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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1404.0783 | Ismail Toroslu | Cem Evrendilek, Ismail Hakki Toroslu, Sasan Hashemi | Task Assignment in Tree-Like Hierarchical Structures | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most large organizations, such as corporations, are hierarchical
organizations. In hierarchical organizations each entity in the organization,
except the root entity, is a sub-part of another entity. In this paper we study
the task assignment problem to the entities of tree-like hierarchical
organizations. The inherent tree structure introduces an interesting and
challenging constraint to the standard assignment problem. When a task is
assigned to an entity in a hierarchical organization, the whole entity,
including its sub-entities, is responsible from the execution of that
particular task. In other words, if an entity has been assigned to a task,
neither its descendants nor its ancestors can be assigned to a task.
Sub-entities cannot be assigned as they have an ancestor already occupied.
Ancestor entities cannot be assigned since one of their sub-entities has
already been employed in an assignment. In the paper, we formally introduce
this new version of the assignment problem called Maximum Weight Tree Matching
($MWTM$), and show its NP-hardness. We also propose an effective heuristic
solution based on an iterative LP-relaxation to it.
| [
{
"created": "Thu, 3 Apr 2014 07:16:49 GMT",
"version": "v1"
}
] | 2014-04-04 | [
[
"Evrendilek",
"Cem",
""
],
[
"Toroslu",
"Ismail Hakki",
""
],
[
"Hashemi",
"Sasan",
""
]
] | Most large organizations, such as corporations, are hierarchical organizations. In hierarchical organizations each entity in the organization, except the root entity, is a sub-part of another entity. In this paper we study the task assignment problem to the entities of tree-like hierarchical organizations. The inherent tree structure introduces an interesting and challenging constraint to the standard assignment problem. When a task is assigned to an entity in a hierarchical organization, the whole entity, including its sub-entities, is responsible from the execution of that particular task. In other words, if an entity has been assigned to a task, neither its descendants nor its ancestors can be assigned to a task. Sub-entities cannot be assigned as they have an ancestor already occupied. Ancestor entities cannot be assigned since one of their sub-entities has already been employed in an assignment. In the paper, we formally introduce this new version of the assignment problem called Maximum Weight Tree Matching ($MWTM$), and show its NP-hardness. We also propose an effective heuristic solution based on an iterative LP-relaxation to it. |
2212.12363 | Zhitong Yang | Zhitong Yang, Xing Ma, Anqi Liu, Zheyu Zhang | Discovering Customer-Service Dialog System with Semi-Supervised Learning
and Coarse-to-Fine Intent Detection | Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems
Co-located with EMNLP 2022, System Description Paper, 5 pages | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Task-oriented dialog(TOD) aims to assist users in achieving specific goals
through multi-turn conversation. Recently, good results have been obtained
based on large pre-trained models. However, the labeled-data scarcity hinders
the efficient development of TOD systems at scale. In this work, we constructed
a weakly supervised dataset based on a teacher/student paradigm that leverages
a large collection of unlabelled dialogues. Furthermore, we built a modular
dialogue system and integrated coarse-to-fine grained classification for user
intent detection. Experiments show that our method can reach the dialog goal
with a higher success rate and generate more coherent responses.
| [
{
"created": "Fri, 23 Dec 2022 14:36:43 GMT",
"version": "v1"
}
] | 2022-12-26 | [
[
"Yang",
"Zhitong",
""
],
[
"Ma",
"Xing",
""
],
[
"Liu",
"Anqi",
""
],
[
"Zhang",
"Zheyu",
""
]
] | Task-oriented dialog(TOD) aims to assist users in achieving specific goals through multi-turn conversation. Recently, good results have been obtained based on large pre-trained models. However, the labeled-data scarcity hinders the efficient development of TOD systems at scale. In this work, we constructed a weakly supervised dataset based on a teacher/student paradigm that leverages a large collection of unlabelled dialogues. Furthermore, we built a modular dialogue system and integrated coarse-to-fine grained classification for user intent detection. Experiments show that our method can reach the dialog goal with a higher success rate and generate more coherent responses. |
1701.00165 | Amit Shaked | Amit Shaked and Lior Wolf | Improved Stereo Matching with Constant Highway Networks and Reflective
Confidence Learning | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an improved three-step pipeline for the stereo matching problem
and introduce multiple novelties at each stage. We propose a new highway
network architecture for computing the matching cost at each possible
disparity, based on multilevel weighted residual shortcuts, trained with a
hybrid loss that supports multilevel comparison of image patches. A novel
post-processing step is then introduced, which employs a second deep
convolutional neural network for pooling global information from multiple
disparities. This network outputs both the image disparity map, which replaces
the conventional "winner takes all" strategy, and a confidence in the
prediction. The confidence score is achieved by training the network with a new
technique that we call the reflective loss. Lastly, the learned confidence is
employed in order to better detect outliers in the refinement step. The
proposed pipeline achieves state of the art accuracy on the largest and most
competitive stereo benchmarks, and the learned confidence is shown to
outperform all existing alternatives.
| [
{
"created": "Sat, 31 Dec 2016 20:24:16 GMT",
"version": "v1"
}
] | 2017-01-03 | [
[
"Shaked",
"Amit",
""
],
[
"Wolf",
"Lior",
""
]
] | We present an improved three-step pipeline for the stereo matching problem and introduce multiple novelties at each stage. We propose a new highway network architecture for computing the matching cost at each possible disparity, based on multilevel weighted residual shortcuts, trained with a hybrid loss that supports multilevel comparison of image patches. A novel post-processing step is then introduced, which employs a second deep convolutional neural network for pooling global information from multiple disparities. This network outputs both the image disparity map, which replaces the conventional "winner takes all" strategy, and a confidence in the prediction. The confidence score is achieved by training the network with a new technique that we call the reflective loss. Lastly, the learned confidence is employed in order to better detect outliers in the refinement step. The proposed pipeline achieves state of the art accuracy on the largest and most competitive stereo benchmarks, and the learned confidence is shown to outperform all existing alternatives. |
2403.01081 | Akash Srivastava | Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, Kai Xu,
David D. Cox, Akash Srivastava | LAB: Large-Scale Alignment for ChatBots | Corresponding Author: Akash Srivastava. Equal Contribution:
Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, Akash Srivastava,
Code: https://github.com/instructlab | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | This work introduces LAB (Large-scale Alignment for chatBots), a novel
methodology designed to overcome the scalability challenges in the
instruction-tuning phase of large language model (LLM) training. Leveraging a
taxonomy-guided synthetic data generation process and a multi-phase tuning
framework, LAB significantly reduces reliance on expensive human annotations
and proprietary models like GPT-4. We demonstrate that LAB-trained models can
achieve competitive performance across several benchmarks compared to models
trained with traditional human-annotated or GPT-4 generated synthetic data.
Thus offering a scalable, cost-effective solution for enhancing LLM
capabilities and instruction-following behaviors without the drawbacks of
catastrophic forgetting, marking a step forward in the efficient training of
LLMs for a wide range of applications.
| [
{
"created": "Sat, 2 Mar 2024 03:48:37 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Mar 2024 22:25:44 GMT",
"version": "v2"
},
{
"created": "Mon, 29 Apr 2024 18:55:34 GMT",
"version": "v3"
}
] | 2024-05-01 | [
[
"Sudalairaj",
"Shivchander",
""
],
[
"Bhandwaldar",
"Abhishek",
""
],
[
"Pareja",
"Aldo",
""
],
[
"Xu",
"Kai",
""
],
[
"Cox",
"David D.",
""
],
[
"Srivastava",
"Akash",
""
]
] | This work introduces LAB (Large-scale Alignment for chatBots), a novel methodology designed to overcome the scalability challenges in the instruction-tuning phase of large language model (LLM) training. Leveraging a taxonomy-guided synthetic data generation process and a multi-phase tuning framework, LAB significantly reduces reliance on expensive human annotations and proprietary models like GPT-4. We demonstrate that LAB-trained models can achieve competitive performance across several benchmarks compared to models trained with traditional human-annotated or GPT-4 generated synthetic data. Thus offering a scalable, cost-effective solution for enhancing LLM capabilities and instruction-following behaviors without the drawbacks of catastrophic forgetting, marking a step forward in the efficient training of LLMs for a wide range of applications. |
2109.07672 | Malek Mouhoub | Munira Al-Ageili and Malek Mouhoub | An Ontology-Based Information Extraction System for Residential Land Use
Suitability Analysis | 17 pages, 18 figures | null | null | null | cs.AI cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We propose an Ontology-Based Information Extraction (OBIE) system to automate
the extraction of the criteria and values applied in Land Use Suitability
Analysis (LUSA) from bylaw and regulation documents related to the geographic
area of interest. The results obtained by our proposed LUSA OBIE system (land
use suitability criteria and their values) are presented as an ontology
populated with instances of the extracted criteria and property values. This
latter output ontology is incorporated into a Multi-Criteria Decision Making
(MCDM) model applied for constructing suitability maps for different kinds of
land uses. The resulting maps may be the final desired product or can be
incorporated into the cellular automata urban modeling and simulation for
predicting future urban growth. A case study has been conducted where the
output from LUSA OBIE is applied to help produce a suitability map for the City
of Regina, Saskatchewan, to assist in the identification of suitable areas for
residential development. A set of Saskatchewan bylaw and regulation documents
were downloaded and input to the LUSA OBIE system. We accessed the extracted
information using both the populated LUSA ontology and the set of annotated
documents. In this regard, the LUSA OBIE system was effective in producing a
final suitability map.
| [
{
"created": "Thu, 16 Sep 2021 02:18:30 GMT",
"version": "v1"
}
] | 2021-09-17 | [
[
"Al-Ageili",
"Munira",
""
],
[
"Mouhoub",
"Malek",
""
]
] | We propose an Ontology-Based Information Extraction (OBIE) system to automate the extraction of the criteria and values applied in Land Use Suitability Analysis (LUSA) from bylaw and regulation documents related to the geographic area of interest. The results obtained by our proposed LUSA OBIE system (land use suitability criteria and their values) are presented as an ontology populated with instances of the extracted criteria and property values. This latter output ontology is incorporated into a Multi-Criteria Decision Making (MCDM) model applied for constructing suitability maps for different kinds of land uses. The resulting maps may be the final desired product or can be incorporated into the cellular automata urban modeling and simulation for predicting future urban growth. A case study has been conducted where the output from LUSA OBIE is applied to help produce a suitability map for the City of Regina, Saskatchewan, to assist in the identification of suitable areas for residential development. A set of Saskatchewan bylaw and regulation documents were downloaded and input to the LUSA OBIE system. We accessed the extracted information using both the populated LUSA ontology and the set of annotated documents. In this regard, the LUSA OBIE system was effective in producing a final suitability map. |
1308.4978 | Daniel Graziotin | Xiaofeng Wang, Daniel Graziotin, Juha Rikkil\"a, and Pekka Abrahamsson
(Free University of Bozen-Bolzano) | Traverse the landscape of the mind by walking: an exploration of a new
brainstorming practice | 12 pages, 2 figures. Pilot study conducted to better understand a new
brainstorming technique. Full study will follow | null | 10.7287/peerj.preprints.51v1 | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Group brainstorming is a well-known idea generation technique, which plays a
key role in software development processes. Despite this, the relevant
literature has had little to offer in advancing our understanding of the
effectiveness of group brainstorming sessions. In this paper we present a
research-in-progress on brainstorming while walking, which is a practice built
upon the relationship between thinking and walking. The objective is to better
understand how to conduct group brainstorming effectively. We compared two
brainstorming sessions, one performed during a mountain walk, the other
traditionally in a room. Three preliminary findings are obtained: walking can
lead to an effective idea generation session; brainstorming while walking can
encourage team members to participate in and contribute to the session in an
equal manner; and it can help a team to maintain sustainable mental energy. Our
study opens up an avenue for future exploration of effective group
brainstorming practices.
| [
{
"created": "Thu, 22 Aug 2013 20:00:21 GMT",
"version": "v1"
}
] | 2013-08-26 | [
[
"Wang",
"Xiaofeng",
"",
"Free University of Bozen-Bolzano"
],
[
"Graziotin",
"Daniel",
"",
"Free University of Bozen-Bolzano"
],
[
"Rikkilä",
"Juha",
"",
"Free University of Bozen-Bolzano"
],
[
"Abrahamsson",
"Pekka",
"",
"Free University of Bozen-Bolzano"
]
] | Group brainstorming is a well-known idea generation technique, which plays a key role in software development processes. Despite this, the relevant literature has had little to offer in advancing our understanding of the effectiveness of group brainstorming sessions. In this paper we present a research-in-progress on brainstorming while walking, which is a practice built upon the relationship between thinking and walking. The objective is to better understand how to conduct group brainstorming effectively. We compared two brainstorming sessions, one performed during a mountain walk, the other traditionally in a room. Three preliminary findings are obtained: walking can lead to an effective idea generation session; brainstorming while walking can encourage team members to participate in and contribute to the session in an equal manner; and it can help a team to maintain sustainable mental energy. Our study opens up an avenue for future exploration of effective group brainstorming practices. |
1801.09036 | Wlodek Zadrozny | Wlodek Zadrozny and Luciana Garbayo | A Sheaf Model of Contradictions and Disagreements. Preliminary Report
and Discussion | This paper was presented at ISAIM 2018, International Symposium on
Artificial Intelligence and Mathematics. Fort Lauderdale, FL. January 3 5,
2018. Minor typographical errors have been corrected | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new formal model -- based on the mathematical construct of
sheaves -- for representing contradictory information in textual sources. This
model has the advantage of letting us (a) identify the causes of the
inconsistency; (b) measure how strong it is; (c) and do something about it,
e.g. suggest ways to reconcile inconsistent advice. This model naturally
represents the distinction between contradictions and disagreements. It is
based on the idea of representing natural language sentences as formulas with
parameters sitting on lattices, creating partial orders based on predicates
shared by theories, and building sheaves on these partial orders with products
of lattices as stalks. Degrees of disagreement are measured by the existence of
global and local sections.
Limitations of the sheaf approach and connections to recent work in natural
language processing, as well as the topics of contextuality in physics, data
fusion, topological data analysis and epistemology are also discussed.
| [
{
"created": "Sat, 27 Jan 2018 05:13:55 GMT",
"version": "v1"
}
] | 2018-01-30 | [
[
"Zadrozny",
"Wlodek",
""
],
[
"Garbayo",
"Luciana",
""
]
] | We introduce a new formal model -- based on the mathematical construct of sheaves -- for representing contradictory information in textual sources. This model has the advantage of letting us (a) identify the causes of the inconsistency; (b) measure how strong it is; (c) and do something about it, e.g. suggest ways to reconcile inconsistent advice. This model naturally represents the distinction between contradictions and disagreements. It is based on the idea of representing natural language sentences as formulas with parameters sitting on lattices, creating partial orders based on predicates shared by theories, and building sheaves on these partial orders with products of lattices as stalks. Degrees of disagreement are measured by the existence of global and local sections. Limitations of the sheaf approach and connections to recent work in natural language processing, as well as the topics of contextuality in physics, data fusion, topological data analysis and epistemology are also discussed. |
2310.07765 | Yonatan Kahn | Hannah Day, Yonatan Kahn, Daniel A. Roberts | Feature Learning and Generalization in Deep Networks with Orthogonal
Weights | v2: numerical experiments updated with more data, plots updated to
match, conclusions unchanged. 30+12 pages, 20 figures | null | null | MIT-CTP/5625 | cs.LG hep-ph hep-th stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fully-connected deep neural networks with weights initialized from
independent Gaussian distributions can be tuned to criticality, which prevents
the exponential growth or decay of signals propagating through the network.
However, such networks still exhibit fluctuations that grow linearly with the
depth of the network, which may impair the training of networks with width
comparable to depth. We show analytically that rectangular networks with tanh
activations and weights initialized from the ensemble of orthogonal matrices
have corresponding preactivation fluctuations which are independent of depth,
to leading order in inverse width. Moreover, we demonstrate numerically that,
at initialization, all correlators involving the neural tangent kernel (NTK)
and its descendants at leading order in inverse width -- which govern the
evolution of observables during training -- saturate at a depth of $\sim 20$,
rather than growing without bound as in the case of Gaussian initializations.
We speculate that this structure preserves finite-width feature learning while
reducing overall noise, thus improving both generalization and training speed
in deep networks with depth comparable to width. We provide some experimental
justification by relating empirical measurements of the NTK to the superior
performance of deep nonlinear orthogonal networks trained under full-batch
gradient descent on the MNIST and CIFAR-10 classification tasks.
| [
{
"created": "Wed, 11 Oct 2023 18:00:02 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Jun 2024 14:57:56 GMT",
"version": "v2"
}
] | 2024-06-13 | [
[
"Day",
"Hannah",
""
],
[
"Kahn",
"Yonatan",
""
],
[
"Roberts",
"Daniel A.",
""
]
] | Fully-connected deep neural networks with weights initialized from independent Gaussian distributions can be tuned to criticality, which prevents the exponential growth or decay of signals propagating through the network. However, such networks still exhibit fluctuations that grow linearly with the depth of the network, which may impair the training of networks with width comparable to depth. We show analytically that rectangular networks with tanh activations and weights initialized from the ensemble of orthogonal matrices have corresponding preactivation fluctuations which are independent of depth, to leading order in inverse width. Moreover, we demonstrate numerically that, at initialization, all correlators involving the neural tangent kernel (NTK) and its descendants at leading order in inverse width -- which govern the evolution of observables during training -- saturate at a depth of $\sim 20$, rather than growing without bound as in the case of Gaussian initializations. We speculate that this structure preserves finite-width feature learning while reducing overall noise, thus improving both generalization and training speed in deep networks with depth comparable to width. We provide some experimental justification by relating empirical measurements of the NTK to the superior performance of deep nonlinear orthogonal networks trained under full-batch gradient descent on the MNIST and CIFAR-10 classification tasks. |
1106.6242 | Sandeep Katta | Sandeep Katta | Visual Secret Sharing Scheme using Grayscale Images | 6 pages, 2 figures | null | null | null | cs.CR cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pixel expansion and the quality of the reconstructed secret image has been a
major issue of visual secret sharing (VSS) schemes. A number of probabilistic
VSS schemes with minimum pixel expansion have been proposed for black and white
(binary) secret images. This paper presents a probabilistic (2, 3)-VSS scheme
for gray scale images. Its pixel expansion is larger in size but the quality of
the image is perfect when it's reconstructed. The construction of the shadow
images (transparent shares) is based on the binary OR operation.
| [
{
"created": "Thu, 30 Jun 2011 14:25:46 GMT",
"version": "v1"
}
] | 2011-07-01 | [
[
"Katta",
"Sandeep",
""
]
] | Pixel expansion and the quality of the reconstructed secret image has been a major issue of visual secret sharing (VSS) schemes. A number of probabilistic VSS schemes with minimum pixel expansion have been proposed for black and white (binary) secret images. This paper presents a probabilistic (2, 3)-VSS scheme for gray scale images. Its pixel expansion is larger in size but the quality of the image is perfect when it's reconstructed. The construction of the shadow images (transparent shares) is based on the binary OR operation. |
2106.04546 | Yuan Yin | Yuan Yin, Ibrahim Ayed, Emmanuel de B\'ezenac, Nicolas Baskiotis,
Patrick Gallinari | LEADS: Learning Dynamical Systems that Generalize Across Environments | Published at NeurIPS 2021 | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When modeling dynamical systems from real-world data samples, the
distribution of data often changes according to the environment in which they
are captured, and the dynamics of the system itself vary from one environment
to another. Generalizing across environments thus challenges the conventional
frameworks. The classical settings suggest either considering data as i.i.d.
and learning a single model to cover all situations or learning
environment-specific models. Both are sub-optimal: the former disregards the
discrepancies between environments leading to biased solutions, while the
latter does not exploit their potential commonalities and is prone to scarcity
problems. We propose LEADS, a novel framework that leverages the commonalities
and discrepancies among known environments to improve model generalization.
This is achieved with a tailored training formulation aiming at capturing
common dynamics within a shared model while additional terms capture
environment-specific dynamics. We ground our approach in theory, exhibiting a
decrease in sample complexity with our approach and corroborate these results
empirically, instantiating it for linear dynamics. Moreover, we concretize this
framework for neural networks and evaluate it experimentally on representative
families of nonlinear dynamics. We show that this new setting can exploit
knowledge extracted from environment-dependent data and improves generalization
for both known and novel environments. Code is available at
https://github.com/yuan-yin/LEADS.
| [
{
"created": "Tue, 8 Jun 2021 17:28:19 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Feb 2022 13:46:57 GMT",
"version": "v2"
}
] | 2022-02-15 | [
[
"Yin",
"Yuan",
""
],
[
"Ayed",
"Ibrahim",
""
],
[
"de Bézenac",
"Emmanuel",
""
],
[
"Baskiotis",
"Nicolas",
""
],
[
"Gallinari",
"Patrick",
""
]
] | When modeling dynamical systems from real-world data samples, the distribution of data often changes according to the environment in which they are captured, and the dynamics of the system itself vary from one environment to another. Generalizing across environments thus challenges the conventional frameworks. The classical settings suggest either considering data as i.i.d. and learning a single model to cover all situations or learning environment-specific models. Both are sub-optimal: the former disregards the discrepancies between environments leading to biased solutions, while the latter does not exploit their potential commonalities and is prone to scarcity problems. We propose LEADS, a novel framework that leverages the commonalities and discrepancies among known environments to improve model generalization. This is achieved with a tailored training formulation aiming at capturing common dynamics within a shared model while additional terms capture environment-specific dynamics. We ground our approach in theory, exhibiting a decrease in sample complexity with our approach and corroborate these results empirically, instantiating it for linear dynamics. Moreover, we concretize this framework for neural networks and evaluate it experimentally on representative families of nonlinear dynamics. We show that this new setting can exploit knowledge extracted from environment-dependent data and improves generalization for both known and novel environments. Code is available at https://github.com/yuan-yin/LEADS. |
2209.09652 | Chengyin Hu | Chengyin Hu, Weiwen Shi, Ling Tian | Adversarial Color Projection: A Projector-based Physical Attack to DNNs | arXiv admin note: substantial text overlap with arXiv:2209.02430 | null | null | null | cs.CR cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent research has demonstrated that deep neural networks (DNNs) are
vulnerable to adversarial perturbations. Therefore, it is imperative to
evaluate the resilience of advanced DNNs to adversarial attacks. However,
traditional methods that use stickers as physical perturbations to deceive
classifiers face challenges in achieving stealthiness and are susceptible to
printing loss. Recently, advancements in physical attacks have utilized light
beams, such as lasers, to perform attacks, where the optical patterns generated
are artificial rather than natural. In this work, we propose a black-box
projector-based physical attack, referred to as adversarial color projection
(AdvCP), which manipulates the physical parameters of color projection to
perform an adversarial attack. We evaluate our approach on three crucial
criteria: effectiveness, stealthiness, and robustness. In the digital
environment, we achieve an attack success rate of 97.60% on a subset of
ImageNet, while in the physical environment, we attain an attack success rate
of 100% in the indoor test and 82.14% in the outdoor test. The adversarial
samples generated by AdvCP are compared with baseline samples to demonstrate
the stealthiness of our approach. When attacking advanced DNNs, experimental
results show that our method can achieve more than 85% attack success rate in
all cases, which verifies the robustness of AdvCP. Finally, we consider the
potential threats posed by AdvCP to future vision-based systems and
applications and suggest some ideas for light-based physical attacks.
| [
{
"created": "Mon, 19 Sep 2022 12:27:32 GMT",
"version": "v1"
},
{
"created": "Tue, 23 May 2023 11:56:41 GMT",
"version": "v2"
}
] | 2023-05-24 | [
[
"Hu",
"Chengyin",
""
],
[
"Shi",
"Weiwen",
""
],
[
"Tian",
"Ling",
""
]
] | Recent research has demonstrated that deep neural networks (DNNs) are vulnerable to adversarial perturbations. Therefore, it is imperative to evaluate the resilience of advanced DNNs to adversarial attacks. However, traditional methods that use stickers as physical perturbations to deceive classifiers face challenges in achieving stealthiness and are susceptible to printing loss. Recently, advancements in physical attacks have utilized light beams, such as lasers, to perform attacks, where the optical patterns generated are artificial rather than natural. In this work, we propose a black-box projector-based physical attack, referred to as adversarial color projection (AdvCP), which manipulates the physical parameters of color projection to perform an adversarial attack. We evaluate our approach on three crucial criteria: effectiveness, stealthiness, and robustness. In the digital environment, we achieve an attack success rate of 97.60% on a subset of ImageNet, while in the physical environment, we attain an attack success rate of 100% in the indoor test and 82.14% in the outdoor test. The adversarial samples generated by AdvCP are compared with baseline samples to demonstrate the stealthiness of our approach. When attacking advanced DNNs, experimental results show that our method can achieve more than 85% attack success rate in all cases, which verifies the robustness of AdvCP. Finally, we consider the potential threats posed by AdvCP to future vision-based systems and applications and suggest some ideas for light-based physical attacks. |
2003.13045 | Liang Liu | Liang Liu, Jiangning Zhang, Ruifei He, Yong Liu, Yabiao Wang, Ying
Tai, Donghao Luo, Chengjie Wang, Jilin Li, Feiyue Huang | Learning by Analogy: Reliable Supervision from Transformations for
Unsupervised Optical Flow Estimation | Accepted to CVPR 2020, https://github.com/lliuz/ARFlow | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Unsupervised learning of optical flow, which leverages the supervision from
view synthesis, has emerged as a promising alternative to supervised methods.
