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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2303.11315
|
Wenxuan Zhou
|
Wenxuan Zhou, Sheng Zhang, Hoifung Poon, Muhao Chen
|
Context-faithful Prompting for Large Language Models
|
Accepted at EMNLP 2023 Findings. Code and data are released at
https://github.com/wzhouad/context-faithful-llm
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large language models (LLMs) encode parametric knowledge about world facts
and have shown remarkable performance in knowledge-driven NLP tasks. However,
their reliance on parametric knowledge may cause them to overlook contextual
cues, leading to incorrect predictions in context-sensitive NLP tasks (e.g.,
knowledge acquisition tasks). In this paper, we seek to assess and enhance
LLMs' contextual faithfulness in two aspects: knowledge conflict and prediction
with abstention. We demonstrate that LLMs' faithfulness can be significantly
improved using carefully designed prompting strategies. In particular, we
identify opinion-based prompts and counterfactual demonstrations as the most
effective methods. Opinion-based prompts reframe the context as a narrator's
statement and inquire about the narrator's opinions, while counterfactual
demonstrations use instances containing false facts to improve faithfulness in
knowledge conflict situations. Neither technique requires additional training.
We conduct experiments on three datasets of two standard NLP tasks, machine
reading comprehension and relation extraction, and the results demonstrate
significant improvement in faithfulness to contexts. Code and data are released
at https://github.com/wzhouad/context-faithful-llm.
|
[
{
"created": "Mon, 20 Mar 2023 17:54:58 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Oct 2023 03:25:13 GMT",
"version": "v2"
}
] |
2023-10-24
|
[
[
"Zhou",
"Wenxuan",
""
],
[
"Zhang",
"Sheng",
""
],
[
"Poon",
"Hoifung",
""
],
[
"Chen",
"Muhao",
""
]
] |
Large language models (LLMs) encode parametric knowledge about world facts and have shown remarkable performance in knowledge-driven NLP tasks. However, their reliance on parametric knowledge may cause them to overlook contextual cues, leading to incorrect predictions in context-sensitive NLP tasks (e.g., knowledge acquisition tasks). In this paper, we seek to assess and enhance LLMs' contextual faithfulness in two aspects: knowledge conflict and prediction with abstention. We demonstrate that LLMs' faithfulness can be significantly improved using carefully designed prompting strategies. In particular, we identify opinion-based prompts and counterfactual demonstrations as the most effective methods. Opinion-based prompts reframe the context as a narrator's statement and inquire about the narrator's opinions, while counterfactual demonstrations use instances containing false facts to improve faithfulness in knowledge conflict situations. Neither technique requires additional training. We conduct experiments on three datasets of two standard NLP tasks, machine reading comprehension and relation extraction, and the results demonstrate significant improvement in faithfulness to contexts. Code and data are released at https://github.com/wzhouad/context-faithful-llm.
|
1804.03357
|
Yasushi Tanaka
|
Yasushi Tanaka, Hajimu Iida, Yasuhiro Takemura
|
A Manga-Driven System Requirements Development PBL Exercise
|
SEEM2018
| null |
10.1145/3194779.3194788
| null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We conducted a Project-Based Learning (PBL)-type exercise incorporating
Japanese cartoon (manga) techniques into Requirements Development (RD)
processes. Manga has established techniques, such as those for character
setting and story development, that we thought are also valid for RD processes.
Using this manga-driven method, students were able to clarify high-level
project goals early in the development life-cycle, and succeeded in defining
high quality and unique system ideas.
|
[
{
"created": "Tue, 10 Apr 2018 06:26:20 GMT",
"version": "v1"
}
] |
2018-04-11
|
[
[
"Tanaka",
"Yasushi",
""
],
[
"Iida",
"Hajimu",
""
],
[
"Takemura",
"Yasuhiro",
""
]
] |
We conducted a Project-Based Learning (PBL)-type exercise incorporating Japanese cartoon (manga) techniques into Requirements Development (RD) processes. Manga has established techniques, such as those for character setting and story development, that we thought are also valid for RD processes. Using this manga-driven method, students were able to clarify high-level project goals early in the development life-cycle, and succeeded in defining high quality and unique system ideas.
|
2408.05452
|
Junjie Jiang
|
Junjie Jiang, Hao Zhuang, Xinjie Huang, Delei Kong, Zheng Fang
|
EV-MGDispNet: Motion-Guided Event-Based Stereo Disparity Estimation
Network with Left-Right Consistency
| null | null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Event cameras have the potential to revolutionize the field of robot vision,
particularly in areas like stereo disparity estimation, owing to their high
temporal resolution and high dynamic range. Many studies use deep learning for
event camera stereo disparity estimation. However, these methods fail to fully
exploit the temporal information in the event stream to acquire clear event
representations. Additionally, there is room for further reduction in pixel
shifts in the feature maps before constructing the cost volume. In this paper,
we propose EV-MGDispNet, a novel event-based stereo disparity estimation
method. Firstly, we propose an edge-aware aggregation (EAA) module, which fuses
event frames and motion confidence maps to generate a novel clear event
representation. Then, we propose a motion-guided attention (MGA) module, where
motion confidence maps utilize deformable transformer encoders to enhance the
feature map with more accurate edges. Finally, we also add a census left-right
consistency loss function to enhance the left-right consistency of stereo event
representation. Through conducting experiments within challenging real-world
driving scenarios, we validate that our method outperforms currently known
state-of-the-art methods in terms of mean absolute error (MAE) and root mean
square error (RMSE) metrics.
|
[
{
"created": "Sat, 10 Aug 2024 06:13:37 GMT",
"version": "v1"
}
] |
2024-08-13
|
[
[
"Jiang",
"Junjie",
""
],
[
"Zhuang",
"Hao",
""
],
[
"Huang",
"Xinjie",
""
],
[
"Kong",
"Delei",
""
],
[
"Fang",
"Zheng",
""
]
] |
Event cameras have the potential to revolutionize the field of robot vision, particularly in areas like stereo disparity estimation, owing to their high temporal resolution and high dynamic range. Many studies use deep learning for event camera stereo disparity estimation. However, these methods fail to fully exploit the temporal information in the event stream to acquire clear event representations. Additionally, there is room for further reduction in pixel shifts in the feature maps before constructing the cost volume. In this paper, we propose EV-MGDispNet, a novel event-based stereo disparity estimation method. Firstly, we propose an edge-aware aggregation (EAA) module, which fuses event frames and motion confidence maps to generate a novel clear event representation. Then, we propose a motion-guided attention (MGA) module, where motion confidence maps utilize deformable transformer encoders to enhance the feature map with more accurate edges. Finally, we also add a census left-right consistency loss function to enhance the left-right consistency of stereo event representation. Through conducting experiments within challenging real-world driving scenarios, we validate that our method outperforms currently known state-of-the-art methods in terms of mean absolute error (MAE) and root mean square error (RMSE) metrics.
|
2404.06357
|
Hyewon Jang
|
Hyewon Jang, Diego Frassinelli
|
Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We tested the robustness of sarcasm detection models by examining their
behavior when fine-tuned on four sarcasm datasets containing varying
characteristics of sarcasm: label source (authors vs. third-party), domain
(social media/online vs. offline conversations/dialogues), style (aggressive
vs. humorous mocking). We tested their prediction performance on the same
dataset (intra-dataset) and across different datasets (cross-dataset). For
intra-dataset predictions, models consistently performed better when fine-tuned
with third-party labels rather than with author labels. For cross-dataset
predictions, most models failed to generalize well to the other datasets,
implying that one type of dataset cannot represent all sorts of sarcasm with
different styles and domains. Compared to the existing datasets, models
fine-tuned on the new dataset we release in this work showed the highest
generalizability to other datasets. With a manual inspection of the datasets
and post-hoc analysis, we attributed the difficulty in generalization to the
fact that sarcasm actually comes in different domains and styles. We argue that
future sarcasm research should take the broad scope of sarcasm into account.
|
[
{
"created": "Tue, 9 Apr 2024 14:48:32 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Apr 2024 07:48:08 GMT",
"version": "v2"
}
] |
2024-04-11
|
[
[
"Jang",
"Hyewon",
""
],
[
"Frassinelli",
"Diego",
""
]
] |
We tested the robustness of sarcasm detection models by examining their behavior when fine-tuned on four sarcasm datasets containing varying characteristics of sarcasm: label source (authors vs. third-party), domain (social media/online vs. offline conversations/dialogues), style (aggressive vs. humorous mocking). We tested their prediction performance on the same dataset (intra-dataset) and across different datasets (cross-dataset). For intra-dataset predictions, models consistently performed better when fine-tuned with third-party labels rather than with author labels. For cross-dataset predictions, most models failed to generalize well to the other datasets, implying that one type of dataset cannot represent all sorts of sarcasm with different styles and domains. Compared to the existing datasets, models fine-tuned on the new dataset we release in this work showed the highest generalizability to other datasets. With a manual inspection of the datasets and post-hoc analysis, we attributed the difficulty in generalization to the fact that sarcasm actually comes in different domains and styles. We argue that future sarcasm research should take the broad scope of sarcasm into account.
|
2402.05012
|
Amir K. Khandani Dr.
|
Amir K. Khandani
|
Information Theoretically Secure Encryption Key Generation over Wireless
Networks by Exploiting Packet Errors
| null | null | null | null |
cs.IT cs.CR math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This article presents a novel method for establishing an information
theoretically secure encryption key over wireless channels. It exploits the
fact that data transmission over wireless links is accompanied by packet error,
while noise terms, and thereby the error events observed by two separate
receivers are independent of each other. A number of data packets, with random
data, are transmitted from a first legitimate node, say Alice, to a second
legitimate node, say Bob. Bob identifies all packets that are received
error-free in the first transmission attempt and sends their indices to Alice
over a public channel. Then, both Alice and Bob mix the contents of identified
packets, e.g., using a hash function, and thereby derive an identical
encryption key. Since error events from Alice to Bob is independent of error
events from Alice to Eve, the chances that Eve has successfully received all
packets used in key generation error-free diminishes as the number of packet
increases. In many wireless standards, the first stage in error detection and
Automatic Repeat Request (ARQ) is deployed at the PHY/MAC (Physical
Layer/Medium Access Control) layer. In such setups, the first re-transmission
is manged by the PHY/MAC layer without informing higher layers. This makes it
impossible to directly access the information related to packet errors through
high-level programming interfaces available to an end-user. A method is
presented for determining packets received error-free in first transmission
attempts through high-level programming. Examples are presented in conjunction
with an LTE cellular network.
|
[
{
"created": "Wed, 7 Feb 2024 16:32:13 GMT",
"version": "v1"
}
] |
2024-02-08
|
[
[
"Khandani",
"Amir K.",
""
]
] |
This article presents a novel method for establishing an information theoretically secure encryption key over wireless channels. It exploits the fact that data transmission over wireless links is accompanied by packet error, while noise terms, and thereby the error events observed by two separate receivers are independent of each other. A number of data packets, with random data, are transmitted from a first legitimate node, say Alice, to a second legitimate node, say Bob. Bob identifies all packets that are received error-free in the first transmission attempt and sends their indices to Alice over a public channel. Then, both Alice and Bob mix the contents of identified packets, e.g., using a hash function, and thereby derive an identical encryption key. Since error events from Alice to Bob is independent of error events from Alice to Eve, the chances that Eve has successfully received all packets used in key generation error-free diminishes as the number of packet increases. In many wireless standards, the first stage in error detection and Automatic Repeat Request (ARQ) is deployed at the PHY/MAC (Physical Layer/Medium Access Control) layer. In such setups, the first re-transmission is manged by the PHY/MAC layer without informing higher layers. This makes it impossible to directly access the information related to packet errors through high-level programming interfaces available to an end-user. A method is presented for determining packets received error-free in first transmission attempts through high-level programming. Examples are presented in conjunction with an LTE cellular network.
|
2312.10019
|
Kwanghee Choi
|
Kwanghee Choi, Jee-weon Jung, Shinji Watanabe
|
Understanding Probe Behaviors through Variational Bounds of Mutual
Information
|
Accepted to ICASSP 2024, implementation available at
https://github.com/juice500ml/information_probing
| null | null | null |
cs.IT cs.LG eess.AS math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
With the success of self-supervised representations, researchers seek a
better understanding of the information encapsulated within a representation.
Among various interpretability methods, we focus on classification-based linear
probing. We aim to foster a solid understanding and provide guidelines for
linear probing by constructing a novel mathematical framework leveraging
information theory. First, we connect probing with the variational bounds of
mutual information (MI) to relax the probe design, equating linear probing with
fine-tuning. Then, we investigate empirical behaviors and practices of probing
through our mathematical framework. We analyze the layer-wise performance curve
being convex, which seemingly violates the data processing inequality. However,
we show that the intermediate representations can have the biggest MI estimate
because of the tradeoff between better separability and decreasing MI. We
further suggest that the margin of linearly separable representations can be a
criterion for measuring the "goodness of representation." We also compare
accuracy with MI as the measuring criteria. Finally, we empirically validate
our claims by observing the self-supervised speech models on retaining word and
phoneme information.
|
[
{
"created": "Fri, 15 Dec 2023 18:38:18 GMT",
"version": "v1"
}
] |
2023-12-18
|
[
[
"Choi",
"Kwanghee",
""
],
[
"Jung",
"Jee-weon",
""
],
[
"Watanabe",
"Shinji",
""
]
] |
With the success of self-supervised representations, researchers seek a better understanding of the information encapsulated within a representation. Among various interpretability methods, we focus on classification-based linear probing. We aim to foster a solid understanding and provide guidelines for linear probing by constructing a novel mathematical framework leveraging information theory. First, we connect probing with the variational bounds of mutual information (MI) to relax the probe design, equating linear probing with fine-tuning. Then, we investigate empirical behaviors and practices of probing through our mathematical framework. We analyze the layer-wise performance curve being convex, which seemingly violates the data processing inequality. However, we show that the intermediate representations can have the biggest MI estimate because of the tradeoff between better separability and decreasing MI. We further suggest that the margin of linearly separable representations can be a criterion for measuring the "goodness of representation." We also compare accuracy with MI as the measuring criteria. Finally, we empirically validate our claims by observing the self-supervised speech models on retaining word and phoneme information.
|
0801.0455
|
Jorg Liebeherr
|
Jorg Liebeherr, Markus Fidler, Shahrokh Valaee
|
A System Theoretic Approach to Bandwidth Estimation
|
23 pages
| null | null | null |
cs.NI cs.PF
| null |
It is shown that bandwidth estimation in packet networks can be viewed in
terms of min-plus linear system theory. The available bandwidth of a link or
complete path is expressed in terms of a {\em service curve}, which is a
function that appears in the network calculus to express the service available
to a traffic flow. The service curve is estimated based on measurements of a
sequence of probing packets or passive measurements of a sample path of
arrivals. It is shown that existing bandwidth estimation methods can be derived
in the min-plus algebra of the network calculus, thus providing further
mathematical justification for these methods. Principal difficulties of
estimating available bandwidth from measurement of network probes are related
to potential non-linearities of the underlying network. When networks are
viewed as systems that operate either in a linear or in a non-linear regime, it
is argued that probing schemes extract the most information at a point when the
network crosses from a linear to a non-linear regime. Experiments on the Emulab
testbed at the University of Utah evaluate the robustness of the system
theoretic interpretation of networks in practice. Multi-node experiments
evaluate how well the convolution operation of the min-plus algebra provides
estimates for the available bandwidth of a path from estimates of individual
links.
|
[
{
"created": "Thu, 3 Jan 2008 00:11:26 GMT",
"version": "v1"
}
] |
2008-01-04
|
[
[
"Liebeherr",
"Jorg",
""
],
[
"Fidler",
"Markus",
""
],
[
"Valaee",
"Shahrokh",
""
]
] |
It is shown that bandwidth estimation in packet networks can be viewed in terms of min-plus linear system theory. The available bandwidth of a link or complete path is expressed in terms of a {\em service curve}, which is a function that appears in the network calculus to express the service available to a traffic flow. The service curve is estimated based on measurements of a sequence of probing packets or passive measurements of a sample path of arrivals. It is shown that existing bandwidth estimation methods can be derived in the min-plus algebra of the network calculus, thus providing further mathematical justification for these methods. Principal difficulties of estimating available bandwidth from measurement of network probes are related to potential non-linearities of the underlying network. When networks are viewed as systems that operate either in a linear or in a non-linear regime, it is argued that probing schemes extract the most information at a point when the network crosses from a linear to a non-linear regime. Experiments on the Emulab testbed at the University of Utah evaluate the robustness of the system theoretic interpretation of networks in practice. Multi-node experiments evaluate how well the convolution operation of the min-plus algebra provides estimates for the available bandwidth of a path from estimates of individual links.
|
2011.05431
|
Nikolaos Stylianou
|
Nikolaos Stylianou, Ioannis Vlahavas
|
E.T.: Entity-Transformers. Coreference augmented Neural Language Model
for richer mention representations via Entity-Transformer blocks
|
10 pages, 4 figures, 5 tables, accepted at CRAC2020
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In the last decade, the field of Neural Language Modelling has witnessed
enormous changes, with the development of novel models through the use of
Transformer architectures. However, even these models struggle to model long
sequences due to memory constraints and increasing computational complexity.
Coreference annotations over the training data can provide context far beyond
the modelling limitations of such language models. In this paper we present an
extension over the Transformer-block architecture used in neural language
models, specifically in GPT2, in order to incorporate entity annotations during
training. Our model, GPT2E, extends the Transformer layers architecture of GPT2
to Entity-Transformers, an architecture designed to handle coreference
information when present. To that end, we achieve richer representations for
entity mentions, with insignificant training cost. We show the comparative
model performance between GPT2 and GPT2E in terms of Perplexity on the CoNLL
2012 and LAMBADA datasets as well as the key differences in the entity
representations and their effects in downstream tasks such as Named Entity
Recognition. Furthermore, our approach can be adopted by the majority of
Transformer-based language models.
|
[
{
"created": "Tue, 10 Nov 2020 22:28:00 GMT",
"version": "v1"
}
] |
2020-11-12
|
[
[
"Stylianou",
"Nikolaos",
""
],
[
"Vlahavas",
"Ioannis",
""
]
] |
In the last decade, the field of Neural Language Modelling has witnessed enormous changes, with the development of novel models through the use of Transformer architectures. However, even these models struggle to model long sequences due to memory constraints and increasing computational complexity. Coreference annotations over the training data can provide context far beyond the modelling limitations of such language models. In this paper we present an extension over the Transformer-block architecture used in neural language models, specifically in GPT2, in order to incorporate entity annotations during training. Our model, GPT2E, extends the Transformer layers architecture of GPT2 to Entity-Transformers, an architecture designed to handle coreference information when present. To that end, we achieve richer representations for entity mentions, with insignificant training cost. We show the comparative model performance between GPT2 and GPT2E in terms of Perplexity on the CoNLL 2012 and LAMBADA datasets as well as the key differences in the entity representations and their effects in downstream tasks such as Named Entity Recognition. Furthermore, our approach can be adopted by the majority of Transformer-based language models.
|
1409.5223
|
Ben Ruijl
|
Ben Ruijl, Aske Plaat, Jos Vermaseren, Jaap van den Herik
|
Why Local Search Excels in Expression Simplification
| null | null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Simplifying expressions is important to make numerical integration of large
expressions from High Energy Physics tractable. To this end, Horner's method
can be used. Finding suitable Horner schemes is assumed to be hard, due to the
lack of local heuristics. Recently, MCTS was reported to be able to find near
optimal schemes. However, several parameters had to be fine-tuned manually. In
this work, we investigate the state space properties of Horner schemes and find
that the domain is relatively flat and contains only a few local minima. As a
result, the Horner space is appropriate to be explored by Stochastic Local
Search (SLS), which has only two parameters: the number of iterations
(computation time) and the neighborhood structure. We found a suitable
neighborhood structure, leaving only the allowed computation time as a
parameter. We performed a range of experiments. The results obtained by SLS are
similar or better than those obtained by MCTS. Furthermore, we show that SLS
obtains the good results at least 10 times faster. Using SLS, we can speed up
numerical integration of many real-world large expressions by at least a factor
of 24. For High Energy Physics this means that numerical integrations that took
weeks can now be done in hours.
|
[
{
"created": "Thu, 18 Sep 2014 08:21:25 GMT",
"version": "v1"
}
] |
2014-09-19
|
[
[
"Ruijl",
"Ben",
""
],
[
"Plaat",
"Aske",
""
],
[
"Vermaseren",
"Jos",
""
],
[
"Herik",
"Jaap van den",
""
]
] |
Simplifying expressions is important to make numerical integration of large expressions from High Energy Physics tractable. To this end, Horner's method can be used. Finding suitable Horner schemes is assumed to be hard, due to the lack of local heuristics. Recently, MCTS was reported to be able to find near optimal schemes. However, several parameters had to be fine-tuned manually. In this work, we investigate the state space properties of Horner schemes and find that the domain is relatively flat and contains only a few local minima. As a result, the Horner space is appropriate to be explored by Stochastic Local Search (SLS), which has only two parameters: the number of iterations (computation time) and the neighborhood structure. We found a suitable neighborhood structure, leaving only the allowed computation time as a parameter. We performed a range of experiments. The results obtained by SLS are similar or better than those obtained by MCTS. Furthermore, we show that SLS obtains the good results at least 10 times faster. Using SLS, we can speed up numerical integration of many real-world large expressions by at least a factor of 24. For High Energy Physics this means that numerical integrations that took weeks can now be done in hours.
|
2105.01772
|
Stephen Gilbert
|
Charles Peasley, Rachel Dianiska, Emily Oldham, Nicholas Wilson,
Stephen Gilbert, Peggy Wu, Brett Israelsen, James Oliver
|
Evaluating Metrics for Standardized Benchmarking of Remote Presence
Systems
| null | null | null | null |
cs.HC cs.CY
|
http://creativecommons.org/licenses/by-sa/4.0/
|
To reduce the need for business-related air travel and its associated energy
consumption and carbon footprint, the U.S. Department of Energy's ARPA-E is
supporting a research project called SCOTTIE - Systematic Communication
Objectives and Telecommunications Technology Investigations and Evaluations.
SCOTTIE tests virtual and augmented reality platforms in a functional
comparison with face-to-face (FtF) interactions to derive travel replacement
thresholds for common industrial training scenarios. The primary goal of Study
1 is to match the communication effectiveness and learning outcomes obtained
from a FtF control using virtual reality (VR) training scenarios in which a
local expert with physical equipment trains a remote apprentice without
physical equipment immediately present. This application scenario is
commonplace in industrial settings where access to expensive equipment and
materials is limited and a number of apprentices must travel to a central
location in order to undergo training. Supplying an empirically validated
virtual training alternative constitutes a readily adoptable use-case for
businesses looking to reduce time and monetary expenditures associated with
travel. The technology used for three different virtual presence technologies
was strategically selected for feasibility, relatively low cost, business
relevance, and potential for impact through transition. The authors suggest
that the results of this study might generalize to the challenge of virtual
conferences.
|
[
{
"created": "Tue, 4 May 2021 21:36:53 GMT",
"version": "v1"
}
] |
2021-05-06
|
[
[
"Peasley",
"Charles",
""
],
[
"Dianiska",
"Rachel",
""
],
[
"Oldham",
"Emily",
""
],
[
"Wilson",
"Nicholas",
""
],
[
"Gilbert",
"Stephen",
""
],
[
"Wu",
"Peggy",
""
],
[
"Israelsen",
"Brett",
""
],
[
"Oliver",
"James",
""
]
] |
To reduce the need for business-related air travel and its associated energy consumption and carbon footprint, the U.S. Department of Energy's ARPA-E is supporting a research project called SCOTTIE - Systematic Communication Objectives and Telecommunications Technology Investigations and Evaluations. SCOTTIE tests virtual and augmented reality platforms in a functional comparison with face-to-face (FtF) interactions to derive travel replacement thresholds for common industrial training scenarios. The primary goal of Study 1 is to match the communication effectiveness and learning outcomes obtained from a FtF control using virtual reality (VR) training scenarios in which a local expert with physical equipment trains a remote apprentice without physical equipment immediately present. This application scenario is commonplace in industrial settings where access to expensive equipment and materials is limited and a number of apprentices must travel to a central location in order to undergo training. Supplying an empirically validated virtual training alternative constitutes a readily adoptable use-case for businesses looking to reduce time and monetary expenditures associated with travel. The technology used for three different virtual presence technologies was strategically selected for feasibility, relatively low cost, business relevance, and potential for impact through transition. The authors suggest that the results of this study might generalize to the challenge of virtual conferences.
|
2303.04598
|
Agi Kurucz
|
Agi Kurucz, Frank Wolter, Michael Zakharyaschev
|
Deciding the Existence of Interpolants and Definitions in First-Order
Modal Logic
| null | null | null | null |
cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
None of the first-order modal logics between $\mathsf{K}$ and $\mathsf{S5}$
under the constant domain semantics enjoys Craig interpolation or projective
Beth definability, even in the language restricted to a single individual
variable. It follows that the existence of a Craig interpolant for a given
implication or of an explicit definition for a given predicate cannot be
directly reduced to validity as in classical first-order and many other logics.
Our concern here is the decidability and computational complexity of the
interpolant and definition existence problems. We first consider two decidable
fragments of first-order modal logic $\mathsf{S5}$: the one-variable fragment
$\mathsf{Q^1S5}$ and its extension $\mathsf{S5}_{\mathcal{ALC}^u}$ that
combines $\mathsf{S5}$ and the description logic$\mathcal{ALC}$ with the
universal role. We prove that interpolant and definition existence in
$\mathsf{Q^1S5}$ and $\mathsf{S5}_{\mathcal{ALC}^u}$ is decidable in
coN2ExpTime, being 2ExpTime-hard, while uniform interpolant existence is
undecidable. These results transfer to the two-variable fragment
$\mathsf{FO^2}$ of classical first-order logic without equality. We also show
that interpolant and definition existence in the one-variable fragment
$\mathsf{Q^1K}$ of first-order modal logic $\mathsf{K}$ is non-elementary
decidable, while uniform interpolant existence is again undecidable.
|
[
{
"created": "Wed, 8 Mar 2023 14:10:59 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Jun 2024 12:03:35 GMT",
"version": "v2"
}
] |
2024-06-06
|
[
[
"Kurucz",
"Agi",
""
],
[
"Wolter",
"Frank",
""
],
[
"Zakharyaschev",
"Michael",
""
]
] |
None of the first-order modal logics between $\mathsf{K}$ and $\mathsf{S5}$ under the constant domain semantics enjoys Craig interpolation or projective Beth definability, even in the language restricted to a single individual variable. It follows that the existence of a Craig interpolant for a given implication or of an explicit definition for a given predicate cannot be directly reduced to validity as in classical first-order and many other logics. Our concern here is the decidability and computational complexity of the interpolant and definition existence problems. We first consider two decidable fragments of first-order modal logic $\mathsf{S5}$: the one-variable fragment $\mathsf{Q^1S5}$ and its extension $\mathsf{S5}_{\mathcal{ALC}^u}$ that combines $\mathsf{S5}$ and the description logic$\mathcal{ALC}$ with the universal role. We prove that interpolant and definition existence in $\mathsf{Q^1S5}$ and $\mathsf{S5}_{\mathcal{ALC}^u}$ is decidable in coN2ExpTime, being 2ExpTime-hard, while uniform interpolant existence is undecidable. These results transfer to the two-variable fragment $\mathsf{FO^2}$ of classical first-order logic without equality. We also show that interpolant and definition existence in the one-variable fragment $\mathsf{Q^1K}$ of first-order modal logic $\mathsf{K}$ is non-elementary decidable, while uniform interpolant existence is again undecidable.
|
2407.17316
|
Niklas B\"oing
|
N. B\"oing, J. Holke, C. Hergl, L. Spataro, G. Gassner, A. Basermann
|
Lossy Data Compression By Adaptive Mesh Coarsening
| null | null | null | null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Today's scientific simulations, for example in the high-performance exascale
sector, produce huge amounts of data. Due to limited I/O bandwidth and
available storage space, there is the necessity to reduce scientific data of
high performance computing applications. Error-bounded lossy compression has
been proven to be an effective approach tackling the trade-off between accuracy
and storage space. Within this work, we are exploring and discussing
error-bounded lossy compression solely based on adaptive mesh refinement
techniques. This compression technique is not only easily integrated into
existing adaptive mesh refinement applications but also suits as a general
lossy compression approach for arbitrary data in form of multi-dimensional
arrays, irrespective of the data type. Moreover, these techniques permit the
exclusion of regions of interest and even allows for nested error domains
during the compression. The described data compression technique is presented
exemplary on ERA5 data.
|
[
{
"created": "Wed, 24 Jul 2024 14:39:24 GMT",
"version": "v1"
}
] |
2024-07-25
|
[
[
"Böing",
"N.",
""
],
[
"Holke",
"J.",
""
],
[
"Hergl",
"C.",
""
],
[
"Spataro",
"L.",
""
],
[
"Gassner",
"G.",
""
],
[
"Basermann",
"A.",
""
]
] |
Today's scientific simulations, for example in the high-performance exascale sector, produce huge amounts of data. Due to limited I/O bandwidth and available storage space, there is the necessity to reduce scientific data of high performance computing applications. Error-bounded lossy compression has been proven to be an effective approach tackling the trade-off between accuracy and storage space. Within this work, we are exploring and discussing error-bounded lossy compression solely based on adaptive mesh refinement techniques. This compression technique is not only easily integrated into existing adaptive mesh refinement applications but also suits as a general lossy compression approach for arbitrary data in form of multi-dimensional arrays, irrespective of the data type. Moreover, these techniques permit the exclusion of regions of interest and even allows for nested error domains during the compression. The described data compression technique is presented exemplary on ERA5 data.
|
2308.10457
|
Xinpeng Ling
|
Xinpeng Ling, Jie Fu, Kuncan Wang, Haitao Liu, Zhili Chen
|
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local
Iterations
| null | null | null | null |
cs.LG cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Federated Learning (FL) is a distributed machine learning technique that
allows model training among multiple devices or organizations by sharing
training parameters instead of raw data. However, adversaries can still infer
individual information through inference attacks (e.g. differential attacks) on
these training parameters. As a result, Differential Privacy (DP) has been
widely used in FL to prevent such attacks.
