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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1807.09163 | Anabik Pal Mr. | Anabik Pal, Sounak Ray and Utpal Garain | Skin disease identification from dermoscopy images using deep
convolutional neural network | Challenge Participation in ISIC 2018: Skin Lesion Analysis Towards
Melanoma Detection | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, a deep neural network based ensemble method is experimented
for automatic identification of skin disease from dermoscopic images. The
developed algorithm is applied on the task3 of the ISIC 2018 challenge dataset
(Skin Lesion Analysis Towards Melanoma Detection).
| [
{
"created": "Tue, 24 Jul 2018 14:48:57 GMT",
"version": "v1"
}
] | 2018-07-25 | [
[
"Pal",
"Anabik",
""
],
[
"Ray",
"Sounak",
""
],
[
"Garain",
"Utpal",
""
]
] | In this paper, a deep neural network based ensemble method is experimented for automatic identification of skin disease from dermoscopic images. The developed algorithm is applied on the task3 of the ISIC 2018 challenge dataset (Skin Lesion Analysis Towards Melanoma Detection). |
2107.01707 | Rasheed el-Bouri | Rasheed el-Bouri, Tingting Zhu, David A. Clifton | Towards Scheduling Federated Deep Learning using Meta-Gradients for
Inter-Hospital Learning | 11 pages, 8 figures | null | null | null | cs.LG cs.CR cs.DC | http://creativecommons.org/licenses/by/4.0/ | Given the abundance and ease of access of personal data today, individual
privacy has become of paramount importance, particularly in the healthcare
domain. In this work, we aim to utilise patient data extracted from multiple
hospital data centres to train a machine learning model without sacrificing
patient privacy. We develop a scheduling algorithm in conjunction with a
student-teacher algorithm that is deployed in a federated manner. This allows a
central model to learn from batches of data at each federal node. The teacher
acts between data centres to update the main task (student) algorithm using the
data that is stored in the various data centres. We show that the scheduler,
trained using meta-gradients, can effectively organise training and as a result
train a machine learning model on a diverse dataset without needing explicit
access to the patient data. We achieve state-of-the-art performance and show
how our method overcomes some of the problems faced in the federated learning
such as node poisoning. We further show how the scheduler can be used as a
mechanism for transfer learning, allowing different teachers to work together
in training a student for state-of-the-art performance.
| [
{
"created": "Sun, 4 Jul 2021 18:45:58 GMT",
"version": "v1"
}
] | 2021-07-06 | [
[
"el-Bouri",
"Rasheed",
""
],
[
"Zhu",
"Tingting",
""
],
[
"Clifton",
"David A.",
""
]
] | Given the abundance and ease of access of personal data today, individual privacy has become of paramount importance, particularly in the healthcare domain. In this work, we aim to utilise patient data extracted from multiple hospital data centres to train a machine learning model without sacrificing patient privacy. We develop a scheduling algorithm in conjunction with a student-teacher algorithm that is deployed in a federated manner. This allows a central model to learn from batches of data at each federal node. The teacher acts between data centres to update the main task (student) algorithm using the data that is stored in the various data centres. We show that the scheduler, trained using meta-gradients, can effectively organise training and as a result train a machine learning model on a diverse dataset without needing explicit access to the patient data. We achieve state-of-the-art performance and show how our method overcomes some of the problems faced in the federated learning such as node poisoning. We further show how the scheduler can be used as a mechanism for transfer learning, allowing different teachers to work together in training a student for state-of-the-art performance. |
1702.01389 | Mohammad Moltafet | Mohammad. Moltafet, Nader. Mokari, Mohammad R. Javan, Paiez. Azmi | Comparison Study between NOMA and SCMA | 5 pages, 2 figures | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, the performance and system complexity of the candidate
multiple access (MA) techniques for the next generation of cellular systems,
namely, non-orthogonal multiple access (NOMA) (in this paper, we consider power
domain MA as NOMA) and sparse code multiple access (SCMA), are investigated. To
this end, for each MA technique, a resource allocation problem considering
heterogeneous cellular networks (HetNet) is formulated. We apply successive
convex approximation (SCA) method to each problem and obtain their solutions.
The simulation results show that SCMA-based system achieves better performance
than NOMA-based one at the cost of more complexity.
| [
{
"created": "Sun, 5 Feb 2017 11:42:00 GMT",
"version": "v1"
}
] | 2017-02-07 | [
[
"Moltafet",
"Mohammad.",
""
],
[
"Mokari",
"Nader.",
""
],
[
"Javan",
"Mohammad R.",
""
],
[
"Azmi",
"Paiez.",
""
]
] | In this paper, the performance and system complexity of the candidate multiple access (MA) techniques for the next generation of cellular systems, namely, non-orthogonal multiple access (NOMA) (in this paper, we consider power domain MA as NOMA) and sparse code multiple access (SCMA), are investigated. To this end, for each MA technique, a resource allocation problem considering heterogeneous cellular networks (HetNet) is formulated. We apply successive convex approximation (SCA) method to each problem and obtain their solutions. The simulation results show that SCMA-based system achieves better performance than NOMA-based one at the cost of more complexity. |
2005.05471 | Lawrence Smolinsky | Lawrence Smolinsky and Aaron J. Lercher | Co-author weighting in bibliometric methodology and subfields of a
scientific discipline | 11 pages, 1 figure, 4 tables | null | null | null | cs.DL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Collaborative work and co-authorship are fundamental to the advancement of
modern science. However, it is not clear how collaboration should be measured
in achievement-based metrics. Co-author weighted credit introduces distortions
into the bibliometric description of a discipline. It puts great weight on
collaboration - not based on the results of collaboration - but purely because
of the existence of collaborations. In terms of publication and citation
impact, it artificially favors some subdisciplines. In order to understand how
credit is given in a co-author weighted system (like the NRC's method), we
introduced credit spaces. We include a study of the discipline of physics to
illustrate the method. Indicators are introduced to measure the proportion of a
credit space awarded to a subfield or a set of authors.
| [
{
"created": "Mon, 11 May 2020 22:40:21 GMT",
"version": "v1"
}
] | 2020-05-13 | [
[
"Smolinsky",
"Lawrence",
""
],
[
"Lercher",
"Aaron J.",
""
]
] | Collaborative work and co-authorship are fundamental to the advancement of modern science. However, it is not clear how collaboration should be measured in achievement-based metrics. Co-author weighted credit introduces distortions into the bibliometric description of a discipline. It puts great weight on collaboration - not based on the results of collaboration - but purely because of the existence of collaborations. In terms of publication and citation impact, it artificially favors some subdisciplines. In order to understand how credit is given in a co-author weighted system (like the NRC's method), we introduced credit spaces. We include a study of the discipline of physics to illustrate the method. Indicators are introduced to measure the proportion of a credit space awarded to a subfield or a set of authors. |
2308.07170 | Jeremy Cochoy | Jeremy Cochoy | Human Voice Pitch Estimation: A Convolutional Network with Auto-Labeled
and Synthetic Data | null | null | null | null | cs.SD cs.LG eess.AS | http://creativecommons.org/licenses/by/4.0/ | In the domain of music and sound processing, pitch extraction plays a pivotal
role. Our research presents a specialized convolutional neural network designed
for pitch extraction, particularly from the human singing voice in acapella
performances. Notably, our approach combines synthetic data with auto-labeled
acapella sung audio, creating a robust training environment. Evaluation across
datasets comprising synthetic sounds, opera recordings, and time-stretched
vowels demonstrates its efficacy. This work paves the way for enhanced pitch
extraction in both music and voice settings.
| [
{
"created": "Mon, 14 Aug 2023 14:26:52 GMT",
"version": "v1"
},
{
"created": "Sun, 17 Dec 2023 17:46:27 GMT",
"version": "v2"
}
] | 2023-12-19 | [
[
"Cochoy",
"Jeremy",
""
]
] | In the domain of music and sound processing, pitch extraction plays a pivotal role. Our research presents a specialized convolutional neural network designed for pitch extraction, particularly from the human singing voice in acapella performances. Notably, our approach combines synthetic data with auto-labeled acapella sung audio, creating a robust training environment. Evaluation across datasets comprising synthetic sounds, opera recordings, and time-stretched vowels demonstrates its efficacy. This work paves the way for enhanced pitch extraction in both music and voice settings. |
1404.6784 | Joao Leite | Martin Slota, Martin Bal\'az, Jo\~ao Leite | On Strong and Default Negation in Logic Program Updates (Extended
Version) | 14 pages, extended version of the paper to appear in the online
supplement of Theory and Practice of Logic Programming (TPLP), and presented
at the 15th International Workshop on Non-Monotonic Reasoning (NMR 2014) and
at the 30th International Conference on Logic Programming (ICLP 2014) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existing semantics for answer-set program updates fall into two categories:
either they consider only strong negation in heads of rules, or they primarily
rely on default negation in heads of rules and optionally provide support for
strong negation by means of a syntactic transformation. In this paper we
pinpoint the limitations of both these approaches and argue that both types of
negation should be first-class citizens in the context of updates. We identify
principles that plausibly constrain their interaction but are not
simultaneously satisfied by any existing rule update semantics. Then we extend
one of the most advanced semantics with direct support for strong negation and
show that it satisfies the outlined principles as well as a variety of other
desirable properties.
| [
{
"created": "Sun, 27 Apr 2014 16:33:42 GMT",
"version": "v1"
},
{
"created": "Thu, 8 May 2014 10:46:56 GMT",
"version": "v2"
},
{
"created": "Wed, 11 Jun 2014 23:30:20 GMT",
"version": "v3"
},
{
"created": "Wed, 9 Jul 2014 16:05:40 GMT",
"version": "v4"
}
] | 2014-07-10 | [
[
"Slota",
"Martin",
""
],
[
"Baláz",
"Martin",
""
],
[
"Leite",
"João",
""
]
] | Existing semantics for answer-set program updates fall into two categories: either they consider only strong negation in heads of rules, or they primarily rely on default negation in heads of rules and optionally provide support for strong negation by means of a syntactic transformation. In this paper we pinpoint the limitations of both these approaches and argue that both types of negation should be first-class citizens in the context of updates. We identify principles that plausibly constrain their interaction but are not simultaneously satisfied by any existing rule update semantics. Then we extend one of the most advanced semantics with direct support for strong negation and show that it satisfies the outlined principles as well as a variety of other desirable properties. |
1610.00813 | Joel Mathias | Joel Mathias, Ana Bu\v{s}i\'c, Sean Meyn | Demand Dispatch with Heterogeneous Intelligent Loads | Extended version of the paper that was published in Proc. 50th Annual
Hawaii International Conference on System Sciences (HICSS), 2017. This
version contains an extended appendix that provides details relevant to the
simulations, including: (i) design of the optimal linear inverse filter in
Appendix A2, and (ii) the creation of nominal transition matrices for TCLs
using Monte Carlo in Appendix A3 | Proc. 50th Annual Hawaii International Conference on System
Sciences (HICSS), 2017 | 10.24251/HICSS.2017.380 | null | cs.SY math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A distributed control architecture is presented that is intended to make a
collection of heterogeneous loads appear to the grid operator as a nearly
perfect battery. Local control is based on randomized decision rules advocated
in prior research, and extended in this paper to any load with a discrete
number of power states. Additional linear filtering at the load ensures that
the input-output dynamics of the aggregate has a nearly flat input-output
response: the behavior of an ideal, multi-GW battery system.
| [
{
"created": "Tue, 4 Oct 2016 01:14:00 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Oct 2019 02:48:49 GMT",
"version": "v2"
}
] | 2019-10-25 | [
[
"Mathias",
"Joel",
""
],
[
"Bušić",
"Ana",
""
],
[
"Meyn",
"Sean",
""
]
] | A distributed control architecture is presented that is intended to make a collection of heterogeneous loads appear to the grid operator as a nearly perfect battery. Local control is based on randomized decision rules advocated in prior research, and extended in this paper to any load with a discrete number of power states. Additional linear filtering at the load ensures that the input-output dynamics of the aggregate has a nearly flat input-output response: the behavior of an ideal, multi-GW battery system. |
2006.08292 | Liangchen Hu | Liangchen Hu and Wensheng Zhang | Robust Locality-Aware Regression for Labeled Data Classification | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the dramatic increase of dimensions in the data representation,
extracting latent low-dimensional features becomes of the utmost importance for
efficient classification. Aiming at the problems of unclear margin
representation and difficulty in revealing the data manifold structure in most
of the existing linear discriminant methods, we propose a new discriminant
feature extraction framework, namely Robust Locality-Aware Regression (RLAR).
In our model, we introduce a retargeted regression to perform the marginal
representation learning adaptively instead of using the general average
inter-class margin. Besides, we formulate a new strategy for enhancing the
local intra-class compactness of the data manifold, which can achieve the joint
learning of locality-aware graph structure and desirable projection matrix. To
alleviate the disturbance of outliers and prevent overfitting, we measure the
regression term and locality-aware term together with the regularization term
by the L2,1 norm. Further, forcing the row sparsity on the projection matrix
through the L2,1 norm achieves the cooperation of feature selection and feature
extraction. Then, we derive an effective iterative algorithm for solving the
proposed model. The experimental results over a range of UCI data sets and
other benchmark databases demonstrate that the proposed RLAR outperforms some
state-of-the-art approaches.
| [
{
"created": "Mon, 15 Jun 2020 11:36:59 GMT",
"version": "v1"
}
] | 2020-06-16 | [
[
"Hu",
"Liangchen",
""
],
[
"Zhang",
"Wensheng",
""
]
] | With the dramatic increase of dimensions in the data representation, extracting latent low-dimensional features becomes of the utmost importance for efficient classification. Aiming at the problems of unclear margin representation and difficulty in revealing the data manifold structure in most of the existing linear discriminant methods, we propose a new discriminant feature extraction framework, namely Robust Locality-Aware Regression (RLAR). In our model, we introduce a retargeted regression to perform the marginal representation learning adaptively instead of using the general average inter-class margin. Besides, we formulate a new strategy for enhancing the local intra-class compactness of the data manifold, which can achieve the joint learning of locality-aware graph structure and desirable projection matrix. To alleviate the disturbance of outliers and prevent overfitting, we measure the regression term and locality-aware term together with the regularization term by the L2,1 norm. Further, forcing the row sparsity on the projection matrix through the L2,1 norm achieves the cooperation of feature selection and feature extraction. Then, we derive an effective iterative algorithm for solving the proposed model. The experimental results over a range of UCI data sets and other benchmark databases demonstrate that the proposed RLAR outperforms some state-of-the-art approaches. |
2012.12411 | Charlie C.L. Wang Prof. Dr. | Rob B.N. Scharff, Guoxin Fang, Yingjun Tian, Jun Wu, Jo M.P. Geraedts,
Charlie C.L. Wang | Sensing and Reconstruction of 3D Deformation on Pneumatic Soft Robots | 8 pages, 10 figures | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real-time proprioception is a challenging problem for soft robots, which have
almost infinite degrees-of-freedom in body deformation. When multiple actuators
are used, it becomes more difficult as deformation can also occur on actuators
caused by interaction between each other. To tackle this problem, we present a
method in this paper to sense and reconstruct 3D deformation on pneumatic soft
robots by first integrating multiple low-cost sensors inside the chambers of
pneumatic actuators and then using machine learning to convert the captured
signals into shape parameters of soft robots. An exterior motion capture system
is employed to generate the datasets for both training and testing. With the
help of good shape parameterization, the 3D shape of a soft robot can be
accurately reconstructed from signals obtained from multiple sensors. We
demonstrate the effectiveness of this approach on two designs of soft robots --
a robotic joint and a deformable membrane. After parameterizing the deformation
of these soft robots into compact shape parameters, we can effectively train
the neural networks to reconstruct the 3D deformation from the sensor signals.
The sensing and shape prediction pipeline can run at 50Hz in real-time on a
consumer-level device.
| [
{
"created": "Tue, 22 Dec 2020 23:18:49 GMT",
"version": "v1"
}
] | 2020-12-24 | [
[
"Scharff",
"Rob B. N.",
""
],
[
"Fang",
"Guoxin",
""
],
[
"Tian",
"Yingjun",
""
],
[
"Wu",
"Jun",
""
],
[
"Geraedts",
"Jo M. P.",
""
],
[
"Wang",
"Charlie C. L.",
""
]
] | Real-time proprioception is a challenging problem for soft robots, which have almost infinite degrees-of-freedom in body deformation. When multiple actuators are used, it becomes more difficult as deformation can also occur on actuators caused by interaction between each other. To tackle this problem, we present a method in this paper to sense and reconstruct 3D deformation on pneumatic soft robots by first integrating multiple low-cost sensors inside the chambers of pneumatic actuators and then using machine learning to convert the captured signals into shape parameters of soft robots. An exterior motion capture system is employed to generate the datasets for both training and testing. With the help of good shape parameterization, the 3D shape of a soft robot can be accurately reconstructed from signals obtained from multiple sensors. We demonstrate the effectiveness of this approach on two designs of soft robots -- a robotic joint and a deformable membrane. After parameterizing the deformation of these soft robots into compact shape parameters, we can effectively train the neural networks to reconstruct the 3D deformation from the sensor signals. The sensing and shape prediction pipeline can run at 50Hz in real-time on a consumer-level device. |
1907.09019 | Eric Sun | Eric D. Sun and Ron Dekel | ImageNet-trained deep neural network exhibits illusion-like response to
the Scintillating Grid | Supplementary material at end of document | null | null | null | cs.CV cs.LG eess.IV q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural network (DNN) models for computer vision are now capable of
human-level object recognition. Consequently, similarities in the performance
and vulnerabilities of DNN and human vision are of great interest. Here we
characterize the response of the VGG-19 DNN to images of the Scintillating Grid
visual illusion, in which white dots are perceived to be partially black. We
observed a significant deviation from the expected monotonic relation between
VGG-19 representational dissimilarity and dot whiteness in the Scintillating
Grid. That is, a linear increase in dot whiteness leads to a non-linear
increase and then, remarkably, a decrease (non-monotonicity) in
representational dissimilarity. In control images, mostly monotonic relations
between representational dissimilarity and dot whiteness were observed.
Furthermore, the dot whiteness level corresponding to the maximal
representational dissimilarity (i.e. onset of non-monotonic dissimilarity)
matched closely with that corresponding to the onset of illusion perception in
human observers. As such, the non-monotonic response in the DNN is a potential
model correlate for human illusion perception.
| [
{
"created": "Sun, 21 Jul 2019 19:14:47 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Aug 2019 02:13:38 GMT",
"version": "v2"
}
] | 2019-08-06 | [
[
"Sun",
"Eric D.",
""
],
[
"Dekel",
"Ron",
""
]
] | Deep neural network (DNN) models for computer vision are now capable of human-level object recognition. Consequently, similarities in the performance and vulnerabilities of DNN and human vision are of great interest. Here we characterize the response of the VGG-19 DNN to images of the Scintillating Grid visual illusion, in which white dots are perceived to be partially black. We observed a significant deviation from the expected monotonic relation between VGG-19 representational dissimilarity and dot whiteness in the Scintillating Grid. That is, a linear increase in dot whiteness leads to a non-linear increase and then, remarkably, a decrease (non-monotonicity) in representational dissimilarity. In control images, mostly monotonic relations between representational dissimilarity and dot whiteness were observed. Furthermore, the dot whiteness level corresponding to the maximal representational dissimilarity (i.e. onset of non-monotonic dissimilarity) matched closely with that corresponding to the onset of illusion perception in human observers. As such, the non-monotonic response in the DNN is a potential model correlate for human illusion perception. |
0709.0426 | Noelle Carbonell | No\"elle Carbonell (INRIA Rocquencourt / INRIA Lorraine - LORIA),
Suzanne Kieffer (INRIA Rocquencourt / INRIA Lorraine - LORIA) | Do oral messages help visual search? | 26 pages | Advances in Natural Multimodal Dialogue Systems, Dordrecht (NL)
Springer (Ed.) (2005) pp. 131-157 | null | null | cs.HC | null | A preliminary experimental study is presented, that aims at eliciting the
contribution of oral messages to facilitating visual search tasks on crowded
visual displays. Results of quantitative and qualitative analyses suggest that
appropriate verbal messages can improve both target selection time and
accuracy. In particular, multimodal messages including a visual presentation of
the isolated target together with absolute spatial oral information on its
location in the displayed scene seem most effective. These messages also got
top-ranking ratings from most subjects.
| [
{
"created": "Tue, 4 Sep 2007 13:23:40 GMT",
"version": "v1"
}
] | 2007-09-05 | [
[
"Carbonell",
"Noëlle",
"",
"INRIA Rocquencourt / INRIA Lorraine - LORIA"
],
[
"Kieffer",
"Suzanne",
"",
"INRIA Rocquencourt / INRIA Lorraine - LORIA"
]
] | A preliminary experimental study is presented, that aims at eliciting the contribution of oral messages to facilitating visual search tasks on crowded visual displays. Results of quantitative and qualitative analyses suggest that appropriate verbal messages can improve both target selection time and accuracy. In particular, multimodal messages including a visual presentation of the isolated target together with absolute spatial oral information on its location in the displayed scene seem most effective. These messages also got top-ranking ratings from most subjects. |
2105.06314 | Ismini Psychoula | Ismini Psychoula, Andreas Gutmann, Pradip Mainali, S. H. Lee, Paul
Dunphy, Fabien A. P. Petitcolas | Explainable Machine Learning for Fraud Detection | To be published in IEEE Computer Special Issue on Explainable AI and
Machine Learning, 12 pages, 7 figures | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The application of machine learning to support the processing of large
datasets holds promise in many industries, including financial services.
However, practical issues for the full adoption of machine learning remain with
the focus being on understanding and being able to explain the decisions and
predictions made by complex models. In this paper, we explore explainability
methods in the domain of real-time fraud detection by investigating the
selection of appropriate background datasets and runtime trade-offs on both
supervised and unsupervised models.
| [
{
"created": "Thu, 13 May 2021 14:12:02 GMT",
"version": "v1"
}
] | 2021-05-14 | [
[
"Psychoula",
"Ismini",
""
],
[
"Gutmann",
"Andreas",
""
],
[
"Mainali",
"Pradip",
""
],
[
"Lee",
"S. H.",
""
],
[
"Dunphy",
"Paul",
""
],
[
"Petitcolas",
"Fabien A. P.",
""
]
] | The application of machine learning to support the processing of large datasets holds promise in many industries, including financial services. However, practical issues for the full adoption of machine learning remain with the focus being on understanding and being able to explain the decisions and predictions made by complex models. In this paper, we explore explainability methods in the domain of real-time fraud detection by investigating the selection of appropriate background datasets and runtime trade-offs on both supervised and unsupervised models. |
0910.1757 | Laurent Tapie | Laurent Tapie (LURPA), Kwamiwi Mawussi (LURPA) | Decomposition of forging die for high speed machining | null | IDMME - Virtual Concept 2008, Beijing : China (2008) | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Today's forging die manufacturing process must be adapted to several
evolutions in machining process generation: CAD/CAM models, CAM software
solutions and High Speed Machining (HSM). In this context, the adequacy between
die shape and HSM process is in the core of machining preparation and process
planning approaches. This paper deals with an original approach of machining
preparation integrating this adequacy in the main tasks carried out. In this
approach, the design of the machining process is based on two levels of
decomposition of the geometrical model of a given die with respect to HSM
cutting conditions (cutting speed and feed rate) and technological constrains
(tool selection, features accessibility). This decomposition assists machining
assistant to generate an HSM process. The result of this decomposition is the
identification of machining features.
| [
{
"created": "Fri, 9 Oct 2009 14:33:17 GMT",
"version": "v1"
}
] | 2009-10-12 | [
[
"Tapie",
"Laurent",
"",
"LURPA"
],
[
"Mawussi",
"Kwamiwi",
"",
"LURPA"
]
] | Today's forging die manufacturing process must be adapted to several evolutions in machining process generation: CAD/CAM models, CAM software solutions and High Speed Machining (HSM). In this context, the adequacy between die shape and HSM process is in the core of machining preparation and process planning approaches. This paper deals with an original approach of machining preparation integrating this adequacy in the main tasks carried out. In this approach, the design of the machining process is based on two levels of decomposition of the geometrical model of a given die with respect to HSM cutting conditions (cutting speed and feed rate) and technological constrains (tool selection, features accessibility). This decomposition assists machining assistant to generate an HSM process. The result of this decomposition is the identification of machining features. |
1503.03244 | Baotian Hu | Baotian Hu, Zhengdong Lu, Hang Li, Qingcai Chen | Convolutional Neural Network Architectures for Matching Natural Language
Sentences | null | null | null | null | cs.CL cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Semantic matching is of central importance to many natural language tasks
\cite{bordes2014semantic,RetrievalQA}. A successful matching algorithm needs to
adequately model the internal structures of language objects and the
interaction between them. As a step toward this goal, we propose convolutional
neural network models for matching two sentences, by adapting the convolutional
strategy in vision and speech. The proposed models not only nicely represent
the hierarchical structures of sentences with their layer-by-layer composition
and pooling, but also capture the rich matching patterns at different levels.
Our models are rather generic, requiring no prior knowledge on language, and
can hence be applied to matching tasks of different nature and in different
languages. The empirical study on a variety of matching tasks demonstrates the
efficacy of the proposed model on a variety of matching tasks and its
superiority to competitor models.
| [
{
"created": "Wed, 11 Mar 2015 09:46:36 GMT",
"version": "v1"
}
] | 2015-03-12 | [
[
"Hu",
"Baotian",
""
],
[
"Lu",
"Zhengdong",
""
],
[
"Li",
"Hang",
""
],
[
"Chen",
"Qingcai",
""
]
] | Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic,RetrievalQA}. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models. |
2104.11670 | Nitzan Tur | Roy Schwartz, Nitzan Tur | The Metric Relaxation for $0$-Extension Admits an
$\Omega(\log^{2/3}{k})$ Gap | 27 pages, 3 figures, will appear in STOC 2021 | null | 10.1145/3406325.3451071 | null | cs.DS math.MG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the $0$-Extension problem, where we are given an undirected graph
$\mathcal{G}=(V,E)$ equipped with non-negative edge weights $w:E\rightarrow
\mathbb{R}^+$, a collection $ T=\{ t_1,\ldots,t_k\}\subseteq V$ of $k$ special
vertices called terminals, and a semi-metric $D$ over $T$. The goal is to
assign every non-terminal vertex to a terminal while minimizing the sum over
all edges of the weight of the edge multiplied by the distance in $D$ between
the terminals to which the endpoints of the edge are assigned. $0$-Extension
admits two known algorithms, achieving approximations of $O(\log{k})$
[C{\u{a}}linescu-Karloff-Rabani SICOMP '05] and $O(\log{k}/\log{\log{k}})$
[Fakcharoenphol-Harrelson-Rao-Talwar SODA '03]. Both known algorithms are based
on rounding a natural linear programming relaxation called the metric
relaxation, in which $D$ is extended from $T$ to the entire of $V$. The current
best known integrality gap for the metric relaxation is $\Omega
(\sqrt{\log{k}})$. In this work we present an improved integrality gap of
$\Omega(\log^{\frac{2}{3}}k)$ for the metric relaxation. Our construction is
based on the randomized extension of one graph by another, a notion that
captures lifts of graphs as a special case and might be of independent
interest. Inspired by algebraic topology, our analysis of the gap instance is
based on proving no continuous section (in the topological sense) exists in the
randomized extension.
| [
{
"created": "Fri, 23 Apr 2021 15:53:06 GMT",
"version": "v1"
}
] | 2021-04-26 | [
[
"Schwartz",
"Roy",
""
],
[
"Tur",
"Nitzan",
""
]
] | We consider the $0$-Extension problem, where we are given an undirected graph $\mathcal{G}=(V,E)$ equipped with non-negative edge weights $w:E\rightarrow \mathbb{R}^+$, a collection $ T=\{ t_1,\ldots,t_k\}\subseteq V$ of $k$ special vertices called terminals, and a semi-metric $D$ over $T$. The goal is to assign every non-terminal vertex to a terminal while minimizing the sum over all edges of the weight of the edge multiplied by the distance in $D$ between the terminals to which the endpoints of the edge are assigned. $0$-Extension admits two known algorithms, achieving approximations of $O(\log{k})$ [C{\u{a}}linescu-Karloff-Rabani SICOMP '05] and $O(\log{k}/\log{\log{k}})$ [Fakcharoenphol-Harrelson-Rao-Talwar SODA '03]. Both known algorithms are based on rounding a natural linear programming relaxation called the metric relaxation, in which $D$ is extended from $T$ to the entire of $V$. The current best known integrality gap for the metric relaxation is $\Omega (\sqrt{\log{k}})$. In this work we present an improved integrality gap of $\Omega(\log^{\frac{2}{3}}k)$ for the metric relaxation. Our construction is based on the randomized extension of one graph by another, a notion that captures lifts of graphs as a special case and might be of independent interest. Inspired by algebraic topology, our analysis of the gap instance is based on proving no continuous section (in the topological sense) exists in the randomized extension. |
2311.02692 | Zhelun Shi | Zhelun Shi, Zhipin Wang, Hongxing Fan, Zhenfei Yin, Lu Sheng, Yu Qiao,
Jing Shao | ChEF: A Comprehensive Evaluation Framework for Standardized Assessment
of Multimodal Large Language Models | 39 pages, 26 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multimodal Large Language Models (MLLMs) have shown impressive abilities in
interacting with visual content with myriad potential downstream tasks.