However, the objective of unsupervised learning is likely to be unreliable in
challenging scenes. In this work, we present a framework to use more reliable
supervision from transformations. It simply twists the general unsupervised
learning pipeline by running another forward pass with transformed data from
augmentation, along with using transformed predictions of original data as the
self-supervision signal. Besides, we further introduce a lightweight network
with multiple frames by a highly-shared flow decoder. Our method consistently
gets a leap of performance on several benchmarks with the best accuracy among
deep unsupervised methods. Also, our method achieves competitive results to
recent fully supervised methods while with much fewer parameters.
| [
{
"created": "Sun, 29 Mar 2020 14:55:24 GMT",
"version": "v1"
},
{
"created": "Sun, 29 Nov 2020 12:26:25 GMT",
"version": "v2"
}
] | 2020-12-01 | [
[
"Liu",
"Liang",
""
],
[
"Zhang",
"Jiangning",
""
],
[
"He",
"Ruifei",
""
],
[
"Liu",
"Yong",
""
],
[
"Wang",
"Yabiao",
""
],
[
"Tai",
"Ying",
""
],
[
"Luo",
"Donghao",
""
],
[
"Wang",
"Chengjie",
""
],
[
"Li",
"Jilin",
""
],
[
"Huang",
"Feiyue",
""
]
] | Unsupervised learning of optical flow, which leverages the supervision from view synthesis, has emerged as a promising alternative to supervised methods. However, the objective of unsupervised learning is likely to be unreliable in challenging scenes. In this work, we present a framework to use more reliable supervision from transformations. It simply twists the general unsupervised learning pipeline by running another forward pass with transformed data from augmentation, along with using transformed predictions of original data as the self-supervision signal. Besides, we further introduce a lightweight network with multiple frames by a highly-shared flow decoder. Our method consistently gets a leap of performance on several benchmarks with the best accuracy among deep unsupervised methods. Also, our method achieves competitive results to recent fully supervised methods while with much fewer parameters. |
2404.01203 | Siddhant Jain | Siddhant Jain, Daniel Watson, Eric Tabellion, Aleksander
Ho{\l}y\'nski, Ben Poole, Janne Kontkanen | Video Interpolation with Diffusion Models | CVPR 2024, Project page at https://vidim-interpolation.github.io/ | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | We present VIDIM, a generative model for video interpolation, which creates
short videos given a start and end frame. In order to achieve high fidelity and
generate motions unseen in the input data, VIDIM uses cascaded diffusion models
to first generate the target video at low resolution, and then generate the
high-resolution video conditioned on the low-resolution generated video. We
compare VIDIM to previous state-of-the-art methods on video interpolation, and
demonstrate how such works fail in most settings where the underlying motion is
complex, nonlinear, or ambiguous while VIDIM can easily handle such cases. We
additionally demonstrate how classifier-free guidance on the start and end
frame and conditioning the super-resolution model on the original
high-resolution frames without additional parameters unlocks high-fidelity
results. VIDIM is fast to sample from as it jointly denoises all the frames to
be generated, requires less than a billion parameters per diffusion model to
produce compelling results, and still enjoys scalability and improved quality
at larger parameter counts.
| [
{
"created": "Mon, 1 Apr 2024 15:59:32 GMT",
"version": "v1"
}
] | 2024-04-02 | [
[
"Jain",
"Siddhant",
""
],
[
"Watson",
"Daniel",
""
],
[
"Tabellion",
"Eric",
""
],
[
"Hołyński",
"Aleksander",
""
],
[
"Poole",
"Ben",
""
],
[
"Kontkanen",
"Janne",
""
]
] | We present VIDIM, a generative model for video interpolation, which creates short videos given a start and end frame. In order to achieve high fidelity and generate motions unseen in the input data, VIDIM uses cascaded diffusion models to first generate the target video at low resolution, and then generate the high-resolution video conditioned on the low-resolution generated video. We compare VIDIM to previous state-of-the-art methods on video interpolation, and demonstrate how such works fail in most settings where the underlying motion is complex, nonlinear, or ambiguous while VIDIM can easily handle such cases. We additionally demonstrate how classifier-free guidance on the start and end frame and conditioning the super-resolution model on the original high-resolution frames without additional parameters unlocks high-fidelity results. VIDIM is fast to sample from as it jointly denoises all the frames to be generated, requires less than a billion parameters per diffusion model to produce compelling results, and still enjoys scalability and improved quality at larger parameter counts. |
1112.1828 | L\'aszl\'o Kozma | Laszlo Kozma | Minimum Average Distance Triangulations | ESA 2012 | null | null | null | cs.CG cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of finding a triangulation T of a planar point set S
such as to minimize the expected distance between two points x and y chosen
uniformly at random from S. By distance we mean the length of the shortest path
between x and y along edges of T. The length of a path is the sum of the
weights of its edges. Edge weights are assumed to be given as part of the
problem for every pair of distinct points (x,y) in S^2.
In a different variant of the problem, the points are vertices of a simple
polygon and we look for a triangulation of the interior of the polygon that is
optimal in the same sense.
We prove that a general formulation of the problem in which the weights are
arbitrary positive numbers is strongly NP-complete. For the case when all the
weights are equal we give polynomial-time algorithms. In the end we mention
several open problems.
| [
{
"created": "Thu, 8 Dec 2011 13:19:24 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Feb 2012 16:35:46 GMT",
"version": "v2"
},
{
"created": "Wed, 20 Jun 2012 13:18:20 GMT",
"version": "v3"
}
] | 2012-06-21 | [
[
"Kozma",
"Laszlo",
""
]
] | We study the problem of finding a triangulation T of a planar point set S such as to minimize the expected distance between two points x and y chosen uniformly at random from S. By distance we mean the length of the shortest path between x and y along edges of T. The length of a path is the sum of the weights of its edges. Edge weights are assumed to be given as part of the problem for every pair of distinct points (x,y) in S^2. In a different variant of the problem, the points are vertices of a simple polygon and we look for a triangulation of the interior of the polygon that is optimal in the same sense. We prove that a general formulation of the problem in which the weights are arbitrary positive numbers is strongly NP-complete. For the case when all the weights are equal we give polynomial-time algorithms. In the end we mention several open problems. |
2312.12466 | Santhosh Pogaku | Santhosh Pogaku | Users Approach on Providing Feedback for Smart Home Devices | arXiv admin note: text overlap with arXiv:2312.11817 | null | null | null | cs.HC cs.CL | http://creativecommons.org/licenses/by/4.0/ | Smart Home technology has accomplished extraordinary interest in making
individuals' lives more straightforward and more relaxing as of late.
Technology as of late brought about delivering numerous savvy and refined
frameworks which advanced clever living innovation. In this paper, we will be
investigating the behavioural intention of user's approach on providing
feedback for smart home devices. We will be conducting an online survey for
sample of three to five students selected by simple random sampling to study
the user's motto for giving feedback on smart home devices and their
expectations. We have observed that most users are ready to share their
feedback on smart home devices actively to improvise the service and quality of
the product to fulfill the user needs and make their lives easier.
| [
{
"created": "Tue, 19 Dec 2023 03:18:12 GMT",
"version": "v1"
}
] | 2023-12-21 | [
[
"Pogaku",
"Santhosh",
""
]
] | Smart Home technology has accomplished extraordinary interest in making individuals' lives more straightforward and more relaxing as of late. Technology as of late brought about delivering numerous savvy and refined frameworks which advanced clever living innovation. In this paper, we will be investigating the behavioural intention of user's approach on providing feedback for smart home devices. We will be conducting an online survey for sample of three to five students selected by simple random sampling to study the user's motto for giving feedback on smart home devices and their expectations. We have observed that most users are ready to share their feedback on smart home devices actively to improvise the service and quality of the product to fulfill the user needs and make their lives easier. |
2404.07336 | Prashant Mathur | Lucas Goncalves, Prashant Mathur, Chandrashekhar Lavania, Metehan
Cekic, Marcello Federico, Kyu J. Han | PEAVS: Perceptual Evaluation of Audio-Visual Synchrony Grounded in
Viewers' Opinion Scores | 24 pages | null | null | null | cs.CV cs.MM eess.AS | http://creativecommons.org/licenses/by/4.0/ | Recent advancements in audio-visual generative modeling have been propelled
by progress in deep learning and the availability of data-rich benchmarks.
However, the growth is not attributed solely to models and benchmarks.
Universally accepted evaluation metrics also play an important role in
advancing the field. While there are many metrics available to evaluate audio
and visual content separately, there is a lack of metrics that offer a
quantitative and interpretable measure of audio-visual synchronization for
videos "in the wild". To address this gap, we first created a large scale human
annotated dataset (100+ hrs) representing nine types of synchronization errors
in audio-visual content and how human perceive them. We then developed a PEAVS
(Perceptual Evaluation of Audio-Visual Synchrony) score, a novel automatic
metric with a 5-point scale that evaluates the quality of audio-visual
synchronization. We validate PEAVS using a newly generated dataset, achieving a
Pearson correlation of 0.79 at the set level and 0.54 at the clip level when
compared to human labels. In our experiments, we observe a relative gain 50%
over a natural extension of Fr\'echet based metrics for Audio-Visual synchrony,
confirming PEAVS efficacy in objectively modeling subjective perceptions of
audio-visual synchronization for videos "in the wild".
| [
{
"created": "Wed, 10 Apr 2024 20:32:24 GMT",
"version": "v1"
}
] | 2024-04-12 | [
[
"Goncalves",
"Lucas",
""
],
[
"Mathur",
"Prashant",
""
],
[
"Lavania",
"Chandrashekhar",
""
],
[
"Cekic",
"Metehan",
""
],
[
"Federico",
"Marcello",
""
],
[
"Han",
"Kyu J.",
""
]
] | Recent advancements in audio-visual generative modeling have been propelled by progress in deep learning and the availability of data-rich benchmarks. However, the growth is not attributed solely to models and benchmarks. Universally accepted evaluation metrics also play an important role in advancing the field. While there are many metrics available to evaluate audio and visual content separately, there is a lack of metrics that offer a quantitative and interpretable measure of audio-visual synchronization for videos "in the wild". To address this gap, we first created a large scale human annotated dataset (100+ hrs) representing nine types of synchronization errors in audio-visual content and how human perceive them. We then developed a PEAVS (Perceptual Evaluation of Audio-Visual Synchrony) score, a novel automatic metric with a 5-point scale that evaluates the quality of audio-visual synchronization. We validate PEAVS using a newly generated dataset, achieving a Pearson correlation of 0.79 at the set level and 0.54 at the clip level when compared to human labels. In our experiments, we observe a relative gain 50% over a natural extension of Fr\'echet based metrics for Audio-Visual synchrony, confirming PEAVS efficacy in objectively modeling subjective perceptions of audio-visual synchronization for videos "in the wild". |
2309.08794 | Deval Mehta | Deval Mehta, Shobi Sivathamboo, Hugh Simpson, Patrick Kwan, Terence
O`Brien, Zongyuan Ge | Privacy-preserving Early Detection of Epileptic Seizures in Videos | Accepted to MICCAI 2023 | null | null | null | cs.AI cs.CV | http://creativecommons.org/licenses/by/4.0/ | In this work, we contribute towards the development of video-based epileptic
seizure classification by introducing a novel framework (SETR-PKD), which could
achieve privacy-preserved early detection of seizures in videos. Specifically,
our framework has two significant components - (1) It is built upon optical
flow features extracted from the video of a seizure, which encodes the seizure
motion semiotics while preserving the privacy of the patient; (2) It utilizes a
transformer based progressive knowledge distillation, where the knowledge is
gradually distilled from networks trained on a longer portion of video samples
to the ones which will operate on shorter portions. Thus, our proposed
framework addresses the limitations of the current approaches which compromise
the privacy of the patients by directly operating on the RGB video of a seizure
as well as impede real-time detection of a seizure by utilizing the full video
sample to make a prediction. Our SETR-PKD framework could detect tonic-clonic
seizures (TCSs) in a privacy-preserving manner with an accuracy of 83.9% while
they are only half-way into their progression. Our data and code is available
at https://github.com/DevD1092/seizure-detection
| [
{
"created": "Fri, 15 Sep 2023 22:29:07 GMT",
"version": "v1"
}
] | 2023-09-19 | [
[
"Mehta",
"Deval",
""
],
[
"Sivathamboo",
"Shobi",
""
],
[
"Simpson",
"Hugh",
""
],
[
"Kwan",
"Patrick",
""
],
[
"O`Brien",
"Terence",
""
],
[
"Ge",
"Zongyuan",
""
]
] | In this work, we contribute towards the development of video-based epileptic seizure classification by introducing a novel framework (SETR-PKD), which could achieve privacy-preserved early detection of seizures in videos. Specifically, our framework has two significant components - (1) It is built upon optical flow features extracted from the video of a seizure, which encodes the seizure motion semiotics while preserving the privacy of the patient; (2) It utilizes a transformer based progressive knowledge distillation, where the knowledge is gradually distilled from networks trained on a longer portion of video samples to the ones which will operate on shorter portions. Thus, our proposed framework addresses the limitations of the current approaches which compromise the privacy of the patients by directly operating on the RGB video of a seizure as well as impede real-time detection of a seizure by utilizing the full video sample to make a prediction. Our SETR-PKD framework could detect tonic-clonic seizures (TCSs) in a privacy-preserving manner with an accuracy of 83.9% while they are only half-way into their progression. Our data and code is available at https://github.com/DevD1092/seizure-detection |
2403.12706 | Shanchuan Lin | Shanchuan Lin, Xiao Yang | AnimateDiff-Lightning: Cross-Model Diffusion Distillation | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present AnimateDiff-Lightning for lightning-fast video generation. Our
model uses progressive adversarial diffusion distillation to achieve new
state-of-the-art in few-step video generation. We discuss our modifications to
adapt it for the video modality. Furthermore, we propose to simultaneously
distill the probability flow of multiple base diffusion models, resulting in a
single distilled motion module with broader style compatibility. We are pleased
to release our distilled AnimateDiff-Lightning model for the community's use.
| [
{
"created": "Tue, 19 Mar 2024 13:08:54 GMT",
"version": "v1"
}
] | 2024-03-20 | [
[
"Lin",
"Shanchuan",
""
],
[
"Yang",
"Xiao",
""
]
] | We present AnimateDiff-Lightning for lightning-fast video generation. Our model uses progressive adversarial diffusion distillation to achieve new state-of-the-art in few-step video generation. We discuss our modifications to adapt it for the video modality. Furthermore, we propose to simultaneously distill the probability flow of multiple base diffusion models, resulting in a single distilled motion module with broader style compatibility. We are pleased to release our distilled AnimateDiff-Lightning model for the community's use. |
cs/0306063 | Dantong Yu | Richard Baker, Dantong Yu, Tomasz Wlodek | A Model for Grid User Management | Talk from the 2003 Computing in High Energy and Nuclear Physics
(CHEP03), La Jolla, Ca, USA, March 2003, 6 pages, 2 figures and 1 style file,
PSN TUBT002 | null | null | null | cs.DC | null | Registration and management of users in a large scale Grid computing
environment presents new challenges that are not well addressed by existing
protocols. Within a single Virtual Organization (VO), thousands of users will
potentially need access to hundreds of computing sites, and the traditional
model where users register for local accounts at each site will present
significant scaling problems. However, computing sites must maintain control
over access to the site and site policies generally require individual local
accounts for every user. We present here a model that allows users to register
once with a VO and yet still provides all of the computing sites the
information they require with the required level of trust. We have developed
tools to allow sites to automate the management of local accounts and the
mappings between Grid identities and local accounts.
| [
{
"created": "Fri, 13 Jun 2003 17:01:45 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Baker",
"Richard",
""
],
[
"Yu",
"Dantong",
""
],
[
"Wlodek",
"Tomasz",
""
]
] | Registration and management of users in a large scale Grid computing environment presents new challenges that are not well addressed by existing protocols. Within a single Virtual Organization (VO), thousands of users will potentially need access to hundreds of computing sites, and the traditional model where users register for local accounts at each site will present significant scaling problems. However, computing sites must maintain control over access to the site and site policies generally require individual local accounts for every user. We present here a model that allows users to register once with a VO and yet still provides all of the computing sites the information they require with the required level of trust. We have developed tools to allow sites to automate the management of local accounts and the mappings between Grid identities and local accounts. |
1903.03036 | David McDonald | David McDonald and Shan He | HEAT: Hyperbolic Embedding of Attributed Networks | 15 pages, 4 figures | null | null | null | cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Finding a low dimensional representation of hierarchical, structured data
described by a network remains a challenging problem in the machine learning
community. An emerging approach is embedding these networks into hyperbolic
space because it can naturally represent a network's hierarchical structure.
However, existing hyperbolic embedding approaches cannot deal with attributed
networks, in which nodes are annotated with additional attributes. These
attributes might provide additional proximity information to constrain the
representations of the nodes, which is important to learn high quality
hyperbolic embeddings. To fill this gap, we introduce HEAT (Hyperbolic
Embedding of ATributed networks), the first method for embedding attributed
networks to a hyperbolic space. HEAT consists of 1) a modified random walk
algorithm to obtain training samples that capture both topological and
attribute similarity; and 2) a learning algorithm for learning hyperboloid
embeddings from the obtained training samples. We show that by leveraging node
attributes, HEAT can outperform a state-of-the-art Hyperbolic embedding
algorithm on several downstream tasks. As a general embedding method, HEAT
opens the door to hyperbolic manifold learning on a wide range of attributed
and unattributed networks.
| [
{
"created": "Thu, 7 Mar 2019 16:50:26 GMT",
"version": "v1"
},
{
"created": "Thu, 2 May 2019 11:17:22 GMT",
"version": "v2"
}
] | 2019-05-03 | [
[
"McDonald",
"David",
""
],
[
"He",
"Shan",
""
]
] | Finding a low dimensional representation of hierarchical, structured data described by a network remains a challenging problem in the machine learning community. An emerging approach is embedding these networks into hyperbolic space because it can naturally represent a network's hierarchical structure. However, existing hyperbolic embedding approaches cannot deal with attributed networks, in which nodes are annotated with additional attributes. These attributes might provide additional proximity information to constrain the representations of the nodes, which is important to learn high quality hyperbolic embeddings. To fill this gap, we introduce HEAT (Hyperbolic Embedding of ATributed networks), the first method for embedding attributed networks to a hyperbolic space. HEAT consists of 1) a modified random walk algorithm to obtain training samples that capture both topological and attribute similarity; and 2) a learning algorithm for learning hyperboloid embeddings from the obtained training samples. We show that by leveraging node attributes, HEAT can outperform a state-of-the-art Hyperbolic embedding algorithm on several downstream tasks. As a general embedding method, HEAT opens the door to hyperbolic manifold learning on a wide range of attributed and unattributed networks. |
2206.12946 | Nimet Kaygusuz | Nimet Kaygusuz, Oscar Mendez, Richard Bowden | AFT-VO: Asynchronous Fusion Transformers for Multi-View Visual Odometry
Estimation | null | null | null | null | cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motion estimation approaches typically employ sensor fusion techniques, such
as the Kalman Filter, to handle individual sensor failures. More recently, deep
learning-based fusion approaches have been proposed, increasing the performance
and requiring less model-specific implementations. However, current deep fusion
approaches often assume that sensors are synchronised, which is not always
practical, especially for low-cost hardware. To address this limitation, in
this work, we propose AFT-VO, a novel transformer-based sensor fusion
architecture to estimate VO from multiple sensors. Our framework combines
predictions from asynchronous multi-view cameras and accounts for the time
discrepancies of measurements coming from different sources.
Our approach first employs a Mixture Density Network (MDN) to estimate the
probability distributions of the 6-DoF poses for every camera in the system.
Then a novel transformer-based fusion module, AFT-VO, is introduced, which
combines these asynchronous pose estimations, along with their confidences.
More specifically, we introduce Discretiser and Source Encoding techniques
which enable the fusion of multi-source asynchronous signals.
We evaluate our approach on the popular nuScenes and KITTI datasets. Our
experiments demonstrate that multi-view fusion for VO estimation provides
robust and accurate trajectories, outperforming the state of the art in both
challenging weather and lighting conditions.
| [
{
"created": "Sun, 26 Jun 2022 19:29:08 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Sep 2022 13:47:18 GMT",
"version": "v2"
}
] | 2022-09-19 | [
[
"Kaygusuz",
"Nimet",
""
],
[
"Mendez",
"Oscar",
""
],
[
"Bowden",
"Richard",
""
]
] | Motion estimation approaches typically employ sensor fusion techniques, such as the Kalman Filter, to handle individual sensor failures. More recently, deep learning-based fusion approaches have been proposed, increasing the performance and requiring less model-specific implementations. However, current deep fusion approaches often assume that sensors are synchronised, which is not always practical, especially for low-cost hardware. To address this limitation, in this work, we propose AFT-VO, a novel transformer-based sensor fusion architecture to estimate VO from multiple sensors. Our framework combines predictions from asynchronous multi-view cameras and accounts for the time discrepancies of measurements coming from different sources. Our approach first employs a Mixture Density Network (MDN) to estimate the probability distributions of the 6-DoF poses for every camera in the system. Then a novel transformer-based fusion module, AFT-VO, is introduced, which combines these asynchronous pose estimations, along with their confidences. More specifically, we introduce Discretiser and Source Encoding techniques which enable the fusion of multi-source asynchronous signals. We evaluate our approach on the popular nuScenes and KITTI datasets. Our experiments demonstrate that multi-view fusion for VO estimation provides robust and accurate trajectories, outperforming the state of the art in both challenging weather and lighting conditions. |
2111.07271 | Auriol Degbelo | Lucas Braun, Auriol Degbelo, Christian Kray | Geofreebie: A Location-Based Freecycling App to Support Forced Migrant
Resettlement | Article accepted for publication in the Journal of Location-based
Services | null | 10.1080/17489725.2021.1874553 | null | cs.HC | http://creativecommons.org/licenses/by/4.0/ | Germany has witnessed an influx of forced migrants in recent years. Promoting
social interaction with the local community is key to supporting the
resettlement of these newcomers. Location-based freecycling services present
important benefits due to freecycling's potential to bolster social engagement
and location-based services' ability to adapt to the user's context. Yet, their
potential to support forced migrants' resettlement is yet to be examined. We
conducted needs assessment interviews with 11 participants in Muenster,
Germany. We analyzed the interview results to develop user requirements for
location-based freecycling services. We then implemented a subset of the user
requirements as a prototype mobile app called Geofreebie. The evaluation of the
app with 22 participants showed that Geofreebie offered two key advantages for
forced migrants' resettlement: it increased the size of their social network,
and created a sense of community on their side. These findings can benefit
researchers and developers of location-based services to support forced migrant
resettlement.
| [
{
"created": "Sun, 14 Nov 2021 08:12:46 GMT",
"version": "v1"
}
] | 2021-11-16 | [
[
"Braun",
"Lucas",
""
],
[
"Degbelo",
"Auriol",
""
],
[
"Kray",
"Christian",
""
]
] | Germany has witnessed an influx of forced migrants in recent years. Promoting social interaction with the local community is key to supporting the resettlement of these newcomers. Location-based freecycling services present important benefits due to freecycling's potential to bolster social engagement and location-based services' ability to adapt to the user's context. Yet, their potential to support forced migrants' resettlement is yet to be examined. We conducted needs assessment interviews with 11 participants in Muenster, Germany. We analyzed the interview results to develop user requirements for location-based freecycling services. We then implemented a subset of the user requirements as a prototype mobile app called Geofreebie. The evaluation of the app with 22 participants showed that Geofreebie offered two key advantages for forced migrants' resettlement: it increased the size of their social network, and created a sense of community on their side. These findings can benefit researchers and developers of location-based services to support forced migrant resettlement. |
cs/0406033 | Manor Mendel | Manor Mendel | Randomized k-server algorithms for growth-rate bounded graphs | The paper is withdrawn | J. Algorithms, 55(2): 192-202, 2005 | 10.1016/j.jalgor.2004.06.002 | null | cs.DS | null | The paper referred to in the title is withdrawn.
| [
{
"created": "Thu, 17 Jun 2004 15:11:54 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Sep 2007 22:31:51 GMT",
"version": "v2"
}
] | 2007-10-01 | [
[
"Mendel",
"Manor",
""
]
] | The paper referred to in the title is withdrawn. |
1901.01651 | Gary Pui-Tung Choi | Gary P. T. Choi, Hei Long Chan, Robin Yong, Sarbin Ranjitkar, Alan
Brook, Grant Townsend, Ke Chen, Lok Ming Lui | Tooth morphometry using quasi-conformal theory | null | Pattern Recognition 99, 107064 (2020) | 10.1016/j.patcog.2019.107064 | null | cs.CV cs.CG q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Shape analysis is important in anthropology, bioarchaeology and forensic
science for interpreting useful information from human remains. In particular,
teeth are morphologically stable and hence well-suited for shape analysis. In
this work, we propose a framework for tooth morphometry using quasi-conformal
theory. Landmark-matching Teichm\"uller maps are used for establishing a 1-1
correspondence between tooth surfaces with prescribed anatomical landmarks.
Then, a quasi-conformal statistical shape analysis model based on the
Teichm\"uller mapping results is proposed for building a tooth classification
scheme. We deploy our framework on a dataset of human premolars to analyze the
tooth shape variation among genders and ancestries. Experimental results show
that our method achieves much higher classification accuracy with respect to
both gender and ancestry when compared to the existing methods. Furthermore,
our model reveals the underlying tooth shape difference between different
genders and ancestries in terms of the local geometric distortion and
curvatures.
| [
{
"created": "Mon, 7 Jan 2019 03:00:12 GMT",
"version": "v1"
}
] | 2020-02-10 | [
[
"Choi",
"Gary P. T.",
""
],
[
"Chan",
"Hei Long",
""
],
[
"Yong",
"Robin",
""
],
[
"Ranjitkar",
"Sarbin",
""
],
[
"Brook",
"Alan",
""
],
[
"Townsend",
"Grant",
""
],
[
"Chen",
"Ke",
""
],
[
"Lui",
"Lok Ming",
""
]
] | Shape analysis is important in anthropology, bioarchaeology and forensic science for interpreting useful information from human remains. In particular, teeth are morphologically stable and hence well-suited for shape analysis. In this work, we propose a framework for tooth morphometry using quasi-conformal theory. Landmark-matching Teichm\"uller maps are used for establishing a 1-1 correspondence between tooth surfaces with prescribed anatomical landmarks. Then, a quasi-conformal statistical shape analysis model based on the Teichm\"uller mapping results is proposed for building a tooth classification scheme. We deploy our framework on a dataset of human premolars to analyze the tooth shape variation among genders and ancestries. Experimental results show that our method achieves much higher classification accuracy with respect to both gender and ancestry when compared to the existing methods. Furthermore, our model reveals the underlying tooth shape difference between different genders and ancestries in terms of the local geometric distortion and curvatures. |
2308.09866 | Junyan Su | Junyan Su, Qiulin Lin, Minghua Chen, Haibo Zeng | Minimizing Carbon Footprint for Timely E-Truck Transportation: Hardness
and Approximation Algorithm | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Carbon footprint optimization (CFO) is important for sustainable heavy-duty
e-truck transportation. We consider the CFO problem for timely transportation
of e-trucks, where the truck travels from an origin to a destination across a
national highway network subject to a deadline. The goal is to minimize the
carbon footprint by orchestrating path planning, speed planning, and
intermediary charging planning. We first show that it is NP-hard even just to
find a feasible CFO solution. We then develop a $(1+\epsilon_F,
1+\epsilon_\beta)$ bi-criteria approximation algorithm that achieves a carbon
footprint within a ratio of $(1+\epsilon_F)$ to the minimum with no deadline
violation and at most a ratio of $(1+\epsilon_\beta)$ battery capacity
violation (for any positive $\epsilon_F$ and $\epsilon_\beta$). Its time
complexity is polynomial in the size of the highway network, $1/\epsilon_F$,
and $1/\epsilon_\beta$. Such algorithmic results are among the best possible
unless P=NP. Simulation results based on real-world traces show that our scheme
reduces up to 11\% carbon footprint as compared to baseline alternatives
considering only energy consumption but not carbon footprint.
| [
{
"created": "Sat, 19 Aug 2023 00:59:17 GMT",
"version": "v1"
}
] | 2023-08-22 | [
[
"Su",
"Junyan",
""
],
[
"Lin",
"Qiulin",
""
],
[
"Chen",
"Minghua",
""
],
[
"Zeng",
"Haibo",
""
]
] | Carbon footprint optimization (CFO) is important for sustainable heavy-duty e-truck transportation. We consider the CFO problem for timely transportation of e-trucks, where the truck travels from an origin to a destination across a national highway network subject to a deadline. The goal is to minimize the carbon footprint by orchestrating path planning, speed planning, and intermediary charging planning. We first show that it is NP-hard even just to find a feasible CFO solution. We then develop a $(1+\epsilon_F, 1+\epsilon_\beta)$ bi-criteria approximation algorithm that achieves a carbon footprint within a ratio of $(1+\epsilon_F)$ to the minimum with no deadline violation and at most a ratio of $(1+\epsilon_\beta)$ battery capacity violation (for any positive $\epsilon_F$ and $\epsilon_\beta$). Its time complexity is polynomial in the size of the highway network, $1/\epsilon_F$, and $1/\epsilon_\beta$. Such algorithmic results are among the best possible unless P=NP. Simulation results based on real-world traces show that our scheme reduces up to 11\% carbon footprint as compared to baseline alternatives considering only energy consumption but not carbon footprint. |
2310.01292 | Luyi Qiu | Luyi Qiu and Dayu Yu and Xiaofeng Zhang and Chenxiao Zhang | Efficient Remote Sensing Segmentation With Generative Adversarial
Transformer | null | null | null | null | cs.CV cs.LG eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most deep learning methods that achieve high segmentation accuracy require
deep network architectures that are too heavy and complex to run on embedded
devices with limited storage and memory space. To address this issue, this
paper proposes an efficient Generative Adversarial Transfomer (GATrans) for
achieving high-precision semantic segmentation while maintaining an extremely
efficient size. The framework utilizes a Global Transformer Network (GTNet) as
the generator, efficiently extracting multi-level features through residual
connections. GTNet employs global transformer blocks with progressively linear
computational complexity to reassign global features based on a learnable
similarity function. To focus on object-level and pixel-level information, the
GATrans optimizes the objective function by combining structural similarity
losses. We validate the effectiveness of our approach through extensive
experiments on the Vaihingen dataset, achieving an average F1 score of 90.17%
and an overall accuracy of 91.92%.