We consider differentially private federated learning in a
resource-constrained scenario, where both privacy budget and communication
rounds are constrained. By theoretically analyzing the convergence, we can find
the optimal number of local DPSGD iterations for clients between any two
sequential global updates. Based on this, we design an algorithm of
Differentially Private Federated Learning with Adaptive Local Iterations
(ALI-DPFL). We experiment our algorithm on the MNIST, FashionMNIST and Cifar10
datasets, and demonstrate significantly better performances than previous work
in the resource-constraint scenario. Code is available at
https://github.com/cheng-t/ALI-DPFL.
|
[
{
"created": "Mon, 21 Aug 2023 04:09:59 GMT",
"version": "v1"
},
{
"created": "Thu, 21 Sep 2023 14:59:28 GMT",
"version": "v2"
},
{
"created": "Fri, 22 Sep 2023 07:59:03 GMT",
"version": "v3"
},
{
"created": "Sun, 25 Feb 2024 06:56:16 GMT",
"version": "v4"
},
{
"created": "Sun, 24 Mar 2024 10:04:37 GMT",
"version": "v5"
},
{
"created": "Tue, 23 Apr 2024 14:34:45 GMT",
"version": "v6"
},
{
"created": "Wed, 24 Apr 2024 06:12:08 GMT",
"version": "v7"
},
{
"created": "Fri, 17 May 2024 03:12:57 GMT",
"version": "v8"
},
{
"created": "Wed, 22 May 2024 04:17:46 GMT",
"version": "v9"
}
] |
2024-05-27
|
[
[
"Ling",
"Xinpeng",
""
],
[
"Fu",
"Jie",
""
],
[
"Wang",
"Kuncan",
""
],
[
"Liu",
"Haitao",
""
],
[
"Chen",
"Zhili",
""
]
] |
Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations by sharing training parameters instead of raw data. However, adversaries can still infer individual information through inference attacks (e.g. differential attacks) on these training parameters. As a result, Differential Privacy (DP) has been widely used in FL to prevent such attacks. We consider differentially private federated learning in a resource-constrained scenario, where both privacy budget and communication rounds are constrained. By theoretically analyzing the convergence, we can find the optimal number of local DPSGD iterations for clients between any two sequential global updates. Based on this, we design an algorithm of Differentially Private Federated Learning with Adaptive Local Iterations (ALI-DPFL). We experiment our algorithm on the MNIST, FashionMNIST and Cifar10 datasets, and demonstrate significantly better performances than previous work in the resource-constraint scenario. Code is available at https://github.com/cheng-t/ALI-DPFL.
|
1707.00338
|
Luciana Foss
|
Leila Ribeiro, Luciana Foss, Simone Andr\'e da Costa Cavalheiro
|
Entendendo o Pensamento Computacional
|
18 pages, in Portuguese
| null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The goal of this article is to clarify the meaning of Computational Thinking.
We differentiate logical from computational reasoning and discuss the
importance of Computational Thinking in solving problems. The three pillars of
Computational Thinking - Abstraction, Automation and Analysis - are outlined,
highlighting the role of each one in developing the skills needed for the
problem-solving process.
-----
O objetivo deste artigo \'e esclarecer o significado de Pensamento
Computacional. Diferencia-se o racioc\'inio l\'ogico do computacional e
discute-se a import\^ancia do Pensamento Computacional na resolu\c{c}\~ao de
problemas. Os tr\^es pilares do Pensamento Computacional - Abstra\c{c}\~ao,
Automa\c{c}\~ao e An\'alise - s\~ao delineados, destacando-se o papel de cada
um deles no desenvolvimento das habilidades necess\'arias para o processo de
solu\c{c}\~ao de problemas.
|
[
{
"created": "Sun, 2 Jul 2017 19:38:55 GMT",
"version": "v1"
}
] |
2017-07-04
|
[
[
"Ribeiro",
"Leila",
""
],
[
"Foss",
"Luciana",
""
],
[
"Cavalheiro",
"Simone André da Costa",
""
]
] |
The goal of this article is to clarify the meaning of Computational Thinking. We differentiate logical from computational reasoning and discuss the importance of Computational Thinking in solving problems. The three pillars of Computational Thinking - Abstraction, Automation and Analysis - are outlined, highlighting the role of each one in developing the skills needed for the problem-solving process. ----- O objetivo deste artigo \'e esclarecer o significado de Pensamento Computacional. Diferencia-se o racioc\'inio l\'ogico do computacional e discute-se a import\^ancia do Pensamento Computacional na resolu\c{c}\~ao de problemas. Os tr\^es pilares do Pensamento Computacional - Abstra\c{c}\~ao, Automa\c{c}\~ao e An\'alise - s\~ao delineados, destacando-se o papel de cada um deles no desenvolvimento das habilidades necess\'arias para o processo de solu\c{c}\~ao de problemas.
|
2101.08032
|
Wanguang Yin
|
Wanguang Yin, Zhengming Ma, Quanying Liu
|
Riemannian Manifold Optimization for Discriminant Subspace Learning
|
13 pages, 4 figures, 6 tables
| null | null | null |
cs.LG eess.IV eess.SP
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Linear discriminant analysis (LDA) is a widely used algorithm in machine
learning to extract a low-dimensional representation of high-dimensional data,
it features to find the orthogonal discriminant projection subspace by using
the Fisher discriminant criterion. However, the traditional Euclidean-based
methods for solving LDA are easily convergent to spurious local minima and
hardly obtain an optimal solution. To address such a problem, in this paper, we
propose a novel algorithm namely Riemannian-based discriminant analysis (RDA)
for subspace learning. In order to obtain an explicit solution, we transform
the traditional Euclidean-based methods to the Riemannian manifold space and
use the trust-region method to learn the discriminant projection subspace. We
compare the proposed algorithm to existing variants of LDA, as well as the
unsupervised tensor decomposition methods on image classification tasks. The
numerical results suggest that RDA achieves state-of-the-art performance in
classification accuracy.
|
[
{
"created": "Wed, 20 Jan 2021 09:13:34 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Jan 2021 07:17:29 GMT",
"version": "v2"
},
{
"created": "Tue, 20 Jul 2021 02:37:14 GMT",
"version": "v3"
}
] |
2021-07-21
|
[
[
"Yin",
"Wanguang",
""
],
[
"Ma",
"Zhengming",
""
],
[
"Liu",
"Quanying",
""
]
] |
Linear discriminant analysis (LDA) is a widely used algorithm in machine learning to extract a low-dimensional representation of high-dimensional data, it features to find the orthogonal discriminant projection subspace by using the Fisher discriminant criterion. However, the traditional Euclidean-based methods for solving LDA are easily convergent to spurious local minima and hardly obtain an optimal solution. To address such a problem, in this paper, we propose a novel algorithm namely Riemannian-based discriminant analysis (RDA) for subspace learning. In order to obtain an explicit solution, we transform the traditional Euclidean-based methods to the Riemannian manifold space and use the trust-region method to learn the discriminant projection subspace. We compare the proposed algorithm to existing variants of LDA, as well as the unsupervised tensor decomposition methods on image classification tasks. The numerical results suggest that RDA achieves state-of-the-art performance in classification accuracy.
|
2404.15385
|
Alaa Elobaid
|
Alaa Elobaid, Nathan Ramoly, Lara Younes, Symeon Papadopoulos, Eirini
Ntoutsi and Ioannis Kompatsiaris
|
Sum of Group Error Differences: A Critical Examination of Bias
Evaluation in Biometric Verification and a Dual-Metric Measure
| null | null | null | null |
cs.CV cs.AI cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Biometric Verification (BV) systems often exhibit accuracy disparities across
different demographic groups, leading to biases in BV applications. Assessing
and quantifying these biases is essential for ensuring the fairness of BV
systems. However, existing bias evaluation metrics in BV have limitations, such
as focusing exclusively on match or non-match error rates, overlooking bias on
demographic groups with performance levels falling between the best and worst
performance levels, and neglecting the magnitude of the bias present.
This paper presents an in-depth analysis of the limitations of current bias
evaluation metrics in BV and, through experimental analysis, demonstrates their
contextual suitability, merits, and limitations. Additionally, it introduces a
novel general-purpose bias evaluation measure for BV, the ``Sum of Group Error
Differences (SEDG)''. Our experimental results on controlled synthetic datasets
demonstrate the effectiveness of demographic bias quantification when using
existing metrics and our own proposed measure. We discuss the applicability of
the bias evaluation metrics in a set of simulated demographic bias scenarios
and provide scenario-based metric recommendations. Our code is publicly
available under \url{https://github.com/alaaobeid/SEDG}.
|
[
{
"created": "Tue, 23 Apr 2024 10:59:44 GMT",
"version": "v1"
}
] |
2024-04-25
|
[
[
"Elobaid",
"Alaa",
""
],
[
"Ramoly",
"Nathan",
""
],
[
"Younes",
"Lara",
""
],
[
"Papadopoulos",
"Symeon",
""
],
[
"Ntoutsi",
"Eirini",
""
],
[
"Kompatsiaris",
"Ioannis",
""
]
] |
Biometric Verification (BV) systems often exhibit accuracy disparities across different demographic groups, leading to biases in BV applications. Assessing and quantifying these biases is essential for ensuring the fairness of BV systems. However, existing bias evaluation metrics in BV have limitations, such as focusing exclusively on match or non-match error rates, overlooking bias on demographic groups with performance levels falling between the best and worst performance levels, and neglecting the magnitude of the bias present. This paper presents an in-depth analysis of the limitations of current bias evaluation metrics in BV and, through experimental analysis, demonstrates their contextual suitability, merits, and limitations. Additionally, it introduces a novel general-purpose bias evaluation measure for BV, the ``Sum of Group Error Differences (SEDG)''. Our experimental results on controlled synthetic datasets demonstrate the effectiveness of demographic bias quantification when using existing metrics and our own proposed measure. We discuss the applicability of the bias evaluation metrics in a set of simulated demographic bias scenarios and provide scenario-based metric recommendations. Our code is publicly available under \url{https://github.com/alaaobeid/SEDG}.
|
0912.4087
|
Wei Ren
|
Wei Ren, Qing Zhao, Ananthram Swami
|
On the Connectivity and Multihop Delay of Ad Hoc Cognitive Radio
Networks
|
28 pages, 9 figures
| null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We analyze the multihop delay of ad hoc cognitive radio networks, where the
transmission delay of each hop consists of the propagation delay and the
waiting time for the availability of the communication channel (i.e., the
occurrence of a spectrum opportunity at this hop). Using theories and
techniques from continuum percolation and ergodicity, we establish the scaling
law of the minimum multihop delay with respect to the source-destination
distance in cognitive radio networks. When the propagation delay is negligible,
we show the starkly different scaling behavior of the minimum multihop delay in
instantaneously connected networks as compared to networks that are only
intermittently connected due to scarcity of spectrum opportunities.
Specifically, if the network is instantaneously connected, the minimum multihop
delay is asymptotically independent of the distance; if the network is only
intermittently connected, the minimum multihop delay scales linearly with the
distance. When the propagation delay is nonnegligible but small, we show that
although the scaling order is always linear, the scaling rate for an
instantaneously connected network can be orders of magnitude smaller than the
one for an intermittently connected network.
|
[
{
"created": "Mon, 21 Dec 2009 06:47:42 GMT",
"version": "v1"
}
] |
2009-12-22
|
[
[
"Ren",
"Wei",
""
],
[
"Zhao",
"Qing",
""
],
[
"Swami",
"Ananthram",
""
]
] |
We analyze the multihop delay of ad hoc cognitive radio networks, where the transmission delay of each hop consists of the propagation delay and the waiting time for the availability of the communication channel (i.e., the occurrence of a spectrum opportunity at this hop). Using theories and techniques from continuum percolation and ergodicity, we establish the scaling law of the minimum multihop delay with respect to the source-destination distance in cognitive radio networks. When the propagation delay is negligible, we show the starkly different scaling behavior of the minimum multihop delay in instantaneously connected networks as compared to networks that are only intermittently connected due to scarcity of spectrum opportunities. Specifically, if the network is instantaneously connected, the minimum multihop delay is asymptotically independent of the distance; if the network is only intermittently connected, the minimum multihop delay scales linearly with the distance. When the propagation delay is nonnegligible but small, we show that although the scaling order is always linear, the scaling rate for an instantaneously connected network can be orders of magnitude smaller than the one for an intermittently connected network.
|
2310.12403
|
Muhammed Fatih Bal{\i}n
|
Muhammed Fatih Balin, Dominique LaSalle, \"Umit V. \c{C}ataly\"urek
|
Cooperative Minibatching in Graph Neural Networks
|
Under submission
| null | null | null |
cs.LG cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Significant computational resources are required to train Graph Neural
Networks (GNNs) at a large scale, and the process is highly data-intensive. One
of the most effective ways to reduce resource requirements is minibatch
training coupled with graph sampling. GNNs have the unique property that items
in a minibatch have overlapping data. However, the commonly implemented
Independent Minibatching approach assigns each Processing Element (PE) its own
minibatch to process, leading to duplicated computations and input data access
across PEs. This amplifies the Neighborhood Explosion Phenomenon (NEP), which
is the main bottleneck limiting scaling. To reduce the effects of NEP in the
multi-PE setting, we propose a new approach called Cooperative Minibatching.
Our approach capitalizes on the fact that the size of the sampled subgraph is a
concave function of the batch size, leading to significant reductions in the
amount of work per seed vertex as batch sizes increase. Hence, it is favorable
for processors equipped with a fast interconnect to work on a large minibatch
together as a single larger processor, instead of working on separate smaller
minibatches, even though global batch size is identical. We also show how to
take advantage of the same phenomenon in serial execution by generating
dependent consecutive minibatches. Our experimental evaluations show up to 4x
bandwidth savings for fetching vertex embeddings, by simply increasing this
dependency without harming model convergence. Combining our proposed
approaches, we achieve up to 64% speedup over Independent Minibatching on
single-node multi-GPU systems.
|
[
{
"created": "Thu, 19 Oct 2023 01:15:24 GMT",
"version": "v1"
},
{
"created": "Sun, 22 Oct 2023 02:01:01 GMT",
"version": "v2"
}
] |
2023-10-24
|
[
[
"Balin",
"Muhammed Fatih",
""
],
[
"LaSalle",
"Dominique",
""
],
[
"Çatalyürek",
"Ümit V.",
""
]
] |
Significant computational resources are required to train Graph Neural Networks (GNNs) at a large scale, and the process is highly data-intensive. One of the most effective ways to reduce resource requirements is minibatch training coupled with graph sampling. GNNs have the unique property that items in a minibatch have overlapping data. However, the commonly implemented Independent Minibatching approach assigns each Processing Element (PE) its own minibatch to process, leading to duplicated computations and input data access across PEs. This amplifies the Neighborhood Explosion Phenomenon (NEP), which is the main bottleneck limiting scaling. To reduce the effects of NEP in the multi-PE setting, we propose a new approach called Cooperative Minibatching. Our approach capitalizes on the fact that the size of the sampled subgraph is a concave function of the batch size, leading to significant reductions in the amount of work per seed vertex as batch sizes increase. Hence, it is favorable for processors equipped with a fast interconnect to work on a large minibatch together as a single larger processor, instead of working on separate smaller minibatches, even though global batch size is identical. We also show how to take advantage of the same phenomenon in serial execution by generating dependent consecutive minibatches. Our experimental evaluations show up to 4x bandwidth savings for fetching vertex embeddings, by simply increasing this dependency without harming model convergence. Combining our proposed approaches, we achieve up to 64% speedup over Independent Minibatching on single-node multi-GPU systems.
|
2101.02847
|
Yunjin Zhang
|
Yunjin Zhang, Rui Wang, Yifan (Evan) Peng, Wei Hua, Hujun Bao
|
Color Contrast Enhanced Rendering for Optical See-through Head-mounted
Displays
|
13 pages, 22 figures, submitted to TVCG
| null | null | null |
cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
Most commercially available optical see-through head-mounted displays
(OST-HMDs) utilize optical combiners to simultaneously visualize the physical
background and virtual objects. The displayed images perceived by users are a
blend of rendered pixels and background colors. Enabling high fidelity color
perception in mixed reality (MR) scenarios using OST-HMDs is an important but
challenging task. We propose a real-time rendering scheme to enhance the color
contrast between virtual objects and the surrounding background for OST-HMDs.
Inspired by the discovery of color perception in psychophysics, we first
formulate the color contrast enhancement as a constrained optimization problem.
We then design an end-to-end algorithm to search the optimal complementary
shift in both chromaticity and luminance of the displayed color. This aims at
enhancing the contrast between virtual objects and the real background as well
as keeping the consistency with the original color. We assess the performance
of our approach using a simulated OST-HMD environment and an off-the-shelf
OST-HMD. Experimental results from objective evaluations and subjective user
studies demonstrate that the proposed approach makes rendered virtual objects
more distinguishable from the surrounding background, thereby bringing a better
visual experience.
|
[
{
"created": "Fri, 8 Jan 2021 04:42:39 GMT",
"version": "v1"
}
] |
2021-01-11
|
[
[
"Zhang",
"Yunjin",
"",
"Evan"
],
[
"Wang",
"Rui",
"",
"Evan"
],
[
"Yifan",
"",
"",
"Evan"
],
[
"Peng",
"",
""
],
[
"Hua",
"Wei",
""
],
[
"Bao",
"Hujun",
""
]
] |
Most commercially available optical see-through head-mounted displays (OST-HMDs) utilize optical combiners to simultaneously visualize the physical background and virtual objects. The displayed images perceived by users are a blend of rendered pixels and background colors. Enabling high fidelity color perception in mixed reality (MR) scenarios using OST-HMDs is an important but challenging task. We propose a real-time rendering scheme to enhance the color contrast between virtual objects and the surrounding background for OST-HMDs. Inspired by the discovery of color perception in psychophysics, we first formulate the color contrast enhancement as a constrained optimization problem. We then design an end-to-end algorithm to search the optimal complementary shift in both chromaticity and luminance of the displayed color. This aims at enhancing the contrast between virtual objects and the real background as well as keeping the consistency with the original color. We assess the performance of our approach using a simulated OST-HMD environment and an off-the-shelf OST-HMD. Experimental results from objective evaluations and subjective user studies demonstrate that the proposed approach makes rendered virtual objects more distinguishable from the surrounding background, thereby bringing a better visual experience.
|
2106.00329
|
Zihao Yan
|
Zihao Yan, Zimu Yi, Ruizhen Hu, Niloy J. Mitra, Daniel Cohen-Or, Hui
Huang
|
Consistent Two-Flow Network for Tele-Registration of Point Clouds
|
Accepted to IEEE TVCG 2021, project page at
https://vcc.tech/research/2021/CTFNet
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Rigid registration of partial observations is a fundamental problem in
various applied fields. In computer graphics, special attention has been given
to the registration between two partial point clouds generated by scanning
devices. State-of-the-art registration techniques still struggle when the
overlap region between the two point clouds is small, and completely fail if
there is no overlap between the scan pairs. In this paper, we present a
learning-based technique that alleviates this problem, and allows registration
between point clouds, presented in arbitrary poses, and having little or even
no overlap, a setting that has been referred to as tele-registration. Our
technique is based on a novel neural network design that learns a prior of a
class of shapes and can complete a partial shape. The key idea is combining the
registration and completion tasks in a way that reinforces each other. In
particular, we simultaneously train the registration network and completion
network using two coupled flows, one that register-and-complete, and one that
complete-and-register, and encourage the two flows to produce a consistent
result. We show that, compared with each separate flow, this two-flow training
leads to robust and reliable tele-registration, and hence to a better point
cloud prediction that completes the registered scans. It is also worth
mentioning that each of the components in our neural network outperforms
state-of-the-art methods in both completion and registration. We further
analyze our network with several ablation studies and demonstrate its
performance on a large number of partial point clouds, both synthetic and
real-world, that have only small or no overlap.
|
[
{
"created": "Tue, 1 Jun 2021 09:03:21 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Jul 2021 09:41:09 GMT",
"version": "v2"
},
{
"created": "Mon, 11 Oct 2021 02:25:04 GMT",
"version": "v3"
}
] |
2021-10-12
|
[
[
"Yan",
"Zihao",
""
],
[
"Yi",
"Zimu",
""
],
[
"Hu",
"Ruizhen",
""
],
[
"Mitra",
"Niloy J.",
""
],
[
"Cohen-Or",
"Daniel",
""
],
[
"Huang",
"Hui",
""
]
] |
Rigid registration of partial observations is a fundamental problem in various applied fields. In computer graphics, special attention has been given to the registration between two partial point clouds generated by scanning devices. State-of-the-art registration techniques still struggle when the overlap region between the two point clouds is small, and completely fail if there is no overlap between the scan pairs. In this paper, we present a learning-based technique that alleviates this problem, and allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap, a setting that has been referred to as tele-registration. Our technique is based on a novel neural network design that learns a prior of a class of shapes and can complete a partial shape. The key idea is combining the registration and completion tasks in a way that reinforces each other. In particular, we simultaneously train the registration network and completion network using two coupled flows, one that register-and-complete, and one that complete-and-register, and encourage the two flows to produce a consistent result. We show that, compared with each separate flow, this two-flow training leads to robust and reliable tele-registration, and hence to a better point cloud prediction that completes the registered scans. It is also worth mentioning that each of the components in our neural network outperforms state-of-the-art methods in both completion and registration. We further analyze our network with several ablation studies and demonstrate its performance on a large number of partial point clouds, both synthetic and real-world, that have only small or no overlap.
|
1804.00101
|
Alan Roytman
|
Mikkel Abrahamsen, Anna Adamaszek, Karl Bringmann, Vincent
Cohen-Addad, Mehran Mehr, Eva Rotenberg, Alan Roytman, Mikkel Thorup
|
Fast Fencing
| null | null | null | null |
cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider very natural "fence enclosure" problems studied by Capoyleas,
Rote, and Woeginger and Arkin, Khuller, and Mitchell in the early 90s. Given a
set $S$ of $n$ points in the plane, we aim at finding a set of closed curves
such that (1) each point is enclosed by a curve and (2) the total length of the
curves is minimized. We consider two main variants. In the first variant, we
pay a unit cost per curve in addition to the total length of the curves. An
equivalent formulation of this version is that we have to enclose $n$ unit
disks, paying only the total length of the enclosing curves. In the other
variant, we are allowed to use at most $k$ closed curves and pay no cost per
curve.
For the variant with at most $k$ closed curves, we present an algorithm that
is polynomial in both $n$ and $k$. For the variant with unit cost per curve, or
unit disks, we present a near-linear time algorithm.
Capoyleas, Rote, and Woeginger solved the problem with at most $k$ curves in
$n^{O(k)}$ time. Arkin, Khuller, and Mitchell used this to solve the unit cost
per curve version in exponential time. At the time, they conjectured that the
problem with $k$ curves is NP-hard for general $k$. Our polynomial time
algorithm refutes this unless P equals NP.
|
[
{
"created": "Sat, 31 Mar 2018 01:17:15 GMT",
"version": "v1"
}
] |
2018-04-03
|
[
[
"Abrahamsen",
"Mikkel",
""
],
[
"Adamaszek",
"Anna",
""
],
[
"Bringmann",
"Karl",
""
],
[
"Cohen-Addad",
"Vincent",
""
],
[
"Mehr",
"Mehran",
""
],
[
"Rotenberg",
"Eva",
""
],
[
"Roytman",
"Alan",
""
],
[
"Thorup",
"Mikkel",
""
]
] |
We consider very natural "fence enclosure" problems studied by Capoyleas, Rote, and Woeginger and Arkin, Khuller, and Mitchell in the early 90s. Given a set $S$ of $n$ points in the plane, we aim at finding a set of closed curves such that (1) each point is enclosed by a curve and (2) the total length of the curves is minimized. We consider two main variants. In the first variant, we pay a unit cost per curve in addition to the total length of the curves. An equivalent formulation of this version is that we have to enclose $n$ unit disks, paying only the total length of the enclosing curves. In the other variant, we are allowed to use at most $k$ closed curves and pay no cost per curve. For the variant with at most $k$ closed curves, we present an algorithm that is polynomial in both $n$ and $k$. For the variant with unit cost per curve, or unit disks, we present a near-linear time algorithm. Capoyleas, Rote, and Woeginger solved the problem with at most $k$ curves in $n^{O(k)}$ time. Arkin, Khuller, and Mitchell used this to solve the unit cost per curve version in exponential time. At the time, they conjectured that the problem with $k$ curves is NP-hard for general $k$. Our polynomial time algorithm refutes this unless P equals NP.
|
2402.07639
|
Nir Weingarten
|
Nir Weingarten, Zohar Yakhini, Moshe Butman, Ran Gilad-Bachrach
|
Tighter Bounds on the Information Bottleneck with Application to Deep
Learning
|
10 pages, 5 figures, code included in github repo
| null | null | null |
cs.LG cs.AI cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Deep Neural Nets (DNNs) learn latent representations induced by their
downstream task, objective function, and other parameters. The quality of the
learned representations impacts the DNN's generalization ability and the
coherence of the emerging latent space. The Information Bottleneck (IB)
provides a hypothetically optimal framework for data modeling, yet it is often
intractable. Recent efforts combined DNNs with the IB by applying VAE-inspired
variational methods to approximate bounds on mutual information, resulting in
improved robustness to adversarial attacks. This work introduces a new and
tighter variational bound for the IB, improving performance of previous
IB-inspired DNNs. These advancements strengthen the case for the IB and its
variational approximations as a data modeling framework, and provide a simple
method to significantly enhance the adversarial robustness of classifier DNNs.
|
[
{
"created": "Mon, 12 Feb 2024 13:24:32 GMT",
"version": "v1"
}
] |
2024-02-13
|
[
[
"Weingarten",
"Nir",
""
],
[
"Yakhini",
"Zohar",
""
],
[
"Butman",
"Moshe",
""
],
[
"Gilad-Bachrach",
"Ran",
""
]
] |
Deep Neural Nets (DNNs) learn latent representations induced by their downstream task, objective function, and other parameters. The quality of the learned representations impacts the DNN's generalization ability and the coherence of the emerging latent space. The Information Bottleneck (IB) provides a hypothetically optimal framework for data modeling, yet it is often intractable. Recent efforts combined DNNs with the IB by applying VAE-inspired variational methods to approximate bounds on mutual information, resulting in improved robustness to adversarial attacks. This work introduces a new and tighter variational bound for the IB, improving performance of previous IB-inspired DNNs. These advancements strengthen the case for the IB and its variational approximations as a data modeling framework, and provide a simple method to significantly enhance the adversarial robustness of classifier DNNs.
|
2205.03464
|
Poorna Dasgupta
|
Poorna Banerjee Dasgupta
|
Comparative Analysis of Non-Blind Deblurring Methods for Noisy Blurred
Images
|
8 pages, Published with International Journal of Computer Trends and
Technology (IJCTT), Volume-70 Issue-3, 2022
| null |
10.14445/22312803/IJCTT-V70I3P101
| null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Image blurring refers to the degradation of an image wherein the image's
overall sharpness decreases. Image blurring is caused by several factors.
Additionally, during the image acquisition process, noise may get added to the
image. Such a noisy and blurred image can be represented as the image resulting
from the convolution of the original image with the associated point spread
function, along with additive noise. However, the blurred image often contains
inadequate information to uniquely determine the plausible original image.
Based on the availability of blurring information, image deblurring methods can
be classified as blind and non-blind. In non-blind image deblurring, some prior
information is known regarding the corresponding point spread function and the
added noise. The objective of this study is to determine the effectiveness of
non-blind image deblurring methods with respect to the identification and
elimination of noise present in blurred images. In this study, three non-blind
image deblurring methods, namely Wiener deconvolution, Lucy-Richardson
deconvolution, and regularized deconvolution were comparatively analyzed for
noisy images featuring salt-and-pepper noise. Two types of blurring effects
were simulated, namely motion blurring and Gaussian blurring. The said three
non-blind deblurring methods were applied under two scenarios: direct
deblurring of noisy blurred images and deblurring of images after denoising
through the application of the adaptive median filter. The obtained results
were then compared for each scenario to determine the best approach for
deblurring noisy images.
|
[
{
"created": "Fri, 6 May 2022 20:07:29 GMT",
"version": "v1"
}
] |
2022-05-10
|
[
[
"Dasgupta",
"Poorna Banerjee",
""
]
] |
Image blurring refers to the degradation of an image wherein the image's overall sharpness decreases. Image blurring is caused by several factors. Additionally, during the image acquisition process, noise may get added to the image. Such a noisy and blurred image can be represented as the image resulting from the convolution of the original image with the associated point spread function, along with additive noise. However, the blurred image often contains inadequate information to uniquely determine the plausible original image. Based on the availability of blurring information, image deblurring methods can be classified as blind and non-blind. In non-blind image deblurring, some prior information is known regarding the corresponding point spread function and the added noise. The objective of this study is to determine the effectiveness of non-blind image deblurring methods with respect to the identification and elimination of noise present in blurred images. In this study, three non-blind image deblurring methods, namely Wiener deconvolution, Lucy-Richardson deconvolution, and regularized deconvolution were comparatively analyzed for noisy images featuring salt-and-pepper noise. Two types of blurring effects were simulated, namely motion blurring and Gaussian blurring. The said three non-blind deblurring methods were applied under two scenarios: direct deblurring of noisy blurred images and deblurring of images after denoising through the application of the adaptive median filter. The obtained results were then compared for each scenario to determine the best approach for deblurring noisy images.
|
1209.3061
|
Sliman Arrag
|
Sliman Arrag, Abdellatif Hamdoun, Abderrahim Tragha and Salah eddine
Khamlich
|
Design and Implementation A different Architectures of mixcolumn in FPGA
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper details Implementation of the Encryption algorithm AES under VHDL
language In FPGA by using different architecture of mixcolumn. We then review
this research investigates the AES algorithm in FPGA and the Very High Speed
Integrated Circuit Hardware Description language (VHDL). Altera Quartus II
software is used for simulation and optimization of the synthesizable VHDL
code. The set of transformations of both Encryptions and decryption are
simulated using an iterative design approach in order to optimize the hardware
consumption. Altera Cyclone III Family devices are utilized for hardware
evaluation.
|
[
{
"created": "Thu, 13 Sep 2012 23:08:53 GMT",
"version": "v1"
}
] |
2012-09-17
|
[
[
"Arrag",
"Sliman",
""
],
[
"Hamdoun",
"Abdellatif",
""
],
[
"Tragha",
"Abderrahim",
""
],
[
"Khamlich",
"Salah eddine",
""
]
] |
This paper details Implementation of the Encryption algorithm AES under VHDL language In FPGA by using different architecture of mixcolumn. We then review this research investigates the AES algorithm in FPGA and the Very High Speed Integrated Circuit Hardware Description language (VHDL). Altera Quartus II software is used for simulation and optimization of the synthesizable VHDL code. The set of transformations of both Encryptions and decryption are simulated using an iterative design approach in order to optimize the hardware consumption. Altera Cyclone III Family devices are utilized for hardware evaluation.
|
1706.03311
|
Kristin Siu
|
Kristin Siu, Alexander Zook, Mark O. Riedl
|
A Framework for Exploring and Evaluating Mechanics in Human Computation
Games
|
11 pages, 5 figures
| null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Human computation games (HCGs) are a crowdsourcing approach to solving
computationally-intractable tasks using games. In this paper, we describe the
need for generalizable HCG design knowledge that accommodates the needs of both
players and tasks. We propose a formal representation of the mechanics in HCGs,
providing a structural breakdown to visualize, compare, and explore the space
of HCG mechanics. We present a methodology based on small-scale design
experiments using fixed tasks while varying game elements to observe effects on
both the player experience and the human computation task completion. Finally
we discuss applications of our framework using comparisons of prior HCGs and
recent design experiments. Ultimately, we wish to enable easier exploration and
development of HCGs, helping these games provide meaningful player experiences
while solving difficult problems.
|
[
{
"created": "Sun, 11 Jun 2017 06:16:49 GMT",
"version": "v1"
}
] |
2017-06-13
|
[
[
"Siu",
"Kristin",
""
],
[
"Zook",
"Alexander",
""
],
[
"Riedl",
"Mark O.",
""
]
] |
Human computation games (HCGs) are a crowdsourcing approach to solving computationally-intractable tasks using games. In this paper, we describe the need for generalizable HCG design knowledge that accommodates the needs of both players and tasks. We propose a formal representation of the mechanics in HCGs, providing a structural breakdown to visualize, compare, and explore the space of HCG mechanics. We present a methodology based on small-scale design experiments using fixed tasks while varying game elements to observe effects on both the player experience and the human computation task completion. Finally we discuss applications of our framework using comparisons of prior HCGs and recent design experiments. Ultimately, we wish to enable easier exploration and development of HCGs, helping these games provide meaningful player experiences while solving difficult problems.
|
2006.00064
|
Scott Schneider
|
Scott Schneider, Xavier Guerin, Shaohan Hu and Kun-Lung Wu
|
A Cloud Native Platform for Stateful Streaming
|
18 pages, 11 figures, submitted to OSDI 2020
| null | null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present the architecture of a cloud native version of IBM Streams, with
Kubernetes as our target platform. Streams is a general purpose streaming
system with its own platform for managing applications and the compute clusters
that execute those applications. Cloud native Streams replaces that platform
with Kubernetes. By using Kubernetes as its platform, Streams is able to
offload job management, life cycle tracking, address translation, fault
tolerance and scheduling. This offloading is possible because we define custom
resources that natively integrate into Kubernetes, allowing Streams to use
Kubernetes' eventing system as its own. We use four design patterns to
implement our system: controllers, conductors, coordinators and causal chains.