However, even though a list of benchmarks has been proposed, the capabilities
and limitations of MLLMs are still not comprehensively understood, due to a
lack of a standardized and holistic evaluation framework. To this end, we
present the first Comprehensive Evaluation Framework (ChEF) that can
holistically profile each MLLM and fairly compare different MLLMs. First, we
structure ChEF as four modular components, i.e., Scenario as scalable
multimodal datasets, Instruction as flexible instruction retrieving formulae,
Inferencer as reliable question answering strategies, and Metric as indicative
task-specific score functions. Based on them, ChEF facilitates versatile
evaluations in a standardized framework, and new evaluations can be built by
designing new Recipes (systematic selection of these four components). Notably,
current MLLM benchmarks can be readily summarized as recipes of ChEF. Second,
we introduce 6 new recipes to quantify competent MLLMs' desired capabilities
(or called desiderata, i.e., calibration, in-context learning, instruction
following, language performance, hallucination, and robustness) as reliable
agents that can perform real-world multimodal interactions. Third, we conduct a
large-scale evaluation of 9 prominent MLLMs on 9 scenarios and 6 desiderata.
Our evaluation summarized over 20 valuable observations concerning the
generalizability of MLLMs across various scenarios and the composite capability
of MLLMs required for multimodal interactions. We will publicly release all the
detailed implementations for further analysis, as well as an easy-to-use
modular toolkit for the integration of new recipes and models, so that ChEF can
be a growing evaluation framework for the MLLM community.
| [
{
"created": "Sun, 5 Nov 2023 16:01:40 GMT",
"version": "v1"
}
] | 2023-11-07 | [
[
"Shi",
"Zhelun",
""
],
[
"Wang",
"Zhipin",
""
],
[
"Fan",
"Hongxing",
""
],
[
"Yin",
"Zhenfei",
""
],
[
"Sheng",
"Lu",
""
],
[
"Qiao",
"Yu",
""
],
[
"Shao",
"Jing",
""
]
] | Multimodal Large Language Models (MLLMs) have shown impressive abilities in interacting with visual content with myriad potential downstream tasks. However, even though a list of benchmarks has been proposed, the capabilities and limitations of MLLMs are still not comprehensively understood, due to a lack of a standardized and holistic evaluation framework. To this end, we present the first Comprehensive Evaluation Framework (ChEF) that can holistically profile each MLLM and fairly compare different MLLMs. First, we structure ChEF as four modular components, i.e., Scenario as scalable multimodal datasets, Instruction as flexible instruction retrieving formulae, Inferencer as reliable question answering strategies, and Metric as indicative task-specific score functions. Based on them, ChEF facilitates versatile evaluations in a standardized framework, and new evaluations can be built by designing new Recipes (systematic selection of these four components). Notably, current MLLM benchmarks can be readily summarized as recipes of ChEF. Second, we introduce 6 new recipes to quantify competent MLLMs' desired capabilities (or called desiderata, i.e., calibration, in-context learning, instruction following, language performance, hallucination, and robustness) as reliable agents that can perform real-world multimodal interactions. Third, we conduct a large-scale evaluation of 9 prominent MLLMs on 9 scenarios and 6 desiderata. Our evaluation summarized over 20 valuable observations concerning the generalizability of MLLMs across various scenarios and the composite capability of MLLMs required for multimodal interactions. We will publicly release all the detailed implementations for further analysis, as well as an easy-to-use modular toolkit for the integration of new recipes and models, so that ChEF can be a growing evaluation framework for the MLLM community. |
1801.01757 | Fulvio Mastrogiovanni | Alessio Capitanelli, Marco Maratea, Fulvio Mastrogiovanni, Mauro
Vallati | On the manipulation of articulated objects in human-robot cooperation
scenarios | null | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Articulated and flexible objects constitute a challenge for robot
manipulation tasks but are present in different real-world settings, including
home and industrial environments. Current approaches to the manipulation of
articulated and flexible objects employ ad hoc strategies to sequence and
perform actions on them depending on a number of physical or geometrical
characteristics related to those objects, as well as on an a priori
classification of target object configurations.
In this paper, we propose an action planning and execution framework, which
(i) considers abstract representations of articulated or flexible objects, (ii)
integrates action planning to reason upon such configurations and to sequence
an appropriate set of actions with the aim of obtaining a target configuration
provided as a goal, and (iii) is able to cooperate with humans to
collaboratively carry out the plan.
On the one hand, we show that a trade-off exists between the way articulated
or flexible objects are perceived and how the system represents them. Such a
trade-off greatly impacts on the complexity of the planning process. On the
other hand, we demonstrate the system's capabilities in allowing humans to
interrupt robot action execution, and - in general - to contribute to the whole
manipulation process.
Results related to planning performance are discussed, and examples of a
Baxter dual-arm manipulator performing actions collaboratively with humans are
shown.
| [
{
"created": "Fri, 5 Jan 2018 14:08:21 GMT",
"version": "v1"
},
{
"created": "Sat, 13 Jan 2018 07:54:49 GMT",
"version": "v2"
}
] | 2018-01-16 | [
[
"Capitanelli",
"Alessio",
""
],
[
"Maratea",
"Marco",
""
],
[
"Mastrogiovanni",
"Fulvio",
""
],
[
"Vallati",
"Mauro",
""
]
] | Articulated and flexible objects constitute a challenge for robot manipulation tasks but are present in different real-world settings, including home and industrial environments. Current approaches to the manipulation of articulated and flexible objects employ ad hoc strategies to sequence and perform actions on them depending on a number of physical or geometrical characteristics related to those objects, as well as on an a priori classification of target object configurations. In this paper, we propose an action planning and execution framework, which (i) considers abstract representations of articulated or flexible objects, (ii) integrates action planning to reason upon such configurations and to sequence an appropriate set of actions with the aim of obtaining a target configuration provided as a goal, and (iii) is able to cooperate with humans to collaboratively carry out the plan. On the one hand, we show that a trade-off exists between the way articulated or flexible objects are perceived and how the system represents them. Such a trade-off greatly impacts on the complexity of the planning process. On the other hand, we demonstrate the system's capabilities in allowing humans to interrupt robot action execution, and - in general - to contribute to the whole manipulation process. Results related to planning performance are discussed, and examples of a Baxter dual-arm manipulator performing actions collaboratively with humans are shown. |
2203.10853 | Mingkui Tan | Shuaicheng Niu and Jiaxiang Wu and Yifan Zhang and Guanghui Xu and
Haokun Li and Peilin Zhao and Junzhou Huang and Yaowei Wang and Mingkui Tan | Boost Test-Time Performance with Closed-Loop Inference | 10 pages, 10 figures, conference | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conventional deep models predict a test sample with a single forward
propagation, which, however, may not be sufficient for predicting
hard-classified samples. On the contrary, we human beings may need to carefully
check the sample many times before making a final decision. During the recheck
process, one may refine/adjust the prediction by referring to related samples.
Motivated by this, we propose to predict those hard-classified test samples in
a looped manner to boost the model performance. However, this idea may pose a
critical challenge: how to construct looped inference, so that the original
erroneous predictions on these hard test samples can be corrected with little
additional effort. To address this, we propose a general Closed-Loop Inference
(CLI) method. Specifically, we first devise a filtering criterion to identify
those hard-classified test samples that need additional inference loops. For
each hard sample, we construct an additional auxiliary learning task based on
its original top-$K$ predictions to calibrate the model, and then use the
calibrated model to obtain the final prediction. Promising results on ImageNet
(in-distribution test samples) and ImageNet-C (out-of-distribution test
samples) demonstrate the effectiveness of CLI in improving the performance of
any pre-trained model.
| [
{
"created": "Mon, 21 Mar 2022 10:20:21 GMT",
"version": "v1"
},
{
"created": "Sat, 26 Mar 2022 12:10:32 GMT",
"version": "v2"
}
] | 2022-03-29 | [
[
"Niu",
"Shuaicheng",
""
],
[
"Wu",
"Jiaxiang",
""
],
[
"Zhang",
"Yifan",
""
],
[
"Xu",
"Guanghui",
""
],
[
"Li",
"Haokun",
""
],
[
"Zhao",
"Peilin",
""
],
[
"Huang",
"Junzhou",
""
],
[
"Wang",
"Yaowei",
""
],
[
"Tan",
"Mingkui",
""
]
] | Conventional deep models predict a test sample with a single forward propagation, which, however, may not be sufficient for predicting hard-classified samples. On the contrary, we human beings may need to carefully check the sample many times before making a final decision. During the recheck process, one may refine/adjust the prediction by referring to related samples. Motivated by this, we propose to predict those hard-classified test samples in a looped manner to boost the model performance. However, this idea may pose a critical challenge: how to construct looped inference, so that the original erroneous predictions on these hard test samples can be corrected with little additional effort. To address this, we propose a general Closed-Loop Inference (CLI) method. Specifically, we first devise a filtering criterion to identify those hard-classified test samples that need additional inference loops. For each hard sample, we construct an additional auxiliary learning task based on its original top-$K$ predictions to calibrate the model, and then use the calibrated model to obtain the final prediction. Promising results on ImageNet (in-distribution test samples) and ImageNet-C (out-of-distribution test samples) demonstrate the effectiveness of CLI in improving the performance of any pre-trained model. |
0706.3412 | Blai Bonet | Nerio Borges, Blai Bonet | On Canonical Forms of Complete Problems via First-order Projections | 9 pages | null | null | null | cs.CC | null | The class of problems complete for NP via first-order reductions is known to
be characterized by existential second-order sentences of a fixed form. All
such sentences are built around the so-called generalized IS-form of the
sentence that defines Independent-Set. This result can also be understood as
that every sentence that defines a NP-complete problem P can be decomposed in
two disjuncts such that the first one characterizes a fragment of P as hard as
Independent-Set and the second the rest of P. That is, a decomposition that
divides every such sentence into a quotient and residue modulo Independent-Set.
In this paper, we show that this result can be generalized over a wide
collection of complexity classes, including the so-called nice classes.
Moreover, we show that such decomposition can be done for any complete problem
with respect to the given class, and that two such decompositions are
non-equivalent in general. Interestingly, our results are based on simple and
well-known properties of first-order reductions.ow that this result can be
generalized over a wide collection of complexity classes, including the
so-called nice classes. Moreover, we show that such decomposition can be done
for any complete problem with respect to the given class, and that two such
decompositions are non-equivalent in general. Interestingly, our results are
based on simple and well-known properties of first-order reductions.
| [
{
"created": "Fri, 22 Jun 2007 21:27:06 GMT",
"version": "v1"
}
] | 2007-06-26 | [
[
"Borges",
"Nerio",
""
],
[
"Bonet",
"Blai",
""
]
] | The class of problems complete for NP via first-order reductions is known to be characterized by existential second-order sentences of a fixed form. All such sentences are built around the so-called generalized IS-form of the sentence that defines Independent-Set. This result can also be understood as that every sentence that defines a NP-complete problem P can be decomposed in two disjuncts such that the first one characterizes a fragment of P as hard as Independent-Set and the second the rest of P. That is, a decomposition that divides every such sentence into a quotient and residue modulo Independent-Set. In this paper, we show that this result can be generalized over a wide collection of complexity classes, including the so-called nice classes. Moreover, we show that such decomposition can be done for any complete problem with respect to the given class, and that two such decompositions are non-equivalent in general. Interestingly, our results are based on simple and well-known properties of first-order reductions.ow that this result can be generalized over a wide collection of complexity classes, including the so-called nice classes. Moreover, we show that such decomposition can be done for any complete problem with respect to the given class, and that two such decompositions are non-equivalent in general. Interestingly, our results are based on simple and well-known properties of first-order reductions. |
1809.07296 | Michael Baddeley | Michael Baddeley, Reza Nejabati, George Oikonomou, Mahesh
Sooriyabandara, Dimitra Simeonidou | Evolving SDN for Low-Power IoT Networks | null | null | 10.1109/NETSOFT.2018.8460125 | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Software Defined Networking (SDN) offers a flexible and scalable architecture
that abstracts decision making away from individual devices and provides a
programmable network platform. However, implementing a centralized SDN
architecture within the constraints of a low-power wireless network faces
considerable challenges. Not only is controller traffic subject to jitter due
to unreliable links and network contention, but the overhead generated by SDN
can severely affect the performance of other traffic. This paper addresses the
challenge of bringing high-overhead SDN architecture to IEEE 802.15.4 networks.
We explore how traditional SDN needs to evolve in order to overcome the
constraints of low-power wireless networks, and discuss protocol and
architectural optimizations necessary to reduce SDN control overhead - the main
barrier to successful implementation. We argue that interoperability with the
existing protocol stack is necessary to provide a platform for controller
discovery and coexistence with legacy networks. We consequently introduce
{\mu}SDN, a lightweight SDN framework for Contiki, with both IPv6 and
underlying routing protocol interoperability, as well as optimizing a number of
elements within the SDN architecture to reduce control overhead to practical
levels. We evaluate {\mu}SDN in terms of latency, energy, and packet delivery.
Through this evaluation we show how the cost of SDN control overhead (both
bootstrapping and management) can be reduced to a point where comparable
performance and scalability is achieved against an IEEE 802.15.4-2012 RPL-based
network. Additionally, we demonstrate {\mu}SDN through simulation: providing a
use-case where the SDN configurability can be used to provide Quality of
Service (QoS) for critical network flows experiencing interference, and we
achieve considerable reductions in delay and jitter in comparison to a scenario
without SDN.
| [
{
"created": "Wed, 19 Sep 2018 16:46:10 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Jan 2019 12:38:59 GMT",
"version": "v2"
},
{
"created": "Wed, 29 May 2019 14:31:21 GMT",
"version": "v3"
}
] | 2019-05-30 | [
[
"Baddeley",
"Michael",
""
],
[
"Nejabati",
"Reza",
""
],
[
"Oikonomou",
"George",
""
],
[
"Sooriyabandara",
"Mahesh",
""
],
[
"Simeonidou",
"Dimitra",
""
]
] | Software Defined Networking (SDN) offers a flexible and scalable architecture that abstracts decision making away from individual devices and provides a programmable network platform. However, implementing a centralized SDN architecture within the constraints of a low-power wireless network faces considerable challenges. Not only is controller traffic subject to jitter due to unreliable links and network contention, but the overhead generated by SDN can severely affect the performance of other traffic. This paper addresses the challenge of bringing high-overhead SDN architecture to IEEE 802.15.4 networks. We explore how traditional SDN needs to evolve in order to overcome the constraints of low-power wireless networks, and discuss protocol and architectural optimizations necessary to reduce SDN control overhead - the main barrier to successful implementation. We argue that interoperability with the existing protocol stack is necessary to provide a platform for controller discovery and coexistence with legacy networks. We consequently introduce {\mu}SDN, a lightweight SDN framework for Contiki, with both IPv6 and underlying routing protocol interoperability, as well as optimizing a number of elements within the SDN architecture to reduce control overhead to practical levels. We evaluate {\mu}SDN in terms of latency, energy, and packet delivery. Through this evaluation we show how the cost of SDN control overhead (both bootstrapping and management) can be reduced to a point where comparable performance and scalability is achieved against an IEEE 802.15.4-2012 RPL-based network. Additionally, we demonstrate {\mu}SDN through simulation: providing a use-case where the SDN configurability can be used to provide Quality of Service (QoS) for critical network flows experiencing interference, and we achieve considerable reductions in delay and jitter in comparison to a scenario without SDN. |
1203.2973 | Sigal Oren | David Bindel, Jon Kleinberg and Sigal Oren | How Bad is Forming Your Own Opinion? | null | null | null | null | cs.GT physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The question of how people form their opinion has fascinated economists and
sociologists for quite some time. In many of the models, a group of people in a
social network, each holding a numerical opinion, arrive at a shared opinion
through repeated averaging with their neighbors in the network. Motivated by
the observation that consensus is rarely reached in real opinion dynamics, we
study a related sociological model in which individuals' intrinsic beliefs
counterbalance the averaging process and yield a diversity of opinions.
By interpreting the repeated averaging as best-response dynamics in an
underlying game with natural payoffs, and the limit of the process as an
equilibrium, we are able to study the cost of disagreement in these models
relative to a social optimum. We provide a tight bound on the cost at
equilibrium relative to the optimum; our analysis draws a connection between
these agreement models and extremal problems that lead to generalized
eigenvalues. We also consider a natural network design problem in this setting:
which links can we add to the underlying network to reduce the cost of
disagreement at equilibrium?
| [
{
"created": "Tue, 13 Mar 2012 23:14:40 GMT",
"version": "v1"
}
] | 2012-03-15 | [
[
"Bindel",
"David",
""
],
[
"Kleinberg",
"Jon",
""
],
[
"Oren",
"Sigal",
""
]
] | The question of how people form their opinion has fascinated economists and sociologists for quite some time. In many of the models, a group of people in a social network, each holding a numerical opinion, arrive at a shared opinion through repeated averaging with their neighbors in the network. Motivated by the observation that consensus is rarely reached in real opinion dynamics, we study a related sociological model in which individuals' intrinsic beliefs counterbalance the averaging process and yield a diversity of opinions. By interpreting the repeated averaging as best-response dynamics in an underlying game with natural payoffs, and the limit of the process as an equilibrium, we are able to study the cost of disagreement in these models relative to a social optimum. We provide a tight bound on the cost at equilibrium relative to the optimum; our analysis draws a connection between these agreement models and extremal problems that lead to generalized eigenvalues. We also consider a natural network design problem in this setting: which links can we add to the underlying network to reduce the cost of disagreement at equilibrium? |
1306.3727 | Lin Chen | Lin Chen, Deshi Ye, Guochuan Zhang | A note on scheduling with low rank processing times | 14 pages | null | null | null | cs.CC cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the classical minimum makespan scheduling problem, where the
processing time of job $j$ on machine $i$ is $p_{ij}$, and the matrix
$P=(p_{ij})_{m\times n}$ is of a low rank. It is proved in (Bhaskara et al.,
SODA 2013) that rank 7 scheduling is NP-hard to approximate to a factor of
$3/2-\epsilon$, and rank 4 scheduling is APX-hard (NP-hard to approximate
within a factor of $1.03-\epsilon$). We improve this result by showing that
rank 4 scheduling is already NP-hard to approximate within a factor of
$3/2-\epsilon$, and meanwhile rank 3 scheduling is APX-hard.
| [
{
"created": "Mon, 17 Jun 2013 02:19:11 GMT",
"version": "v1"
}
] | 2013-06-18 | [
[
"Chen",
"Lin",
""
],
[
"Ye",
"Deshi",
""
],
[
"Zhang",
"Guochuan",
""
]
] | We consider the classical minimum makespan scheduling problem, where the processing time of job $j$ on machine $i$ is $p_{ij}$, and the matrix $P=(p_{ij})_{m\times n}$ is of a low rank. It is proved in (Bhaskara et al., SODA 2013) that rank 7 scheduling is NP-hard to approximate to a factor of $3/2-\epsilon$, and rank 4 scheduling is APX-hard (NP-hard to approximate within a factor of $1.03-\epsilon$). We improve this result by showing that rank 4 scheduling is already NP-hard to approximate within a factor of $3/2-\epsilon$, and meanwhile rank 3 scheduling is APX-hard. |
2404.05281 | Boshko Koloski | Syrielle Montariol and Matej Martinc and Andra\v{z} Pelicon and Senja
Pollak and Boshko Koloski and Igor Lon\v{c}arski and Aljo\v{s}a
Valentin\v{c}i\v{c} | Multi-Task Learning for Features Extraction in Financial Annual Reports | Accepted at MIDAS Workshop at ECML-PKDD 2022 | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | For assessing various performance indicators of companies, the focus is
shifting from strictly financial (quantitative) publicly disclosed information
to qualitative (textual) information. This textual data can provide valuable
weak signals, for example through stylistic features, which can complement the
quantitative data on financial performance or on Environmental, Social and
Governance (ESG) criteria. In this work, we use various multi-task learning
methods for financial text classification with the focus on financial
sentiment, objectivity, forward-looking sentence prediction and ESG-content
detection. We propose different methods to combine the information extracted
from training jointly on different tasks; our best-performing method highlights
the positive effect of explicitly adding auxiliary task predictions as features
for the final target task during the multi-task training. Next, we use these
classifiers to extract textual features from annual reports of FTSE350
companies and investigate the link between ESG quantitative scores and these
features.
| [
{
"created": "Mon, 8 Apr 2024 08:13:40 GMT",
"version": "v1"
}
] | 2024-04-09 | [
[
"Montariol",
"Syrielle",
""
],
[
"Martinc",
"Matej",
""
],
[
"Pelicon",
"Andraž",
""
],
[
"Pollak",
"Senja",
""
],
[
"Koloski",
"Boshko",
""
],
[
"Lončarski",
"Igor",
""
],
[
"Valentinčič",
"Aljoša",
""
]
] | For assessing various performance indicators of companies, the focus is shifting from strictly financial (quantitative) publicly disclosed information to qualitative (textual) information. This textual data can provide valuable weak signals, for example through stylistic features, which can complement the quantitative data on financial performance or on Environmental, Social and Governance (ESG) criteria. In this work, we use various multi-task learning methods for financial text classification with the focus on financial sentiment, objectivity, forward-looking sentence prediction and ESG-content detection. We propose different methods to combine the information extracted from training jointly on different tasks; our best-performing method highlights the positive effect of explicitly adding auxiliary task predictions as features for the final target task during the multi-task training. Next, we use these classifiers to extract textual features from annual reports of FTSE350 companies and investigate the link between ESG quantitative scores and these features. |
2304.12139 | Jimmy Lin | Xueguang Ma, Tommaso Teofili, Jimmy Lin | Anserini Gets Dense Retrieval: Integration of Lucene's HNSW Indexes | null | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Anserini is a Lucene-based toolkit for reproducible information retrieval
research in Java that has been gaining traction in the community. It provides
retrieval capabilities for both "traditional" bag-of-words retrieval models
such as BM25 as well as retrieval using learned sparse representations such as
SPLADE. With Pyserini, which provides a Python interface to Anserini, users
gain access to both sparse and dense retrieval models, as Pyserini implements
bindings to the Faiss vector search library alongside Lucene inverted indexes
in a uniform, consistent interface. Nevertheless, hybrid fusion techniques that
integrate sparse and dense retrieval models need to stitch together results
from two completely different "software stacks", which creates unnecessary
complexities and inefficiencies. However, the introduction of HNSW indexes for
dense vector search in Lucene promises the integration of both dense and sparse
retrieval within a single software framework. We explore exactly this
integration in the context of Anserini. Experiments on the MS MARCO passage and
BEIR datasets show that our Anserini HNSW integration supports (reasonably)
effective and (reasonably) efficient approximate nearest neighbor search for
dense retrieval models, using only Lucene.
| [
{
"created": "Mon, 24 Apr 2023 14:44:27 GMT",
"version": "v1"
}
] | 2023-04-25 | [
[
"Ma",
"Xueguang",
""
],
[
"Teofili",
"Tommaso",
""
],
[
"Lin",
"Jimmy",
""
]
] | Anserini is a Lucene-based toolkit for reproducible information retrieval research in Java that has been gaining traction in the community. It provides retrieval capabilities for both "traditional" bag-of-words retrieval models such as BM25 as well as retrieval using learned sparse representations such as SPLADE. With Pyserini, which provides a Python interface to Anserini, users gain access to both sparse and dense retrieval models, as Pyserini implements bindings to the Faiss vector search library alongside Lucene inverted indexes in a uniform, consistent interface. Nevertheless, hybrid fusion techniques that integrate sparse and dense retrieval models need to stitch together results from two completely different "software stacks", which creates unnecessary complexities and inefficiencies. However, the introduction of HNSW indexes for dense vector search in Lucene promises the integration of both dense and sparse retrieval within a single software framework. We explore exactly this integration in the context of Anserini. Experiments on the MS MARCO passage and BEIR datasets show that our Anserini HNSW integration supports (reasonably) effective and (reasonably) efficient approximate nearest neighbor search for dense retrieval models, using only Lucene. |
2404.03704 | Luis Sigcha | Luis Sigcha, Luigi Borz\`i, Ignacio Pav\'on, N\'elson Costa, Susana
Costa, Pedro Arezes, Juan-Manuel L\'opez, Guillermo De Arcas | Improvement of Performance in Freezing of Gait detection in Parkinsons
Disease using Transformer networks and a single waist worn triaxial
accelerometer | null | Engineering Applications of Artificial Intelligence Volume 116,
November 2022, 105482 | 10.1016/j.engappai.2022.105482 | null | cs.LG cs.AI eess.SP | http://creativecommons.org/licenses/by/4.0/ | Freezing of gait (FOG) is one of the most incapacitating symptoms in
Parkinsons disease, affecting more than 50 percent of patients in advanced
stages of the disease. The presence of FOG may lead to falls and a loss of
independence with a consequent reduction in the quality of life. Wearable
technology and artificial intelligence have been used for automatic FOG
detection to optimize monitoring. However, differences between laboratory and
daily-life conditions present challenges for the implementation of reliable
detection systems. Consequently, improvement of FOG detection methods remains
important to provide accurate monitoring mechanisms intended for free-living
and real-time use. This paper presents advances in automatic FOG detection
using a single body-worn triaxial accelerometer and a novel classification
algorithm based on Transformers and convolutional networks. This study was
performed with data from 21 patients who manifested FOG episodes while
performing activities of daily living in a home setting. Results indicate that
the proposed FOG-Transformer can bring a significant improvement in FOG
detection using leave-one-subject-out cross-validation (LOSO CV). These results
bring opportunities for the implementation of accurate monitoring systems for
use in ambulatory or home settings.
| [
{
"created": "Thu, 4 Apr 2024 09:02:17 GMT",
"version": "v1"
}
] | 2024-04-08 | [
[
"Sigcha",
"Luis",
""
],
[
"Borzì",
"Luigi",
""
],
[
"Pavón",
"Ignacio",
""
],
[
"Costa",
"Nélson",
""
],
[
"Costa",
"Susana",
""
],
[
"Arezes",
"Pedro",
""
],
[
"López",
"Juan-Manuel",
""
],
[
"De Arcas",
"Guillermo",
""
]
] | Freezing of gait (FOG) is one of the most incapacitating symptoms in Parkinsons disease, affecting more than 50 percent of patients in advanced stages of the disease. The presence of FOG may lead to falls and a loss of independence with a consequent reduction in the quality of life. Wearable technology and artificial intelligence have been used for automatic FOG detection to optimize monitoring. However, differences between laboratory and daily-life conditions present challenges for the implementation of reliable detection systems. Consequently, improvement of FOG detection methods remains important to provide accurate monitoring mechanisms intended for free-living and real-time use. This paper presents advances in automatic FOG detection using a single body-worn triaxial accelerometer and a novel classification algorithm based on Transformers and convolutional networks. This study was performed with data from 21 patients who manifested FOG episodes while performing activities of daily living in a home setting. Results indicate that the proposed FOG-Transformer can bring a significant improvement in FOG detection using leave-one-subject-out cross-validation (LOSO CV). These results bring opportunities for the implementation of accurate monitoring systems for use in ambulatory or home settings. |
2306.09298 | Leonhard Horstmeyer | Leonhard Horstmeyer | Lakat: An open and permissionless architecture for continuous
integration academic publishing | 23 pages, 5 figures, 1 table | null | null | null | cs.NI | http://creativecommons.org/licenses/by-sa/4.0/ | In this paper, we present three contributions to the field of academic
publishing. Firstly, we introduce Lakat, a novel base layer for a publishing
system that fosters collaboration, pluralism and permissionless participation.