| [
{
"created": "Mon, 2 Oct 2023 15:46:59 GMT",
"version": "v1"
}
] | 2023-10-03 | [
[
"Qiu",
"Luyi",
""
],
[
"Yu",
"Dayu",
""
],
[
"Zhang",
"Xiaofeng",
""
],
[
"Zhang",
"Chenxiao",
""
]
] | Most deep learning methods that achieve high segmentation accuracy require deep network architectures that are too heavy and complex to run on embedded devices with limited storage and memory space. To address this issue, this paper proposes an efficient Generative Adversarial Transfomer (GATrans) for achieving high-precision semantic segmentation while maintaining an extremely efficient size. The framework utilizes a Global Transformer Network (GTNet) as the generator, efficiently extracting multi-level features through residual connections. GTNet employs global transformer blocks with progressively linear computational complexity to reassign global features based on a learnable similarity function. To focus on object-level and pixel-level information, the GATrans optimizes the objective function by combining structural similarity losses. We validate the effectiveness of our approach through extensive experiments on the Vaihingen dataset, achieving an average F1 score of 90.17% and an overall accuracy of 91.92%. |
2103.13188 | Alexander Venus MSc | Alexander Venus, Erik Leitinger, Stefan Tertinek and Klaus Witrisal | A Message Passing based Adaptive PDA Algorithm for Robust Radio-based
Localization and Tracking | 6 pages (two column), 6 figures, IEEE RadarConf 2021: Synergistic
Radar Signal Processing and Tracking | null | null | null | cs.IT math.IT | http://creativecommons.org/licenses/by/4.0/ | We present a message passing algorithm for localization and tracking in
multipath-prone environments that implicitly considers obstructed line-of-sight
situations. The proposed adaptive probabilistic data association algorithm
infers the position of a mobile agent using multiple anchors by utilizing delay
and amplitude of the multipath components (MPCs) as well as their respective
uncertainties. By employing a nonuniform clutter model, we enable the algorithm
to facilitate the position information contained in the MPCs to support the
estimation of the agent position without exact knowledge about the environment
geometry. Our algorithm adapts in an online manner to both, the time-varying
signal-to-noise-ratio and line-of-sight (LOS) existence probability of each
anchor. In a numerical analysis we show that the algorithm is able to operate
reliably in environments characterized by strong multipath propagation, even if
a temporary obstruction of all anchors occurs simultaneously.
| [
{
"created": "Wed, 24 Mar 2021 13:43:34 GMT",
"version": "v1"
},
{
"created": "Thu, 25 Mar 2021 12:06:28 GMT",
"version": "v2"
}
] | 2021-03-26 | [
[
"Venus",
"Alexander",
""
],
[
"Leitinger",
"Erik",
""
],
[
"Tertinek",
"Stefan",
""
],
[
"Witrisal",
"Klaus",
""
]
] | We present a message passing algorithm for localization and tracking in multipath-prone environments that implicitly considers obstructed line-of-sight situations. The proposed adaptive probabilistic data association algorithm infers the position of a mobile agent using multiple anchors by utilizing delay and amplitude of the multipath components (MPCs) as well as their respective uncertainties. By employing a nonuniform clutter model, we enable the algorithm to facilitate the position information contained in the MPCs to support the estimation of the agent position without exact knowledge about the environment geometry. Our algorithm adapts in an online manner to both, the time-varying signal-to-noise-ratio and line-of-sight (LOS) existence probability of each anchor. In a numerical analysis we show that the algorithm is able to operate reliably in environments characterized by strong multipath propagation, even if a temporary obstruction of all anchors occurs simultaneously. |
2104.02995 | Qingqing Long | Qingqing Long, Yilun Jin, Yi Wu, Guojie Song | Theoretically Improving Graph Neural Networks via Anonymous Walk Graph
Kernels | 11 pages | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graph neural networks (GNNs) have achieved tremendous success in graph
mining. However, the inability of GNNs to model substructures in graphs remains
a significant drawback. Specifically, message-passing GNNs (MPGNNs), as the
prevailing type of GNNs, have been theoretically shown unable to distinguish,
detect or count many graph substructures. While efforts have been paid to
complement the inability, existing works either rely on pre-defined
substructure sets, thus being less flexible, or are lacking in theoretical
insights. In this paper, we propose GSKN, a GNN model with a theoretically
stronger ability to distinguish graph structures. Specifically, we design GSKN
based on anonymous walks (AWs), flexible substructure units, and derive it upon
feature mappings of graph kernels (GKs). We theoretically show that GSKN
provably extends the 1-WL test, and hence the maximally powerful MPGNNs from
both graph-level and node-level viewpoints. Correspondingly, various
experiments are leveraged to evaluate GSKN, where GSKN outperforms a wide range
of baselines, endorsing the analysis.
| [
{
"created": "Wed, 7 Apr 2021 08:50:34 GMT",
"version": "v1"
}
] | 2021-04-08 | [
[
"Long",
"Qingqing",
""
],
[
"Jin",
"Yilun",
""
],
[
"Wu",
"Yi",
""
],
[
"Song",
"Guojie",
""
]
] | Graph neural networks (GNNs) have achieved tremendous success in graph mining. However, the inability of GNNs to model substructures in graphs remains a significant drawback. Specifically, message-passing GNNs (MPGNNs), as the prevailing type of GNNs, have been theoretically shown unable to distinguish, detect or count many graph substructures. While efforts have been paid to complement the inability, existing works either rely on pre-defined substructure sets, thus being less flexible, or are lacking in theoretical insights. In this paper, we propose GSKN, a GNN model with a theoretically stronger ability to distinguish graph structures. Specifically, we design GSKN based on anonymous walks (AWs), flexible substructure units, and derive it upon feature mappings of graph kernels (GKs). We theoretically show that GSKN provably extends the 1-WL test, and hence the maximally powerful MPGNNs from both graph-level and node-level viewpoints. Correspondingly, various experiments are leveraged to evaluate GSKN, where GSKN outperforms a wide range of baselines, endorsing the analysis. |
2311.10785 | Federico Albanese | Federico Albanese and Daniel Ciolek and Nicolas D'Ippolito | Text Sanitization Beyond Specific Domains: Zero-Shot Redaction &
Substitution with Large Language Models | null | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the context of information systems, text sanitization techniques are used
to identify and remove sensitive data to comply with security and regulatory
requirements. Even though many methods for privacy preservation have been
proposed, most of them are focused on the detection of entities from specific
domains (e.g., credit card numbers, social security numbers), lacking
generality and requiring customization for each desirable domain. Moreover,
removing words is, in general, a drastic measure, as it can degrade text
coherence and contextual information. Less severe measures include substituting
a word for a safe alternative, yet it can be challenging to automatically find
meaningful substitutions. We present a zero-shot text sanitization technique
that detects and substitutes potentially sensitive information using Large
Language Models. Our evaluation shows that our method excels at protecting
privacy while maintaining text coherence and contextual information, preserving
data utility for downstream tasks.
| [
{
"created": "Thu, 16 Nov 2023 18:42:37 GMT",
"version": "v1"
}
] | 2023-11-21 | [
[
"Albanese",
"Federico",
""
],
[
"Ciolek",
"Daniel",
""
],
[
"D'Ippolito",
"Nicolas",
""
]
] | In the context of information systems, text sanitization techniques are used to identify and remove sensitive data to comply with security and regulatory requirements. Even though many methods for privacy preservation have been proposed, most of them are focused on the detection of entities from specific domains (e.g., credit card numbers, social security numbers), lacking generality and requiring customization for each desirable domain. Moreover, removing words is, in general, a drastic measure, as it can degrade text coherence and contextual information. Less severe measures include substituting a word for a safe alternative, yet it can be challenging to automatically find meaningful substitutions. We present a zero-shot text sanitization technique that detects and substitutes potentially sensitive information using Large Language Models. Our evaluation shows that our method excels at protecting privacy while maintaining text coherence and contextual information, preserving data utility for downstream tasks. |
2401.03552 | Vinod Puthuvath | Sameera K. M., Serena Nicolazzo, Marco Arazzi, Antonino Nocera,
Rafidha Rehiman K. A., Vinod P and Mauro Conti | Privacy-Preserving in Blockchain-based Federated Learning Systems | 44 pages, 11 figures | null | null | null | cs.CR cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Federated Learning (FL) has recently arisen as a revolutionary approach to
collaborative training Machine Learning models. According to this novel
framework, multiple participants train a global model collaboratively,
coordinating with a central aggregator without sharing their local data. As FL
gains popularity in diverse domains, security, and privacy concerns arise due
to the distributed nature of this solution. Therefore, integrating this
strategy with Blockchain technology has been consolidated as a preferred choice
to ensure the privacy and security of participants.
This paper explores the research efforts carried out by the scientific
community to define privacy solutions in scenarios adopting Blockchain-Enabled
FL. It comprehensively summarizes the background related to FL and Blockchain,
evaluates existing architectures for their integration, and the primary attacks
and possible countermeasures to guarantee privacy in this setting. Finally, it
reviews the main application scenarios where Blockchain-Enabled FL approaches
have been proficiently applied. This survey can help academia and industry
practitioners understand which theories and techniques exist to improve the
performance of FL through Blockchain to preserve privacy and which are the main
challenges and future directions in this novel and still under-explored
context. We believe this work provides a novel contribution respect to the
previous surveys and is a valuable tool to explore the current landscape,
understand perspectives, and pave the way for advancements or improvements in
this amalgamation of Blockchain and Federated Learning.
| [
{
"created": "Sun, 7 Jan 2024 17:23:55 GMT",
"version": "v1"
}
] | 2024-01-09 | [
[
"M.",
"Sameera K.",
""
],
[
"Nicolazzo",
"Serena",
""
],
[
"Arazzi",
"Marco",
""
],
[
"Nocera",
"Antonino",
""
],
[
"A.",
"Rafidha Rehiman K.",
""
],
[
"P",
"Vinod",
""
],
[
"Conti",
"Mauro",
""
]
] | Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants train a global model collaboratively, coordinating with a central aggregator without sharing their local data. As FL gains popularity in diverse domains, security, and privacy concerns arise due to the distributed nature of this solution. Therefore, integrating this strategy with Blockchain technology has been consolidated as a preferred choice to ensure the privacy and security of participants. This paper explores the research efforts carried out by the scientific community to define privacy solutions in scenarios adopting Blockchain-Enabled FL. It comprehensively summarizes the background related to FL and Blockchain, evaluates existing architectures for their integration, and the primary attacks and possible countermeasures to guarantee privacy in this setting. Finally, it reviews the main application scenarios where Blockchain-Enabled FL approaches have been proficiently applied. This survey can help academia and industry practitioners understand which theories and techniques exist to improve the performance of FL through Blockchain to preserve privacy and which are the main challenges and future directions in this novel and still under-explored context. We believe this work provides a novel contribution respect to the previous surveys and is a valuable tool to explore the current landscape, understand perspectives, and pave the way for advancements or improvements in this amalgamation of Blockchain and Federated Learning. |
1905.11471 | Jasdeep Singh | Jasdeep Singh, Bryan McCann, Nitish Shirish Keskar, Caiming Xiong,
Richard Socher | XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and
Question Answering | null | null | null | null | cs.CL cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While natural language processing systems often focus on a single language,
multilingual transfer learning has the potential to improve performance,
especially for low-resource languages. We introduce XLDA, cross-lingual data
augmentation, a method that replaces a segment of the input text with its
translation in another language. XLDA enhances performance of all 14 tested
languages of the cross-lingual natural language inference (XNLI) benchmark.
With improvements of up to $4.8\%$, training with XLDA achieves
state-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrast
to, and performs markedly better than, a more naive approach that aggregates
examples in various languages in a way that each example is solely in one
language. On the SQuAD question answering task, we see that XLDA provides a
$1.0\%$ performance increase on the English evaluation set. Comprehensive
experiments suggest that most languages are effective as cross-lingual
augmentors, that XLDA is robust to a wide range of translation quality, and
that XLDA is even more effective for randomly initialized models than for
pretrained models.
| [
{
"created": "Mon, 27 May 2019 19:44:33 GMT",
"version": "v1"
}
] | 2019-05-29 | [
[
"Singh",
"Jasdeep",
""
],
[
"McCann",
"Bryan",
""
],
[
"Keskar",
"Nitish Shirish",
""
],
[
"Xiong",
"Caiming",
""
],
[
"Socher",
"Richard",
""
]
] | While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language. XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark. With improvements of up to $4.8\%$, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language. On the SQuAD question answering task, we see that XLDA provides a $1.0\%$ performance increase on the English evaluation set. Comprehensive experiments suggest that most languages are effective as cross-lingual augmentors, that XLDA is robust to a wide range of translation quality, and that XLDA is even more effective for randomly initialized models than for pretrained models. |
2210.07071 | Yunhua Zhou | Yunhua Zhou, Pengyu Wang, Peiju Liu, Yuxin Wang, Xipeng Qiu | The Open-World Lottery Ticket Hypothesis for OOD Intent Classification | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most existing methods of Out-of-Domain (OOD) intent classification rely on
extensive auxiliary OOD corpora or specific training paradigms. However, they
are underdeveloped in the underlying principle that the models should have
differentiated confidence in In- and Out-of-domain intent. In this work, we
shed light on the fundamental cause of model overconfidence on OOD and
demonstrate that calibrated subnetworks can be uncovered by pruning the
overparameterized model. Calibrated confidence provided by the subnetwork can
better distinguish In- and Out-of-domain, which can be a benefit for almost all
post hoc methods. In addition to bringing fundamental insights, we also extend
the Lottery Ticket Hypothesis to open-world scenarios. We conduct extensive
experiments on four real-world datasets to demonstrate our approach can
establish consistent improvements compared with a suite of competitive
baselines.
| [
{
"created": "Thu, 13 Oct 2022 14:58:35 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Apr 2024 13:42:13 GMT",
"version": "v2"
},
{
"created": "Wed, 24 Apr 2024 02:37:55 GMT",
"version": "v3"
}
] | 2024-04-25 | [
[
"Zhou",
"Yunhua",
""
],
[
"Wang",
"Pengyu",
""
],
[
"Liu",
"Peiju",
""
],
[
"Wang",
"Yuxin",
""
],
[
"Qiu",
"Xipeng",
""
]
] | Most existing methods of Out-of-Domain (OOD) intent classification rely on extensive auxiliary OOD corpora or specific training paradigms. However, they are underdeveloped in the underlying principle that the models should have differentiated confidence in In- and Out-of-domain intent. In this work, we shed light on the fundamental cause of model overconfidence on OOD and demonstrate that calibrated subnetworks can be uncovered by pruning the overparameterized model. Calibrated confidence provided by the subnetwork can better distinguish In- and Out-of-domain, which can be a benefit for almost all post hoc methods. In addition to bringing fundamental insights, we also extend the Lottery Ticket Hypothesis to open-world scenarios. We conduct extensive experiments on four real-world datasets to demonstrate our approach can establish consistent improvements compared with a suite of competitive baselines. |
2006.03659 | John Giorgi | John Giorgi, Osvald Nitski, Bo Wang, Gary Bader | DeCLUTR: Deep Contrastive Learning for Unsupervised Textual
Representations | ACL2021 Camera Ready V2 | null | null | null | cs.CL cs.LG | http://creativecommons.org/publicdomain/zero/1.0/ | Sentence embeddings are an important component of many natural language
processing (NLP) systems. Like word embeddings, sentence embeddings are
typically learned on large text corpora and then transferred to various
downstream tasks, such as clustering and retrieval. Unlike word embeddings, the
highest performing solutions for learning sentence embeddings require labelled
data, limiting their usefulness to languages and domains where labelled data is
abundant. In this paper, we present DeCLUTR: Deep Contrastive Learning for
Unsupervised Textual Representations. Inspired by recent advances in deep
metric learning (DML), we carefully design a self-supervised objective for
learning universal sentence embeddings that does not require labelled training
data. When used to extend the pretraining of transformer-based language models,
our approach closes the performance gap between unsupervised and supervised
pretraining for universal sentence encoders. Importantly, our experiments
suggest that the quality of the learned embeddings scale with both the number
of trainable parameters and the amount of unlabelled training data. Our code
and pretrained models are publicly available and can be easily adapted to new
domains or used to embed unseen text.
| [
{
"created": "Fri, 5 Jun 2020 20:00:28 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Jun 2020 20:24:17 GMT",
"version": "v2"
},
{
"created": "Thu, 20 May 2021 19:47:53 GMT",
"version": "v3"
},
{
"created": "Thu, 27 May 2021 14:57:02 GMT",
"version": "v4"
}
] | 2021-05-28 | [
[
"Giorgi",
"John",
""
],
[
"Nitski",
"Osvald",
""
],
[
"Wang",
"Bo",
""
],
[
"Bader",
"Gary",
""
]
] | Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solutions for learning sentence embeddings require labelled data, limiting their usefulness to languages and domains where labelled data is abundant. In this paper, we present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. Inspired by recent advances in deep metric learning (DML), we carefully design a self-supervised objective for learning universal sentence embeddings that does not require labelled training data. When used to extend the pretraining of transformer-based language models, our approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders. Importantly, our experiments suggest that the quality of the learned embeddings scale with both the number of trainable parameters and the amount of unlabelled training data. Our code and pretrained models are publicly available and can be easily adapted to new domains or used to embed unseen text. |
1910.13181 | Talip Ucar | Talip Ucar | Bridging the ELBO and MMD | 14 pages, 11 figures | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the challenges in training generative models such as the variational
auto encoder (VAE) is avoiding posterior collapse. When the generator has too
much capacity, it is prone to ignoring latent code. This problem is exacerbated
when the dataset is small, and the latent dimension is high. The root of the
problem is the ELBO objective, specifically the Kullback-Leibler (KL)
divergence term in objective function \citep{zhao2019infovae}. This paper
proposes a new objective function to replace the KL term with one that emulates
the maximum mean discrepancy (MMD) objective. It also introduces a new
technique, named latent clipping, that is used to control distance between
samples in latent space. A probabilistic autoencoder model, named $\mu$-VAE, is
designed and trained on MNIST and MNIST Fashion datasets, using the new
objective function and is shown to outperform models trained with ELBO and
$\beta$-VAE objective. The $\mu$-VAE is less prone to posterior collapse, and
can generate reconstructions and new samples in good quality. Latent
representations learned by $\mu$-VAE are shown to be good and can be used for
downstream tasks such as classification.
| [
{
"created": "Tue, 29 Oct 2019 10:32:40 GMT",
"version": "v1"
}
] | 2019-10-30 | [
[
"Ucar",
"Talip",
""
]
] | One of the challenges in training generative models such as the variational auto encoder (VAE) is avoiding posterior collapse. When the generator has too much capacity, it is prone to ignoring latent code. This problem is exacerbated when the dataset is small, and the latent dimension is high. The root of the problem is the ELBO objective, specifically the Kullback-Leibler (KL) divergence term in objective function \citep{zhao2019infovae}. This paper proposes a new objective function to replace the KL term with one that emulates the maximum mean discrepancy (MMD) objective. It also introduces a new technique, named latent clipping, that is used to control distance between samples in latent space. A probabilistic autoencoder model, named $\mu$-VAE, is designed and trained on MNIST and MNIST Fashion datasets, using the new objective function and is shown to outperform models trained with ELBO and $\beta$-VAE objective. The $\mu$-VAE is less prone to posterior collapse, and can generate reconstructions and new samples in good quality. Latent representations learned by $\mu$-VAE are shown to be good and can be used for downstream tasks such as classification. |
1707.06070 | Nicolas Robinson-Garcia | Nicolas Robinson-Garcia, Philippe Mongeon, Wei Jeng and Rodrigo Costas | DataCite as a novel bibliometric source: Coverage, strengths and
limitations | Paper accepted for publication in Journal of Informetrics | Journal of Informetrics, 11(3), 841-854 (2017) | 10.1016/j.joi.2017.07.003 | null | cs.DL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper explores the characteristics of DataCite to determine its
possibilities and potential as a new bibliometric data source to analyze the
scholarly production of open data. Open science and the increasing data sharing
requirements from governments, funding bodies, institutions and scientific
journals has led to a pressing demand for the development of data metrics. As a
very first step towards reliable data metrics, we need to better comprehend the
limitations and caveats of the information provided by sources of open data. In
this paper, we critically examine records downloaded from the DataCite's OAI
API and elaborate a series of recommendations regarding the use of this source
for bibliometric analyses of open data. We highlight issues related to metadata
incompleteness, lack of standardization, and ambiguous definitions of several
fields. Despite these limitations, we emphasize DataCite's value and potential
to become one of the main sources for data metrics development.
| [
{
"created": "Wed, 19 Jul 2017 13:14:44 GMT",
"version": "v1"
}
] | 2017-10-13 | [
[
"Robinson-Garcia",
"Nicolas",
""
],
[
"Mongeon",
"Philippe",
""
],
[
"Jeng",
"Wei",
""
],
[
"Costas",
"Rodrigo",
""
]
] | This paper explores the characteristics of DataCite to determine its possibilities and potential as a new bibliometric data source to analyze the scholarly production of open data. Open science and the increasing data sharing requirements from governments, funding bodies, institutions and scientific journals has led to a pressing demand for the development of data metrics. As a very first step towards reliable data metrics, we need to better comprehend the limitations and caveats of the information provided by sources of open data. In this paper, we critically examine records downloaded from the DataCite's OAI API and elaborate a series of recommendations regarding the use of this source for bibliometric analyses of open data. We highlight issues related to metadata incompleteness, lack of standardization, and ambiguous definitions of several fields. Despite these limitations, we emphasize DataCite's value and potential to become one of the main sources for data metrics development. |
1605.00031 | Thomas Wiatowski | Philipp Grohs, Thomas Wiatowski, Helmut B\"olcskei | Deep Convolutional Neural Networks on Cartoon Functions | This is a slightly updated version of the paper published in the ISIT
proceedings. Specifically, we corrected errors in the arguments on the volume
of tubes. Note that this correction does not affect the main statements of
the paper | Proc. of IEEE International Symposium on Information Theory
(ISIT), Barcelona, Spain, pp. 1163-1167, July 2016 | 10.1109/ISIT.2016.7541482 | null | cs.LG cs.CV math.NA stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wiatowski and B\"olcskei, 2015, proved that deformation stability and
vertical translation invariance of deep convolutional neural network-based
feature extractors are guaranteed by the network structure per se rather than
the specific convolution kernels and non-linearities. While the translation
invariance result applies to square-integrable functions, the deformation
stability bound holds for band-limited functions only. Many signals of
practical relevance (such as natural images) exhibit, however, sharp and curved
discontinuities and are, hence, not band-limited. The main contribution of this
paper is a deformation stability result that takes these structural properties
into account. Specifically, we establish deformation stability bounds for the
class of cartoon functions introduced by Donoho, 2001.
| [
{
"created": "Fri, 29 Apr 2016 21:40:16 GMT",
"version": "v1"
},
{
"created": "Mon, 12 Feb 2018 13:47:49 GMT",
"version": "v2"
}
] | 2018-02-13 | [
[
"Grohs",
"Philipp",
""
],
[
"Wiatowski",
"Thomas",
""
],
[
"Bölcskei",
"Helmut",
""
]
] | Wiatowski and B\"olcskei, 2015, proved that deformation stability and vertical translation invariance of deep convolutional neural network-based feature extractors are guaranteed by the network structure per se rather than the specific convolution kernels and non-linearities. While the translation invariance result applies to square-integrable functions, the deformation stability bound holds for band-limited functions only. Many signals of practical relevance (such as natural images) exhibit, however, sharp and curved discontinuities and are, hence, not band-limited. The main contribution of this paper is a deformation stability result that takes these structural properties into account. Specifically, we establish deformation stability bounds for the class of cartoon functions introduced by Donoho, 2001. |
cs/0605041 | Jian Cao | Jian Cao, Edmund M. Yeh | Asymptotically Optimal Multiple-access Communication via Distributed
Rate Splitting | Submitted to the IEEE Transactions on Information Theory. 15 Pages | null | 10.1109/TIT.2006.887497 | null | cs.IT math.IT | null | We consider the multiple-access communication problem in a distributed
setting for both the additive white Gaussian noise channel and the discrete
memoryless channel. We propose a scheme called Distributed Rate Splitting to
achieve the optimal rates allowed by information theory in a distributed
manner. In this scheme, each real user creates a number of virtual users via a
power/rate splitting mechanism in the M-user Gaussian channel or via a random
switching mechanism in the M-user discrete memoryless channel. At the receiver,
all virtual users are successively decoded. Compared with other multiple-access
techniques, Distributed Rate Splitting can be implemented with lower complexity
and less coordination. Furthermore, in a symmetric setting, we show that the
rate tuple achieved by this scheme converges to the maximum equal rate point
allowed by the information-theoretic bound as the number of virtual users per
real user tends to infinity. When the capacity regions are asymmetric, we show
that a point on the dominant face can be achieved asymptotically. Finally, when
there is an unequal number of virtual users per real user, we show that
differential user rate requirements can be accommodated in a distributed
fashion.
| [
{
"created": "Tue, 9 May 2006 14:32:51 GMT",
"version": "v1"
},
{
"created": "Tue, 3 Oct 2006 19:48:15 GMT",
"version": "v2"
}
] | 2016-11-18 | [
[
"Cao",
"Jian",
""
],
[
"Yeh",
"Edmund M.",
""
]
] | We consider the multiple-access communication problem in a distributed setting for both the additive white Gaussian noise channel and the discrete memoryless channel. We propose a scheme called Distributed Rate Splitting to achieve the optimal rates allowed by information theory in a distributed manner. In this scheme, each real user creates a number of virtual users via a power/rate splitting mechanism in the M-user Gaussian channel or via a random switching mechanism in the M-user discrete memoryless channel. At the receiver, all virtual users are successively decoded. Compared with other multiple-access techniques, Distributed Rate Splitting can be implemented with lower complexity and less coordination. Furthermore, in a symmetric setting, we show that the rate tuple achieved by this scheme converges to the maximum equal rate point allowed by the information-theoretic bound as the number of virtual users per real user tends to infinity. When the capacity regions are asymmetric, we show that a point on the dominant face can be achieved asymptotically. Finally, when there is an unequal number of virtual users per real user, we show that differential user rate requirements can be accommodated in a distributed fashion. |
2206.03657 | Zhuoling Li | Zhuoling Li, Chuanrui Zhang, En Yu, Haoqian Wang | Delving into the Pre-training Paradigm of Monocular 3D Object Detection | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The labels of monocular 3D object detection (M3OD) are expensive to obtain.