Composing controllers, conductors and coordinators allows us to build
deterministic state machines out of an asynchronous distributed system. The
resulting implementation eliminates 75% of the original platform code. Our
experimental results show that the performance of Kubernetes is an adequate
replacement in most cases, but it has problems with oversubscription,
networking latency, garbage collection and pod recovery.
|
[
{
"created": "Fri, 29 May 2020 20:18:43 GMT",
"version": "v1"
}
] |
2020-06-02
|
[
[
"Schneider",
"Scott",
""
],
[
"Guerin",
"Xavier",
""
],
[
"Hu",
"Shaohan",
""
],
[
"Wu",
"Kun-Lung",
""
]
] |
We present the architecture of a cloud native version of IBM Streams, with Kubernetes as our target platform. Streams is a general purpose streaming system with its own platform for managing applications and the compute clusters that execute those applications. Cloud native Streams replaces that platform with Kubernetes. By using Kubernetes as its platform, Streams is able to offload job management, life cycle tracking, address translation, fault tolerance and scheduling. This offloading is possible because we define custom resources that natively integrate into Kubernetes, allowing Streams to use Kubernetes' eventing system as its own. We use four design patterns to implement our system: controllers, conductors, coordinators and causal chains. Composing controllers, conductors and coordinators allows us to build deterministic state machines out of an asynchronous distributed system. The resulting implementation eliminates 75% of the original platform code. Our experimental results show that the performance of Kubernetes is an adequate replacement in most cases, but it has problems with oversubscription, networking latency, garbage collection and pod recovery.
|
2012.05359
|
Jaydeep Rade
|
Jaydeep Rade, Aditya Balu, Ethan Herron, Jay Pathak, Rishikesh Ranade,
Soumik Sarkar, Adarsh Krishnamurthy
|
Algorithmically-Consistent Deep Learning Frameworks for Structural
Topology Optimization
|
29 pages, 28 figures, 9 tables
|
Engineering Applications of Artificial Intelligence, 2021, Volume
106,104483
|
10.1016/j.engappai.2021.104483
| null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Topology optimization has emerged as a popular approach to refine a
component's design and increase its performance. However, current
state-of-the-art topology optimization frameworks are compute-intensive, mainly
due to multiple finite element analysis iterations required to evaluate the
component's performance during the optimization process. Recently, machine
learning (ML)-based topology optimization methods have been explored by
researchers to alleviate this issue. However, previous ML approaches have
mainly been demonstrated on simple two-dimensional applications with
low-resolution geometry. Further, current methods are based on a single ML
model for end-to-end prediction, which requires a large dataset for training.
These challenges make it non-trivial to extend current approaches to higher
resolutions. In this paper, we develop deep learning-based frameworks
consistent with traditional topology optimization algorithms for 3D topology
optimization with a reasonably fine (high) resolution. We achieve this by
training multiple networks, each learning a different step of the overall
topology optimization methodology, making the framework more consistent with
the topology optimization algorithm. We demonstrate the application of our
framework on both 2D and 3D geometries. The results show that our approach
predicts the final optimized design better (5.76x reduction in total compliance
MSE in 2D; 2.03x reduction in total compliance MSE in 3D) than current ML-based
topology optimization methods.
|
[
{
"created": "Wed, 9 Dec 2020 23:05:55 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Oct 2022 03:50:31 GMT",
"version": "v2"
}
] |
2022-10-27
|
[
[
"Rade",
"Jaydeep",
""
],
[
"Balu",
"Aditya",
""
],
[
"Herron",
"Ethan",
""
],
[
"Pathak",
"Jay",
""
],
[
"Ranade",
"Rishikesh",
""
],
[
"Sarkar",
"Soumik",
""
],
[
"Krishnamurthy",
"Adarsh",
""
]
] |
Topology optimization has emerged as a popular approach to refine a component's design and increase its performance. However, current state-of-the-art topology optimization frameworks are compute-intensive, mainly due to multiple finite element analysis iterations required to evaluate the component's performance during the optimization process. Recently, machine learning (ML)-based topology optimization methods have been explored by researchers to alleviate this issue. However, previous ML approaches have mainly been demonstrated on simple two-dimensional applications with low-resolution geometry. Further, current methods are based on a single ML model for end-to-end prediction, which requires a large dataset for training. These challenges make it non-trivial to extend current approaches to higher resolutions. In this paper, we develop deep learning-based frameworks consistent with traditional topology optimization algorithms for 3D topology optimization with a reasonably fine (high) resolution. We achieve this by training multiple networks, each learning a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm. We demonstrate the application of our framework on both 2D and 3D geometries. The results show that our approach predicts the final optimized design better (5.76x reduction in total compliance MSE in 2D; 2.03x reduction in total compliance MSE in 3D) than current ML-based topology optimization methods.
|
1509.05589
|
Lorenzo Saino
|
Ioannis Psaras, Konstantinos V. Katsaros, Lorenzo Saino and George
Pavlou
|
LIRA: A Location Independent Routing Layer based on Source-Provided
Ephemeral Names
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We identify the obstacles hindering the deployment of Information Centric
Networking (ICN) and the shift from the current IP architecture. In particular,
we argue that scalability of name resolution and the lack of control of content
access from content providers are two important barriers that keep ICN away
from deployment. We design solutions to incentivise ICN deployment and present
a new network architecture that incorporates an extra layer in the protocol
stack (the Location Independent Routing Layer, LIRA) to integrate
location-independent content delivery. According to our design, content names
need not (and should not) be permanent, but rather should be ephemeral.
Resolution of non-permanent names requires the involvement of content
providers, enabling desirable features such as request logging and cache
purging, while avoiding the need for the deployment of a new name resolution
infrastructure. Our results show that with half of the network's nodes
operating under the LIRA framework, we can get the full gain of the ICN mode of
operation.
|
[
{
"created": "Fri, 18 Sep 2015 11:12:58 GMT",
"version": "v1"
}
] |
2015-09-21
|
[
[
"Psaras",
"Ioannis",
""
],
[
"Katsaros",
"Konstantinos V.",
""
],
[
"Saino",
"Lorenzo",
""
],
[
"Pavlou",
"George",
""
]
] |
We identify the obstacles hindering the deployment of Information Centric Networking (ICN) and the shift from the current IP architecture. In particular, we argue that scalability of name resolution and the lack of control of content access from content providers are two important barriers that keep ICN away from deployment. We design solutions to incentivise ICN deployment and present a new network architecture that incorporates an extra layer in the protocol stack (the Location Independent Routing Layer, LIRA) to integrate location-independent content delivery. According to our design, content names need not (and should not) be permanent, but rather should be ephemeral. Resolution of non-permanent names requires the involvement of content providers, enabling desirable features such as request logging and cache purging, while avoiding the need for the deployment of a new name resolution infrastructure. Our results show that with half of the network's nodes operating under the LIRA framework, we can get the full gain of the ICN mode of operation.
|
1808.08665
|
Mehdi Ganji
|
Mehdi Ganji and Hamid Jafarkhani
|
Novel Time Asynchronous NOMA schemes for Downlink Transmissions
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we investigate the effect of time asynchrony in non-orthogonal
multiple access (NOMA) schemes for downlink transmissions. First, we analyze
the benefit of adding intentional timing offsets to the conventional power
domain-NOMA (P-NOMA). This method which is called Asynchronous-Power
Domain-NOMA (AP-NOMA) introduces artificial symbol-offsets between packets
destined for different users. It reduces the mutual interference which results
in enlarging the achievable rate-region of the conventional P-NOMA. Then, we
propose a precoding scheme which fully exploits the degrees of freedom provided
by the time asynchrony. We call this multiple access scheme T-NOMA which
provides higher degrees of freedom for users compared to the conventional
P-NOMA or even the modified AP-NOMA. T-NOMA adopts a precoding at the base
station and a linear preprocessing scheme at the receiving user which
decomposes the broadcast channel into parallel channels circumventing the need
for Successive Interference Cancellation (SIC). The numerical results show that
T-NOMA outperforms AP-NOMA and both outperform the conventional P-NOMA. We also
compare the maximum sum-rate and fairness provided by these methods. Moreover,
the impact of pulse shape and symbol offset on the performance of AP-NOMA and
T-NOMA schemes are investigated.
|
[
{
"created": "Mon, 27 Aug 2018 02:12:03 GMT",
"version": "v1"
}
] |
2018-08-28
|
[
[
"Ganji",
"Mehdi",
""
],
[
"Jafarkhani",
"Hamid",
""
]
] |
In this work, we investigate the effect of time asynchrony in non-orthogonal multiple access (NOMA) schemes for downlink transmissions. First, we analyze the benefit of adding intentional timing offsets to the conventional power domain-NOMA (P-NOMA). This method which is called Asynchronous-Power Domain-NOMA (AP-NOMA) introduces artificial symbol-offsets between packets destined for different users. It reduces the mutual interference which results in enlarging the achievable rate-region of the conventional P-NOMA. Then, we propose a precoding scheme which fully exploits the degrees of freedom provided by the time asynchrony. We call this multiple access scheme T-NOMA which provides higher degrees of freedom for users compared to the conventional P-NOMA or even the modified AP-NOMA. T-NOMA adopts a precoding at the base station and a linear preprocessing scheme at the receiving user which decomposes the broadcast channel into parallel channels circumventing the need for Successive Interference Cancellation (SIC). The numerical results show that T-NOMA outperforms AP-NOMA and both outperform the conventional P-NOMA. We also compare the maximum sum-rate and fairness provided by these methods. Moreover, the impact of pulse shape and symbol offset on the performance of AP-NOMA and T-NOMA schemes are investigated.
|
1911.05921
|
Van-Dang Tran
|
Van-Dang Tran, Hiroyuki Kato, Zhenjiang Hu
|
Programmable View Update Strategies on Relations
| null | null | null | null |
cs.DB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
View update is an important mechanism that allows updates on a view by
translating them into the corresponding updates on the base relations. The
existing literature has shown the ambiguity of translating view updates. To
address this ambiguity, we propose a robust language-based approach for making
view update strategies programmable and validatable. Specifically, we introduce
a novel approach to use Datalog to describe these update strategies. We propose
a validation algorithm to check the well-behavedness of the written Datalog
programs. We present a fragment of the Datalog language for which our
validation is both sound and complete. This fragment not only has good
properties in theory but is also useful for solving practical view updates.
Furthermore, we develop an algorithm for optimizing user-written programs to
efficiently implement updatable views in relational database management
systems. We have implemented our proposed approach. The experimental results
show that our framework is feasible and efficient in practice.
|
[
{
"created": "Thu, 14 Nov 2019 03:40:32 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Jan 2020 04:08:05 GMT",
"version": "v2"
},
{
"created": "Mon, 31 Aug 2020 16:07:10 GMT",
"version": "v3"
}
] |
2020-09-01
|
[
[
"Tran",
"Van-Dang",
""
],
[
"Kato",
"Hiroyuki",
""
],
[
"Hu",
"Zhenjiang",
""
]
] |
View update is an important mechanism that allows updates on a view by translating them into the corresponding updates on the base relations. The existing literature has shown the ambiguity of translating view updates. To address this ambiguity, we propose a robust language-based approach for making view update strategies programmable and validatable. Specifically, we introduce a novel approach to use Datalog to describe these update strategies. We propose a validation algorithm to check the well-behavedness of the written Datalog programs. We present a fragment of the Datalog language for which our validation is both sound and complete. This fragment not only has good properties in theory but is also useful for solving practical view updates. Furthermore, we develop an algorithm for optimizing user-written programs to efficiently implement updatable views in relational database management systems. We have implemented our proposed approach. The experimental results show that our framework is feasible and efficient in practice.
|
2206.12795
|
Lloyd Allison
|
Lloyd Allison
|
Applications of Recursively Defined Data Structures
|
The paper originally appeared in the Australian Computer Journal
(ISSN 0004-8917). The journal was published by the Australian Computer
Society from 1967 to 1999
|
Australian Computer Journal, 25(1):14-20,February 1993
| null | null |
cs.DS cs.PL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
A circular program contains a data structure whose definition is
self-referential or recursive. The use of such a definition allows efficient
functional programs to be written and can avoid repeated evaluations and the
creation of intermediate data structures that would have to be garbage
collected. This paper uses circular programs in various ways, to implement
memo-structures and explicit search-trees to hold solutions to
constraint-satisfaction problems.
|
[
{
"created": "Sun, 26 Jun 2022 06:02:06 GMT",
"version": "v1"
}
] |
2022-06-28
|
[
[
"Allison",
"Lloyd",
""
]
] |
A circular program contains a data structure whose definition is self-referential or recursive. The use of such a definition allows efficient functional programs to be written and can avoid repeated evaluations and the creation of intermediate data structures that would have to be garbage collected. This paper uses circular programs in various ways, to implement memo-structures and explicit search-trees to hold solutions to constraint-satisfaction problems.
|
2309.07974
|
Jack Lanchantin
|
Jack Lanchantin, Sainbayar Sukhbaatar, Gabriel Synnaeve, Yuxuan Sun,
Kavya Srinet, Arthur Szlam
|
A Data Source for Reasoning Embodied Agents
| null | null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Recent progress in using machine learning models for reasoning tasks has been
driven by novel model architectures, large-scale pre-training protocols, and
dedicated reasoning datasets for fine-tuning. In this work, to further pursue
these advances, we introduce a new data generator for machine reasoning that
integrates with an embodied agent. The generated data consists of templated
text queries and answers, matched with world-states encoded into a database.
The world-states are a result of both world dynamics and the actions of the
agent. We show the results of several baseline models on instantiations of
train sets. These include pre-trained language models fine-tuned on a
text-formatted representation of the database, and graph-structured
Transformers operating on a knowledge-graph representation of the database. We
find that these models can answer some questions about the world-state, but
struggle with others. These results hint at new research directions in
designing neural reasoning models and database representations. Code to
generate the data will be released at github.com/facebookresearch/neuralmemory
|
[
{
"created": "Thu, 14 Sep 2023 18:17:16 GMT",
"version": "v1"
}
] |
2023-09-18
|
[
[
"Lanchantin",
"Jack",
""
],
[
"Sukhbaatar",
"Sainbayar",
""
],
[
"Synnaeve",
"Gabriel",
""
],
[
"Sun",
"Yuxuan",
""
],
[
"Srinet",
"Kavya",
""
],
[
"Szlam",
"Arthur",
""
]
] |
Recent progress in using machine learning models for reasoning tasks has been driven by novel model architectures, large-scale pre-training protocols, and dedicated reasoning datasets for fine-tuning. In this work, to further pursue these advances, we introduce a new data generator for machine reasoning that integrates with an embodied agent. The generated data consists of templated text queries and answers, matched with world-states encoded into a database. The world-states are a result of both world dynamics and the actions of the agent. We show the results of several baseline models on instantiations of train sets. These include pre-trained language models fine-tuned on a text-formatted representation of the database, and graph-structured Transformers operating on a knowledge-graph representation of the database. We find that these models can answer some questions about the world-state, but struggle with others. These results hint at new research directions in designing neural reasoning models and database representations. Code to generate the data will be released at github.com/facebookresearch/neuralmemory
|
2308.13074
|
Srivathsan Gnanasekaran Morkonda
|
Srivathsan G. Morkonda, Sonia Chiasson, Paul C. van Oorschot
|
Influences of Displaying Permission-related Information on Web Single
Sign-On Login Decisions
| null | null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Web users are increasingly presented with multiple login options, including
password-based login and common web single sign-on (SSO) login options such as
"Login with Google" and "Login with Facebook". There has been little focus in
previous studies on how users choose from a list of login options and how to
better inform users about privacy issues in web SSO systems. In this paper, we
conducted a 200-participant study to understand factors that influence
participants' login decisions, and how they are affected by displaying
permission differences across login options; permissions in SSO result in
release of user personal information to third-party web sites through SSO
identity providers. We compare and report on login decisions made by
participants before and after viewing permission-related information, examine
self-reported responses for reasons related to their login decisions, and
report on the factors that motivated their choices. We find that usability
preferences and inertia (habituation) were among the dominant factors
influencing login decisions. After participants viewed permission-related
information, many prioritised privacy over other factors, changing their login
decisions to more privacy-friendly alternatives. Displaying permission-related
information also influenced some participants to make tradeoffs between privacy
and usability preferences.
|
[
{
"created": "Thu, 24 Aug 2023 20:35:09 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Dec 2023 18:30:36 GMT",
"version": "v2"
}
] |
2023-12-29
|
[
[
"Morkonda",
"Srivathsan G.",
""
],
[
"Chiasson",
"Sonia",
""
],
[
"van Oorschot",
"Paul C.",
""
]
] |
Web users are increasingly presented with multiple login options, including password-based login and common web single sign-on (SSO) login options such as "Login with Google" and "Login with Facebook". There has been little focus in previous studies on how users choose from a list of login options and how to better inform users about privacy issues in web SSO systems. In this paper, we conducted a 200-participant study to understand factors that influence participants' login decisions, and how they are affected by displaying permission differences across login options; permissions in SSO result in release of user personal information to third-party web sites through SSO identity providers. We compare and report on login decisions made by participants before and after viewing permission-related information, examine self-reported responses for reasons related to their login decisions, and report on the factors that motivated their choices. We find that usability preferences and inertia (habituation) were among the dominant factors influencing login decisions. After participants viewed permission-related information, many prioritised privacy over other factors, changing their login decisions to more privacy-friendly alternatives. Displaying permission-related information also influenced some participants to make tradeoffs between privacy and usability preferences.
|
1304.1128
|
Robert Fung
|
Robert Fung, S. L. Crawford, Lee A. Appelbaum, Richard M. Tong
|
An Architecture for Probabilistic Concept-Based Information Retrieval
|
Appears in Proceedings of the Sixth Conference on Uncertainty in
Artificial Intelligence (UAI1990)
| null | null |
UAI-P-1990-PG-392-404
|
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While concept-based methods for information retrieval can provide improved
performance over more conventional techniques, they require large amounts of
effort to acquire the concepts and their qualitative and quantitative
relationships. This paper discusses an architecture for probabilistic
concept-based information retrieval which addresses the knowledge acquisition
problem. The architecture makes use of the probabilistic networks technology
for representing and reasoning about concepts and includes a knowledge
acquisition component which partially automates the construction of concept
knowledge bases from data. We describe two experiments that apply the
architecture to the task of retrieving documents about terrorism from a set of
documents from the Reuters news service. The experiments provide positive
evidence that the architecture design is feasible and that there are advantages
to concept-based methods.
|
[
{
"created": "Wed, 27 Mar 2013 13:58:58 GMT",
"version": "v1"
}
] |
2013-04-05
|
[
[
"Fung",
"Robert",
""
],
[
"Crawford",
"S. L.",
""
],
[
"Appelbaum",
"Lee A.",
""
],
[
"Tong",
"Richard M.",
""
]
] |
While concept-based methods for information retrieval can provide improved performance over more conventional techniques, they require large amounts of effort to acquire the concepts and their qualitative and quantitative relationships. This paper discusses an architecture for probabilistic concept-based information retrieval which addresses the knowledge acquisition problem. The architecture makes use of the probabilistic networks technology for representing and reasoning about concepts and includes a knowledge acquisition component which partially automates the construction of concept knowledge bases from data. We describe two experiments that apply the architecture to the task of retrieving documents about terrorism from a set of documents from the Reuters news service. The experiments provide positive evidence that the architecture design is feasible and that there are advantages to concept-based methods.
|
2306.12310
|
Pranauv Aj
|
Niketha Sabesan, Nivethitha, J.N Shreyah, Pranauv A J, Shyam R
|
Medical ministrations through web scraping
| null | null | null | null |
cs.CL
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Web scraping is a technique that allows us to extract data from websites
automatically. in the field of medicine, web scraping can be used to collect
information about medical procedures, treatments, and healthcare providers.
this information can be used to improve patient care, monitor the quality of
healthcare services, and identify areas for improvement. one area where web
scraping can be particularly useful is in medical ministrations. medical
ministrations are the actions taken to provide medical care to patients, and
web scraping can help healthcare providers identify the most effective
ministrations for their patients. for example, healthcare providers can use web
scraping to collect data about the symptoms and medical histories of their
patients, and then use this information to determine the most appropriate
ministrations. they can also use web scraping to gather information about the
latest medical research and clinical trials, which can help them stay
up-to-date with the latest treatments and procedures.
|
[
{
"created": "Wed, 21 Jun 2023 14:43:25 GMT",
"version": "v1"
}
] |
2023-06-22
|
[
[
"Sabesan",
"Niketha",
""
],
[
"Nivethitha",
"",
""
],
[
"Shreyah",
"J. N",
""
],
[
"J",
"Pranauv A",
""
],
[
"R",
"Shyam",
""
]
] |
Web scraping is a technique that allows us to extract data from websites automatically. in the field of medicine, web scraping can be used to collect information about medical procedures, treatments, and healthcare providers. this information can be used to improve patient care, monitor the quality of healthcare services, and identify areas for improvement. one area where web scraping can be particularly useful is in medical ministrations. medical ministrations are the actions taken to provide medical care to patients, and web scraping can help healthcare providers identify the most effective ministrations for their patients. for example, healthcare providers can use web scraping to collect data about the symptoms and medical histories of their patients, and then use this information to determine the most appropriate ministrations. they can also use web scraping to gather information about the latest medical research and clinical trials, which can help them stay up-to-date with the latest treatments and procedures.
|
2312.12908
|
Pau Torras
|
Pau Torras and Sanket Biswas and Alicia Forn\'es
|
The Common Optical Music Recognition Evaluation Framework
|
18 pages, 4 figures, 3 tables, submitted (under review) for the
International Journal in Document Analysis and Recognition
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The quality of Optical Music Recognition (OMR) systems is a rather difficult
magnitude to measure. There is no lingua franca shared among OMR datasets that
allows to compare systems' performance on equal grounds, since most of them are
specialised on certain approaches. As a result, most state-of-the-art works
currently report metrics that cannot be compared directly. In this paper we
identify the need of a common music representation language and propose the
Music Tree Notation (MTN) format, thanks to which the definition of standard
metrics is possible. This format represents music as a set of primitives that
group together into higher-abstraction nodes, a compromise between the
expression of fully graph-based and sequential notation formats. We have also
developed a specific set of OMR metrics and a typeset score dataset as a proof
of concept of this idea.
|
[
{
"created": "Wed, 20 Dec 2023 10:45:22 GMT",
"version": "v1"
}
] |
2023-12-21
|
[
[
"Torras",
"Pau",
""
],
[
"Biswas",
"Sanket",
""
],
[
"Fornés",
"Alicia",
""
]
] |
The quality of Optical Music Recognition (OMR) systems is a rather difficult magnitude to measure. There is no lingua franca shared among OMR datasets that allows to compare systems' performance on equal grounds, since most of them are specialised on certain approaches. As a result, most state-of-the-art works currently report metrics that cannot be compared directly. In this paper we identify the need of a common music representation language and propose the Music Tree Notation (MTN) format, thanks to which the definition of standard metrics is possible. This format represents music as a set of primitives that group together into higher-abstraction nodes, a compromise between the expression of fully graph-based and sequential notation formats. We have also developed a specific set of OMR metrics and a typeset score dataset as a proof of concept of this idea.
|
2004.00865
|
Timotej Ga\v{s}par
|
Timotej Ga\v{s}par, Miha Deni\v{s}a and Ale\v{s} Ude
|
A reconfigurable robot workcell for quick set-up of assembly processes
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
High volume production has been a prerequisite in order to invest into
automation of the manufacturing process for decades. The high cost of setup and
the inflexibility of classical automation meant that low batch productions,
often present in Small and Medium-sized Enterprises (SMEs), were dismissed as
potential end user of automation technologies. In this extended abstract we
present the results of the ReconCell project whose objective was to develop a
new type of highly reconfigurable robot workcell for fast set-up of automated
assembly processes in SMEs. The high degree of reconfigurability was achieved
by the developed reconfigurable hardware and the complementary reconfigurable
software, while fast set-up was achieved with technologies for fast robot
programming.
|
[
{
"created": "Thu, 2 Apr 2020 08:26:23 GMT",
"version": "v1"
}
] |
2020-04-03
|
[
[
"Gašpar",
"Timotej",
""
],
[
"Deniša",
"Miha",
""
],
[
"Ude",
"Aleš",
""
]
] |
High volume production has been a prerequisite in order to invest into automation of the manufacturing process for decades. The high cost of setup and the inflexibility of classical automation meant that low batch productions, often present in Small and Medium-sized Enterprises (SMEs), were dismissed as potential end user of automation technologies. In this extended abstract we present the results of the ReconCell project whose objective was to develop a new type of highly reconfigurable robot workcell for fast set-up of automated assembly processes in SMEs. The high degree of reconfigurability was achieved by the developed reconfigurable hardware and the complementary reconfigurable software, while fast set-up was achieved with technologies for fast robot programming.
|
1309.4616
|
Lukas Einkemmer
|
Lukas Einkemmer and Alexander Ostermann
|
Exponential Integrators on Graphic Processing Units
|
To appear in: Proceedings of the 2013 International Conference on
High Performance Computing Simulation (HPCS 2013), IEEE (2013)
|
High Performance Computing and Simulation (HPCS), 2013
International Conference on, pp. 490-496
|
10.1109/HPCSim.2013.6641458
| null |
cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we revisit stencil methods on GPUs in the context of
exponential integrators. We further discuss boundary conditions, in the same
context, and show that simple boundary conditions (for example, homogeneous
Dirichlet or homogeneous Neumann boundary conditions) do not affect the
performance if implemented directly into the CUDA kernel. In addition, we show
that stencil methods with position-dependent coefficients can be implemented
efficiently as well.