Drawing inspiration from the philosophy of Imre Lakatos, Lakat is designed as a
peer-to-peer process- and conflict-oriented system that supports continuous
integration across multiple branches. This architecture provides a robust
foundation for the integration of existing reputation systems and incentive
structures or the development of new ones. Secondly, we propose a new consensus
mechanism, called Proof of Review, which ensures the integrity and quality of
the content while promoting active participation from the community. Lastly, we
present Lignification, a new finality gadget specifically designed for
branched, permissionless systems. Lignification provides a deterministic way to
find the consensual state in these systems, ensuring the system's robustness
and reliability in handling complex scenarios where multiple contributors may
be proposing changes simultaneously. Together, these contributions aim to
provide a convenient starting point to tackle some of the issues in traditional
paper-formatted publishing of research output. By prioritizing collaboration,
process-orientation, and pluralism, Lakat aims to improve the way research is
conducted and disseminated and ultimately hopes to contribute to a healthier
and more productive academic culture.
| [
{
"created": "Thu, 15 Jun 2023 17:27:16 GMT",
"version": "v1"
}
] | 2023-06-16 | [
[
"Horstmeyer",
"Leonhard",
""
]
] | In this paper, we present three contributions to the field of academic publishing. Firstly, we introduce Lakat, a novel base layer for a publishing system that fosters collaboration, pluralism and permissionless participation. Drawing inspiration from the philosophy of Imre Lakatos, Lakat is designed as a peer-to-peer process- and conflict-oriented system that supports continuous integration across multiple branches. This architecture provides a robust foundation for the integration of existing reputation systems and incentive structures or the development of new ones. Secondly, we propose a new consensus mechanism, called Proof of Review, which ensures the integrity and quality of the content while promoting active participation from the community. Lastly, we present Lignification, a new finality gadget specifically designed for branched, permissionless systems. Lignification provides a deterministic way to find the consensual state in these systems, ensuring the system's robustness and reliability in handling complex scenarios where multiple contributors may be proposing changes simultaneously. Together, these contributions aim to provide a convenient starting point to tackle some of the issues in traditional paper-formatted publishing of research output. By prioritizing collaboration, process-orientation, and pluralism, Lakat aims to improve the way research is conducted and disseminated and ultimately hopes to contribute to a healthier and more productive academic culture. |
1708.01944 | Abram Handler | Abram Handler, Brendan O'Connor | Rookie: A unique approach for exploring news archives | Presented at KDD 2017: Data Science + Journalism workshop | null | null | null | cs.HC cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | News archives are an invaluable primary source for placing current events in
historical context. But current search engine tools do a poor job at uncovering
broad themes and narratives across documents. We present Rookie: a practical
software system which uses natural language processing (NLP) to help readers,
reporters and editors uncover broad stories in news archives. Unlike prior
work, Rookie's design emerged from 18 months of iterative development in
consultation with editors and computational journalists. This process lead to a
dramatically different approach from previous academic systems with similar
goals. Our efforts offer a generalizable case study for others building
real-world journalism software using NLP.
| [
{
"created": "Sun, 6 Aug 2017 22:20:02 GMT",
"version": "v1"
}
] | 2017-08-08 | [
[
"Handler",
"Abram",
""
],
[
"O'Connor",
"Brendan",
""
]
] | News archives are an invaluable primary source for placing current events in historical context. But current search engine tools do a poor job at uncovering broad themes and narratives across documents. We present Rookie: a practical software system which uses natural language processing (NLP) to help readers, reporters and editors uncover broad stories in news archives. Unlike prior work, Rookie's design emerged from 18 months of iterative development in consultation with editors and computational journalists. This process lead to a dramatically different approach from previous academic systems with similar goals. Our efforts offer a generalizable case study for others building real-world journalism software using NLP. |
1809.10508 | Sebastien Ratel | Victor Chepoi and Arnaud Labourel and Sebastien Ratel | Distance and routing labeling schemes for cube-free median graphs | 34 pages, 10 figures | null | null | null | cs.DM cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Distance labeling schemes are schemes that label the vertices of a graph with
short labels in such a way that the distance between any two vertices $u$ and
$v$ can be determined efficiently by merely inspecting the labels of $u$ and
$v$, without using any other information. Similarly, routing labeling schemes
label the vertices of a graph in a such a way that given the labels of a source
node and a destination node, it is possible to compute efficiently the port
number of the edge from the source that heads in the direction of the
destination. One of important problems is finding natural classes of graphs
admitting distance and/or routing labeling schemes with labels of
polylogarithmic size. In this paper, we show that the class of cube-free median
graphs on $n$ nodes enjoys distance and routing labeling schemes with labels of
$O(\log^3 n)$ bits.
| [
{
"created": "Thu, 27 Sep 2018 13:30:48 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Jul 2020 13:20:44 GMT",
"version": "v2"
}
] | 2020-07-07 | [
[
"Chepoi",
"Victor",
""
],
[
"Labourel",
"Arnaud",
""
],
[
"Ratel",
"Sebastien",
""
]
] | Distance labeling schemes are schemes that label the vertices of a graph with short labels in such a way that the distance between any two vertices $u$ and $v$ can be determined efficiently by merely inspecting the labels of $u$ and $v$, without using any other information. Similarly, routing labeling schemes label the vertices of a graph in a such a way that given the labels of a source node and a destination node, it is possible to compute efficiently the port number of the edge from the source that heads in the direction of the destination. One of important problems is finding natural classes of graphs admitting distance and/or routing labeling schemes with labels of polylogarithmic size. In this paper, we show that the class of cube-free median graphs on $n$ nodes enjoys distance and routing labeling schemes with labels of $O(\log^3 n)$ bits. |
2306.14401 | Jon Butler | Jon T. Butler, Tsutomu Sasao, and Shinobu Nagayama | On the distribution of sensitivities of symmetric Boolean functions | 5 pages, 0 figures The submitted paper is a journal version of
"Enumeration of Symmetric Boolean Functions By Sensitivity" by J. Butler, T.
Sasao, and S. Nagayama presented at the Reed-Muller Workshop, Matsue, Japan
on May 24, 2023. Paper was presented, but not distributed. Authors retained
copyright | null | null | null | cs.CC | http://creativecommons.org/licenses/by/4.0/ | A Boolean function $f({\vec x})$ is sensitive to bit $x_i$ if there is at
least one input vector $\vec x$ and one bit $x_i$ in $\vec x$, such that
changing $x_i$ changes $f$. A function has sensitivity $s$ if among all input
vectors, the largest number of bits to which $f$ is sensitive is $s$. We count
the $n$-variable symmetric Boolean functions that have maximum sensitivity. We
show that most such functions have the largest possible sensitivity, $n$. This
suggests sensitivity is limited as a complexity measure for symmetric Boolean
functions.
| [
{
"created": "Mon, 26 Jun 2023 03:29:54 GMT",
"version": "v1"
}
] | 2023-06-27 | [
[
"Butler",
"Jon T.",
""
],
[
"Sasao",
"Tsutomu",
""
],
[
"Nagayama",
"Shinobu",
""
]
] | A Boolean function $f({\vec x})$ is sensitive to bit $x_i$ if there is at least one input vector $\vec x$ and one bit $x_i$ in $\vec x$, such that changing $x_i$ changes $f$. A function has sensitivity $s$ if among all input vectors, the largest number of bits to which $f$ is sensitive is $s$. We count the $n$-variable symmetric Boolean functions that have maximum sensitivity. We show that most such functions have the largest possible sensitivity, $n$. This suggests sensitivity is limited as a complexity measure for symmetric Boolean functions. |
1404.7610 | Takeaki Uno | Takeaki Uno, Hiroko Satoh | An Efficient Algorithm for Enumerating Chordless Cycles and Chordless
Paths | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A chordless cycle (induced cycle) $C$ of a graph is a cycle without any
chord, meaning that there is no edge outside the cycle connecting two vertices
of the cycle. A chordless path is defined similarly. In this paper, we consider
the problems of enumerating chordless cycles/paths of a given graph $G=(V,E),$
and propose algorithms taking $O(|E|)$ time for each chordless cycle/path. In
the existing studies, the problems had not been deeply studied in the
theoretical computer science area, and no output polynomial time algorithm has
been proposed. Our experiments showed that the computation time of our
algorithms is constant per chordless cycle/path for non-dense random graphs and
real-world graphs. They also show that the number of chordless cycles is much
smaller than the number of cycles. We applied the algorithm to prediction of
NMR (Nuclear Magnetic Resonance) spectra, and increased the accuracy of the
prediction.
| [
{
"created": "Wed, 30 Apr 2014 06:57:09 GMT",
"version": "v1"
}
] | 2014-05-01 | [
[
"Uno",
"Takeaki",
""
],
[
"Satoh",
"Hiroko",
""
]
] | A chordless cycle (induced cycle) $C$ of a graph is a cycle without any chord, meaning that there is no edge outside the cycle connecting two vertices of the cycle. A chordless path is defined similarly. In this paper, we consider the problems of enumerating chordless cycles/paths of a given graph $G=(V,E),$ and propose algorithms taking $O(|E|)$ time for each chordless cycle/path. In the existing studies, the problems had not been deeply studied in the theoretical computer science area, and no output polynomial time algorithm has been proposed. Our experiments showed that the computation time of our algorithms is constant per chordless cycle/path for non-dense random graphs and real-world graphs. They also show that the number of chordless cycles is much smaller than the number of cycles. We applied the algorithm to prediction of NMR (Nuclear Magnetic Resonance) spectra, and increased the accuracy of the prediction. |
1202.4833 | EPTCS | Vanda Santos (CISUC/ESTGV - IPV), Pedro Quaresma (CISUC/Department of
Mathematics, University of Coimbra) | Integrating DGSs and GATPs in an Adaptative and Collaborative
Blended-Learning Web-Environment | In Proceedings THedu'11, arXiv:1202.4535 | EPTCS 79, 2012, pp. 111-123 | 10.4204/EPTCS.79.7 | null | cs.CG cs.MS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The area of geometry with its very strong and appealing visual contents and
its also strong and appealing connection between the visual content and its
formal specification, is an area where computational tools can enhance, in a
significant way, the learning environments.
The dynamic geometry software systems (DGSs) can be used to explore the
visual contents of geometry. This already mature tools allows an easy
construction of geometric figures build from free objects and elementary
constructions. The geometric automated theorem provers (GATPs) allows formal
deductive reasoning about geometric constructions, extending the reasoning via
concrete instances in a given model to formal deductive reasoning in a
geometric theory.
An adaptative and collaborative blended-learning environment where the DGS
and GATP features could be fully explored would be, in our opinion a very rich
and challenging learning environment for teachers and students.
In this text we will describe the Web Geometry Laboratory a Web environment
incorporating a DGS and a repository of geometric problems, that can be used in
a synchronous and asynchronous fashion and with some adaptative and
collaborative features.
As future work we want to enhance the adaptative and collaborative aspects of
the environment and also to incorporate a GATP, constructing a dynamic and
individualised learning environment for geometry.
| [
{
"created": "Wed, 22 Feb 2012 06:42:02 GMT",
"version": "v1"
}
] | 2012-02-23 | [
[
"Santos",
"Vanda",
"",
"CISUC/ESTGV - IPV"
],
[
"Quaresma",
"Pedro",
"",
"CISUC/Department of\n Mathematics, University of Coimbra"
]
] | The area of geometry with its very strong and appealing visual contents and its also strong and appealing connection between the visual content and its formal specification, is an area where computational tools can enhance, in a significant way, the learning environments. The dynamic geometry software systems (DGSs) can be used to explore the visual contents of geometry. This already mature tools allows an easy construction of geometric figures build from free objects and elementary constructions. The geometric automated theorem provers (GATPs) allows formal deductive reasoning about geometric constructions, extending the reasoning via concrete instances in a given model to formal deductive reasoning in a geometric theory. An adaptative and collaborative blended-learning environment where the DGS and GATP features could be fully explored would be, in our opinion a very rich and challenging learning environment for teachers and students. In this text we will describe the Web Geometry Laboratory a Web environment incorporating a DGS and a repository of geometric problems, that can be used in a synchronous and asynchronous fashion and with some adaptative and collaborative features. As future work we want to enhance the adaptative and collaborative aspects of the environment and also to incorporate a GATP, constructing a dynamic and individualised learning environment for geometry. |
1901.03768 | Taejoon Byun | Taejoon Byun, Vaibhav Sharma, Abhishek Vijayakumar, Sanjai Rayadurgam,
Darren Cofer | Input Prioritization for Testing Neural Networks | null | null | null | null | cs.SE cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks (DNNs) are increasingly being adopted for sensing and
control functions in a variety of safety and mission-critical systems such as
self-driving cars, autonomous air vehicles, medical diagnostics, and industrial
robotics. Failures of such systems can lead to loss of life or property, which
necessitates stringent verification and validation for providing high
assurance. Though formal verification approaches are being investigated,
testing remains the primary technique for assessing the dependability of such
systems. Due to the nature of the tasks handled by DNNs, the cost of obtaining
test oracle data---the expected output, a.k.a. label, for a given input---is
high, which significantly impacts the amount and quality of testing that can be
performed. Thus, prioritizing input data for testing DNNs in meaningful ways to
reduce the cost of labeling can go a long way in increasing testing efficacy.
This paper proposes using gauges of the DNN's sentiment derived from the
computation performed by the model, as a means to identify inputs that are
likely to reveal weaknesses. We empirically assessed the efficacy of three such
sentiment measures for prioritization---confidence, uncertainty, and
surprise---and compare their effectiveness in terms of their fault-revealing
capability and retraining effectiveness. The results indicate that sentiment
measures can effectively flag inputs that expose unacceptable DNN behavior. For
MNIST models, the average percentage of inputs correctly flagged ranged from
88% to 94.8%.
| [
{
"created": "Fri, 11 Jan 2019 23:13:47 GMT",
"version": "v1"
}
] | 2019-01-15 | [
[
"Byun",
"Taejoon",
""
],
[
"Sharma",
"Vaibhav",
""
],
[
"Vijayakumar",
"Abhishek",
""
],
[
"Rayadurgam",
"Sanjai",
""
],
[
"Cofer",
"Darren",
""
]
] | Deep neural networks (DNNs) are increasingly being adopted for sensing and control functions in a variety of safety and mission-critical systems such as self-driving cars, autonomous air vehicles, medical diagnostics, and industrial robotics. Failures of such systems can lead to loss of life or property, which necessitates stringent verification and validation for providing high assurance. Though formal verification approaches are being investigated, testing remains the primary technique for assessing the dependability of such systems. Due to the nature of the tasks handled by DNNs, the cost of obtaining test oracle data---the expected output, a.k.a. label, for a given input---is high, which significantly impacts the amount and quality of testing that can be performed. Thus, prioritizing input data for testing DNNs in meaningful ways to reduce the cost of labeling can go a long way in increasing testing efficacy. This paper proposes using gauges of the DNN's sentiment derived from the computation performed by the model, as a means to identify inputs that are likely to reveal weaknesses. We empirically assessed the efficacy of three such sentiment measures for prioritization---confidence, uncertainty, and surprise---and compare their effectiveness in terms of their fault-revealing capability and retraining effectiveness. The results indicate that sentiment measures can effectively flag inputs that expose unacceptable DNN behavior. For MNIST models, the average percentage of inputs correctly flagged ranged from 88% to 94.8%. |
2007.02833 | Amelia Pollard | Amelia Elizabeth Pollard and Jonathan L. Shapiro | Eliminating Catastrophic Interference with Biased Competition | null | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present here a model to take advantage of the multi-task nature of complex
datasets by learning to separate tasks and subtasks in and end to end manner by
biasing competitive interactions in the network. This method does not require
additional labelling or reformatting of data in a dataset. We propose an
alternate view to the monolithic one-task-fits-all learning of multi-task
problems, and describe a model based on a theory of neuronal attention from
neuroscience, proposed by Desimone. We create and exhibit a new toy dataset,
based on the MNIST dataset, which we call MNIST-QA, for testing Visual Question
Answering architectures in a low-dimensional environment while preserving the
more difficult components of the Visual Question Answering task, and
demonstrate the proposed network architecture on this new dataset, as well as
on COCO-QA and DAQUAR-FULL. We then demonstrate that this model eliminates
catastrophic interference between tasks on a newly created toy dataset and
provides competitive results in the Visual Question Answering space. We provide
further evidence that Visual Question Answering can be approached as a
multi-task problem, and demonstrate that this new architecture based on the
Biased Competition model is capable of learning to separate and learn the tasks
in an end-to-end fashion without the need for task labels.
| [
{
"created": "Fri, 3 Jul 2020 16:15:15 GMT",
"version": "v1"
}
] | 2020-07-07 | [
[
"Pollard",
"Amelia Elizabeth",
""
],
[
"Shapiro",
"Jonathan L.",
""
]
] | We present here a model to take advantage of the multi-task nature of complex datasets by learning to separate tasks and subtasks in and end to end manner by biasing competitive interactions in the network. This method does not require additional labelling or reformatting of data in a dataset. We propose an alternate view to the monolithic one-task-fits-all learning of multi-task problems, and describe a model based on a theory of neuronal attention from neuroscience, proposed by Desimone. We create and exhibit a new toy dataset, based on the MNIST dataset, which we call MNIST-QA, for testing Visual Question Answering architectures in a low-dimensional environment while preserving the more difficult components of the Visual Question Answering task, and demonstrate the proposed network architecture on this new dataset, as well as on COCO-QA and DAQUAR-FULL. We then demonstrate that this model eliminates catastrophic interference between tasks on a newly created toy dataset and provides competitive results in the Visual Question Answering space. We provide further evidence that Visual Question Answering can be approached as a multi-task problem, and demonstrate that this new architecture based on the Biased Competition model is capable of learning to separate and learn the tasks in an end-to-end fashion without the need for task labels. |
1609.09253 | Ivan Grechikhin | Ivan S. Grechikhin | Heuristic with elements of tabu search for Truck and Trailer Routing
Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Vehicle Routing Problem is a well-known problem in logistics and
transportation, and the variety of such problems is explained by the fact that
it occurs in many real-life situations. It is an NP-hard combinatorial
optimization problem and finding an exact optimal solution is practically
impossible. In this work, Site-Dependent Truck and Trailer Routing Problem with
hard and soft Time Windows and Split Deliveries is considered (SDTTRPTWSD). In
this article, we develop a heuristic with the elements of Tabu Search for
solving SDTTRPTWSD. The heuristic uses the concept of neighborhoods and visits
infeasible solutions during the search. A greedy heuristic is applied to
construct an initial solution.
| [
{
"created": "Thu, 29 Sep 2016 08:37:48 GMT",
"version": "v1"
}
] | 2016-09-30 | [
[
"Grechikhin",
"Ivan S.",
""
]
] | Vehicle Routing Problem is a well-known problem in logistics and transportation, and the variety of such problems is explained by the fact that it occurs in many real-life situations. It is an NP-hard combinatorial optimization problem and finding an exact optimal solution is practically impossible. In this work, Site-Dependent Truck and Trailer Routing Problem with hard and soft Time Windows and Split Deliveries is considered (SDTTRPTWSD). In this article, we develop a heuristic with the elements of Tabu Search for solving SDTTRPTWSD. The heuristic uses the concept of neighborhoods and visits infeasible solutions during the search. A greedy heuristic is applied to construct an initial solution. |
2203.13412 | Zengjie Song | Zengjie Song, Yuxi Wang, Junsong Fan, Tieniu Tan, Zhaoxiang Zhang | Self-Supervised Predictive Learning: A Negative-Free Method for Sound
Source Localization in Visual Scenes | Camera-ready, CVPR 2022. Code: https://github.com/zjsong/SSPL | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sound source localization in visual scenes aims to localize objects emitting
the sound in a given image. Recent works showing impressive localization
performance typically rely on the contrastive learning framework. However, the
random sampling of negatives, as commonly adopted in these methods, can result
in misalignment between audio and visual features and thus inducing ambiguity
in localization. In this paper, instead of following previous literature, we
propose Self-Supervised Predictive Learning (SSPL), a negative-free method for
sound localization via explicit positive mining. Specifically, we first devise
a three-stream network to elegantly associate sound source with two augmented
views of one corresponding video frame, leading to semantically coherent
similarities between audio and visual features. Second, we introduce a novel
predictive coding module for audio-visual feature alignment. Such a module
assists SSPL to focus on target objects in a progressive manner and effectively
lowers the positive-pair learning difficulty. Experiments show surprising
results that SSPL outperforms the state-of-the-art approach on two standard
sound localization benchmarks. In particular, SSPL achieves significant
improvements of 8.6% cIoU and 3.4% AUC on SoundNet-Flickr compared to the
previous best. Code is available at: https://github.com/zjsong/SSPL.
| [
{
"created": "Fri, 25 Mar 2022 01:42:42 GMT",
"version": "v1"
}
] | 2022-03-28 | [
[
"Song",
"Zengjie",
""
],
[
"Wang",
"Yuxi",
""
],
[
"Fan",
"Junsong",
""
],
[
"Tan",
"Tieniu",
""
],
[
"Zhang",
"Zhaoxiang",
""
]
] | Sound source localization in visual scenes aims to localize objects emitting the sound in a given image. Recent works showing impressive localization performance typically rely on the contrastive learning framework. However, the random sampling of negatives, as commonly adopted in these methods, can result in misalignment between audio and visual features and thus inducing ambiguity in localization. In this paper, instead of following previous literature, we propose Self-Supervised Predictive Learning (SSPL), a negative-free method for sound localization via explicit positive mining. Specifically, we first devise a three-stream network to elegantly associate sound source with two augmented views of one corresponding video frame, leading to semantically coherent similarities between audio and visual features. Second, we introduce a novel predictive coding module for audio-visual feature alignment. Such a module assists SSPL to focus on target objects in a progressive manner and effectively lowers the positive-pair learning difficulty. Experiments show surprising results that SSPL outperforms the state-of-the-art approach on two standard sound localization benchmarks. In particular, SSPL achieves significant improvements of 8.6% cIoU and 3.4% AUC on SoundNet-Flickr compared to the previous best. Code is available at: https://github.com/zjsong/SSPL. |
1709.06745 | Huiju Wang Dr | Huiju Wang, Zhengkui Wang, Kian-Lee Tan, Chee-Yong Chan, Qi Fan, Xiao
Yue | VCExplorer: A Interactive Graph Exploration Framework Based on Hub
Vertices with Graph Consolidation | 11 pages, 8 figures and 2 tables | null | null | null | cs.DB cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graphs have been widely used to model different information networks, such as
the Web, biological networks and social networks (e.g. Twitter). Due to the
size and complexity of these graphs, how to explore and utilize these graphs
has become a very challenging problem. In this paper, we propose, VCExplorer, a
new interactive graph exploration framework that integrates the strengths of
graph visualization and graph summarization. Unlike existing graph
visualization tools where vertices of a graph may be clustered into a smaller
collection of super/virtual vertices, VCExplorer displays a small number of
actual source graph vertices (called hubs) and summaries of the information
between these vertices. We refer to such a graph as a HA-graph (Hub-based
Aggregation Graph). This allows users to appreciate the relationship between
the hubs, rather than super/virtual vertices. Users can navigate through the
HA- graph by "drilling down" into the summaries between hubs to display more
hubs. We illustrate how the graph aggregation techniques can be integrated into
the exploring framework as the consolidated information to users. In addition,
we propose efficient graph aggregation algorithms over multiple subgraphs via
computation sharing. Extensive experimental evaluations have been conducted
using both real and synthetic datasets and the results indicate the
effectiveness and efficiency of VCExplorer for exploration.
| [
{
"created": "Wed, 20 Sep 2017 07:23:03 GMT",
"version": "v1"
}
] | 2017-09-21 | [
[
"Wang",
"Huiju",
""
],
[
"Wang",
"Zhengkui",
""
],
[
"Tan",
"Kian-Lee",
""
],
[
"Chan",
"Chee-Yong",
""
],
[
"Fan",
"Qi",
""
],
[
"Yue",
"Xiao",
""
]
] | Graphs have been widely used to model different information networks, such as the Web, biological networks and social networks (e.g. Twitter). Due to the size and complexity of these graphs, how to explore and utilize these graphs has become a very challenging problem. In this paper, we propose, VCExplorer, a new interactive graph exploration framework that integrates the strengths of graph visualization and graph summarization. Unlike existing graph visualization tools where vertices of a graph may be clustered into a smaller collection of super/virtual vertices, VCExplorer displays a small number of actual source graph vertices (called hubs) and summaries of the information between these vertices. We refer to such a graph as a HA-graph (Hub-based Aggregation Graph). This allows users to appreciate the relationship between the hubs, rather than super/virtual vertices. Users can navigate through the HA- graph by "drilling down" into the summaries between hubs to display more hubs. We illustrate how the graph aggregation techniques can be integrated into the exploring framework as the consolidated information to users. In addition, we propose efficient graph aggregation algorithms over multiple subgraphs via computation sharing. Extensive experimental evaluations have been conducted using both real and synthetic datasets and the results indicate the effectiveness and efficiency of VCExplorer for exploration. |
2407.06624 | EPTCS | Gabriele Cecilia (Universit\`a degli Studi di Milano), Alberto
Momigliano (Universit\`a degli Studi di Milano) | A Beluga Formalization of the Harmony Lemma in the $\pi$-Calculus | In Proceedings LFMTP 2024, arXiv:2407.05822 | EPTCS 404, 2024, pp. 1-17 | 10.4204/EPTCS.404.1 | null | cs.LO | http://creativecommons.org/licenses/by/4.0/ | The "Harmony Lemma", as formulated by Sangiorgi & Walker, establishes the
equivalence between the labelled transition semantics and the reduction
semantics in the $\pi$-calculus. Despite being a widely known and accepted
result for the standard $\pi$-calculus, this assertion has never been
rigorously proven, formally or informally. Hence, its validity may not be
immediately apparent when considering extensions of the $\pi$-calculus.
Contributing to the second challenge of the Concurrent Calculi Formalization
Benchmark -- a set of challenges tackling the main issues related to the
mechanization of concurrent systems -- we present a formalization of this
result for the fragment of the $\pi$-calculus examined in the Benchmark. Our
formalization is implemented in Beluga and draws inspiration from the HOAS
formalization of the LTS semantics popularized by Honsell et al. In passing, we
introduce a couple of useful encoding techniques for handling telescopes and
lexicographic induction.
| [
{
"created": "Tue, 9 Jul 2024 07:51:33 GMT",
"version": "v1"
}
] | 2024-07-10 | [
[
"Cecilia",
"Gabriele",
"",
"Università degli Studi di Milano"
],
[
"Momigliano",
"Alberto",
"",
"Università degli Studi di Milano"
]
] | The "Harmony Lemma", as formulated by Sangiorgi & Walker, establishes the equivalence between the labelled transition semantics and the reduction semantics in the $\pi$-calculus. Despite being a widely known and accepted result for the standard $\pi$-calculus, this assertion has never been rigorously proven, formally or informally. Hence, its validity may not be immediately apparent when considering extensions of the $\pi$-calculus. Contributing to the second challenge of the Concurrent Calculi Formalization Benchmark -- a set of challenges tackling the main issues related to the mechanization of concurrent systems -- we present a formalization of this result for the fragment of the $\pi$-calculus examined in the Benchmark. Our formalization is implemented in Beluga and draws inspiration from the HOAS formalization of the LTS semantics popularized by Honsell et al. In passing, we introduce a couple of useful encoding techniques for handling telescopes and lexicographic induction. |
2110.11269 | Alyssa Kody | Alyssa Kody, Samuel Chevalier, Spyros Chatzivasileiadis, Daniel
Molzahn | Modeling the AC Power Flow Equations with Optimally Compact Neural
Networks: Application to Unit Commitment | added acknowledgement, first two authors equally contributed, 8
pages, 3 figures, 1 table | null | null | null | cs.LG cs.SY eess.SY math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nonlinear power flow constraints render a variety of power system
optimization problems computationally intractable. Emerging research shows,
however, that the nonlinear AC power flow equations can be successfully modeled
using Neural Networks (NNs). These NNs can be exactly transformed into Mixed
Integer Linear Programs (MILPs) and embedded inside challenging optimization
problems, thus replacing nonlinearities that are intractable for many
applications with tractable piecewise linear approximations. Such approaches,
though, suffer from an explosion of the number of binary variables needed to
represent the NN. Accordingly, this paper develops a technique for training an
"optimally compact" NN, i.e., one that can represent the power flow equations
with a sufficiently high degree of accuracy while still maintaining a tractable
number of binary variables. We show that the resulting NN model is more
expressive than both the DC and linearized power flow approximations when
embedded inside of a challenging optimization problem (i.e., the AC unit
commitment problem).
| [
{
"created": "Thu, 21 Oct 2021 16:51:43 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Oct 2021 18:18:59 GMT",
"version": "v2"
}
] | 2021-11-01 | [
[
"Kody",
"Alyssa",
""
],
[
"Chevalier",
"Samuel",
""
],
[
"Chatzivasileiadis",
"Spyros",
""
],
[
"Molzahn",
"Daniel",
""
]
] | Nonlinear power flow constraints render a variety of power system optimization problems computationally intractable. Emerging research shows, however, that the nonlinear AC power flow equations can be successfully modeled using Neural Networks (NNs). These NNs can be exactly transformed into Mixed Integer Linear Programs (MILPs) and embedded inside challenging optimization problems, thus replacing nonlinearities that are intractable for many applications with tractable piecewise linear approximations. Such approaches, though, suffer from an explosion of the number of binary variables needed to represent the NN. Accordingly, this paper develops a technique for training an "optimally compact" NN, i.e., one that can represent the power flow equations with a sufficiently high degree of accuracy while still maintaining a tractable number of binary variables. We show that the resulting NN model is more expressive than both the DC and linearized power flow approximations when embedded inside of a challenging optimization problem (i.e., the AC unit commitment problem). |
2403.06100 | Yusuke Yasuda | Yusuke Yasuda and Tomoki Toda | Automatic design optimization of preference-based subjective evaluation
with online learning in crowdsourcing environment | null | null | null | null | cs.HC cs.CL cs.LG eess.AS stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A preference-based subjective evaluation is a key method for evaluating
generative media reliably. However, its huge combinations of pairs prohibit it
from being applied to large-scale evaluation using crowdsourcing. To address
this issue, we propose an automatic optimization method for preference-based
subjective evaluation in terms of pair combination selections and allocation of
evaluation volumes with online learning in a crowdsourcing environment. We use
a preference-based online learning method based on a sorting algorithm to
identify the total order of evaluation targets with minimum sample volumes. Our
online learning algorithm supports parallel and asynchronous execution under
fixed-budget conditions required for crowdsourcing. Our experiment on
preference-based subjective evaluation of synthetic speech shows that our
method successfully optimizes the test by reducing pair combinations from 351
to 83 and allocating optimal evaluation volumes for each pair ranging from 30
to 663 without compromising evaluation accuracies and wasting budget
allocations.
| [
{
"created": "Sun, 10 Mar 2024 05:55:00 GMT",
"version": "v1"
}
] | 2024-03-12 | [
[
"Yasuda",
"Yusuke",
""
],
[
"Toda",
"Tomoki",
""
]
] | A preference-based subjective evaluation is a key method for evaluating generative media reliably. However, its huge combinations of pairs prohibit it from being applied to large-scale evaluation using crowdsourcing. To address this issue, we propose an automatic optimization method for preference-based subjective evaluation in terms of pair combination selections and allocation of evaluation volumes with online learning in a crowdsourcing environment. We use a preference-based online learning method based on a sorting algorithm to identify the total order of evaluation targets with minimum sample volumes. Our online learning algorithm supports parallel and asynchronous execution under fixed-budget conditions required for crowdsourcing. Our experiment on preference-based subjective evaluation of synthetic speech shows that our method successfully optimizes the test by reducing pair combinations from 351 to 83 and allocating optimal evaluation volumes for each pair ranging from 30 to 663 without compromising evaluation accuracies and wasting budget allocations. |
2112.02380 | Salman Parsa | Erin Wolf Chambers, Salman Parsa, Hannah Schreiber | On Complexity of Computing Bottleneck and Lexicographic Optimal Cycles
in a Homology Class | null | null | null | null | cs.CG cs.CC | http://creativecommons.org/licenses/by/4.0/ | Homology features of spaces which appear in applications, for instance 3D
meshes, are among the most important topological properties of these objects.
