new intermediate problem for PPAD, which can be thought of as $\varepsilon$-GCircuit pushed to the limit as $\varepsilon \rightarrow 1$, and we show that the problem is PPAD-complete. We then prove that $\varepsilon$-GCircuit is PPAD-hard for all $\varepsilon < 0.1$ by a reduction from Pure-Circuit, and thus strengthen all prior work that has used GCircuit as an intermediate problem from the existential-constant regime to the large-constant regime. We show that stronger inapproximability results can be derived by reducing directly from Pure-Circuit. In particular, we prove tight inapproximability results for computing $\varepsilon$-well-supported Nash equilibria in two-action polymatrix games, as well as for finding approximate equilibria in threshold games. |
1804.07899 | Markus Freitag | Markus Freitag, Scott Roy | Unsupervised Natural Language Generation with Denoising Autoencoders | Accepted at EMNLP 2018 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generating text from structured data is important for various tasks such as
question answering and dialog systems. We show that in at least one domain,
without any supervision and only based on unlabeled text, we are able to build
a Natural Language Generation (NLG) system with higher performance than
supervised approaches. In our approach, we interpret the structured data as a
corrupt representation of the desired output and use a denoising auto-encoder
to reconstruct the sentence. We show how to introduce noise into training
examples that do not contain structured data, and that the resulting denoising
auto-encoder generalizes to generate correct sentences when given structured
data.
| [
{
"created": "Sat, 21 Apr 2018 06:16:57 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Aug 2018 19:53:33 GMT",
"version": "v2"
}
] | 2018-08-28 | [
[
"Freitag",
"Markus",
""
],
[
"Roy",
"Scott",
""
]
] | Generating text from structured data is important for various tasks such as question answering and dialog systems. We show that in at least one domain, without any supervision and only based on unlabeled text, we are able to build a Natural Language Generation (NLG) system with higher performance than supervised approaches. In our approach, we interpret the structured data as a corrupt representation of the desired output and use a denoising auto-encoder to reconstruct the sentence. We show how to introduce noise into training examples that do not contain structured data, and that the resulting denoising auto-encoder generalizes to generate correct sentences when given structured data. |
2404.10789 | Dipkamal Bhusal | Dipkamal Bhusal, Md Tanvirul Alam, Monish K. Veerabhadran, Michael
Clifford, Sara Rampazzi, Nidhi Rastogi | PASA: Attack Agnostic Unsupervised Adversarial Detection using
Prediction & Attribution Sensitivity Analysis | 9th IEEE European Symposium on Security and Privacy | null | null | null | cs.CR cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Deep neural networks for classification are vulnerable to adversarial
attacks, where small perturbations to input samples lead to incorrect
predictions. This susceptibility, combined with the black-box nature of such
networks, limits their adoption in critical applications like autonomous
driving. Feature-attribution-based explanation methods provide relevance of
input features for model predictions on input samples, thus explaining model
decisions. However, we observe that both model predictions and feature
attributions for input samples are sensitive to noise. We develop a practical
method for this characteristic of model prediction and feature attribution to
detect adversarial samples. Our method, PASA, requires the computation of two
test statistics using model prediction and feature attribution and can reliably
detect adversarial samples using thresholds learned from benign samples. We
validate our lightweight approach by evaluating the performance of PASA on
varying strengths of FGSM, PGD, BIM, and CW attacks on multiple image and
non-image datasets. On average, we outperform state-of-the-art statistical
unsupervised adversarial detectors on CIFAR-10 and ImageNet by 14\% and 35\%
ROC-AUC scores, respectively. Moreover, our approach demonstrates competitive
performance even when an adversary is aware of the defense mechanism.
| [
{
"created": "Fri, 12 Apr 2024 21:22:21 GMT",
"version": "v1"
}
] | 2024-04-18 | [
[
"Bhusal",
"Dipkamal",
""
],
[
"Alam",
"Md Tanvirul",
""
],
[
"Veerabhadran",
"Monish K.",
""
],
[
"Clifford",
"Michael",
""
],
[
"Rampazzi",
"Sara",
""
],
[
"Rastogi",
"Nidhi",
""
]
] | Deep neural networks for classification are vulnerable to adversarial attacks, where small perturbations to input samples lead to incorrect predictions. This susceptibility, combined with the black-box nature of such networks, limits their adoption in critical applications like autonomous driving. Feature-attribution-based explanation methods provide relevance of input features for model predictions on input samples, thus explaining model decisions. However, we observe that both model predictions and feature attributions for input samples are sensitive to noise. We develop a practical method for this characteristic of model prediction and feature attribution to detect adversarial samples. Our method, PASA, requires the computation of two test statistics using model prediction and feature attribution and can reliably detect adversarial samples using thresholds learned from benign samples. We validate our lightweight approach by evaluating the performance of PASA on varying strengths of FGSM, PGD, BIM, and CW attacks on multiple image and non-image datasets. On average, we outperform state-of-the-art statistical unsupervised adversarial detectors on CIFAR-10 and ImageNet by 14\% and 35\% ROC-AUC scores, respectively. Moreover, our approach demonstrates competitive performance even when an adversary is aware of the defense mechanism. |
2308.08302 | Ribhu Chopra | Ashish Pratap Singh, Ribhu Chopra | PSA Based Power Control for Cell-Free Massive MIMO under LoS/NLoS
Channels | 10 pages, 10 figures | null | null | null | cs.IT eess.SP math.IT | http://creativecommons.org/licenses/by/4.0/ | A primary design goal of the cell-free~(CF) massive MIMO architecture is to
provide uniformly good coverage to all the user equipments~(UEs) connected to
the network. However, it has been found that this requirement may not be
satisfied in case the channels between the access points~(APs) and the UEs are
mixed LoS/NLoS. In this paper, we try to address this issue via the use of
appropriate power control in both the uplink and downlink of a CF massive MIMO
system under mixed LoS/NLoS channels. We find that simplistic power control
techniques, such as channel inversion-based power control perform sub-optimally
as compared to max-min power control. As a consequence, we propose a particle
swarm algorithm~(PSA) based power control algorithm to optimize the performance
of the system under study. We then use numerical simulations to evaluate the
performance of the proposed PSA-based solution and show that it results in a
significant improvement in the fairness of the underlying system while
incurring a lower computational complexity.