As an application, we discuss the implementation of exponential integrators
for different classes of problems in a single and multi GPU setup (up to 4
GPUs). We further show that for stencil based methods such parallelization can
be done very efficiently, while for some unstructured matrices the
parallelization to multiple GPUs is severely limited by the throughput of the
PCIe bus.
|
[
{
"created": "Wed, 18 Sep 2013 11:21:05 GMT",
"version": "v1"
}
] |
2014-05-27
|
[
[
"Einkemmer",
"Lukas",
""
],
[
"Ostermann",
"Alexander",
""
]
] |
In this paper we revisit stencil methods on GPUs in the context of exponential integrators. We further discuss boundary conditions, in the same context, and show that simple boundary conditions (for example, homogeneous Dirichlet or homogeneous Neumann boundary conditions) do not affect the performance if implemented directly into the CUDA kernel. In addition, we show that stencil methods with position-dependent coefficients can be implemented efficiently as well. As an application, we discuss the implementation of exponential integrators for different classes of problems in a single and multi GPU setup (up to 4 GPUs). We further show that for stencil based methods such parallelization can be done very efficiently, while for some unstructured matrices the parallelization to multiple GPUs is severely limited by the throughput of the PCIe bus.
|
1910.05291
|
Serhii Havrylov
|
Shangmin Guo, Yi Ren, Serhii Havrylov, Stella Frank, Ivan Titov, Kenny
Smith
|
The Emergence of Compositional Languages for Numeric Concepts Through
Iterated Learning in Neural Agents
| null | null | null | null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Since first introduced, computer simulation has been an increasingly
important tool in evolutionary linguistics. Recently, with the development of
deep learning techniques, research in grounded language learning has also
started to focus on facilitating the emergence of compositional languages
without pre-defined elementary linguistic knowledge. In this work, we explore
the emergence of compositional languages for numeric concepts in multi-agent
communication systems. We demonstrate that compositional language for encoding
numeric concepts can emerge through iterated learning in populations of deep
neural network agents. However, language properties greatly depend on the input
representations given to agents. We found that compositional languages only
emerge if they require less iterations to be fully learnt than other
non-degenerate languages for agents on a given input representation.
|
[
{
"created": "Fri, 11 Oct 2019 16:34:01 GMT",
"version": "v1"
}
] |
2019-10-14
|
[
[
"Guo",
"Shangmin",
""
],
[
"Ren",
"Yi",
""
],
[
"Havrylov",
"Serhii",
""
],
[
"Frank",
"Stella",
""
],
[
"Titov",
"Ivan",
""
],
[
"Smith",
"Kenny",
""
]
] |
Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics. Recently, with the development of deep learning techniques, research in grounded language learning has also started to focus on facilitating the emergence of compositional languages without pre-defined elementary linguistic knowledge. In this work, we explore the emergence of compositional languages for numeric concepts in multi-agent communication systems. We demonstrate that compositional language for encoding numeric concepts can emerge through iterated learning in populations of deep neural network agents. However, language properties greatly depend on the input representations given to agents. We found that compositional languages only emerge if they require less iterations to be fully learnt than other non-degenerate languages for agents on a given input representation.
|
2205.12443
|
Kaiyu Yang
|
Kaiyu Yang and Jia Deng and Danqi Chen
|
Generating Natural Language Proofs with Verifier-Guided Search
|
EMNLP 2022. Code and models are available at
https://github.com/princeton-nlp/NLProofS. v3 added evaluation of GPT-3 and
Codex
| null | null | null |
cs.CL cs.LG cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reasoning over natural language is a challenging problem in NLP. In this
work, we focus on proof generation: Given a hypothesis and a set of supporting
facts, the model generates a proof tree indicating how to derive the hypothesis
from supporting facts. Compared to generating the entire proof in one shot,
stepwise generation can better exploit the compositionality and generalize to
longer proofs but has achieved limited success on real-world data. Existing
stepwise methods struggle to generate proof steps that are both logically valid
and relevant to the hypothesis. Instead, they tend to hallucinate invalid steps
given the hypothesis. In this paper, we present a novel stepwise method,
NLProofS (Natural Language Proof Search), which learns to generate relevant
steps conditioning on the hypothesis. At the core of our approach, we train an
independent verifier to check the validity of the proof steps to prevent
hallucination. Instead of generating steps greedily, we search for proofs
maximizing a global proof score judged by the verifier. NLProofS achieves
state-of-the-art performance on EntailmentBank and RuleTaker. Specifically, it
improves the correctness of predicted proofs from 27.7% to 33.3% in the
distractor setting of EntailmentBank, demonstrating the effectiveness of
NLProofS in generating challenging human-authored proofs.
|
[
{
"created": "Wed, 25 May 2022 02:22:30 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Oct 2022 17:33:26 GMT",
"version": "v2"
},
{
"created": "Fri, 21 Oct 2022 20:08:11 GMT",
"version": "v3"
}
] |
2022-10-25
|
[
[
"Yang",
"Kaiyu",
""
],
[
"Deng",
"Jia",
""
],
[
"Chen",
"Danqi",
""
]
] |
Reasoning over natural language is a challenging problem in NLP. In this work, we focus on proof generation: Given a hypothesis and a set of supporting facts, the model generates a proof tree indicating how to derive the hypothesis from supporting facts. Compared to generating the entire proof in one shot, stepwise generation can better exploit the compositionality and generalize to longer proofs but has achieved limited success on real-world data. Existing stepwise methods struggle to generate proof steps that are both logically valid and relevant to the hypothesis. Instead, they tend to hallucinate invalid steps given the hypothesis. In this paper, we present a novel stepwise method, NLProofS (Natural Language Proof Search), which learns to generate relevant steps conditioning on the hypothesis. At the core of our approach, we train an independent verifier to check the validity of the proof steps to prevent hallucination. Instead of generating steps greedily, we search for proofs maximizing a global proof score judged by the verifier. NLProofS achieves state-of-the-art performance on EntailmentBank and RuleTaker. Specifically, it improves the correctness of predicted proofs from 27.7% to 33.3% in the distractor setting of EntailmentBank, demonstrating the effectiveness of NLProofS in generating challenging human-authored proofs.
|
0906.4618
|
Pedro Peris-Lopez
|
Pedro Peris-Lopez, Julio C. Hernandez-Castro, Christos Dimitrakakis,
Aikaterini Mitrokotsa, Juan M. E. Tapiador
|
Shedding Light on RFID Distance Bounding Protocols and Terrorist Fraud
Attacks
|
31 pages, 10 figures, 1 table
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The vast majority of RFID authentication protocols assume the proximity
between readers and tags due to the limited range of the radio channel.
However, in real scenarios an intruder can be located between the prover (tag)
and the verifier (reader) and trick this last one into thinking that the prover
is in close proximity. This attack is generally known as a relay attack in
which scope distance fraud, mafia fraud and terrorist attacks are included.
Distance bounding protocols represent a promising countermeasure to hinder
relay attacks. Several protocols have been proposed during the last years but
vulnerabilities of major or minor relevance have been identified in most of
them. In 2008, Kim et al. [1] proposed a new distance bounding protocol with
the objective of being the best in terms of security, privacy, tag
computational overhead and fault tolerance. In this paper, we analyze this
protocol and we present a passive full disclosure attack, which allows an
adversary to discover the long-term secret key of the tag. The presented attack
is very relevant, since no security objectives are met in Kim et al.'s
protocol. Then, design guidelines are introduced with the aim of facilitating
protocol designers the stimulating task of designing secure and efficient
schemes against relay attacks. Finally a new protocol, named Hitomi and
inspired by [1], is designed conforming the guidelines proposed previously.
|
[
{
"created": "Thu, 25 Jun 2009 07:12:26 GMT",
"version": "v1"
},
{
"created": "Sun, 20 Jun 2010 19:35:19 GMT",
"version": "v2"
}
] |
2010-06-22
|
[
[
"Peris-Lopez",
"Pedro",
""
],
[
"Hernandez-Castro",
"Julio C.",
""
],
[
"Dimitrakakis",
"Christos",
""
],
[
"Mitrokotsa",
"Aikaterini",
""
],
[
"Tapiador",
"Juan M. E.",
""
]
] |
The vast majority of RFID authentication protocols assume the proximity between readers and tags due to the limited range of the radio channel. However, in real scenarios an intruder can be located between the prover (tag) and the verifier (reader) and trick this last one into thinking that the prover is in close proximity. This attack is generally known as a relay attack in which scope distance fraud, mafia fraud and terrorist attacks are included. Distance bounding protocols represent a promising countermeasure to hinder relay attacks. Several protocols have been proposed during the last years but vulnerabilities of major or minor relevance have been identified in most of them. In 2008, Kim et al. [1] proposed a new distance bounding protocol with the objective of being the best in terms of security, privacy, tag computational overhead and fault tolerance. In this paper, we analyze this protocol and we present a passive full disclosure attack, which allows an adversary to discover the long-term secret key of the tag. The presented attack is very relevant, since no security objectives are met in Kim et al.'s protocol. Then, design guidelines are introduced with the aim of facilitating protocol designers the stimulating task of designing secure and efficient schemes against relay attacks. Finally a new protocol, named Hitomi and inspired by [1], is designed conforming the guidelines proposed previously.
|
2010.01385
|
Purnata Ghosal
|
Purnata Ghosal and B. V. Raghavendra Rao
|
Limitations of Sums of Bounded-Read Formulas
|
20 pages, 3 figures
| null | null | null |
cs.CC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Proving super polynomial size lower bounds for various classes of arithmetic
circuits computing explicit polynomials is a very important and challenging
task in algebraic complexity theory. We study representation of polynomials as
sums of weaker models such as read once formulas (ROFs) and read once oblivious
algebraic branching programs (ROABPs). We prove:
(1) An exponential separation between sum of ROFs and read-$k$ formulas for
some constant $k$. (2) A sub-exponential separation between sum of ROABPs and
syntactic multilinear ABPs.
Our results are based on analysis of the partial derivative matrix under
different distributions. These results highlight richness of bounded read
restrictions in arithmetic formulas and ABPs.
Finally, we consider a generalization of multilinear ROABPs known as
strict-interval ABPs defined in [Ramya-Rao, MFCS2019]. We show that
strict-interval ABPs are equivalent to ROABPs upto a polynomial size blow up.
In contrast, we show that interval formulas are different from ROFs and also
admit depth reduction which is not known in the case of strict-interval ABPs.
|
[
{
"created": "Sat, 3 Oct 2020 16:41:22 GMT",
"version": "v1"
}
] |
2020-10-06
|
[
[
"Ghosal",
"Purnata",
""
],
[
"Rao",
"B. V. Raghavendra",
""
]
] |
Proving super polynomial size lower bounds for various classes of arithmetic circuits computing explicit polynomials is a very important and challenging task in algebraic complexity theory. We study representation of polynomials as sums of weaker models such as read once formulas (ROFs) and read once oblivious algebraic branching programs (ROABPs). We prove: (1) An exponential separation between sum of ROFs and read-$k$ formulas for some constant $k$. (2) A sub-exponential separation between sum of ROABPs and syntactic multilinear ABPs. Our results are based on analysis of the partial derivative matrix under different distributions. These results highlight richness of bounded read restrictions in arithmetic formulas and ABPs. Finally, we consider a generalization of multilinear ROABPs known as strict-interval ABPs defined in [Ramya-Rao, MFCS2019]. We show that strict-interval ABPs are equivalent to ROABPs upto a polynomial size blow up. In contrast, we show that interval formulas are different from ROFs and also admit depth reduction which is not known in the case of strict-interval ABPs.
|
1605.03269
|
Junpei Zhong
|
Junpei Zhong and Rony Novianto and Mingjun Dai and Xinzheng Zhang and
Angelo Cangelosi
|
A Hierarchical Emotion Regulated Sensorimotor Model: Case Studies
|
Accepted at The 5th International Conference on Data-Driven Control
and Learning Systems. 2016
| null | null | null |
cs.RO cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Inspired by the hierarchical cognitive architecture and the perception-action
model (PAM), we propose that the internal status acts as a kind of
common-coding representation which affects, mediates and even regulates the
sensorimotor behaviours. These regulation can be depicted in the Bayesian
framework, that is why cognitive agents are able to generate behaviours with
subtle differences according to their emotion or recognize the emotion by
perception. A novel recurrent neural network called recurrent neural network
with parametric bias units (RNNPB) runs in three modes, constructing a
two-level emotion regulated learning model, was further applied to testify this
theory in two different cases.
|
[
{
"created": "Wed, 11 May 2016 03:22:13 GMT",
"version": "v1"
}
] |
2016-05-12
|
[
[
"Zhong",
"Junpei",
""
],
[
"Novianto",
"Rony",
""
],
[
"Dai",
"Mingjun",
""
],
[
"Zhang",
"Xinzheng",
""
],
[
"Cangelosi",
"Angelo",
""
]
] |
Inspired by the hierarchical cognitive architecture and the perception-action model (PAM), we propose that the internal status acts as a kind of common-coding representation which affects, mediates and even regulates the sensorimotor behaviours. These regulation can be depicted in the Bayesian framework, that is why cognitive agents are able to generate behaviours with subtle differences according to their emotion or recognize the emotion by perception. A novel recurrent neural network called recurrent neural network with parametric bias units (RNNPB) runs in three modes, constructing a two-level emotion regulated learning model, was further applied to testify this theory in two different cases.
|
1211.3666
|
Shuang Li
|
Shuang Li, Zizhan Zheng, Eylem Ekici and Ness B. Shroff
|
Maximizing System Throughput Using Cooperative Sensing in Multi-Channel
Cognitive Radio Networks
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In Cognitive Radio Networks (CRNs), unlicensed users are allowed to access
the licensed spectrum when it is not currently being used by primary users
(PUs). In this paper, we study the throughput maximization problem for a
multi-channel CRN where each SU can only sense a limited number of channels. We
show that this problem is strongly NP-hard, and propose an approximation
algorithm with a factor at least $1/2\mu$ where $\mu \in [1,2]$ is a system
parameter reflecting the sensing capability of SUs across channels and their
sensing budgets. This performance guarantee is achieved by exploiting a nice
structural property of the objective function and constructing a particular
matching. Our numerical results demonstrate the advantage of our algorithm
compared with both a random and a greedy sensing assignment algorithm.
|
[
{
"created": "Thu, 15 Nov 2012 17:22:33 GMT",
"version": "v1"
}
] |
2012-11-16
|
[
[
"Li",
"Shuang",
""
],
[
"Zheng",
"Zizhan",
""
],
[
"Ekici",
"Eylem",
""
],
[
"Shroff",
"Ness B.",
""
]
] |
In Cognitive Radio Networks (CRNs), unlicensed users are allowed to access the licensed spectrum when it is not currently being used by primary users (PUs). In this paper, we study the throughput maximization problem for a multi-channel CRN where each SU can only sense a limited number of channels. We show that this problem is strongly NP-hard, and propose an approximation algorithm with a factor at least $1/2\mu$ where $\mu \in [1,2]$ is a system parameter reflecting the sensing capability of SUs across channels and their sensing budgets. This performance guarantee is achieved by exploiting a nice structural property of the objective function and constructing a particular matching. Our numerical results demonstrate the advantage of our algorithm compared with both a random and a greedy sensing assignment algorithm.
|
2302.05442
|
Mostafa Dehghani
|
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski,
Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert
Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael
Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan
Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F.
Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn
Bastings, Mark Patrick Collier, Alexey Gritsenko, Vighnesh Birodkar, Cristina
Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Paveti\'c,
Dustin Tran, Thomas Kipf, Mario Lu\v{c}i\'c, Xiaohua Zhai, Daniel Keysers,
Jeremiah Harmsen, Neil Houlsby
|
Scaling Vision Transformers to 22 Billion Parameters
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The scaling of Transformers has driven breakthrough capabilities for language
models. At present, the largest large language models (LLMs) contain upwards of
100B parameters. Vision Transformers (ViT) have introduced the same
architecture to image and video modelling, but these have not yet been
successfully scaled to nearly the same degree; the largest dense ViT contains
4B parameters (Chen et al., 2022). We present a recipe for highly efficient and
stable training of a 22B-parameter ViT (ViT-22B) and perform a wide variety of
experiments on the resulting model. When evaluated on downstream tasks (often
with a lightweight linear model on frozen features), ViT-22B demonstrates
increasing performance with scale. We further observe other interesting
benefits of scale, including an improved tradeoff between fairness and
performance, state-of-the-art alignment to human visual perception in terms of
shape/texture bias, and improved robustness. ViT-22B demonstrates the potential
for "LLM-like" scaling in vision, and provides key steps towards getting there.
|
[
{
"created": "Fri, 10 Feb 2023 18:58:21 GMT",
"version": "v1"
}
] |
2023-02-13
|
[
[
"Dehghani",
"Mostafa",
""
],
[
"Djolonga",
"Josip",
""
],
[
"Mustafa",
"Basil",
""
],
[
"Padlewski",
"Piotr",
""
],
[
"Heek",
"Jonathan",
""
],
[
"Gilmer",
"Justin",
""
],
[
"Steiner",
"Andreas",
""
],
[
"Caron",
"Mathilde",
""
],
[
"Geirhos",
"Robert",
""
],
[
"Alabdulmohsin",
"Ibrahim",
""
],
[
"Jenatton",
"Rodolphe",
""
],
[
"Beyer",
"Lucas",
""
],
[
"Tschannen",
"Michael",
""
],
[
"Arnab",
"Anurag",
""
],
[
"Wang",
"Xiao",
""
],
[
"Riquelme",
"Carlos",
""
],
[
"Minderer",
"Matthias",
""
],
[
"Puigcerver",
"Joan",
""
],
[
"Evci",
"Utku",
""
],
[
"Kumar",
"Manoj",
""
],
[
"van Steenkiste",
"Sjoerd",
""
],
[
"Elsayed",
"Gamaleldin F.",
""
],
[
"Mahendran",
"Aravindh",
""
],
[
"Yu",
"Fisher",
""
],
[
"Oliver",
"Avital",
""
],
[
"Huot",
"Fantine",
""
],
[
"Bastings",
"Jasmijn",
""
],
[
"Collier",
"Mark Patrick",
""
],
[
"Gritsenko",
"Alexey",
""
],
[
"Birodkar",
"Vighnesh",
""
],
[
"Vasconcelos",
"Cristina",
""
],
[
"Tay",
"Yi",
""
],
[
"Mensink",
"Thomas",
""
],
[
"Kolesnikov",
"Alexander",
""
],
[
"Pavetić",
"Filip",
""
],
[
"Tran",
"Dustin",
""
],
[
"Kipf",
"Thomas",
""
],
[
"Lučić",
"Mario",
""
],
[
"Zhai",
"Xiaohua",
""
],
[
"Keysers",
"Daniel",
""
],
[
"Harmsen",
"Jeremiah",
""
],
[
"Houlsby",
"Neil",
""
]
] |
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Vision Transformers (ViT) have introduced the same architecture to image and video modelling, but these have not yet been successfully scaled to nearly the same degree; the largest dense ViT contains 4B parameters (Chen et al., 2022). We present a recipe for highly efficient and stable training of a 22B-parameter ViT (ViT-22B) and perform a wide variety of experiments on the resulting model. When evaluated on downstream tasks (often with a lightweight linear model on frozen features), ViT-22B demonstrates increasing performance with scale. We further observe other interesting benefits of scale, including an improved tradeoff between fairness and performance, state-of-the-art alignment to human visual perception in terms of shape/texture bias, and improved robustness. ViT-22B demonstrates the potential for "LLM-like" scaling in vision, and provides key steps towards getting there.
|
2104.03841
|
Daniela Massiceti
|
Daniela Massiceti, Luisa Zintgraf, John Bronskill, Lida Theodorou,
Matthew Tobias Harris, Edward Cutrell, Cecily Morrison, Katja Hofmann, Simone
Stumpf
|
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition
|
IEEE/CVF International Conference on Computer Vision (ICCV), 2021
| null |
10.25383/city.14294597
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Object recognition has made great advances in the last decade, but
predominately still relies on many high-quality training examples per object
category. In contrast, learning new objects from only a few examples could
enable many impactful applications from robotics to user personalization. Most
few-shot learning research, however, has been driven by benchmark datasets that
lack the high variation that these applications will face when deployed in the
real-world. To close this gap, we present the ORBIT dataset and benchmark,
grounded in the real-world application of teachable object recognizers for
people who are blind/low-vision. The dataset contains 3,822 videos of 486
objects recorded by people who are blind/low-vision on their mobile phones. The
benchmark reflects a realistic, highly challenging recognition problem,
providing a rich playground to drive research in robustness to few-shot,
high-variation conditions. We set the benchmark's first state-of-the-art and
show there is massive scope for further innovation, holding the potential to
impact a broad range of real-world vision applications including tools for the
blind/low-vision community. We release the dataset at
https://doi.org/10.25383/city.14294597 and benchmark code at
https://github.com/microsoft/ORBIT-Dataset.
|
[
{
"created": "Thu, 8 Apr 2021 15:32:01 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Apr 2021 16:56:43 GMT",
"version": "v2"
},
{
"created": "Thu, 10 Jun 2021 14:50:34 GMT",
"version": "v3"
},
{
"created": "Mon, 16 Aug 2021 16:19:12 GMT",
"version": "v4"
},
{
"created": "Fri, 8 Oct 2021 13:20:52 GMT",
"version": "v5"
}
] |
2021-10-11
|
[
[
"Massiceti",
"Daniela",
""
],
[
"Zintgraf",
"Luisa",
""
],
[
"Bronskill",
"John",
""
],
[
"Theodorou",
"Lida",
""
],
[
"Harris",
"Matthew Tobias",
""
],
[
"Cutrell",
"Edward",
""
],
[
"Morrison",
"Cecily",
""
],
[
"Hofmann",
"Katja",
""
],
[
"Stumpf",
"Simone",
""
]
] |
Object recognition has made great advances in the last decade, but predominately still relies on many high-quality training examples per object category. In contrast, learning new objects from only a few examples could enable many impactful applications from robotics to user personalization. Most few-shot learning research, however, has been driven by benchmark datasets that lack the high variation that these applications will face when deployed in the real-world. To close this gap, we present the ORBIT dataset and benchmark, grounded in the real-world application of teachable object recognizers for people who are blind/low-vision. The dataset contains 3,822 videos of 486 objects recorded by people who are blind/low-vision on their mobile phones. The benchmark reflects a realistic, highly challenging recognition problem, providing a rich playground to drive research in robustness to few-shot, high-variation conditions. We set the benchmark's first state-of-the-art and show there is massive scope for further innovation, holding the potential to impact a broad range of real-world vision applications including tools for the blind/low-vision community. We release the dataset at https://doi.org/10.25383/city.14294597 and benchmark code at https://github.com/microsoft/ORBIT-Dataset.
|
2111.09625
|
Diego Garbervetsky
|
Saikat Dutta, Diego Garbervetsky, Shuvendu Lahiri, Max Sch\"afer
|
InspectJS: Leveraging Code Similarity and User-Feedback for Effective
Taint Specification Inference for JavaScript
|
11 pages, sent to Software Engineering in Practice track at ICSE'2022
| null | null | null |
cs.CR cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Static analysis has established itself as a weapon of choice for detecting
security vulnerabilities. Taint analysis in particular is a very general and
powerful technique, where security policies are expressed in terms of forbidden
flows, either from untrusted input sources to sensitive sinks (in integrity
policies) or from sensitive sources to untrusted sinks (in confidentiality
policies). The appeal of this approach is that the taint-tracking mechanism has
to be implemented only once, and can then be parameterized with different taint
specifications (that is, sets of sources and sinks, as well as any sanitizers
that render otherwise problematic flows innocuous) to detect many different
kinds of vulnerabilities.
But while techniques for implementing scalable inter-procedural static taint
tracking are fairly well established, crafting taint specifications is still
more of an art than a science, and in practice tends to involve a lot of manual
effort.
Past work has focussed on automated techniques for inferring taint
specifications for libraries either from their implementation or from the way
they tend to be used in client code. Among the latter, machine learning-based
approaches have shown great promise.
In this work we present our experience combining an existing machine-learning
approach to mining sink specifications for JavaScript libraries with manual
taint modelling in the context of GitHub's CodeQL analysis framework. We show
that the machine-learning component can successfully infer many new taint sinks
that either are not part of the manual modelling or are not detected due to
analysis incompleteness. Moreover, we present techniques for organizing sink
predictions using automated ranking and code-similarity metrics that allow an
analysis engineer to efficiently sift through large numbers of predictions to
identify true positives.
|
[
{
"created": "Thu, 18 Nov 2021 11:10:04 GMT",
"version": "v1"
}
] |
2021-11-19
|
[
[
"Dutta",
"Saikat",
""
],
[
"Garbervetsky",
"Diego",
""
],
[
"Lahiri",
"Shuvendu",
""
],
[
"Schäfer",
"Max",
""
]
] |
Static analysis has established itself as a weapon of choice for detecting security vulnerabilities. Taint analysis in particular is a very general and powerful technique, where security policies are expressed in terms of forbidden flows, either from untrusted input sources to sensitive sinks (in integrity policies) or from sensitive sources to untrusted sinks (in confidentiality policies). The appeal of this approach is that the taint-tracking mechanism has to be implemented only once, and can then be parameterized with different taint specifications (that is, sets of sources and sinks, as well as any sanitizers that render otherwise problematic flows innocuous) to detect many different kinds of vulnerabilities. But while techniques for implementing scalable inter-procedural static taint tracking are fairly well established, crafting taint specifications is still more of an art than a science, and in practice tends to involve a lot of manual effort. Past work has focussed on automated techniques for inferring taint specifications for libraries either from their implementation or from the way they tend to be used in client code. Among the latter, machine learning-based approaches have shown great promise. In this work we present our experience combining an existing machine-learning approach to mining sink specifications for JavaScript libraries with manual taint modelling in the context of GitHub's CodeQL analysis framework. We show that the machine-learning component can successfully infer many new taint sinks that either are not part of the manual modelling or are not detected due to analysis incompleteness. Moreover, we present techniques for organizing sink predictions using automated ranking and code-similarity metrics that allow an analysis engineer to efficiently sift through large numbers of predictions to identify true positives.
|
2006.13534
|
Ehsan Asali
|
Ehsan Asali, Farzin Negahbani, Shahriyar Bamaei, Zahra Abbasi
|
Namira Soccer 2D Simulation Team Description Paper 2020
| null | null | null | null |
cs.RO cs.AI cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this article, we will discuss methods and ideas which are implemented on
Namira 2D Soccer Simulation team in the recent year. Numerous scientific and
programming activities were done in the process of code development, but we
will mention the most outstanding ones in details. A Kalman filtering method
for localization and two helpful software packages will be discussed here.
Namira uses agent2d-3.1.1 as base code and librcsc-4.1.0 as library with some
deliberate changes.
|
[
{
"created": "Wed, 24 Jun 2020 07:40:44 GMT",
"version": "v1"
}
] |
2020-06-25
|
[
[
"Asali",
"Ehsan",
""
],
[
"Negahbani",
"Farzin",
""
],
[
"Bamaei",
"Shahriyar",
""
],
[
"Abbasi",
"Zahra",
""
]
] |
In this article, we will discuss methods and ideas which are implemented on Namira 2D Soccer Simulation team in the recent year. Numerous scientific and programming activities were done in the process of code development, but we will mention the most outstanding ones in details. A Kalman filtering method for localization and two helpful software packages will be discussed here. Namira uses agent2d-3.1.1 as base code and librcsc-4.1.0 as library with some deliberate changes.
|
cs/0403027
|
Francesc Rossello
|
Jaume Casasnovas, Joe Miro, Manuel Moya, Francesc Rossello
|
An approach to membrane computing under inexactitude
|
20 pages, 0 figures
| null | null | null |
cs.OH cs.NE
| null |
In this paper we introduce a fuzzy version of symport/antiport membrane
systems. Our fuzzy membrane systems handle possibly inexact copies of reactives
and their rules are endowed with threshold functions that determine whether a
rule can be applied or not to a given set of objects, depending of the degree
of accuracy of these objects to the reactives specified in the rule. We prove
that these fuzzy membrane systems generate exactly the recursively enumerable
finite-valued fuzzy sets of natural numbers.
|
[
{
"created": "Tue, 16 Mar 2004 09:02:39 GMT",
"version": "v1"
},
{
"created": "Tue, 11 May 2004 08:27:15 GMT",
"version": "v2"
}
] |
2007-05-23
|
[
[
"Casasnovas",
"Jaume",
""
],
[
"Miro",
"Joe",
""
],
[
"Moya",
"Manuel",
""
],
[
"Rossello",
"Francesc",
""
]
] |
In this paper we introduce a fuzzy version of symport/antiport membrane systems. Our fuzzy membrane systems handle possibly inexact copies of reactives and their rules are endowed with threshold functions that determine whether a rule can be applied or not to a given set of objects, depending of the degree of accuracy of these objects to the reactives specified in the rule. We prove that these fuzzy membrane systems generate exactly the recursively enumerable finite-valued fuzzy sets of natural numbers.
|
2401.05509
|
MohammadNoor Injadat
|
MohammadNoor Injadat
|
Optimized Ensemble Model Towards Secured Industrial IoT Devices
|
Accepted and presented in 24th International Arab Conference on
Information Technology (ACIT'2023)
| null | null | null |
cs.CR cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The continued growth in the deployment of Internet-of-Things (IoT) devices
has been fueled by the increased connectivity demand, particularly in
industrial environments. However, this has led to an increase in the number of
network related attacks due to the increased number of potential attack
surfaces. Industrial IoT (IIoT) devices are prone to various network related
attacks that can have severe consequences on the manufacturing process as well
as on the safety of the workers in the manufacturing plant. One promising
solution that has emerged in recent years for attack detection is Machine
learning (ML). More specifically, ensemble learning models have shown great
promise in improving the performance of the underlying ML models. Accordingly,
this paper proposes a framework based on the combined use of Bayesian
Optimization-Gaussian Process (BO-GP) with an ensemble tree-based learning
model to improve the performance of intrusion and attack detection in IIoT
environments. The proposed framework's performance is evaluated using the
Windows 10 dataset collected by the Cyber Range and IoT labs at University of
New South Wales. Experimental results illustrate the improvement in detection
accuracy, precision, and F-score when compared to standard tree and ensemble
tree models.
|
[
{
"created": "Wed, 10 Jan 2024 19:06:39 GMT",
"version": "v1"
}
] |
2024-01-12
|
[
[
"Injadat",
"MohammadNoor",
""
]
] |
The continued growth in the deployment of Internet-of-Things (IoT) devices has been fueled by the increased connectivity demand, particularly in industrial environments. However, this has led to an increase in the number of network related attacks due to the increased number of potential attack surfaces. Industrial IoT (IIoT) devices are prone to various network related attacks that can have severe consequences on the manufacturing process as well as on the safety of the workers in the manufacturing plant. One promising solution that has emerged in recent years for attack detection is Machine learning (ML). More specifically, ensemble learning models have shown great promise in improving the performance of the underlying ML models. Accordingly, this paper proposes a framework based on the combined use of Bayesian Optimization-Gaussian Process (BO-GP) with an ensemble tree-based learning model to improve the performance of intrusion and attack detection in IIoT environments. The proposed framework's performance is evaluated using the Windows 10 dataset collected by the Cyber Range and IoT labs at University of New South Wales. Experimental results illustrate the improvement in detection accuracy, precision, and F-score when compared to standard tree and ensemble tree models.
|
1809.01898
|
Jo\~ao R. Campos
|
Jo\~ao R. Campos, Marco Vieira, Ernesto Costa
|
Propheticus: Generalizable Machine Learning Framework
| null | null | null | null |
cs.LG cs.AI stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Due to recent technological developments, Machine Learning (ML), a subfield
of Artificial Intelligence (AI), has been successfully used to process and
extract knowledge from a variety of complex problems. However, a thorough ML
approach is complex and highly dependent on the problem at hand. Additionally,
implementing the logic required to execute the experiments is no small nor
trivial deed, consequentially increasing the probability of faulty code which
can compromise the results. Propheticus is a data-driven framework which
results of the need for a tool that abstracts some of the inherent complexity
of ML, whilst being easy to understand and use, as well as to adapt and expand
to assist the user's specific needs. Propheticus systematizes and enforces
various complex concepts of an ML experiment workflow, taking into account the
nature of both the problem and the data. It contains functionalities to execute
all the different tasks, from data preprocessing, to results analysis and
comparison. Notwithstanding, it can be fairly easily adapted to different
problems due to its flexible architecture, and customized as needed to address
the user's needs.
|
[
{
"created": "Thu, 6 Sep 2018 09:26:03 GMT",
"version": "v1"
}
] |
2018-09-10
|
[
[
"Campos",
"João R.",
""
],
[
"Vieira",
"Marco",
""
],
[
"Costa",
"Ernesto",
""
]
] |
Due to recent technological developments, Machine Learning (ML), a subfield of Artificial Intelligence (AI), has been successfully used to process and extract knowledge from a variety of complex problems. However, a thorough ML approach is complex and highly dependent on the problem at hand. Additionally, implementing the logic required to execute the experiments is no small nor trivial deed, consequentially increasing the probability of faulty code which can compromise the results. Propheticus is a data-driven framework which results of the need for a tool that abstracts some of the inherent complexity of ML, whilst being easy to understand and use, as well as to adapt and expand to assist the user's specific needs. Propheticus systematizes and enforces various complex concepts of an ML experiment workflow, taking into account the nature of both the problem and the data. It contains functionalities to execute all the different tasks, from data preprocessing, to results analysis and comparison. Notwithstanding, it can be fairly easily adapted to different problems due to its flexible architecture, and customized as needed to address the user's needs.
|
1911.04382
|
Zhuo Feng
|
Zhuo Feng
|
GRASS: Graph Spectral Sparsification Leveraging Scalable Spectral
Perturbation Analysis
|
14 pages, 13 figures. arXiv admin note: substantial text overlap with
arXiv:1711.05135
| null | null | null |
cs.DS cs.NA cs.SI math.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Spectral graph sparsification aims to find ultra-sparse subgraphs whose
Laplacian matrix can well approximate the original Laplacian eigenvalues and
eigenvectors. In recent years, spectral sparsification techniques have been
extensively studied for accelerating various numerical and graph-related
applications. Prior nearly-linear-time spectral sparsification methods first
extract low-stretch spanning tree from the original graph to form the backbone
of the sparsifier, and then recover small portions of spectrally-critical
off-tree edges to the spanning tree to significantly improve the approximation
quality. However, it is not clear how many off-tree edges should be recovered
for achieving a desired spectral similarity level within the sparsifier.