Meanwhile, there usually exists numerous unlabeled data in practical
applications, and pre-training is an efficient way of exploiting the knowledge
in unlabeled data. However, the pre-training paradigm for M3OD is hardly
studied. We aim to bridge this gap in this work. To this end, we first draw two
observations: (1) The guideline of devising pre-training tasks is imitating the
representation of the target task. (2) Combining depth estimation and 2D object
detection is a promising M3OD pre-training baseline. Afterwards, following the
guideline, we propose several strategies to further improve this baseline,
which mainly include target guided semi-dense depth estimation, keypoint-aware
2D object detection, and class-level loss adjustment. Combining all the
developed techniques, the obtained pre-training framework produces pre-trained
backbones that improve M3OD performance significantly on both the KITTI-3D and
nuScenes benchmarks. For example, by applying a DLA34 backbone to a naive
center-based M3OD detector, the moderate ${\rm AP}_{3D}70$ score of Car on the
KITTI-3D testing set is boosted by 18.71\% and the NDS score on the nuScenes
validation set is improved by 40.41\% relatively.
| [
{
"created": "Wed, 8 Jun 2022 03:01:13 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Jun 2022 02:50:31 GMT",
"version": "v2"
}
] | 2022-06-16 | [
[
"Li",
"Zhuoling",
""
],
[
"Zhang",
"Chuanrui",
""
],
[
"Yu",
"En",
""
],
[
"Wang",
"Haoqian",
""
]
] | The labels of monocular 3D object detection (M3OD) are expensive to obtain. Meanwhile, there usually exists numerous unlabeled data in practical applications, and pre-training is an efficient way of exploiting the knowledge in unlabeled data. However, the pre-training paradigm for M3OD is hardly studied. We aim to bridge this gap in this work. To this end, we first draw two observations: (1) The guideline of devising pre-training tasks is imitating the representation of the target task. (2) Combining depth estimation and 2D object detection is a promising M3OD pre-training baseline. Afterwards, following the guideline, we propose several strategies to further improve this baseline, which mainly include target guided semi-dense depth estimation, keypoint-aware 2D object detection, and class-level loss adjustment. Combining all the developed techniques, the obtained pre-training framework produces pre-trained backbones that improve M3OD performance significantly on both the KITTI-3D and nuScenes benchmarks. For example, by applying a DLA34 backbone to a naive center-based M3OD detector, the moderate ${\rm AP}_{3D}70$ score of Car on the KITTI-3D testing set is boosted by 18.71\% and the NDS score on the nuScenes validation set is improved by 40.41\% relatively. |
2101.02028 | Ye Tian | Ye Tian | A Multilayer Correlated Topic Model | 11 pages, 4 figures | null | null | null | cs.IR cs.LG stat.CO stat.ME stat.ML | http://creativecommons.org/licenses/by/4.0/ | We proposed a novel multilayer correlated topic model (MCTM) to analyze how
the main ideas inherit and vary between a document and its different segments,
which helps understand an article's structure. The variational
expectation-maximization (EM) algorithm was derived to estimate the posterior
and parameters in MCTM. We introduced two potential applications of MCTM,
including the paragraph-level document analysis and market basket data
analysis. The effectiveness of MCTM in understanding the document structure has
been verified by the great predictive performance on held-out documents and
intuitive visualization. We also showed that MCTM could successfully capture
customers' popular shopping patterns in the market basket analysis.
| [
{
"created": "Sat, 2 Jan 2021 21:50:36 GMT",
"version": "v1"
}
] | 2021-01-07 | [
[
"Tian",
"Ye",
""
]
] | We proposed a novel multilayer correlated topic model (MCTM) to analyze how the main ideas inherit and vary between a document and its different segments, which helps understand an article's structure. The variational expectation-maximization (EM) algorithm was derived to estimate the posterior and parameters in MCTM. We introduced two potential applications of MCTM, including the paragraph-level document analysis and market basket data analysis. The effectiveness of MCTM in understanding the document structure has been verified by the great predictive performance on held-out documents and intuitive visualization. We also showed that MCTM could successfully capture customers' popular shopping patterns in the market basket analysis. |
2002.10451 | Mayank Mittal | Mayank Mittal, Marco Gallieri, Alessio Quaglino, Seyed Sina Mirrazavi
Salehian, Jan Koutn\'ik | Neural Lyapunov Model Predictive Control: Learning Safe Global
Controllers from Sub-optimal Examples | null | null | null | null | cs.AI cs.NE cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With a growing interest in data-driven control techniques, Model Predictive
Control (MPC) provides an opportunity to exploit the surplus of data reliably,
particularly while taking safety and stability into account. In many real-world
and industrial applications, it is typical to have an existing control
strategy, for instance, execution from a human operator. The objective of this
work is to improve upon this unknown, safe but suboptimal policy by learning a
new controller that retains safety and stability. Learning how to be safe is
achieved directly from data and from a knowledge of the system constraints. The
proposed algorithm alternatively learns the terminal cost and updates the MPC
parameters according to a stability metric. The terminal cost is constructed as
a Lyapunov function neural network with the aim of recovering or extending the
stable region of the initial demonstrator using a short prediction horizon.
Theorems that characterize the stability and performance of the learned MPC in
the bearing of model uncertainties and sub-optimality due to function
approximation are presented. The efficacy of the proposed algorithm is
demonstrated on non-linear continuous control tasks with soft constraints. The
proposed approach can improve upon the initial demonstrator also in practice
and achieve better stability than popular reinforcement learning baselines.
| [
{
"created": "Fri, 21 Feb 2020 16:57:38 GMT",
"version": "v1"
},
{
"created": "Thu, 3 Jun 2021 14:37:05 GMT",
"version": "v2"
}
] | 2021-06-04 | [
[
"Mittal",
"Mayank",
""
],
[
"Gallieri",
"Marco",
""
],
[
"Quaglino",
"Alessio",
""
],
[
"Salehian",
"Seyed Sina Mirrazavi",
""
],
[
"Koutník",
"Jan",
""
]
] | With a growing interest in data-driven control techniques, Model Predictive Control (MPC) provides an opportunity to exploit the surplus of data reliably, particularly while taking safety and stability into account. In many real-world and industrial applications, it is typical to have an existing control strategy, for instance, execution from a human operator. The objective of this work is to improve upon this unknown, safe but suboptimal policy by learning a new controller that retains safety and stability. Learning how to be safe is achieved directly from data and from a knowledge of the system constraints. The proposed algorithm alternatively learns the terminal cost and updates the MPC parameters according to a stability metric. The terminal cost is constructed as a Lyapunov function neural network with the aim of recovering or extending the stable region of the initial demonstrator using a short prediction horizon. Theorems that characterize the stability and performance of the learned MPC in the bearing of model uncertainties and sub-optimality due to function approximation are presented. The efficacy of the proposed algorithm is demonstrated on non-linear continuous control tasks with soft constraints. The proposed approach can improve upon the initial demonstrator also in practice and achieve better stability than popular reinforcement learning baselines. |
1401.7828 | Cristina Flaut | Cristina Flaut | Codes over a subset of Octonion Integers | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we define codes over some Octonion integers. We prove that in
some conditions these codes can correct up to two errors for a transmitted
vector and the code rate of the codes is grater than the code rate of the codes
defined on some subset of Quaternion integers.
| [
{
"created": "Thu, 30 Jan 2014 12:38:13 GMT",
"version": "v1"
}
] | 2014-01-31 | [
[
"Flaut",
"Cristina",
""
]
] | In this paper we define codes over some Octonion integers. We prove that in some conditions these codes can correct up to two errors for a transmitted vector and the code rate of the codes is grater than the code rate of the codes defined on some subset of Quaternion integers. |
2005.00247 | Jonas Pfeiffer | Jonas Pfeiffer, Aishwarya Kamath, Andreas R\"uckl\'e, Kyunghyun Cho,
Iryna Gurevych | AdapterFusion: Non-Destructive Task Composition for Transfer Learning | null | Proceedings of EACL 2021 | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequential fine-tuning and multi-task learning are methods aiming to
incorporate knowledge from multiple tasks; however, they suffer from
catastrophic forgetting and difficulties in dataset balancing. To address these
shortcomings, we propose AdapterFusion, a new two stage learning algorithm that
leverages knowledge from multiple tasks. First, in the knowledge extraction
stage we learn task specific parameters called adapters, that encapsulate the
task-specific information. We then combine the adapters in a separate knowledge
composition step. We show that by separating the two stages, i.e., knowledge
extraction and knowledge composition, the classifier can effectively exploit
the representations learned from multiple tasks in a non-destructive manner. We
empirically evaluate AdapterFusion on 16 diverse NLU tasks, and find that it
effectively combines various types of knowledge at different layers of the
model. We show that our approach outperforms traditional strategies such as
full fine-tuning as well as multi-task learning. Our code and adapters are
available at AdapterHub.ml.
| [
{
"created": "Fri, 1 May 2020 07:03:42 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Jan 2021 14:34:32 GMT",
"version": "v2"
},
{
"created": "Tue, 26 Jan 2021 12:54:33 GMT",
"version": "v3"
}
] | 2021-01-27 | [
[
"Pfeiffer",
"Jonas",
""
],
[
"Kamath",
"Aishwarya",
""
],
[
"Rücklé",
"Andreas",
""
],
[
"Cho",
"Kyunghyun",
""
],
[
"Gurevych",
"Iryna",
""
]
] | Sequential fine-tuning and multi-task learning are methods aiming to incorporate knowledge from multiple tasks; however, they suffer from catastrophic forgetting and difficulties in dataset balancing. To address these shortcomings, we propose AdapterFusion, a new two stage learning algorithm that leverages knowledge from multiple tasks. First, in the knowledge extraction stage we learn task specific parameters called adapters, that encapsulate the task-specific information. We then combine the adapters in a separate knowledge composition step. We show that by separating the two stages, i.e., knowledge extraction and knowledge composition, the classifier can effectively exploit the representations learned from multiple tasks in a non-destructive manner. We empirically evaluate AdapterFusion on 16 diverse NLU tasks, and find that it effectively combines various types of knowledge at different layers of the model. We show that our approach outperforms traditional strategies such as full fine-tuning as well as multi-task learning. Our code and adapters are available at AdapterHub.ml. |
2212.04634 | Yuxin Wang | Yuxin Wang, Jieru Lin, Zhiwei Yu, Wei Hu, B\"orje F. Karlsson | Open-world Story Generation with Structured Knowledge Enhancement: A
Comprehensive Survey | Accepted in Neurocomputing | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Storytelling and narrative are fundamental to human experience, intertwined
with our social and cultural engagement. As such, researchers have long
attempted to create systems that can generate stories automatically. In recent
years, powered by deep learning and massive data resources, automatic story
generation has shown significant advances. However, considerable challenges,
like the need for global coherence in generated stories, still hamper
generative models from reaching the same storytelling ability as human
narrators. To tackle these challenges, many studies seek to inject structured
knowledge into the generation process, which is referred to as structured
knowledge-enhanced story generation. Incorporating external knowledge can
enhance the logical coherence among story events, achieve better knowledge
grounding, and alleviate over-generalization and repetition problems in
stories. This survey provides the latest and comprehensive review of this
research field: (i) we present a systematic taxonomy regarding how existing
methods integrate structured knowledge into story generation; (ii) we summarize
involved story corpora, structured knowledge datasets, and evaluation metrics;
(iii) we give multidimensional insights into the challenges of
knowledge-enhanced story generation and cast light on promising directions for
future study.
| [
{
"created": "Fri, 9 Dec 2022 02:19:07 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Mar 2023 13:20:05 GMT",
"version": "v2"
},
{
"created": "Tue, 12 Sep 2023 17:38:30 GMT",
"version": "v3"
}
] | 2023-09-13 | [
[
"Wang",
"Yuxin",
""
],
[
"Lin",
"Jieru",
""
],
[
"Yu",
"Zhiwei",
""
],
[
"Hu",
"Wei",
""
],
[
"Karlsson",
"Börje F.",
""
]
] | Storytelling and narrative are fundamental to human experience, intertwined with our social and cultural engagement. As such, researchers have long attempted to create systems that can generate stories automatically. In recent years, powered by deep learning and massive data resources, automatic story generation has shown significant advances. However, considerable challenges, like the need for global coherence in generated stories, still hamper generative models from reaching the same storytelling ability as human narrators. To tackle these challenges, many studies seek to inject structured knowledge into the generation process, which is referred to as structured knowledge-enhanced story generation. Incorporating external knowledge can enhance the logical coherence among story events, achieve better knowledge grounding, and alleviate over-generalization and repetition problems in stories. This survey provides the latest and comprehensive review of this research field: (i) we present a systematic taxonomy regarding how existing methods integrate structured knowledge into story generation; (ii) we summarize involved story corpora, structured knowledge datasets, and evaluation metrics; (iii) we give multidimensional insights into the challenges of knowledge-enhanced story generation and cast light on promising directions for future study. |
cs/0606100 | Marco Cuturi | Marco Cuturi | The generating function of the polytope of transport matrices $U(r,c)$
as a positive semidefinite kernel of the marginals $r$ and $c$ | This paper has been withdrawn | null | null | null | cs.LG cs.DM | null | This paper has been withdrawn by the author due to a crucial error in the
proof of Lemma 5.
| [
{
"created": "Fri, 23 Jun 2006 10:19:40 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Jun 2006 05:46:00 GMT",
"version": "v2"
},
{
"created": "Tue, 4 Jan 2011 08:26:13 GMT",
"version": "v3"
},
{
"created": "Tue, 11 Oct 2011 10:21:45 GMT",
"version": "v4"
}
] | 2011-10-12 | [
[
"Cuturi",
"Marco",
""
]
] | This paper has been withdrawn by the author due to a crucial error in the proof of Lemma 5. |
2311.06362 | Yunting Yin | Yunting Yin and Steven Skiena | Word Definitions from Large Language Models | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dictionary definitions are historically the arbitrator of what words mean,
but this primacy has come under threat by recent progress in NLP, including
word embeddings and generative models like ChatGPT. We present an exploratory
study of the degree of alignment between word definitions from classical
dictionaries and these newer computational artifacts. Specifically, we compare
definitions from three published dictionaries to those generated from variants
of ChatGPT. We show that (i) definitions from different traditional
dictionaries exhibit more surface form similarity than do model-generated
definitions, (ii) that the ChatGPT definitions are highly accurate, comparable
to traditional dictionaries, and (iii) ChatGPT-based embedding definitions
retain their accuracy even on low frequency words, much better than GloVE and
FastText word embeddings.
| [
{
"created": "Fri, 10 Nov 2023 19:27:20 GMT",
"version": "v1"
}
] | 2023-11-14 | [
[
"Yin",
"Yunting",
""
],
[
"Skiena",
"Steven",
""
]
] | Dictionary definitions are historically the arbitrator of what words mean, but this primacy has come under threat by recent progress in NLP, including word embeddings and generative models like ChatGPT. We present an exploratory study of the degree of alignment between word definitions from classical dictionaries and these newer computational artifacts. Specifically, we compare definitions from three published dictionaries to those generated from variants of ChatGPT. We show that (i) definitions from different traditional dictionaries exhibit more surface form similarity than do model-generated definitions, (ii) that the ChatGPT definitions are highly accurate, comparable to traditional dictionaries, and (iii) ChatGPT-based embedding definitions retain their accuracy even on low frequency words, much better than GloVE and FastText word embeddings. |
2012.11339 | Anh Tong | Anh Tong, Toan Tran, Hung Bui, Jaesik Choi | Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior | AAAI 2021 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Choosing a proper set of kernel functions is an important problem in learning
Gaussian Process (GP) models since each kernel structure has different model
complexity and data fitness. Recently, automatic kernel composition methods
provide not only accurate prediction but also attractive interpretability
through search-based methods. However, existing methods suffer from slow kernel
composition learning. To tackle large-scaled data, we propose a new sparse
approximate posterior for GPs, MultiSVGP, constructed from groups of inducing
points associated with individual additive kernels in compositional kernels. We
demonstrate that this approximation provides a better fit to learn
compositional kernels given empirical observations. We also provide
theoretically justification on error bound when compared to the traditional
sparse GP. In contrast to the search-based approach, we present a novel
probabilistic algorithm to learn a kernel composition by handling the sparsity
in the kernel selection with Horseshoe prior. We demonstrate that our model can
capture characteristics of time series with significant reductions in
computational time and have competitive regression performance on real-world
data sets.
| [
{
"created": "Mon, 21 Dec 2020 13:41:15 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Feb 2021 07:11:56 GMT",
"version": "v2"
}
] | 2021-02-25 | [
[
"Tong",
"Anh",
""
],
[
"Tran",
"Toan",
""
],
[
"Bui",
"Hung",
""
],
[
"Choi",
"Jaesik",
""
]
] | Choosing a proper set of kernel functions is an important problem in learning Gaussian Process (GP) models since each kernel structure has different model complexity and data fitness. Recently, automatic kernel composition methods provide not only accurate prediction but also attractive interpretability through search-based methods. However, existing methods suffer from slow kernel composition learning. To tackle large-scaled data, we propose a new sparse approximate posterior for GPs, MultiSVGP, constructed from groups of inducing points associated with individual additive kernels in compositional kernels. We demonstrate that this approximation provides a better fit to learn compositional kernels given empirical observations. We also provide theoretically justification on error bound when compared to the traditional sparse GP. In contrast to the search-based approach, we present a novel probabilistic algorithm to learn a kernel composition by handling the sparsity in the kernel selection with Horseshoe prior. We demonstrate that our model can capture characteristics of time series with significant reductions in computational time and have competitive regression performance on real-world data sets. |
0710.4318 | Evelyne Hubert | Evelyne Hubert | Differential invariants of a Lie group action: syzygies on a generating
set | Journal of Symbolic Computation (2008) | null | 10.1016/j.jsc.2008.08.003 | null | cs.SC math.DG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given a group action, known by its infinitesimal generators, we exhibit a
complete set of syzygies on a generating set of differential invariants. For
that we elaborate on the reinterpretation of Cartan's moving frame by Fels and
Olver (1999). This provides constructive tools for exploring algebras of
differential invariants.
| [
{
"created": "Tue, 23 Oct 2007 19:20:10 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Dec 2007 15:49:21 GMT",
"version": "v2"
},
{
"created": "Thu, 4 Sep 2008 15:06:25 GMT",
"version": "v3"
},
{
"created": "Mon, 3 Nov 2008 10:42:30 GMT",
"version": "v4"
}
] | 2008-11-03 | [
[
"Hubert",
"Evelyne",
""
]
] | Given a group action, known by its infinitesimal generators, we exhibit a complete set of syzygies on a generating set of differential invariants. For that we elaborate on the reinterpretation of Cartan's moving frame by Fels and Olver (1999). This provides constructive tools for exploring algebras of differential invariants. |
2406.14150 | Guillaume Richard | Juan Jose Garau-Luis, Patrick Bordes, Liam Gonzalez, Masa Roller,
Bernardo P. de Almeida, Lorenz Hexemer, Christopher Blum, Stefan Laurent, Jan
Grzegorzewski, Maren Lang, Thomas Pierrot, Guillaume Richard | Multi-modal Transfer Learning between Biological Foundation Models | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Biological sequences encode fundamental instructions for the building blocks
of life, in the form of DNA, RNA, and proteins. Modeling these sequences is key
to understand disease mechanisms and is an active research area in
computational biology. Recently, Large Language Models have shown great promise
in solving certain biological tasks but current approaches are limited to a
single sequence modality (DNA, RNA, or protein). Key problems in genomics
intrinsically involve multiple modalities, but it remains unclear how to adapt
general-purpose sequence models to those cases. In this work we propose a
multi-modal model that connects DNA, RNA, and proteins by leveraging
information from different pre-trained modality-specific encoders. We
demonstrate its capabilities by applying it to the largely unsolved problem of
predicting how multiple RNA transcript isoforms originate from the same gene
(i.e. same DNA sequence) and map to different transcription expression levels
across various human tissues. We show that our model, dubbed IsoFormer, is able
to accurately predict differential transcript expression, outperforming
existing methods and leveraging the use of multiple modalities. Our framework
also achieves efficient transfer knowledge from the encoders pre-training as
well as in between modalities. We open-source our model, paving the way for new
multi-modal gene expression approaches.
| [
{
"created": "Thu, 20 Jun 2024 09:44:53 GMT",
"version": "v1"
}
] | 2024-06-21 | [
[
"Garau-Luis",
"Juan Jose",
""
],
[
"Bordes",
"Patrick",
""
],
[
"Gonzalez",
"Liam",
""
],
[
"Roller",
"Masa",
""
],
[
"de Almeida",
"Bernardo P.",
""
],
[
"Hexemer",
"Lorenz",
""
],
[
"Blum",
"Christopher",
""
],
[
"Laurent",
"Stefan",
""
],
[
"Grzegorzewski",
"Jan",
""
],
[
"Lang",
"Maren",
""
],
[
"Pierrot",
"Thomas",
""
],
[
"Richard",
"Guillaume",
""
]
] | Biological sequences encode fundamental instructions for the building blocks of life, in the form of DNA, RNA, and proteins. Modeling these sequences is key to understand disease mechanisms and is an active research area in computational biology. Recently, Large Language Models have shown great promise in solving certain biological tasks but current approaches are limited to a single sequence modality (DNA, RNA, or protein). Key problems in genomics intrinsically involve multiple modalities, but it remains unclear how to adapt general-purpose sequence models to those cases. In this work we propose a multi-modal model that connects DNA, RNA, and proteins by leveraging information from different pre-trained modality-specific encoders. We demonstrate its capabilities by applying it to the largely unsolved problem of predicting how multiple RNA transcript isoforms originate from the same gene (i.e. same DNA sequence) and map to different transcription expression levels across various human tissues. We show that our model, dubbed IsoFormer, is able to accurately predict differential transcript expression, outperforming existing methods and leveraging the use of multiple modalities. Our framework also achieves efficient transfer knowledge from the encoders pre-training as well as in between modalities. We open-source our model, paving the way for new multi-modal gene expression approaches. |
2406.07840 | Shubham Dokania | Abhay Rawat, Shubham Dokania, Astitva Srivastava, Shuaib Ahmed, Haiwen
Feng, Rahul Tallamraju | SynthForge: Synthesizing High-Quality Face Dataset with Controllable 3D
Generative Models | 11 pages, 4 figures, 3 tables. Under Review | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recent advancements in generative models have unlocked the capabilities to
render photo-realistic data in a controllable fashion. Trained on the real
data, these generative models are capable of producing realistic samples with
minimal to no domain gap, as compared to the traditional graphics rendering.
However, using the data generated using such models for training downstream
tasks remains under-explored, mainly due to the lack of 3D consistent
annotations. Moreover, controllable generative models are learned from massive
data and their latent space is often too vast to obtain meaningful sample
distributions for downstream task with limited generation. To overcome these
challenges, we extract 3D consistent annotations from an existing controllable
generative model, making the data useful for downstream tasks. Our experiments
show competitive performance against state-of-the-art models using only
generated synthetic data, demonstrating potential for solving downstream tasks.
Project page: https://synth-forge.github.io
| [
{
"created": "Wed, 12 Jun 2024 03:15:15 GMT",
"version": "v1"
}
] | 2024-06-13 | [
[
"Rawat",
"Abhay",
""
],
[
"Dokania",
"Shubham",
""
],
[
"Srivastava",
"Astitva",
""
],
[
"Ahmed",
"Shuaib",
""
],
[
"Feng",
"Haiwen",
""
],
[
"Tallamraju",
"Rahul",
""
]
] | Recent advancements in generative models have unlocked the capabilities to render photo-realistic data in a controllable fashion. Trained on the real data, these generative models are capable of producing realistic samples with minimal to no domain gap, as compared to the traditional graphics rendering. However, using the data generated using such models for training downstream tasks remains under-explored, mainly due to the lack of 3D consistent annotations. Moreover, controllable generative models are learned from massive data and their latent space is often too vast to obtain meaningful sample distributions for downstream task with limited generation. To overcome these challenges, we extract 3D consistent annotations from an existing controllable generative model, making the data useful for downstream tasks. Our experiments show competitive performance against state-of-the-art models using only generated synthetic data, demonstrating potential for solving downstream tasks. Project page: https://synth-forge.github.io |
1712.07752 | Rajesh Chidambaram | Rajesh Chidambaram | Towards an unanimous international regulatory body for responsible use
of Artificial Intelligence [UIRB-AI] | The paper covers a diverse range of topics but doesn't get into the
details of any and hence the proposals remain pragmatically irrelevant | null | null | null | cs.AI cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence (AI), is once again in the phase of drastic
advancements. Unarguably, the technology itself can revolutionize the way we
live our everyday life. But the exponential growth of technology poses a
daunting task for policy researchers and law makers in making amendments to the
existing norms. In addition, not everyone in the society is studying the
potential socio-economic intricacies and cultural drifts that AI can bring
about. It is prudence to reflect from our historical past to propel the
development of technology in the right direction. To benefit the society of the
present and future, I scientifically explore the societal impact of AI. While
there are many public and private partnerships working on similar aspects, here
I describe the necessity for an Unanimous International Regulatory Body for all
applications of AI (UIRB-AI). I also discuss the benefits and drawbacks of such
an organization. To combat any drawbacks in the formation of an UIRB-AI, both
idealistic and pragmatic perspectives are discussed alternatively. The paper
further advances the discussion by proposing novel policies on how such
organization should be structured and how it can bring about a win-win
situation for everyone in the society.
| [
{
"created": "Thu, 21 Dec 2017 00:29:48 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Dec 2017 16:39:50 GMT",
"version": "v2"
},
{
"created": "Thu, 28 Jun 2018 22:24:09 GMT",
"version": "v3"
}
] | 2018-07-02 | [
[
"Chidambaram",
"Rajesh",
""
]
] | Artificial Intelligence (AI), is once again in the phase of drastic advancements. Unarguably, the technology itself can revolutionize the way we live our everyday life. But the exponential growth of technology poses a daunting task for policy researchers and law makers in making amendments to the existing norms. In addition, not everyone in the society is studying the potential socio-economic intricacies and cultural drifts that AI can bring about. It is prudence to reflect from our historical past to propel the development of technology in the right direction. To benefit the society of the present and future, I scientifically explore the societal impact of AI. While there are many public and private partnerships working on similar aspects, here I describe the necessity for an Unanimous International Regulatory Body for all applications of AI (UIRB-AI). I also discuss the benefits and drawbacks of such an organization. To combat any drawbacks in the formation of an UIRB-AI, both idealistic and pragmatic perspectives are discussed alternatively. The paper further advances the discussion by proposing novel policies on how such organization should be structured and how it can bring about a win-win situation for everyone in the society. |
2402.18511 | Thiago Eustaquio Alves De Oliveira Dr. | Laurent Yves Emile Ramos Cheret, Vinicius Prado da Fonseca, Thiago
Eustaquio Alves de Oliveira | Leveraging Compliant Tactile Perception for Haptic Blind Surface
Reconstruction | 7 pages, 9 figures, 2024 IEEE International Conference on Robotics
and Automation (ICRA 2024) | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Non-flat surfaces pose difficulties for robots operating in unstructured
environments. Reconstructions of uneven surfaces may only be partially possible
due to non-compliant end-effectors and limitations on vision systems such as
transparency, reflections, and occlusions. This study achieves blind surface
reconstruction by harnessing the robotic manipulator's kinematic data and a
compliant tactile sensing module, which incorporates inertial, magnetic, and
pressure sensors. The module's flexibility enables us to estimate contact
positions and surface normals by analyzing its deformation during interactions
with unknown objects. While previous works collect only positional information,
we include the local normals in a geometrical approach to estimate curvatures
between adjacent contact points. These parameters then guide a spline-based
patch generation, which allows us to recreate larger surfaces without an
increase in complexity while reducing the time-consuming step of probing the
surface. Experimental validation demonstrates that this approach outperforms an
off-the-shelf vision system in estimation accuracy. Moreover, this compliant
haptic method works effectively even when the manipulator's approach angle is
not aligned with the surface normals, which is ideal for unknown non-flat
surfaces.
| [
{
"created": "Wed, 28 Feb 2024 17:40:01 GMT",
"version": "v1"
}
] | 2024-02-29 | [
[
"Cheret",
"Laurent Yves Emile Ramos",
""
],
[
"da Fonseca",
"Vinicius Prado",
""
],
[
"de Oliveira",
"Thiago Eustaquio Alves",
""
]
] | Non-flat surfaces pose difficulties for robots operating in unstructured environments. Reconstructions of uneven surfaces may only be partially possible due to non-compliant end-effectors and limitations on vision systems such as transparency, reflections, and occlusions. This study achieves blind surface reconstruction by harnessing the robotic manipulator's kinematic data and a compliant tactile sensing module, which incorporates inertial, magnetic, and pressure sensors. The module's flexibility enables us to estimate contact positions and surface normals by analyzing its deformation during interactions with unknown objects. While previous works collect only positional information, we include the local normals in a geometrical approach to estimate curvatures between adjacent contact points. These parameters then guide a spline-based patch generation, which allows us to recreate larger surfaces without an increase in complexity while reducing the time-consuming step of probing the surface. Experimental validation demonstrates that this approach outperforms an off-the-shelf vision system in estimation accuracy. Moreover, this compliant haptic method works effectively even when the manipulator's approach angle is not aligned with the surface normals, which is ideal for unknown non-flat surfaces. |
1808.06303 | Ian Schmutte | John M. Abowd and Ian M. Schmutte | An Economic Analysis of Privacy Protection and Statistical Accuracy as
Social Choices | Forthcoming in American Economic Review | null | 10.1257/aer.20170627 | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Statistical agencies face a dual mandate to publish accurate statistics while
protecting respondent privacy. Increasing privacy protection requires decreased
accuracy. Recognizing this as a resource allocation problem, we propose an
economic solution: operate where the marginal cost of increasing privacy equals
the marginal benefit. Our model of production, from computer science, assumes
data are published using an efficient differentially private algorithm. Optimal
choice weighs the demand for accurate statistics against the demand for
privacy. Examples from U.S.\ statistical programs show how our framework can
guide decision-making. Further progress requires a better understanding of
willingness-to-pay for privacy and statistical accuracy.
| [
{
"created": "Mon, 20 Aug 2018 04:34:43 GMT",
"version": "v1"
}
] | 2019-03-12 | [
[
"Abowd",
"John M.",
""
],
[
"Schmutte",
"Ian M.",
""
]
] | Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from U.S.\ statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy. |
1512.08292 | Saeed Mehrabi | Saeed Mehrabi | Guarding the Vertices of an Orthogonal Terrain using Vertex Guards | null | null | null | null | cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A terrain T is an x-monotone polygonal chain in the plane; T is orthogonal if
each edge of T is either horizontal or vertical. In this paper, we give an
exact algorithm for the problem of guarding the convex vertices of an
orthogonal terrain with the minimum number of reflex vertices.
| [
{
"created": "Mon, 28 Dec 2015 00:01:52 GMT",
"version": "v1"
}
] | 2015-12-29 | [
[
"Mehrabi",
"Saeed",
""
]
] | A terrain T is an x-monotone polygonal chain in the plane; T is orthogonal if each edge of T is either horizontal or vertical. In this paper, we give an exact algorithm for the problem of guarding the convex vertices of an orthogonal terrain with the minimum number of reflex vertices. |
2306.11879 | Sidi Lu | Sidi Lu and Hongyi Liu and Asli Celikyilmaz and Tianlu Wang and Nanyun
Peng | Open-Domain Text Evaluation via Contrastive Distribution Methods | Accepted to ICML 2024 | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Recent advancements in open-domain text generation, driven by the power of
large pre-trained language models (LLMs), have demonstrated remarkable
performance. However, assessing these models' generation quality remains a
challenge. In this paper, we introduce a novel method for evaluating
open-domain text generation called Contrastive Distribution Methods (CDM).
