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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2201.00968
|
Thomas Watson
|
Md Lutfar Rahman and Thomas Watson
|
Erd\H{o}s-Selfridge Theorem for Nonmonotone CNFs
| null | null | null | null |
cs.DM cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In an influential paper, Erd\H{o}s and Selfridge introduced the Maker-Breaker
game played on a hypergraph, or equivalently, on a monotone CNF. The players
take turns assigning values to variables of their choosing, and Breaker's goal
is to satisfy the CNF, while Maker's goal is to falsify it. The
Erd\H{o}s-Selfridge Theorem says that the least number of clauses in any
monotone CNF with $k$ literals per clause where Maker has a winning strategy is
$\Theta(2^k)$.
We study the analogous question when the CNF is not necessarily monotone. We
prove bounds of $\Theta(\sqrt{2}\,^k)$ when Maker plays last, and
$\Omega(1.5^k)$ and $O(r^k)$ when Breaker plays last, where
$r=(1+\sqrt{5})/2\approx 1.618$ is the golden ratio.
|
[
{
"version": "v1",
"created": "Tue, 4 Jan 2022 04:15:11 GMT"
}
] | 2022-01-05T00:00:00 |
[
[
"Rahman",
"Md Lutfar",
""
],
[
"Watson",
"Thomas",
""
]
] |
new_dataset
| 0.971482 |
2201.00987
|
Yongchun Zhu
|
Qiong Nan, Juan Cao, Yongchun Zhu, Yanyan Wang, Jintao Li
|
MDFEND: Multi-domain Fake News Detection
|
CIKM 2021 short paper. 5 pages
| null |
10.1145/3459637.3482139
| null |
cs.CL cs.AI cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fake news spread widely on social media in various domains, which lead to
real-world threats in many aspects like politics, disasters, and finance. Most
existing approaches focus on single-domain fake news detection (SFND), which
leads to unsatisfying performance when these methods are applied to
multi-domain fake news detection. As an emerging field, multi-domain fake news
detection (MFND) is increasingly attracting attention. However, data
distributions, such as word frequency and propagation patterns, vary from
domain to domain, namely domain shift. Facing the challenge of serious domain
shift, existing fake news detection techniques perform poorly for multi-domain
scenarios. Therefore, it is demanding to design a specialized model for MFND.
In this paper, we first design a benchmark of fake news dataset for MFND with
domain label annotated, namely Weibo21, which consists of 4,488 fake news and
4,640 real news from 9 different domains. We further propose an effective
Multi-domain Fake News Detection Model (MDFEND) by utilizing a domain gate to
aggregate multiple representations extracted by a mixture of experts. The
experiments show that MDFEND can significantly improve the performance of
multi-domain fake news detection. Our dataset and code are available at
https://github.com/kennqiang/MDFEND-Weibo21.
|
[
{
"version": "v1",
"created": "Tue, 4 Jan 2022 05:28:25 GMT"
}
] | 2022-01-05T00:00:00 |
[
[
"Nan",
"Qiong",
""
],
[
"Cao",
"Juan",
""
],
[
"Zhu",
"Yongchun",
""
],
[
"Wang",
"Yanyan",
""
],
[
"Li",
"Jintao",
""
]
] |
new_dataset
| 0.999724 |
2201.01031
|
Madhu Raka
|
Swati Bhardwaj and Madhu Raka
|
Multi-dimensional Constacyclic Codes of Arbitrary Length over Finite
Fields
|
21 pages. arXiv admin note: text overlap with arXiv:2007.14921
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Multi-dimensional cyclic code is a natural generalization of cyclic code. In
an earlier paper we explored two-dimensional constacyclic codes over finite
fields. Following the same technique, here we characterize the algebraic
structure of multi-dimensional constacyclic codes, in particular
three-dimensional $(\alpha,\beta,\gamma)$- constacyclic codes of arbitrary
length $s\ell k$ and their duals over a finite field $\mathbb{F}_q$, where
$\alpha,\beta,\gamma$ are non zero elements of $\mathbb{F}_q$. We give
necessary and sufficient conditions for a three-dimensional
$(\alpha,\beta,\gamma)$- constacyclic code to be self-dual.