Given a non-trivial cycle in a homology class, we consider the problem of
computing a representative in that homology class which is optimal. We study
two measures of optimality, namely, the lexicographic order of cycles (the
lex-optimal cycle) and the bottleneck norm (a bottleneck-optimal cycle). We
give a simple algorithm for computing the lex-optimal cycle for a 1-homology
lass in a closed orientable surface. In contrast to this, our main result is
that, in the case of 3-Manifolds of size $n^2$ in the Euclidean 3-space, the
problem of finding a bottleneck optimal cycle cannot be solved more efficiently
than solving a system of linear equations with an $n \times n$ sparse matrix.
From this reduction, we deduce several hardness results. Most notably, we show
that for 3-manifolds given as a subset of the 3-space of size $n^2$, persistent
homology computations are at least as hard as rank computation (for sparse
matrices) while ordinary homology computations can be done in $O(n^2 \log n)$
time. This is the first such distinction between these two computations.
Moreover, it follows that the same disparity exists between the height
persistent homology computation and general sub-level set persistent homology
computation for simplicial complexes in the 3-space.
| [
{
"created": "Sat, 4 Dec 2021 16:42:48 GMT",
"version": "v1"
},
{
"created": "Wed, 16 Mar 2022 21:20:21 GMT",
"version": "v2"
}
] | 2022-03-18 | [
[
"Chambers",
"Erin Wolf",
""
],
[
"Parsa",
"Salman",
""
],
[
"Schreiber",
"Hannah",
""
]
] | Homology features of spaces which appear in applications, for instance 3D meshes, are among the most important topological properties of these objects. Given a non-trivial cycle in a homology class, we consider the problem of computing a representative in that homology class which is optimal. We study two measures of optimality, namely, the lexicographic order of cycles (the lex-optimal cycle) and the bottleneck norm (a bottleneck-optimal cycle). We give a simple algorithm for computing the lex-optimal cycle for a 1-homology lass in a closed orientable surface. In contrast to this, our main result is that, in the case of 3-Manifolds of size $n^2$ in the Euclidean 3-space, the problem of finding a bottleneck optimal cycle cannot be solved more efficiently than solving a system of linear equations with an $n \times n$ sparse matrix. From this reduction, we deduce several hardness results. Most notably, we show that for 3-manifolds given as a subset of the 3-space of size $n^2$, persistent homology computations are at least as hard as rank computation (for sparse matrices) while ordinary homology computations can be done in $O(n^2 \log n)$ time. This is the first such distinction between these two computations. Moreover, it follows that the same disparity exists between the height persistent homology computation and general sub-level set persistent homology computation for simplicial complexes in the 3-space. |
1312.0932 | Inaki Estella | I\~naki Estella Aguerri and Deniz G\"und\"uz | Joint Source-Channel Coding with Time-Varying Channel and
Side-Information | Submitted to IEEE Transactions on Information Theory | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transmission of a Gaussian source over a time-varying Gaussian channel is
studied in the presence of time-varying correlated side information at the
receiver. A block fading model is considered for both the channel and the side
information, whose states are assumed to be known only at the receiver. The
optimality of separate source and channel coding in terms of average end-to-end
distortion is shown when the channel is static while the side information state
follows a discrete or a continuous and quasiconcave distribution. When both the
channel and side information states are time-varying, separate source and
channel coding is suboptimal in general. A partially informed encoder lower
bound is studied by providing the channel state information to the encoder.
Several achievable transmission schemes are proposed based on uncoded
transmission, separate source and channel coding, joint decoding as well as
hybrid digital-analog transmission. Uncoded transmission is shown to be optimal
for a class of continuous and quasiconcave side information state
distributions, while the channel gain may have an arbitrary distribution. To
the best of our knowledge, this is the first example in which the uncoded
transmission achieves the optimal performance thanks to the time-varying nature
of the states, while it is suboptimal in the static version of the same
problem. Then, the optimal \emph{distortion exponent}, that quantifies the
exponential decay rate of the expected distortion in the high SNR regime, is
characterized for Nakagami distributed channel and side information states, and
it is shown to be achieved by hybrid digital-analog and joint decoding schemes
in certain cases, illustrating the suboptimality of pure digital or analog
transmission in general.
| [
{
"created": "Tue, 3 Dec 2013 20:53:25 GMT",
"version": "v1"
},
{
"created": "Tue, 26 May 2015 12:22:25 GMT",
"version": "v2"
}
] | 2015-05-27 | [
[
"Aguerri",
"Iñaki Estella",
""
],
[
"Gündüz",
"Deniz",
""
]
] | Transmission of a Gaussian source over a time-varying Gaussian channel is studied in the presence of time-varying correlated side information at the receiver. A block fading model is considered for both the channel and the side information, whose states are assumed to be known only at the receiver. The optimality of separate source and channel coding in terms of average end-to-end distortion is shown when the channel is static while the side information state follows a discrete or a continuous and quasiconcave distribution. When both the channel and side information states are time-varying, separate source and channel coding is suboptimal in general. A partially informed encoder lower bound is studied by providing the channel state information to the encoder. Several achievable transmission schemes are proposed based on uncoded transmission, separate source and channel coding, joint decoding as well as hybrid digital-analog transmission. Uncoded transmission is shown to be optimal for a class of continuous and quasiconcave side information state distributions, while the channel gain may have an arbitrary distribution. To the best of our knowledge, this is the first example in which the uncoded transmission achieves the optimal performance thanks to the time-varying nature of the states, while it is suboptimal in the static version of the same problem. Then, the optimal \emph{distortion exponent}, that quantifies the exponential decay rate of the expected distortion in the high SNR regime, is characterized for Nakagami distributed channel and side information states, and it is shown to be achieved by hybrid digital-analog and joint decoding schemes in certain cases, illustrating the suboptimality of pure digital or analog transmission in general. |
1608.07872 | Monowar Hasan | Monowar Hasan, Sibin Mohan, Rakesh B. Bobba and Rodolfo Pellizzoni | Exploring Opportunistic Execution for Integrating Security into Legacy
Hard Real-Time Systems | Accepted for publication, IEEE RTSS 2016 | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to physical isolation as well as use of proprietary hardware and
protocols, traditional real-time systems (RTS) were considered to be
invulnerable to security breaches and external attacks. However, this
assumption is being challenged by recent attacks that highlight the
vulnerabilities in such systems. In this paper, we focus on integrating
security mechanisms into RTS (especially legacy RTS) and provide a metric to
measure the effectiveness of such mechanisms. We combine opportunistic
execution with hierarchical scheduling to maintain compatibility with legacy
systems while still providing flexibility. The proposed approach is shown to
increase the security posture of RTS systems without impacting their temporal
constraints.
| [
{
"created": "Mon, 29 Aug 2016 00:27:53 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Aug 2016 00:28:11 GMT",
"version": "v2"
}
] | 2016-08-31 | [
[
"Hasan",
"Monowar",
""
],
[
"Mohan",
"Sibin",
""
],
[
"Bobba",
"Rakesh B.",
""
],
[
"Pellizzoni",
"Rodolfo",
""
]
] | Due to physical isolation as well as use of proprietary hardware and protocols, traditional real-time systems (RTS) were considered to be invulnerable to security breaches and external attacks. However, this assumption is being challenged by recent attacks that highlight the vulnerabilities in such systems. In this paper, we focus on integrating security mechanisms into RTS (especially legacy RTS) and provide a metric to measure the effectiveness of such mechanisms. We combine opportunistic execution with hierarchical scheduling to maintain compatibility with legacy systems while still providing flexibility. The proposed approach is shown to increase the security posture of RTS systems without impacting their temporal constraints. |
2010.13540 | Zhesong Yu | Zhesong Yu, Xingjian Du, Bilei Zhu, Zejun Ma | Contrastive Unsupervised Learning for Audio Fingerprinting | 5 pages | null | null | null | cs.SD cs.LG cs.MM eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rise of video-sharing platforms has attracted more and more people to
shoot videos and upload them to the Internet. These videos mostly contain a
carefully-edited background audio track, where serious speech change, pitch
shifting and various types of audio effects may involve, and existing audio
identification systems may fail to recognize the audio. To solve this problem,
in this paper, we introduce the idea of contrastive learning to the task of
audio fingerprinting (AFP). Contrastive learning is an unsupervised approach to
learn representations that can effectively group similar samples and
discriminate dissimilar ones. In our work, we consider an audio track and its
differently distorted versions as similar while considering different audio
tracks as dissimilar. Based on the momentum contrast (MoCo) framework, we
devise a contrastive learning method for AFP, which can generate fingerprints
that are both discriminative and robust. A set of experiments showed that our
AFP method is effective for audio identification, with robustness to serious
audio distortions, including the challenging speed change and pitch shifting.
| [
{
"created": "Mon, 26 Oct 2020 12:49:39 GMT",
"version": "v1"
}
] | 2020-10-27 | [
[
"Yu",
"Zhesong",
""
],
[
"Du",
"Xingjian",
""
],
[
"Zhu",
"Bilei",
""
],
[
"Ma",
"Zejun",
""
]
] | The rise of video-sharing platforms has attracted more and more people to shoot videos and upload them to the Internet. These videos mostly contain a carefully-edited background audio track, where serious speech change, pitch shifting and various types of audio effects may involve, and existing audio identification systems may fail to recognize the audio. To solve this problem, in this paper, we introduce the idea of contrastive learning to the task of audio fingerprinting (AFP). Contrastive learning is an unsupervised approach to learn representations that can effectively group similar samples and discriminate dissimilar ones. In our work, we consider an audio track and its differently distorted versions as similar while considering different audio tracks as dissimilar. Based on the momentum contrast (MoCo) framework, we devise a contrastive learning method for AFP, which can generate fingerprints that are both discriminative and robust. A set of experiments showed that our AFP method is effective for audio identification, with robustness to serious audio distortions, including the challenging speed change and pitch shifting. |
0912.3098 | Loet Leydesdorff | Loet Leydesdorff and Alkim Almila Akdag Salah | Maps on the basis of the Arts & Humanities Citation Index: The journals
Leonardo and Art Journal versus "Digital Humanities" as a topic | null | null | null | null | cs.DL physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The possibilities of using the Arts & Humanities Citation Index (A&HCI) for
journal mapping have not been sufficiently recognized because of the absence of
a Journal Citations Report (JCR) for this database. A quasi-JCR for the A&HCI
(2008) was constructed from the data contained in the Web-of-Science and is
used for the evaluation of two journals as examples: Leonardo and Art Journal.
The maps on the basis of the aggregated journal-journal citations within this
domain can be compared with maps including references to journals in the
Science Citation Index and Social Science Citation Index. Art journals are
cited by (social) science journals more than by other art journals, but these
journals draw upon one another in terms of their own references. This cultural
impact in terms of being cited is not found when documents with a topic such as
"digital humanities" are analyzed. This community of practice functions more as
an intellectual organizer than a journal.
| [
{
"created": "Wed, 16 Dec 2009 10:53:03 GMT",
"version": "v1"
}
] | 2009-12-17 | [
[
"Leydesdorff",
"Loet",
""
],
[
"Salah",
"Alkim Almila Akdag",
""
]
] | The possibilities of using the Arts & Humanities Citation Index (A&HCI) for journal mapping have not been sufficiently recognized because of the absence of a Journal Citations Report (JCR) for this database. A quasi-JCR for the A&HCI (2008) was constructed from the data contained in the Web-of-Science and is used for the evaluation of two journals as examples: Leonardo and Art Journal. The maps on the basis of the aggregated journal-journal citations within this domain can be compared with maps including references to journals in the Science Citation Index and Social Science Citation Index. Art journals are cited by (social) science journals more than by other art journals, but these journals draw upon one another in terms of their own references. This cultural impact in terms of being cited is not found when documents with a topic such as "digital humanities" are analyzed. This community of practice functions more as an intellectual organizer than a journal. |
0909.2058 | Sihem Amer-Yahia | Sihem Amer-Yahia (Yahoo! Research), Laks Lakshmanan (UBC), Cong Yu
(Yahoo! Research) | SocialScope: Enabling Information Discovery on Social Content Sites | CIDR 2009 | null | null | null | cs.DB cs.HC cs.IR cs.PL | http://creativecommons.org/licenses/by/3.0/ | Recently, many content sites have started encouraging their users to engage
in social activities such as adding buddies on Yahoo! Travel and sharing
articles with their friends on New York Times. This has led to the emergence of
{\em social content sites}, which is being facilitated by initiatives like
OpenID (http://www.openid.net/) and OpenSocial (http://www.opensocial.org/).
These community standards enable the open access to users' social profiles and
connections by individual content sites and are bringing content-oriented sites
and social networking sites ever closer. The integration of content and social
information raises new challenges for {\em information management and
discovery} over such sites. We propose a logical architecture, named
\kw{SocialScope}, consisting of three layers, for tackling the challenges. The
{\em content management} layer is responsible for integrating, maintaining and
physically accessing the content and social data. The {\em information
discovery} layer takes care of analyzing content to derive interesting new
information, and interpreting and processing the user's information need to
identify relevant information. Finally, the {\em information presentation}
layer explores the discovered information and helps users better understand it
in a principled way. We describe the challenges in each layer and propose
solutions for some of those challenges. In particular, we propose a uniform
algebraic framework, which can be leveraged to uniformly and flexibly specify
many of the information discovery and analysis tasks and provide the foundation
for the optimization of those tasks.
| [
{
"created": "Thu, 10 Sep 2009 22:08:17 GMT",
"version": "v1"
}
] | 2016-09-08 | [
[
"Amer-Yahia",
"Sihem",
"",
"Yahoo! Research"
],
[
"Lakshmanan",
"Laks",
"",
"UBC"
],
[
"Yu",
"Cong",
"",
"Yahoo! Research"
]
] | Recently, many content sites have started encouraging their users to engage in social activities such as adding buddies on Yahoo! Travel and sharing articles with their friends on New York Times. This has led to the emergence of {\em social content sites}, which is being facilitated by initiatives like OpenID (http://www.openid.net/) and OpenSocial (http://www.opensocial.org/). These community standards enable the open access to users' social profiles and connections by individual content sites and are bringing content-oriented sites and social networking sites ever closer. The integration of content and social information raises new challenges for {\em information management and discovery} over such sites. We propose a logical architecture, named \kw{SocialScope}, consisting of three layers, for tackling the challenges. The {\em content management} layer is responsible for integrating, maintaining and physically accessing the content and social data. The {\em information discovery} layer takes care of analyzing content to derive interesting new information, and interpreting and processing the user's information need to identify relevant information. Finally, the {\em information presentation} layer explores the discovered information and helps users better understand it in a principled way. We describe the challenges in each layer and propose solutions for some of those challenges. In particular, we propose a uniform algebraic framework, which can be leveraged to uniformly and flexibly specify many of the information discovery and analysis tasks and provide the foundation for the optimization of those tasks. |
1608.00936 | Saad Nadeem | Saad Nadeem and Arie Kaufman | Multimodal Brain Visualization | SPIE Medical Imaging 2016, Proc. SPIE Medical Imaging: Biomedical
Applications in Molecular, Structural, and Functional Imaging, 2016 | SPIE Medical Imaging, pp. 97881Y-97881Y. 2016 | 10.1117/12.2217003 | null | cs.GR q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current connectivity diagrams of human brain image data are either overly
complex or overly simplistic. In this work we introduce simple yet accurate
interactive visual representations of multiple brain image structures and the
connectivity among them. We map cortical surfaces extracted from human brain
magnetic resonance imaging (MRI) data onto 2D surfaces that preserve shape
(angle), extent (area), and spatial (neighborhood) information for 2D (circular
disk) and 3D (spherical) mapping, split these surfaces into separate patches,
and cluster functional and diffusion tractography MRI connections between pairs
of these patches. The resulting visualizations are easier to compute on and
more visually intuitive to interact with than the original data, and facilitate
simultaneous exploration of multiple data sets, modalities, and statistical
maps.
| [
{
"created": "Tue, 2 Aug 2016 19:02:40 GMT",
"version": "v1"
},
{
"created": "Sat, 6 Aug 2016 17:01:31 GMT",
"version": "v2"
},
{
"created": "Tue, 9 Aug 2016 19:55:31 GMT",
"version": "v3"
},
{
"created": "Thu, 1 Sep 2016 14:51:28 GMT",
"version": "v4"
}
] | 2016-09-02 | [
[
"Nadeem",
"Saad",
""
],
[
"Kaufman",
"Arie",
""
]
] | Current connectivity diagrams of human brain image data are either overly complex or overly simplistic. In this work we introduce simple yet accurate interactive visual representations of multiple brain image structures and the connectivity among them. We map cortical surfaces extracted from human brain magnetic resonance imaging (MRI) data onto 2D surfaces that preserve shape (angle), extent (area), and spatial (neighborhood) information for 2D (circular disk) and 3D (spherical) mapping, split these surfaces into separate patches, and cluster functional and diffusion tractography MRI connections between pairs of these patches. The resulting visualizations are easier to compute on and more visually intuitive to interact with than the original data, and facilitate simultaneous exploration of multiple data sets, modalities, and statistical maps. |
1911.12525 | Min Ye | Min Ye | New constructions of cooperative MSR codes: Reducing node size to
$\exp(O(n))$ | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of multiple-node repair in distributed storage
systems under the cooperative model, where the repair bandwidth includes the
amount of data exchanged between any two different storage nodes. Recently,
explicit constructions of MDS codes with optimal cooperative repair bandwidth
for all possible parameters were given by Ye and Barg (IEEE Transactions on
Information Theory, 2019). The node size (or sub-packetization) in this
construction scales as $\exp(\Theta(n^h))$, where $h$ is the number of failed
nodes and $n$ is the code length.
In this paper, we give new explicit constructions of optimal MDS codes for
all possible parameters under the cooperative model, and the node size of our
new constructions only scales as $\exp(O(n))$ for any number of failed nodes.
Furthermore, it is known that any optimal MDS code under the cooperative model
(including, in particular, our new code construction) also achieves optimal
repair bandwidth under the centralized model, where the amount of data
exchanged between failed nodes is not included in the repair bandwidth. We
further show that the node size of our new construction is also much smaller
than that of the best known MDS code constructions for the centralized model.
| [
{
"created": "Thu, 28 Nov 2019 04:41:10 GMT",
"version": "v1"
}
] | 2019-12-02 | [
[
"Ye",
"Min",
""
]
] | We consider the problem of multiple-node repair in distributed storage systems under the cooperative model, where the repair bandwidth includes the amount of data exchanged between any two different storage nodes. Recently, explicit constructions of MDS codes with optimal cooperative repair bandwidth for all possible parameters were given by Ye and Barg (IEEE Transactions on Information Theory, 2019). The node size (or sub-packetization) in this construction scales as $\exp(\Theta(n^h))$, where $h$ is the number of failed nodes and $n$ is the code length. In this paper, we give new explicit constructions of optimal MDS codes for all possible parameters under the cooperative model, and the node size of our new constructions only scales as $\exp(O(n))$ for any number of failed nodes. Furthermore, it is known that any optimal MDS code under the cooperative model (including, in particular, our new code construction) also achieves optimal repair bandwidth under the centralized model, where the amount of data exchanged between failed nodes is not included in the repair bandwidth. We further show that the node size of our new construction is also much smaller than that of the best known MDS code constructions for the centralized model. |
2101.09333 | Shenjie Huang | Shenjie Huang and Majid Safari | SPAD-Based Optical Wireless Communication with Signal Pre-Distortion and
Noise Normalization | null | null | null | null | cs.IT math.IT | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In recent years, there has been a growing interest in exploring the
application of single-photon avalanche diode (SPAD) in optical wireless
communication (OWC). As a photon counting detector, SPAD can provide much
higher sensitivity compared to the other commonly used photodetectors. However,
SPAD-based receivers suffer from significant dead-time-induced non-linear
distortion and signal dependent noise. In this work, we propose a novel
SPAD-based OWC system in which the non-linear distortion caused by dead time
can be successfully eliminated by the pre-distortion of the signal at the
transmitter. In addition, another system with joint pre-distortion and noise
normalization functionality is proposed. Thanks to the additional noise
normalization process, for the transformed signal at the receiver, the
originally signal dependent noise becomes signal independent so that the
conventional signal detection techniques designed for AWGN channels can be
employed to decode the signal. Our numerical results demonstrate the
superiority of the proposed SPAD-based systems compared to the existing systems
in terms of BER performance and achievable data rate.
| [
{
"created": "Fri, 22 Jan 2021 21:11:27 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Feb 2022 22:36:20 GMT",
"version": "v2"
}
] | 2022-02-14 | [
[
"Huang",
"Shenjie",
""
],
[
"Safari",
"Majid",
""
]
] | In recent years, there has been a growing interest in exploring the application of single-photon avalanche diode (SPAD) in optical wireless communication (OWC). As a photon counting detector, SPAD can provide much higher sensitivity compared to the other commonly used photodetectors. However, SPAD-based receivers suffer from significant dead-time-induced non-linear distortion and signal dependent noise. In this work, we propose a novel SPAD-based OWC system in which the non-linear distortion caused by dead time can be successfully eliminated by the pre-distortion of the signal at the transmitter. In addition, another system with joint pre-distortion and noise normalization functionality is proposed. Thanks to the additional noise normalization process, for the transformed signal at the receiver, the originally signal dependent noise becomes signal independent so that the conventional signal detection techniques designed for AWGN channels can be employed to decode the signal. Our numerical results demonstrate the superiority of the proposed SPAD-based systems compared to the existing systems in terms of BER performance and achievable data rate. |
1602.04878 | Clayton Davis | Clayton A Davis, Julia Heiman, Erick Janssen, Stephanie Sanders,
Justin Garcia, Filippo Menczer | Kinsey Reporter: Citizen Science for Sex Research | Let's Talk About Sex (Apps) Workshop at CSCW 2015 | null | null | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Kinsey Reporter is a global mobile app to share, explore, and visualize
anonymous data about sex. Reports are submitted via smartphone, then visualized
on a website or downloaded for offline analysis. In this paper we present the
major features of the Kinsey Reporter citizen science platform designed to
preserve the anonymity of its contributors, and preliminary data analyses that
suggest questions for future research.
| [
{
"created": "Tue, 16 Feb 2016 01:07:32 GMT",
"version": "v1"
}
] | 2016-02-17 | [
[
"Davis",
"Clayton A",
""
],
[
"Heiman",
"Julia",
""
],
[
"Janssen",
"Erick",
""
],
[
"Sanders",
"Stephanie",
""
],
[
"Garcia",
"Justin",
""
],
[
"Menczer",
"Filippo",
""
]
] | Kinsey Reporter is a global mobile app to share, explore, and visualize anonymous data about sex. Reports are submitted via smartphone, then visualized on a website or downloaded for offline analysis. In this paper we present the major features of the Kinsey Reporter citizen science platform designed to preserve the anonymity of its contributors, and preliminary data analyses that suggest questions for future research. |
2304.10391 | Avital Boruchovsky | Avital Boruchovsky, Daniella Bar-Lev and Eitan Yaakobi | DNA-Correcting Codes: End-to-end Correction in DNA Storage Systems | Extended version of the paper that appeared in ISIT 2023 | null | null | null | cs.IT math.IT | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper introduces a new solution to DNA storage that integrates all three
steps of retrieval, namely clustering, reconstruction, and error correction.