Leveraging the connection between increasing model parameters and enhanced LLM
performance, CDM creates a mapping from the _contrast_ of two probabilistic
distributions -- one known to be superior to the other -- to quality measures.
We investigate CDM for open-domain text generation evaluation under two
paradigms: 1) _Generative_ CDM, which harnesses the contrast of two language
models' distributions to generate synthetic examples for training
discriminator-based metrics; 2) _Discriminative_ CDM, which directly uses
distribution disparities between two language models for evaluation. Our
experiments on coherence evaluation for multi-turn dialogue and commonsense
evaluation for controllable generation demonstrate CDM's superior correlate
with human judgment than existing automatic evaluation metrics, highlighting
the strong performance and generalizability of our approach.
| [
{
"created": "Tue, 20 Jun 2023 20:37:54 GMT",
"version": "v1"
},
{
"created": "Fri, 3 May 2024 23:21:45 GMT",
"version": "v2"
},
{
"created": "Thu, 6 Jun 2024 21:24:17 GMT",
"version": "v3"
},
{
"created": "Mon, 10 Jun 2024 00:44:32 GMT",
"version": "v4"
}
] | 2024-06-11 | [
[
"Lu",
"Sidi",
""
],
[
"Liu",
"Hongyi",
""
],
[
"Celikyilmaz",
"Asli",
""
],
[
"Wang",
"Tianlu",
""
],
[
"Peng",
"Nanyun",
""
]
] | Recent advancements in open-domain text generation, driven by the power of large pre-trained language models (LLMs), have demonstrated remarkable performance. However, assessing these models' generation quality remains a challenge. In this paper, we introduce a novel method for evaluating open-domain text generation called Contrastive Distribution Methods (CDM). Leveraging the connection between increasing model parameters and enhanced LLM performance, CDM creates a mapping from the _contrast_ of two probabilistic distributions -- one known to be superior to the other -- to quality measures. We investigate CDM for open-domain text generation evaluation under two paradigms: 1) _Generative_ CDM, which harnesses the contrast of two language models' distributions to generate synthetic examples for training discriminator-based metrics; 2) _Discriminative_ CDM, which directly uses distribution disparities between two language models for evaluation. Our experiments on coherence evaluation for multi-turn dialogue and commonsense evaluation for controllable generation demonstrate CDM's superior correlate with human judgment than existing automatic evaluation metrics, highlighting the strong performance and generalizability of our approach. |
2401.08073 | Alagappan Ramanathan | Alagappan Ramanathan, Rishika Sankaran, Sangeetha Abdu Jyothi | Xaminer: An Internet Cross-Layer Resilience Analysis Tool | null | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A resilient Internet infrastructure is critical in our highly interconnected
society. However, the Internet faces several vulnerabilities, ranging from
natural disasters to human activities, that can impact the physical layer and,
in turn, the higher network layers, such as IP links. In this paper, we
introduce Xaminer, the first Internet cross-layer resilience analysis tool, to
evaluate the interplay between physical- and network-layer failures. Using a
cross-layer Internet map and a failure event model, Xaminer generates a risk
profile encompassing a cross-layer impact report, critical infrastructure
identification at each layer, and the discovery of trends and patterns under
different failure event settings. Xaminer's key strengths lie in its
adaptability to diverse disaster scenarios, the ability to assess risks at
various granularities, and the capability to generate joint risk profiles for
multiple events. We demonstrate Xaminer's capabilities in cross-layer analysis
across a spectrum of disaster event models and regions, showcasing its
potential role in facilitating well-informed decision-making for resilience
planning and deployments.
| [
{
"created": "Tue, 16 Jan 2024 02:58:27 GMT",
"version": "v1"
}
] | 2024-01-17 | [
[
"Ramanathan",
"Alagappan",
""
],
[
"Sankaran",
"Rishika",
""
],
[
"Jyothi",
"Sangeetha Abdu",
""
]
] | A resilient Internet infrastructure is critical in our highly interconnected society. However, the Internet faces several vulnerabilities, ranging from natural disasters to human activities, that can impact the physical layer and, in turn, the higher network layers, such as IP links. In this paper, we introduce Xaminer, the first Internet cross-layer resilience analysis tool, to evaluate the interplay between physical- and network-layer failures. Using a cross-layer Internet map and a failure event model, Xaminer generates a risk profile encompassing a cross-layer impact report, critical infrastructure identification at each layer, and the discovery of trends and patterns under different failure event settings. Xaminer's key strengths lie in its adaptability to diverse disaster scenarios, the ability to assess risks at various granularities, and the capability to generate joint risk profiles for multiple events. We demonstrate Xaminer's capabilities in cross-layer analysis across a spectrum of disaster event models and regions, showcasing its potential role in facilitating well-informed decision-making for resilience planning and deployments. |
1902.00172 | Shikhar Vashishth | Shikhar Vashishth, Prince Jain, Partha Talukdar | CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side
Information | Accepted at WWW 2018 | International World Wide Web Conferences Steering Committee 2018 | 10.1145/3178876.3186030 | null | cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Open Information Extraction (OpenIE) methods extract (noun phrase, relation
phrase, noun phrase) triples from text, resulting in the construction of large
Open Knowledge Bases (Open KBs). The noun phrases (NPs) and relation phrases in
such Open KBs are not canonicalized, leading to the storage of redundant and
ambiguous facts. Recent research has posed canonicalization of Open KBs as
clustering over manuallydefined feature spaces. Manual feature engineering is
expensive and often sub-optimal. In order to overcome this challenge, we
propose Canonicalization using Embeddings and Side Information (CESI) - a novel
approach which performs canonicalization over learned embeddings of Open KBs.
CESI extends recent advances in KB embedding by incorporating relevant NP and
relation phrase side information in a principled manner. Through extensive
experiments on multiple real-world datasets, we demonstrate CESI's
effectiveness.
| [
{
"created": "Fri, 1 Feb 2019 04:18:49 GMT",
"version": "v1"
}
] | 2019-02-04 | [
[
"Vashishth",
"Shikhar",
""
],
[
"Jain",
"Prince",
""
],
[
"Talukdar",
"Partha",
""
]
] | Open Information Extraction (OpenIE) methods extract (noun phrase, relation phrase, noun phrase) triples from text, resulting in the construction of large Open Knowledge Bases (Open KBs). The noun phrases (NPs) and relation phrases in such Open KBs are not canonicalized, leading to the storage of redundant and ambiguous facts. Recent research has posed canonicalization of Open KBs as clustering over manuallydefined feature spaces. Manual feature engineering is expensive and often sub-optimal. In order to overcome this challenge, we propose Canonicalization using Embeddings and Side Information (CESI) - a novel approach which performs canonicalization over learned embeddings of Open KBs. CESI extends recent advances in KB embedding by incorporating relevant NP and relation phrase side information in a principled manner. Through extensive experiments on multiple real-world datasets, we demonstrate CESI's effectiveness. |
2006.04611 | Kaustubh Yadav | Kaustubh Yadav | A Comprehensive Survey on Aspect Based Sentiment Analysis | null | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Aspect Based Sentiment Analysis (ABSA) is the sub-field of Natural Language
Processing that deals with essentially splitting our data into aspects ad
finally extracting the sentiment information. ABSA is known to provide more
information about the context than general sentiment analysis. In this study,
our aim is to explore the various methodologies practiced while performing
ABSA, and providing a comparative study. This survey paper discusses various
solutions in-depth and gives a comparison between them. And is conveniently
divided into sections to get a holistic view on the process.
| [
{
"created": "Mon, 8 Jun 2020 14:07:58 GMT",
"version": "v1"
}
] | 2020-06-09 | [
[
"Yadav",
"Kaustubh",
""
]
] | Aspect Based Sentiment Analysis (ABSA) is the sub-field of Natural Language Processing that deals with essentially splitting our data into aspects ad finally extracting the sentiment information. ABSA is known to provide more information about the context than general sentiment analysis. In this study, our aim is to explore the various methodologies practiced while performing ABSA, and providing a comparative study. This survey paper discusses various solutions in-depth and gives a comparison between them. And is conveniently divided into sections to get a holistic view on the process. |
2406.09095 | Yuhao Dan | Yuhao Dan, Junfeng Tian, Jie Zhou, Ming Yan, Ji Zhang, Qin Chen, Liang
He | Modeling Comparative Logical Relation with Contrastive Learning for Text
Generation | NLPCC 2024 | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Data-to-Text Generation (D2T), a classic natural language generation problem,
aims at producing fluent descriptions for structured input data, such as a
table. Existing D2T works mainly focus on describing the superficial
associative relations among entities, while ignoring the deep comparative
logical relations, such as A is better than B in a certain aspect with a
corresponding opinion, which is quite common in our daily life. In this paper,
we introduce a new D2T task named comparative logical relation generation
(CLRG). Additionally, we propose a Comparative Logic (CoLo) based text
generation method, which generates texts following specific comparative logical
relations with contrastive learning. Specifically, we first construct various
positive and negative samples by fine-grained perturbations in entities,
aspects and opinions. Then, we perform contrastive learning in the encoder
layer to have a better understanding of the comparative logical relations, and
integrate it in the decoder layer to guide the model to correctly generate the
relations. Noting the data scarcity problem, we construct a Chinese Comparative
Logical Relation Dataset (CLRD), which is a high-quality human-annotated
dataset and challenging for text generation with descriptions of multiple
entities and annotations on their comparative logical relations. Extensive
experiments show that our method achieves impressive performance in both
automatic and human evaluations.
| [
{
"created": "Thu, 13 Jun 2024 13:25:50 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Aug 2024 04:47:29 GMT",
"version": "v2"
}
] | 2024-08-16 | [
[
"Dan",
"Yuhao",
""
],
[
"Tian",
"Junfeng",
""
],
[
"Zhou",
"Jie",
""
],
[
"Yan",
"Ming",
""
],
[
"Zhang",
"Ji",
""
],
[
"Chen",
"Qin",
""
],
[
"He",
"Liang",
""
]
] | Data-to-Text Generation (D2T), a classic natural language generation problem, aims at producing fluent descriptions for structured input data, such as a table. Existing D2T works mainly focus on describing the superficial associative relations among entities, while ignoring the deep comparative logical relations, such as A is better than B in a certain aspect with a corresponding opinion, which is quite common in our daily life. In this paper, we introduce a new D2T task named comparative logical relation generation (CLRG). Additionally, we propose a Comparative Logic (CoLo) based text generation method, which generates texts following specific comparative logical relations with contrastive learning. Specifically, we first construct various positive and negative samples by fine-grained perturbations in entities, aspects and opinions. Then, we perform contrastive learning in the encoder layer to have a better understanding of the comparative logical relations, and integrate it in the decoder layer to guide the model to correctly generate the relations. Noting the data scarcity problem, we construct a Chinese Comparative Logical Relation Dataset (CLRD), which is a high-quality human-annotated dataset and challenging for text generation with descriptions of multiple entities and annotations on their comparative logical relations. Extensive experiments show that our method achieves impressive performance in both automatic and human evaluations. |
1012.5506 | Adrian Paschke | Alejandra Gonzalez-Beltran, Ben Tagger, and Anthony Finkelstein | Ontology-based Queries over Cancer Data | in Adrian Paschke, Albert Burger, Andrea Splendiani, M. Scott
Marshall, Paolo Romano: Proceedings of the 3rd International Workshop on
Semantic Web Applications and Tools for the Life Sciences, Berlin,Germany,
December 8-10, 2010 | null | null | SWAT4LS 2010 | cs.AI cs.DB cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ever-increasing amount of data in biomedical research, and in cancer
research in particular, needs to be managed to support efficient data access,
exchange and integration. Existing software infrastructures, such caGrid,
support access to distributed information annotated with a domain ontology.
However, caGrid's current querying functionality depends on the structure of
individual data resources without exploiting the semantic annotations. In this
paper, we present the design and development of an ontology-based querying
functionality that consists of: the generation of OWL2 ontologies from the
underlying data resources metadata and a query rewriting and translation
process based on reasoning, which converts a query at the domain ontology level
into queries at the software infrastructure level. We present a detailed
analysis of our approach as well as an extensive performance evaluation. While
the implementation and evaluation was performed for the caGrid infrastructure,
the approach could be applicable to other model and metadata-driven
environments for data sharing.
| [
{
"created": "Sun, 26 Dec 2010 10:49:52 GMT",
"version": "v1"
}
] | 2010-12-30 | [
[
"Gonzalez-Beltran",
"Alejandra",
""
],
[
"Tagger",
"Ben",
""
],
[
"Finkelstein",
"Anthony",
""
]
] | The ever-increasing amount of data in biomedical research, and in cancer research in particular, needs to be managed to support efficient data access, exchange and integration. Existing software infrastructures, such caGrid, support access to distributed information annotated with a domain ontology. However, caGrid's current querying functionality depends on the structure of individual data resources without exploiting the semantic annotations. In this paper, we present the design and development of an ontology-based querying functionality that consists of: the generation of OWL2 ontologies from the underlying data resources metadata and a query rewriting and translation process based on reasoning, which converts a query at the domain ontology level into queries at the software infrastructure level. We present a detailed analysis of our approach as well as an extensive performance evaluation. While the implementation and evaluation was performed for the caGrid infrastructure, the approach could be applicable to other model and metadata-driven environments for data sharing. |
1707.05468 | Elena Mikhalkova | Elena Mikhalkova and Yuri Karyakin | Detecting Intentional Lexical Ambiguity in English Puns | In Proceedings of the International Conference "Dialogue 2017"
Moscow, May 31-June 3, 2017 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The article describes a model of automatic analysis of puns, where a word is
intentionally used in two meanings at the same time (the target word). We
employ Roget's Thesaurus to discover two groups of words which, in a pun, form
around two abstract bits of meaning (semes). They become a semantic vector,
based on which an SVM classifier learns to recognize puns, reaching a score
0.73 for F-measure. We apply several rule-based methods to locate intentionally
ambiguous (target) words, based on structural and semantic criteria. It appears
that the structural criterion is more effective, although it possibly
characterizes only the tested dataset. The results we get correlate with the
results of other teams at SemEval-2017 competition (Task 7 Detection and
Interpretation of English Puns) considering effects of using supervised
learning models and word statistics.
| [
{
"created": "Tue, 18 Jul 2017 05:04:03 GMT",
"version": "v1"
}
] | 2017-07-19 | [
[
"Mikhalkova",
"Elena",
""
],
[
"Karyakin",
"Yuri",
""
]
] | The article describes a model of automatic analysis of puns, where a word is intentionally used in two meanings at the same time (the target word). We employ Roget's Thesaurus to discover two groups of words which, in a pun, form around two abstract bits of meaning (semes). They become a semantic vector, based on which an SVM classifier learns to recognize puns, reaching a score 0.73 for F-measure. We apply several rule-based methods to locate intentionally ambiguous (target) words, based on structural and semantic criteria. It appears that the structural criterion is more effective, although it possibly characterizes only the tested dataset. The results we get correlate with the results of other teams at SemEval-2017 competition (Task 7 Detection and Interpretation of English Puns) considering effects of using supervised learning models and word statistics. |
2407.02775 | Ying Zhang | Ying Zhang and Ziheng Yang and Shufan Ji | MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language
Models | null | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Knowledge distillation is an effective technique for pre-trained language
model compression. Although existing knowledge distillation methods perform
well for the most typical model BERT, they could be further improved in two
aspects: the relation-level knowledge could be further explored to improve
model performance; and the setting of student attention head number could be
more flexible to decrease inference time. Therefore, we are motivated to
propose a novel knowledge distillation method MLKD-BERT to distill multi-level
knowledge in teacher-student framework. Extensive experiments on GLUE benchmark
and extractive question answering tasks demonstrate that our method outperforms
state-of-the-art knowledge distillation methods on BERT. In addition, MLKD-BERT
can flexibly set student attention head number, allowing for substantial
inference time decrease with little performance drop.
| [
{
"created": "Wed, 3 Jul 2024 03:03:30 GMT",
"version": "v1"
}
] | 2024-07-04 | [
[
"Zhang",
"Ying",
""
],
[
"Yang",
"Ziheng",
""
],
[
"Ji",
"Shufan",
""
]
] | Knowledge distillation is an effective technique for pre-trained language model compression. Although existing knowledge distillation methods perform well for the most typical model BERT, they could be further improved in two aspects: the relation-level knowledge could be further explored to improve model performance; and the setting of student attention head number could be more flexible to decrease inference time. Therefore, we are motivated to propose a novel knowledge distillation method MLKD-BERT to distill multi-level knowledge in teacher-student framework. Extensive experiments on GLUE benchmark and extractive question answering tasks demonstrate that our method outperforms state-of-the-art knowledge distillation methods on BERT. In addition, MLKD-BERT can flexibly set student attention head number, allowing for substantial inference time decrease with little performance drop. |
2102.06774 | Hanieh Rafiee | Haniyeh Rafiee and Mohammad Fakhredanesh | Presenting a Method for Improving Echo Hiding | 14 page, This paper is printed in Journal of Computer and Knowledge
Engineering, Vol. 2, No. 1 | null | 10.22067/CKE.V2I1.74388 | null | cs.CR cs.MM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In this article, one of the most important methods of steganography on VoIP
called echo hiding is improved. This method has advantages in maintaining the
statistical and perceptual characteristics of audio signals as well as security
against the sensitivity of the human audio system (HAS). However, it has lots
of errors in detecting coded and hidden messages, which is detectable using
existing steganalysis methods. The percentage of extracting messages in these
improved methods of echo hiding is high, but they lower the security of the
method. In this article, a method is presented to improve the method of
extracting echo hiding, and enhance its security through a combined method
based on spread spectrum. To improve the extraction, a wrong hypothesis is
corrected and substituted. To improve security using a pseudo-random key
generation algorithm, spread spectrum and echo hiding methods are used
randomly. To evaluate the proposed extraction, numerous extraction tests are
carried out in the normal state and in the event of attacks. A steganalyser has
also been used to assess security improvements. The results gained through
different experiments on the security of steganography indicate a 3-percent
increase in steganalysis errors. The proposed extraction method was modified
based on the main method and resulted in more than 10% improvement.
| [
{
"created": "Fri, 12 Feb 2021 21:09:36 GMT",
"version": "v1"
}
] | 2021-02-16 | [
[
"Rafiee",
"Haniyeh",
""
],
[
"Fakhredanesh",
"Mohammad",
""
]
] | In this article, one of the most important methods of steganography on VoIP called echo hiding is improved. This method has advantages in maintaining the statistical and perceptual characteristics of audio signals as well as security against the sensitivity of the human audio system (HAS). However, it has lots of errors in detecting coded and hidden messages, which is detectable using existing steganalysis methods. The percentage of extracting messages in these improved methods of echo hiding is high, but they lower the security of the method. In this article, a method is presented to improve the method of extracting echo hiding, and enhance its security through a combined method based on spread spectrum. To improve the extraction, a wrong hypothesis is corrected and substituted. To improve security using a pseudo-random key generation algorithm, spread spectrum and echo hiding methods are used randomly. To evaluate the proposed extraction, numerous extraction tests are carried out in the normal state and in the event of attacks. A steganalyser has also been used to assess security improvements. The results gained through different experiments on the security of steganography indicate a 3-percent increase in steganalysis errors. The proposed extraction method was modified based on the main method and resulted in more than 10% improvement. |
1005.5489 | Constantin Jucovschi | Constantin Jucovschi, Michael Kohlhase | sTeXIDE: An Integrated Development Environment for sTeX Collections | To appear in The 9th International Conference on Mathematical
Knowledge Management: MKM 2010 | null | null | null | cs.OH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Authoring documents in MKM formats like OMDoc is a very tedious task. After
years of working on a semantically annotated corpus of sTeX documents (GenCS),
we identified a set of common, time-consuming subtasks, which can be supported
in an integrated authoring environment. We have adapted the modular Eclipse IDE
into sTeXIDE, an authoring solution for enhancing productivity in contributing
to sTeX based corpora. sTeXIDE supports context-aware command completion,
module management, semantic macro retrieval, and theory graph navigation.
| [
{
"created": "Sat, 29 May 2010 22:31:05 GMT",
"version": "v1"
}
] | 2010-06-01 | [
[
"Jucovschi",
"Constantin",
""
],
[
"Kohlhase",
"Michael",
""
]
] | Authoring documents in MKM formats like OMDoc is a very tedious task. After years of working on a semantically annotated corpus of sTeX documents (GenCS), we identified a set of common, time-consuming subtasks, which can be supported in an integrated authoring environment. We have adapted the modular Eclipse IDE into sTeXIDE, an authoring solution for enhancing productivity in contributing to sTeX based corpora. sTeXIDE supports context-aware command completion, module management, semantic macro retrieval, and theory graph navigation. |
2206.07089 | Boyang Li | Boyang Li, Qing Lu, Weiwen Jiang, Taeho Jung, Yiyu Shi | A Collaboration Strategy in the Mining Pool for
Proof-of-Neural-Architecture Consensus | null | null | null | null | cs.DC cs.LG | http://creativecommons.org/publicdomain/zero/1.0/ | In most popular public accessible cryptocurrency systems, the mining pool
plays a key role because mining cryptocurrency with the mining pool turns the
non-profitable situation into profitable for individual miners. In many recent
novel blockchain consensuses, the deep learning training procedure becomes the
task for miners to prove their workload, thus the computation power of miners
will not purely be spent on the hash puzzle. In this way, the hardware and
energy will support the blockchain service and deep learning training
simultaneously. While the incentive of miners is to earn tokens, individual
miners are motivated to join mining pools to become more competitive. In this
paper, we are the first to demonstrate a mining pool solution for novel
consensuses based on deep learning.