| [
{
"created": "Wed, 16 Aug 2023 12:05:16 GMT",
"version": "v1"
}
] | 2023-08-17 | [
[
"Singh",
"Ashish Pratap",
""
],
[
"Chopra",
"Ribhu",
""
]
] | A primary design goal of the cell-free~(CF) massive MIMO architecture is to provide uniformly good coverage to all the user equipments~(UEs) connected to the network. However, it has been found that this requirement may not be satisfied in case the channels between the access points~(APs) and the UEs are mixed LoS/NLoS. In this paper, we try to address this issue via the use of appropriate power control in both the uplink and downlink of a CF massive MIMO system under mixed LoS/NLoS channels. We find that simplistic power control techniques, such as channel inversion-based power control perform sub-optimally as compared to max-min power control. As a consequence, we propose a particle swarm algorithm~(PSA) based power control algorithm to optimize the performance of the system under study. We then use numerical simulations to evaluate the performance of the proposed PSA-based solution and show that it results in a significant improvement in the fairness of the underlying system while incurring a lower computational complexity. |
2106.00934 | Nada Almarwani | Nada Almarwani and Mona Diab | Discrete Cosine Transform as Universal Sentence Encoder | to be published in ACL-IJCNLP 2021 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modern sentence encoders are used to generate dense vector representations
that capture the underlying linguistic characteristics for a sequence of words,
including phrases, sentences, or paragraphs. These kinds of representations are
ideal for training a classifier for an end task such as sentiment analysis,
question answering and text classification. Different models have been proposed
to efficiently generate general purpose sentence representations to be used in
pretraining protocols. While averaging is the most commonly used efficient
sentence encoder, Discrete Cosine Transform (DCT) was recently proposed as an
alternative that captures the underlying syntactic characteristics of a given
text without compromising practical efficiency compared to averaging. However,
as with most other sentence encoders, the DCT sentence encoder was only
evaluated in English. To this end, we utilize DCT encoder to generate universal
sentence representation for different languages such as German, French, Spanish
and Russian. The experimental results clearly show the superior effectiveness
of DCT encoding in which consistent performance improvements are achieved over
strong baselines on multiple standardized datasets.
| [
{
"created": "Wed, 2 Jun 2021 04:43:54 GMT",
"version": "v1"
}
] | 2021-06-03 | [
[
"Almarwani",
"Nada",
""
],
[
"Diab",
"Mona",
""
]
] | Modern sentence encoders are used to generate dense vector representations that capture the underlying linguistic characteristics for a sequence of words, including phrases, sentences, or paragraphs. These kinds of representations are ideal for training a classifier for an end task such as sentiment analysis, question answering and text classification. Different models have been proposed to efficiently generate general purpose sentence representations to be used in pretraining protocols. While averaging is the most commonly used efficient sentence encoder, Discrete Cosine Transform (DCT) was recently proposed as an alternative that captures the underlying syntactic characteristics of a given text without compromising practical efficiency compared to averaging. However, as with most other sentence encoders, the DCT sentence encoder was only evaluated in English. To this end, we utilize DCT encoder to generate universal sentence representation for different languages such as German, French, Spanish and Russian. The experimental results clearly show the superior effectiveness of DCT encoding in which consistent performance improvements are achieved over strong baselines on multiple standardized datasets. |
2011.08463 | R\'emy Portelas | R\'emy Portelas, Cl\'ement Romac, Katja Hofmann, Pierre-Yves Oudeyer | Meta Automatic Curriculum Learning | This paper extends and generalizes work in arXiv:2004.03168 | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major challenge in the Deep RL (DRL) community is to train agents able to
generalize their control policy over situations never seen in training.
Training on diverse tasks has been identified as a key ingredient for good
generalization, which pushed researchers towards using rich procedural task
generation systems controlled through complex continuous parameter spaces. In
such complex task spaces, it is essential to rely on some form of Automatic
Curriculum Learning (ACL) to adapt the task sampling distribution to a given
learning agent, instead of randomly sampling tasks, as many could end up being
either trivial or unfeasible. Since it is hard to get prior knowledge on such
task spaces, many ACL algorithms explore the task space to detect progress
niches over time, a costly tabula-rasa process that needs to be performed for
each new learning agents, although they might have similarities in their
capabilities profiles. To address this limitation, we introduce the concept of
Meta-ACL, and formalize it in the context of black-box RL learners, i.e.
algorithms seeking to generalize curriculum generation to an (unknown)
distribution of learners. In this work, we present AGAIN, a first instantiation
of Meta-ACL, and showcase its benefits for curriculum generation over classical
ACL in multiple simulated environments including procedurally generated parkour
environments with learners of varying morphologies. Videos and code are
available at https://sites.google.com/view/meta-acl .