DNA-correcting codes are presented as a unique solution to the problem of
ensuring that the output of the storage system is unique for any valid set of
input strands. To this end, we introduce a novel distance metric to capture the
unique behavior of the DNA storage system and provide necessary and sufficient
conditions for DNA-correcting codes. The paper also includes several bounds and
constructions of DNA-correcting codes.
| [
{
"created": "Thu, 20 Apr 2023 15:27:14 GMT",
"version": "v1"
},
{
"created": "Sun, 30 Jun 2024 10:56:10 GMT",
"version": "v2"
}
] | 2024-07-02 | [
[
"Boruchovsky",
"Avital",
""
],
[
"Bar-Lev",
"Daniella",
""
],
[
"Yaakobi",
"Eitan",
""
]
] | This paper introduces a new solution to DNA storage that integrates all three steps of retrieval, namely clustering, reconstruction, and error correction. DNA-correcting codes are presented as a unique solution to the problem of ensuring that the output of the storage system is unique for any valid set of input strands. To this end, we introduce a novel distance metric to capture the unique behavior of the DNA storage system and provide necessary and sufficient conditions for DNA-correcting codes. The paper also includes several bounds and constructions of DNA-correcting codes. |
2306.01346 | Beatriz Soret | Beatriz Soret, Israel Leyva-Mayorga, Federico Lozano-Cuadra, and
Mathias D. Thorsager | Q-learning for distributed routing in LEO satellite constellations | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | End-to-end routing in Low Earth Orbit (LEO) satellite constellations (LSatCs)
is a complex and dynamic problem. The topology, of finite size, is dynamic and
predictable, the traffic from/to Earth and transiting the space segment is
highly imbalanced, and the delay is dominated by the propagation time in
non-congested routes and by the queueing time at Inter-Satellite Links (ISLs)
in congested routes. Traditional routing algorithms depend on excessive
communication with ground or other satellites, and oversimplify the
characterization of the path links towards the destination. We model the
problem as a multi-agent Partially Observable Markov Decision Problem (POMDP)
where the nodes (i.e., the satellites) interact only with nearby nodes. We
propose a distributed Q-learning solution that leverages on the knowledge of
the neighbours and the correlation of the routing decisions of each node. We
compare our results to two centralized algorithms based on the shortest path:
one aiming at using the highest data rate links and a second genie algorithm
that knows the instantaneous queueing delays at all satellites. The results of
our proposal are positive on every front: (1) it experiences delays that are
comparable to the benchmarks in steady-state conditions; (2) it increases the
supported traffic load without congestion; and (3) it can be easily implemented
in a LSatC as it does not depend on the ground segment and minimizes the
signaling overhead among satellites.
| [
{
"created": "Fri, 2 Jun 2023 08:18:43 GMT",
"version": "v1"
}
] | 2023-06-05 | [
[
"Soret",
"Beatriz",
""
],
[
"Leyva-Mayorga",
"Israel",
""
],
[
"Lozano-Cuadra",
"Federico",
""
],
[
"Thorsager",
"Mathias D.",
""
]
] | End-to-end routing in Low Earth Orbit (LEO) satellite constellations (LSatCs) is a complex and dynamic problem. The topology, of finite size, is dynamic and predictable, the traffic from/to Earth and transiting the space segment is highly imbalanced, and the delay is dominated by the propagation time in non-congested routes and by the queueing time at Inter-Satellite Links (ISLs) in congested routes. Traditional routing algorithms depend on excessive communication with ground or other satellites, and oversimplify the characterization of the path links towards the destination. We model the problem as a multi-agent Partially Observable Markov Decision Problem (POMDP) where the nodes (i.e., the satellites) interact only with nearby nodes. We propose a distributed Q-learning solution that leverages on the knowledge of the neighbours and the correlation of the routing decisions of each node. We compare our results to two centralized algorithms based on the shortest path: one aiming at using the highest data rate links and a second genie algorithm that knows the instantaneous queueing delays at all satellites. The results of our proposal are positive on every front: (1) it experiences delays that are comparable to the benchmarks in steady-state conditions; (2) it increases the supported traffic load without congestion; and (3) it can be easily implemented in a LSatC as it does not depend on the ground segment and minimizes the signaling overhead among satellites. |
1906.01010 | Glorianna Jagfeld | Glorianna Jagfeld | A computational linguistic study of personal recovery in bipolar
disorder | ACL Student Research Workshop 2019, research proposal | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mental health research can benefit increasingly fruitfully from computational
linguistics methods, given the abundant availability of language data in the
internet and advances of computational tools. This interdisciplinary project
will collect and analyse social media data of individuals diagnosed with
bipolar disorder with regard to their recovery experiences. Personal recovery -
living a satisfying and contributing life along symptoms of severe mental
health issues - so far has only been investigated qualitatively with structured
interviews and quantitatively with standardised questionnaires with mainly
English-speaking participants in Western countries. Complementary to this
evidence, computational linguistic methods allow us to analyse first-person
accounts shared online in large quantities, representing unstructured settings
and a more heterogeneous, multilingual population, to draw a more complete
picture of the aspects and mechanisms of personal recovery in bipolar disorder.
| [
{
"created": "Mon, 3 Jun 2019 18:17:09 GMT",
"version": "v1"
}
] | 2019-06-05 | [
[
"Jagfeld",
"Glorianna",
""
]
] | Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools. This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences. Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants in Western countries. Complementary to this evidence, computational linguistic methods allow us to analyse first-person accounts shared online in large quantities, representing unstructured settings and a more heterogeneous, multilingual population, to draw a more complete picture of the aspects and mechanisms of personal recovery in bipolar disorder. |
2008.02840 | Siddharth Reddy | Siddharth Reddy, Sergey Levine, Anca D. Dragan | Assisted Perception: Optimizing Observations to Communicate State | null | null | null | null | cs.LG cs.HC cs.RO stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We aim to help users estimate the state of the world in tasks like robotic
teleoperation and navigation with visual impairments, where users may have
systematic biases that lead to suboptimal behavior: they might struggle to
process observations from multiple sensors simultaneously, receive delayed
observations, or overestimate distances to obstacles. While we cannot directly
change the user's internal beliefs or their internal state estimation process,
our insight is that we can still assist them by modifying the user's
observations. Instead of showing the user their true observations, we
synthesize new observations that lead to more accurate internal state estimates
when processed by the user. We refer to this method as assistive state
estimation (ASE): an automated assistant uses the true observations to infer
the state of the world, then generates a modified observation for the user to
consume (e.g., through an augmented reality interface), and optimizes the
modification to induce the user's new beliefs to match the assistant's current
beliefs. We evaluate ASE in a user study with 12 participants who each perform
four tasks: two tasks with known user biases -- bandwidth-limited image
classification and a driving video game with observation delay -- and two with
unknown biases that our method has to learn -- guided 2D navigation and a lunar
lander teleoperation video game. A different assistance strategy emerges in
each domain, such as quickly revealing informative pixels to speed up image
classification, using a dynamics model to undo observation delay in driving,
identifying nearby landmarks for navigation, and exaggerating a visual
indicator of tilt in the lander game. The results show that ASE substantially
improves the task performance of users with bandwidth constraints, observation
delay, and other unknown biases.
| [
{
"created": "Thu, 6 Aug 2020 19:08:05 GMT",
"version": "v1"
}
] | 2020-08-10 | [
[
"Reddy",
"Siddharth",
""
],
[
"Levine",
"Sergey",
""
],
[
"Dragan",
"Anca D.",
""
]
] | We aim to help users estimate the state of the world in tasks like robotic teleoperation and navigation with visual impairments, where users may have systematic biases that lead to suboptimal behavior: they might struggle to process observations from multiple sensors simultaneously, receive delayed observations, or overestimate distances to obstacles. While we cannot directly change the user's internal beliefs or their internal state estimation process, our insight is that we can still assist them by modifying the user's observations. Instead of showing the user their true observations, we synthesize new observations that lead to more accurate internal state estimates when processed by the user. We refer to this method as assistive state estimation (ASE): an automated assistant uses the true observations to infer the state of the world, then generates a modified observation for the user to consume (e.g., through an augmented reality interface), and optimizes the modification to induce the user's new beliefs to match the assistant's current beliefs. We evaluate ASE in a user study with 12 participants who each perform four tasks: two tasks with known user biases -- bandwidth-limited image classification and a driving video game with observation delay -- and two with unknown biases that our method has to learn -- guided 2D navigation and a lunar lander teleoperation video game. A different assistance strategy emerges in each domain, such as quickly revealing informative pixels to speed up image classification, using a dynamics model to undo observation delay in driving, identifying nearby landmarks for navigation, and exaggerating a visual indicator of tilt in the lander game. The results show that ASE substantially improves the task performance of users with bandwidth constraints, observation delay, and other unknown biases. |
2310.19360 | Yifei Wang | Yifei Wang, Liangchen Li, Jiansheng Yang, Zhouchen Lin, Yisen Wang | Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from
a Minimax Game Perspective | Accepted by NeurIPS 2023 | null | null | null | cs.LG cs.AI cs.CV stat.ML | http://creativecommons.org/licenses/by/4.0/ | Adversarial Training (AT) has become arguably the state-of-the-art algorithm
for extracting robust features. However, researchers recently notice that AT
suffers from severe robust overfitting problems, particularly after learning
rate (LR) decay. In this paper, we explain this phenomenon by viewing
adversarial training as a dynamic minimax game between the model trainer and
the attacker. Specifically, we analyze how LR decay breaks the balance between
the minimax game by empowering the trainer with a stronger memorization
ability, and show such imbalance induces robust overfitting as a result of
memorizing non-robust features. We validate this understanding with extensive
experiments, and provide a holistic view of robust overfitting from the
dynamics of both the two game players. This understanding further inspires us
to alleviate robust overfitting by rebalancing the two players by either
regularizing the trainer's capacity or improving the attack strength.
Experiments show that the proposed ReBalanced Adversarial Training (ReBAT) can
attain good robustness and does not suffer from robust overfitting even after
very long training. Code is available at https://github.com/PKU-ML/ReBAT.
| [
{
"created": "Mon, 30 Oct 2023 09:00:11 GMT",
"version": "v1"
}
] | 2023-10-31 | [
[
"Wang",
"Yifei",
""
],
[
"Li",
"Liangchen",
""
],
[
"Yang",
"Jiansheng",
""
],
[
"Lin",
"Zhouchen",
""
],
[
"Wang",
"Yisen",
""
]
] | Adversarial Training (AT) has become arguably the state-of-the-art algorithm for extracting robust features. However, researchers recently notice that AT suffers from severe robust overfitting problems, particularly after learning rate (LR) decay. In this paper, we explain this phenomenon by viewing adversarial training as a dynamic minimax game between the model trainer and the attacker. Specifically, we analyze how LR decay breaks the balance between the minimax game by empowering the trainer with a stronger memorization ability, and show such imbalance induces robust overfitting as a result of memorizing non-robust features. We validate this understanding with extensive experiments, and provide a holistic view of robust overfitting from the dynamics of both the two game players. This understanding further inspires us to alleviate robust overfitting by rebalancing the two players by either regularizing the trainer's capacity or improving the attack strength. Experiments show that the proposed ReBalanced Adversarial Training (ReBAT) can attain good robustness and does not suffer from robust overfitting even after very long training. Code is available at https://github.com/PKU-ML/ReBAT. |
1810.04650 | Ozan Sener | Ozan Sener, Vladlen Koltun | Multi-Task Learning as Multi-Objective Optimization | In Neural Information Processing Systems (NeurIPS) 2018 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In multi-task learning, multiple tasks are solved jointly, sharing inductive
bias between them. Multi-task learning is inherently a multi-objective problem
because different tasks may conflict, necessitating a trade-off. A common
compromise is to optimize a proxy objective that minimizes a weighted linear
combination of per-task losses. However, this workaround is only valid when the
tasks do not compete, which is rarely the case. In this paper, we explicitly
cast multi-task learning as multi-objective optimization, with the overall
objective of finding a Pareto optimal solution. To this end, we use algorithms
developed in the gradient-based multi-objective optimization literature. These
algorithms are not directly applicable to large-scale learning problems since
they scale poorly with the dimensionality of the gradients and the number of
tasks. We therefore propose an upper bound for the multi-objective loss and
show that it can be optimized efficiently. We further prove that optimizing
this upper bound yields a Pareto optimal solution under realistic assumptions.
We apply our method to a variety of multi-task deep learning problems including
digit classification, scene understanding (joint semantic segmentation,
instance segmentation, and depth estimation), and multi-label classification.
Our method produces higher-performing models than recent multi-task learning
formulations or per-task training.
| [
{
"created": "Wed, 10 Oct 2018 17:18:09 GMT",
"version": "v1"
},
{
"created": "Fri, 11 Jan 2019 12:57:32 GMT",
"version": "v2"
}
] | 2019-01-14 | [
[
"Sener",
"Ozan",
""
],
[
"Koltun",
"Vladlen",
""
]
] | In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them. Multi-task learning is inherently a multi-objective problem because different tasks may conflict, necessitating a trade-off. A common compromise is to optimize a proxy objective that minimizes a weighted linear combination of per-task losses. However, this workaround is only valid when the tasks do not compete, which is rarely the case. In this paper, we explicitly cast multi-task learning as multi-objective optimization, with the overall objective of finding a Pareto optimal solution. To this end, we use algorithms developed in the gradient-based multi-objective optimization literature. These algorithms are not directly applicable to large-scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks. We therefore propose an upper bound for the multi-objective loss and show that it can be optimized efficiently. We further prove that optimizing this upper bound yields a Pareto optimal solution under realistic assumptions. We apply our method to a variety of multi-task deep learning problems including digit classification, scene understanding (joint semantic segmentation, instance segmentation, and depth estimation), and multi-label classification. Our method produces higher-performing models than recent multi-task learning formulations or per-task training. |
2005.05276 | Raoul Heese | Raoul Heese, Lukas Morand, Dirk Helm, Michael Bortz | CupNet -- Pruning a network for geometric data | 4 pages, 2 figures, 1 table | Artificial Neural Networks and Machine Learning - ICANN 2021, pp
29-33 | 10.1007/978-3-030-86380-7_3 | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using data from a simulated cup drawing process, we demonstrate how the
inherent geometrical structure of cup meshes can be used to effectively prune
an artificial neural network in a straightforward way.
| [
{
"created": "Mon, 11 May 2020 17:21:23 GMT",
"version": "v1"
},
{
"created": "Mon, 13 Sep 2021 13:37:31 GMT",
"version": "v2"
}
] | 2021-09-14 | [
[
"Heese",
"Raoul",
""
],
[
"Morand",
"Lukas",
""
],
[
"Helm",
"Dirk",
""
],
[
"Bortz",
"Michael",
""
]
] | Using data from a simulated cup drawing process, we demonstrate how the inherent geometrical structure of cup meshes can be used to effectively prune an artificial neural network in a straightforward way. |
1904.11580 | Matthias Kahl | Matthias Kahl, Thomas Kriechbaumer, Daniel Jorde, Anwar Ul Haq and
Hans-Arno Jacobsen | Appliance Event Detection -- A Multivariate, Supervised Classification
Approach | null | null | null | null | cs.OH cs.SY eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Non-intrusive load monitoring (NILM) is a modern and still expanding
technique, helping to understand fundamental energy consumption patterns and
appliance characteristics. Appliance event detection is an elementary step in
the NILM pipeline. Unfortunately, several types of appliances (e.g., switching
mode power supply (SMPS) or multi-state) are known to challenge
state-of-the-art event detection systems due to their noisy consumption
profiles. Classical rule-based event detection system become infeasible and
complex for these appliances. By stepping away from distinct event definitions,
we can learn from a consumer-configured event model to differentiate between
relevant and irrelevant event transients.
We introduce a boosting oriented adaptive training, that uses false positives
from the initial training area to reduce the number of false positives on the
test area substantially. The results show a false positive decrease by more
than a factor of eight on a dataset that has a strong focus on SMPS-driven
appliances. To obtain a stable event detection system, we applied several
experiments on different parameters to measure its performance. These
experiments include the evaluation of six event features from the spectral and
time domain, different types of feature space normalization to eliminate
undesired feature weighting, the conventional and adaptive training, and two
common classifiers with its optimal parameter settings. The evaluations are
performed on two publicly available energy datasets with high sampling rates:
BLUED and BLOND-50.
| [
{
"created": "Wed, 24 Apr 2019 15:17:55 GMT",
"version": "v1"
}
] | 2019-04-29 | [
[
"Kahl",
"Matthias",
""
],
[
"Kriechbaumer",
"Thomas",
""
],
[
"Jorde",
"Daniel",
""
],
[
"Haq",
"Anwar Ul",
""
],
[
"Jacobsen",
"Hans-Arno",
""
]
] | Non-intrusive load monitoring (NILM) is a modern and still expanding technique, helping to understand fundamental energy consumption patterns and appliance characteristics. Appliance event detection is an elementary step in the NILM pipeline. Unfortunately, several types of appliances (e.g., switching mode power supply (SMPS) or multi-state) are known to challenge state-of-the-art event detection systems due to their noisy consumption profiles. Classical rule-based event detection system become infeasible and complex for these appliances. By stepping away from distinct event definitions, we can learn from a consumer-configured event model to differentiate between relevant and irrelevant event transients. We introduce a boosting oriented adaptive training, that uses false positives from the initial training area to reduce the number of false positives on the test area substantially. The results show a false positive decrease by more than a factor of eight on a dataset that has a strong focus on SMPS-driven appliances. To obtain a stable event detection system, we applied several experiments on different parameters to measure its performance. These experiments include the evaluation of six event features from the spectral and time domain, different types of feature space normalization to eliminate undesired feature weighting, the conventional and adaptive training, and two common classifiers with its optimal parameter settings. The evaluations are performed on two publicly available energy datasets with high sampling rates: BLUED and BLOND-50. |
2001.05609 | Silei Xu | Silei Xu, Giovanni Campagna, Jian Li and Monica S. Lam | Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured Web | null | null | 10.1145/3340531.3411974 | null | cs.CL | http://creativecommons.org/licenses/by-sa/4.0/ | Building a question-answering agent currently requires large annotated
datasets, which are prohibitively expensive. This paper proposes Schema2QA, an
open-source toolkit that can generate a Q&A system from a database schema
augmented with a few annotations for each field. The key concept is to cover
the space of possible compound queries on the database with a large number of
in-domain questions synthesized with the help of a corpus of generic query
templates. The synthesized data and a small paraphrase set are used to train a
novel neural network based on the BERT pretrained model. We use Schema2QA to
generate Q&A systems for five Schema.org domains, restaurants, people, movies,
books and music, and obtain an overall accuracy between 64% and 75% on
crowdsourced questions for these domains. Once annotations and paraphrases are
obtained for a Schema.org schema, no additional manual effort is needed to
create a Q&A agent for any website that uses the same schema. Furthermore, we
demonstrate that learning can be transferred from the restaurant to the hotel
domain, obtaining a 64% accuracy on crowdsourced questions with no manual
effort. Schema2QA achieves an accuracy of 60% on popular restaurant questions
that can be answered using Schema.org. Its performance is comparable to Google
Assistant, 7% lower than Siri, and 15% higher than Alexa. It outperforms all
these assistants by at least 18% on more complex, long-tail questions.
| [
{
"created": "Thu, 16 Jan 2020 01:49:16 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Jan 2020 20:32:49 GMT",
"version": "v2"
},
{
"created": "Sun, 17 May 2020 19:44:06 GMT",
"version": "v3"
},
{
"created": "Tue, 19 May 2020 17:13:27 GMT",
"version": "v4"
},
{
"created": "Mon, 24 Aug 2020 21:35:26 GMT",
"version": "v5"
},
{
"created": "Tue, 8 Jun 2021 01:30:11 GMT",
"version": "v6"
}
] | 2023-05-03 | [
[
"Xu",
"Silei",
""
],
[
"Campagna",
"Giovanni",
""
],
[
"Li",
"Jian",
""
],
[
"Lam",
"Monica S.",
""
]
] | Building a question-answering agent currently requires large annotated datasets, which are prohibitively expensive. This paper proposes Schema2QA, an open-source toolkit that can generate a Q&A system from a database schema augmented with a few annotations for each field. The key concept is to cover the space of possible compound queries on the database with a large number of in-domain questions synthesized with the help of a corpus of generic query templates. The synthesized data and a small paraphrase set are used to train a novel neural network based on the BERT pretrained model. We use Schema2QA to generate Q&A systems for five Schema.org domains, restaurants, people, movies, books and music, and obtain an overall accuracy between 64% and 75% on crowdsourced questions for these domains. Once annotations and paraphrases are obtained for a Schema.org schema, no additional manual effort is needed to create a Q&A agent for any website that uses the same schema. Furthermore, we demonstrate that learning can be transferred from the restaurant to the hotel domain, obtaining a 64% accuracy on crowdsourced questions with no manual effort. Schema2QA achieves an accuracy of 60% on popular restaurant questions that can be answered using Schema.org. Its performance is comparable to Google Assistant, 7% lower than Siri, and 15% higher than Alexa. It outperforms all these assistants by at least 18% on more complex, long-tail questions. |
2110.05183 | Chuanting Zhang | Chuanting Zhang, Shuping Dang, Basem Shihada, Mohamed-Slim Alouini | Dual Attention-Based Federated Learning for Wireless Traffic Prediction | IEEE INFOCOM 2021 - IEEE Conference on Computer Communications | null | 10.1109/INFOCOM42981.2021.9488883 | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wireless traffic prediction is essential for cellular networks to realize
intelligent network operations, such as load-aware resource management and
predictive control. Existing prediction approaches usually adopt centralized
training architectures and require the transferring of huge amounts of traffic
data, which may raise delay and privacy concerns for certain scenarios. In this
work, we propose a novel wireless traffic prediction framework named
\textit{Dual Attention-Based Federated Learning} (FedDA), by which a
high-quality prediction model is trained collaboratively by multiple edge
clients. To simultaneously capture the various wireless traffic patterns and
keep raw data locally, FedDA first groups the clients into different clusters
by using a small augmentation dataset. Then, a quasi-global model is trained
and shared among clients as prior knowledge, aiming to solve the statistical
heterogeneity challenge confronted with federated learning. To construct the
global model, a dual attention scheme is further proposed by aggregating the
intra- and inter-cluster models, instead of simply averaging the weights of
local models. We conduct extensive experiments on two real-world wireless
traffic datasets and results show that FedDA outperforms state-of-the-art
methods. The average mean squared error performance gains on the two datasets
are up to 10\% and 30\%, respectively.
| [
{
"created": "Mon, 11 Oct 2021 12:00:21 GMT",
"version": "v1"
}
] | 2021-10-12 | [
[
"Zhang",
"Chuanting",
""
],
[
"Dang",
"Shuping",
""
],
[
"Shihada",
"Basem",
""
],
[
"Alouini",
"Mohamed-Slim",
""
]
] | Wireless traffic prediction is essential for cellular networks to realize intelligent network operations, such as load-aware resource management and predictive control. Existing prediction approaches usually adopt centralized training architectures and require the transferring of huge amounts of traffic data, which may raise delay and privacy concerns for certain scenarios. In this work, we propose a novel wireless traffic prediction framework named \textit{Dual Attention-Based Federated Learning} (FedDA), by which a high-quality prediction model is trained collaboratively by multiple edge clients. To simultaneously capture the various wireless traffic patterns and keep raw data locally, FedDA first groups the clients into different clusters by using a small augmentation dataset. Then, a quasi-global model is trained and shared among clients as prior knowledge, aiming to solve the statistical heterogeneity challenge confronted with federated learning. To construct the global model, a dual attention scheme is further proposed by aggregating the intra- and inter-cluster models, instead of simply averaging the weights of local models. We conduct extensive experiments on two real-world wireless traffic datasets and results show that FedDA outperforms state-of-the-art methods. The average mean squared error performance gains on the two datasets are up to 10\% and 30\%, respectively. |
2405.18375 | Phakphum Artkaew | Phakphum Artkaew | Thai Winograd Schemas: A Benchmark for Thai Commonsense Reasoning | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Commonsense reasoning is one of the important aspect of natural language
understanding, with several benchmarks developed to evaluate it. However, only
a few of these benchmarks are available in languages other than English.
Developing parallel benchmarks facilitates cross-lingual evaluation, enabling a
better understanding of different languages. This research introduces a
collection of Winograd Schemas in Thai, a novel dataset designed to evaluate
commonsense reasoning capabilities in the context of the Thai language.
Through a methodology involving native speakers, professional translators,
and thorough validation, the schemas aim to closely reflect Thai language
nuances, idioms, and cultural references while maintaining ambiguity and
commonsense challenges. We evaluate the performance of popular large language
models on this benchmark, revealing their strengths, limitations, and providing
insights into the current state-of-the-art. Results indicate that while models
like GPT-4 and Claude-3-Opus achieve high accuracy in English, their
performance significantly drops in Thai, highlighting the need for further
advancements in multilingual commonsense reasoning.
| [
{
"created": "Tue, 28 May 2024 17:14:02 GMT",
"version": "v1"
}
] | 2024-05-29 | [
[
"Artkaew",
"Phakphum",
""
]
] | Commonsense reasoning is one of the important aspect of natural language understanding, with several benchmarks developed to evaluate it. However, only a few of these benchmarks are available in languages other than English. Developing parallel benchmarks facilitates cross-lingual evaluation, enabling a better understanding of different languages. This research introduces a collection of Winograd Schemas in Thai, a novel dataset designed to evaluate commonsense reasoning capabilities in the context of the Thai language. Through a methodology involving native speakers, professional translators, and thorough validation, the schemas aim to closely reflect Thai language nuances, idioms, and cultural references while maintaining ambiguity and commonsense challenges. We evaluate the performance of popular large language models on this benchmark, revealing their strengths, limitations, and providing insights into the current state-of-the-art. Results indicate that while models like GPT-4 and Claude-3-Opus achieve high accuracy in English, their performance significantly drops in Thai, highlighting the need for further advancements in multilingual commonsense reasoning. |
1611.02806 | Yu Wang | Yu Wang and Yang Feng and Xiyang Zhang and Jiebo Luo | Gender Politics in the 2016 U.S. Presidential Election: A Computer
Vision Approach | null | null | null | null | cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gender is playing an important role in the 2016 U.S. presidential election,
especially with Hillary Clinton becoming the first female presidential nominee
and Donald Trump being frequently accused of sexism. In this paper, we
introduce computer vision to the study of gender politics and present an
image-driven method that can measure the effects of gender in an accurate and
timely manner. We first collect all the profile images of the candidates'
Twitter followers. Then we train a convolutional neural network using images
that contain gender labels. Lastly, we classify all the follower and unfollower
images. Through two case studies, one on the `woman card' controversy and one
on Sanders followers, we demonstrate how gender is informing the 2016
presidential election. Our framework of analysis can be readily generalized to
other case studies and elections.
| [
{
"created": "Wed, 9 Nov 2016 03:42:13 GMT",
"version": "v1"
}
] | 2016-11-10 | [
[
"Wang",
"Yu",
""
],
[
"Feng",
"Yang",
""
],
[
"Zhang",
"Xiyang",
""
],
[
"Luo",
"Jiebo",
""
]
] | Gender is playing an important role in the 2016 U.S. presidential election, especially with Hillary Clinton becoming the first female presidential nominee and Donald Trump being frequently accused of sexism. In this paper, we introduce computer vision to the study of gender politics and present an image-driven method that can measure the effects of gender in an accurate and timely manner. We first collect all the profile images of the candidates' Twitter followers. Then we train a convolutional neural network using images that contain gender labels. Lastly, we classify all the follower and unfollower images. Through two case studies, one on the `woman card' controversy and one on Sanders followers, we demonstrate how gender is informing the 2016 presidential election. Our framework of analysis can be readily generalized to other case studies and elections. |
cs/9907012 | Guido Minnen | Guido Minnen (University of Sussex) | Selective Magic HPSG Parsing | 9 pages, LaTeX with 4 postscript figures (uses avm.sty, eaclap.sty
and psfig-scale.sty) | Proceedings of EACL99, Bergen, Norway, June 8-11 | null | null | cs.CL | null | We propose a parser for constraint-logic grammars implementing HPSG that
combines the advantages of dynamic bottom-up and advanced top-down control. The
parser allows the user to apply magic compilation to specific constraints in a
grammar which as a result can be processed dynamically in a bottom-up and
goal-directed fashion. State of the art top-down processing techniques are used
to deal with the remaining constraints. We discuss various aspects concerning
the implementation of the parser as part of a grammar development system.
| [
{
"created": "Thu, 8 Jul 1999 09:46:37 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Minnen",
"Guido",
"",
"University of Sussex"
]
] | We propose a parser for constraint-logic grammars implementing HPSG that combines the advantages of dynamic bottom-up and advanced top-down control. The parser allows the user to apply magic compilation to specific constraints in a grammar which as a result can be processed dynamically in a bottom-up and goal-directed fashion. State of the art top-down processing techniques are used to deal with the remaining constraints. We discuss various aspects concerning the implementation of the parser as part of a grammar development system. |
2010.05953 | Jena Hwang | Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke
Sakaguchi, Antoine Bosselut, Yejin Choi | COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs | null | Proceedings of the AAAI Conference on Artificial Intelligence
(2021), 35(7), 6384-6392 | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Recent years have brought about a renewed interest in commonsense
representation and reasoning in the field of natural language understanding.
The development of new commonsense knowledge graphs (CSKG) has been central to
these advances as their diverse facts can be used and referenced by machine
learning models for tackling new and challenging tasks. At the same time, there
remain questions about the quality and coverage of these resources due to the
massive scale required to comprehensively encompass general commonsense
knowledge.
In this work, we posit that manually constructed CSKGs will never achieve the
coverage necessary to be applicable in all situations encountered by NLP
agents. Therefore, we propose a new evaluation framework for testing the
utility of KGs based on how effectively implicit knowledge representations can
be learned from them.
With this new goal, we propose ATOMIC 2020, a new CSKG of general-purpose
commonsense knowledge containing knowledge that is not readily available in
pretrained language models. We evaluate its properties in comparison with other
leading CSKGs, performing the first large-scale pairwise study of commonsense
knowledge resources. Next, we show that ATOMIC 2020 is better suited for
training knowledge models that can generate accurate, representative knowledge
for new, unseen entities and events. Finally, through human evaluation, we show
that the few-shot performance of GPT-3 (175B parameters), while impressive,
remains ~12 absolute points lower than a BART-based knowledge model trained on
ATOMIC 2020 despite using over 430x fewer parameters.
| [
{
"created": "Mon, 12 Oct 2020 18:27:05 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Dec 2021 18:57:18 GMT",
"version": "v2"
}
] | 2021-12-17 | [
[
"Hwang",
"Jena D.",
""
],
[
"Bhagavatula",
"Chandra",
""
],
[
"Bras",
"Ronan Le",
""
],
[
"Da",
"Jeff",
""
],
[
"Sakaguchi",
"Keisuke",
""
],
[
"Bosselut",
"Antoine",
""
],
[
"Choi",
"Yejin",
""
]
] | Recent years have brought about a renewed interest in commonsense representation and reasoning in the field of natural language understanding. The development of new commonsense knowledge graphs (CSKG) has been central to these advances as their diverse facts can be used and referenced by machine learning models for tackling new and challenging tasks. At the same time, there remain questions about the quality and coverage of these resources due to the massive scale required to comprehensively encompass general commonsense knowledge. In this work, we posit that manually constructed CSKGs will never achieve the coverage necessary to be applicable in all situations encountered by NLP agents. Therefore, we propose a new evaluation framework for testing the utility of KGs based on how effectively implicit knowledge representations can be learned from them. With this new goal, we propose ATOMIC 2020, a new CSKG of general-purpose commonsense knowledge containing knowledge that is not readily available in pretrained language models. We evaluate its properties in comparison with other leading CSKGs, performing the first large-scale pairwise study of commonsense knowledge resources. Next, we show that ATOMIC 2020 is better suited for training knowledge models that can generate accurate, representative knowledge for new, unseen entities and events. Finally, through human evaluation, we show that the few-shot performance of GPT-3 (175B parameters), while impressive, remains ~12 absolute points lower than a BART-based knowledge model trained on ATOMIC 2020 despite using over 430x fewer parameters. |
1809.10745 | Shafie Gholizadeh | Shafie Gholizadeh and Wlodek Zadrozny | A Short Survey of Topological Data Analysis in Time Series and Systems
Analysis | null | null | null | null | cs.IR cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Topological Data Analysis (TDA) is the collection of mathematical tools that
capture the structure of shapes in data. Despite computational topology and
computational geometry, the utilization of TDA in time series and signal
processing is relatively new. In some recent contributions, TDA has been
utilized as an alternative to the conventional signal processing methods.