The mining pool manager partitions the full searching space into subspaces
and all miners are scheduled to collaborate on the Neural Architecture Search
(NAS) tasks in the assigned subspace. Experiments demonstrate that the
performance of this type of mining pool is more competitive than an individual
miner. Due to the uncertainty of miners' behaviors, the mining pool manager
checks the standard deviation of the performance of high reward miners and
prepares backup miners to ensure the completion of the tasks of high reward
miners.
| [
{
"created": "Thu, 5 May 2022 17:08:02 GMT",
"version": "v1"
}
] | 2022-06-16 | [
[
"Li",
"Boyang",
""
],
[
"Lu",
"Qing",
""
],
[
"Jiang",
"Weiwen",
""
],
[
"Jung",
"Taeho",
""
],
[
"Shi",
"Yiyu",
""
]
] | In most popular public accessible cryptocurrency systems, the mining pool plays a key role because mining cryptocurrency with the mining pool turns the non-profitable situation into profitable for individual miners. In many recent novel blockchain consensuses, the deep learning training procedure becomes the task for miners to prove their workload, thus the computation power of miners will not purely be spent on the hash puzzle. In this way, the hardware and energy will support the blockchain service and deep learning training simultaneously. While the incentive of miners is to earn tokens, individual miners are motivated to join mining pools to become more competitive. In this paper, we are the first to demonstrate a mining pool solution for novel consensuses based on deep learning. The mining pool manager partitions the full searching space into subspaces and all miners are scheduled to collaborate on the Neural Architecture Search (NAS) tasks in the assigned subspace. Experiments demonstrate that the performance of this type of mining pool is more competitive than an individual miner. Due to the uncertainty of miners' behaviors, the mining pool manager checks the standard deviation of the performance of high reward miners and prepares backup miners to ensure the completion of the tasks of high reward miners. |
2209.07367 | Turgay Pamuklu | Anne Catherine Nguyen, Turgay Pamuklu, Aisha Syed, W. Sean Kennedy,
Melike Erol-Kantarci | Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm
Networks | Accepted Paper | null | null | null | cs.NI cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The fifth and sixth generations of wireless communication networks are
enabling tools such as internet of things devices, unmanned aerial vehicles
(UAVs), and artificial intelligence, to improve the agricultural landscape
using a network of devices to automatically monitor farmlands. Surveying a
large area requires performing a lot of image classification tasks within a
specific period of time in order to prevent damage to the farm in case of an
incident, such as fire or flood. UAVs have limited energy and computing power,
and may not be able to perform all of the intense image classification tasks
locally and within an appropriate amount of time. Hence, it is assumed that the
UAVs are able to partially offload their workload to nearby multi-access edge
computing devices. The UAVs need a decision-making algorithm that will decide
where the tasks will be performed, while also considering the time constraints
and energy level of the other UAVs in the network. In this paper, we introduce
a Deep Q-Learning (DQL) approach to solve this multi-objective problem. The
proposed method is compared with Q-Learning and three heuristic baselines, and
the simulation results show that our proposed DQL-based method achieves
comparable results when it comes to the UAVs' remaining battery levels and
percentage of deadline violations. In addition, our method is able to reach
convergence 13 times faster than Q-Learning.
| [
{
"created": "Thu, 15 Sep 2022 15:29:57 GMT",
"version": "v1"
}
] | 2022-09-16 | [
[
"Nguyen",
"Anne Catherine",
""
],
[
"Pamuklu",
"Turgay",
""
],
[
"Syed",
"Aisha",
""
],
[
"Kennedy",
"W. Sean",
""
],
[
"Erol-Kantarci",
"Melike",
""
]
] | The fifth and sixth generations of wireless communication networks are enabling tools such as internet of things devices, unmanned aerial vehicles (UAVs), and artificial intelligence, to improve the agricultural landscape using a network of devices to automatically monitor farmlands. Surveying a large area requires performing a lot of image classification tasks within a specific period of time in order to prevent damage to the farm in case of an incident, such as fire or flood. UAVs have limited energy and computing power, and may not be able to perform all of the intense image classification tasks locally and within an appropriate amount of time. Hence, it is assumed that the UAVs are able to partially offload their workload to nearby multi-access edge computing devices. The UAVs need a decision-making algorithm that will decide where the tasks will be performed, while also considering the time constraints and energy level of the other UAVs in the network. In this paper, we introduce a Deep Q-Learning (DQL) approach to solve this multi-objective problem. The proposed method is compared with Q-Learning and three heuristic baselines, and the simulation results show that our proposed DQL-based method achieves comparable results when it comes to the UAVs' remaining battery levels and percentage of deadline violations. In addition, our method is able to reach convergence 13 times faster than Q-Learning. |
2105.06714 | Peijia Chen | Peijia Chen, Jianhuang Lai, Guangcong Wang, Huajun Zhou | Confidence-guided Adaptive Gate and Dual Differential Enhancement for
Video Salient Object Detection | Accepted by ICME2021 as oral | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Video salient object detection (VSOD) aims to locate and segment the most
attractive object by exploiting both spatial cues and temporal cues hidden in
video sequences. However, spatial and temporal cues are often unreliable in
real-world scenarios, such as low-contrast foreground, fast motion, and
multiple moving objects. To address these problems, we propose a new framework
to adaptively capture available information from spatial and temporal cues,
which contains Confidence-guided Adaptive Gate (CAG) modules and Dual
Differential Enhancement (DDE) modules. For both RGB features and optical flow
features, CAG estimates confidence scores supervised by the IoU between
predictions and the ground truths to re-calibrate the information with a gate
mechanism. DDE captures the differential feature representation to enrich the
spatial and temporal information and generate the fused features. Experimental
results on four widely used datasets demonstrate the effectiveness of the
proposed method against thirteen state-of-the-art methods.
| [
{
"created": "Fri, 14 May 2021 08:49:37 GMT",
"version": "v1"
}
] | 2021-05-17 | [
[
"Chen",
"Peijia",
""
],
[
"Lai",
"Jianhuang",
""
],
[
"Wang",
"Guangcong",
""
],
[
"Zhou",
"Huajun",
""
]
] | Video salient object detection (VSOD) aims to locate and segment the most attractive object by exploiting both spatial cues and temporal cues hidden in video sequences. However, spatial and temporal cues are often unreliable in real-world scenarios, such as low-contrast foreground, fast motion, and multiple moving objects. To address these problems, we propose a new framework to adaptively capture available information from spatial and temporal cues, which contains Confidence-guided Adaptive Gate (CAG) modules and Dual Differential Enhancement (DDE) modules. For both RGB features and optical flow features, CAG estimates confidence scores supervised by the IoU between predictions and the ground truths to re-calibrate the information with a gate mechanism. DDE captures the differential feature representation to enrich the spatial and temporal information and generate the fused features. Experimental results on four widely used datasets demonstrate the effectiveness of the proposed method against thirteen state-of-the-art methods. |
2403.10746 | Matthijs Douze | Gergely Szilvasy and Pierre-Emmanuel Mazar\'e and Matthijs Douze | Vector search with small radiuses | null | null | null | null | cs.CV cs.DB | http://creativecommons.org/licenses/by/4.0/ | In recent years, the dominant accuracy metric for vector search is the recall
of a result list of fixed size (top-k retrieval), considering as ground truth
the exact vector retrieval results. Although convenient to compute, this metric
is distantly related to the end-to-end accuracy of a full system that
integrates vector search. In this paper we focus on the common case where a
hard decision needs to be taken depending on the vector retrieval results, for
example, deciding whether a query image matches a database image or not. We
solve this as a range search task, where all vectors within a certain radius
from the query are returned.
We show that the value of a range search result can be modeled rigorously
based on the query-to-vector distance. This yields a metric for range search,
RSM, that is both principled and easy to compute without running an end-to-end
evaluation. We apply this metric to the case of image retrieval. We show that
indexing methods that are adapted for top-k retrieval do not necessarily
maximize the RSM. In particular, for inverted file based indexes, we show that
visiting a limited set of clusters and encoding vectors compactly yields near
optimal results.
| [
{
"created": "Sat, 16 Mar 2024 00:34:25 GMT",
"version": "v1"
}
] | 2024-03-19 | [
[
"Szilvasy",
"Gergely",
""
],
[
"Mazaré",
"Pierre-Emmanuel",
""
],
[
"Douze",
"Matthijs",
""
]
] | In recent years, the dominant accuracy metric for vector search is the recall of a result list of fixed size (top-k retrieval), considering as ground truth the exact vector retrieval results. Although convenient to compute, this metric is distantly related to the end-to-end accuracy of a full system that integrates vector search. In this paper we focus on the common case where a hard decision needs to be taken depending on the vector retrieval results, for example, deciding whether a query image matches a database image or not. We solve this as a range search task, where all vectors within a certain radius from the query are returned. We show that the value of a range search result can be modeled rigorously based on the query-to-vector distance. This yields a metric for range search, RSM, that is both principled and easy to compute without running an end-to-end evaluation. We apply this metric to the case of image retrieval. We show that indexing methods that are adapted for top-k retrieval do not necessarily maximize the RSM. In particular, for inverted file based indexes, we show that visiting a limited set of clusters and encoding vectors compactly yields near optimal results. |
2312.06795 | MohammadReza Davari | MohammadReza Davari and Eugene Belilovsky | Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks | Published in ECCV 2024 | null | null | null | cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The rapid development of AI systems has been greatly influenced by the
emergence of foundation models. A common approach for targeted problems
involves fine-tuning these pre-trained foundation models for specific target
tasks, resulting in a rapid spread of models fine-tuned across a diverse array
of tasks. This work focuses on the problem of merging multiple fine-tunings of
the same foundation model derived from a spectrum of auxiliary tasks. We
introduce a new simple method, Model Breadcrumbs, which consists of a sparsely
defined weight set that guides model adaptation within the weight space of a
pre-trained model. These breadcrumbs are constructed by subtracting the weights
from a pre-trained model before and after fine-tuning, followed by a
sparsification process that eliminates weight outliers and negligible
perturbations. Our experiments demonstrate the effectiveness of Model
Breadcrumbs to simultaneously improve performance across multiple tasks. This
contribution aligns with the evolving paradigm of updatable machine learning,
reminiscent of the collaborative principles underlying open-source software
development, fostering a community-driven effort to reliably update machine
learning models. Our method is shown to be more efficient and unlike previous
proposals does not require hyperparameter tuning for each new task added.
Through extensive experimentation involving various models, tasks, and
modalities we establish that integrating Model Breadcrumbs offers a simple,
efficient, and highly effective approach for constructing multi-task models and
facilitating updates to foundation models.
| [
{
"created": "Mon, 11 Dec 2023 19:10:55 GMT",
"version": "v1"
},
{
"created": "Sat, 10 Aug 2024 00:02:00 GMT",
"version": "v2"
}
] | 2024-08-13 | [
[
"Davari",
"MohammadReza",
""
],
[
"Belilovsky",
"Eugene",
""
]
] | The rapid development of AI systems has been greatly influenced by the emergence of foundation models. A common approach for targeted problems involves fine-tuning these pre-trained foundation models for specific target tasks, resulting in a rapid spread of models fine-tuned across a diverse array of tasks. This work focuses on the problem of merging multiple fine-tunings of the same foundation model derived from a spectrum of auxiliary tasks. We introduce a new simple method, Model Breadcrumbs, which consists of a sparsely defined weight set that guides model adaptation within the weight space of a pre-trained model. These breadcrumbs are constructed by subtracting the weights from a pre-trained model before and after fine-tuning, followed by a sparsification process that eliminates weight outliers and negligible perturbations. Our experiments demonstrate the effectiveness of Model Breadcrumbs to simultaneously improve performance across multiple tasks. This contribution aligns with the evolving paradigm of updatable machine learning, reminiscent of the collaborative principles underlying open-source software development, fostering a community-driven effort to reliably update machine learning models. Our method is shown to be more efficient and unlike previous proposals does not require hyperparameter tuning for each new task added. Through extensive experimentation involving various models, tasks, and modalities we establish that integrating Model Breadcrumbs offers a simple, efficient, and highly effective approach for constructing multi-task models and facilitating updates to foundation models. |
2208.08612 | Chen Yang | Chen Yang and Yupeng Hou and Yang Song and Tao Zhang and Ji-Rong Wen
and Wayne Xin Zhao | Modeling Two-Way Selection Preference for Person-Job Fit | 10 pages, Accepted by RecSys 2022 | null | 10.1145/3523227.3546752 | null | cs.IR | http://creativecommons.org/licenses/by/4.0/ | Person-job fit is the core technique of online recruitment platforms, which
can improve the efficiency of recruitment by accurately matching the job
positions with the job seekers. Existing works mainly focus on modeling the
unidirectional process or overall matching. However, recruitment is a two-way
selection process, which means that both candidate and employer involved in the
interaction should meet the expectation of each other, instead of unilateral
satisfaction. In this paper, we propose a dual-perspective graph representation
learning approach to model directed interactions between candidates and jobs.
To model the two-way selection preference from the dual-perspective of job
seekers and employers, we incorporate two different nodes for each candidate
(or job) and characterize both successful matching and failed matching via a
unified dual-perspective interaction graph. To learn dual-perspective node
representations effectively, we design an effective optimization algorithm,
which involves a quadruple-based loss and a dual-perspective contrastive
learning loss. Extensive experiments on three large real-world recruitment
datasets have shown the effectiveness of our approach.
| [
{
"created": "Thu, 18 Aug 2022 03:16:11 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Aug 2022 15:10:46 GMT",
"version": "v2"
}
] | 2022-08-22 | [
[
"Yang",
"Chen",
""
],
[
"Hou",
"Yupeng",
""
],
[
"Song",
"Yang",
""
],
[
"Zhang",
"Tao",
""
],
[
"Wen",
"Ji-Rong",
""
],
[
"Zhao",
"Wayne Xin",
""
]
] | Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers. Existing works mainly focus on modeling the unidirectional process or overall matching. However, recruitment is a two-way selection process, which means that both candidate and employer involved in the interaction should meet the expectation of each other, instead of unilateral satisfaction. In this paper, we propose a dual-perspective graph representation learning approach to model directed interactions between candidates and jobs. To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph. To learn dual-perspective node representations effectively, we design an effective optimization algorithm, which involves a quadruple-based loss and a dual-perspective contrastive learning loss. Extensive experiments on three large real-world recruitment datasets have shown the effectiveness of our approach. |
2212.00501 | Lb Luo | Linbo Luo, Yuanjing Li, Haiyan Yin, Shangwei Xie, Ruimin Hu, Wentong
Cai | Crowd-level Abnormal Behavior Detection via Multi-scale Motion
Consistency Learning | Version with appendix for the AAAI-23 publication | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Detecting abnormal crowd motion emerging from complex interactions of
individuals is paramount to ensure the safety of crowds. Crowd-level abnormal
behaviors (CABs), e.g., counter flow and crowd turbulence, are proven to be the
crucial causes of many crowd disasters. In the recent decade, video anomaly
detection (VAD) techniques have achieved remarkable success in detecting
individual-level abnormal behaviors (e.g., sudden running, fighting and
stealing), but research on VAD for CABs is rather limited. Unlike
individual-level anomaly, CABs usually do not exhibit salient difference from
the normal behaviors when observed locally, and the scale of CABs could vary
from one scenario to another. In this paper, we present a systematic study to
tackle the important problem of VAD for CABs with a novel crowd motion learning
framework, multi-scale motion consistency network (MSMC-Net). MSMC-Net first
captures the spatial and temporal crowd motion consistency information in a
graph representation. Then, it simultaneously trains multiple feature graphs
constructed at different scales to capture rich crowd patterns. An attention
network is used to adaptively fuse the multi-scale features for better CAB
detection. For the empirical study, we consider three large-scale crowd event
datasets, UMN, Hajj and Love Parade. Experimental results show that MSMC-Net
could substantially improve the state-of-the-art performance on all the
datasets.
| [
{
"created": "Thu, 1 Dec 2022 13:52:32 GMT",
"version": "v1"
}
] | 2022-12-02 | [
[
"Luo",
"Linbo",
""
],
[
"Li",
"Yuanjing",
""
],
[
"Yin",
"Haiyan",
""
],
[
"Xie",
"Shangwei",
""
],
[
"Hu",
"Ruimin",
""
],
[
"Cai",
"Wentong",
""
]
] | Detecting abnormal crowd motion emerging from complex interactions of individuals is paramount to ensure the safety of crowds. Crowd-level abnormal behaviors (CABs), e.g., counter flow and crowd turbulence, are proven to be the crucial causes of many crowd disasters. In the recent decade, video anomaly detection (VAD) techniques have achieved remarkable success in detecting individual-level abnormal behaviors (e.g., sudden running, fighting and stealing), but research on VAD for CABs is rather limited. Unlike individual-level anomaly, CABs usually do not exhibit salient difference from the normal behaviors when observed locally, and the scale of CABs could vary from one scenario to another. In this paper, we present a systematic study to tackle the important problem of VAD for CABs with a novel crowd motion learning framework, multi-scale motion consistency network (MSMC-Net). MSMC-Net first captures the spatial and temporal crowd motion consistency information in a graph representation. Then, it simultaneously trains multiple feature graphs constructed at different scales to capture rich crowd patterns. An attention network is used to adaptively fuse the multi-scale features for better CAB detection. For the empirical study, we consider three large-scale crowd event datasets, UMN, Hajj and Love Parade. Experimental results show that MSMC-Net could substantially improve the state-of-the-art performance on all the datasets. |
2405.19743 | Hyemin Ahn | Hyemin Ahn | May the Dance be with You: Dance Generation Framework for Non-Humanoids | 13 pages, 6 Figures, Rejected at Neurips 2023 | null | null | null | cs.CV cs.AI cs.RO | http://creativecommons.org/licenses/by/4.0/ | We hypothesize dance as a motion that forms a visual rhythm from music, where
the visual rhythm can be perceived from an optical flow. If an agent can
recognize the relationship between visual rhythm and music, it will be able to
dance by generating a motion to create a visual rhythm that matches the music.
Based on this, we propose a framework for any kind of non-humanoid agents to
learn how to dance from human videos. Our framework works in two processes: (1)
training a reward model which perceives the relationship between optical flow
(visual rhythm) and music from human dance videos, (2) training the
non-humanoid dancer based on that reward model, and reinforcement learning. Our
reward model consists of two feature encoders for optical flow and music. They
are trained based on contrastive learning which makes the higher similarity
between concurrent optical flow and music features. With this reward model, the
agent learns dancing by getting a higher reward when its action creates an
optical flow whose feature has a higher similarity with the given music
feature. Experiment results show that generated dance motion can align with the
music beat properly, and user study result indicates that our framework is more
preferred by humans compared to the baselines. To the best of our knowledge,
our work of non-humanoid agents which learn dance from human videos is
unprecedented. An example video can be found at https://youtu.be/dOUPvo-O3QY.
| [
{
"created": "Thu, 30 May 2024 06:43:55 GMT",
"version": "v1"
}
] | 2024-05-31 | [
[
"Ahn",
"Hyemin",
""
]
] | We hypothesize dance as a motion that forms a visual rhythm from music, where the visual rhythm can be perceived from an optical flow. If an agent can recognize the relationship between visual rhythm and music, it will be able to dance by generating a motion to create a visual rhythm that matches the music. Based on this, we propose a framework for any kind of non-humanoid agents to learn how to dance from human videos. Our framework works in two processes: (1) training a reward model which perceives the relationship between optical flow (visual rhythm) and music from human dance videos, (2) training the non-humanoid dancer based on that reward model, and reinforcement learning. Our reward model consists of two feature encoders for optical flow and music. They are trained based on contrastive learning which makes the higher similarity between concurrent optical flow and music features. With this reward model, the agent learns dancing by getting a higher reward when its action creates an optical flow whose feature has a higher similarity with the given music feature. Experiment results show that generated dance motion can align with the music beat properly, and user study result indicates that our framework is more preferred by humans compared to the baselines. To the best of our knowledge, our work of non-humanoid agents which learn dance from human videos is unprecedented. An example video can be found at https://youtu.be/dOUPvo-O3QY. |
2212.11756 | Yuanbo Li | Yuanbo Li, Chong Han, Yi Chen, Ziming Yu, and Xuefeng Yin | DSS-o-SAGE: Direction-Scan Sounding-Oriented SAGE Algorithm for Channel
Parameter Estimation in mmWave and THz Bands | 15 pages, 10 figures, 3 tables | null | null | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Investigation of millimeter (mmWave) and Terahertz (THz) channels relies on
channel measurements and estimation of multi-path component (MPC) parameters.
As a common measurement technique in the mmWave and THz bands, direction-scan
sounding (DSS) resolves angular information and increases the measurable
distance. Through mechanical rotation, the DSS creates a virtual multi-antenna
sounding system, which however incurs signal phase instability and large data
sizes, which are not fully considered in existing estimation algorithms and
thus make them ineffective. To tackle this research gap, in this paper, a
DSS-oriented space-alternating generalized expectation-maximization
(DSS-o-SAGE) algorithm is proposed for channel parameter estimation in mmWave
and THz bands. To appropriately capture the measured data in mmWave and THz
DSS, the phase instability is modeled by the scanning-direction-dependent
signal phases. Furthermore, based on the signal model, the DSS-o-SAGE algorithm
is developed, which not only addresses the problems brought by phase
instability, but also achieves ultra-low computational complexity by exploiting
the narrow antenna beam property of DSS. Simulations in synthetic channels are
conducted to demonstrate the efficacy of the proposed algorithm and explore the
applicable region of the far-field approximation in DSS-o-SAGE. Last but not
least, the proposed DSS-o-SAGE algorithm is applied in real measurements in an
indoor corridor scenario at 300~GHz. Compared with results using the baseline
noise-elimination method, the channel is characterized more correctly and
reasonably based on the DSS-o-SAGE.
| [
{
"created": "Mon, 28 Nov 2022 16:11:52 GMT",
"version": "v1"
},
{
"created": "Mon, 4 Mar 2024 06:41:17 GMT",
"version": "v2"
}
] | 2024-03-05 | [
[
"Li",
"Yuanbo",
""
],
[
"Han",
"Chong",
""
],
[
"Chen",
"Yi",
""
],
[
"Yu",
"Ziming",
""
],
[
"Yin",
"Xuefeng",
""
]
] | Investigation of millimeter (mmWave) and Terahertz (THz) channels relies on channel measurements and estimation of multi-path component (MPC) parameters. As a common measurement technique in the mmWave and THz bands, direction-scan sounding (DSS) resolves angular information and increases the measurable distance. Through mechanical rotation, the DSS creates a virtual multi-antenna sounding system, which however incurs signal phase instability and large data sizes, which are not fully considered in existing estimation algorithms and thus make them ineffective. To tackle this research gap, in this paper, a DSS-oriented space-alternating generalized expectation-maximization (DSS-o-SAGE) algorithm is proposed for channel parameter estimation in mmWave and THz bands. To appropriately capture the measured data in mmWave and THz DSS, the phase instability is modeled by the scanning-direction-dependent signal phases. Furthermore, based on the signal model, the DSS-o-SAGE algorithm is developed, which not only addresses the problems brought by phase instability, but also achieves ultra-low computational complexity by exploiting the narrow antenna beam property of DSS. Simulations in synthetic channels are conducted to demonstrate the efficacy of the proposed algorithm and explore the applicable region of the far-field approximation in DSS-o-SAGE. Last but not least, the proposed DSS-o-SAGE algorithm is applied in real measurements in an indoor corridor scenario at 300~GHz. Compared with results using the baseline noise-elimination method, the channel is characterized more correctly and reasonably based on the DSS-o-SAGE. |
1807.05153 | Hongwei Li | Hongwei Li, Jianguo Zhang, Mark Muehlau, Jan Kirschke and Bjoern Menze | Multi-Scale Convolutional-Stack Aggregation for Robust White Matter
Hyperintensities Segmentation | accepted by MICCAI brain lesion workshop | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Segmentation of both large and small white matter hyperintensities/lesions in
brain MR images is a challenging task which has drawn much attention in recent
years. We propose a multi-scale aggregation model framework to deal with
volume-varied lesions. Firstly, we present a specifically-designed network for
small lesion segmentation called Stack-Net, in which multiple convolutional
layers are connected, aiming to preserve rich local spatial information of
small lesions before the sub-sampling layer. Secondly, we aggregate multi-scale
Stack-Nets with different receptive fields to learn multi-scale contextual
information of both large and small lesions. Our model is evaluated on recent
MICCAI WMH Challenge Dataset and outperforms the state-of-the-art on lesion
recall and lesion F1-score under 5-fold cross validation. In addition, we
further test our pre-trained models on a Multiple Sclerosis lesion dataset with
30 subjects under cross-center evaluation. Results show that the aggregation
model is effective in learning multi-scale spatial information.It claimed the
first place on the hidden test set after independent evaluation by the
challenge organizer. In addition, we further test our pre-trained models on a
Multiple Sclerosis lesion dataset with 30 subjects under cross-center
evaluation. Results show that the aggregation model is effective in learning
multi-scale spatial information.
| [
{
"created": "Fri, 13 Jul 2018 15:56:20 GMT",
"version": "v1"
},
{
"created": "Wed, 29 Aug 2018 21:55:37 GMT",
"version": "v2"
},
{
"created": "Wed, 27 Feb 2019 14:57:19 GMT",
"version": "v3"
}
] | 2019-02-28 | [
[
"Li",
"Hongwei",
""
],
[
"Zhang",
"Jianguo",
""
],
[
"Muehlau",
"Mark",
""
],
[
"Kirschke",
"Jan",
""
],
[
"Menze",
"Bjoern",
""
]
] | Segmentation of both large and small white matter hyperintensities/lesions in brain MR images is a challenging task which has drawn much attention in recent years. We propose a multi-scale aggregation model framework to deal with volume-varied lesions. Firstly, we present a specifically-designed network for small lesion segmentation called Stack-Net, in which multiple convolutional layers are connected, aiming to preserve rich local spatial information of small lesions before the sub-sampling layer. Secondly, we aggregate multi-scale Stack-Nets with different receptive fields to learn multi-scale contextual information of both large and small lesions. Our model is evaluated on recent MICCAI WMH Challenge Dataset and outperforms the state-of-the-art on lesion recall and lesion F1-score under 5-fold cross validation. In addition, we further test our pre-trained models on a Multiple Sclerosis lesion dataset with 30 subjects under cross-center evaluation. Results show that the aggregation model is effective in learning multi-scale spatial information.It claimed the first place on the hidden test set after independent evaluation by the challenge organizer. In addition, we further test our pre-trained models on a Multiple Sclerosis lesion dataset with 30 subjects under cross-center evaluation. Results show that the aggregation model is effective in learning multi-scale spatial information. |
1910.08248 | Duong Nguyen | Duong Nguyen, Sandeep S. Kulkarni | Benefits of Stabilization versus Rollback in Eventually Consistent
Key-Value Stores | null | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we evaluate and compare the performance of two approaches,
namely self-stabilization and rollback, to handling consistency violation
faults (cvf) that occurred when a distributed program is executed on eventually
consistent key-value store. We observe that self-stabilization is usually
better than rollbacks in our experiments. Moreover, when we aggressively allow
more cvf in exchange of eliminating mechanisms for guaranteeing atomicity
requirements of actions, we observe the programs in our case studies achieve a
speedup between 2--15 times compared with the standard implementation. We also
analyze different factors that contribute to the results. Our results and
analysis are useful in helping a system designer choose proper design options
for their program.
| [
{
"created": "Fri, 18 Oct 2019 03:53:11 GMT",
"version": "v1"
}
] | 2019-10-21 | [
[
"Nguyen",
"Duong",
""
],
[
"Kulkarni",
"Sandeep S.",
""
]
] | In this paper, we evaluate and compare the performance of two approaches, namely self-stabilization and rollback, to handling consistency violation faults (cvf) that occurred when a distributed program is executed on eventually consistent key-value store. We observe that self-stabilization is usually better than rollbacks in our experiments. Moreover, when we aggressively allow more cvf in exchange of eliminating mechanisms for guaranteeing atomicity requirements of actions, we observe the programs in our case studies achieve a speedup between 2--15 times compared with the standard implementation. We also analyze different factors that contribute to the results. Our results and analysis are useful in helping a system designer choose proper design options for their program. |
1702.00855 | Enno Shioji | Enno Shioji, Masayuki Arai | Neural Feature Embedding for User Response Prediction in Real-Time
Bidding (RTB) | null | Proc. of the Workshop on Social Media for Personalization and
Search (2017) 8-13 | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the area of ad-targeting, predicting user responses is essential for many
applications such as Real-Time Bidding (RTB). Many of the features available in
this domain are sparse categorical features. This presents a challenge
especially when the user responses to be predicted are rare, because each
feature will only have very few positive examples. Recently, neural embedding
techniques such as word2vec which learn distributed representations of words
using occurrence statistics in the corpus have been shown to be effective in
many Natural Language Processing tasks. In this paper, we use real-world data
set to show that a similar technique can be used to learn distributed
representations of features from users' web history, and that such
representations can be used to improve the accuracy of commonly used models for
predicting rare user responses.
| [
{
"created": "Thu, 2 Feb 2017 22:32:29 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Feb 2017 10:37:24 GMT",
"version": "v2"
},
{
"created": "Wed, 26 Apr 2017 17:01:40 GMT",
"version": "v3"
},
{
"created": "Tue, 9 May 2017 11:21:42 GMT",
"version": "v4"
},
{
"created": "Wed, 17 May 2017 07:05:42 GMT",
"version": "v5"
},
{
"created": "Thu, 18 May 2017 17:35:36 GMT",
"version": "v6"
}
] | 2017-05-19 | [
[
"Shioji",
"Enno",
""
],
[
"Arai",
"Masayuki",
""
]
] | In the area of ad-targeting, predicting user responses is essential for many applications such as Real-Time Bidding (RTB). Many of the features available in this domain are sparse categorical features. This presents a challenge especially when the user responses to be predicted are rare, because each feature will only have very few positive examples. Recently, neural embedding techniques such as word2vec which learn distributed representations of words using occurrence statistics in the corpus have been shown to be effective in many Natural Language Processing tasks. In this paper, we use real-world data set to show that a similar technique can be used to learn distributed representations of features from users' web history, and that such representations can be used to improve the accuracy of commonly used models for predicting rare user responses. |
1809.07948 | Michael Fulton | Michael Fulton, Chelsey Edge, Junaed Sattar | Robot Communication Via Motion: Closing the Underwater Human-Robot
Interaction Loop | Under review for ICRA 2019 | null | null | null | cs.RO cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a novel method for underwater robot-to-human
communication using the motion of the robot as "body language". To evaluate
this system, we develop simulated examples of the system's body language
gestures, called kinemes, and compare them to a baseline system using flashing
colored lights through a user study. Our work shows evidence that motion can be
used as a successful communication vector which is accurate, easy to learn, and
quick enough to be used, all without requiring any additional hardware to be
added to our platform. We thus contribute to "closing the loop" for human-robot
interaction underwater by proposing and testing this system, suggesting a
library of possible body language gestures for underwater robots, and offering
insight on the design of nonverbal robot-to-human communication methods.
| [
{
"created": "Fri, 21 Sep 2018 05:22:58 GMT",
"version": "v1"
}
] | 2018-09-24 | [
[
"Fulton",
"Michael",
""
],
[
"Edge",
"Chelsey",
""
],
[
"Sattar",
"Junaed",
""
]
] | In this paper, we propose a novel method for underwater robot-to-human communication using the motion of the robot as "body language". To evaluate this system, we develop simulated examples of the system's body language gestures, called kinemes, and compare them to a baseline system using flashing colored lights through a user study. Our work shows evidence that motion can be used as a successful communication vector which is accurate, easy to learn, and quick enough to be used, all without requiring any additional hardware to be added to our platform. We thus contribute to "closing the loop" for human-robot interaction underwater by proposing and testing this system, suggesting a library of possible body language gestures for underwater robots, and offering insight on the design of nonverbal robot-to-human communication methods. |
1711.01306 | Aidin Ferdowsi | Aidin Ferdowsi and Walid Saad | Deep Learning-Based Dynamic Watermarking for Secure Signal
Authentication in the Internet of Things | 6 pages, 9 figures | null | null | null | cs.IT cs.CR cs.MM math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Securing the Internet of Things (IoT) is a necessary milestone toward
expediting the deployment of its applications and services. In particular, the
functionality of the IoT devices is extremely dependent on the reliability of
their message transmission. Cyber attacks such as data injection,
eavesdropping, and man-in-the-middle threats can lead to security challenges.