| [
{
"created": "Mon, 16 Nov 2020 14:56:42 GMT",
"version": "v1"
},
{
"created": "Thu, 4 Mar 2021 16:19:46 GMT",
"version": "v2"
},
{
"created": "Wed, 1 Sep 2021 15:41:34 GMT",
"version": "v3"
}
] | 2021-09-02 | [
[
"Portelas",
"Rémy",
""
],
[
"Romac",
"Clément",
""
],
[
"Hofmann",
"Katja",
""
],
[
"Oudeyer",
"Pierre-Yves",
""
]
] | A major challenge in the Deep RL (DRL) community is to train agents able to generalize their control policy over situations never seen in training. Training on diverse tasks has been identified as a key ingredient for good generalization, which pushed researchers towards using rich procedural task generation systems controlled through complex continuous parameter spaces. In such complex task spaces, it is essential to rely on some form of Automatic Curriculum Learning (ACL) to adapt the task sampling distribution to a given learning agent, instead of randomly sampling tasks, as many could end up being either trivial or unfeasible. Since it is hard to get prior knowledge on such task spaces, many ACL algorithms explore the task space to detect progress niches over time, a costly tabula-rasa process that needs to be performed for each new learning agents, although they might have similarities in their capabilities profiles. To address this limitation, we introduce the concept of Meta-ACL, and formalize it in the context of black-box RL learners, i.e. algorithms seeking to generalize curriculum generation to an (unknown) distribution of learners. In this work, we present AGAIN, a first instantiation of Meta-ACL, and showcase its benefits for curriculum generation over classical ACL in multiple simulated environments including procedurally generated parkour environments with learners of varying morphologies. Videos and code are available at https://sites.google.com/view/meta-acl . |
2302.03640 | Junwen Huang | Junwen Huang, Alexey Artemov, Yujin Chen, Shuaifeng Zhi, Kai Xu,
Matthias Nie{\ss}ner | SSR-2D: Semantic 3D Scene Reconstruction from 2D Images | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Most deep learning approaches to comprehensive semantic modeling of 3D indoor
spaces require costly dense annotations in the 3D domain. In this work, we
explore a central 3D scene modeling task, namely, semantic scene reconstruction
without using any 3D annotations. The key idea of our approach is to design a
trainable model that employs both incomplete 3D reconstructions and their
corresponding source RGB-D images, fusing cross-domain features into volumetric
embeddings to predict complete 3D geometry, color, and semantics with only 2D
labeling which can be either manual or machine-generated. Our key technical
innovation is to leverage differentiable rendering of color and semantics to
bridge 2D observations and unknown 3D space, using the observed RGB images and
2D semantics as supervision, respectively. We additionally develop a learning
pipeline and corresponding method to enable learning from imperfect predicted
2D labels, which could be additionally acquired by synthesizing in an augmented
set of virtual training views complementing the original real captures,
enabling more efficient self-supervision loop for semantics. As a result, our
end-to-end trainable solution jointly addresses geometry completion,
colorization, and semantic mapping from limited RGB-D images, without relying
on any 3D ground-truth information. Our method achieves the state-of-the-art
performance of semantic scene completion on two large-scale benchmark datasets
MatterPort3D and ScanNet, surpasses baselines even with costly 3D annotations
in predicting both geometry and semantics. To our knowledge, our method is also
the first 2D-driven method addressing completion and semantic segmentation of
real-world 3D scans simultaneously.
| [
{
"created": "Tue, 7 Feb 2023 17:47:52 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Feb 2023 20:50:33 GMT",
"version": "v2"
},
{
"created": "Thu, 20 Apr 2023 19:20:30 GMT",
"version": "v3"
},
{
"created": "Wed, 5 Jun 2024 12:02:12 GMT",
"version": "v4"
}
] | 2024-06-06 | [
[
"Huang",
"Junwen",
""
],
[
"Artemov",
"Alexey",
""
],
[
"Chen",
"Yujin",
""
],
[
"Zhi",
"Shuaifeng",
""
],
[
"Xu",
"Kai",
""
],
[
"Nießner",
"Matthias",
""
]
] | Most deep learning approaches to comprehensive semantic modeling of 3D indoor spaces require costly dense annotations in the 3D domain. In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations. The key idea of our approach is to design a trainable model that employs both incomplete 3D reconstructions and their corresponding source RGB-D images, fusing cross-domain features into volumetric embeddings to predict complete 3D geometry, color, and semantics with only 2D labeling which can be either manual or machine-generated. Our key technical innovation is to leverage differentiable rendering of color and semantics to bridge 2D observations and unknown 3D space, using the observed RGB images and 2D semantics as supervision, respectively. We additionally develop a learning pipeline and corresponding method to enable learning from imperfect predicted 2D labels, which could be additionally acquired by synthesizing in an augmented set of virtual training views complementing the original real captures, enabling more efficient self-supervision loop for semantics. As a result, our end-to-end trainable solution jointly addresses geometry completion, colorization, and semantic mapping from limited RGB-D images, without relying on any 3D ground-truth information. Our method achieves the state-of-the-art performance of semantic scene completion on two large-scale benchmark datasets MatterPort3D and ScanNet, surpasses baselines even with costly 3D annotations in predicting both geometry and semantics. To our knowledge, our method is also the first 2D-driven method addressing completion and semantic segmentation of real-world 3D scans simultaneously. |
1212.2450 | Salem Benferhat | Salem Benferhat, Sylvain Lagrue, Odile Papini | A possibilistic handling of partially ordered information | Appears in Proceedings of the Nineteenth Conference on Uncertainty in
Artificial Intelligence (UAI2003) | null | null | UAI-P-2003-PG-29-36 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a standard possibilistic logic, prioritized information are encoded by
means of weighted knowledge base. This paper proposes an extension of
possibilistic logic for dealing with partially ordered information. We Show
that all basic notions of standard possibilitic logic (sumbsumption, syntactic
and semantic inference, etc.) have natural couterparts when dealing with
partially ordered information. We also propose an algorithm which computes
possibilistic conclusions of a partial knowledge base of a partially ordered
knowlege base.
| [
{
"created": "Fri, 19 Oct 2012 15:03:38 GMT",
"version": "v1"
}
] | 2012-12-12 | [
[
"Benferhat",
"Salem",
""
],
[
"Lagrue",
"Sylvain",
""
],
[
"Papini",
"Odile",
""
]
] | In a standard possibilistic logic, prioritized information are encoded by means of weighted knowledge base. This paper proposes an extension of possibilistic logic for dealing with partially ordered information. We Show that all basic notions of standard possibilitic logic (sumbsumption, syntactic and semantic inference, etc.) have natural couterparts when dealing with partially ordered information. We also propose an algorithm which computes possibilistic conclusions of a partial knowledge base of a partially ordered knowlege base. |
1801.01615 | Hanbyul Joo | Hanbyul Joo, Tomas Simon, Yaser Sheikh | Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and
Bodies | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a unified deformation model for the markerless capture of multiple
scales of human movement, including facial expressions, body motion, and hand
gestures. An initial model is generated by locally stitching together models of
the individual parts of the human body, which we refer to as the "Frankenstein"
model. This model enables the full expression of part movements, including face
and hands by a single seamless model. Using a large-scale capture of people
wearing everyday clothes, we optimize the Frankenstein model to create "Adam".