Motivated by recent graph signal processing techniques, this paper proposes a
similarity-aware spectral graph sparsification framework that leverages
efficient spectral off-tree edge embedding and filtering schemes to construct
spectral sparsifiers with guaranteed spectral similarity (relative condition
number) level. An iterative graph densification scheme is also introduced to
facilitate efficient and effective filtering of off-tree edges for highly
ill-conditioned problems. The proposed method has been validated using various
kinds of graphs obtained from public domain sparse matrix collections relevant
to VLSI CAD, finite element analysis, as well as social and data networks
frequently studied in many machine learning and data mining applications. For
instance, a sparse SDD matrix with 40 million unknowns and 180 million nonzeros
can be solved (1E-3 accuracy level) within two minutes using a single CPU core
and about 6GB memory.
|
[
{
"created": "Mon, 4 Nov 2019 00:47:36 GMT",
"version": "v1"
},
{
"created": "Thu, 21 Nov 2019 12:33:59 GMT",
"version": "v2"
},
{
"created": "Wed, 29 Apr 2020 01:17:42 GMT",
"version": "v3"
}
] |
2020-04-30
|
[
[
"Feng",
"Zhuo",
""
]
] |
Spectral graph sparsification aims to find ultra-sparse subgraphs whose Laplacian matrix can well approximate the original Laplacian eigenvalues and eigenvectors. In recent years, spectral sparsification techniques have been extensively studied for accelerating various numerical and graph-related applications. Prior nearly-linear-time spectral sparsification methods first extract low-stretch spanning tree from the original graph to form the backbone of the sparsifier, and then recover small portions of spectrally-critical off-tree edges to the spanning tree to significantly improve the approximation quality. However, it is not clear how many off-tree edges should be recovered for achieving a desired spectral similarity level within the sparsifier. Motivated by recent graph signal processing techniques, this paper proposes a similarity-aware spectral graph sparsification framework that leverages efficient spectral off-tree edge embedding and filtering schemes to construct spectral sparsifiers with guaranteed spectral similarity (relative condition number) level. An iterative graph densification scheme is also introduced to facilitate efficient and effective filtering of off-tree edges for highly ill-conditioned problems. The proposed method has been validated using various kinds of graphs obtained from public domain sparse matrix collections relevant to VLSI CAD, finite element analysis, as well as social and data networks frequently studied in many machine learning and data mining applications. For instance, a sparse SDD matrix with 40 million unknowns and 180 million nonzeros can be solved (1E-3 accuracy level) within two minutes using a single CPU core and about 6GB memory.
|
2301.10638
|
Johanni Brea
|
Johanni Brea, Flavio Martinelli, Berfin \c{S}im\c{s}ek, Wulfram
Gerstner
|
MLPGradientFlow: going with the flow of multilayer perceptrons (and
finding minima fast and accurately)
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
MLPGradientFlow is a software package to solve numerically the gradient flow
differential equation $\dot \theta = -\nabla \mathcal L(\theta; \mathcal D)$,
where $\theta$ are the parameters of a multi-layer perceptron, $\mathcal D$ is
some data set, and $\nabla \mathcal L$ is the gradient of a loss function. We
show numerically that adaptive first- or higher-order integration methods based
on Runge-Kutta schemes have better accuracy and convergence speed than gradient
descent with the Adam optimizer. However, we find Newton's method and
approximations like BFGS preferable to find fixed points (local and global
minima of $\mathcal L$) efficiently and accurately. For small networks and data
sets, gradients are usually computed faster than in pytorch and Hessian are
computed at least $5\times$ faster. Additionally, the package features an
integrator for a teacher-student setup with bias-free, two-layer networks
trained with standard Gaussian input in the limit of infinite data. The code is
accessible at https://github.com/jbrea/MLPGradientFlow.jl.
|
[
{
"created": "Wed, 25 Jan 2023 15:21:44 GMT",
"version": "v1"
}
] |
2023-01-26
|
[
[
"Brea",
"Johanni",
""
],
[
"Martinelli",
"Flavio",
""
],
[
"Şimşek",
"Berfin",
""
],
[
"Gerstner",
"Wulfram",
""
]
] |
MLPGradientFlow is a software package to solve numerically the gradient flow differential equation $\dot \theta = -\nabla \mathcal L(\theta; \mathcal D)$, where $\theta$ are the parameters of a multi-layer perceptron, $\mathcal D$ is some data set, and $\nabla \mathcal L$ is the gradient of a loss function. We show numerically that adaptive first- or higher-order integration methods based on Runge-Kutta schemes have better accuracy and convergence speed than gradient descent with the Adam optimizer. However, we find Newton's method and approximations like BFGS preferable to find fixed points (local and global minima of $\mathcal L$) efficiently and accurately. For small networks and data sets, gradients are usually computed faster than in pytorch and Hessian are computed at least $5\times$ faster. Additionally, the package features an integrator for a teacher-student setup with bias-free, two-layer networks trained with standard Gaussian input in the limit of infinite data. The code is accessible at https://github.com/jbrea/MLPGradientFlow.jl.
|
1901.00295
|
Xingjian Du
|
Xingjian Du, Mengyao Zhu, Xuan Shi, Xinpeng Zhang, Wen Zhang, Jingdong
Chen
|
End-to-End Model for Speech Enhancement by Consistent Spectrogram
Masking
| null | null | null | null |
cs.SD cs.AI cs.MM eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, phase processing is attracting increasinginterest in speech
enhancement community. Some researchersintegrate phase estimations module into
speech enhancementmodels by using complex-valued short-time Fourier
transform(STFT) spectrogram based training targets, e.g. Complex RatioMask
(cRM) [1]. However, masking on spectrogram would violentits consistency
constraints. In this work, we prove that theinconsistent problem enlarges the
solution space of the speechenhancement model and causes unintended artifacts.
ConsistencySpectrogram Masking (CSM) is proposed to estimate the
complexspectrogram of a signal with the consistency constraint in asimple but
not trivial way. The experiments comparing ourCSM based end-to-end model with
other methods are conductedto confirm that the CSM accelerate the model
training andhave significant improvements in speech quality. From
ourexperimental results, we assured that our method could enha
|
[
{
"created": "Wed, 2 Jan 2019 08:39:05 GMT",
"version": "v1"
}
] |
2019-01-03
|
[
[
"Du",
"Xingjian",
""
],
[
"Zhu",
"Mengyao",
""
],
[
"Shi",
"Xuan",
""
],
[
"Zhang",
"Xinpeng",
""
],
[
"Zhang",
"Wen",
""
],
[
"Chen",
"Jingdong",
""
]
] |
Recently, phase processing is attracting increasinginterest in speech enhancement community. Some researchersintegrate phase estimations module into speech enhancementmodels by using complex-valued short-time Fourier transform(STFT) spectrogram based training targets, e.g. Complex RatioMask (cRM) [1]. However, masking on spectrogram would violentits consistency constraints. In this work, we prove that theinconsistent problem enlarges the solution space of the speechenhancement model and causes unintended artifacts. ConsistencySpectrogram Masking (CSM) is proposed to estimate the complexspectrogram of a signal with the consistency constraint in asimple but not trivial way. The experiments comparing ourCSM based end-to-end model with other methods are conductedto confirm that the CSM accelerate the model training andhave significant improvements in speech quality. From ourexperimental results, we assured that our method could enha
|
2012.01101
|
Kim Phuc Tran
|
Zhenglei He, Kim Phuc Tran (GEMTEX), Sebastien Thomassey, Xianyi Zeng,
Jie Xu, Changhai Yi
|
Multi-Objective Optimization of the Textile Manufacturing Process Using
Deep-Q-Network Based Multi-Agent Reinforcement Learning
| null | null | null | null |
cs.AI cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multi-objective optimization of the textile manufacturing process is an
increasing challenge because of the growing complexity involved in the
development of the textile industry. The use of intelligent techniques has been
often discussed in this domain, although a significant improvement from certain
successful applications has been reported, the traditional methods failed to
work with high-as well as human intervention. Upon which, this paper proposed a
multi-agent reinforcement learning (MARL) framework to transform the
optimization process into a stochastic game and introduced the deep Q-networks
algorithm to train the multiple agents. A utilitarian selection mechanism was
employed in the stochastic game, which (-greedy policy) in each state to avoid
the interruption of multiple equilibria and achieve the correlated equilibrium
optimal solutions of the optimizing process. The case study result reflects
that the proposed MARL system is possible to achieve the optimal solutions for
the textile ozonation process and it performs better than the traditional
approaches.
|
[
{
"created": "Wed, 2 Dec 2020 11:37:44 GMT",
"version": "v1"
}
] |
2020-12-03
|
[
[
"He",
"Zhenglei",
"",
"GEMTEX"
],
[
"Tran",
"Kim Phuc",
"",
"GEMTEX"
],
[
"Thomassey",
"Sebastien",
""
],
[
"Zeng",
"Xianyi",
""
],
[
"Xu",
"Jie",
""
],
[
"Yi",
"Changhai",
""
]
] |
Multi-objective optimization of the textile manufacturing process is an increasing challenge because of the growing complexity involved in the development of the textile industry. The use of intelligent techniques has been often discussed in this domain, although a significant improvement from certain successful applications has been reported, the traditional methods failed to work with high-as well as human intervention. Upon which, this paper proposed a multi-agent reinforcement learning (MARL) framework to transform the optimization process into a stochastic game and introduced the deep Q-networks algorithm to train the multiple agents. A utilitarian selection mechanism was employed in the stochastic game, which (-greedy policy) in each state to avoid the interruption of multiple equilibria and achieve the correlated equilibrium optimal solutions of the optimizing process. The case study result reflects that the proposed MARL system is possible to achieve the optimal solutions for the textile ozonation process and it performs better than the traditional approaches.
|
2303.11546
|
Sunghwan Kim
|
Sunghwan Kim, Dae-hwan Kim, Hoseong Kim
|
Texture Learning Domain Randomization for Domain Generalized
Segmentation
|
ICCV 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep Neural Networks (DNNs)-based semantic segmentation models trained on a
source domain often struggle to generalize to unseen target domains, i.e., a
domain gap problem. Texture often contributes to the domain gap, making DNNs
vulnerable to domain shift because they are prone to be texture-biased.
Existing Domain Generalized Semantic Segmentation (DGSS) methods have
alleviated the domain gap problem by guiding models to prioritize shape over
texture. On the other hand, shape and texture are two prominent and
complementary cues in semantic segmentation. This paper argues that leveraging
texture is crucial for improving performance in DGSS. Specifically, we propose
a novel framework, coined Texture Learning Domain Randomization (TLDR). TLDR
includes two novel losses to effectively enhance texture learning in DGSS: (1)
a texture regularization loss to prevent overfitting to source domain textures
by using texture features from an ImageNet pre-trained model and (2) a texture
generalization loss that utilizes random style images to learn diverse texture
representations in a self-supervised manner. Extensive experimental results
demonstrate the superiority of the proposed TLDR; e.g., TLDR achieves 46.5 mIoU
on GTA-to-Cityscapes using ResNet-50, which improves the prior state-of-the-art
method by 1.9 mIoU. The source code is available at
https://github.com/ssssshwan/TLDR.
|
[
{
"created": "Tue, 21 Mar 2023 02:23:26 GMT",
"version": "v1"
},
{
"created": "Thu, 17 Aug 2023 10:39:37 GMT",
"version": "v2"
}
] |
2023-08-21
|
[
[
"Kim",
"Sunghwan",
""
],
[
"Kim",
"Dae-hwan",
""
],
[
"Kim",
"Hoseong",
""
]
] |
Deep Neural Networks (DNNs)-based semantic segmentation models trained on a source domain often struggle to generalize to unseen target domains, i.e., a domain gap problem. Texture often contributes to the domain gap, making DNNs vulnerable to domain shift because they are prone to be texture-biased. Existing Domain Generalized Semantic Segmentation (DGSS) methods have alleviated the domain gap problem by guiding models to prioritize shape over texture. On the other hand, shape and texture are two prominent and complementary cues in semantic segmentation. This paper argues that leveraging texture is crucial for improving performance in DGSS. Specifically, we propose a novel framework, coined Texture Learning Domain Randomization (TLDR). TLDR includes two novel losses to effectively enhance texture learning in DGSS: (1) a texture regularization loss to prevent overfitting to source domain textures by using texture features from an ImageNet pre-trained model and (2) a texture generalization loss that utilizes random style images to learn diverse texture representations in a self-supervised manner. Extensive experimental results demonstrate the superiority of the proposed TLDR; e.g., TLDR achieves 46.5 mIoU on GTA-to-Cityscapes using ResNet-50, which improves the prior state-of-the-art method by 1.9 mIoU. The source code is available at https://github.com/ssssshwan/TLDR.
|
2302.06396
|
Thibaut Verron
|
Manuel Kauers, Christoph Koutschan, Thibaut Verron
|
Transcendence Certificates for D-finite Functions
|
9 pages, 1 figure
|
Proceedings of International Symposium on Symbolic and Algebraic
Computation 2023
|
10.1145/3597066.3597091
| null |
cs.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Although in theory we can decide whether a given D-finite function is
transcendental, transcendence proofs remain a challenge in practice. Typically,
transcendence is certified by checking certain incomplete sufficient
conditions. In this paper we propose an additional such condition which catches
some cases on which other tests fail.
|
[
{
"created": "Mon, 13 Feb 2023 14:36:20 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Sep 2023 12:14:17 GMT",
"version": "v2"
}
] |
2023-09-20
|
[
[
"Kauers",
"Manuel",
""
],
[
"Koutschan",
"Christoph",
""
],
[
"Verron",
"Thibaut",
""
]
] |
Although in theory we can decide whether a given D-finite function is transcendental, transcendence proofs remain a challenge in practice. Typically, transcendence is certified by checking certain incomplete sufficient conditions. In this paper we propose an additional such condition which catches some cases on which other tests fail.
|
1712.01794
|
Svetlana Kiritchenko
|
Svetlana Kiritchenko and Saif M. Mohammad
|
The Effect of Negators, Modals, and Degree Adverbs on Sentiment
Composition
|
In Proceedings of the 7th Workshop on Computational Approaches to
Subjectivity, Sentiment and Social Media Analysis (WASSA), San Diego,
California, 2016
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Negators, modals, and degree adverbs can significantly affect the sentiment
of the words they modify. Often, their impact is modeled with simple
heuristics; although, recent work has shown that such heuristics do not capture
the true sentiment of multi-word phrases. We created a dataset of phrases that
include various negators, modals, and degree adverbs, as well as their
combinations. Both the phrases and their constituent content words were
annotated with real-valued scores of sentiment association. Using phrasal terms
in the created dataset, we analyze the impact of individual modifiers and the
average effect of the groups of modifiers on overall sentiment. We find that
the effect of modifiers varies substantially among the members of the same
group. Furthermore, each individual modifier can affect sentiment words in
different ways. Therefore, solutions based on statistical learning seem more
promising than fixed hand-crafted rules on the task of automatic sentiment
prediction.
|
[
{
"created": "Tue, 5 Dec 2017 18:17:43 GMT",
"version": "v1"
}
] |
2017-12-06
|
[
[
"Kiritchenko",
"Svetlana",
""
],
[
"Mohammad",
"Saif M.",
""
]
] |
Negators, modals, and degree adverbs can significantly affect the sentiment of the words they modify. Often, their impact is modeled with simple heuristics; although, recent work has shown that such heuristics do not capture the true sentiment of multi-word phrases. We created a dataset of phrases that include various negators, modals, and degree adverbs, as well as their combinations. Both the phrases and their constituent content words were annotated with real-valued scores of sentiment association. Using phrasal terms in the created dataset, we analyze the impact of individual modifiers and the average effect of the groups of modifiers on overall sentiment. We find that the effect of modifiers varies substantially among the members of the same group. Furthermore, each individual modifier can affect sentiment words in different ways. Therefore, solutions based on statistical learning seem more promising than fixed hand-crafted rules on the task of automatic sentiment prediction.
|
2310.05932
|
Mian Ibad Ali Shah
|
Mian Ibad Ali Shah, Abdul Wahid, Enda Barrett, Karl Mason
|
A Multi-Agent Systems Approach for Peer-to-Peer Energy Trading in Dairy
Farming
|
Proc. of the Artificial Intelligence for Sustainability, ECAI 2023,
Eunika et al. (eds.), Sep 30- Oct 1, 2023,
https://sites.google.com/view/ai4s. 2023
| null | null | null |
cs.MA cs.AI cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To achieve desired carbon emission reductions, integrating renewable
generation and accelerating the adoption of peer-to-peer energy trading is
crucial. This is especially important for energy-intensive farming, like dairy
farming. However, integrating renewables and peer-to-peer trading presents
challenges. To address this, we propose the Multi-Agent Peer-to-Peer Dairy Farm
Energy Simulator (MAPDES), enabling dairy farms to participate in peer-to-peer
markets. Our strategy reduces electricity costs and peak demand by
approximately 30% and 24% respectively, while increasing energy sales by 37%
compared to the baseline scenario without P2P trading. This demonstrates the
effectiveness of our approach.
|
[
{
"created": "Mon, 21 Aug 2023 13:22:20 GMT",
"version": "v1"
}
] |
2023-10-11
|
[
[
"Shah",
"Mian Ibad Ali",
""
],
[
"Wahid",
"Abdul",
""
],
[
"Barrett",
"Enda",
""
],
[
"Mason",
"Karl",
""
]
] |
To achieve desired carbon emission reductions, integrating renewable generation and accelerating the adoption of peer-to-peer energy trading is crucial. This is especially important for energy-intensive farming, like dairy farming. However, integrating renewables and peer-to-peer trading presents challenges. To address this, we propose the Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator (MAPDES), enabling dairy farms to participate in peer-to-peer markets. Our strategy reduces electricity costs and peak demand by approximately 30% and 24% respectively, while increasing energy sales by 37% compared to the baseline scenario without P2P trading. This demonstrates the effectiveness of our approach.
|
1910.00974
|
Mutaz Melhem
|
Mutaz Y. Melhem and Laszlo B. Kish
|
Generalized DC loop current attack against the KLJN secure key exchange
scheme
|
11 pages, 6 Figures, and Journal paper
|
Metrol. Meas. Syst., Vol. 26 (2019) No. 4, pp. 607-616
|
10.24425/mms.2019.130571
| null |
cs.ET cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A new attack against the Kirchhoff Law Johnson Noise (KLJN) secure key
distribution system is studied with unknown parasitic DC voltage sources at
both Alices and Bobs ends. This paper is the generalization of our earlier
investigation with a single end parasitic source. Under the assumption that Eve
does not know the values of the parasitic sources, a new attack, utilizing the
current generated by the parasitic dc voltage sources, is introduced. The
attack is mathematically analyzed and demonstrated by computer simulations.
Simple defense methods against the attack are shown. The earlier defense method
based solely on the comparison of current/voltage data at Alice's and Bob's
terminals is useless here since the wire currents and voltages are equal at
both ends. However, the more expensive version of the earlier defense method,
which is based on in situ system simulation and comparison with measurements,
works efficiently.
|
[
{
"created": "Mon, 30 Sep 2019 19:34:32 GMT",
"version": "v1"
}
] |
2020-04-07
|
[
[
"Melhem",
"Mutaz Y.",
""
],
[
"Kish",
"Laszlo B.",
""
]
] |
A new attack against the Kirchhoff Law Johnson Noise (KLJN) secure key distribution system is studied with unknown parasitic DC voltage sources at both Alices and Bobs ends. This paper is the generalization of our earlier investigation with a single end parasitic source. Under the assumption that Eve does not know the values of the parasitic sources, a new attack, utilizing the current generated by the parasitic dc voltage sources, is introduced. The attack is mathematically analyzed and demonstrated by computer simulations. Simple defense methods against the attack are shown. The earlier defense method based solely on the comparison of current/voltage data at Alice's and Bob's terminals is useless here since the wire currents and voltages are equal at both ends. However, the more expensive version of the earlier defense method, which is based on in situ system simulation and comparison with measurements, works efficiently.
|
2305.03130
|
Kaixin Ma
|
Kaixin Ma, Hao Cheng, Yu Zhang, Xiaodong Liu, Eric Nyberg, Jianfeng
Gao
|
Chain-of-Skills: A Configurable Model for Open-domain Question Answering
|
ACL 2023
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The retrieval model is an indispensable component for real-world
knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As
separate retrieval skills are annotated for different datasets, recent work
focuses on customized methods, limiting the model transferability and
scalability. In this work, we propose a modular retriever where individual
modules correspond to key skills that can be reused across datasets. Our
approach supports flexible skill configurations based on the target domain to
boost performance. To mitigate task interference, we design a novel
modularization parameterization inspired by sparse Transformer. We demonstrate
that our model can benefit from self-supervised pretraining on Wikipedia and
fine-tuning using multiple ODQA datasets, both in a multi-task fashion. Our
approach outperforms recent self-supervised retrievers in zero-shot evaluations
and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA
and OTT-QA.
|
[
{
"created": "Thu, 4 May 2023 20:19:39 GMT",
"version": "v1"
},
{
"created": "Fri, 26 May 2023 17:19:58 GMT",
"version": "v2"
}
] |
2023-05-29
|
[
[
"Ma",
"Kaixin",
""
],
[
"Cheng",
"Hao",
""
],
[
"Zhang",
"Yu",
""
],
[
"Liu",
"Xiaodong",
""
],
[
"Nyberg",
"Eric",
""
],
[
"Gao",
"Jianfeng",
""
]
] |
The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As separate retrieval skills are annotated for different datasets, recent work focuses on customized methods, limiting the model transferability and scalability. In this work, we propose a modular retriever where individual modules correspond to key skills that can be reused across datasets. Our approach supports flexible skill configurations based on the target domain to boost performance. To mitigate task interference, we design a novel modularization parameterization inspired by sparse Transformer. We demonstrate that our model can benefit from self-supervised pretraining on Wikipedia and fine-tuning using multiple ODQA datasets, both in a multi-task fashion. Our approach outperforms recent self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA.
|
1808.01990
|
Fatih Cakir
|
Fatih Cakir, Kun He, Stan Sclaroff
|
Hashing with Binary Matrix Pursuit
|
23 pages, 4 figures. In Proceedings of European Conference on
Computer Vision (ECCV), 2018
| null | null | null |
cs.LG cs.CV stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose theoretical and empirical improvements for two-stage hashing
methods. We first provide a theoretical analysis on the quality of the binary
codes and show that, under mild assumptions, a residual learning scheme can
construct binary codes that fit any neighborhood structure with arbitrary
accuracy. Secondly, we show that with high-capacity hash functions such as
CNNs, binary code inference can be greatly simplified for many standard
neighborhood definitions, yielding smaller optimization problems and more
robust codes. Incorporating our findings, we propose a novel two-stage hashing
method that significantly outperforms previous hashing studies on widely used
image retrieval benchmarks.
|
[
{
"created": "Mon, 6 Aug 2018 16:51:36 GMT",
"version": "v1"
}
] |
2018-08-07
|
[
[
"Cakir",
"Fatih",
""
],
[
"He",
"Kun",
""
],
[
"Sclaroff",
"Stan",
""
]
] |
We propose theoretical and empirical improvements for two-stage hashing methods. We first provide a theoretical analysis on the quality of the binary codes and show that, under mild assumptions, a residual learning scheme can construct binary codes that fit any neighborhood structure with arbitrary accuracy. Secondly, we show that with high-capacity hash functions such as CNNs, binary code inference can be greatly simplified for many standard neighborhood definitions, yielding smaller optimization problems and more robust codes. Incorporating our findings, we propose a novel two-stage hashing method that significantly outperforms previous hashing studies on widely used image retrieval benchmarks.
|
1701.07193
|
Leon Abdillah
|
Leon Andretti Abdillah
|
Exploring Students Blended Learning Through Social Media
|
10 pages
|
ComTech (Computer, Mathematics and Engineering Applications),
7(4), 245-254 (2016)
|
10.21512/comtech.v7i4.2495
| null |
cs.CY cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Information technology (IT) has been used widely in many aspects of our daily
life. After discuss politics related aspects for some articles. In this article
author would like to discuss social media for students learning environment.
Social media as a leading application on the internet has changed many aspects
of life become more globalized. This article discusses the use of social media
to support learning activities for students in the faculty of computer science.
The author uses Facebook and WordPress as an alternative to electronic
learning: 1) online attendance tool, 2) media storage and dissemination of
course materials, 3) event scheduling for the lectures. Social media succeed to
change the way of modern learning styles and environment. The results of this
study are some learning activities such as : 1) Preparation, 2) Weekly meeting
activities, 3) Course Page, 4) Social Media as Online Attendance Tool, 5)
Social Media as Learning Repository and Dissemination, and 6) Social Media as
Online Event Scheduling.
|
[
{
"created": "Wed, 25 Jan 2017 07:41:05 GMT",
"version": "v1"
}
] |
2018-04-24
|
[
[
"Abdillah",
"Leon Andretti",
""
]
] |
Information technology (IT) has been used widely in many aspects of our daily life. After discuss politics related aspects for some articles. In this article author would like to discuss social media for students learning environment. Social media as a leading application on the internet has changed many aspects of life become more globalized. This article discusses the use of social media to support learning activities for students in the faculty of computer science. The author uses Facebook and WordPress as an alternative to electronic learning: 1) online attendance tool, 2) media storage and dissemination of course materials, 3) event scheduling for the lectures. Social media succeed to change the way of modern learning styles and environment. The results of this study are some learning activities such as : 1) Preparation, 2) Weekly meeting activities, 3) Course Page, 4) Social Media as Online Attendance Tool, 5) Social Media as Learning Repository and Dissemination, and 6) Social Media as Online Event Scheduling.
|
2007.15879
|
Sina Sharif Mansouri
|
Sina Sharif Mansouri, Christoforos Kanellakis, Bjorn Lindqvist, Farhad
Pourkamali-Anaraki, Ali-akbar Agha-mohammadi, Joel Burdick and George
Nikolakopoulos
|
A Unified NMPC Scheme for MAVs Navigation with 3D Collision Avoidance
under Position Uncertainty
| null |
IEEE Robotics and Automation Letters, Volume 5, Issue 4, On
Page(s) 5740-5747, October 2020
|
10.1109/LRA.2020.3010485
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This article proposes a novel Nonlinear Model Predictive Control (NMPC)
framework for Micro Aerial Vehicle (MAV) autonomous navigation in constrained
environments. The introduced framework allows us to consider the nonlinear
dynamics of MAVs and guarantees real-time performance. Our first contribution
is to design a computationally efficient subspace clustering method to reveal
from geometrical constraints to underlying constraint planes within a 3D point
cloud, obtained from a 3D lidar scanner. The second contribution of our work is
to incorporate the extracted information into the nonlinear constraints of NMPC
for avoiding collisions. Our third contribution focuses on making the
controller robust by considering the uncertainty of localization and NMPC using
the Shannon entropy. This step enables us to track either the position or
velocity references, or none of them if necessary. As a result, the collision
avoidance constraints are defined in the local coordinates of MAVs and it
remains active and guarantees collision avoidance, despite localization
uncertainties, e.g., position estimation drifts. Additionally, as the platform
continues the mission, this will result in less uncertain position estimations,
due to the feature extraction and loop closure. The efficacy of the suggested
framework has been evaluated using various simulations in the Gazebo
environment.
|
[
{
"created": "Fri, 31 Jul 2020 07:26:49 GMT",
"version": "v1"
}
] |
2020-08-03
|
[
[
"Mansouri",
"Sina Sharif",
""
],
[
"Kanellakis",
"Christoforos",
""
],
[
"Lindqvist",
"Bjorn",
""
],
[
"Pourkamali-Anaraki",
"Farhad",
""
],
[
"Agha-mohammadi",
"Ali-akbar",
""
],
[
"Burdick",
"Joel",
""
],
[
"Nikolakopoulos",
"George",
""
]
] |
This article proposes a novel Nonlinear Model Predictive Control (NMPC) framework for Micro Aerial Vehicle (MAV) autonomous navigation in constrained environments. The introduced framework allows us to consider the nonlinear dynamics of MAVs and guarantees real-time performance. Our first contribution is to design a computationally efficient subspace clustering method to reveal from geometrical constraints to underlying constraint planes within a 3D point cloud, obtained from a 3D lidar scanner. The second contribution of our work is to incorporate the extracted information into the nonlinear constraints of NMPC for avoiding collisions. Our third contribution focuses on making the controller robust by considering the uncertainty of localization and NMPC using the Shannon entropy. This step enables us to track either the position or velocity references, or none of them if necessary. As a result, the collision avoidance constraints are defined in the local coordinates of MAVs and it remains active and guarantees collision avoidance, despite localization uncertainties, e.g., position estimation drifts. Additionally, as the platform continues the mission, this will result in less uncertain position estimations, due to the feature extraction and loop closure. The efficacy of the suggested framework has been evaluated using various simulations in the Gazebo environment.
|
2401.17699
|
Jun Wan
|
Hao Fang, Ajian Liu, Haocheng Yuan, Junze Zheng, Dingheng Zeng,
Yanhong Liu, Jiankang Deng, Sergio Escalera, Xiaoming Liu, Jun Wan, Zhen Lei
|
Unified Physical-Digital Face Attack Detection
|
12 pages, 8 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Face Recognition (FR) systems can suffer from physical (i.e., print photo)
and digital (i.e., DeepFake) attacks. However, previous related work rarely
considers both situations at the same time. This implies the deployment of
multiple models and thus more computational burden. The main reasons for this
lack of an integrated model are caused by two factors: (1) The lack of a
dataset including both physical and digital attacks with ID consistency which
means the same ID covers the real face and all attack types; (2) Given the
large intra-class variance between these two attacks, it is difficult to learn
a compact feature space to detect both attacks simultaneously. To address these
issues, we collect a Unified physical-digital Attack dataset, called
UniAttackData. The dataset consists of $1,800$ participations of 2 and 12
physical and digital attacks, respectively, resulting in a total of 29,706
videos. Then, we propose a Unified Attack Detection framework based on
Vision-Language Models (VLMs), namely UniAttackDetection, which includes three
main modules: the Teacher-Student Prompts (TSP) module, focused on acquiring
unified and specific knowledge respectively; the Unified Knowledge Mining (UKM)
module, designed to capture a comprehensive feature space; and the Sample-Level
Prompt Interaction (SLPI) module, aimed at grasping sample-level semantics.