Specifically, TDA is been considered to deal with noisy signals and time
series. In these applications, TDA is used to find the shapes in data as the
main properties, while the other properties are assumed much less informative.
In this paper, we will review recent developments and contributions where
topological data analysis especially persistent homology has been applied to
time series analysis, dynamical systems and signal processing. We will cover
problem statements such as stability determination, risk analysis, systems
behaviour, and predicting critical transitions in financial markets.
| [
{
"created": "Thu, 27 Sep 2018 19:53:16 GMT",
"version": "v1"
},
{
"created": "Sat, 20 Oct 2018 17:40:31 GMT",
"version": "v2"
}
] | 2018-10-23 | [
[
"Gholizadeh",
"Shafie",
""
],
[
"Zadrozny",
"Wlodek",
""
]
] | Topological Data Analysis (TDA) is the collection of mathematical tools that capture the structure of shapes in data. Despite computational topology and computational geometry, the utilization of TDA in time series and signal processing is relatively new. In some recent contributions, TDA has been utilized as an alternative to the conventional signal processing methods. Specifically, TDA is been considered to deal with noisy signals and time series. In these applications, TDA is used to find the shapes in data as the main properties, while the other properties are assumed much less informative. In this paper, we will review recent developments and contributions where topological data analysis especially persistent homology has been applied to time series analysis, dynamical systems and signal processing. We will cover problem statements such as stability determination, risk analysis, systems behaviour, and predicting critical transitions in financial markets. |
2002.06512 | Vinod Ganapathy | Rakesh Rajan Beck and Abhishek Vijeev and Vinod Ganapathy | Privaros: A Framework for Privacy-Compliant Delivery Drones | null | null | 10.1145/3372297.3417858 | null | cs.CR cs.OS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present Privaros, a framework to enforce privacy policies on drones.
Privaros is designed for commercial delivery drones, such as the ones that will
likely be used by Amazon Prime Air. Such drones visit a number of host
airspaces, each of which may have different privacy requirements. Privaros
provides an information flow control framework to enforce the policies of these
hosts on the guest delivery drones. The mechanisms in Privaros are built on top
of ROS, a middleware popular in many drone platforms. This paper presents the
design and implementation of these mechanisms, describes how policies are
specified, and shows that Privaros's policy specification can be integrated
with India's Digital Sky portal. Our evaluation shows that a drone running
Privaros can robustly enforce various privacy policies specified by hosts, and
that its core mechanisms only marginally increase communication latency and
power consumption.
| [
{
"created": "Sun, 16 Feb 2020 05:51:41 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Aug 2020 04:42:57 GMT",
"version": "v2"
},
{
"created": "Thu, 13 Aug 2020 18:00:46 GMT",
"version": "v3"
}
] | 2020-08-17 | [
[
"Beck",
"Rakesh Rajan",
""
],
[
"Vijeev",
"Abhishek",
""
],
[
"Ganapathy",
"Vinod",
""
]
] | We present Privaros, a framework to enforce privacy policies on drones. Privaros is designed for commercial delivery drones, such as the ones that will likely be used by Amazon Prime Air. Such drones visit a number of host airspaces, each of which may have different privacy requirements. Privaros provides an information flow control framework to enforce the policies of these hosts on the guest delivery drones. The mechanisms in Privaros are built on top of ROS, a middleware popular in many drone platforms. This paper presents the design and implementation of these mechanisms, describes how policies are specified, and shows that Privaros's policy specification can be integrated with India's Digital Sky portal. Our evaluation shows that a drone running Privaros can robustly enforce various privacy policies specified by hosts, and that its core mechanisms only marginally increase communication latency and power consumption. |
1301.0552 | Ionut Aron | Ionut Aron, Pascal Van Hentenryck | A constraint satisfaction approach to the robust spanning tree problem
with interval data | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-18-25 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robust optimization is one of the fundamental approaches to deal with
uncertainty in combinatorial optimization. This paper considers the robust
spanning tree problem with interval data, which arises in a variety of
telecommunication applications. It proposes a constraint satisfaction approach
using a combinatorial lower bound, a pruning component that removes infeasible
and suboptimal edges, as well as a search strategy exploring the most uncertain
edges first. The resulting algorithm is shown to produce very dramatic
improvements over the mathematical programming approach of Yaman et al. and to
enlarge considerably the class of problems amenable to effective solutions
| [
{
"created": "Wed, 12 Dec 2012 15:55:09 GMT",
"version": "v1"
}
] | 2013-01-07 | [
[
"Aron",
"Ionut",
""
],
[
"Van Hentenryck",
"Pascal",
""
]
] | Robust optimization is one of the fundamental approaches to deal with uncertainty in combinatorial optimization. This paper considers the robust spanning tree problem with interval data, which arises in a variety of telecommunication applications. It proposes a constraint satisfaction approach using a combinatorial lower bound, a pruning component that removes infeasible and suboptimal edges, as well as a search strategy exploring the most uncertain edges first. The resulting algorithm is shown to produce very dramatic improvements over the mathematical programming approach of Yaman et al. and to enlarge considerably the class of problems amenable to effective solutions |
2211.16891 | Lennart Reimann | Lennart M. Reimann, Sarp Erd\"onmez, Dominik Sisejkovic and Rainer
Leupers | Quantitative Information Flow for Hardware: Advancing the Attack
Landscape | 4 pages, accepted at IEEE Latin American Symposium on Circuits and
Systems (LASCAS), 2023 | null | null | null | cs.CR cs.AR | http://creativecommons.org/licenses/by/4.0/ | Security still remains an afterthought in modern Electronic Design Automation
(EDA) tools, which solely focus on enhancing performance and reducing the chip
size. Typically, the security analysis is conducted by hand, leading to
vulnerabilities in the design remaining unnoticed. Security-aware EDA tools
assist the designer in the identification and removal of security threats while
keeping performance and area in mind. State-of-the-art approaches utilize
information flow analysis to spot unintended information leakages in design
structures. However, the classification of such threats is binary, resulting in
negligible leakages being listed as well. A novel quantitative analysis allows
the application of a metric to determine a numeric value for a leakage.
Nonetheless, current approximations to quantify the leakage are still prone to
overlooking leakages. The mathematical model 2D-QModel introduced in this work
aims to overcome this shortcoming. Additionally, as previous work only includes
a limited threat model, multiple threat models can be applied using the
provided approach. Open-source benchmarks are used to show the capabilities of
2D-QModel to identify hardware Trojans in the design while ignoring
insignificant leakages.
| [
{
"created": "Wed, 30 Nov 2022 10:44:54 GMT",
"version": "v1"
}
] | 2022-12-01 | [
[
"Reimann",
"Lennart M.",
""
],
[
"Erdönmez",
"Sarp",
""
],
[
"Sisejkovic",
"Dominik",
""
],
[
"Leupers",
"Rainer",
""
]
] | Security still remains an afterthought in modern Electronic Design Automation (EDA) tools, which solely focus on enhancing performance and reducing the chip size. Typically, the security analysis is conducted by hand, leading to vulnerabilities in the design remaining unnoticed. Security-aware EDA tools assist the designer in the identification and removal of security threats while keeping performance and area in mind. State-of-the-art approaches utilize information flow analysis to spot unintended information leakages in design structures. However, the classification of such threats is binary, resulting in negligible leakages being listed as well. A novel quantitative analysis allows the application of a metric to determine a numeric value for a leakage. Nonetheless, current approximations to quantify the leakage are still prone to overlooking leakages. The mathematical model 2D-QModel introduced in this work aims to overcome this shortcoming. Additionally, as previous work only includes a limited threat model, multiple threat models can be applied using the provided approach. Open-source benchmarks are used to show the capabilities of 2D-QModel to identify hardware Trojans in the design while ignoring insignificant leakages. |
1910.03422 | Katja Tuma | Katja Tuma, Christian Sandberg, Urban Thorsson, Mathias Widman,
Riccardo Scandariato | Finding Security Threats That Matter: An Industrial Case Study | null | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent trends in the software engineering (i.e., Agile, DevOps) have
shortened the development life-cycle limiting resources spent on security
analysis of software designs. In this context, architecture models are (often
manually) analyzed for potential security threats. Risk-last threat analysis
suggests identifying all security threats before prioritizing them. In
contrast, risk-first threat analysis suggests identifying the risks before the
threats, by-passing threat prioritization. This seems promising for
organizations where developing speed is of great importance. Yet, little
empirical evidence exists about the effect of sacrificing systematicity for
high-priority threats on the performance and execution of threat analysis. To
this aim, we conduct a case study with industrial experts from the automotive
domain, where we empirically compare a risk-first technique to a risk-last
technique. In this study, we consciously trade the amount of participants for a
more realistic simulation of threat analysis sessions in practice. This allows
us to closely observe industrial experts and gain deep insights into the
industrial practice. This work contributes with: (i) a quantitative comparison
of performance, (ii) a quantitative and qualitative comparison of execution,
and (iii) a comparative discussion of the two techniques. We find no
differences in the productivity and timeliness of discovering high-priority
security threats. Yet, we find differences in analysis execution. In
particular, participants using the risk-first technique found twice as many
high-priority threats, developed detailed attack scenarios, and discussed
threat feasibility in detail. On the other hand, participants using the
risk-last technique found more medium and low-priority threats and finished
early.
| [
{
"created": "Tue, 8 Oct 2019 14:29:21 GMT",
"version": "v1"
}
] | 2019-10-09 | [
[
"Tuma",
"Katja",
""
],
[
"Sandberg",
"Christian",
""
],
[
"Thorsson",
"Urban",
""
],
[
"Widman",
"Mathias",
""
],
[
"Scandariato",
"Riccardo",
""
]
] | Recent trends in the software engineering (i.e., Agile, DevOps) have shortened the development life-cycle limiting resources spent on security analysis of software designs. In this context, architecture models are (often manually) analyzed for potential security threats. Risk-last threat analysis suggests identifying all security threats before prioritizing them. In contrast, risk-first threat analysis suggests identifying the risks before the threats, by-passing threat prioritization. This seems promising for organizations where developing speed is of great importance. Yet, little empirical evidence exists about the effect of sacrificing systematicity for high-priority threats on the performance and execution of threat analysis. To this aim, we conduct a case study with industrial experts from the automotive domain, where we empirically compare a risk-first technique to a risk-last technique. In this study, we consciously trade the amount of participants for a more realistic simulation of threat analysis sessions in practice. This allows us to closely observe industrial experts and gain deep insights into the industrial practice. This work contributes with: (i) a quantitative comparison of performance, (ii) a quantitative and qualitative comparison of execution, and (iii) a comparative discussion of the two techniques. We find no differences in the productivity and timeliness of discovering high-priority security threats. Yet, we find differences in analysis execution. In particular, participants using the risk-first technique found twice as many high-priority threats, developed detailed attack scenarios, and discussed threat feasibility in detail. On the other hand, participants using the risk-last technique found more medium and low-priority threats and finished early. |
2402.14536 | Siyin Wang | Siyin Wang, Jie Zhou, Qin Chen, Qi Zhang, Tao Gui, Xuanjing Huang | Domain Generalization via Causal Adjustment for Cross-Domain Sentiment
Analysis | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Domain adaption has been widely adapted for cross-domain sentiment analysis
to transfer knowledge from the source domain to the target domain. Whereas,
most methods are proposed under the assumption that the target (test) domain is
known, making them fail to generalize well on unknown test data that is not
always available in practice. In this paper, we focus on the problem of domain
generalization for cross-domain sentiment analysis. Specifically, we propose a
backdoor adjustment-based causal model to disentangle the domain-specific and
domain-invariant representations that play essential roles in tackling domain
shift. First, we rethink the cross-domain sentiment analysis task in a causal
view to model the causal-and-effect relationships among different variables.
Then, to learn an invariant feature representation, we remove the effect of
domain confounders (e.g., domain knowledge) using the backdoor adjustment. A
series of experiments over many homologous and diverse datasets show the great
performance and robustness of our model by comparing it with the
state-of-the-art domain generalization baselines.
| [
{
"created": "Thu, 22 Feb 2024 13:26:56 GMT",
"version": "v1"
}
] | 2024-02-23 | [
[
"Wang",
"Siyin",
""
],
[
"Zhou",
"Jie",
""
],
[
"Chen",
"Qin",
""
],
[
"Zhang",
"Qi",
""
],
[
"Gui",
"Tao",
""
],
[
"Huang",
"Xuanjing",
""
]
] | Domain adaption has been widely adapted for cross-domain sentiment analysis to transfer knowledge from the source domain to the target domain. Whereas, most methods are proposed under the assumption that the target (test) domain is known, making them fail to generalize well on unknown test data that is not always available in practice. In this paper, we focus on the problem of domain generalization for cross-domain sentiment analysis. Specifically, we propose a backdoor adjustment-based causal model to disentangle the domain-specific and domain-invariant representations that play essential roles in tackling domain shift. First, we rethink the cross-domain sentiment analysis task in a causal view to model the causal-and-effect relationships among different variables. Then, to learn an invariant feature representation, we remove the effect of domain confounders (e.g., domain knowledge) using the backdoor adjustment. A series of experiments over many homologous and diverse datasets show the great performance and robustness of our model by comparing it with the state-of-the-art domain generalization baselines. |
2012.13219 | Mustafa Hashmi | Ho-Pun Lam and Mustafa Hashmi and Akhil Kumar | Towards a Formal Framework for Partial Compliance of Business Processes | 15 page, 4 figures, 2 tables; Under consideration at AICOL 2020,
co-located with Jurix | null | null | null | cs.AI cs.LO | http://creativecommons.org/licenses/by/4.0/ | Binary "YES-NO" notions of process compliance are not very helpful to
managers for assessing the operational performance of their company because a
large number of cases fall in the grey area of partial compliance. Hence, it is
necessary to have ways to quantify partial compliance in terms of metrics and
be able to classify actual cases by assigning a numeric value of compliance to
them. In this paper, we formulate an evaluation framework to quantify the level
of compliance of business processes across different levels of abstraction
(such as task,trace and process level) and across multiple dimensions of each
task (such as temporal, monetary, role-, data-, and quality-related) to provide
managers more useful information about their operations and to help them
improve their decision making processes. Our approach can also add social value
by making social services provided by local, state and federal governments more
flexible and improving the lives of citizens.
| [
{
"created": "Thu, 24 Dec 2020 12:38:40 GMT",
"version": "v1"
}
] | 2020-12-25 | [
[
"Lam",
"Ho-Pun",
""
],
[
"Hashmi",
"Mustafa",
""
],
[
"Kumar",
"Akhil",
""
]
] | Binary "YES-NO" notions of process compliance are not very helpful to managers for assessing the operational performance of their company because a large number of cases fall in the grey area of partial compliance. Hence, it is necessary to have ways to quantify partial compliance in terms of metrics and be able to classify actual cases by assigning a numeric value of compliance to them. In this paper, we formulate an evaluation framework to quantify the level of compliance of business processes across different levels of abstraction (such as task,trace and process level) and across multiple dimensions of each task (such as temporal, monetary, role-, data-, and quality-related) to provide managers more useful information about their operations and to help them improve their decision making processes. Our approach can also add social value by making social services provided by local, state and federal governments more flexible and improving the lives of citizens. |
2010.10573 | Hoang Nguyen Hung Van | Hoang Van, David Kauchak, Gondy Leroy | AutoMeTS: The Autocomplete for Medical Text Simplification | 9 pages, 3 figures, and 8 tables, Accpeted to COLING 2020 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of text simplification (TS) is to transform difficult text into a
version that is easier to understand and more broadly accessible to a wide
variety of readers. In some domains, such as healthcare, fully automated
approaches cannot be used since information must be accurately preserved.
Instead, semi-automated approaches can be used that assist a human writer in
simplifying text faster and at a higher quality. In this paper, we examine the
application of autocomplete to text simplification in the medical domain. We
introduce a new parallel medical data set consisting of aligned English
Wikipedia with Simple English Wikipedia sentences and examine the application
of pretrained neural language models (PNLMs) on this dataset. We compare four
PNLMs(BERT, RoBERTa, XLNet, and GPT-2), and show how the additional context of
the sentence to be simplified can be incorporated to achieve better results
(6.17% absolute improvement over the best individual model). We also introduce
an ensemble model that combines the four PNLMs and outperforms the best
individual model by 2.1%, resulting in an overall word prediction accuracy of
64.52%.
| [
{
"created": "Tue, 20 Oct 2020 19:20:29 GMT",
"version": "v1"
}
] | 2020-10-22 | [
[
"Van",
"Hoang",
""
],
[
"Kauchak",
"David",
""
],
[
"Leroy",
"Gondy",
""
]
] | The goal of text simplification (TS) is to transform difficult text into a version that is easier to understand and more broadly accessible to a wide variety of readers. In some domains, such as healthcare, fully automated approaches cannot be used since information must be accurately preserved. Instead, semi-automated approaches can be used that assist a human writer in simplifying text faster and at a higher quality. In this paper, we examine the application of autocomplete to text simplification in the medical domain. We introduce a new parallel medical data set consisting of aligned English Wikipedia with Simple English Wikipedia sentences and examine the application of pretrained neural language models (PNLMs) on this dataset. We compare four PNLMs(BERT, RoBERTa, XLNet, and GPT-2), and show how the additional context of the sentence to be simplified can be incorporated to achieve better results (6.17% absolute improvement over the best individual model). We also introduce an ensemble model that combines the four PNLMs and outperforms the best individual model by 2.1%, resulting in an overall word prediction accuracy of 64.52%. |
2404.05073 | Stefano Scanzio | Stefano Scanzio, Gianluca Cena, Adriano Valenzano | QRscript: Embedding a Programming Language in QR codes to support
Decision and Management | preprint, 8 pages | 27th IEEE International Conference on Emerging Technologies and
Factory Automation (ETFA 2022) | 10.1109/ETFA52439.2022.9921530 | null | cs.NI cs.CL | http://creativecommons.org/licenses/by/4.0/ | Embedding a programming language in a QR code is a new and extremely
promising opportunity, as it makes devices and objects smarter without
necessarily requiring an Internet connection. In this paper, all the steps
needed to translate a program written in a high-level programming language to
its binary representation encoded in a QR code, and the opposite process that,
starting from the QR code, executes it by means of a virtual machine, have been
carefully detailed. The proposed programming language was named QRscript, and
can be easily extended so as to integrate new features. One of the main design
goals was to produce a very compact target binary code. In particular, in this
work we propose a specific sub-language (a dialect) that is aimed at encoding
decision trees. Besides industrial scenarios, this is useful in many other
application fields. The reported example, related to the configuration of an
industrial networked device, highlights the potential of the proposed
technology, and permits to better understand all the translation steps.
| [
{
"created": "Sun, 7 Apr 2024 21:02:55 GMT",
"version": "v1"
}
] | 2024-04-09 | [
[
"Scanzio",
"Stefano",
""
],
[
"Cena",
"Gianluca",
""
],
[
"Valenzano",
"Adriano",
""
]
] | Embedding a programming language in a QR code is a new and extremely promising opportunity, as it makes devices and objects smarter without necessarily requiring an Internet connection. In this paper, all the steps needed to translate a program written in a high-level programming language to its binary representation encoded in a QR code, and the opposite process that, starting from the QR code, executes it by means of a virtual machine, have been carefully detailed. The proposed programming language was named QRscript, and can be easily extended so as to integrate new features. One of the main design goals was to produce a very compact target binary code. In particular, in this work we propose a specific sub-language (a dialect) that is aimed at encoding decision trees. Besides industrial scenarios, this is useful in many other application fields. The reported example, related to the configuration of an industrial networked device, highlights the potential of the proposed technology, and permits to better understand all the translation steps. |
2309.03720 | Radek Svoboda | Radek Svoboda, Sebastian Basterrech, Jedrzej Kozal, Jan Platos, Michal
Wozniak | A Natural Gas Consumption Forecasting System for Continual Learning
Scenarios based on Hoeffding Trees with Change Point Detection Mechanism | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Forecasting natural gas consumption, considering seasonality and trends, is
crucial in planning its supply and consumption and optimizing the cost of
obtaining it, mainly by industrial entities. However, in times of threats to
its supply, it is also a critical element that guarantees the supply of this
raw material to meet individual consumers' needs, ensuring society's energy
security. This article introduces a novel multistep ahead forecasting of
natural gas consumption with change point detection integration for model
collection selection with continual learning capabilities using data stream
processing. The performance of the forecasting models based on the proposed
approach is evaluated in a complex real-world use case of natural gas
consumption forecasting. We employed Hoeffding tree predictors as forecasting
models and the Pruned Exact Linear Time (PELT) algorithm for the change point
detection procedure. The change point detection integration enables selecting a
different model collection for successive time frames. Thus, three model
collection selection procedures (with and without an error feedback loop) are
defined and evaluated for forecasting scenarios with various densities of
detected change points. These models were compared with change point agnostic
baseline approaches. Our experiments show that fewer change points result in a
lower forecasting error regardless of the model collection selection procedure
employed. Also, simpler model collection selection procedures omitting
forecasting error feedback leads to more robust forecasting models suitable for
continual learning tasks.
| [
{
"created": "Thu, 7 Sep 2023 13:52:20 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Nov 2023 12:48:13 GMT",
"version": "v2"
},
{
"created": "Mon, 4 Mar 2024 13:52:35 GMT",
"version": "v3"
},
{
"created": "Mon, 12 Aug 2024 08:27:48 GMT",
"version": "v4"
}
] | 2024-08-13 | [
[
"Svoboda",
"Radek",
""
],
[
"Basterrech",
"Sebastian",
""
],
[
"Kozal",
"Jedrzej",
""
],
[
"Platos",
"Jan",
""
],
[
"Wozniak",
"Michal",
""
]
] | Forecasting natural gas consumption, considering seasonality and trends, is crucial in planning its supply and consumption and optimizing the cost of obtaining it, mainly by industrial entities. However, in times of threats to its supply, it is also a critical element that guarantees the supply of this raw material to meet individual consumers' needs, ensuring society's energy security. This article introduces a novel multistep ahead forecasting of natural gas consumption with change point detection integration for model collection selection with continual learning capabilities using data stream processing. The performance of the forecasting models based on the proposed approach is evaluated in a complex real-world use case of natural gas consumption forecasting. We employed Hoeffding tree predictors as forecasting models and the Pruned Exact Linear Time (PELT) algorithm for the change point detection procedure. The change point detection integration enables selecting a different model collection for successive time frames. Thus, three model collection selection procedures (with and without an error feedback loop) are defined and evaluated for forecasting scenarios with various densities of detected change points. These models were compared with change point agnostic baseline approaches. Our experiments show that fewer change points result in a lower forecasting error regardless of the model collection selection procedure employed. Also, simpler model collection selection procedures omitting forecasting error feedback leads to more robust forecasting models suitable for continual learning tasks. |
1703.06941 | Kostas Peppas P | K. Denia Kanellopoulou and Kostas P. Peppas and P. Takis Mathiopoulos | A Unified Effective Capacity Performance Analysis of Lp-norm Diversity
Reception over Arbitrary and Correlated Generalized Fading Channels | This manuscript was submitted on Sept. 30, 2017, for possible
publication in the IEEE TCOM as TCOM-TPS-17-1021 | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The effective capacity (EC) has been recently established as a rigorous
alternative to the classical Shannon's ergodic capacity since it accounts for
the delay constraints imposed by future wireless applications and their impact
on the overall system performance. This paper presents a novel moment
generating function (MGF)-based framework for the unified EC performance
analysis of a generic Lp-norm diversity combining scheme operating over
arbitrary and correlated generalized fading channels and a maximum delay
constraint. The Lp-norm diversity is a generic diversity structure which
includes as special cases various well-known diversity schemes such as equal
gain combining (EGC) and maximal ratio combining (MRC). For MRC, the proposed
methodology reduces to a previously published MGF-based approach for the
evaluation of the EC, whereas, for EGC, analytical approach presented is novel
and the associated performance evaluation results have not been published
previously in the open technical literature. Based on this methodology, novel
analytical closed-form expressions for the EC performance of dual branch
Lp-norm diversity receivers operating over Gamma shadowed generalized
Nakagami-m fading channels are deduced. For diversity order greater than two, a
novel analytical approach for the asymptotic EC performance analysis is also
developed and evaluated, revealing how basic system parameters affect the
overall system performance. The overall mathematical formalism is validated
with selected numerical and equivalent simulation performance evaluation
results thus confirming the correctness of the proposed unified analytical
methodology.
| [
{
"created": "Mon, 20 Mar 2017 19:34:29 GMT",
"version": "v1"
},
{
"created": "Sat, 2 Feb 2019 21:45:50 GMT",
"version": "v2"
},
{
"created": "Wed, 23 Oct 2019 19:10:20 GMT",
"version": "v3"
}
] | 2019-10-25 | [
[
"Kanellopoulou",
"K. Denia",
""
],
[
"Peppas",
"Kostas P.",
""
],
[
"Mathiopoulos",
"P. Takis",
""
]
] | The effective capacity (EC) has been recently established as a rigorous alternative to the classical Shannon's ergodic capacity since it accounts for the delay constraints imposed by future wireless applications and their impact on the overall system performance. This paper presents a novel moment generating function (MGF)-based framework for the unified EC performance analysis of a generic Lp-norm diversity combining scheme operating over arbitrary and correlated generalized fading channels and a maximum delay constraint. The Lp-norm diversity is a generic diversity structure which includes as special cases various well-known diversity schemes such as equal gain combining (EGC) and maximal ratio combining (MRC). For MRC, the proposed methodology reduces to a previously published MGF-based approach for the evaluation of the EC, whereas, for EGC, analytical approach presented is novel and the associated performance evaluation results have not been published previously in the open technical literature. Based on this methodology, novel analytical closed-form expressions for the EC performance of dual branch Lp-norm diversity receivers operating over Gamma shadowed generalized Nakagami-m fading channels are deduced. For diversity order greater than two, a novel analytical approach for the asymptotic EC performance analysis is also developed and evaluated, revealing how basic system parameters affect the overall system performance. The overall mathematical formalism is validated with selected numerical and equivalent simulation performance evaluation results thus confirming the correctness of the proposed unified analytical methodology. |
1704.03105 | EPTCS | Yingfu Zeng (Rice University), Ferenc Bartha (Rice University), Walid
Taha (Halmstad University) | Compile-Time Extensions to Hybrid ODEs | In Proceedings SNR 2017, arXiv:1704.02421 | EPTCS 247, 2017, pp. 52-70 | 10.4204/EPTCS.247.5 | null | cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reachability analysis for hybrid systems is an active area of development and
has resulted in many promising prototype tools. Most of these tools allow users
to express hybrid system as automata with a set of ordinary differential
equations (ODEs) associated with each state, as well as rules for transitions
between states. Significant effort goes into developing and verifying and
correctly implementing those tools. As such, it is desirable to expand the
scope of applicability tools of such as far as possible. With this goal, we
show how compile-time transformations can be used to extend the basic hybrid
ODE formalism traditionally supported in hybrid reachability tools such as
SpaceEx or Flow*. The extension supports certain types of partial derivatives
and equational constraints. These extensions allow users to express, among
other things, the Euler-Lagrangian equation, and to capture practically
relevant constraints that arise naturally in mechanical systems. Achieving this
level of expressiveness requires using a binding time-analysis (BTA), program
differentiation, symbolic Gaussian elimination, and abstract interpretation
using interval analysis. Except for BTA, the other components are either
readily available or can be easily added to most reachability tools. The paper
therefore focuses on presenting both the declarative and algorithmic
specifications for the BTA phase, and establishes the soundness of the
algorithmic specifications with respect to the declarative one.