Adam is a calibrated model that shares the same skeleton hierarchy as the
initial model but can express hair and clothing geometry, making it directly
usable for fitting people as they normally appear in everyday life. Finally, we
demonstrate the use of these models for total motion tracking, simultaneously
capturing the large-scale body movements and the subtle face and hand motion of
a social group of people.
| [
{
"created": "Fri, 5 Jan 2018 02:41:54 GMT",
"version": "v1"
}
] | 2018-01-08 | [
[
"Joo",
"Hanbyul",
""
],
[
"Simon",
"Tomas",
""
],
[
"Sheikh",
"Yaser",
""
]
] | We present a unified deformation model for the markerless capture of multiple scales of human movement, including facial expressions, body motion, and hand gestures. An initial model is generated by locally stitching together models of the individual parts of the human body, which we refer to as the "Frankenstein" model. This model enables the full expression of part movements, including face and hands by a single seamless model. Using a large-scale capture of people wearing everyday clothes, we optimize the Frankenstein model to create "Adam". Adam is a calibrated model that shares the same skeleton hierarchy as the initial model but can express hair and clothing geometry, making it directly usable for fitting people as they normally appear in everyday life. Finally, we demonstrate the use of these models for total motion tracking, simultaneously capturing the large-scale body movements and the subtle face and hand motion of a social group of people. |
2404.15980 | Ali Ebnenasir | Ali Ebnenasir and Kieran Young | Minimizing the Number of Teleportations in Distributed Quantum Computing
Using Alloy | null | null | null | null | cs.ET cs.DC quant-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper presents a novel approach for minimizing the number of
teleportations in Distributed Quantum Computing (DQC) using formal methods.
Quantum teleportation plays a major role in communicating quantum information.
As such, it is desirable to perform as few teleportations as possible when
distributing a quantum algorithm on a network of quantum machines. Contrary to
most existing methods which rely on graph-theoretic or heuristic search
techniques, we propose a drastically different approach for minimizing the
number of teleportations through utilizing formal methods. Specifically, the
contributions of this paper include: the formal specification of the
teleportation minimization problem in Alloy, the generalizability of the
proposed Alloy specifications to quantum circuits with $n$-ary gates, the
reusability of the Alloy specifications for different quantum circuits and
networks, the simplicity of specifying and solving other problems such as load
balancing and heterogeneity, and the compositionality of the proposed approach.
We also develop a software tool, called qcAlloy, that takes as input the
textual description of a quantum circuit, generates the corresponding Alloy
model, and finally solves the minimization problem using the Alloy analyzer. We
have experimentally evaluated qcAlloy for some of the circuits in the RevLib
benchmark with more than 100 qubits and 1200 layers, and have demonstrated that
qcAlloy outperforms one of the most efficient existing methods for most
benchmark circuits in terms of minimizing the number of teleportations.
| [
{
"created": "Wed, 24 Apr 2024 16:55:29 GMT",
"version": "v1"
}
] | 2024-04-25 | [
[
"Ebnenasir",
"Ali",
""
],
[
"Young",
"Kieran",
""
]
] | This paper presents a novel approach for minimizing the number of teleportations in Distributed Quantum Computing (DQC) using formal methods. Quantum teleportation plays a major role in communicating quantum information. As such, it is desirable to perform as few teleportations as possible when distributing a quantum algorithm on a network of quantum machines. Contrary to most existing methods which rely on graph-theoretic or heuristic search techniques, we propose a drastically different approach for minimizing the number of teleportations through utilizing formal methods. Specifically, the contributions of this paper include: the formal specification of the teleportation minimization problem in Alloy, the generalizability of the proposed Alloy specifications to quantum circuits with $n$-ary gates, the reusability of the Alloy specifications for different quantum circuits and networks, the simplicity of specifying and solving other problems such as load balancing and heterogeneity, and the compositionality of the proposed approach. We also develop a software tool, called qcAlloy, that takes as input the textual description of a quantum circuit, generates the corresponding Alloy model, and finally solves the minimization problem using the Alloy analyzer. We have experimentally evaluated qcAlloy for some of the circuits in the RevLib benchmark with more than 100 qubits and 1200 layers, and have demonstrated that qcAlloy outperforms one of the most efficient existing methods for most benchmark circuits in terms of minimizing the number of teleportations. |
2105.00572 | Alexis Conneau | Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau | Larger-Scale Transformers for Multilingual Masked Language Modeling | 4 pages | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Recent work has demonstrated the effectiveness of cross-lingual language
model pretraining for cross-lingual understanding. In this study, we present
the results of two larger multilingual masked language models, with 3.5B and
10.7B parameters. Our two new models dubbed XLM-R XL and XLM-R XXL outperform
XLM-R by 1.8% and 2.4% average accuracy on XNLI. Our model also outperforms the
RoBERTa-Large model on several English tasks of the GLUE benchmark by 0.3% on
average while handling 99 more languages. This suggests pretrained models with
larger capacity may obtain both strong performance on high-resource languages
while greatly improving low-resource languages. We make our code and models
publicly available.