These three modules seamlessly form a robust unified attack detection
framework. Extensive experiments on UniAttackData and three other datasets
demonstrate the superiority of our approach for unified face attack detection.
|
[
{
"created": "Wed, 31 Jan 2024 09:38:44 GMT",
"version": "v1"
}
] |
2024-02-01
|
[
[
"Fang",
"Hao",
""
],
[
"Liu",
"Ajian",
""
],
[
"Yuan",
"Haocheng",
""
],
[
"Zheng",
"Junze",
""
],
[
"Zeng",
"Dingheng",
""
],
[
"Liu",
"Yanhong",
""
],
[
"Deng",
"Jiankang",
""
],
[
"Escalera",
"Sergio",
""
],
[
"Liu",
"Xiaoming",
""
],
[
"Wan",
"Jun",
""
],
[
"Lei",
"Zhen",
""
]
] |
Face Recognition (FR) systems can suffer from physical (i.e., print photo) and digital (i.e., DeepFake) attacks. However, previous related work rarely considers both situations at the same time. This implies the deployment of multiple models and thus more computational burden. The main reasons for this lack of an integrated model are caused by two factors: (1) The lack of a dataset including both physical and digital attacks with ID consistency which means the same ID covers the real face and all attack types; (2) Given the large intra-class variance between these two attacks, it is difficult to learn a compact feature space to detect both attacks simultaneously. To address these issues, we collect a Unified physical-digital Attack dataset, called UniAttackData. The dataset consists of $1,800$ participations of 2 and 12 physical and digital attacks, respectively, resulting in a total of 29,706 videos. Then, we propose a Unified Attack Detection framework based on Vision-Language Models (VLMs), namely UniAttackDetection, which includes three main modules: the Teacher-Student Prompts (TSP) module, focused on acquiring unified and specific knowledge respectively; the Unified Knowledge Mining (UKM) module, designed to capture a comprehensive feature space; and the Sample-Level Prompt Interaction (SLPI) module, aimed at grasping sample-level semantics. These three modules seamlessly form a robust unified attack detection framework. Extensive experiments on UniAttackData and three other datasets demonstrate the superiority of our approach for unified face attack detection.
|
2107.05138
|
S. Rasoul Etesami
|
S. Rasoul Etesami
|
Open-Loop Equilibrium Strategies for Dynamic Influence Maximization Game
Over Social Networks
| null | null | null | null |
cs.GT cs.MA cs.SY eess.SY math.OC
|
http://creativecommons.org/publicdomain/zero/1.0/
|
We consider the problem of budget allocation for competitive influence
maximization over social networks. In this problem, multiple competing parties
(players) want to distribute their limited advertising resources over a set of
social individuals to maximize their long-run cumulative payoffs. It is assumed
that the individuals are connected via a social network and update their
opinions based on the classical DeGroot model. The players must decide the
budget distribution among the individuals at a finite number of campaign times
to maximize their overall payoff given as a function of individuals' opinions.
We show that i) the optimal investment strategy for the case of a single-player
can be found in polynomial time by solving a concave program, and ii) the
open-loop equilibrium strategies for the multiplayer dynamic game can be
computed efficiently by following natural regret minimization dynamics. Our
results extend the earlier work on the static version of the problem to a
dynamic multistage game.
|
[
{
"created": "Sun, 11 Jul 2021 22:31:08 GMT",
"version": "v1"
},
{
"created": "Mon, 30 Aug 2021 04:07:57 GMT",
"version": "v2"
}
] |
2021-08-31
|
[
[
"Etesami",
"S. Rasoul",
""
]
] |
We consider the problem of budget allocation for competitive influence maximization over social networks. In this problem, multiple competing parties (players) want to distribute their limited advertising resources over a set of social individuals to maximize their long-run cumulative payoffs. It is assumed that the individuals are connected via a social network and update their opinions based on the classical DeGroot model. The players must decide the budget distribution among the individuals at a finite number of campaign times to maximize their overall payoff given as a function of individuals' opinions. We show that i) the optimal investment strategy for the case of a single-player can be found in polynomial time by solving a concave program, and ii) the open-loop equilibrium strategies for the multiplayer dynamic game can be computed efficiently by following natural regret minimization dynamics. Our results extend the earlier work on the static version of the problem to a dynamic multistage game.
|
2102.01173
|
Tony Zhao
|
Tony Zhao, Irving Fang, Jeffrey Kim, Gerald Friedland
|
Multi-modal Ensemble Models for Predicting Video Memorability
| null | null | null | null |
cs.LG cs.AI cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
Modeling media memorability has been a consistent challenge in the field of
machine learning. The Predicting Media Memorability task in MediaEval2020 is
the latest benchmark among similar challenges addressing this topic. Building
upon techniques developed in previous iterations of the challenge, we developed
ensemble methods with the use of extracted video, image, text, and audio
features. Critically, in this work we introduce and demonstrate the efficacy
and high generalizability of extracted audio embeddings as a feature for the
task of predicting media memorability.
|
[
{
"created": "Mon, 1 Feb 2021 21:16:52 GMT",
"version": "v1"
}
] |
2021-02-03
|
[
[
"Zhao",
"Tony",
""
],
[
"Fang",
"Irving",
""
],
[
"Kim",
"Jeffrey",
""
],
[
"Friedland",
"Gerald",
""
]
] |
Modeling media memorability has been a consistent challenge in the field of machine learning. The Predicting Media Memorability task in MediaEval2020 is the latest benchmark among similar challenges addressing this topic. Building upon techniques developed in previous iterations of the challenge, we developed ensemble methods with the use of extracted video, image, text, and audio features. Critically, in this work we introduce and demonstrate the efficacy and high generalizability of extracted audio embeddings as a feature for the task of predicting media memorability.
|
1911.10835
|
Vil\'em Zouhar
|
Vil\'em Zouhar and Ond\v{r}ej Bojar
|
Outbound Translation User Interface Ptakopet: A Pilot Study
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
It is not uncommon for Internet users to have to produce a text in a foreign
language they have very little knowledge of and are unable to verify the
translation quality. We call the task "outbound translation" and explore it by
introducing an open-source modular system Ptakop\v{e}t. Its main purpose is to
inspect human interaction with MT systems enhanced with additional subsystems,
such as backward translation and quality estimation. We follow up with an
experiment on (Czech) human annotators tasked to produce questions in a
language they do not speak (German), with the help of Ptakop\v{e}t. We focus on
three real-world use cases (communication with IT support, describing
administrative issues and asking encyclopedic questions) from which we gain
insight into different strategies users take when faced with outbound
translation tasks. Round trip translation is known to be unreliable for
evaluating MT systems but our experimental evaluation documents that it works
very well for users, at least on MT systems of mid-range quality.
|
[
{
"created": "Mon, 25 Nov 2019 11:22:45 GMT",
"version": "v1"
},
{
"created": "Thu, 5 Mar 2020 17:40:27 GMT",
"version": "v2"
}
] |
2020-03-06
|
[
[
"Zouhar",
"Vilém",
""
],
[
"Bojar",
"Ondřej",
""
]
] |
It is not uncommon for Internet users to have to produce a text in a foreign language they have very little knowledge of and are unable to verify the translation quality. We call the task "outbound translation" and explore it by introducing an open-source modular system Ptakop\v{e}t. Its main purpose is to inspect human interaction with MT systems enhanced with additional subsystems, such as backward translation and quality estimation. We follow up with an experiment on (Czech) human annotators tasked to produce questions in a language they do not speak (German), with the help of Ptakop\v{e}t. We focus on three real-world use cases (communication with IT support, describing administrative issues and asking encyclopedic questions) from which we gain insight into different strategies users take when faced with outbound translation tasks. Round trip translation is known to be unreliable for evaluating MT systems but our experimental evaluation documents that it works very well for users, at least on MT systems of mid-range quality.
|
2005.13820
|
Huaxi Huang
|
Huaxi Huang, Junjie Zhang, Jian Zhang, Qiang Wu, Chang Xu
|
TOAN: Target-Oriented Alignment Network for Fine-Grained Image
Categorization with Few Labeled Samples
|
T-CSVT Accepted
| null |
10.1109/TCSVT.2021.3065693
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The challenges of high intra-class variance yet low inter-class fluctuations
in fine-grained visual categorization are more severe with few labeled samples,
\textit{i.e.,} Fine-Grained categorization problems under the Few-Shot setting
(FGFS). High-order features are usually developed to uncover subtle differences
between sub-categories in FGFS, but they are less effective in handling the
high intra-class variance. In this paper, we propose a Target-Oriented
Alignment Network (TOAN) to investigate the fine-grained relation between the
target query image and support classes. The feature of each support image is
transformed to match the query ones in the embedding feature space, which
reduces the disparity explicitly within each category. Moreover, different from
existing FGFS approaches devise the high-order features over the global image
with less explicit consideration of discriminative parts, we generate
discriminative fine-grained features by integrating compositional concept
representations to global second-order pooling. Extensive experiments are
conducted on four fine-grained benchmarks to demonstrate the effectiveness of
TOAN compared with the state-of-the-art models.
|
[
{
"created": "Thu, 28 May 2020 07:48:44 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Mar 2021 05:40:46 GMT",
"version": "v2"
}
] |
2021-04-02
|
[
[
"Huang",
"Huaxi",
""
],
[
"Zhang",
"Junjie",
""
],
[
"Zhang",
"Jian",
""
],
[
"Wu",
"Qiang",
""
],
[
"Xu",
"Chang",
""
]
] |
The challenges of high intra-class variance yet low inter-class fluctuations in fine-grained visual categorization are more severe with few labeled samples, \textit{i.e.,} Fine-Grained categorization problems under the Few-Shot setting (FGFS). High-order features are usually developed to uncover subtle differences between sub-categories in FGFS, but they are less effective in handling the high intra-class variance. In this paper, we propose a Target-Oriented Alignment Network (TOAN) to investigate the fine-grained relation between the target query image and support classes. The feature of each support image is transformed to match the query ones in the embedding feature space, which reduces the disparity explicitly within each category. Moreover, different from existing FGFS approaches devise the high-order features over the global image with less explicit consideration of discriminative parts, we generate discriminative fine-grained features by integrating compositional concept representations to global second-order pooling. Extensive experiments are conducted on four fine-grained benchmarks to demonstrate the effectiveness of TOAN compared with the state-of-the-art models.
|
2206.13829
|
Davide Alessandro Coccomini
|
Davide Alessandro Coccomini, Roberto Caldelli, Fabrizio Falchi,
Claudio Gennaro, Giuseppe Amato
|
Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake
Image Detection
| null | null |
10.1145/3512732.3533582
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deepfake Generation Techniques are evolving at a rapid pace, making it
possible to create realistic manipulated images and videos and endangering the
serenity of modern society. The continual emergence of new and varied
techniques brings with it a further problem to be faced, namely the ability of
deepfake detection models to update themselves promptly in order to be able to
identify manipulations carried out using even the most recent methods. This is
an extremely complex problem to solve, as training a model requires large
amounts of data, which are difficult to obtain if the deepfake generation
method is too recent. Moreover, continuously retraining a network would be
unfeasible. In this paper, we ask ourselves if, among the various deep learning
techniques, there is one that is able to generalise the concept of deepfake to
such an extent that it does not remain tied to one or more specific deepfake
generation methods used in the training set. We compared a Vision Transformer
with an EfficientNetV2 on a cross-forgery context based on the ForgeryNet
dataset. From our experiments, It emerges that EfficientNetV2 has a greater
tendency to specialize often obtaining better results on training methods while
Vision Transformers exhibit a superior generalization ability that makes them
more competent even on images generated with new methodologies.
|
[
{
"created": "Tue, 28 Jun 2022 08:50:22 GMT",
"version": "v1"
}
] |
2022-06-29
|
[
[
"Coccomini",
"Davide Alessandro",
""
],
[
"Caldelli",
"Roberto",
""
],
[
"Falchi",
"Fabrizio",
""
],
[
"Gennaro",
"Claudio",
""
],
[
"Amato",
"Giuseppe",
""
]
] |
Deepfake Generation Techniques are evolving at a rapid pace, making it possible to create realistic manipulated images and videos and endangering the serenity of modern society. The continual emergence of new and varied techniques brings with it a further problem to be faced, namely the ability of deepfake detection models to update themselves promptly in order to be able to identify manipulations carried out using even the most recent methods. This is an extremely complex problem to solve, as training a model requires large amounts of data, which are difficult to obtain if the deepfake generation method is too recent. Moreover, continuously retraining a network would be unfeasible. In this paper, we ask ourselves if, among the various deep learning techniques, there is one that is able to generalise the concept of deepfake to such an extent that it does not remain tied to one or more specific deepfake generation methods used in the training set. We compared a Vision Transformer with an EfficientNetV2 on a cross-forgery context based on the ForgeryNet dataset. From our experiments, It emerges that EfficientNetV2 has a greater tendency to specialize often obtaining better results on training methods while Vision Transformers exhibit a superior generalization ability that makes them more competent even on images generated with new methodologies.
|
2312.01256
|
Chenglu Jin
|
Niloufar Sayadi, Phuong Ha Nguyen, Marten van Dijk, Chenglu Jin
|
Breaking XOR Arbiter PUFs without Reliability Information
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unreliable XOR Arbiter PUFs were broken by a machine learning attack, which
targets the underlying Arbiter PUFs individually. However, reliability
information from the PUF was required for this attack.
We show that, for the first time, a perfectly reliable XOR Arbiter PUF, where
no reliability information is accessible, can be efficiently attacked in the
same divide-and-conquer manner. Our key insight is that the responses of
correlated challenges also reveal their distance to the decision boundary. This
leads to a chosen challenge attack on XOR Arbiter PUFs. The effectiveness of
our attack is confirmed through PUF simulation and FPGA implementation.
|
[
{
"created": "Sun, 3 Dec 2023 01:39:09 GMT",
"version": "v1"
}
] |
2023-12-05
|
[
[
"Sayadi",
"Niloufar",
""
],
[
"Nguyen",
"Phuong Ha",
""
],
[
"van Dijk",
"Marten",
""
],
[
"Jin",
"Chenglu",
""
]
] |
Unreliable XOR Arbiter PUFs were broken by a machine learning attack, which targets the underlying Arbiter PUFs individually. However, reliability information from the PUF was required for this attack. We show that, for the first time, a perfectly reliable XOR Arbiter PUF, where no reliability information is accessible, can be efficiently attacked in the same divide-and-conquer manner. Our key insight is that the responses of correlated challenges also reveal their distance to the decision boundary. This leads to a chosen challenge attack on XOR Arbiter PUFs. The effectiveness of our attack is confirmed through PUF simulation and FPGA implementation.
|
2204.05575
|
Haibao Yu
|
Haibao Yu, Yizhen Luo, Mao Shu, Yiyi Huo, Zebang Yang, Yifeng Shi,
Zhenglong Guo, Hanyu Li, Xing Hu, Jirui Yuan, Zaiqing Nie
|
DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative
3D Object Detection
|
CVPR2022
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Autonomous driving faces great safety challenges for a lack of global
perspective and the limitation of long-range perception capabilities. It has
been widely agreed that vehicle-infrastructure cooperation is required to
achieve Level 5 autonomy. However, there is still NO dataset from real
scenarios available for computer vision researchers to work on
vehicle-infrastructure cooperation-related problems. To accelerate computer
vision research and innovation for Vehicle-Infrastructure Cooperative
Autonomous Driving (VICAD), we release DAIR-V2X Dataset, which is the first
large-scale, multi-modality, multi-view dataset from real scenarios for VICAD.
DAIR-V2X comprises 71254 LiDAR frames and 71254 Camera frames, and all frames
are captured from real scenes with 3D annotations. The Vehicle-Infrastructure
Cooperative 3D Object Detection problem (VIC3D) is introduced, formulating the
problem of collaboratively locating and identifying 3D objects using sensory
inputs from both vehicle and infrastructure. In addition to solving traditional
3D object detection problems, the solution of VIC3D needs to consider the
temporal asynchrony problem between vehicle and infrastructure sensors and the
data transmission cost between them. Furthermore, we propose Time Compensation
Late Fusion (TCLF), a late fusion framework for the VIC3D task as a benchmark
based on DAIR-V2X. Find data, code, and more up-to-date information at
https://thudair.baai.ac.cn/index and https://github.com/AIR-THU/DAIR-V2X.
|
[
{
"created": "Tue, 12 Apr 2022 07:13:33 GMT",
"version": "v1"
}
] |
2022-04-13
|
[
[
"Yu",
"Haibao",
""
],
[
"Luo",
"Yizhen",
""
],
[
"Shu",
"Mao",
""
],
[
"Huo",
"Yiyi",
""
],
[
"Yang",
"Zebang",
""
],
[
"Shi",
"Yifeng",
""
],
[
"Guo",
"Zhenglong",
""
],
[
"Li",
"Hanyu",
""
],
[
"Hu",
"Xing",
""
],
[
"Yuan",
"Jirui",
""
],
[
"Nie",
"Zaiqing",
""
]
] |
Autonomous driving faces great safety challenges for a lack of global perspective and the limitation of long-range perception capabilities. It has been widely agreed that vehicle-infrastructure cooperation is required to achieve Level 5 autonomy. However, there is still NO dataset from real scenarios available for computer vision researchers to work on vehicle-infrastructure cooperation-related problems. To accelerate computer vision research and innovation for Vehicle-Infrastructure Cooperative Autonomous Driving (VICAD), we release DAIR-V2X Dataset, which is the first large-scale, multi-modality, multi-view dataset from real scenarios for VICAD. DAIR-V2X comprises 71254 LiDAR frames and 71254 Camera frames, and all frames are captured from real scenes with 3D annotations. The Vehicle-Infrastructure Cooperative 3D Object Detection problem (VIC3D) is introduced, formulating the problem of collaboratively locating and identifying 3D objects using sensory inputs from both vehicle and infrastructure. In addition to solving traditional 3D object detection problems, the solution of VIC3D needs to consider the temporal asynchrony problem between vehicle and infrastructure sensors and the data transmission cost between them. Furthermore, we propose Time Compensation Late Fusion (TCLF), a late fusion framework for the VIC3D task as a benchmark based on DAIR-V2X. Find data, code, and more up-to-date information at https://thudair.baai.ac.cn/index and https://github.com/AIR-THU/DAIR-V2X.
|
2105.09932
|
Zhijian Liu
|
Zhijian Liu, Alexander Amini, Sibo Zhu, Sertac Karaman, Song Han,
Daniela Rus
|
Efficient and Robust LiDAR-Based End-to-End Navigation
|
ICRA 2021. The first two authors contributed equally to this work.
Project page: https://le2ed.mit.edu/
| null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep learning has been used to demonstrate end-to-end neural network learning
for autonomous vehicle control from raw sensory input. While LiDAR sensors
provide reliably accurate information, existing end-to-end driving solutions
are mainly based on cameras since processing 3D data requires a large memory
footprint and computation cost. On the other hand, increasing the robustness of
these systems is also critical; however, even estimating the model's
uncertainty is very challenging due to the cost of sampling-based methods. In
this paper, we present an efficient and robust LiDAR-based end-to-end
navigation framework. We first introduce Fast-LiDARNet that is based on sparse
convolution kernel optimization and hardware-aware model design. We then
propose Hybrid Evidential Fusion that directly estimates the uncertainty of the
prediction from only a single forward pass and then fuses the control
predictions intelligently. We evaluate our system on a full-scale vehicle and
demonstrate lane-stable as well as navigation capabilities. In the presence of
out-of-distribution events (e.g., sensor failures), our system significantly
improves robustness and reduces the number of takeovers in the real world.
|
[
{
"created": "Thu, 20 May 2021 17:52:37 GMT",
"version": "v1"
}
] |
2021-05-21
|
[
[
"Liu",
"Zhijian",
""
],
[
"Amini",
"Alexander",
""
],
[
"Zhu",
"Sibo",
""
],
[
"Karaman",
"Sertac",
""
],
[
"Han",
"Song",
""
],
[
"Rus",
"Daniela",
""
]
] |
Deep learning has been used to demonstrate end-to-end neural network learning for autonomous vehicle control from raw sensory input. While LiDAR sensors provide reliably accurate information, existing end-to-end driving solutions are mainly based on cameras since processing 3D data requires a large memory footprint and computation cost. On the other hand, increasing the robustness of these systems is also critical; however, even estimating the model's uncertainty is very challenging due to the cost of sampling-based methods. In this paper, we present an efficient and robust LiDAR-based end-to-end navigation framework. We first introduce Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass and then fuses the control predictions intelligently. We evaluate our system on a full-scale vehicle and demonstrate lane-stable as well as navigation capabilities. In the presence of out-of-distribution events (e.g., sensor failures), our system significantly improves robustness and reduces the number of takeovers in the real world.
|
1712.08409
|
Nils Bore
|
Nils Bore, Johan Ekekrantz, Patric Jensfelt, John Folkesson
|
Detection and Tracking of General Movable Objects in Large 3D Maps
|
Submitted for peer review
| null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper studies the problem of detection and tracking of general objects
with long-term dynamics, observed by a mobile robot moving in a large
environment. A key problem is that due to the environment scale, it can only
observe a subset of the objects at any given time. Since some time passes
between observations of objects in different places, the objects might be moved
when the robot is not there. We propose a model for this movement in which the
objects typically only move locally, but with some small probability they jump
longer distances, through what we call global motion. For filtering, we
decompose the posterior over local and global movements into two linked
processes. The posterior over the global movements and measurement associations
is sampled, while we track the local movement analytically using Kalman
filters. This novel filter is evaluated on point cloud data gathered
autonomously by a mobile robot over an extended period of time. We show that
tracking jumping objects is feasible, and that the proposed probabilistic
treatment outperforms previous methods when applied to real world data. The key
to efficient probabilistic tracking in this scenario is focused sampling of the
object posteriors.
|
[
{
"created": "Fri, 22 Dec 2017 11:53:52 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Jan 2018 09:31:47 GMT",
"version": "v2"
}
] |
2018-01-31
|
[
[
"Bore",
"Nils",
""
],
[
"Ekekrantz",
"Johan",
""
],
[
"Jensfelt",
"Patric",
""
],
[
"Folkesson",
"John",
""
]
] |
This paper studies the problem of detection and tracking of general objects with long-term dynamics, observed by a mobile robot moving in a large environment. A key problem is that due to the environment scale, it can only observe a subset of the objects at any given time. Since some time passes between observations of objects in different places, the objects might be moved when the robot is not there. We propose a model for this movement in which the objects typically only move locally, but with some small probability they jump longer distances, through what we call global motion. For filtering, we decompose the posterior over local and global movements into two linked processes. The posterior over the global movements and measurement associations is sampled, while we track the local movement analytically using Kalman filters. This novel filter is evaluated on point cloud data gathered autonomously by a mobile robot over an extended period of time. We show that tracking jumping objects is feasible, and that the proposed probabilistic treatment outperforms previous methods when applied to real world data. The key to efficient probabilistic tracking in this scenario is focused sampling of the object posteriors.
|
2305.13168
|
Ningyu Zhang
|
Yuqi Zhu, Xiaohan Wang, Jing Chen, Shuofei Qiao, Yixin Ou, Yunzhi Yao,
Shumin Deng, Huajun Chen, Ningyu Zhang
|
LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities
and Future Opportunities
|
Work in progress
| null | null | null |
cs.CL cs.AI cs.DB cs.IR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents an exhaustive quantitative and qualitative evaluation of
Large Language Models (LLMs) for Knowledge Graph (KG) construction and
reasoning. We engage in experiments across eight diverse datasets, focusing on
four representative tasks encompassing entity and relation extraction, event
extraction, link prediction, and question-answering, thereby thoroughly
exploring LLMs' performance in the domain of construction and inference.
Empirically, our findings suggest that LLMs, represented by GPT-4, are more
suited as inference assistants rather than few-shot information extractors.
Specifically, while GPT-4 exhibits good performance in tasks related to KG
construction, it excels further in reasoning tasks, surpassing fine-tuned
models in certain cases. Moreover, our investigation extends to the potential
generalization ability of LLMs for information extraction, leading to the
proposition of a Virtual Knowledge Extraction task and the development of the
corresponding VINE dataset. Based on these empirical findings, we further
propose AutoKG, a multi-agent-based approach employing LLMs and external
sources for KG construction and reasoning. We anticipate that this research can
provide invaluable insights for future undertakings in the field of knowledge
graphs. The code and datasets are in https://github.com/zjunlp/AutoKG.
|
[
{
"created": "Mon, 22 May 2023 15:56:44 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Feb 2024 10:15:25 GMT",
"version": "v2"
}
] |
2024-02-23
|
[
[
"Zhu",
"Yuqi",
""
],
[
"Wang",
"Xiaohan",
""
],
[
"Chen",
"Jing",
""
],
[
"Qiao",
"Shuofei",
""
],
[
"Ou",
"Yixin",
""
],
[
"Yao",
"Yunzhi",
""
],
[
"Deng",
"Shumin",
""
],
[
"Chen",
"Huajun",
""
],
[
"Zhang",
"Ningyu",
""
]
] |
This paper presents an exhaustive quantitative and qualitative evaluation of Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We engage in experiments across eight diverse datasets, focusing on four representative tasks encompassing entity and relation extraction, event extraction, link prediction, and question-answering, thereby thoroughly exploring LLMs' performance in the domain of construction and inference. Empirically, our findings suggest that LLMs, represented by GPT-4, are more suited as inference assistants rather than few-shot information extractors. Specifically, while GPT-4 exhibits good performance in tasks related to KG construction, it excels further in reasoning tasks, surpassing fine-tuned models in certain cases. Moreover, our investigation extends to the potential generalization ability of LLMs for information extraction, leading to the proposition of a Virtual Knowledge Extraction task and the development of the corresponding VINE dataset. Based on these empirical findings, we further propose AutoKG, a multi-agent-based approach employing LLMs and external sources for KG construction and reasoning. We anticipate that this research can provide invaluable insights for future undertakings in the field of knowledge graphs. The code and datasets are in https://github.com/zjunlp/AutoKG.
|
1705.04678
|
Nikhil Galagali
|
Nikhil Galagali and Youssef M. Marzouk
|
Exploiting network topology for large-scale inference of nonlinear
reaction models
| null | null | null | null |
cs.CE q-bio.QM stat.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The development of chemical reaction models aids understanding and prediction
in areas ranging from biology to electrochemistry and combustion. A systematic
approach to building reaction network models uses observational data not only
to estimate unknown parameters, but also to learn model structure. Bayesian
inference provides a natural approach to this data-driven construction of
models. Yet traditional Bayesian model inference methodologies that numerically
evaluate the evidence for each model are often infeasible for nonlinear
reaction network inference, as the number of plausible models can be
combinatorially large. Alternative approaches based on model-space sampling can
enable large-scale network inference, but their realization presents many
challenges. In this paper, we present new computational methods that make
large-scale nonlinear network inference tractable. First, we exploit the
topology of networks describing potential interactions among chemical species
to design improved "between-model" proposals for reversible-jump Markov chain
Monte Carlo. Second, we introduce a sensitivity-based determination of move
types which, when combined with network-aware proposals, yields significant
additional gains in sampling performance. These algorithms are demonstrated on
inference problems drawn from systems biology, with nonlinear differential
equation models of species interactions.
|
[
{
"created": "Fri, 12 May 2017 17:55:44 GMT",
"version": "v1"
},
{
"created": "Thu, 19 Jul 2018 18:35:07 GMT",
"version": "v2"
},
{
"created": "Sun, 14 Oct 2018 16:11:46 GMT",
"version": "v3"
},
{
"created": "Tue, 16 Oct 2018 01:26:55 GMT",
"version": "v4"
},
{
"created": "Sat, 19 Jan 2019 03:43:48 GMT",
"version": "v5"
}
] |
2019-01-23
|
[
[
"Galagali",
"Nikhil",
""
],
[
"Marzouk",
"Youssef M.",
""
]
] |
The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters, but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved "between-model" proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.
|
1806.09171
|
Vitaly Petrov
|
Vitaly Petrov, Sergey Andreev, Mario Gerla, Yevgeni Koucheryavy
|
Breaking the Limits in Urban Video Monitoring: Massive Crowd Sourced
Surveillance over Vehicles
|
8 pages, 5 figures, accepted to IEEE Wireless Communications, 2019
| null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Contemporary urban environments are in prompt need of means for intelligent
decision-making, where a crucial role belongs to smart video surveillance
systems. While existing deployments of stationary monitoring cameras already
deliver notable societal benefits, the proposed concept of massive video
surveillance over connected vehicles that we contribute in this paper may
further augment these important capabilities. We therefore introduce the
envisioned system concept, discuss its implementation, outline the high-level
architecture, and identify major data flows, while also offering insights into
the corresponding design and deployment aspects. Our conducted case study
confirms the potential of the described crowd sourced vehicular system to
effectively complement and eventually surpass even the best of today's static
video surveillance setups. We expect that our proposal will become of value and
integrate seamlessly into the future Internet-of-Things landscape, thus
enabling a plethora of advanced urban applications.
|
[
{
"created": "Sun, 24 Jun 2018 16:28:22 GMT",
"version": "v1"
}
] |
2018-06-26
|
[
[
"Petrov",
"Vitaly",
""
],
[
"Andreev",
"Sergey",
""
],
[
"Gerla",
"Mario",
""
],
[
"Koucheryavy",
"Yevgeni",
""
]
] |
Contemporary urban environments are in prompt need of means for intelligent decision-making, where a crucial role belongs to smart video surveillance systems. While existing deployments of stationary monitoring cameras already deliver notable societal benefits, the proposed concept of massive video surveillance over connected vehicles that we contribute in this paper may further augment these important capabilities. We therefore introduce the envisioned system concept, discuss its implementation, outline the high-level architecture, and identify major data flows, while also offering insights into the corresponding design and deployment aspects. Our conducted case study confirms the potential of the described crowd sourced vehicular system to effectively complement and eventually surpass even the best of today's static video surveillance setups. We expect that our proposal will become of value and integrate seamlessly into the future Internet-of-Things landscape, thus enabling a plethora of advanced urban applications.
|
2105.14478
|
Yian Li
|
Yian Li, Hai Zhao
|
Pre-training Universal Language Representation
|
Accepted by ACL-IJCNLP 2021 main conference
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Despite the well-developed cut-edge representation learning for language,
most language representation models usually focus on specific levels of
linguistic units. This work introduces universal language representation
learning, i.e., embeddings of different levels of linguistic units or text with
quite diverse lengths in a uniform vector space. We propose the training
objective MiSAD that utilizes meaningful n-grams extracted from large unlabeled
corpus by a simple but effective algorithm for pre-trained language models.