Securing IoT devices against such attacks requires accounting for their
stringent computational power and need for low-latency operations. In this
paper, a novel deep learning method is proposed for dynamic watermarking of IoT
signals to detect cyber attacks. The proposed learning framework, based on a
long short-term memory (LSTM) structure, enables the IoT devices to extract a
set of stochastic features from their generated signal and dynamically
watermark these features into the signal. This method enables the IoT's cloud
center, which collects signals from the IoT devices, to effectively
authenticate the reliability of the signals. Furthermore, the proposed method
prevents complicated attack scenarios such as eavesdropping in which the cyber
attacker collects the data from the IoT devices and aims to break the
watermarking algorithm. Simulation results show that, with an attack detection
delay of under 1 second the messages can be transmitted from IoT devices with
an almost 100% reliability.
| [
{
"created": "Fri, 3 Nov 2017 19:12:23 GMT",
"version": "v1"
}
] | 2017-11-07 | [
[
"Ferdowsi",
"Aidin",
""
],
[
"Saad",
"Walid",
""
]
] | Securing the Internet of Things (IoT) is a necessary milestone toward expediting the deployment of its applications and services. In particular, the functionality of the IoT devices is extremely dependent on the reliability of their message transmission. Cyber attacks such as data injection, eavesdropping, and man-in-the-middle threats can lead to security challenges. Securing IoT devices against such attacks requires accounting for their stringent computational power and need for low-latency operations. In this paper, a novel deep learning method is proposed for dynamic watermarking of IoT signals to detect cyber attacks. The proposed learning framework, based on a long short-term memory (LSTM) structure, enables the IoT devices to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the IoT's cloud center, which collects signals from the IoT devices, to effectively authenticate the reliability of the signals. Furthermore, the proposed method prevents complicated attack scenarios such as eavesdropping in which the cyber attacker collects the data from the IoT devices and aims to break the watermarking algorithm. Simulation results show that, with an attack detection delay of under 1 second the messages can be transmitted from IoT devices with an almost 100% reliability. |
2005.05487 | Takashi Morita | Takashi Morita and Hiroki Koda | Exploring TTS without T Using Biologically/Psychologically Motivated
Neural Network Modules (ZeroSpeech 2020) | Accepted in INTERSPEECH 2020 | null | 10.21437/Interspeech.2020-3127 | null | cs.CL cs.LG cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this study, we reported our exploration of Text-To-Speech without Text
(TTS without T) in the Zero Resource Speech Challenge 2020, in which
participants proposed an end-to-end, unsupervised system that learned speech
recognition and TTS together. We addressed the challenge using
biologically/psychologically motivated modules of Artificial Neural Networks
(ANN), with a particular interest in unsupervised learning of human language as
a biological/psychological problem. The system first processes Mel Frequency
Cepstral Coefficient (MFCC) frames with an Echo-State Network (ESN), and
simulates computations in cortical microcircuits. The outcome is discretized by
our original Variational Autoencoder (VAE) that implements the Dirichlet-based
Bayesian clustering widely accepted in computational linguistics and cognitive
science. The discretized signal is then reverted into sound waveform via a
neural-network implementation of the source-filter model for speech production.
| [
{
"created": "Mon, 11 May 2020 23:44:37 GMT",
"version": "v1"
},
{
"created": "Fri, 15 May 2020 09:18:57 GMT",
"version": "v2"
},
{
"created": "Mon, 10 Aug 2020 09:13:40 GMT",
"version": "v3"
}
] | 2020-11-03 | [
[
"Morita",
"Takashi",
""
],
[
"Koda",
"Hiroki",
""
]
] | In this study, we reported our exploration of Text-To-Speech without Text (TTS without T) in the Zero Resource Speech Challenge 2020, in which participants proposed an end-to-end, unsupervised system that learned speech recognition and TTS together. We addressed the challenge using biologically/psychologically motivated modules of Artificial Neural Networks (ANN), with a particular interest in unsupervised learning of human language as a biological/psychological problem. The system first processes Mel Frequency Cepstral Coefficient (MFCC) frames with an Echo-State Network (ESN), and simulates computations in cortical microcircuits. The outcome is discretized by our original Variational Autoencoder (VAE) that implements the Dirichlet-based Bayesian clustering widely accepted in computational linguistics and cognitive science. The discretized signal is then reverted into sound waveform via a neural-network implementation of the source-filter model for speech production. |
2003.05171 | Peter beim Graben | Peter beim Graben, Markus Huber, Werner Meyer, Ronald R\"omer and
Matthias Wolff | Vector symbolic architectures for context-free grammars | 36 pages, 3 figures | null | null | null | cs.CL q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background / introduction. Vector symbolic architectures (VSA) are a viable
approach for the hyperdimensional representation of symbolic data, such as
documents, syntactic structures, or semantic frames. Methods. We present a
rigorous mathematical framework for the representation of phrase structure
trees and parse trees of context-free grammars (CFG) in Fock space, i.e.
infinite-dimensional Hilbert space as being used in quantum field theory. We
define a novel normal form for CFG by means of term algebras. Using a recently
developed software toolbox, called FockBox, we construct Fock space
representations for the trees built up by a CFG left-corner (LC) parser.
Results. We prove a universal representation theorem for CFG term algebras in
Fock space and illustrate our findings through a low-dimensional principal
component projection of the LC parser states. Conclusions. Our approach could
leverage the development of VSA for explainable artificial intelligence (XAI)
by means of hyperdimensional deep neural computation. It could be of
significance for the improvement of cognitive user interfaces and other
applications of VSA in machine learning.
| [
{
"created": "Wed, 11 Mar 2020 09:07:02 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Sep 2020 08:34:46 GMT",
"version": "v2"
}
] | 2020-09-28 | [
[
"Graben",
"Peter beim",
""
],
[
"Huber",
"Markus",
""
],
[
"Meyer",
"Werner",
""
],
[
"Römer",
"Ronald",
""
],
[
"Wolff",
"Matthias",
""
]
] | Background / introduction. Vector symbolic architectures (VSA) are a viable approach for the hyperdimensional representation of symbolic data, such as documents, syntactic structures, or semantic frames. Methods. We present a rigorous mathematical framework for the representation of phrase structure trees and parse trees of context-free grammars (CFG) in Fock space, i.e. infinite-dimensional Hilbert space as being used in quantum field theory. We define a novel normal form for CFG by means of term algebras. Using a recently developed software toolbox, called FockBox, we construct Fock space representations for the trees built up by a CFG left-corner (LC) parser. Results. We prove a universal representation theorem for CFG term algebras in Fock space and illustrate our findings through a low-dimensional principal component projection of the LC parser states. Conclusions. Our approach could leverage the development of VSA for explainable artificial intelligence (XAI) by means of hyperdimensional deep neural computation. It could be of significance for the improvement of cognitive user interfaces and other applications of VSA in machine learning. |
2405.16103 | Andrzej Lingas | Andrzej Lingas | Boolean Matrix Multiplication for Highly Clustered Data on the Congested
Clique | To appear in Euro-Par 2024 proceedings, 14 pages | null | null | null | cs.DS cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a protocol for the Boolean matrix product of two $n\times b$
Boolean matrices on the congested clique designed for the situation when the
rows of the first matrix or the columns of the second matrix are highly
clustered in the space $\{0,1\}^n.$ With high probability (w.h.p), it uses
$\tilde{O}\left(\sqrt {\frac M n+1}\right)$ rounds on the congested clique with
$n$ nodes, where $M$ is the minimum of the cost of a minimum spanning tree
(MST) of the rows of the first input matrix and the cost of an MST of the
columns of the second input matrix in the Hamming space $\{0,1\}^n.$ A key step
in our protocol is the computation of an approximate minimum spanning tree of a
set of $n$ points in the space $\{0,1\}^n$. We provide a protocol for this
problem (of interest in its own rights) based on a known randomized technique
of dimension reduction in Hamming spaces. W.h.p., it constructs an
$O(1)$-factor approximation of an MST of $n$ points in the Hamming space $\{
0,\ 1\}^n$ using $O(\log^3 n)$ rounds on the congested clique with $n$ nodes.
| [
{
"created": "Sat, 25 May 2024 07:31:05 GMT",
"version": "v1"
}
] | 2024-05-28 | [
[
"Lingas",
"Andrzej",
""
]
] | We present a protocol for the Boolean matrix product of two $n\times b$ Boolean matrices on the congested clique designed for the situation when the rows of the first matrix or the columns of the second matrix are highly clustered in the space $\{0,1\}^n.$ With high probability (w.h.p), it uses $\tilde{O}\left(\sqrt {\frac M n+1}\right)$ rounds on the congested clique with $n$ nodes, where $M$ is the minimum of the cost of a minimum spanning tree (MST) of the rows of the first input matrix and the cost of an MST of the columns of the second input matrix in the Hamming space $\{0,1\}^n.$ A key step in our protocol is the computation of an approximate minimum spanning tree of a set of $n$ points in the space $\{0,1\}^n$. We provide a protocol for this problem (of interest in its own rights) based on a known randomized technique of dimension reduction in Hamming spaces. W.h.p., it constructs an $O(1)$-factor approximation of an MST of $n$ points in the Hamming space $\{ 0,\ 1\}^n$ using $O(\log^3 n)$ rounds on the congested clique with $n$ nodes. |
1904.00510 | Gustavo Gil | Gustavo D. Gil, Julie M. Walker, Nabil Zemiti, Allison M. Okamura,
Philippe Poignet | How to enhance learning of robotic surgery gestures? A tactile cue
saliency investigation for 3D hand guidance | HSMR: 12th Hamlyn Symposium on Medical Robotics (London, 24th-26th
June 2019) | null | 10.31256/HSMR2019.9 | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The current generation of surgeons requires extensive training in
teleoperation to develop specific dexterous skills, which are independent of
medical knowledge. Training curricula progress from manipulation tasks to
simulated surgical tasks but are limited in time. To tackle this, we propose to
integrate surgical robotic training together with Haptic Feedback (HF) to
improve skill acquisition. This paper present the initial but promising results
of our haptic device designed to support in the training of surgical gestures.
Our ongoing work is related to integrate the HF in the RAVEN II platform.
| [
{
"created": "Sun, 31 Mar 2019 23:36:31 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Apr 2019 16:15:42 GMT",
"version": "v2"
},
{
"created": "Fri, 10 May 2019 09:18:31 GMT",
"version": "v3"
},
{
"created": "Fri, 19 Jul 2019 10:15:43 GMT",
"version": "v4"
}
] | 2019-07-23 | [
[
"Gil",
"Gustavo D.",
""
],
[
"Walker",
"Julie M.",
""
],
[
"Zemiti",
"Nabil",
""
],
[
"Okamura",
"Allison M.",
""
],
[
"Poignet",
"Philippe",
""
]
] | The current generation of surgeons requires extensive training in teleoperation to develop specific dexterous skills, which are independent of medical knowledge. Training curricula progress from manipulation tasks to simulated surgical tasks but are limited in time. To tackle this, we propose to integrate surgical robotic training together with Haptic Feedback (HF) to improve skill acquisition. This paper present the initial but promising results of our haptic device designed to support in the training of surgical gestures. Our ongoing work is related to integrate the HF in the RAVEN II platform. |
2305.09442 | Jake Welde | Jake Welde and Vijay Kumar | Towards Automatic Identification of Globally Valid Geometric Flat
Outputs via Numerical Optimization | To appear as a contributed paper in the "Geometric Representations"
workshop at the 2023 International Conference on Robotics and Automation
(ICRA) | null | null | null | cs.RO math.DG math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Differential flatness enables efficient planning and control for
underactuated robotic systems, but we lack a systematic and practical means of
identifying a flat output (or determining whether one exists) for an arbitrary
robotic system. In this work, we leverage recent results elucidating the role
of symmetry in constructing flat outputs for free-flying robotic systems. Using
the tools of Riemannian geometry, Lie group theory, and differential forms, we
cast the search for a globally valid, equivariant flat output as an
optimization problem. An approximate transcription of this continuum
formulation to a quadratic program is performed, and its solutions for two
example systems achieve precise agreement with the known closed-form flat
outputs. Our results point towards a systematic, automated approach to
numerically identify geometric flat outputs directly from the system model,
particularly useful when complexity renders pen and paper analysis intractable.
| [
{
"created": "Tue, 16 May 2023 13:58:40 GMT",
"version": "v1"
}
] | 2023-05-17 | [
[
"Welde",
"Jake",
""
],
[
"Kumar",
"Vijay",
""
]
] | Differential flatness enables efficient planning and control for underactuated robotic systems, but we lack a systematic and practical means of identifying a flat output (or determining whether one exists) for an arbitrary robotic system. In this work, we leverage recent results elucidating the role of symmetry in constructing flat outputs for free-flying robotic systems. Using the tools of Riemannian geometry, Lie group theory, and differential forms, we cast the search for a globally valid, equivariant flat output as an optimization problem. An approximate transcription of this continuum formulation to a quadratic program is performed, and its solutions for two example systems achieve precise agreement with the known closed-form flat outputs. Our results point towards a systematic, automated approach to numerically identify geometric flat outputs directly from the system model, particularly useful when complexity renders pen and paper analysis intractable. |
1107.3759 | Dejan Munjin | Dejan Munjin, Jean-Henry Morin | User Empowerment in the Internet of Things | null | null | null | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper focuses on the characteristics of two big triggers that
facilitated wide user adoption of the Internet: Web 2.0 and online social
networks. We detect brakes for reproduction of these events in Internet of
things. To support our hypothesis we first compare the difference between the
ways of use of the Internet with the future scenarios of Internet of things. We
detect barriers that could slow down apparition of this kind of social events
during user adoption of Internet of Things and we propose a conceptual
framework to solve these problems.
| [
{
"created": "Tue, 19 Jul 2011 16:09:07 GMT",
"version": "v1"
}
] | 2011-07-20 | [
[
"Munjin",
"Dejan",
""
],
[
"Morin",
"Jean-Henry",
""
]
] | This paper focuses on the characteristics of two big triggers that facilitated wide user adoption of the Internet: Web 2.0 and online social networks. We detect brakes for reproduction of these events in Internet of things. To support our hypothesis we first compare the difference between the ways of use of the Internet with the future scenarios of Internet of things. We detect barriers that could slow down apparition of this kind of social events during user adoption of Internet of Things and we propose a conceptual framework to solve these problems. |
1510.00132 | MIkhail Hushchyn | Mikhail Hushchyn, Philippe Charpentier, Andrey Ustyuzhanin | Disk storage management for LHCb based on Data Popularity estimator | null | null | 10.1088/1742-6596/664/4/042026 | null | cs.DC cs.LG physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an algorithm providing recommendations for optimizing the
LHCb data storage. The LHCb data storage system is a hybrid system. All
datasets are kept as archives on magnetic tapes. The most popular datasets are
kept on disks. The algorithm takes the dataset usage history and metadata
(size, type, configuration etc.) to generate a recommendation report. This
article presents how we use machine learning algorithms to predict future data
popularity. Using these predictions it is possible to estimate which datasets
should be removed from disk. We use regression algorithms and time series
analysis to find the optimal number of replicas for datasets that are kept on
disk. Based on the data popularity and the number of replicas optimization, the
algorithm minimizes a loss function to find the optimal data distribution. The
loss function represents all requirements for data distribution in the data
storage system. We demonstrate how our algorithm helps to save disk space and
to reduce waiting times for jobs using this data.
| [
{
"created": "Thu, 1 Oct 2015 07:40:37 GMT",
"version": "v1"
}
] | 2016-01-20 | [
[
"Hushchyn",
"Mikhail",
""
],
[
"Charpentier",
"Philippe",
""
],
[
"Ustyuzhanin",
"Andrey",
""
]
] | This paper presents an algorithm providing recommendations for optimizing the LHCb data storage. The LHCb data storage system is a hybrid system. All datasets are kept as archives on magnetic tapes. The most popular datasets are kept on disks. The algorithm takes the dataset usage history and metadata (size, type, configuration etc.) to generate a recommendation report. This article presents how we use machine learning algorithms to predict future data popularity. Using these predictions it is possible to estimate which datasets should be removed from disk. We use regression algorithms and time series analysis to find the optimal number of replicas for datasets that are kept on disk. Based on the data popularity and the number of replicas optimization, the algorithm minimizes a loss function to find the optimal data distribution. The loss function represents all requirements for data distribution in the data storage system. We demonstrate how our algorithm helps to save disk space and to reduce waiting times for jobs using this data. |
1412.2662 | Dmitry Zakablukov | Dmitry V. Zakablukov | On Gate Complexity of Reversible Circuits Consisting of NOT, CNOT and
2-CNOT Gates | In Russian, 18 pages, 1 figure | null | 10.4213/dm1365 | null | cs.ET | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper discusses the gate complexity of reversible circuits consisting of
NOT, CNOT and 2-CNOT gates. The Shannon gate complexity function $L(n, q)$ for
a reversible circuit, implementing a Boolean transformation $f\colon \mathbb
Z_2^n \to \mathbb Z_2^n$, is defined as a function of $n$ and the number of
additional inputs $q$. The general lower bound $L(n,q) \geq
\frac{2^n(n-2)}{3\log_2(n+q)} - \frac{n}{3}$ for the gate complexity of a
reversible circuit is proved. An upper bound $L(n,0) \leqslant
3n2^{n+4}(1+o(1)) \mathop / \log_2n$ for the gate complexity of a reversible
circuit without additional inputs is proved. An upper bound $L(n,q_0) \lesssim
2^n$ for the gate complexity of a reversible circuit with $q_0 \sim
n2^{n-o(n)}$ additional inputs is proved.
| [
{
"created": "Mon, 8 Dec 2014 16:58:12 GMT",
"version": "v1"
},
{
"created": "Sat, 13 Feb 2016 12:15:30 GMT",
"version": "v2"
}
] | 2016-07-08 | [
[
"Zakablukov",
"Dmitry V.",
""
]
] | The paper discusses the gate complexity of reversible circuits consisting of NOT, CNOT and 2-CNOT gates. The Shannon gate complexity function $L(n, q)$ for a reversible circuit, implementing a Boolean transformation $f\colon \mathbb Z_2^n \to \mathbb Z_2^n$, is defined as a function of $n$ and the number of additional inputs $q$. The general lower bound $L(n,q) \geq \frac{2^n(n-2)}{3\log_2(n+q)} - \frac{n}{3}$ for the gate complexity of a reversible circuit is proved. An upper bound $L(n,0) \leqslant 3n2^{n+4}(1+o(1)) \mathop / \log_2n$ for the gate complexity of a reversible circuit without additional inputs is proved. An upper bound $L(n,q_0) \lesssim 2^n$ for the gate complexity of a reversible circuit with $q_0 \sim n2^{n-o(n)}$ additional inputs is proved. |
1305.2704 | Ahmad Alamgir Khan Mr | Ahmad Alamgir Khan | Preventing Phishing Attacks using One Time Password and User Machine
Identification | 5 Pages, 8 Figures, Published with International Journal of Computer
Applications 0975 8887 Volume 68 No.3, April 2013 | International Journal of Computer Applications 68(3):7-11, April
2013 | 10.5120/11557-6839 | null | cs.CR | http://creativecommons.org/licenses/by/3.0/ | Phishing is a type of attack in which cyber criminals tricks the victims to
steal their personal and financial data. It has become an organized criminal
activity. Spoofed emails claiming to be from legitimate source are crafted in a
way to lead victims to reveal their personal, financial data by misdirecting
them to the counterfeit website.
This research paper presents a novel approach to combat the Phishing attacks.
An approach is proposed where user will retrieve the one time password by SMS
or by alternate email address. After receiving the one time password the web
server will create an encrypted token for the users computer or device for
authentication. The encrypted token will be used for identification, any time
user wishes to access the website he or she must request the new password. The
one time password as name implies will expire after single use. The one time
password and encrypted token is a smart way to tackle this problem.
| [
{
"created": "Mon, 13 May 2013 08:41:13 GMT",
"version": "v1"
}
] | 2013-05-14 | [
[
"Khan",
"Ahmad Alamgir",
""
]
] | Phishing is a type of attack in which cyber criminals tricks the victims to steal their personal and financial data. It has become an organized criminal activity. Spoofed emails claiming to be from legitimate source are crafted in a way to lead victims to reveal their personal, financial data by misdirecting them to the counterfeit website. This research paper presents a novel approach to combat the Phishing attacks. An approach is proposed where user will retrieve the one time password by SMS or by alternate email address. After receiving the one time password the web server will create an encrypted token for the users computer or device for authentication. The encrypted token will be used for identification, any time user wishes to access the website he or she must request the new password. The one time password as name implies will expire after single use. The one time password and encrypted token is a smart way to tackle this problem. |
1406.3969 | Siddhartha Ghosh | Siddhartha Ghosh, Sujata Thamke and Kalyani U.R.S | Translation Of Telugu-Marathi and Vice-Versa using Rule Based Machine
Translation | 13 pages, Fourth International Conference on Advances in Computing
and Information Technology (ACITY 2014) Delhi, India - May 2014 | null | 10.5121/csit.2014.4501 | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In todays digital world automated Machine Translation of one language to
another has covered a long way to achieve different kinds of success stories.
Whereas Babel Fish supports a good number of foreign languages and only Hindi
from Indian languages, the Google Translator takes care of about 10 Indian
languages. Though most of the Automated Machine Translation Systems are doing
well but handling Indian languages needs a major care while handling the local
proverbs/ idioms. Most of the Machine Translation system follows the direct
translation approach while translating one Indian language to other. Our
research at KMIT R&D Lab found that handling the local proverbs/idioms is not
given enough attention by the earlier research work. This paper focuses on two
of the majorly spoken Indian languages Marathi and Telugu, and translation
between them. Handling proverbs and idioms of both the languages have been
given a special care, and the research outcome shows a significant achievement
in this direction.
| [
{
"created": "Mon, 16 Jun 2014 10:59:03 GMT",
"version": "v1"
}
] | 2014-06-17 | [
[
"Ghosh",
"Siddhartha",
""
],
[
"Thamke",
"Sujata",
""
],
[
"S",
"Kalyani U. R.",
""
]
] | In todays digital world automated Machine Translation of one language to another has covered a long way to achieve different kinds of success stories. Whereas Babel Fish supports a good number of foreign languages and only Hindi from Indian languages, the Google Translator takes care of about 10 Indian languages. Though most of the Automated Machine Translation Systems are doing well but handling Indian languages needs a major care while handling the local proverbs/ idioms. Most of the Machine Translation system follows the direct translation approach while translating one Indian language to other. Our research at KMIT R&D Lab found that handling the local proverbs/idioms is not given enough attention by the earlier research work. This paper focuses on two of the majorly spoken Indian languages Marathi and Telugu, and translation between them. Handling proverbs and idioms of both the languages have been given a special care, and the research outcome shows a significant achievement in this direction. |
1911.01763 | Rahat Yeasin Emon | Rahat Yeasin Emon, Sharmistha Chanda Tista | An Efficient Word Lookup System by using Improved Trie Algorithm | 6 pages | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Efficiently word storing and searching is an important task in computer
science. An application space complexity, time complexity, and overall
performance depend on this string data. Many word searching data structures and
algorithms exist in the current world but few of them have space compress
ability. Trie is a popular data structure for word searching for its linear
searching capability. It is the basic and important part of various computer
applications such as information retrieval, natural language processing,
database system, compiler, and computer network. But currently, the available
version of trie tree cannot be used widely because of its high memory
requirement. This paper proposes a new Radix trie based data structure for word
storing and searching which can share not only just prefix but also infix and
suffix and thus reduces memory requirement. We propose a new emptiness property
to Radix trie. Proposed trie has character cell reduction capability and it can
dramatically reduce any application runtime memory size. Using it as data tank
to an operating system the overall main memory requirement of a device can be
reduced to a large extent.
| [
{
"created": "Tue, 5 Nov 2019 13:36:15 GMT",
"version": "v1"
}
] | 2019-11-06 | [
[
"Emon",
"Rahat Yeasin",
""
],
[
"Tista",
"Sharmistha Chanda",
""
]
] | Efficiently word storing and searching is an important task in computer science. An application space complexity, time complexity, and overall performance depend on this string data. Many word searching data structures and algorithms exist in the current world but few of them have space compress ability. Trie is a popular data structure for word searching for its linear searching capability. It is the basic and important part of various computer applications such as information retrieval, natural language processing, database system, compiler, and computer network. But currently, the available version of trie tree cannot be used widely because of its high memory requirement. This paper proposes a new Radix trie based data structure for word storing and searching which can share not only just prefix but also infix and suffix and thus reduces memory requirement. We propose a new emptiness property to Radix trie. Proposed trie has character cell reduction capability and it can dramatically reduce any application runtime memory size. Using it as data tank to an operating system the overall main memory requirement of a device can be reduced to a large extent. |
1910.14673 | Tiantian Fang | Tiantian Fang and Alexander G. Schwing | Co-Generation with GANs using AIS based HMC | Accepted to NeurIPS 2019 | null | null | null | cs.CV cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Inferring the most likely configuration for a subset of variables of a joint
distribution given the remaining ones - which we refer to as co-generation - is
an important challenge that is computationally demanding for all but the
simplest settings. This task has received a considerable amount of attention,
particularly for classical ways of modeling distributions like structured
prediction. In contrast, almost nothing is known about this task when
considering recently proposed techniques for modeling high-dimensional
distributions, particularly generative adversarial nets (GANs). Therefore, in
this paper, we study the occurring challenges for co-generation with GANs. To
address those challenges we develop an annealed importance sampling based
Hamiltonian Monte Carlo co-generation algorithm. The presented approach
significantly outperforms classical gradient based methods on a synthetic and
on the CelebA and LSUN datasets.
| [
{
"created": "Thu, 31 Oct 2019 17:59:59 GMT",
"version": "v1"
}
] | 2019-11-01 | [
[
"Fang",
"Tiantian",
""
],
[
"Schwing",
"Alexander G.",
""
]
] | Inferring the most likely configuration for a subset of variables of a joint distribution given the remaining ones - which we refer to as co-generation - is an important challenge that is computationally demanding for all but the simplest settings. This task has received a considerable amount of attention, particularly for classical ways of modeling distributions like structured prediction. In contrast, almost nothing is known about this task when considering recently proposed techniques for modeling high-dimensional distributions, particularly generative adversarial nets (GANs). Therefore, in this paper, we study the occurring challenges for co-generation with GANs. To address those challenges we develop an annealed importance sampling based Hamiltonian Monte Carlo co-generation algorithm. The presented approach significantly outperforms classical gradient based methods on a synthetic and on the CelebA and LSUN datasets. |
1811.10376 | Yi-Te Hsu | Yi-Te Hsu, Zining Zhu, Chi-Te Wang, Shih-Hau Fang, Frank Rudzicz, Yu
Tsao | Robustness against the channel effect in pathological voice detection | Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
arXiv:1811.07216 | null | null | ML4H/2018/200 | cs.LG cs.SD eess.AS stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many people are suffering from voice disorders, which can adversely affect
the quality of their lives. In response, some researchers have proposed
algorithms for automatic assessment of these disorders, based on voice signals.