| [
{
"created": "Sun, 2 May 2021 23:15:02 GMT",
"version": "v1"
}
] | 2021-05-04 | [
[
"Goyal",
"Naman",
""
],
[
"Du",
"Jingfei",
""
],
[
"Ott",
"Myle",
""
],
[
"Anantharaman",
"Giri",
""
],
[
"Conneau",
"Alexis",
""
]
] | Recent work has demonstrated the effectiveness of cross-lingual language model pretraining for cross-lingual understanding. In this study, we present the results of two larger multilingual masked language models, with 3.5B and 10.7B parameters. Our two new models dubbed XLM-R XL and XLM-R XXL outperform XLM-R by 1.8% and 2.4% average accuracy on XNLI. Our model also outperforms the RoBERTa-Large model on several English tasks of the GLUE benchmark by 0.3% on average while handling 99 more languages. This suggests pretrained models with larger capacity may obtain both strong performance on high-resource languages while greatly improving low-resource languages. We make our code and models publicly available. |
1803.10815 | Piotr Mardziel | Shayak Sen and Piotr Mardziel and Anupam Datta and Matthew Fredrikson | Supervising Feature Influence | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Causal influence measures for machine learnt classifiers shed light on the
reasons behind classification, and aid in identifying influential input
features and revealing their biases. However, such analyses involve evaluating
the classifier using datapoints that may be atypical of its training
distribution. Standard methods for training classifiers that minimize empirical
risk do not constrain the behavior of the classifier on such datapoints. As a
result, training to minimize empirical risk does not distinguish among
classifiers that agree on predictions in the training distribution but have
wildly different causal influences. We term this problem covariate shift in
causal testing and formally characterize conditions under which it arises. As a
solution to this problem, we propose a novel active learning algorithm that
constrains the influence measures of the trained model. We prove that any two
predictors whose errors are close on both the original training distribution
and the distribution of atypical points are guaranteed to have causal
influences that are also close. Further, we empirically demonstrate with
synthetic labelers that our algorithm trains models that (i) have similar
causal influences as the labeler's model, and (ii) generalize better to
out-of-distribution points while (iii) retaining their accuracy on
in-distribution points.
| [
{
"created": "Wed, 28 Mar 2018 19:16:39 GMT",
"version": "v1"
},
{
"created": "Sat, 7 Apr 2018 23:46:15 GMT",
"version": "v2"
}
] | 2018-04-10 | [
[
"Sen",
"Shayak",
""
],
[
"Mardziel",
"Piotr",
""
],
[
"Datta",
"Anupam",
""
],
[
"Fredrikson",
"Matthew",
""
]
] | Causal influence measures for machine learnt classifiers shed light on the reasons behind classification, and aid in identifying influential input features and revealing their biases. However, such analyses involve evaluating the classifier using datapoints that may be atypical of its training distribution. Standard methods for training classifiers that minimize empirical risk do not constrain the behavior of the classifier on such datapoints. As a result, training to minimize empirical risk does not distinguish among classifiers that agree on predictions in the training distribution but have wildly different causal influences. We term this problem covariate shift in causal testing and formally characterize conditions under which it arises. As a solution to this problem, we propose a novel active learning algorithm that constrains the influence measures of the trained model. We prove that any two predictors whose errors are close on both the original training distribution and the distribution of atypical points are guaranteed to have causal influences that are also close. Further, we empirically demonstrate with synthetic labelers that our algorithm trains models that (i) have similar causal influences as the labeler's model, and (ii) generalize better to out-of-distribution points while (iii) retaining their accuracy on in-distribution points. |
2312.14030 | Erik Frisk | Fatemeh Hashemniya, Beno\"it Caillaud, Erik Frisk, Mattias Krysander,
Mathias Malandain | Fault Diagnosability Analysis of Multi-Mode Systems | null | null | null | null | cs.LO cs.SY eess.SY | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Multi-mode systems can operate in different modes, leading to large numbers
of different dynamics. Consequently, applying traditional structural
diagnostics to such systems is often untractable. To address this challenge, we
present a multi-mode diagnostics algorithm that relies on a multi-mode
extension of the Dulmage-Mendelsohn decomposition. We introduce two
methodologies for modeling faults, either as signals or as Boolean variables,
and apply them to a modular switched battery system in order to demonstrate
their effectiveness and discuss their respective advantages.
| [
{
"created": "Thu, 21 Dec 2023 17:00:37 GMT",
"version": "v1"
}
] | 2023-12-22 | [
[
"Hashemniya",
"Fatemeh",
""
],
[
"Caillaud",
"Benoït",
""
],
[
"Frisk",
"Erik",
""
],
[
"Krysander",
"Mattias",
""
],
[
"Malandain",
"Mathias",
""
]
] | Multi-mode systems can operate in different modes, leading to large numbers of different dynamics. Consequently, applying traditional structural diagnostics to such systems is often untractable. To address this challenge, we present a multi-mode diagnostics algorithm that relies on a multi-mode extension of the Dulmage-Mendelsohn decomposition. We introduce two methodologies for modeling faults, either as signals or as Boolean variables, and apply them to a modular switched battery system in order to demonstrate their effectiveness and discuss their respective advantages. |
2302.00089 | Hussein Hazimeh | Hussein Hazimeh, Natalia Ponomareva | Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for
Adversarial Nets | Accepted to AISTATS 2023 | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Adversarial nets have proved to be powerful in various domains including
generative modeling (GANs), transfer learning, and fairness. However,
successfully training adversarial nets using first-order methods remains a
major challenge. Typically, careful choices of the learning rates are needed to
maintain the delicate balance between the competing networks. In this paper, we
design a novel learning rate scheduler that dynamically adapts the learning
rate of the adversary to maintain the right balance. The scheduler is driven by
the fact that the loss of an ideal adversarial net is a constant known a
priori. The scheduler is thus designed to keep the loss of the optimized
adversarial net close to that of an ideal network. We run large-scale
experiments to study the effectiveness of the scheduler on two popular
applications: GANs for image generation and adversarial nets for domain
adaptation. Our experiments indicate that adversarial nets trained with the
scheduler are less likely to diverge and require significantly less tuning. For
example, on CelebA, a GAN with the scheduler requires only one-tenth of the
tuning budget needed without a scheduler. Moreover, the scheduler leads to
statistically significant improvements in model quality, reaching up to $27\%$
in Frechet Inception Distance for image generation and $3\%$ in test accuracy
for domain adaptation.