Then we empirically verify that well designed pre-training scheme may
effectively yield universal language representation, which will bring great
convenience when handling multiple layers of linguistic objects in a unified
way. Especially, our model achieves the highest accuracy on analogy tasks in
different language levels and significantly improves the performance on
downstream tasks in the GLUE benchmark and a question answering dataset.
|
[
{
"created": "Sun, 30 May 2021 09:29:01 GMT",
"version": "v1"
}
] |
2021-06-01
|
[
[
"Li",
"Yian",
""
],
[
"Zhao",
"Hai",
""
]
] |
Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific levels of linguistic units. This work introduces universal language representation learning, i.e., embeddings of different levels of linguistic units or text with quite diverse lengths in a uniform vector space. We propose the training objective MiSAD that utilizes meaningful n-grams extracted from large unlabeled corpus by a simple but effective algorithm for pre-trained language models. Then we empirically verify that well designed pre-training scheme may effectively yield universal language representation, which will bring great convenience when handling multiple layers of linguistic objects in a unified way. Especially, our model achieves the highest accuracy on analogy tasks in different language levels and significantly improves the performance on downstream tasks in the GLUE benchmark and a question answering dataset.
|
2305.06900
|
Huzaifa Mustafa Unjhawala
|
Huzaifa Mustafa Unjhawala, Ruochun Zhang, Wei Hu, Jinlong Wu, Radu
Serban, Dan Negrut
|
Using a Bayesian-Inference Approach to Calibrating Models for Simulation
in Robotics
|
19 pages, 42 figures
|
061004-18 / Vol. 18, JUNE 2023
|
10.1115/1.4062199
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In robotics, simulation has the potential to reduce design time and costs,
and lead to a more robust engineered solution and a safer development process.
However, the use of simulators is predicated on the availability of good
models. This contribution is concerned with improving the quality of these
models via calibration, which is cast herein in a Bayesian framework. First, we
discuss the Bayesian machinery involved in model calibration. Then, we
demonstrate it in one example: calibration of a vehicle dynamics model that has
low degree of freedom count and can be used for state estimation, model
predictive control, or path planning. A high fidelity simulator is used to
emulate the ``experiments'' and generate the data for the calibration. The
merit of this work is not tied to a new Bayesian methodology for calibration,
but to the demonstration of how the Bayesian machinery can establish
connections among models in computational dynamics, even when the data in use
is noisy. The software used to generate the results reported herein is
available in a public repository for unfettered use and distribution.
|
[
{
"created": "Thu, 11 May 2023 15:41:59 GMT",
"version": "v1"
}
] |
2023-05-12
|
[
[
"Unjhawala",
"Huzaifa Mustafa",
""
],
[
"Zhang",
"Ruochun",
""
],
[
"Hu",
"Wei",
""
],
[
"Wu",
"Jinlong",
""
],
[
"Serban",
"Radu",
""
],
[
"Negrut",
"Dan",
""
]
] |
In robotics, simulation has the potential to reduce design time and costs, and lead to a more robust engineered solution and a safer development process. However, the use of simulators is predicated on the availability of good models. This contribution is concerned with improving the quality of these models via calibration, which is cast herein in a Bayesian framework. First, we discuss the Bayesian machinery involved in model calibration. Then, we demonstrate it in one example: calibration of a vehicle dynamics model that has low degree of freedom count and can be used for state estimation, model predictive control, or path planning. A high fidelity simulator is used to emulate the ``experiments'' and generate the data for the calibration. The merit of this work is not tied to a new Bayesian methodology for calibration, but to the demonstration of how the Bayesian machinery can establish connections among models in computational dynamics, even when the data in use is noisy. The software used to generate the results reported herein is available in a public repository for unfettered use and distribution.
|
2111.04382
|
Raghavendra Sridharamurthy
|
Raghavendra Sridharamurthy and Vijay Natarajan
|
Comparative Analysis of Merge Trees using Local Tree Edit Distance
| null |
IEEE Transactions on Visualization and Computer Graphics, 29 (2),
2023, 1518--1530
|
10.1109/TVCG.2021.3122176
| null |
cs.GR cs.CG
|
http://creativecommons.org/licenses/by/4.0/
|
Comparative analysis of scalar fields is an important problem with various
applications including feature-directed visualization and feature tracking in
time-varying data. Comparing topological structures that are abstract and
succinct representations of the scalar fields lead to faster and meaningful
comparison. While there are many distance or similarity measures to compare
topological structures in a global context, there are no known measures for
comparing topological structures locally. While the global measures have many
applications, they do not directly lend themselves to fine-grained analysis
across multiple scales. We define a local variant of the tree edit distance and
apply it towards local comparative analysis of merge trees with support for
finer analysis. We also present experimental results on time-varying scalar
fields, 3D cryo-electron microscopy data, and other synthetic data sets to show
the utility of this approach in applications like symmetry detection and
feature tracking.
|
[
{
"created": "Mon, 8 Nov 2021 11:02:36 GMT",
"version": "v1"
}
] |
2024-06-06
|
[
[
"Sridharamurthy",
"Raghavendra",
""
],
[
"Natarajan",
"Vijay",
""
]
] |
Comparative analysis of scalar fields is an important problem with various applications including feature-directed visualization and feature tracking in time-varying data. Comparing topological structures that are abstract and succinct representations of the scalar fields lead to faster and meaningful comparison. While there are many distance or similarity measures to compare topological structures in a global context, there are no known measures for comparing topological structures locally. While the global measures have many applications, they do not directly lend themselves to fine-grained analysis across multiple scales. We define a local variant of the tree edit distance and apply it towards local comparative analysis of merge trees with support for finer analysis. We also present experimental results on time-varying scalar fields, 3D cryo-electron microscopy data, and other synthetic data sets to show the utility of this approach in applications like symmetry detection and feature tracking.
|
1704.05617
|
Chun-Nan Hsu
|
Sanjeev Shenoy, Tsung-Ting Kuo, Rodney Gabriel, Julian McAuley and
Chun-Nan Hsu
|
Deduplication in a massive clinical note dataset
|
Extended from the Master project report of Sanjeev Shenoy, Department
of Computer Science and Engineering, University of California, San Diego.
June 2016
| null | null | null |
cs.DB cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Duplication, whether exact or partial, is a common issue in many datasets. In
clinical notes data, duplication (and near duplication) can arise for many
reasons, such as the pervasive use of templates, copy-pasting, or notes being
generated by automated procedures. A key challenge in removing such near
duplicates is the size of such datasets; our own dataset consists of more than
10 million notes. To detect and correct such duplicates requires algorithms
that both accurate and highly scalable. We describe a solution based on
Minhashing with Locality Sensitive Hashing. In this paper, we present the
theory behind this method and present a database-inspired approach to make the
method scalable. We also present a clustering technique using disjoint sets to
produce dense clusters, which speeds up our algorithm.
|
[
{
"created": "Wed, 19 Apr 2017 05:33:21 GMT",
"version": "v1"
}
] |
2017-04-20
|
[
[
"Shenoy",
"Sanjeev",
""
],
[
"Kuo",
"Tsung-Ting",
""
],
[
"Gabriel",
"Rodney",
""
],
[
"McAuley",
"Julian",
""
],
[
"Hsu",
"Chun-Nan",
""
]
] |
Duplication, whether exact or partial, is a common issue in many datasets. In clinical notes data, duplication (and near duplication) can arise for many reasons, such as the pervasive use of templates, copy-pasting, or notes being generated by automated procedures. A key challenge in removing such near duplicates is the size of such datasets; our own dataset consists of more than 10 million notes. To detect and correct such duplicates requires algorithms that both accurate and highly scalable. We describe a solution based on Minhashing with Locality Sensitive Hashing. In this paper, we present the theory behind this method and present a database-inspired approach to make the method scalable. We also present a clustering technique using disjoint sets to produce dense clusters, which speeds up our algorithm.
|
2404.03943
|
Chen Wang
|
Chen Wang, Haoxiang Luo, Kun Zhang, Hua Chen, Jia Pan, Wei Zhang
|
POMDP-Guided Active Force-Based Search for Robotic Insertion
| null | null |
10.1109/IROS55552.2023.10342421
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In robotic insertion tasks where the uncertainty exceeds the allowable
tolerance, a good search strategy is essential for successful insertion and
significantly influences efficiency. The commonly used blind search method is
time-consuming and does not exploit the rich contact information. In this
paper, we propose a novel search strategy that actively utilizes the
information contained in the contact configuration and shows high efficiency.
In particular, we formulate this problem as a Partially Observable Markov
Decision Process (POMDP) with carefully designed primitives based on an
in-depth analysis of the contact configuration's static stability. From the
formulated POMDP, we can derive a novel search strategy. Thanks to its
simplicity, this search strategy can be incorporated into a
Finite-State-Machine (FSM) controller. The behaviors of the FSM controller are
realized through a low-level Cartesian Impedance Controller. Our method is
based purely on the robot's proprioceptive sensing and does not need visual or
tactile sensors. To evaluate the effectiveness of our proposed strategy and
control framework, we conduct extensive comparison experiments in simulation,
where we compare our method with the baseline approach. The results demonstrate
that our proposed method achieves a higher success rate with a shorter search
time and search trajectory length compared to the baseline method.
Additionally, we show that our method is robust to various initial displacement
errors.
|
[
{
"created": "Fri, 5 Apr 2024 08:17:03 GMT",
"version": "v1"
}
] |
2024-04-08
|
[
[
"Wang",
"Chen",
""
],
[
"Luo",
"Haoxiang",
""
],
[
"Zhang",
"Kun",
""
],
[
"Chen",
"Hua",
""
],
[
"Pan",
"Jia",
""
],
[
"Zhang",
"Wei",
""
]
] |
In robotic insertion tasks where the uncertainty exceeds the allowable tolerance, a good search strategy is essential for successful insertion and significantly influences efficiency. The commonly used blind search method is time-consuming and does not exploit the rich contact information. In this paper, we propose a novel search strategy that actively utilizes the information contained in the contact configuration and shows high efficiency. In particular, we formulate this problem as a Partially Observable Markov Decision Process (POMDP) with carefully designed primitives based on an in-depth analysis of the contact configuration's static stability. From the formulated POMDP, we can derive a novel search strategy. Thanks to its simplicity, this search strategy can be incorporated into a Finite-State-Machine (FSM) controller. The behaviors of the FSM controller are realized through a low-level Cartesian Impedance Controller. Our method is based purely on the robot's proprioceptive sensing and does not need visual or tactile sensors. To evaluate the effectiveness of our proposed strategy and control framework, we conduct extensive comparison experiments in simulation, where we compare our method with the baseline approach. The results demonstrate that our proposed method achieves a higher success rate with a shorter search time and search trajectory length compared to the baseline method. Additionally, we show that our method is robust to various initial displacement errors.
|
2110.00480
|
Yifan Song
|
Kevin K\"oser, Yifan Song, Lasse Petersen, Emanuel Wenzlaff, Felix
Woelk
|
Robustly Removing Deep Sea Lighting Effects for Visual Mapping of
Abyssal Plains
| null | null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The majority of Earth's surface lies deep in the oceans, where no surface
light reaches. Robots diving down to great depths must bring light sources that
create moving illumination patterns in the darkness, such that the same 3D
point appears with different color in each image. On top, scattering and
attenuation of light in the water makes images appear foggy and typically
blueish, the degradation depending on each pixel's distance to its observed
seafloor patch, on the local composition of the water and the relative poses
and cones of the light sources. Consequently, visual mapping, including image
matching and surface albedo estimation, severely suffers from the effects that
co-moving light sources produce, and larger mosaic maps from photos are often
dominated by lighting effects that obscure the actual seafloor structure. In
this contribution a practical approach to estimating and compensating these
lighting effects on predominantly homogeneous, flat seafloor regions, as can be
found in the Abyssal plains of our oceans, is presented. The method is
essentially parameter-free and intended as a preprocessing step to facilitate
visual mapping, but already produces convincing lighting artefact compensation
up to a global white balance factor. It does not require to be trained
beforehand on huge sets of annotated images, which are not available for the
deep sea. Rather, we motivate our work by physical models of light propagation,
perform robust statistics-based estimates of additive and multiplicative
nuisances that avoid explicit parameters for light, camera, water or scene,
discuss the breakdown point of the algorithms and show results on imagery
captured by robots in several kilometer water depth.
|
[
{
"created": "Fri, 1 Oct 2021 15:28:07 GMT",
"version": "v1"
}
] |
2021-10-04
|
[
[
"Köser",
"Kevin",
""
],
[
"Song",
"Yifan",
""
],
[
"Petersen",
"Lasse",
""
],
[
"Wenzlaff",
"Emanuel",
""
],
[
"Woelk",
"Felix",
""
]
] |
The majority of Earth's surface lies deep in the oceans, where no surface light reaches. Robots diving down to great depths must bring light sources that create moving illumination patterns in the darkness, such that the same 3D point appears with different color in each image. On top, scattering and attenuation of light in the water makes images appear foggy and typically blueish, the degradation depending on each pixel's distance to its observed seafloor patch, on the local composition of the water and the relative poses and cones of the light sources. Consequently, visual mapping, including image matching and surface albedo estimation, severely suffers from the effects that co-moving light sources produce, and larger mosaic maps from photos are often dominated by lighting effects that obscure the actual seafloor structure. In this contribution a practical approach to estimating and compensating these lighting effects on predominantly homogeneous, flat seafloor regions, as can be found in the Abyssal plains of our oceans, is presented. The method is essentially parameter-free and intended as a preprocessing step to facilitate visual mapping, but already produces convincing lighting artefact compensation up to a global white balance factor. It does not require to be trained beforehand on huge sets of annotated images, which are not available for the deep sea. Rather, we motivate our work by physical models of light propagation, perform robust statistics-based estimates of additive and multiplicative nuisances that avoid explicit parameters for light, camera, water or scene, discuss the breakdown point of the algorithms and show results on imagery captured by robots in several kilometer water depth.
|
2406.20092
|
Jie-Neng Chen
|
Jieneng Chen, Luoxin Ye, Ju He, Zhao-Yang Wang, Daniel Khashabi, Alan
Yuille
|
LLaVolta: Efficient Multi-modal Models via Stage-wise Visual Context
Compression
|
Code is available at https://github.com/Beckschen/LLaVolta
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While significant advancements have been made in compressed representations
for text embeddings in large language models (LLMs), the compression of visual
tokens in large multi-modal models (LMMs) has remained a largely overlooked
area. In this work, we present the study on the analysis of redundancy
concerning visual tokens and efficient training within these models. Our
initial experiments show that eliminating up to 70% of visual tokens at the
testing stage by simply average pooling only leads to a minimal 3% reduction in
visual question answering accuracy on the GQA benchmark, indicating significant
redundancy in visual context. Addressing this, we introduce Visual Context
Compressor, which reduces the number of visual tokens during training to
enhance training efficiency without sacrificing performance. To minimize
information loss caused by the compression on visual tokens while maintaining
training efficiency, we develop LLaVolta as a lite training scheme. LLaVolta
incorporates stage-wise visual context compression to progressively compress
the visual tokens from heavily to lightly, and finally no compression at the
end of training, yielding no loss of information when testing. Extensive
experiments demonstrate that our approach enhances the performance of MLLMs in
both image-language and video-language understanding, while also significantly
cutting training costs. Code is available at
https://github.com/Beckschen/LLaVolta
|
[
{
"created": "Fri, 28 Jun 2024 17:57:14 GMT",
"version": "v1"
}
] |
2024-07-01
|
[
[
"Chen",
"Jieneng",
""
],
[
"Ye",
"Luoxin",
""
],
[
"He",
"Ju",
""
],
[
"Wang",
"Zhao-Yang",
""
],
[
"Khashabi",
"Daniel",
""
],
[
"Yuille",
"Alan",
""
]
] |
While significant advancements have been made in compressed representations for text embeddings in large language models (LLMs), the compression of visual tokens in large multi-modal models (LMMs) has remained a largely overlooked area. In this work, we present the study on the analysis of redundancy concerning visual tokens and efficient training within these models. Our initial experiments show that eliminating up to 70% of visual tokens at the testing stage by simply average pooling only leads to a minimal 3% reduction in visual question answering accuracy on the GQA benchmark, indicating significant redundancy in visual context. Addressing this, we introduce Visual Context Compressor, which reduces the number of visual tokens during training to enhance training efficiency without sacrificing performance. To minimize information loss caused by the compression on visual tokens while maintaining training efficiency, we develop LLaVolta as a lite training scheme. LLaVolta incorporates stage-wise visual context compression to progressively compress the visual tokens from heavily to lightly, and finally no compression at the end of training, yielding no loss of information when testing. Extensive experiments demonstrate that our approach enhances the performance of MLLMs in both image-language and video-language understanding, while also significantly cutting training costs. Code is available at https://github.com/Beckschen/LLaVolta
|
2101.11939
|
Wenguan Wang
|
Wenguan Wang, Tianfei Zhou, Fisher Yu, Jifeng Dai, Ender Konukoglu,
Luc Van Gool
|
Exploring Cross-Image Pixel Contrast for Semantic Segmentation
|
Our code will be available at
https://github.com/tfzhou/ContrastiveSeg
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current semantic segmentation methods focus only on mining "local" context,
i.e., dependencies between pixels within individual images, by
context-aggregation modules (e.g., dilated convolution, neural attention) or
structure-aware optimization criteria (e.g., IoU-like loss). However, they
ignore "global" context of the training data, i.e., rich semantic relations
between pixels across different images. Inspired by the recent advance in
unsupervised contrastive representation learning, we propose a pixel-wise
contrastive framework for semantic segmentation in the fully supervised
setting. The core idea is to enforce pixel embeddings belonging to a same
semantic class to be more similar than embeddings from different classes. It
raises a pixel-wise metric learning paradigm for semantic segmentation, by
explicitly exploring the structures of labeled pixels, which were rarely
explored before. Our method can be effortlessly incorporated into existing
segmentation frameworks without extra overhead during testing. We
experimentally show that, with famous segmentation models (i.e., DeepLabV3,
HRNet, OCR) and backbones (i.e., ResNet, HR-Net), our method brings consistent
performance improvements across diverse datasets (i.e., Cityscapes,
PASCAL-Context, COCO-Stuff, CamVid). We expect this work will encourage our
community to rethink the current de facto training paradigm in fully supervised
semantic segmentation.
|
[
{
"created": "Thu, 28 Jan 2021 11:35:32 GMT",
"version": "v1"
},
{
"created": "Sat, 30 Jan 2021 23:41:45 GMT",
"version": "v2"
},
{
"created": "Thu, 11 Feb 2021 20:35:21 GMT",
"version": "v3"
},
{
"created": "Tue, 30 Mar 2021 15:16:23 GMT",
"version": "v4"
}
] |
2021-03-31
|
[
[
"Wang",
"Wenguan",
""
],
[
"Zhou",
"Tianfei",
""
],
[
"Yu",
"Fisher",
""
],
[
"Dai",
"Jifeng",
""
],
[
"Konukoglu",
"Ender",
""
],
[
"Van Gool",
"Luc",
""
]
] |
Current semantic segmentation methods focus only on mining "local" context, i.e., dependencies between pixels within individual images, by context-aggregation modules (e.g., dilated convolution, neural attention) or structure-aware optimization criteria (e.g., IoU-like loss). However, they ignore "global" context of the training data, i.e., rich semantic relations between pixels across different images. Inspired by the recent advance in unsupervised contrastive representation learning, we propose a pixel-wise contrastive framework for semantic segmentation in the fully supervised setting. The core idea is to enforce pixel embeddings belonging to a same semantic class to be more similar than embeddings from different classes. It raises a pixel-wise metric learning paradigm for semantic segmentation, by explicitly exploring the structures of labeled pixels, which were rarely explored before. Our method can be effortlessly incorporated into existing segmentation frameworks without extra overhead during testing. We experimentally show that, with famous segmentation models (i.e., DeepLabV3, HRNet, OCR) and backbones (i.e., ResNet, HR-Net), our method brings consistent performance improvements across diverse datasets (i.e., Cityscapes, PASCAL-Context, COCO-Stuff, CamVid). We expect this work will encourage our community to rethink the current de facto training paradigm in fully supervised semantic segmentation.
|
1205.5199
|
Ashwin Ganesan
|
Ashwin Ganesan
|
Automorphism groups of Cayley graphs generated by connected
transposition sets
| null |
Discrete Mathematics, vol. 313, no. 21, pp. 2482-2485, November
2013
|
10.1016/j.disc.2013.07.013
| null |
cs.DM math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Let $S$ be a set of transpositions that generates the symmetric group $S_n$,
where $n \ge 3$. The transposition graph $T(S)$ is defined to be the graph with
vertex set $\{1,\ldots,n\}$ and with vertices $i$ and $j$ being adjacent in
$T(S)$ whenever $(i,j) \in S$. We prove that if the girth of the transposition
graph $T(S)$ is at least 5, then the automorphism group of the Cayley graph
$\Cay(S_n,S)$ is the semidirect product $R(S_n) \rtimes \Aut(S_n,S)$, where
$\Aut(S_n,S)$ is the set of automorphisms of $S_n$ that fixes $S$. This
strengthens a result of Feng on transposition graphs that are trees. We also
prove that if the transposition graph $T(S)$ is a 4-cycle, then the set of
automorphisms of the Cayley graph $\Cay(S_4,S)$ that fixes a vertex and each of
its neighbors is isomorphic to the Klein 4-group and hence is nontrivial. We
thus identify the existence of 4-cycles in the transposition graph as being an
important factor in causing a potentially larger automorphism group of the
Cayley graph.
|
[
{
"created": "Wed, 23 May 2012 15:20:17 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Sep 2012 18:24:08 GMT",
"version": "v2"
},
{
"created": "Sat, 1 Dec 2012 19:55:22 GMT",
"version": "v3"
},
{
"created": "Sun, 23 Jun 2013 12:48:04 GMT",
"version": "v4"
}
] |
2015-12-11
|
[
[
"Ganesan",
"Ashwin",
""
]
] |
Let $S$ be a set of transpositions that generates the symmetric group $S_n$, where $n \ge 3$. The transposition graph $T(S)$ is defined to be the graph with vertex set $\{1,\ldots,n\}$ and with vertices $i$ and $j$ being adjacent in $T(S)$ whenever $(i,j) \in S$. We prove that if the girth of the transposition graph $T(S)$ is at least 5, then the automorphism group of the Cayley graph $\Cay(S_n,S)$ is the semidirect product $R(S_n) \rtimes \Aut(S_n,S)$, where $\Aut(S_n,S)$ is the set of automorphisms of $S_n$ that fixes $S$. This strengthens a result of Feng on transposition graphs that are trees. We also prove that if the transposition graph $T(S)$ is a 4-cycle, then the set of automorphisms of the Cayley graph $\Cay(S_4,S)$ that fixes a vertex and each of its neighbors is isomorphic to the Klein 4-group and hence is nontrivial. We thus identify the existence of 4-cycles in the transposition graph as being an important factor in causing a potentially larger automorphism group of the Cayley graph.
|
1905.06109
|
Xiaosen Wang
|
Kun He and Wu Wang and Xiaosen Wang and John E. Hopcroft
|
A New Anchor Word Selection Method for the Separable Topic Discovery
|
18 pages, 4 figures
| null | null | null |
cs.IR cs.CL cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Separable Non-negative Matrix Factorization (SNMF) is an important method for
topic modeling, where "separable" assumes every topic contains at least one
anchor word, defined as a word that has non-zero probability only on that
topic. SNMF focuses on the word co-occurrence patterns to reveal topics by two
steps: anchor word selection and topic recovery. The quality of the anchor
words strongly influences the quality of the extracted topics. Existing anchor
word selection algorithm is to greedily find an approximate convex hull in a
high-dimensional word co-occurrence space. In this work, we propose a new
method for the anchor word selection by associating the word co-occurrence
probability with the words similarity and assuming that the most different
words on semantic are potential candidates for the anchor words. Therefore, if
the similarity of a word-pair is very low, then the two words are very likely
to be the anchor words. According to the statistical information of text
corpora, we can get the similarity of all word-pairs. We build the word
similarity graph where the nodes correspond to words and weights on edges stand
for the word-pair similarity. Following this way, we design a greedy method to
find a minimum edge-weight anchor clique of a given size in the graph for the
anchor word selection. Extensive experiments on real-world corpus demonstrate
the effectiveness of the proposed anchor word selection method that outperforms
the common convex hull-based methods on the revealed topic quality. Meanwhile,
our method is much faster than typical SNMF based method.
|
[
{
"created": "Fri, 10 May 2019 12:16:10 GMT",
"version": "v1"
}
] |
2019-05-16
|
[
[
"He",
"Kun",
""
],
[
"Wang",
"Wu",
""
],
[
"Wang",
"Xiaosen",
""
],
[
"Hopcroft",
"John E.",
""
]
] |
Separable Non-negative Matrix Factorization (SNMF) is an important method for topic modeling, where "separable" assumes every topic contains at least one anchor word, defined as a word that has non-zero probability only on that topic. SNMF focuses on the word co-occurrence patterns to reveal topics by two steps: anchor word selection and topic recovery. The quality of the anchor words strongly influences the quality of the extracted topics. Existing anchor word selection algorithm is to greedily find an approximate convex hull in a high-dimensional word co-occurrence space. In this work, we propose a new method for the anchor word selection by associating the word co-occurrence probability with the words similarity and assuming that the most different words on semantic are potential candidates for the anchor words. Therefore, if the similarity of a word-pair is very low, then the two words are very likely to be the anchor words. According to the statistical information of text corpora, we can get the similarity of all word-pairs. We build the word similarity graph where the nodes correspond to words and weights on edges stand for the word-pair similarity. Following this way, we design a greedy method to find a minimum edge-weight anchor clique of a given size in the graph for the anchor word selection. Extensive experiments on real-world corpus demonstrate the effectiveness of the proposed anchor word selection method that outperforms the common convex hull-based methods on the revealed topic quality. Meanwhile, our method is much faster than typical SNMF based method.
|
1201.2531
|
Gergely Acs
|
Gergely Acs and Claude Castelluccia
|
DREAM: DiffeRentially privatE smArt Metering
|
Shorter version appeared on Information Hiding Conference 2011
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/publicdomain/
|
This paper presents a new privacy-preserving smart metering system. Our
scheme is private under the differential privacy model and therefore provides
strong and provable guarantees. With our scheme, an (electricity) supplier can
periodically collect data from smart meters and derive aggregated statistics
while learning only limited information about the activities of individual
households. For example, a supplier cannot tell from a user's trace when he
watched TV or turned on heating. Our scheme is simple, efficient and practical.