However, these signals can be sensitive to the recording devices. Indeed, the
channel effect is a pervasive problem in machine learning for healthcare. In
this study, we propose a detection system for pathological voice, which is
robust against the channel effect. This system is based on a bidirectional LSTM
network. To increase the performance robustness against channel mismatch, we
integrate domain adversarial training (DAT) to eliminate the differences
between the devices. When we train on data recorded on a high-quality
microphone and evaluate on smartphone data without labels, our robust detection
system increases the PR-AUC from 0.8448 to 0.9455 (and 0.9522 with target
sample labels). To the best of our knowledge, this is the first study applying
unsupervised domain adaptation to pathological voice detection. Notably, our
system does not need target device sample labels, which allows for
generalization to many new devices.
| [
{
"created": "Mon, 26 Nov 2018 14:11:12 GMT",
"version": "v1"
},
{
"created": "Sun, 2 Dec 2018 14:52:39 GMT",
"version": "v2"
}
] | 2018-12-04 | [
[
"Hsu",
"Yi-Te",
""
],
[
"Zhu",
"Zining",
""
],
[
"Wang",
"Chi-Te",
""
],
[
"Fang",
"Shih-Hau",
""
],
[
"Rudzicz",
"Frank",
""
],
[
"Tsao",
"Yu",
""
]
] | Many people are suffering from voice disorders, which can adversely affect the quality of their lives. In response, some researchers have proposed algorithms for automatic assessment of these disorders, based on voice signals. However, these signals can be sensitive to the recording devices. Indeed, the channel effect is a pervasive problem in machine learning for healthcare. In this study, we propose a detection system for pathological voice, which is robust against the channel effect. This system is based on a bidirectional LSTM network. To increase the performance robustness against channel mismatch, we integrate domain adversarial training (DAT) to eliminate the differences between the devices. When we train on data recorded on a high-quality microphone and evaluate on smartphone data without labels, our robust detection system increases the PR-AUC from 0.8448 to 0.9455 (and 0.9522 with target sample labels). To the best of our knowledge, this is the first study applying unsupervised domain adaptation to pathological voice detection. Notably, our system does not need target device sample labels, which allows for generalization to many new devices. |
0905.0079 | Thorsten Hehn | Thorsten Hehn, Johannes B. Huber, Olgica Milenkovic, Stefan Laendner | Multiple-Bases Belief-Propagation Decoding of High-Density Cyclic Codes | This full paper accompanies a letter submitted to "IEEE Transactions
on Communications". It is intended to provide detailed information for
interested readers of the letter. 24 pages, 6 figures | null | 10.1109/TCOMM.2010.01.070468 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new method for decoding short and moderate length linear block
codes with dense parity-check matrix representations of cyclic form, termed
multiple-bases belief-propagation (MBBP). The proposed iterative scheme makes
use of the fact that a code has many structurally diverse parity-check
matrices, capable of detecting different error patterns. We show that this
inherent code property leads to decoding algorithms with significantly better
performance when compared to standard BP decoding. Furthermore, we describe how
to choose sets of parity-check matrices of cyclic form amenable for
multiple-bases decoding, based on analytical studies performed for the binary
erasure channel. For several cyclic and extended cyclic codes, the MBBP
decoding performance can be shown to closely follow that of maximum-likelihood
decoders.
| [
{
"created": "Fri, 1 May 2009 11:15:25 GMT",
"version": "v1"
}
] | 2016-11-15 | [
[
"Hehn",
"Thorsten",
""
],
[
"Huber",
"Johannes B.",
""
],
[
"Milenkovic",
"Olgica",
""
],
[
"Laendner",
"Stefan",
""
]
] | We introduce a new method for decoding short and moderate length linear block codes with dense parity-check matrix representations of cyclic form, termed multiple-bases belief-propagation (MBBP). The proposed iterative scheme makes use of the fact that a code has many structurally diverse parity-check matrices, capable of detecting different error patterns. We show that this inherent code property leads to decoding algorithms with significantly better performance when compared to standard BP decoding. Furthermore, we describe how to choose sets of parity-check matrices of cyclic form amenable for multiple-bases decoding, based on analytical studies performed for the binary erasure channel. For several cyclic and extended cyclic codes, the MBBP decoding performance can be shown to closely follow that of maximum-likelihood decoders. |
1306.6657 | Markus Rabe | Bernd Finkbeiner, Markus N. Rabe, and C\'esar S\'anchez | A Temporal Logic for Hyperproperties | null | null | null | null | cs.LO cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hyperproperties, as introduced by Clarkson and Schneider, characterize the
correctness of a computer program as a condition on its set of computation
paths. Standard temporal logics can only refer to a single path at a time, and
therefore cannot express many hyperproperties of interest, including
noninterference and other important properties in security and coding theory.
In this paper, we investigate an extension of temporal logic with explicit path
variables. We show that the quantification over paths naturally subsumes other
extensions of temporal logic with operators for information flow and knowledge.
The model checking problem for temporal logic with path quantification is
decidable. For alternation depth 1, the complexity is PSPACE in the length of
the formula and NLOGSPACE in the size of the system, as for linear-time
temporal logic.
| [
{
"created": "Thu, 27 Jun 2013 20:39:03 GMT",
"version": "v1"
}
] | 2013-07-01 | [
[
"Finkbeiner",
"Bernd",
""
],
[
"Rabe",
"Markus N.",
""
],
[
"Sánchez",
"César",
""
]
] | Hyperproperties, as introduced by Clarkson and Schneider, characterize the correctness of a computer program as a condition on its set of computation paths. Standard temporal logics can only refer to a single path at a time, and therefore cannot express many hyperproperties of interest, including noninterference and other important properties in security and coding theory. In this paper, we investigate an extension of temporal logic with explicit path variables. We show that the quantification over paths naturally subsumes other extensions of temporal logic with operators for information flow and knowledge. The model checking problem for temporal logic with path quantification is decidable. For alternation depth 1, the complexity is PSPACE in the length of the formula and NLOGSPACE in the size of the system, as for linear-time temporal logic. |
2308.00931 | Risheng Liu | Zengxi Zhang, Zhiying Jiang, Jinyuan Liu, Xin Fan, Risheng Liu | WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement
and Beyond | 10 pages, 13 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Underwater images suffer from light refraction and absorption, which impairs
visibility and interferes the subsequent applications. Existing underwater
image enhancement methods mainly focus on image quality improvement, ignoring
the effect on practice. To balance the visual quality and application, we
propose a heuristic normalizing flow for detection-driven underwater image
enhancement, dubbed WaterFlow. Specifically, we first develop an invertible
mapping to achieve the translation between the degraded image and its clear
counterpart. Considering the differentiability and interpretability, we
incorporate the heuristic prior into the data-driven mapping procedure, where
the ambient light and medium transmission coefficient benefit credible
generation. Furthermore, we introduce a detection perception module to transmit
the implicit semantic guidance into the enhancement procedure, where the
enhanced images hold more detection-favorable features and are able to promote
the detection performance. Extensive experiments prove the superiority of our
WaterFlow, against state-of-the-art methods quantitatively and qualitatively.
| [
{
"created": "Wed, 2 Aug 2023 04:17:35 GMT",
"version": "v1"
}
] | 2023-08-03 | [
[
"Zhang",
"Zengxi",
""
],
[
"Jiang",
"Zhiying",
""
],
[
"Liu",
"Jinyuan",
""
],
[
"Fan",
"Xin",
""
],
[
"Liu",
"Risheng",
""
]
] | Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications. Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practice. To balance the visual quality and application, we propose a heuristic normalizing flow for detection-driven underwater image enhancement, dubbed WaterFlow. Specifically, we first develop an invertible mapping to achieve the translation between the degraded image and its clear counterpart. Considering the differentiability and interpretability, we incorporate the heuristic prior into the data-driven mapping procedure, where the ambient light and medium transmission coefficient benefit credible generation. Furthermore, we introduce a detection perception module to transmit the implicit semantic guidance into the enhancement procedure, where the enhanced images hold more detection-favorable features and are able to promote the detection performance. Extensive experiments prove the superiority of our WaterFlow, against state-of-the-art methods quantitatively and qualitatively. |
2312.10099 | Shun Liu | Shun Liu, Jianan Zhang, Ruocheng Song, Teik Toe Teoh | ADA-YOLO: Dynamic Fusion of YOLOv8 and Adaptive Heads for Precise Image
Detection and Diagnosis | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Object detection and localization are crucial tasks for biomedical image
analysis, particularly in the field of hematology where the detection and
recognition of blood cells are essential for diagnosis and treatment decisions.
While attention-based methods have shown significant progress in object
detection in various domains, their application in medical object detection has
been limited due to the unique challenges posed by medical imaging datasets. To
address this issue, we propose ADA-YOLO, a light-weight yet effective method
for medical object detection that integrates attention-based mechanisms with
the YOLOv8 architecture. Our proposed method leverages the dynamic feature
localisation and parallel regression for computer vision tasks through
\textit{adaptive head} module. Empirical experiments were conducted on the
Blood Cell Count and Detection (BCCD) dataset to evaluate the effectiveness of
ADA-YOLO. The results showed that ADA-YOLO outperforms the YOLOv8 model in mAP
(mean average precision) on the BCCD dataset by using more than 3 times less
space than YOLOv8. This indicates that our proposed method is effective.
Moreover, the light-weight nature of our proposed method makes it suitable for
deployment in resource-constrained environments such as mobile devices or edge
computing systems. which could ultimately lead to improved diagnosis and
treatment outcomes in the field of hematology.
| [
{
"created": "Thu, 14 Dec 2023 18:27:13 GMT",
"version": "v1"
}
] | 2023-12-19 | [
[
"Liu",
"Shun",
""
],
[
"Zhang",
"Jianan",
""
],
[
"Song",
"Ruocheng",
""
],
[
"Teoh",
"Teik Toe",
""
]
] | Object detection and localization are crucial tasks for biomedical image analysis, particularly in the field of hematology where the detection and recognition of blood cells are essential for diagnosis and treatment decisions. While attention-based methods have shown significant progress in object detection in various domains, their application in medical object detection has been limited due to the unique challenges posed by medical imaging datasets. To address this issue, we propose ADA-YOLO, a light-weight yet effective method for medical object detection that integrates attention-based mechanisms with the YOLOv8 architecture. Our proposed method leverages the dynamic feature localisation and parallel regression for computer vision tasks through \textit{adaptive head} module. Empirical experiments were conducted on the Blood Cell Count and Detection (BCCD) dataset to evaluate the effectiveness of ADA-YOLO. The results showed that ADA-YOLO outperforms the YOLOv8 model in mAP (mean average precision) on the BCCD dataset by using more than 3 times less space than YOLOv8. This indicates that our proposed method is effective. Moreover, the light-weight nature of our proposed method makes it suitable for deployment in resource-constrained environments such as mobile devices or edge computing systems. which could ultimately lead to improved diagnosis and treatment outcomes in the field of hematology. |
2311.17795 | Guy Hay | Guy Hay and Ohad Volk | Marginal Laplacian Score | 10 pages | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | High-dimensional imbalanced data poses a machine learning challenge. In the
absence of sufficient or high-quality labels, unsupervised feature selection
methods are crucial for the success of subsequent algorithms. Therefore, we
introduce a Marginal Laplacian Score (MLS), a modification of the well known
Laplacian Score (LS) tailored to better address imbalanced data. We introduce
an assumption that the minority class or anomalous appear more frequently in
the margin of the features. Consequently, MLS aims to preserve the local
structure of the dataset's margin. We propose its integration into modern
feature selection methods that utilize the Laplacian score. We integrate the
MLS algorithm into the Differentiable Unsupervised Feature Selection (DUFS),
resulting in DUFS-MLS. The proposed methods demonstrate robust and improved
performance on synthetic and public datasets.
| [
{
"created": "Wed, 29 Nov 2023 16:45:43 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Feb 2024 08:06:51 GMT",
"version": "v2"
}
] | 2024-02-05 | [
[
"Hay",
"Guy",
""
],
[
"Volk",
"Ohad",
""
]
] | High-dimensional imbalanced data poses a machine learning challenge. In the absence of sufficient or high-quality labels, unsupervised feature selection methods are crucial for the success of subsequent algorithms. Therefore, we introduce a Marginal Laplacian Score (MLS), a modification of the well known Laplacian Score (LS) tailored to better address imbalanced data. We introduce an assumption that the minority class or anomalous appear more frequently in the margin of the features. Consequently, MLS aims to preserve the local structure of the dataset's margin. We propose its integration into modern feature selection methods that utilize the Laplacian score. We integrate the MLS algorithm into the Differentiable Unsupervised Feature Selection (DUFS), resulting in DUFS-MLS. The proposed methods demonstrate robust and improved performance on synthetic and public datasets. |
1905.10902 | Darren Strash | Damir Ferizovic and Demian Hespe and Sebastian Lamm and Matthias Mnich
and Christian Schulz and Darren Strash | Engineering Kernelization for Maximum Cut | 16 pages, 4 tables, 2 figures | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Kernelization is a general theoretical framework for preprocessing instances
of NP-hard problems into (generally smaller) instances with bounded size, via
the repeated application of data reduction rules. For the fundamental Max Cut
problem, kernelization algorithms are theoretically highly efficient for
various parameterizations. However, the efficacy of these reduction rules in
practice---to aid solving highly challenging benchmark instances to
optimality---remains entirely unexplored.
We engineer a new suite of efficient data reduction rules that subsume most
of the previously published rules, and demonstrate their significant impact on
benchmark data sets, including synthetic instances, and data sets from the VLSI
and image segmentation application domains. Our experiments reveal that current
state-of-the-art solvers can be sped up by up to multiple orders of magnitude
when combined with our data reduction rules. On social and biological networks
in particular, kernelization enables us to solve four instances that were
previously unsolved in a ten-hour time limit with state-of-the-art solvers;
three of these instances are now solved in less than two seconds.
| [
{
"created": "Sun, 26 May 2019 23:12:33 GMT",
"version": "v1"
}
] | 2019-05-28 | [
[
"Ferizovic",
"Damir",
""
],
[
"Hespe",
"Demian",
""
],
[
"Lamm",
"Sebastian",
""
],
[
"Mnich",
"Matthias",
""
],
[
"Schulz",
"Christian",
""
],
[
"Strash",
"Darren",
""
]
] | Kernelization is a general theoretical framework for preprocessing instances of NP-hard problems into (generally smaller) instances with bounded size, via the repeated application of data reduction rules. For the fundamental Max Cut problem, kernelization algorithms are theoretically highly efficient for various parameterizations. However, the efficacy of these reduction rules in practice---to aid solving highly challenging benchmark instances to optimality---remains entirely unexplored. We engineer a new suite of efficient data reduction rules that subsume most of the previously published rules, and demonstrate their significant impact on benchmark data sets, including synthetic instances, and data sets from the VLSI and image segmentation application domains. Our experiments reveal that current state-of-the-art solvers can be sped up by up to multiple orders of magnitude when combined with our data reduction rules. On social and biological networks in particular, kernelization enables us to solve four instances that were previously unsolved in a ten-hour time limit with state-of-the-art solvers; three of these instances are now solved in less than two seconds. |
2404.19048 | Ximing Dong | Ximing Dong, Dayi Lin, Shaowei Wang, Ahmed E. Hassan | A Framework for Real-time Safeguarding the Text Generation of Large
Language Model | null | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) have significantly advanced natural language
processing (NLP) tasks but also pose ethical and societal risks due to their
propensity to generate harmful content. To address this, various approaches
have been developed to safeguard LLMs from producing unsafe content. However,
existing methods have limitations, including the need for training specific
control models and proactive intervention during text generation, that lead to
quality degradation and increased computational overhead. To mitigate those
limitations, we propose LLMSafeGuard, a lightweight framework to safeguard LLM
text generation in real-time. LLMSafeGuard integrates an external validator
into the beam search algorithm during decoding, rejecting candidates that
violate safety constraints while allowing valid ones to proceed. We introduce a
similarity based validation approach, simplifying constraint introduction and
eliminating the need for control model training. Additionally, LLMSafeGuard
employs a context-wise timing selection strategy, intervening LLMs only when
necessary. We evaluate LLMSafeGuard on two tasks, detoxification and copyright
safeguarding, and demonstrate its superior performance over SOTA baselines. For
instance, LLMSafeGuard reduces the average toxic score of. LLM output by 29.7%
compared to the best baseline meanwhile preserving similar linguistic quality
as natural output in detoxification task. Similarly, in the copyright task,
LLMSafeGuard decreases the Longest Common Subsequence (LCS) by 56.2% compared
to baselines. Moreover, our context-wise timing selection strategy reduces
inference time by at least 24% meanwhile maintaining comparable effectiveness
as validating each time step. LLMSafeGuard also offers tunable parameters to
balance its effectiveness and efficiency.
| [
{
"created": "Mon, 29 Apr 2024 18:40:01 GMT",
"version": "v1"
},
{
"created": "Wed, 1 May 2024 19:53:12 GMT",
"version": "v2"
}
] | 2024-05-03 | [
[
"Dong",
"Ximing",
""
],
[
"Lin",
"Dayi",
""
],
[
"Wang",
"Shaowei",
""
],
[
"Hassan",
"Ahmed E.",
""
]
] | Large Language Models (LLMs) have significantly advanced natural language processing (NLP) tasks but also pose ethical and societal risks due to their propensity to generate harmful content. To address this, various approaches have been developed to safeguard LLMs from producing unsafe content. However, existing methods have limitations, including the need for training specific control models and proactive intervention during text generation, that lead to quality degradation and increased computational overhead. To mitigate those limitations, we propose LLMSafeGuard, a lightweight framework to safeguard LLM text generation in real-time. LLMSafeGuard integrates an external validator into the beam search algorithm during decoding, rejecting candidates that violate safety constraints while allowing valid ones to proceed. We introduce a similarity based validation approach, simplifying constraint introduction and eliminating the need for control model training. Additionally, LLMSafeGuard employs a context-wise timing selection strategy, intervening LLMs only when necessary. We evaluate LLMSafeGuard on two tasks, detoxification and copyright safeguarding, and demonstrate its superior performance over SOTA baselines. For instance, LLMSafeGuard reduces the average toxic score of. LLM output by 29.7% compared to the best baseline meanwhile preserving similar linguistic quality as natural output in detoxification task. Similarly, in the copyright task, LLMSafeGuard decreases the Longest Common Subsequence (LCS) by 56.2% compared to baselines. Moreover, our context-wise timing selection strategy reduces inference time by at least 24% meanwhile maintaining comparable effectiveness as validating each time step. LLMSafeGuard also offers tunable parameters to balance its effectiveness and efficiency. |
2104.03879 | Dat Quoc Nguyen | Thinh Hung Truong, Mai Hoang Dao, Dat Quoc Nguyen | COVID-19 Named Entity Recognition for Vietnamese | To appear in Proceedings of NAACL 2021 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The current COVID-19 pandemic has lead to the creation of many corpora that
facilitate NLP research and downstream applications to help fight the pandemic.
However, most of these corpora are exclusively for English. As the pandemic is
a global problem, it is worth creating COVID-19 related datasets for languages
other than English. In this paper, we present the first manually-annotated
COVID-19 domain-specific dataset for Vietnamese. Particularly, our dataset is
annotated for the named entity recognition (NER) task with newly-defined entity
types that can be used in other future epidemics. Our dataset also contains the
largest number of entities compared to existing Vietnamese NER datasets. We
empirically conduct experiments using strong baselines on our dataset, and find
that: automatic Vietnamese word segmentation helps improve the NER results and
the highest performances are obtained by fine-tuning pre-trained language
models where the monolingual model PhoBERT for Vietnamese (Nguyen and Nguyen,
2020) produces higher results than the multilingual model XLM-R (Conneau et
al., 2020). We publicly release our dataset at:
https://github.com/VinAIResearch/PhoNER_COVID19
| [
{
"created": "Thu, 8 Apr 2021 16:35:34 GMT",
"version": "v1"
}
] | 2021-04-09 | [
[
"Truong",
"Thinh Hung",
""
],
[
"Dao",
"Mai Hoang",
""
],
[
"Nguyen",
"Dat Quoc",
""
]
] | The current COVID-19 pandemic has lead to the creation of many corpora that facilitate NLP research and downstream applications to help fight the pandemic. However, most of these corpora are exclusively for English. As the pandemic is a global problem, it is worth creating COVID-19 related datasets for languages other than English. In this paper, we present the first manually-annotated COVID-19 domain-specific dataset for Vietnamese. Particularly, our dataset is annotated for the named entity recognition (NER) task with newly-defined entity types that can be used in other future epidemics. Our dataset also contains the largest number of entities compared to existing Vietnamese NER datasets. We empirically conduct experiments using strong baselines on our dataset, and find that: automatic Vietnamese word segmentation helps improve the NER results and the highest performances are obtained by fine-tuning pre-trained language models where the monolingual model PhoBERT for Vietnamese (Nguyen and Nguyen, 2020) produces higher results than the multilingual model XLM-R (Conneau et al., 2020). We publicly release our dataset at: https://github.com/VinAIResearch/PhoNER_COVID19 |
1811.11262 | Pieter Stroobant | Pieter Stroobant, Sergi Abadal, Wouter Tavernier, Eduard Alarc\'on,
Didier Colle, and Mario Pickavet | A General, Fault tolerant, Adaptive, Deadlock-free Routing Protocol for
Network-on-chip | Presented at 11th International Workshop on Network on Chip
Architectures (NoCArc 2018) | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper presents a topology-agnostic greedy protocol for network-on-chip
routing. The proposed routing algorithm can tolerate any number of permanent
faults, and is proven to be deadlock-free. We introduce a specialized variant
of the algorithm, which is optimized for 2D mesh networks, both flat and
wireless. The adaptiveness and minimality of several variants this algorithm
are analyzed through graph-based simulations.
| [
{
"created": "Thu, 25 Oct 2018 04:59:37 GMT",
"version": "v1"
}
] | 2018-11-29 | [
[
"Stroobant",
"Pieter",
""
],
[
"Abadal",
"Sergi",
""
],
[
"Tavernier",
"Wouter",
""
],
[
"Alarcón",
"Eduard",
""
],
[
"Colle",
"Didier",
""
],
[
"Pickavet",
"Mario",
""
]
] | The paper presents a topology-agnostic greedy protocol for network-on-chip routing. The proposed routing algorithm can tolerate any number of permanent faults, and is proven to be deadlock-free. We introduce a specialized variant of the algorithm, which is optimized for 2D mesh networks, both flat and wireless. The adaptiveness and minimality of several variants this algorithm are analyzed through graph-based simulations. |
1708.08551 | Mohammad Amin Nabian | Mohammad Amin Nabian, Hadi Meidani | Deep Learning for Accelerated Reliability Analysis of Infrastructure
Networks | null | Nabian, M. A. and Meidani, H. (2018), Deep Learning for
Accelerated Seismic Reliability Analysis of Transportation Networks. Computer
Aided Civil and Infrastructure Engineering, 33: 443-458 | 10.1111/mice.12359 | null | cs.CE cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Natural disasters can have catastrophic impacts on the functionality of
infrastructure systems and cause severe physical and socio-economic losses.
Given budget constraints, it is crucial to optimize decisions regarding
mitigation, preparedness, response, and recovery practices for these systems.
This requires accurate and efficient means to evaluate the infrastructure
system reliability. While numerous research efforts have addressed and
quantified the impact of natural disasters on infrastructure systems, typically
using the Monte Carlo approach, they still suffer from high computational cost
and, thus, are of limited applicability to large systems. This paper presents a
deep learning framework for accelerating infrastructure system reliability
analysis. In particular, two distinct deep neural network surrogates are
constructed and studied: (1) A classifier surrogate which speeds up the
connectivity determination of networks, and (2) An end-to-end surrogate that
replaces a number of components such as roadway status realization,
connectivity determination, and connectivity averaging. The proposed approach
is applied to a simulation-based study of the two-terminal connectivity of a
California transportation network subject to extreme probabilistic earthquake
events. Numerical results highlight the effectiveness of the proposed approach
in accelerating the transportation system two-terminal reliability analysis
with extremely high prediction accuracy.
| [
{
"created": "Mon, 28 Aug 2017 22:41:11 GMT",
"version": "v1"
}
] | 2018-06-11 | [
[
"Nabian",
"Mohammad Amin",
""
],
[
"Meidani",
"Hadi",
""
]
] | Natural disasters can have catastrophic impacts on the functionality of infrastructure systems and cause severe physical and socio-economic losses. Given budget constraints, it is crucial to optimize decisions regarding mitigation, preparedness, response, and recovery practices for these systems. This requires accurate and efficient means to evaluate the infrastructure system reliability. While numerous research efforts have addressed and quantified the impact of natural disasters on infrastructure systems, typically using the Monte Carlo approach, they still suffer from high computational cost and, thus, are of limited applicability to large systems. This paper presents a deep learning framework for accelerating infrastructure system reliability analysis. In particular, two distinct deep neural network surrogates are constructed and studied: (1) A classifier surrogate which speeds up the connectivity determination of networks, and (2) An end-to-end surrogate that replaces a number of components such as roadway status realization, connectivity determination, and connectivity averaging. The proposed approach is applied to a simulation-based study of the two-terminal connectivity of a California transportation network subject to extreme probabilistic earthquake events. Numerical results highlight the effectiveness of the proposed approach in accelerating the transportation system two-terminal reliability analysis with extremely high prediction accuracy. |
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