| [
{
"created": "Tue, 31 Jan 2023 20:36:40 GMT",
"version": "v1"
}
] | 2023-02-02 | [
[
"Hazimeh",
"Hussein",
""
],
[
"Ponomareva",
"Natalia",
""
]
] | Adversarial nets have proved to be powerful in various domains including generative modeling (GANs), transfer learning, and fairness. However, successfully training adversarial nets using first-order methods remains a major challenge. Typically, careful choices of the learning rates are needed to maintain the delicate balance between the competing networks. In this paper, we design a novel learning rate scheduler that dynamically adapts the learning rate of the adversary to maintain the right balance. The scheduler is driven by the fact that the loss of an ideal adversarial net is a constant known a priori. The scheduler is thus designed to keep the loss of the optimized adversarial net close to that of an ideal network. We run large-scale experiments to study the effectiveness of the scheduler on two popular applications: GANs for image generation and adversarial nets for domain adaptation. Our experiments indicate that adversarial nets trained with the scheduler are less likely to diverge and require significantly less tuning. For example, on CelebA, a GAN with the scheduler requires only one-tenth of the tuning budget needed without a scheduler. Moreover, the scheduler leads to statistically significant improvements in model quality, reaching up to $27\%$ in Frechet Inception Distance for image generation and $3\%$ in test accuracy for domain adaptation. |
2209.01211 | Yaping Zhao | Yaping Zhao, Haitian Zheng, Mengqi Ji, Ruqi Huang | Cross-Camera Deep Colorization | 12 pages, 6 figures | null | null | null | cs.CV eess.IV | http://creativecommons.org/licenses/by/4.0/ | In this paper, we consider the color-plus-mono dual-camera system and propose
an end-to-end convolutional neural network to align and fuse images from it in
an efficient and cost-effective way. Our method takes cross-domain and
cross-scale images as input, and consequently synthesizes HR colorization
results to facilitate the trade-off between spatial-temporal resolution and
color depth in the single-camera imaging system. In contrast to the previous
colorization methods, ours can adapt to color and monochrome cameras with
distinctive spatial-temporal resolutions, rendering the flexibility and
robustness in practical applications. The key ingredient of our method is a
cross-camera alignment module that generates multi-scale correspondences for
cross-domain image alignment. Through extensive experiments on various datasets
and multiple settings, we validate the flexibility and effectiveness of our
approach. Remarkably, our method consistently achieves substantial
improvements, i.e., around 10dB PSNR gain, upon the state-of-the-art methods.
Code is at: https://github.com/IndigoPurple/CCDC
| [
{
"created": "Fri, 26 Aug 2022 11:02:14 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Sep 2022 04:00:27 GMT",
"version": "v2"
}
] | 2022-09-08 | [
[
"Zhao",
"Yaping",
""
],
[
"Zheng",
"Haitian",
""
],
[
"Ji",
"Mengqi",
""
],
[
"Huang",
"Ruqi",
""
]
] | In this paper, we consider the color-plus-mono dual-camera system and propose an end-to-end convolutional neural network to align and fuse images from it in an efficient and cost-effective way. Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilitate the trade-off between spatial-temporal resolution and color depth in the single-camera imaging system. In contrast to the previous colorization methods, ours can adapt to color and monochrome cameras with distinctive spatial-temporal resolutions, rendering the flexibility and robustness in practical applications. The key ingredient of our method is a cross-camera alignment module that generates multi-scale correspondences for cross-domain image alignment. Through extensive experiments on various datasets and multiple settings, we validate the flexibility and effectiveness of our approach. Remarkably, our method consistently achieves substantial improvements, i.e., around 10dB PSNR gain, upon the state-of-the-art methods. Code is at: https://github.com/IndigoPurple/CCDC |
2404.06721 | Norrathep Rattanavipanon | Norrathep Rattanavipanon and Ivan De Oliveira Nunes | Poisoning Prevention in Federated Learning and Differential Privacy via
Stateful Proofs of Execution | null | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | The rise in IoT-driven distributed data analytics, coupled with increasing
privacy concerns, has led to a demand for effective privacy-preserving and
federated data collection/model training mechanisms. In response, approaches
such as Federated Learning (FL) and Local Differential Privacy (LDP) have been
proposed and attracted much attention over the past few years. However, they
still share the common limitation of being vulnerable to poisoning attacks
wherein adversaries compromising edge devices feed forged (a.k.a. poisoned)
data to aggregation back-ends, undermining the integrity of FL/LDP results.
In this work, we propose a system-level approach to remedy this issue based
on a novel security notion of Proofs of Stateful Execution (PoSX) for
IoT/embedded devices' software. To realize the PoSX concept, we design SLAPP: a
System-Level Approach for Poisoning Prevention. SLAPP leverages commodity
security features of embedded devices - in particular ARM TrustZoneM security
extensions - to verifiably bind raw sensed data to their correct usage as part
of FL/LDP edge device routines. As a consequence, it offers robust security
guarantees against poisoning. Our evaluation, based on real-world prototypes
featuring multiple cryptographic primitives and data collection schemes,
showcases SLAPP's security and low overhead.