Processing cost is very limited: smart meters only have to add noise to their
data and encrypt the results with an efficient stream cipher.
|
[
{
"created": "Thu, 12 Jan 2012 11:15:02 GMT",
"version": "v1"
}
] |
2012-01-13
|
[
[
"Acs",
"Gergely",
""
],
[
"Castelluccia",
"Claude",
""
]
] |
This paper presents a new privacy-preserving smart metering system. Our scheme is private under the differential privacy model and therefore provides strong and provable guarantees. With our scheme, an (electricity) supplier can periodically collect data from smart meters and derive aggregated statistics while learning only limited information about the activities of individual households. For example, a supplier cannot tell from a user's trace when he watched TV or turned on heating. Our scheme is simple, efficient and practical. Processing cost is very limited: smart meters only have to add noise to their data and encrypt the results with an efficient stream cipher.
|
2012.13196
|
Daniel O'Connor
|
Daniel O'Connor, Walter Vinci
|
RBM-Flow and D-Flow: Invertible Flows with Discrete Energy Base Spaces
| null | null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Efficient sampling of complex data distributions can be achieved using
trained invertible flows (IF), where the model distribution is generated by
pushing a simple base distribution through multiple non-linear bijective
transformations. However, the iterative nature of the transformations in IFs
can limit the approximation to the target distribution. In this paper we seek
to mitigate this by implementing RBM-Flow, an IF model whose base distribution
is a Restricted Boltzmann Machine (RBM) with a continuous smoothing applied. We
show that by using RBM-Flow we are able to improve the quality of samples
generated, quantified by the Inception Scores (IS) and Frechet Inception
Distance (FID), over baseline models with the same IF transformations, but with
less expressive base distributions. Furthermore, we also obtain D-Flow, an IF
model with uncorrelated discrete latent variables. We show that D-Flow achieves
similar likelihoods and FID/IS scores to those of a typical IF with Gaussian
base variables, but with the additional benefit that global features are
meaningfully encoded as discrete labels in the latent space.
|
[
{
"created": "Thu, 24 Dec 2020 11:05:27 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Jan 2021 16:03:39 GMT",
"version": "v2"
},
{
"created": "Mon, 12 Jul 2021 10:00:47 GMT",
"version": "v3"
}
] |
2021-07-13
|
[
[
"O'Connor",
"Daniel",
""
],
[
"Vinci",
"Walter",
""
]
] |
Efficient sampling of complex data distributions can be achieved using trained invertible flows (IF), where the model distribution is generated by pushing a simple base distribution through multiple non-linear bijective transformations. However, the iterative nature of the transformations in IFs can limit the approximation to the target distribution. In this paper we seek to mitigate this by implementing RBM-Flow, an IF model whose base distribution is a Restricted Boltzmann Machine (RBM) with a continuous smoothing applied. We show that by using RBM-Flow we are able to improve the quality of samples generated, quantified by the Inception Scores (IS) and Frechet Inception Distance (FID), over baseline models with the same IF transformations, but with less expressive base distributions. Furthermore, we also obtain D-Flow, an IF model with uncorrelated discrete latent variables. We show that D-Flow achieves similar likelihoods and FID/IS scores to those of a typical IF with Gaussian base variables, but with the additional benefit that global features are meaningfully encoded as discrete labels in the latent space.
|
1909.10407
|
Mandar Gogate
|
Mandar Gogate, Kia Dashtipour, Ahsan Adeel, Amir Hussain
|
CochleaNet: A Robust Language-independent Audio-Visual Model for Speech
Enhancement
|
34 pages, 11 figures, Submitted to Information Fusion
| null | null | null |
cs.SD cs.CV cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Noisy situations cause huge problems for suffers of hearing loss as hearing
aids often make the signal more audible but do not always restore the
intelligibility. In noisy settings, humans routinely exploit the audio-visual
(AV) nature of the speech to selectively suppress the background noise and to
focus on the target speaker. In this paper, we present a causal, language,
noise and speaker independent AV deep neural network (DNN) architecture for
speech enhancement (SE). The model exploits the noisy acoustic cues and noise
robust visual cues to focus on the desired speaker and improve the speech
intelligibility. To evaluate the proposed SE framework a first of its kind AV
binaural speech corpus, called ASPIRE, is recorded in real noisy environments
including cafeteria and restaurant. We demonstrate superior performance of our
approach in terms of objective measures and subjective listening tests over the
state-of-the-art SE approaches as well as recent DNN based SE models. In
addition, our work challenges a popular belief that a scarcity of
multi-language large vocabulary AV corpus and wide variety of noises is a major
bottleneck to build a robust language, speaker and noise independent SE
systems. We show that a model trained on synthetic mixture of Grid corpus (with
33 speakers and a small English vocabulary) and ChiME 3 Noises (consisting of
only bus, pedestrian, cafeteria, and street noises) generalise well not only on
large vocabulary corpora but also on completely unrelated languages (such as
Mandarin), wide variety of speakers and noises.
|
[
{
"created": "Mon, 23 Sep 2019 14:59:47 GMT",
"version": "v1"
}
] |
2019-09-24
|
[
[
"Gogate",
"Mandar",
""
],
[
"Dashtipour",
"Kia",
""
],
[
"Adeel",
"Ahsan",
""
],
[
"Hussain",
"Amir",
""
]
] |
Noisy situations cause huge problems for suffers of hearing loss as hearing aids often make the signal more audible but do not always restore the intelligibility. In noisy settings, humans routinely exploit the audio-visual (AV) nature of the speech to selectively suppress the background noise and to focus on the target speaker. In this paper, we present a causal, language, noise and speaker independent AV deep neural network (DNN) architecture for speech enhancement (SE). The model exploits the noisy acoustic cues and noise robust visual cues to focus on the desired speaker and improve the speech intelligibility. To evaluate the proposed SE framework a first of its kind AV binaural speech corpus, called ASPIRE, is recorded in real noisy environments including cafeteria and restaurant. We demonstrate superior performance of our approach in terms of objective measures and subjective listening tests over the state-of-the-art SE approaches as well as recent DNN based SE models. In addition, our work challenges a popular belief that a scarcity of multi-language large vocabulary AV corpus and wide variety of noises is a major bottleneck to build a robust language, speaker and noise independent SE systems. We show that a model trained on synthetic mixture of Grid corpus (with 33 speakers and a small English vocabulary) and ChiME 3 Noises (consisting of only bus, pedestrian, cafeteria, and street noises) generalise well not only on large vocabulary corpora but also on completely unrelated languages (such as Mandarin), wide variety of speakers and noises.
|
1205.7031
|
Fabian Schuh
|
Fabian Schuh, Johannes B. Huber
|
Nonlinear Trellis Description for Convolutionally Encoded Transmission
Over ISI-channels with Applications for CPM
|
6 pages, 13 figures, submitted for IEEE-SCC-13
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by-nc-sa/3.0/
|
In this paper we propose a matched decoding scheme for convolutionally
encoded transmission over intersymbol interference (ISI) channels and devise a
nonlinear trellis description. As an application we show that for coded
continuous phase modulation (CPM) using a non-coherent receiver the number of
states of the super trellis can be significantly reduced by means of a matched
non-linear trellis encoder.
|
[
{
"created": "Thu, 31 May 2012 16:19:28 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Aug 2012 10:55:22 GMT",
"version": "v2"
}
] |
2012-08-02
|
[
[
"Schuh",
"Fabian",
""
],
[
"Huber",
"Johannes B.",
""
]
] |
In this paper we propose a matched decoding scheme for convolutionally encoded transmission over intersymbol interference (ISI) channels and devise a nonlinear trellis description. As an application we show that for coded continuous phase modulation (CPM) using a non-coherent receiver the number of states of the super trellis can be significantly reduced by means of a matched non-linear trellis encoder.
|
2006.14765
|
Tingmin Wu
|
Tingmin Wu, Wanlun Ma, Sheng Wen, Xin Xia, Cecile Paris, Surya Nepal,
Yang Xiang
|
Analysis of Trending Topics and Text-based Channels of Information
Delivery in Cybersecurity
|
13 pages (main content) + 4 pages (references and appendix)
| null | null | null |
cs.CR cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Computer users are generally faced with difficulties in making correct
security decisions. While an increasingly fewer number of people are trying or
willing to take formal security training, online sources including news,
security blogs, and websites are continuously making security knowledge more
accessible. Analysis of cybersecurity texts can provide insights into the
trending topics and identify current security issues as well as how cyber
attacks evolve over time. These in turn can support researchers and
practitioners in predicting and preparing for these attacks. Comparing
different sources may facilitate the learning process for normal users by
persisting the security knowledge gained from different cybersecurity context.
Prior studies neither systematically analysed the wide-range of digital sources
nor provided any standardisation in analysing the trending topics from recent
security texts. Although LDA has been widely adopted in topic generation, its
generated topics cannot cover the cybersecurity concepts completely and
considerably overlap. To address this issue, we propose a semi-automated
classification method to generate comprehensive security categories instead of
LDA-generated topics. We further compare the identified 16 security categories
across different sources based on their popularity and impact. We have revealed
several surprising findings. (1) The impact reflected from cyber-security texts
strongly correlates with the monetary loss caused by cybercrimes. (2) For most
categories, security blogs share the largest popularity and largest
absolute/relative impact over time. (3) Websites deliver security information
without caring about timeliness much, where one third of the articles do not
specify the date and the rest have a time lag in posting emerging security
issues.
|
[
{
"created": "Fri, 26 Jun 2020 03:00:04 GMT",
"version": "v1"
}
] |
2020-06-29
|
[
[
"Wu",
"Tingmin",
""
],
[
"Ma",
"Wanlun",
""
],
[
"Wen",
"Sheng",
""
],
[
"Xia",
"Xin",
""
],
[
"Paris",
"Cecile",
""
],
[
"Nepal",
"Surya",
""
],
[
"Xiang",
"Yang",
""
]
] |
Computer users are generally faced with difficulties in making correct security decisions. While an increasingly fewer number of people are trying or willing to take formal security training, online sources including news, security blogs, and websites are continuously making security knowledge more accessible. Analysis of cybersecurity texts can provide insights into the trending topics and identify current security issues as well as how cyber attacks evolve over time. These in turn can support researchers and practitioners in predicting and preparing for these attacks. Comparing different sources may facilitate the learning process for normal users by persisting the security knowledge gained from different cybersecurity context. Prior studies neither systematically analysed the wide-range of digital sources nor provided any standardisation in analysing the trending topics from recent security texts. Although LDA has been widely adopted in topic generation, its generated topics cannot cover the cybersecurity concepts completely and considerably overlap. To address this issue, we propose a semi-automated classification method to generate comprehensive security categories instead of LDA-generated topics. We further compare the identified 16 security categories across different sources based on their popularity and impact. We have revealed several surprising findings. (1) The impact reflected from cyber-security texts strongly correlates with the monetary loss caused by cybercrimes. (2) For most categories, security blogs share the largest popularity and largest absolute/relative impact over time. (3) Websites deliver security information without caring about timeliness much, where one third of the articles do not specify the date and the rest have a time lag in posting emerging security issues.
|
2207.08803
|
Hashmat Shadab Malik
|
Hashmat Shadab Malik, Shahina K Kunhimon, Muzammal Naseer, Salman
Khan, Fahad Shahbaz Khan
|
Adversarial Pixel Restoration as a Pretext Task for Transferable
Perturbations
|
Accepted at BMVC'22 (Oral)
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Transferable adversarial attacks optimize adversaries from a pretrained
surrogate model and known label space to fool the unknown black-box models.
Therefore, these attacks are restricted by the availability of an effective
surrogate model. In this work, we relax this assumption and propose Adversarial
Pixel Restoration as a self-supervised alternative to train an effective
surrogate model from scratch under the condition of no labels and few data
samples. Our training approach is based on a min-max scheme which reduces
overfitting via an adversarial objective and thus optimizes for a more
generalizable surrogate model. Our proposed attack is complimentary to the
adversarial pixel restoration and is independent of any task specific objective
as it can be launched in a self-supervised manner. We successfully demonstrate
the adversarial transferability of our approach to Vision Transformers as well
as Convolutional Neural Networks for the tasks of classification, object
detection, and video segmentation. Our training approach improves the
transferability of the baseline unsupervised training method by 16.4% on
ImageNet val. set. Our codes & pre-trained surrogate models are available at:
https://github.com/HashmatShadab/APR
|
[
{
"created": "Mon, 18 Jul 2022 17:59:58 GMT",
"version": "v1"
},
{
"created": "Mon, 8 Aug 2022 07:52:11 GMT",
"version": "v2"
},
{
"created": "Fri, 14 Oct 2022 08:27:49 GMT",
"version": "v3"
}
] |
2022-10-17
|
[
[
"Malik",
"Hashmat Shadab",
""
],
[
"Kunhimon",
"Shahina K",
""
],
[
"Naseer",
"Muzammal",
""
],
[
"Khan",
"Salman",
""
],
[
"Khan",
"Fahad Shahbaz",
""
]
] |
Transferable adversarial attacks optimize adversaries from a pretrained surrogate model and known label space to fool the unknown black-box models. Therefore, these attacks are restricted by the availability of an effective surrogate model. In this work, we relax this assumption and propose Adversarial Pixel Restoration as a self-supervised alternative to train an effective surrogate model from scratch under the condition of no labels and few data samples. Our training approach is based on a min-max scheme which reduces overfitting via an adversarial objective and thus optimizes for a more generalizable surrogate model. Our proposed attack is complimentary to the adversarial pixel restoration and is independent of any task specific objective as it can be launched in a self-supervised manner. We successfully demonstrate the adversarial transferability of our approach to Vision Transformers as well as Convolutional Neural Networks for the tasks of classification, object detection, and video segmentation. Our training approach improves the transferability of the baseline unsupervised training method by 16.4% on ImageNet val. set. Our codes & pre-trained surrogate models are available at: https://github.com/HashmatShadab/APR
|
2402.13126
|
Yan Pang
|
Yan Pang, Yang Zhang, Tianhao Wang
|
VGMShield: Mitigating Misuse of Video Generative Models
|
17 pages, 10 figures
| null | null | null |
cs.CR cs.AI cs.CV cs.LG eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the rapid advancement in video generation, people can conveniently
utilize video generation models to create videos tailored to their specific
desires. Nevertheless, there are also growing concerns about their potential
misuse in creating and disseminating false information.
In this work, we introduce VGMShield: a set of three straightforward but
pioneering mitigations through the lifecycle of fake video generation. We start
from \textit{fake video detection} trying to understand whether there is
uniqueness in generated videos and whether we can differentiate them from real
videos; then, we investigate the \textit{tracing} problem, which maps a fake
video back to a model that generates it. Towards these, we propose to leverage
pre-trained models that focus on {\it spatial-temporal dynamics} as the
backbone to identify inconsistencies in videos. Through experiments on seven
state-of-the-art open-source models, we demonstrate that current models still
cannot perfectly handle spatial-temporal relationships, and thus, we can
accomplish detection and tracing with nearly perfect accuracy.
Furthermore, anticipating future generative model improvements, we propose a
{\it prevention} method that adds invisible perturbations to images to make the
generated videos look unreal. Together with fake video detection and tracing,
our multi-faceted set of solutions can effectively mitigate misuse of video
generative models.
|
[
{
"created": "Tue, 20 Feb 2024 16:39:23 GMT",
"version": "v1"
}
] |
2024-02-21
|
[
[
"Pang",
"Yan",
""
],
[
"Zhang",
"Yang",
""
],
[
"Wang",
"Tianhao",
""
]
] |
With the rapid advancement in video generation, people can conveniently utilize video generation models to create videos tailored to their specific desires. Nevertheless, there are also growing concerns about their potential misuse in creating and disseminating false information. In this work, we introduce VGMShield: a set of three straightforward but pioneering mitigations through the lifecycle of fake video generation. We start from \textit{fake video detection} trying to understand whether there is uniqueness in generated videos and whether we can differentiate them from real videos; then, we investigate the \textit{tracing} problem, which maps a fake video back to a model that generates it. Towards these, we propose to leverage pre-trained models that focus on {\it spatial-temporal dynamics} as the backbone to identify inconsistencies in videos. Through experiments on seven state-of-the-art open-source models, we demonstrate that current models still cannot perfectly handle spatial-temporal relationships, and thus, we can accomplish detection and tracing with nearly perfect accuracy. Furthermore, anticipating future generative model improvements, we propose a {\it prevention} method that adds invisible perturbations to images to make the generated videos look unreal. Together with fake video detection and tracing, our multi-faceted set of solutions can effectively mitigate misuse of video generative models.
|
2407.02304
|
Bas Van Den Heuvel
|
Bas van den Heuvel, Farzaneh Derakhshan, Stephanie Balzer
|
Information Flow Control in Cyclic Process Networks
|
Extended version of ECOOP24 paper
| null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
Protection of confidential data is an important security consideration of
today's applications. Of particular concern is to guard against unintentional
leakage to a (malicious) observer, who may interact with the program and draw
inference from made observations. Information flow control (IFC) type systems
address this concern by statically ruling out such leakage. This paper
contributes an IFC type system for message-passing concurrent programs, the
computational model of choice for many of today's applications such as cloud
computing and IoT applications. Such applications typically either implicitly
or explicitly codify protocols according to which message exchange must happen,
and to statically ensure protocol safety, behavioral type systems such as
session types can be used. This paper marries IFC with session typing and
contributes over prior work in the following regards: (1) support of realistic
cyclic process networks as opposed to the restriction to tree-shaped networks,
(2) more permissive, yet entirely secure, IFC control, exploiting cyclic
process networks, and (3) considering deadlocks as another form of side
channel, and asserting deadlock-sensitive noninterference (DSNI) for well-typed
programs. To prove DSNI, the paper develops a novel logical relation that
accounts for cyclic process networks. The logical relation is rooted in linear
logic, but drops the tree-topology restriction imposed by prior work.
|
[
{
"created": "Tue, 2 Jul 2024 14:37:17 GMT",
"version": "v1"
}
] |
2024-07-03
|
[
[
"Heuvel",
"Bas van den",
""
],
[
"Derakhshan",
"Farzaneh",
""
],
[
"Balzer",
"Stephanie",
""
]
] |
Protection of confidential data is an important security consideration of today's applications. Of particular concern is to guard against unintentional leakage to a (malicious) observer, who may interact with the program and draw inference from made observations. Information flow control (IFC) type systems address this concern by statically ruling out such leakage. This paper contributes an IFC type system for message-passing concurrent programs, the computational model of choice for many of today's applications such as cloud computing and IoT applications. Such applications typically either implicitly or explicitly codify protocols according to which message exchange must happen, and to statically ensure protocol safety, behavioral type systems such as session types can be used. This paper marries IFC with session typing and contributes over prior work in the following regards: (1) support of realistic cyclic process networks as opposed to the restriction to tree-shaped networks, (2) more permissive, yet entirely secure, IFC control, exploiting cyclic process networks, and (3) considering deadlocks as another form of side channel, and asserting deadlock-sensitive noninterference (DSNI) for well-typed programs. To prove DSNI, the paper develops a novel logical relation that accounts for cyclic process networks. The logical relation is rooted in linear logic, but drops the tree-topology restriction imposed by prior work.
|
2105.11628
|
Guoqing Zhang
|
Yuhao Chen, Guoqing Zhang, Yujiang Lu, Zhenxing Wang, Yuhui Zheng,
Ruili Wang
|
TIPCB: A Simple but Effective Part-based Convolutional Baseline for
Text-based Person Search
|
27 pages
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Text-based person search is a sub-task in the field of image retrieval, which
aims to retrieve target person images according to a given textual description.
The significant feature gap between two modalities makes this task very
challenging. Many existing methods attempt to utilize local alignment to
address this problem in the fine-grained level. However, most relevant methods
introduce additional models or complicated training and evaluation strategies,
which are hard to use in realistic scenarios. In order to facilitate the
practical application, we propose a simple but effective end-to-end learning
framework for text-based person search named TIPCB (i.e., Text-Image Part-based
Convolutional Baseline). Firstly, a novel dual-path local alignment network
structure is proposed to extract visual and textual local representations, in
which images are segmented horizontally and texts are aligned adaptively. Then,
we propose a multi-stage cross-modal matching strategy, which eliminates the
modality gap from three feature levels, including low level, local level and
global level. Extensive experiments are conducted on the widely-used benchmark
dataset (CUHK-PEDES) and verify that our method outperforms the
state-of-the-art methods by 3.69%, 2.95% and 2.31% in terms of Top-1, Top-5 and
Top-10. Our code has been released in https://github.com/OrangeYHChen/TIPCB.
|
[
{
"created": "Tue, 25 May 2021 03:00:21 GMT",
"version": "v1"
}
] |
2021-05-26
|
[
[
"Chen",
"Yuhao",
""
],
[
"Zhang",
"Guoqing",
""
],
[
"Lu",
"Yujiang",
""
],
[
"Wang",
"Zhenxing",
""
],
[
"Zheng",
"Yuhui",
""
],
[
"Wang",
"Ruili",
""
]
] |
Text-based person search is a sub-task in the field of image retrieval, which aims to retrieve target person images according to a given textual description. The significant feature gap between two modalities makes this task very challenging. Many existing methods attempt to utilize local alignment to address this problem in the fine-grained level. However, most relevant methods introduce additional models or complicated training and evaluation strategies, which are hard to use in realistic scenarios. In order to facilitate the practical application, we propose a simple but effective end-to-end learning framework for text-based person search named TIPCB (i.e., Text-Image Part-based Convolutional Baseline). Firstly, a novel dual-path local alignment network structure is proposed to extract visual and textual local representations, in which images are segmented horizontally and texts are aligned adaptively. Then, we propose a multi-stage cross-modal matching strategy, which eliminates the modality gap from three feature levels, including low level, local level and global level. Extensive experiments are conducted on the widely-used benchmark dataset (CUHK-PEDES) and verify that our method outperforms the state-of-the-art methods by 3.69%, 2.95% and 2.31% in terms of Top-1, Top-5 and Top-10. Our code has been released in https://github.com/OrangeYHChen/TIPCB.
|
1312.0641
|
Samet Oymak
|
Samet Oymak, Christos Thrampoulidis, Babak Hassibi
|
Simple Bounds for Noisy Linear Inverse Problems with Exact Side
Information
|
13 pages
| null | null | null |
cs.IT math.IT math.OC math.ST stat.TH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper considers the linear inverse problem where we wish to estimate a
structured signal $x$ from its corrupted observations. When the problem is
ill-posed, it is natural to make use of a convex function $f(\cdot)$ that
exploits the structure of the signal. For example, $\ell_1$ norm can be used
for sparse signals. To carry out the estimation, we consider two well-known
convex programs: 1) Second order cone program (SOCP), and, 2) Lasso. Assuming
Gaussian measurements, we show that, if precise information about the value
$f(x)$ or the $\ell_2$-norm of the noise is available, one can do a
particularly good job at estimation. In particular, the reconstruction error
becomes proportional to the "sparsity" of the signal rather than the ambient
dimension of the noise vector. We connect our results to existing works and
provide a discussion on the relation of our results to the standard
least-squares problem. Our error bounds are non-asymptotic and sharp, they
apply to arbitrary convex functions and do not assume any distribution on the
noise.
|
[
{
"created": "Mon, 2 Dec 2013 22:07:05 GMT",
"version": "v1"
},
{
"created": "Thu, 5 Dec 2013 20:58:46 GMT",
"version": "v2"
}
] |
2013-12-06
|
[
[
"Oymak",
"Samet",
""
],
[
"Thrampoulidis",
"Christos",
""
],
[
"Hassibi",
"Babak",
""
]
] |
This paper considers the linear inverse problem where we wish to estimate a structured signal $x$ from its corrupted observations. When the problem is ill-posed, it is natural to make use of a convex function $f(\cdot)$ that exploits the structure of the signal. For example, $\ell_1$ norm can be used for sparse signals. To carry out the estimation, we consider two well-known convex programs: 1) Second order cone program (SOCP), and, 2) Lasso. Assuming Gaussian measurements, we show that, if precise information about the value $f(x)$ or the $\ell_2$-norm of the noise is available, one can do a particularly good job at estimation. In particular, the reconstruction error becomes proportional to the "sparsity" of the signal rather than the ambient dimension of the noise vector. We connect our results to existing works and provide a discussion on the relation of our results to the standard least-squares problem. Our error bounds are non-asymptotic and sharp, they apply to arbitrary convex functions and do not assume any distribution on the noise.
|
2310.10330
|
Guillermo Encinas-Lago
|
Guillermo Encinas-Lago, Antonio Albanese, Vincenzo Sciancalepore,
Marco Di Renzo, Xavier Costa-P\'erez
|
Unlocking Metasurface Practicality for B5G Networks: AI-assisted RIS
Planning
| null | null | null | null |
cs.NI cs.AI eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
The advent of reconfigurable intelligent surfaces(RISs) brings along
significant improvements for wireless technology on the verge of
beyond-fifth-generation networks (B5G).The proven flexibility in influencing
the propagation environment opens up the possibility of programmatically
altering the wireless channel to the advantage of network designers, enabling
the exploitation of higher-frequency bands for superior throughput overcoming
the challenging electromagnetic (EM) propagation properties at these frequency
bands.
However, RISs are not magic bullets. Their employment comes with significant
complexity, requiring ad-hoc deployments and management operations to come to
fruition. In this paper, we tackle the open problem of bringing RISs to the
field, focusing on areas with little or no coverage. In fact, we present a
first-of-its-kind deep reinforcement learning (DRL) solution, dubbed as D-RISA,
which trains a DRL agent and, in turn, obtain san optimal RIS deployment. We
validate our framework in the indoor scenario of the Rennes railway station in
France, assessing the performance of our algorithm against state-of-the-art
(SOA) approaches. Our benchmarks showcase better coverage, i.e., 10-dB increase
in minimum signal-to-noise ratio (SNR), at lower computational time (up to -25
percent) while improving scalability towards denser network deployments.
|
[
{
"created": "Mon, 16 Oct 2023 12:14:42 GMT",
"version": "v1"
}
] |
2023-10-17
|
[
[
"Encinas-Lago",
"Guillermo",
""
],
[
"Albanese",
"Antonio",
""
],
[
"Sciancalepore",
"Vincenzo",
""
],
[
"Di Renzo",
"Marco",
""
],
[
"Costa-Pérez",
"Xavier",
""
]
] |
The advent of reconfigurable intelligent surfaces(RISs) brings along significant improvements for wireless technology on the verge of beyond-fifth-generation networks (B5G).The proven flexibility in influencing the propagation environment opens up the possibility of programmatically altering the wireless channel to the advantage of network designers, enabling the exploitation of higher-frequency bands for superior throughput overcoming the challenging electromagnetic (EM) propagation properties at these frequency bands. However, RISs are not magic bullets. Their employment comes with significant complexity, requiring ad-hoc deployments and management operations to come to fruition. In this paper, we tackle the open problem of bringing RISs to the field, focusing on areas with little or no coverage. In fact, we present a first-of-its-kind deep reinforcement learning (DRL) solution, dubbed as D-RISA, which trains a DRL agent and, in turn, obtain san optimal RIS deployment. We validate our framework in the indoor scenario of the Rennes railway station in France, assessing the performance of our algorithm against state-of-the-art (SOA) approaches. Our benchmarks showcase better coverage, i.e., 10-dB increase in minimum signal-to-noise ratio (SNR), at lower computational time (up to -25 percent) while improving scalability towards denser network deployments.
|
2212.03371
|
Shiqing Wu
|
Guan Wang, Weihua Li, Edmund Lai, Jianhua Jiang
|
KATSum: Knowledge-aware Abstractive Text Summarization
|
Presented at PKAW 2022 (arXiv:2211.03888) Report-no: PKAW/2022/02
| null | null |
Report-no: PKAW/2022/02
|
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Text Summarization is recognised as one of the NLP downstream tasks and it
has been extensively investigated in recent years. It can assist people with
perceiving the information rapidly from the Internet, including news articles,
social posts, videos, etc. Most existing research works attempt to develop
summarization models to produce a better output. However, advent limitations of
most existing models emerge, including unfaithfulness and factual errors. In
this paper, we propose a novel model, named as Knowledge-aware Abstractive Text
Summarization, which leverages the advantages offered by Knowledge Graph to
enhance the standard Seq2Seq model. On top of that, the Knowledge Graph
triplets are extracted from the source text and utilised to provide keywords
with relational information, producing coherent and factually errorless
summaries. We conduct extensive experiments by using real-world data sets. The
results reveal that the proposed framework can effectively utilise the
information from Knowledge Graph and significantly reduce the factual errors in
the summary.
|
[
{
"created": "Tue, 6 Dec 2022 23:43:50 GMT",
"version": "v1"
}
] |
2022-12-08
|
[
[
"Wang",
"Guan",
""
],
[
"Li",
"Weihua",
""
],
[
"Lai",
"Edmund",
""
],
[
"Jiang",
"Jianhua",
""
]
] |
Text Summarization is recognised as one of the NLP downstream tasks and it has been extensively investigated in recent years. It can assist people with perceiving the information rapidly from the Internet, including news articles, social posts, videos, etc. Most existing research works attempt to develop summarization models to produce a better output. However, advent limitations of most existing models emerge, including unfaithfulness and factual errors. In this paper, we propose a novel model, named as Knowledge-aware Abstractive Text Summarization, which leverages the advantages offered by Knowledge Graph to enhance the standard Seq2Seq model. On top of that, the Knowledge Graph triplets are extracted from the source text and utilised to provide keywords with relational information, producing coherent and factually errorless summaries. We conduct extensive experiments by using real-world data sets. The results reveal that the proposed framework can effectively utilise the information from Knowledge Graph and significantly reduce the factual errors in the summary.
|
2101.04690
|
Matthias Frey
|
Matthias Frey, Igor Bjelakovic, Slawomir Stanczak
|
Over-The-Air Computation in Correlated Channels
|
Extended version can be found at arXiv:2007.02648
|
2020 IEEE Information Theory Workshop (ITW), Riva del Garda,
Italy, 11-15 April, 2021
|
10.1109/TSP.2021.3106115
| null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper addresses the problem of Over-The-Air (OTA) computation in
wireless networks which has the potential to realize huge efficiency gains for
instance in training of distributed ML models. We provide non-asymptotic,
theoretical guarantees for OTA computation in fast-fading wireless channels
where the fading and noise may be correlated. The distributions of fading and
noise are not restricted to Gaussian distributions, but instead are assumed to
follow a distribution in the more general sub-gaussian class. Furthermore, our
result does not make any assumptions on the distribution of the sources and
therefore, it can, e.g., be applied to arbitrarily correlated sources. We
illustrate our analysis with numerical evaluations for OTA computation of two
example functions in large wireless networks: the arithmetic mean and the
Euclidean norm.
|
[
{
"created": "Tue, 12 Jan 2021 19:00:02 GMT",
"version": "v1"
}
] |
2021-12-01
|
[
[
"Frey",
"Matthias",
""
],
[
"Bjelakovic",
"Igor",
""
],
[
"Stanczak",
"Slawomir",
""
]
] |
This paper addresses the problem of Over-The-Air (OTA) computation in wireless networks which has the potential to realize huge efficiency gains for instance in training of distributed ML models. We provide non-asymptotic, theoretical guarantees for OTA computation in fast-fading wireless channels where the fading and noise may be correlated. The distributions of fading and noise are not restricted to Gaussian distributions, but instead are assumed to follow a distribution in the more general sub-gaussian class. Furthermore, our result does not make any assumptions on the distribution of the sources and therefore, it can, e.g., be applied to arbitrarily correlated sources. We illustrate our analysis with numerical evaluations for OTA computation of two example functions in large wireless networks: the arithmetic mean and the Euclidean norm.
|
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