| [
{
"created": "Tue, 11 Apr 2017 00:57:40 GMT",
"version": "v1"
}
] | 2017-04-12 | [
[
"Zeng",
"Yingfu",
"",
"Rice University"
],
[
"Bartha",
"Ferenc",
"",
"Rice University"
],
[
"Taha",
"Walid",
"",
"Halmstad University"
]
] | Reachability analysis for hybrid systems is an active area of development and has resulted in many promising prototype tools. Most of these tools allow users to express hybrid system as automata with a set of ordinary differential equations (ODEs) associated with each state, as well as rules for transitions between states. Significant effort goes into developing and verifying and correctly implementing those tools. As such, it is desirable to expand the scope of applicability tools of such as far as possible. With this goal, we show how compile-time transformations can be used to extend the basic hybrid ODE formalism traditionally supported in hybrid reachability tools such as SpaceEx or Flow*. The extension supports certain types of partial derivatives and equational constraints. These extensions allow users to express, among other things, the Euler-Lagrangian equation, and to capture practically relevant constraints that arise naturally in mechanical systems. Achieving this level of expressiveness requires using a binding time-analysis (BTA), program differentiation, symbolic Gaussian elimination, and abstract interpretation using interval analysis. Except for BTA, the other components are either readily available or can be easily added to most reachability tools. The paper therefore focuses on presenting both the declarative and algorithmic specifications for the BTA phase, and establishes the soundness of the algorithmic specifications with respect to the declarative one. |
1804.05212 | Avi Segal | Avi Segal, Yossi Ben David, Joseph Jay Williams, Kobi Gal, Yaar Shalom | Combining Difficulty Ranking with Multi-Armed Bandits to Sequence
Educational Content | null | null | 10.1016/j.physletb.2019.04.047 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As e-learning systems become more prevalent, there is a growing need for them
to accommodate individual differences between students. This paper addresses
the problem of how to personalize educational content to students in order to
maximize their learning gains over time. We present a new computational
approach to this problem called MAPLE (Multi-Armed Bandits based
Personalization for Learning Environments) that combines difficulty ranking
with multi-armed bandits. Given a set of target questions MAPLE estimates the
expected learning gains for each question and uses an exploration-exploitation
strategy to choose the next question to pose to the student. It maintains a
personalized ranking over the difficulties of question in the target set which
is used in two ways: First, to obtain initial estimates over the learning gains
for the set of questions. Second, to update the estimates over time based on
the students responses. We show in simulations that MAPLE was able to improve
students' learning gains compared to approaches that sequence questions in
increasing level of difficulty, or rely on content experts. When implemented in
a live e-learning system in the wild, MAPLE showed promising results. This work
demonstrates the efficacy of using stochastic approaches to the sequencing
problem when augmented with information about question difficulty.
| [
{
"created": "Sat, 14 Apr 2018 12:36:00 GMT",
"version": "v1"
}
] | 2019-04-24 | [
[
"Segal",
"Avi",
""
],
[
"David",
"Yossi Ben",
""
],
[
"Williams",
"Joseph Jay",
""
],
[
"Gal",
"Kobi",
""
],
[
"Shalom",
"Yaar",
""
]
] | As e-learning systems become more prevalent, there is a growing need for them to accommodate individual differences between students. This paper addresses the problem of how to personalize educational content to students in order to maximize their learning gains over time. We present a new computational approach to this problem called MAPLE (Multi-Armed Bandits based Personalization for Learning Environments) that combines difficulty ranking with multi-armed bandits. Given a set of target questions MAPLE estimates the expected learning gains for each question and uses an exploration-exploitation strategy to choose the next question to pose to the student. It maintains a personalized ranking over the difficulties of question in the target set which is used in two ways: First, to obtain initial estimates over the learning gains for the set of questions. Second, to update the estimates over time based on the students responses. We show in simulations that MAPLE was able to improve students' learning gains compared to approaches that sequence questions in increasing level of difficulty, or rely on content experts. When implemented in a live e-learning system in the wild, MAPLE showed promising results. This work demonstrates the efficacy of using stochastic approaches to the sequencing problem when augmented with information about question difficulty. |
2007.13809 | Andra Lutu | Andra Lutu, Byunjin Jun, Fabian Bustamante, Diego Perino, Marcelo
Bagnulo, Carlos Gamboa Bontje | A first look at the IP eXchange Ecosystem | null | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The IPX Network interconnects about 800 Mobile Network Operators (MNOs)
worldwide and a range of other service providers (such as cloud and content
providers). It forms the core that enables global data roaming while supporting
emerging applications, from VoLTE and video streaming to IoT verticals. This
paper presents the first characterization of this, so-far opaque, IPX ecosystem
and a first-of-its-kind in-depth analysis of ann IPX Provider (IPX-P). The IPX
Network is a private network formed by a small set of tightly interconnected
IPX-Ps. We analyze an operational dataset from a large IPX-P that includes BGP
data as well as statistics from signaling. We shed light on the structure of
the IPX Network as well as on the temporal, structural and geographic features
of the IPX traffic. Our results are a first step in understanding the IPX
Network at its core, key to fully understand the global mobile Internet.
| [
{
"created": "Mon, 27 Jul 2020 18:48:49 GMT",
"version": "v1"
}
] | 2020-07-29 | [
[
"Lutu",
"Andra",
""
],
[
"Jun",
"Byunjin",
""
],
[
"Bustamante",
"Fabian",
""
],
[
"Perino",
"Diego",
""
],
[
"Bagnulo",
"Marcelo",
""
],
[
"Bontje",
"Carlos Gamboa",
""
]
] | The IPX Network interconnects about 800 Mobile Network Operators (MNOs) worldwide and a range of other service providers (such as cloud and content providers). It forms the core that enables global data roaming while supporting emerging applications, from VoLTE and video streaming to IoT verticals. This paper presents the first characterization of this, so-far opaque, IPX ecosystem and a first-of-its-kind in-depth analysis of ann IPX Provider (IPX-P). The IPX Network is a private network formed by a small set of tightly interconnected IPX-Ps. We analyze an operational dataset from a large IPX-P that includes BGP data as well as statistics from signaling. We shed light on the structure of the IPX Network as well as on the temporal, structural and geographic features of the IPX traffic. Our results are a first step in understanding the IPX Network at its core, key to fully understand the global mobile Internet. |
1412.5619 | Yoshihiro Terasawa | Yoshihiro Terasawa | A Simple construction of the Pseudorandom Generator from Permutation | I want to rewrite | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A simple construction of pseudorandom generator is appear.This pseudorandom
generator is always passed by NIST statistical test.This paper reports a
pseudorandom number generator which has good property is able to construct
using only permutation and data rewriting by XOR.
| [
{
"created": "Tue, 16 Dec 2014 13:44:39 GMT",
"version": "v1"
},
{
"created": "Sun, 13 Aug 2023 11:45:40 GMT",
"version": "v2"
}
] | 2023-08-15 | [
[
"Terasawa",
"Yoshihiro",
""
]
] | A simple construction of pseudorandom generator is appear.This pseudorandom generator is always passed by NIST statistical test.This paper reports a pseudorandom number generator which has good property is able to construct using only permutation and data rewriting by XOR. |
2112.08460 | Molly Jane Nicholas | Molly Jane Nicholas, Brian A. Smith, Rajan Vaish | Friendscope: Exploring In-the-Moment Experience Sharing on Camera
Glasses via a Shared Camera | ACM CSCW 2022 | null | null | null | cs.HC | http://creativecommons.org/licenses/by/4.0/ | We introduce Friendscope, an instant, in-the-moment experience sharing system
for lightweight commercial camera glasses. Friendscope explores a new concept
called a shared camera. This concept allows a wearer to share control of their
camera with a remote friend, making it possible for both people to capture
photos/videos from the camera in the moment. Through a user study with 48
participants, we found that users felt connected to each other, describing the
shared camera as a more intimate form of livestreaming. Moreover, even
privacy-sensitive users were able to retain their sense of privacy and control
with the shared camera. Friendscope's different shared camera configurations
give wearers ultimate control over who they share the camera with and what
photos/videos they share. We conclude with design implications for future
experience sharing systems.
| [
{
"created": "Wed, 15 Dec 2021 20:15:11 GMT",
"version": "v1"
}
] | 2021-12-17 | [
[
"Nicholas",
"Molly Jane",
""
],
[
"Smith",
"Brian A.",
""
],
[
"Vaish",
"Rajan",
""
]
] | We introduce Friendscope, an instant, in-the-moment experience sharing system for lightweight commercial camera glasses. Friendscope explores a new concept called a shared camera. This concept allows a wearer to share control of their camera with a remote friend, making it possible for both people to capture photos/videos from the camera in the moment. Through a user study with 48 participants, we found that users felt connected to each other, describing the shared camera as a more intimate form of livestreaming. Moreover, even privacy-sensitive users were able to retain their sense of privacy and control with the shared camera. Friendscope's different shared camera configurations give wearers ultimate control over who they share the camera with and what photos/videos they share. We conclude with design implications for future experience sharing systems. |
2205.13248 | Qingpeng Cai | Qingpeng Cai, Ruohan Zhan, Chi Zhang, Jie Zheng, Guangwei Ding,
Pinghua Gong, Dong Zheng, Peng Jiang | Constrained Reinforcement Learning for Short Video Recommendation | null | null | null | null | cs.LG cs.IR | http://creativecommons.org/licenses/by/4.0/ | The wide popularity of short videos on social media poses new opportunities
and challenges to optimize recommender systems on the video-sharing platforms.
Users provide complex and multi-faceted responses towards recommendations,
including watch time and various types of interactions with videos. As a
result, established recommendation algorithms that concern a single objective
are not adequate to meet this new demand of optimizing comprehensive user
experiences. In this paper, we formulate the problem of short video
recommendation as a constrained Markov Decision Process (MDP), where platforms
want to optimize the main goal of user watch time in long term, with the
constraint of accommodating the auxiliary responses of user interactions such
as sharing/downloading videos.
To solve the constrained MDP, we propose a two-stage reinforcement learning
approach based on actor-critic framework. At stage one, we learn individual
policies to optimize each auxiliary response. At stage two, we learn a policy
to (i) optimize the main response and (ii) stay close to policies learned at
the first stage, which effectively guarantees the performance of this main
policy on the auxiliaries. Through extensive simulations, we demonstrate
effectiveness of our approach over alternatives in both optimizing the main
goal as well as balancing the others. We further show the advantage of our
approach in live experiments of short video recommendations, where it
significantly outperforms other baselines in terms of watch time and
interactions from video views. Our approach has been fully launched in the
production system to optimize user experiences on the platform.
| [
{
"created": "Thu, 26 May 2022 09:36:20 GMT",
"version": "v1"
}
] | 2022-05-27 | [
[
"Cai",
"Qingpeng",
""
],
[
"Zhan",
"Ruohan",
""
],
[
"Zhang",
"Chi",
""
],
[
"Zheng",
"Jie",
""
],
[
"Ding",
"Guangwei",
""
],
[
"Gong",
"Pinghua",
""
],
[
"Zheng",
"Dong",
""
],
[
"Jiang",
"Peng",
""
]
] | The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users provide complex and multi-faceted responses towards recommendations, including watch time and various types of interactions with videos. As a result, established recommendation algorithms that concern a single objective are not adequate to meet this new demand of optimizing comprehensive user experiences. In this paper, we formulate the problem of short video recommendation as a constrained Markov Decision Process (MDP), where platforms want to optimize the main goal of user watch time in long term, with the constraint of accommodating the auxiliary responses of user interactions such as sharing/downloading videos. To solve the constrained MDP, we propose a two-stage reinforcement learning approach based on actor-critic framework. At stage one, we learn individual policies to optimize each auxiliary response. At stage two, we learn a policy to (i) optimize the main response and (ii) stay close to policies learned at the first stage, which effectively guarantees the performance of this main policy on the auxiliaries. Through extensive simulations, we demonstrate effectiveness of our approach over alternatives in both optimizing the main goal as well as balancing the others. We further show the advantage of our approach in live experiments of short video recommendations, where it significantly outperforms other baselines in terms of watch time and interactions from video views. Our approach has been fully launched in the production system to optimize user experiences on the platform. |
2102.03237 | Jinseok Kim | Jinseok Kim and Jason Owen-Smith | ORCID-linked labeled data for evaluating author name disambiguation at
scale | A pre-print of a paper accepted for publication in the journal
Scientometrics | null | 10.1007/s11192-020-03826-6 | null | cs.DL cs.IR | http://creativecommons.org/licenses/by/4.0/ | How can we evaluate the performance of a disambiguation method implemented on
big bibliographic data? This study suggests that the open researcher profile
system, ORCID, can be used as an authority source to label name instances at
scale. This study demonstrates the potential by evaluating the disambiguation
performances of Author-ity2009 (which algorithmically disambiguates author
names in MEDLINE) using 3 million name instances that are automatically labeled
through linkage to 5 million ORCID researcher profiles. Results show that
although ORCID-linked labeled data do not effectively represent the population
of name instances in Author-ity2009, they do effectively capture the 'high
precision over high recall' performances of Author-ity2009. In addition,
ORCID-linked labeled data can provide nuanced details about the
Author-ity2009's performance when name instances are evaluated within and
across ethnicity categories. As ORCID continues to be expanded to include more
researchers, labeled data via ORCID-linkage can be improved in representing the
population of a whole disambiguated data and updated on a regular basis. This
can benefit author name disambiguation researchers and practitioners who need
large-scale labeled data but lack resources for manual labeling or access to
other authority sources for linkage-based labeling. The ORCID-linked labeled
data for Author-tiy2009 are publicly available for validation and reuse.
| [
{
"created": "Fri, 5 Feb 2021 15:34:08 GMT",
"version": "v1"
}
] | 2021-02-08 | [
[
"Kim",
"Jinseok",
""
],
[
"Owen-Smith",
"Jason",
""
]
] | How can we evaluate the performance of a disambiguation method implemented on big bibliographic data? This study suggests that the open researcher profile system, ORCID, can be used as an authority source to label name instances at scale. This study demonstrates the potential by evaluating the disambiguation performances of Author-ity2009 (which algorithmically disambiguates author names in MEDLINE) using 3 million name instances that are automatically labeled through linkage to 5 million ORCID researcher profiles. Results show that although ORCID-linked labeled data do not effectively represent the population of name instances in Author-ity2009, they do effectively capture the 'high precision over high recall' performances of Author-ity2009. In addition, ORCID-linked labeled data can provide nuanced details about the Author-ity2009's performance when name instances are evaluated within and across ethnicity categories. As ORCID continues to be expanded to include more researchers, labeled data via ORCID-linkage can be improved in representing the population of a whole disambiguated data and updated on a regular basis. This can benefit author name disambiguation researchers and practitioners who need large-scale labeled data but lack resources for manual labeling or access to other authority sources for linkage-based labeling. The ORCID-linked labeled data for Author-tiy2009 are publicly available for validation and reuse. |
1806.01214 | Ivan Geffner | Ittai Abraham, Danny Dolev, Ivan Geffner, Joseph Y. Halpern | Implementing Mediators with Asynchronous Cheap Talk | null | null | null | null | cs.DC cs.CR cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A mediator can help non-cooperative agents obtain an equilibrium that may
otherwise not be possible. We study the ability of players to obtain the same
equilibrium without a mediator, using only cheap talk, that is, nonbinding
pre-play communication. Previous work has considered this problem in a
synchronous setting. Here we consider the effect of asynchrony on the problem,
and provide upper bounds for implementing mediators. Considering asynchronous
environments introduces new subtleties, including exactly what solution concept
is most appropriate and determining what move is played if the cheap talk goes
on forever. Different results are obtained depending on whether the move after
such "infinite play" is under the control of the players or part of the
description of the game.
| [
{
"created": "Mon, 4 Jun 2018 16:55:07 GMT",
"version": "v1"
}
] | 2018-06-05 | [
[
"Abraham",
"Ittai",
""
],
[
"Dolev",
"Danny",
""
],
[
"Geffner",
"Ivan",
""
],
[
"Halpern",
"Joseph Y.",
""
]
] | A mediator can help non-cooperative agents obtain an equilibrium that may otherwise not be possible. We study the ability of players to obtain the same equilibrium without a mediator, using only cheap talk, that is, nonbinding pre-play communication. Previous work has considered this problem in a synchronous setting. Here we consider the effect of asynchrony on the problem, and provide upper bounds for implementing mediators. Considering asynchronous environments introduces new subtleties, including exactly what solution concept is most appropriate and determining what move is played if the cheap talk goes on forever. Different results are obtained depending on whether the move after such "infinite play" is under the control of the players or part of the description of the game. |
2112.09690 | Yinghao Xu | Yinghao Xu, Fangyun Wei, Xiao Sun, Ceyuan Yang, Yujun Shen, Bo Dai,
Bolei Zhou, Stephen Lin | Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition | CVPR 2022 camera-ready, Project webpage:
https://justimyhxu.github.io/projects/cmpl/ | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Semi-supervised action recognition is a challenging but important task due to
the high cost of data annotation. A common approach to this problem is to
assign unlabeled data with pseudo-labels, which are then used as additional
supervision in training. Typically in recent work, the pseudo-labels are
obtained by training a model on the labeled data, and then using confident
predictions from the model to teach itself. In this work, we propose a more
effective pseudo-labeling scheme, called Cross-Model Pseudo-Labeling (CMPL).
Concretely, we introduce a lightweight auxiliary network in addition to the
primary backbone, and ask them to predict pseudo-labels for each other. We
observe that, due to their different structural biases, these two models tend
to learn complementary representations from the same video clips. Each model
can thus benefit from its counterpart by utilizing cross-model predictions as
supervision. Experiments on different data partition protocols demonstrate the
significant improvement of our framework over existing alternatives. For
example, CMPL achieves $17.6\%$ and $25.1\%$ Top-1 accuracy on Kinetics-400 and
UCF-101 using only the RGB modality and $1\%$ labeled data, outperforming our
baseline model, FixMatch, by $9.0\%$ and $10.3\%$, respectively.
| [
{
"created": "Fri, 17 Dec 2021 18:59:41 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Apr 2022 12:03:08 GMT",
"version": "v2"
}
] | 2022-04-19 | [
[
"Xu",
"Yinghao",
""
],
[
"Wei",
"Fangyun",
""
],
[
"Sun",
"Xiao",
""
],
[
"Yang",
"Ceyuan",
""
],
[
"Shen",
"Yujun",
""
],
[
"Dai",
"Bo",
""
],
[
"Zhou",
"Bolei",
""
],
[
"Lin",
"Stephen",
""
]
] | Semi-supervised action recognition is a challenging but important task due to the high cost of data annotation. A common approach to this problem is to assign unlabeled data with pseudo-labels, which are then used as additional supervision in training. Typically in recent work, the pseudo-labels are obtained by training a model on the labeled data, and then using confident predictions from the model to teach itself. In this work, we propose a more effective pseudo-labeling scheme, called Cross-Model Pseudo-Labeling (CMPL). Concretely, we introduce a lightweight auxiliary network in addition to the primary backbone, and ask them to predict pseudo-labels for each other. We observe that, due to their different structural biases, these two models tend to learn complementary representations from the same video clips. Each model can thus benefit from its counterpart by utilizing cross-model predictions as supervision. Experiments on different data partition protocols demonstrate the significant improvement of our framework over existing alternatives. For example, CMPL achieves $17.6\%$ and $25.1\%$ Top-1 accuracy on Kinetics-400 and UCF-101 using only the RGB modality and $1\%$ labeled data, outperforming our baseline model, FixMatch, by $9.0\%$ and $10.3\%$, respectively. |
2102.06944 | Saman Motamed | Saman Motamed and Farzad Khalvati | Multi-class Generative Adversarial Nets for Semi-supervised Image
Classification | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | From generating never-before-seen images to domain adaptation, applications
of Generative Adversarial Networks (GANs) spread wide in the domain of vision
and graphics problems. With the remarkable ability of GANs in learning the
distribution and generating images of a particular class, they can be used for
semi-supervised classification tasks. However, the problem is that if two
classes of images share similar characteristics, the GAN might learn to
generalize and hinder the classification of the two classes. In this paper, we
use various images from MNIST and Fashion-MNIST datasets to illustrate how
similar images cause the GAN to generalize, leading to the poor classification
of images. We propose a modification to the traditional training of GANs that
allows for improved multi-class classification in similar classes of images in
a semi-supervised learning framework.
| [
{
"created": "Sat, 13 Feb 2021 15:26:17 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Feb 2021 16:25:31 GMT",
"version": "v2"
}
] | 2021-02-23 | [
[
"Motamed",
"Saman",
""
],
[
"Khalvati",
"Farzad",
""
]
] | From generating never-before-seen images to domain adaptation, applications of Generative Adversarial Networks (GANs) spread wide in the domain of vision and graphics problems. With the remarkable ability of GANs in learning the distribution and generating images of a particular class, they can be used for semi-supervised classification tasks. However, the problem is that if two classes of images share similar characteristics, the GAN might learn to generalize and hinder the classification of the two classes. In this paper, we use various images from MNIST and Fashion-MNIST datasets to illustrate how similar images cause the GAN to generalize, leading to the poor classification of images. We propose a modification to the traditional training of GANs that allows for improved multi-class classification in similar classes of images in a semi-supervised learning framework. |
1707.05589 | G\'abor Melis | G\'abor Melis, Chris Dyer, Phil Blunsom | On the State of the Art of Evaluation in Neural Language Models | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ongoing innovations in recurrent neural network architectures have provided a
steady influx of apparently state-of-the-art results on language modelling
benchmarks. However, these have been evaluated using differing code bases and
limited computational resources, which represent uncontrolled sources of
experimental variation. We reevaluate several popular architectures and
regularisation methods with large-scale automatic black-box hyperparameter
tuning and arrive at the somewhat surprising conclusion that standard LSTM
architectures, when properly regularised, outperform more recent models. We
establish a new state of the art on the Penn Treebank and Wikitext-2 corpora,
as well as strong baselines on the Hutter Prize dataset.
| [
{
"created": "Tue, 18 Jul 2017 12:35:53 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Nov 2017 17:57:58 GMT",
"version": "v2"
}
] | 2017-11-21 | [
[
"Melis",
"Gábor",
""
],
[
"Dyer",
"Chris",
""
],
[
"Blunsom",
"Phil",
""
]
] | Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these have been evaluated using differing code bases and limited computational resources, which represent uncontrolled sources of experimental variation. We reevaluate several popular architectures and regularisation methods with large-scale automatic black-box hyperparameter tuning and arrive at the somewhat surprising conclusion that standard LSTM architectures, when properly regularised, outperform more recent models. We establish a new state of the art on the Penn Treebank and Wikitext-2 corpora, as well as strong baselines on the Hutter Prize dataset. |
0909.4756 | Brendan Lucier | Jason D. Hartline and Brendan Lucier | Bayesian Algorithmic Mechanism Design | null | null | 10.1257/aer.20130712 | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The principal problem in algorithmic mechanism design is in merging the
incentive constraints imposed by selfish behavior with the algorithmic
constraints imposed by computational intractability. This field is motivated by
the observation that the preeminent approach for designing incentive compatible
mechanisms, namely that of Vickrey, Clarke, and Groves; and the central
approach for circumventing computational obstacles, that of approximation
algorithms, are fundamentally incompatible: natural applications of the VCG
approach to an approximation algorithm fails to yield an incentive compatible
mechanism. We consider relaxing the desideratum of (ex post) incentive
compatibility (IC) to Bayesian incentive compatibility (BIC), where
truthtelling is a Bayes-Nash equilibrium (the standard notion of incentive
compatibility in economics). For welfare maximization in single-parameter agent
settings, we give a general black-box reduction that turns any approximation
algorithm into a Bayesian incentive compatible mechanism with essentially the
same approximation factor.
| [
{
"created": "Fri, 25 Sep 2009 18:00:59 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Feb 2011 22:16:38 GMT",
"version": "v2"
}
] | 2017-08-21 | [
[
"Hartline",
"Jason D.",
""
],
[
"Lucier",
"Brendan",
""
]
] | The principal problem in algorithmic mechanism design is in merging the incentive constraints imposed by selfish behavior with the algorithmic constraints imposed by computational intractability. This field is motivated by the observation that the preeminent approach for designing incentive compatible mechanisms, namely that of Vickrey, Clarke, and Groves; and the central approach for circumventing computational obstacles, that of approximation algorithms, are fundamentally incompatible: natural applications of the VCG approach to an approximation algorithm fails to yield an incentive compatible mechanism. We consider relaxing the desideratum of (ex post) incentive compatibility (IC) to Bayesian incentive compatibility (BIC), where truthtelling is a Bayes-Nash equilibrium (the standard notion of incentive compatibility in economics). For welfare maximization in single-parameter agent settings, we give a general black-box reduction that turns any approximation algorithm into a Bayesian incentive compatible mechanism with essentially the same approximation factor. |
1910.12073 | Jillian Tompkins | Jillian Tompkins | Disinformation Detection: A review of linguistic feature selection and
classification models in news veracity assessments | null | null | null | null | cs.CL cs.CY cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Over the past couple of years, the topic of "fake news" and its influence
over people's opinions has become a growing cause for concern. Although the
spread of disinformation on the Internet is not a new phenomenon, the
widespread use of social media has exacerbated its effects, providing more
channels for dissemination and the potential to "go viral." Nowhere was this
more evident than during the 2016 United States Presidential Election. Although
the current of disinformation spread via trolls, bots, and hyperpartisan media
outlets likely reinforced existing biases rather than sway undecided voters,
the effects of this deluge of disinformation are by no means trivial. The
consequences range in severity from an overall distrust in news media, to an
ill-informed citizenry, and in extreme cases, provocation of violent action. It
is clear that human ability to discern lies from truth is flawed at best. As
such, greater attention has been given towards applying machine learning
approaches to detect deliberately deceptive news articles. This paper looks at
the work that has already been done in this area.
| [
{
"created": "Sat, 26 Oct 2019 14:29:37 GMT",
"version": "v1"
}
] | 2019-10-29 | [
[
"Tompkins",
"Jillian",
""
]
] | Over the past couple of years, the topic of "fake news" and its influence over people's opinions has become a growing cause for concern. Although the spread of disinformation on the Internet is not a new phenomenon, the widespread use of social media has exacerbated its effects, providing more channels for dissemination and the potential to "go viral." Nowhere was this more evident than during the 2016 United States Presidential Election. Although the current of disinformation spread via trolls, bots, and hyperpartisan media outlets likely reinforced existing biases rather than sway undecided voters, the effects of this deluge of disinformation are by no means trivial. The consequences range in severity from an overall distrust in news media, to an ill-informed citizenry, and in extreme cases, provocation of violent action. It is clear that human ability to discern lies from truth is flawed at best. As such, greater attention has been given towards applying machine learning approaches to detect deliberately deceptive news articles. This paper looks at the work that has already been done in this area. |
0804.4750 | Icius Committee | Heru Tjahjana, Iwan Pranoto, Hari Muhammad, J. Naiborhu, and Miswanto | The Numerical Control Design for a Pair of Dubins Vehicles | Uploaded by ICIUS2007 Conference Organizer on behalf of the
author(s). 3 pages, 2 figures | Proceedings of the International Conference on Intelligent
Unmanned System (ICIUS 2007), Bali, Indonesia, October 24-25, 2007, Paper No.