| [
{
"created": "Wed, 10 Apr 2024 04:18:26 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Apr 2024 12:05:52 GMT",
"version": "v2"
},
{
"created": "Wed, 19 Jun 2024 03:01:31 GMT",
"version": "v3"
}
] | 2024-06-21 | [
[
"Rattanavipanon",
"Norrathep",
""
],
[
"Nunes",
"Ivan De Oliveira",
""
]
] | The rise in IoT-driven distributed data analytics, coupled with increasing privacy concerns, has led to a demand for effective privacy-preserving and federated data collection/model training mechanisms. In response, approaches such as Federated Learning (FL) and Local Differential Privacy (LDP) have been proposed and attracted much attention over the past few years. However, they still share the common limitation of being vulnerable to poisoning attacks wherein adversaries compromising edge devices feed forged (a.k.a. poisoned) data to aggregation back-ends, undermining the integrity of FL/LDP results. In this work, we propose a system-level approach to remedy this issue based on a novel security notion of Proofs of Stateful Execution (PoSX) for IoT/embedded devices' software. To realize the PoSX concept, we design SLAPP: a System-Level Approach for Poisoning Prevention. SLAPP leverages commodity security features of embedded devices - in particular ARM TrustZoneM security extensions - to verifiably bind raw sensed data to their correct usage as part of FL/LDP edge device routines. As a consequence, it offers robust security guarantees against poisoning. Our evaluation, based on real-world prototypes featuring multiple cryptographic primitives and data collection schemes, showcases SLAPP's security and low overhead. |
1910.03126 | Jiunn-Kai Huang | Jiunn-Kai Huang and Jessy W. Grizzle | Improvements to Target-Based 3D LiDAR to Camera Calibration | null | IEEE Access, vol. 8, 2020, pp. 134101-134110 | 10.1109/ACCESS.2020.3010734 | null | cs.RO cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The homogeneous transformation between a LiDAR and monocular camera is
required for sensor fusion tasks, such as SLAM. While determining such a
transformation is not considered glamorous in any sense of the word, it is
nonetheless crucial for many modern autonomous systems. Indeed, an error of a
few degrees in rotation or a few percent in translation can lead to 20 cm
translation errors at a distance of 5 m when overlaying a LiDAR image on a
camera image. The biggest impediments to determining the transformation
accurately are the relative sparsity of LiDAR point clouds and systematic
errors in their distance measurements. This paper proposes (1) the use of
targets of known dimension and geometry to ameliorate target pose estimation in
face of the quantization and systematic errors inherent in a LiDAR image of a
target, and (2) a fitting method for the LiDAR to monocular camera
transformation that fundamentally assumes the camera image data is the most
accurate information in one's possession.
| [
{
"created": "Mon, 7 Oct 2019 23:03:16 GMT",
"version": "v1"
},
{
"created": "Wed, 11 Mar 2020 20:05:04 GMT",
"version": "v2"
},
{
"created": "Sat, 18 Jul 2020 15:07:13 GMT",
"version": "v3"
}
] | 2020-07-30 | [
[
"Huang",
"Jiunn-Kai",
""
],
[
"Grizzle",
"Jessy W.",
""
]
] | The homogeneous transformation between a LiDAR and monocular camera is required for sensor fusion tasks, such as SLAM. While determining such a transformation is not considered glamorous in any sense of the word, it is nonetheless crucial for many modern autonomous systems. Indeed, an error of a few degrees in rotation or a few percent in translation can lead to 20 cm translation errors at a distance of 5 m when overlaying a LiDAR image on a camera image. The biggest impediments to determining the transformation accurately are the relative sparsity of LiDAR point clouds and systematic errors in their distance measurements. This paper proposes (1) the use of targets of known dimension and geometry to ameliorate target pose estimation in face of the quantization and systematic errors inherent in a LiDAR image of a target, and (2) a fitting method for the LiDAR to monocular camera transformation that fundamentally assumes the camera image data is the most accurate information in one's possession. |
1710.07096 | Ribana Roscher | Anika Bettge, Ribana Roscher, Susanne Wenzel | Deep Self-taught Learning for Remote Sensing Image Classification | This is a corrected version of the final paper published in the
proceedings | Proceedings of the 2017 conference on Big Data from Space | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses the land cover classification task for remote sensing
images by deep self-taught learning. Our self-taught learning approach learns
suitable feature representations of the input data using sparse representation
and undercomplete dictionary learning. We propose a deep learning framework
which extracts representations in multiple layers and use the output of the
deepest layer as input to a classification algorithm. We evaluate our approach
using a multispectral Landsat 5 TM image of a study area in the North of Novo
Progresso (South America) and the Zurich Summer Data Set provided by the
University of Zurich. Experiments indicate that features learned by a deep
self-taught learning framework can be used for classification and improve the
results compared to classification results using the original feature
representation.
| [
{
"created": "Thu, 19 Oct 2017 11:32:53 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Dec 2017 20:55:12 GMT",
"version": "v2"
}
] | 2017-12-21 | [
[
"Bettge",
"Anika",
""
],
[
"Roscher",
"Ribana",
""
],
[
"Wenzel",
"Susanne",
""
]
] | This paper addresses the land cover classification task for remote sensing images by deep self-taught learning. Our self-taught learning approach learns suitable feature representations of the input data using sparse representation and undercomplete dictionary learning. We propose a deep learning framework which extracts representations in multiple layers and use the output of the deepest layer as input to a classification algorithm. We evaluate our approach using a multispectral Landsat 5 TM image of a study area in the North of Novo Progresso (South America) and the Zurich Summer Data Set provided by the University of Zurich. Experiments indicate that features learned by a deep self-taught learning framework can be used for classification and improve the results compared to classification results using the original feature representation. |
cs/0509002 | Zsolt I. L\'az\'ar | Zsolt I. L\'az\'ar, Jouke R. Heringa, Bazil P\^arv, Simon W. de Leeuw | Component Based Programming in Scientific Computing: The Viable Approach | null | null | null | null | cs.CE | null | Computational scientists are facing a new era where the old ways of
developing and reusing code have to be left behind and a few daring steps are
to be made towards new horizons. The present work analyzes the needs that drive
this change, the factors that contribute to the inertia of the community and
slow the transition, the status and perspective of present attempts, the
principle, practical and technical problems that are to be addressed in the
short and long run.
| [
{
"created": "Wed, 31 Aug 2005 21:57:04 GMT",
"version": "v1"
}
] | 2021-08-23 | [
[
"Lázár",
"Zsolt I.",
""
],
[
"Heringa",
"Jouke R.",
""
],
[
"Pârv",
"Bazil",
""
],
[
"de Leeuw",
"Simon W.",
""
]
] | Computational scientists are facing a new era where the old ways of developing and reusing code have to be left behind and a few daring steps are to be made towards new horizons. The present work analyzes the needs that drive this change, the factors that contribute to the inertia of the community and slow the transition, the status and perspective of present attempts, the principle, practical and technical problems that are to be addressed in the short and long run. |
1103.4875 | Isabelle Stanton | Isabelle Stanton and Ali Pinar | Constructing and Sampling Graphs with a Prescribed Joint Degree
Distribution | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the most influential recent results in network analysis is that many
natural networks exhibit a power-law or log-normal degree distribution. This
has inspired numerous generative models that match this property. However, more
recent work has shown that while these generative models do have the right
degree distribution, they are not good models for real life networks due to
their differences on other important metrics like conductance. We believe this
is, in part, because many of these real-world networks have very different
joint degree distributions, i.e. the probability that a randomly selected edge
will be between nodes of degree k and l. Assortativity is a sufficient
statistic of the joint degree distribution, and it has been previously noted
that social networks tend to be assortative, while biological and technological
networks tend to be disassortative.
We suggest understanding the relationship between network structure and the
joint degree distribution of graphs is an interesting avenue of further
research. An important tool for such studies are algorithms that can generate
random instances of graphs with the same joint degree distribution. This is the
main topic of this paper and we study the problem from both a theoretical and
practical perspective. We provide an algorithm for constructing simple graphs
from a given joint degree distribution, and a Monte Carlo Markov Chain method
for sampling them. We also show that the state space of simple graphs with a
fixed degree distribution is connected via end point switches. We empirically
evaluate the mixing time of this Markov Chain by using experiments based on the
autocorrelation of each edge. These experiments show that our Markov Chain
mixes quickly on real graphs, allowing for utilization of our techniques in
practice.
| [
{
"created": "Thu, 24 Mar 2011 21:05:17 GMT",
"version": "v1"
},
{
"created": "Wed, 31 Aug 2011 18:41:54 GMT",
"version": "v2"
}
] | 2011-09-01 | [
[
"Stanton",
"Isabelle",
""
],
[
"Pinar",
"Ali",
""
]
] | One of the most influential recent results in network analysis is that many natural networks exhibit a power-law or log-normal degree distribution. This has inspired numerous generative models that match this property. However, more recent work has shown that while these generative models do have the right degree distribution, they are not good models for real life networks due to their differences on other important metrics like conductance. We believe this is, in part, because many of these real-world networks have very different joint degree distributions, i.e. the probability that a randomly selected edge will be between nodes of degree k and l. Assortativity is a sufficient statistic of the joint degree distribution, and it has been previously noted that social networks tend to be assortative, while biological and technological networks tend to be disassortative. We suggest understanding the relationship between network structure and the joint degree distribution of graphs is an interesting avenue of further research. An important tool for such studies are algorithms that can generate random instances of graphs with the same joint degree distribution. This is the main topic of this paper and we study the problem from both a theoretical and practical perspective. We provide an algorithm for constructing simple graphs from a given joint degree distribution, and a Monte Carlo Markov Chain method for sampling them. We also show that the state space of simple graphs with a fixed degree distribution is connected via end point switches. We empirically evaluate the mixing time of this Markov Chain by using experiments based on the autocorrelation of each edge. These experiments show that our Markov Chain mixes quickly on real graphs, allowing for utilization of our techniques in practice. |
1401.7583 | Daniel Kulesz | Daniel Kulesz, Jan-Peter Ostberg | Practical Challenges with Spreadsheet Auditing Tools | 13 Pages. 3 Detailed Colour Figures, Proc. European Spreadsheet Risks
Int. Grp. (EuSpRIG) 2013, ISBN: 978-1-9054045-1-3 | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Just like other software, spreadsheets can contain significant faults. Static
analysis is an accepted and well-established technique in software engineering
known for its capability to discover faults. In recent years, a growing number
of tool vendors started offering tools that allow casual end-users to run
various static analyses on spreadsheets as well. We supervised a study where
three undergraduate software engineering students examined a selection of 14
spreadsheet auditing tools, trying to give a concrete recommendation for an
industry partner. Reflecting on the study's results, we found that most of
these tools do provide useful aids in finding problems in spreadsheets, but we
have also spotted several areas where tools had significant issues. Some of
these issues could be remedied if spreadsheet auditing tool vendors would pick
up some ideas of static analysis tools for traditional software development and
adopt some of their solution approaches.
| [
{
"created": "Tue, 28 Jan 2014 20:51:32 GMT",
"version": "v1"
}
] | 2014-01-30 | [
[
"Kulesz",
"Daniel",
""
],
[
"Ostberg",
"Jan-Peter",
""
]
] | Just like other software, spreadsheets can contain significant faults. Static analysis is an accepted and well-established technique in software engineering known for its capability to discover faults. In recent years, a growing number of tool vendors started offering tools that allow casual end-users to run various static analyses on spreadsheets as well. We supervised a study where three undergraduate software engineering students examined a selection of 14 spreadsheet auditing tools, trying to give a concrete recommendation for an industry partner. Reflecting on the study's results, we found that most of these tools do provide useful aids in finding problems in spreadsheets, but we have also spotted several areas where tools had significant issues. Some of these issues could be remedied if spreadsheet auditing tool vendors would pick up some ideas of static analysis tools for traditional software development and adopt some of their solution approaches. |
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