ICIUS2007-C003 | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, a model of a pair of Dubins vehicles is considered. The
vehicles move from an initial position and orientation to final position and
orientation. A long the motion, the two vehicles are not allowed to collide
however the two vehicles cant to far each other. The optimal control of the
vehicle is found using the Pontryagins Maximum Principle (PMP). This PMP leads
to a Hamiltonian system consisting of a system of differential equation and its
adjoint. The originally differential equation has initial and final condition
but the adjoint system doesn't have one. The classical difficulty is solved
numerically by the greatest gradient descent method. Some simulation results
are presented in this paper.
| [
{
"created": "Wed, 30 Apr 2008 08:03:05 GMT",
"version": "v1"
}
] | 2008-05-01 | [
[
"Tjahjana",
"Heru",
""
],
[
"Pranoto",
"Iwan",
""
],
[
"Muhammad",
"Hari",
""
],
[
"Naiborhu",
"J.",
""
],
[
"Miswanto",
"",
""
]
] | In this paper, a model of a pair of Dubins vehicles is considered. The vehicles move from an initial position and orientation to final position and orientation. A long the motion, the two vehicles are not allowed to collide however the two vehicles cant to far each other. The optimal control of the vehicle is found using the Pontryagins Maximum Principle (PMP). This PMP leads to a Hamiltonian system consisting of a system of differential equation and its adjoint. The originally differential equation has initial and final condition but the adjoint system doesn't have one. The classical difficulty is solved numerically by the greatest gradient descent method. Some simulation results are presented in this paper. |
2203.15636 | Guillaume Jeanneret | Guillaume Jeanneret, Lo\"ic Simon and Fr\'ed\'eric Jurie | Diffusion Models for Counterfactual Explanations | null | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Counterfactual explanations have shown promising results as a post-hoc
framework to make image classifiers more explainable. In this paper, we propose
DiME, a method allowing the generation of counterfactual images using the
recent diffusion models. By leveraging the guided generative diffusion process,
our proposed methodology shows how to use the gradients of the target
classifier to generate counterfactual explanations of input instances. Further,
we analyze current approaches to evaluate spurious correlations and extend the
evaluation measurements by proposing a new metric: Correlation Difference. Our
experimental validations show that the proposed algorithm surpasses previous
State-of-the-Art results on 5 out of 6 metrics on CelebA.
| [
{
"created": "Tue, 29 Mar 2022 14:59:31 GMT",
"version": "v1"
}
] | 2022-03-30 | [
[
"Jeanneret",
"Guillaume",
""
],
[
"Simon",
"Loïc",
""
],
[
"Jurie",
"Frédéric",
""
]
] | Counterfactual explanations have shown promising results as a post-hoc framework to make image classifiers more explainable. In this paper, we propose DiME, a method allowing the generation of counterfactual images using the recent diffusion models. By leveraging the guided generative diffusion process, our proposed methodology shows how to use the gradients of the target classifier to generate counterfactual explanations of input instances. Further, we analyze current approaches to evaluate spurious correlations and extend the evaluation measurements by proposing a new metric: Correlation Difference. Our experimental validations show that the proposed algorithm surpasses previous State-of-the-Art results on 5 out of 6 metrics on CelebA. |
2212.05063 | Asma Bensalah | Alicia Forn\'es, Asma Bensalah, Cristina Carmona-Duarte, Jialuo Chen,
Miguel A. Ferrer, Andreas Fischer, Josep Llad\'os, Cristina Mart\'in, Eloy
Opisso, R\'ejean Plamondon, Anna Scius-Bertrand, and Josep Maria Tormos | The RPM3D project: 3D Kinematics for Remote Patient Monitoring | null | null | 10.1007/978-3-031-19745-1_16 | null | cs.HC cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This project explores the feasibility of remote patient monitoring based on
the analysis of 3D movements captured with smartwatches. We base our analysis
on the Kinematic Theory of Rapid Human Movement. We have validated our research
in a real case scenario for stroke rehabilitation at the Guttmann Institute5
(neurorehabilitation hospital), showing promising results. Our work could have
a great impact in remote healthcare applications, improving the medical
efficiency and reducing the healthcare costs. Future steps include more
clinical validation, developing multi-modal analysis architectures (analysing
data from sensors, images, audio, etc.), and exploring the application of our
technology to monitor other neurodegenerative diseases.
| [
{
"created": "Fri, 9 Dec 2022 14:16:32 GMT",
"version": "v1"
}
] | 2022-12-13 | [
[
"Fornés",
"Alicia",
""
],
[
"Bensalah",
"Asma",
""
],
[
"Carmona-Duarte",
"Cristina",
""
],
[
"Chen",
"Jialuo",
""
],
[
"Ferrer",
"Miguel A.",
""
],
[
"Fischer",
"Andreas",
""
],
[
"Lladós",
"Josep",
""
],
[
"Martín",
"Cristina",
""
],
[
"Opisso",
"Eloy",
""
],
[
"Plamondon",
"Réjean",
""
],
[
"Scius-Bertrand",
"Anna",
""
],
[
"Tormos",
"Josep Maria",
""
]
] | This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of Rapid Human Movement. We have validated our research in a real case scenario for stroke rehabilitation at the Guttmann Institute5 (neurorehabilitation hospital), showing promising results. Our work could have a great impact in remote healthcare applications, improving the medical efficiency and reducing the healthcare costs. Future steps include more clinical validation, developing multi-modal analysis architectures (analysing data from sensors, images, audio, etc.), and exploring the application of our technology to monitor other neurodegenerative diseases. |
1811.07468 | Jianing Li | Jianing Li, Shiliang Zhang, Tiejun Huang | Multi-scale 3D Convolution Network for Video Based Person
Re-Identification | AAAI, 2019 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a two-stream convolution network to extract spatial and
temporal cues for video based person Re-Identification (ReID). A temporal
stream in this network is constructed by inserting several Multi-scale 3D (M3D)
convolution layers into a 2D CNN network. The resulting M3D convolution network
introduces a fraction of parameters into the 2D CNN, but gains the ability of
multi-scale temporal feature learning. With this compact architecture, M3D
convolution network is also more efficient and easier to optimize than existing
3D convolution networks. The temporal stream further involves Residual
Attention Layers (RAL) to refine the temporal features. By jointly learning
spatial-temporal attention masks in a residual manner, RAL identifies the
discriminative spatial regions and temporal cues. The other stream in our
network is implemented with a 2D CNN for spatial feature extraction. The
spatial and temporal features from two streams are finally fused for the video
based person ReID. Evaluations on three widely used benchmarks datasets, i.e.,
MARS, PRID2011, and iLIDS-VID demonstrate the substantial advantages of our
method over existing 3D convolution networks and state-of-art methods.
| [
{
"created": "Mon, 19 Nov 2018 02:40:32 GMT",
"version": "v1"
}
] | 2018-11-20 | [
[
"Li",
"Jianing",
""
],
[
"Zhang",
"Shiliang",
""
],
[
"Huang",
"Tiejun",
""
]
] | This paper proposes a two-stream convolution network to extract spatial and temporal cues for video based person Re-Identification (ReID). A temporal stream in this network is constructed by inserting several Multi-scale 3D (M3D) convolution layers into a 2D CNN network. The resulting M3D convolution network introduces a fraction of parameters into the 2D CNN, but gains the ability of multi-scale temporal feature learning. With this compact architecture, M3D convolution network is also more efficient and easier to optimize than existing 3D convolution networks. The temporal stream further involves Residual Attention Layers (RAL) to refine the temporal features. By jointly learning spatial-temporal attention masks in a residual manner, RAL identifies the discriminative spatial regions and temporal cues. The other stream in our network is implemented with a 2D CNN for spatial feature extraction. The spatial and temporal features from two streams are finally fused for the video based person ReID. Evaluations on three widely used benchmarks datasets, i.e., MARS, PRID2011, and iLIDS-VID demonstrate the substantial advantages of our method over existing 3D convolution networks and state-of-art methods. |
0712.2959 | Te Sun Han | Te Sun Han | Joint Source-Channel Coding Revisited: Information-Spectrum Approach | null | null | null | null | cs.IT math.IT | null | Given a general source with countably infinite source alphabet and a general
channel with arbitrary abstract channel input/channel output alphabets, we
study the joint source-channel coding problem from the information-spectrum
point of view. First, we generalize Feinstein's lemma (direct part) and
Verdu-Han's lemma (converse part) so as to be applicable to the general joint
source-channel coding problem. Based on these lemmas, we establish a sufficient
condition as well as a necessary condition for the source to be reliably
transmissible over the channel with asymptotically vanishing probability of
error. It is shown that our sufficient condition is equivalent to the
sufficient condition derived by Vembu, Verdu and Steinberg, whereas our
necessary condition is shown to be stronger than or equivalent to the necessary
condition derived by them. It turns out, as a direct consequence, that
separation principle in a relevantly generalized sense holds for a wide class
of sources and channels, as was shown in a quite dfifferent manner by Vembu,
Verdu and Steinberg. It should also be remarked that a nice duality is found
between our necessary and sufficient conditions, whereas we cannot fully enjoy
such a duality between the necessary condition and the sufficient condition by
Vembu, Verdu and Steinberg. In addition, we demonstrate a sufficient condition
as well as a necessary condition for the epsilon-transmissibility. Finally, the
separation theorem of the traditional standard form is shown to hold for the
class of sources and channels that satisfy the semi-strong converse property.
| [
{
"created": "Tue, 18 Dec 2007 13:33:58 GMT",
"version": "v1"
}
] | 2007-12-19 | [
[
"Han",
"Te Sun",
""
]
] | Given a general source with countably infinite source alphabet and a general channel with arbitrary abstract channel input/channel output alphabets, we study the joint source-channel coding problem from the information-spectrum point of view. First, we generalize Feinstein's lemma (direct part) and Verdu-Han's lemma (converse part) so as to be applicable to the general joint source-channel coding problem. Based on these lemmas, we establish a sufficient condition as well as a necessary condition for the source to be reliably transmissible over the channel with asymptotically vanishing probability of error. It is shown that our sufficient condition is equivalent to the sufficient condition derived by Vembu, Verdu and Steinberg, whereas our necessary condition is shown to be stronger than or equivalent to the necessary condition derived by them. It turns out, as a direct consequence, that separation principle in a relevantly generalized sense holds for a wide class of sources and channels, as was shown in a quite dfifferent manner by Vembu, Verdu and Steinberg. It should also be remarked that a nice duality is found between our necessary and sufficient conditions, whereas we cannot fully enjoy such a duality between the necessary condition and the sufficient condition by Vembu, Verdu and Steinberg. In addition, we demonstrate a sufficient condition as well as a necessary condition for the epsilon-transmissibility. Finally, the separation theorem of the traditional standard form is shown to hold for the class of sources and channels that satisfy the semi-strong converse property. |
2404.19467 | Harshini Gangapuram | Harshini Gangapuram and Vidya Manian | Bayesian Functional Connectivity and Graph Convolutional Network for
Working Memory Load Classification | null | null | null | null | cs.LG eess.SP q-bio.NC | http://creativecommons.org/licenses/by-sa/4.0/ | Brain responses related to working memory originate from distinct brain areas
and oscillate at different frequencies. EEG signals with high temporal
correlation can effectively capture these responses. Therefore, estimating the
functional connectivity of EEG for working memory protocols in different
frequency bands plays a significant role in analyzing the brain dynamics with
increasing memory and cognitive loads, which remains largely unexplored. The
present study introduces a Bayesian structure learning algorithm to learn the
functional connectivity of EEG in sensor space. Next, the functional
connectivity graphs are taken as input to the graph convolutional network to
classify the working memory loads. The intrasubject (subject-specific)
classification performed on 154 subjects for six different verbal working
memory loads produced the highest classification accuracy of 96% and average
classification accuracy of 89%, outperforming state-of-the-art classification
models proposed in the literature. Furthermore, the proposed Bayesian structure
learning algorithm is compared with state-of-the-art functional connectivity
estimation methods through intersubject and intrasubject statistical analysis
of variance. The results also show that the alpha and theta bands have better
classification accuracy than the beta band.
| [
{
"created": "Tue, 30 Apr 2024 11:31:07 GMT",
"version": "v1"
}
] | 2024-05-01 | [
[
"Gangapuram",
"Harshini",
""
],
[
"Manian",
"Vidya",
""
]
] | Brain responses related to working memory originate from distinct brain areas and oscillate at different frequencies. EEG signals with high temporal correlation can effectively capture these responses. Therefore, estimating the functional connectivity of EEG for working memory protocols in different frequency bands plays a significant role in analyzing the brain dynamics with increasing memory and cognitive loads, which remains largely unexplored. The present study introduces a Bayesian structure learning algorithm to learn the functional connectivity of EEG in sensor space. Next, the functional connectivity graphs are taken as input to the graph convolutional network to classify the working memory loads. The intrasubject (subject-specific) classification performed on 154 subjects for six different verbal working memory loads produced the highest classification accuracy of 96% and average classification accuracy of 89%, outperforming state-of-the-art classification models proposed in the literature. Furthermore, the proposed Bayesian structure learning algorithm is compared with state-of-the-art functional connectivity estimation methods through intersubject and intrasubject statistical analysis of variance. The results also show that the alpha and theta bands have better classification accuracy than the beta band. |
1404.7060 | Mitsuru Kusumoto | Mitsuru Kusumoto and Yuichi Yoshida | Testing Forest-Isomorphism in the Adjacency List Model | ICALP 2014 to appear | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of testing if two input forests are isomorphic or are
far from being so. An algorithm is called an $\varepsilon$-tester for
forest-isomorphism if given an oracle access to two forests $G$ and $H$ in the
adjacency list model, with high probability, accepts if $G$ and $H$ are
isomorphic and rejects if we must modify at least $\varepsilon n$ edges to make
$G$ isomorphic to $H$. We show an $\varepsilon$-tester for forest-isomorphism
with a query complexity $\mathrm{polylog}(n)$ and a lower bound of
$\Omega(\sqrt{\log{n}})$. Further, with the aid of the tester, we show that
every graph property is testable in the adjacency list model with
$\mathrm{polylog}(n)$ queries if the input graph is a forest.
| [
{
"created": "Mon, 28 Apr 2014 17:10:35 GMT",
"version": "v1"
}
] | 2014-04-29 | [
[
"Kusumoto",
"Mitsuru",
""
],
[
"Yoshida",
"Yuichi",
""
]
] | We consider the problem of testing if two input forests are isomorphic or are far from being so. An algorithm is called an $\varepsilon$-tester for forest-isomorphism if given an oracle access to two forests $G$ and $H$ in the adjacency list model, with high probability, accepts if $G$ and $H$ are isomorphic and rejects if we must modify at least $\varepsilon n$ edges to make $G$ isomorphic to $H$. We show an $\varepsilon$-tester for forest-isomorphism with a query complexity $\mathrm{polylog}(n)$ and a lower bound of $\Omega(\sqrt{\log{n}})$. Further, with the aid of the tester, we show that every graph property is testable in the adjacency list model with $\mathrm{polylog}(n)$ queries if the input graph is a forest. |
2310.16677 | Niki Maria Foteinopoulou | Niki Maria Foteinopoulou, Ioannis Patras | Machine Learning Approaches for Fine-Grained Symptom Estimation in
Schizophrenia: A Comprehensive Review | 19 pages, 5 figures | null | null | null | cs.HC | http://creativecommons.org/licenses/by/4.0/ | Schizophrenia is a severe yet treatable mental disorder, it is diagnosed
using a multitude of primary and secondary symptoms. Diagnosis and treatment
for each individual depends on the severity of the symptoms, therefore there is
a need for accurate, personalised assessments. However, the process can be both
time-consuming and subjective; hence, there is a motivation to explore
automated methods that can offer consistent diagnosis and precise symptom
assessments, thereby complementing the work of healthcare practitioners.
Machine Learning has demonstrated impressive capabilities across numerous
domains, including medicine; the use of Machine Learning in patient assessment
holds great promise for healthcare professionals and patients alike, as it can
lead to more consistent and accurate symptom estimation.This survey aims to
review methodologies that utilise Machine Learning for diagnosis and assessment
of schizophrenia. Contrary to previous reviews that primarily focused on binary
classification, this work recognises the complexity of the condition and
instead, offers an overview of Machine Learning methods designed for
fine-grained symptom estimation. We cover multiple modalities, namely Medical
Imaging, Electroencephalograms and Audio-Visual, as the illness symptoms can
manifest themselves both in a patient's pathology and behaviour. Finally, we
analyse the datasets and methodologies used in the studies and identify trends,
gaps as well as opportunities for future research.
| [
{
"created": "Wed, 25 Oct 2023 14:42:58 GMT",
"version": "v1"
}
] | 2023-10-26 | [
[
"Foteinopoulou",
"Niki Maria",
""
],
[
"Patras",
"Ioannis",
""
]
] | Schizophrenia is a severe yet treatable mental disorder, it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatment for each individual depends on the severity of the symptoms, therefore there is a need for accurate, personalised assessments. However, the process can be both time-consuming and subjective; hence, there is a motivation to explore automated methods that can offer consistent diagnosis and precise symptom assessments, thereby complementing the work of healthcare practitioners. Machine Learning has demonstrated impressive capabilities across numerous domains, including medicine; the use of Machine Learning in patient assessment holds great promise for healthcare professionals and patients alike, as it can lead to more consistent and accurate symptom estimation.This survey aims to review methodologies that utilise Machine Learning for diagnosis and assessment of schizophrenia. Contrary to previous reviews that primarily focused on binary classification, this work recognises the complexity of the condition and instead, offers an overview of Machine Learning methods designed for fine-grained symptom estimation. We cover multiple modalities, namely Medical Imaging, Electroencephalograms and Audio-Visual, as the illness symptoms can manifest themselves both in a patient's pathology and behaviour. Finally, we analyse the datasets and methodologies used in the studies and identify trends, gaps as well as opportunities for future research. |
2109.02625 | Guande Wu | Guande Wu, Jianzhe Lin, Claudio T. Silva | ERA: Entity Relationship Aware Video Summarization with Wasserstein GAN | 8 pages, 3 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Video summarization aims to simplify large scale video browsing by generating
concise, short summaries that diver from but well represent the original video.
Due to the scarcity of video annotations, recent progress for video
summarization concentrates on unsupervised methods, among which the GAN based
methods are most prevalent. This type of methods includes a summarizer and a
discriminator. The summarized video from the summarizer will be assumed as the
final output, only if the video reconstructed from this summary cannot be
discriminated from the original one by the discriminator. The primary problems
of this GAN based methods are two folds. First, the summarized video in this
way is a subset of original video with low redundancy and contains high
priority events/entities. This summarization criterion is not enough. Second,
the training of the GAN framework is not stable. This paper proposes a novel
Entity relationship Aware video summarization method (ERA) to address the above
problems. To be more specific, we introduce an Adversarial Spatio Temporal
network to construct the relationship among entities, which we think should
also be given high priority in the summarization. The GAN training problem is
solved by introducing the Wasserstein GAN and two newly proposed video
patch/score sum losses. In addition, the score sum loss can also relieve the
model sensitivity to the varying video lengths, which is an inherent problem
for most current video analysis tasks. Our method substantially lifts the
performance on the target benchmark datasets and exceeds the current
leaderboard Rank 1 state of the art CSNet (2.1% F1 score increase on TVSum and
3.1% F1 score increase on SumMe). We hope our straightforward yet effective
approach will shed some light on the future research of unsupervised video
summarization.
| [
{
"created": "Mon, 6 Sep 2021 17:46:59 GMT",
"version": "v1"
}
] | 2021-09-07 | [
[
"Wu",
"Guande",
""
],
[
"Lin",
"Jianzhe",
""
],
[
"Silva",
"Claudio T.",
""
]
] | Video summarization aims to simplify large scale video browsing by generating concise, short summaries that diver from but well represent the original video. Due to the scarcity of video annotations, recent progress for video summarization concentrates on unsupervised methods, among which the GAN based methods are most prevalent. This type of methods includes a summarizer and a discriminator. The summarized video from the summarizer will be assumed as the final output, only if the video reconstructed from this summary cannot be discriminated from the original one by the discriminator. The primary problems of this GAN based methods are two folds. First, the summarized video in this way is a subset of original video with low redundancy and contains high priority events/entities. This summarization criterion is not enough. Second, the training of the GAN framework is not stable. This paper proposes a novel Entity relationship Aware video summarization method (ERA) to address the above problems. To be more specific, we introduce an Adversarial Spatio Temporal network to construct the relationship among entities, which we think should also be given high priority in the summarization. The GAN training problem is solved by introducing the Wasserstein GAN and two newly proposed video patch/score sum losses. In addition, the score sum loss can also relieve the model sensitivity to the varying video lengths, which is an inherent problem for most current video analysis tasks. Our method substantially lifts the performance on the target benchmark datasets and exceeds the current leaderboard Rank 1 state of the art CSNet (2.1% F1 score increase on TVSum and 3.1% F1 score increase on SumMe). We hope our straightforward yet effective approach will shed some light on the future research of unsupervised video summarization. |
2209.07302 | Xiaomin Li | Jianrong Wang, Xiaomin Li, Xuewei Li, Mei Yu, Qiang Fang, Li Liu | MVNet: Memory Assistance and Vocal Reinforcement Network for Speech
Enhancement | ICONIP 2022 | null | null | null | cs.SD eess.AS | http://creativecommons.org/publicdomain/zero/1.0/ | Speech enhancement improves speech quality and promotes the performance of
various downstream tasks. However, most current speech enhancement work was
mainly devoted to improving the performance of downstream automatic speech
recognition (ASR), only a relatively small amount of work focused on the
automatic speaker verification (ASV) task. In this work, we propose a MVNet
consisted of a memory assistance module which improves the performance of
downstream ASR and a vocal reinforcement module which boosts the performance of
ASV. In addition, we design a new loss function to improve speaker vocal
similarity. Experimental results on the Libri2mix dataset show that our method
outperforms baseline methods in several metrics, including speech quality,
intelligibility, and speaker vocal similarity et al.
| [
{
"created": "Thu, 15 Sep 2022 13:57:48 GMT",
"version": "v1"
}
] | 2022-09-16 | [
[
"Wang",
"Jianrong",
""
],
[
"Li",
"Xiaomin",
""
],
[
"Li",
"Xuewei",
""
],
[
"Yu",
"Mei",
""
],
[
"Fang",
"Qiang",
""
],
[
"Liu",
"Li",
""
]
] | Speech enhancement improves speech quality and promotes the performance of various downstream tasks. However, most current speech enhancement work was mainly devoted to improving the performance of downstream automatic speech recognition (ASR), only a relatively small amount of work focused on the automatic speaker verification (ASV) task. In this work, we propose a MVNet consisted of a memory assistance module which improves the performance of downstream ASR and a vocal reinforcement module which boosts the performance of ASV. In addition, we design a new loss function to improve speaker vocal similarity. Experimental results on the Libri2mix dataset show that our method outperforms baseline methods in several metrics, including speech quality, intelligibility, and speaker vocal similarity et al. |
2301.02711 | Thomas Thuesen Enevoldsen | Thomas T. Enevoldsen, Mogens Blanke, Roberto Galeazzi | Autonomy for Ferries and Harbour Buses: a Collision Avoidance
Perspective | Accepted for presentation at the IFAC World Congress 2023 | null | null | null | cs.RO cs.SY eess.SY | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper provides a collision avoidance perspective to maritime autonomy,
in the shift towards Maritime Autonomous Surface Ships (MASS). In particular,
the paper presents the developments related to the Greenhopper, Denmark's first
autonomous harbour bus. The collision and grounding avoidance scheme, called
the Short Horizon Planner (SHP), is described and discussed in detail.
Furthermore, the required autonomy stack for facilitating safe and
rule-compliant collision avoidance is presented. The inherent difficulties
related to adhering to the COLREGs are outlined, highlighting some of the
operational constraints and challenges within the space of autonomous ferries
and harbour buses. Finally, collision and grounding avoidance is demonstrated
using a simulation of the whole Greenhopper autonomy stack.
| [
{
"created": "Fri, 6 Jan 2023 20:57:47 GMT",
"version": "v1"
},
{
"created": "Thu, 20 Apr 2023 09:05:48 GMT",
"version": "v2"
}
] | 2023-04-21 | [
[
"Enevoldsen",
"Thomas T.",
""
],
[
"Blanke",
"Mogens",
""
],
[
"Galeazzi",
"Roberto",
""
]
] | This paper provides a collision avoidance perspective to maritime autonomy, in the shift towards Maritime Autonomous Surface Ships (MASS). In particular, the paper presents the developments related to the Greenhopper, Denmark's first autonomous harbour bus. The collision and grounding avoidance scheme, called the Short Horizon Planner (SHP), is described and discussed in detail. Furthermore, the required autonomy stack for facilitating safe and rule-compliant collision avoidance is presented. The inherent difficulties related to adhering to the COLREGs are outlined, highlighting some of the operational constraints and challenges within the space of autonomous ferries and harbour buses. Finally, collision and grounding avoidance is demonstrated using a simulation of the whole Greenhopper autonomy stack. |
2110.05747 | Khizar Hayat | Tanzila Qazi, Mushtaq Ali and Khizar Hayat | Seamless Copy Move Manipulation in Digital Images | 9 pages and 9 figures (most having subfigures) | J. Imaging 2022, 8(3), 69 | 10.3390/jimaging8030069 | null | cs.CV cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The importance and relevance of digital image forensics has attracted
researchers to establish different techniques for creating as well as detecting
forgeries. The core category in passive image forgery is copy-move image
forgery that affects the originality of image by applying a different
transformation. In this paper frequency domain image manipulation method is
being presented.The method exploits the localized nature of discrete wavelet
transform (DWT) to get hold of the region of the host image to be manipulated.
Both the patch and host image are subjected to DWT at the same level $l$ to get
$3l + 1$ sub-bands and each sub-band of the patch is pasted to the identified
region in the corresponding sub-band of the host image. The resultant
manipulated host sub-bands are then subjected to inverse DWT to get the final
manipulated host image. The proposed method shows good resistance against
detection by two frequency domain forgery detection methods from the
literature. The purpose of this research work is to create the forgery and
highlight the need to produce forgery detection methods that are robust against
the malicious copy-move forgery.
| [
{
"created": "Tue, 12 Oct 2021 05:35:26 GMT",
"version": "v1"
}
] | 2022-03-11 | [
[
"Qazi",
"Tanzila",
""
],
[
"Ali",
"Mushtaq",
""
],
[
"Hayat",
"Khizar",
""
]
] | The importance and relevance of digital image forensics has attracted researchers to establish different techniques for creating as well as detecting forgeries. The core category in passive image forgery is copy-move image forgery that affects the originality of image by applying a different transformation. In this paper frequency domain image manipulation method is being presented.The method exploits the localized nature of discrete wavelet transform (DWT) to get hold of the region of the host image to be manipulated. Both the patch and host image are subjected to DWT at the same level $l$ to get $3l + 1$ sub-bands and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. The resultant manipulated host sub-bands are then subjected to inverse DWT to get the final manipulated host image. The proposed method shows good resistance against detection by two frequency domain forgery detection methods from the literature. The purpose of this research work is to create the forgery and highlight the need to produce forgery detection methods that are robust against the malicious copy-move forgery. |
2006.16471 | Mazin Hnewa | Mazin Hnewa and Hayder Radha | Object Detection Under Rainy Conditions for Autonomous Vehicles: A
Review of State-of-the-Art and Emerging Techniques | null | IEEE Signal Processing Magazine, vol. 38, no. 1, pp. 53-67, Jan.
2021 | 10.1109/MSP.2020.2984801 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Advanced automotive active-safety systems, in general, and autonomous
vehicles, in particular, rely heavily on visual data to classify and localize
objects such as pedestrians, traffic signs and lights, and other nearby cars,
to assist the corresponding vehicles maneuver safely in their environments.
However, the performance of object detection methods could degrade rather
significantly under challenging weather scenarios including rainy conditions.
Despite major advancements in the development of deraining approaches, the
impact of rain on object detection has largely been understudied, especially in
the context of autonomous driving. The main objective of this paper is to
present a tutorial on state-of-the-art and emerging techniques that represent
leading candidates for mitigating the influence of rainy conditions on an
autonomous vehicle's ability to detect objects. Our goal includes surveying and
analyzing the performance of object detection methods trained and tested using
visual data captured under clear and rainy conditions. Moreover, we survey and
evaluate the efficacy and limitations of leading deraining approaches,
deep-learning based domain adaptation, and image translation frameworks that
are being considered for addressing the problem of object detection under rainy
conditions. Experimental results of a variety of the surveyed techniques are
presented as part of this tutorial.
| [
{
"created": "Tue, 30 Jun 2020 02:05:10 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Jul 2020 19:06:43 GMT",
"version": "v2"
},
{
"created": "Fri, 10 Jul 2020 19:51:52 GMT",
"version": "v3"
},
{
"created": "Fri, 12 Feb 2021 02:16:15 GMT",
"version": "v4"
}
] | 2021-02-17 | [
[
"Hnewa",
"Mazin",
""
],
[
"Radha",
"Hayder",
""
]
] | Advanced automotive active-safety systems, in general, and autonomous vehicles, in particular, rely heavily on visual data to classify and localize objects such as pedestrians, traffic signs and lights, and other nearby cars, to assist the corresponding vehicles maneuver safely in their environments. However, the performance of object detection methods could degrade rather significantly under challenging weather scenarios including rainy conditions. Despite major advancements in the development of deraining approaches, the impact of rain on object detection has largely been understudied, especially in the context of autonomous driving. The main objective of this paper is to present a tutorial on state-of-the-art and emerging techniques that represent leading candidates for mitigating the influence of rainy conditions on an autonomous vehicle's ability to detect objects. Our goal includes surveying and analyzing the performance of object detection methods trained and tested using visual data captured under clear and rainy conditions. Moreover, we survey and evaluate the efficacy and limitations of leading deraining approaches, deep-learning based domain adaptation, and image translation frameworks that are being considered for addressing the problem of object detection under rainy conditions. Experimental results of a variety of the surveyed techniques are presented as part of this tutorial. |
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