|
[
{
"version": "v1",
"created": "Tue, 4 Jan 2022 08:15:25 GMT"
}
] | 2022-01-05T00:00:00 |
[
[
"Bhardwaj",
"Swati",
""
],
[
"Raka",
"Madhu",
""
]
] |
new_dataset
| 0.999097 |
2201.01069
|
Damien Chablat
|
Liang Ma, Damien Chablat (ReV, LS2N), Fouad Bennis, Wei Zhang
|
A new simple dynamic muscle fatigue model and its validation
|
arXiv admin note: substantial text overlap with arXiv:0901.0222
|
International Journal of Industrial Ergonomics, Elsevier, 2009, 39
(1), pp.211-220
|
10.1016/j.ergon.2008.04.004
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Musculoskeletal disorder (MSD) is one of the major health problems in
physical work especially in manual handling jobs. In several literatures,
muscle fatigue is considered to be closely related to MSD, especially for
muscle related disorders. In addition to many existing analysis techniques for
muscle fatigue assessment and MSD risk analysis, in this paper, a new muscle
fatigue model was proposed. The new proposed model reflects the influence of
external load, workload history, and individual differences. This model is
simple in mathematics and can be easily applied in realtime calculation, such
as the application in realtime virtual work simulation and evaluation. The new
model was mathematically validated with 24 existing static models by comparing
the calculated METs, and qualitatively or quantitatively validated with 3
existing dynamic models. The proposed model shows high or moderate similarities
in predicting the METs with all the 24 static models. Validation results with
the three dynamic models were also promising. The main limitation of the model
is that it still lacks experimental validation for more dynamic situations.
Relevance to industry Muscle fatigue is one of the main reasons causing MSDs in
industry, especially for physical work. Correct evaluation of muscle fatigue is
necessary to determine work-rest regimens and reduce the risks of MSD.
|
[
{
"version": "v1",
"created": "Tue, 4 Jan 2022 10:09:42 GMT"
}
] | 2022-01-05T00:00:00 |
[
[
"Ma",
"Liang",
"",
"ReV, LS2N"
],
[
"Chablat",
"Damien",
"",
"ReV, LS2N"
],
[
"Bennis",
"Fouad",
""
],
[
"Zhang",
"Wei",
""
]
] |
new_dataset
| 0.996437 |
2201.01095
|
Mostafa Faraji
|
Mostafa Faraji, Alexander Seitz, Christoph Meier, Wolfgang A. Wall
|
A Mortar Finite Element Formulation for Large Deformation Lubricated
Contact Problems with Smooth Transition Between Mixed, Elasto-Hydrodynamic
and Full Hydrodynamic Lubrication
|
31 pages, 15 figures
| null | null | null |
cs.CE cs.NA math.NA physics.app-ph physics.flu-dyn
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work proposes a novel model and numerical formulation for lubricated
contact problems describing the mutual interaction between two deformable 3D
solid bodies and an interposed fluid film. The solid bodies are consistently
described based on nonlinear continuum mechanics allowing for finite
deformations and arbitrary constitutive laws. The fluid film is modelled as a
quasi-2D flow problem on the interface between the solids governed by the
averaged Reynolds equation. The averaged Reynolds equation accounts for surface
roughness utilizing spatially homogenized, effective fluid parameters and for
cavitation through a positivity constraint imposed on the pressure field. In
contrast to existing approaches, the proposed model accounts for the
co-existence of frictional contact tractions and hydrodynamic fluid tractions
at every local point on the contact surface of the interacting bodies and
covers the entire range from boundary lubrication to mixed, elastohydrodynamic,
and eventually to full film hydrodynamic lubrication in one unified modelling
framework with smooth transition between these different regimes. Critically,
the model relies on a recently proposed regularization scheme for the
mechanical contact constraint combining the advantages of classical penalty and
Lagrange multiplier approaches by expressing the mechanical contact pressure as
a function of the effective gap between the solid bodies while at the same time
limiting the minimal gap value occurring at the (theoretical) limit of
infinitely high contact pressures. From a physical point of view, this approach
can be considered as a model for the elastic deformation of surface asperities,
with a bounded magnitude depending on the interacting solids' surface
roughness. A consistent and accurate model behavior is demonstrated and
validated by employing several challenging and practically relevant benchmark
test cases.
|
[
{
"version": "v1",
"created": "Tue, 4 Jan 2022 11:43:00 GMT"
}
] | 2022-01-05T00:00:00 |
[
[
"Faraji",
"Mostafa",
""
],
[
"Seitz",
"Alexander",
""
],
[
"Meier",
"Christoph",
""
],
[
"Wall",
"Wolfgang A.",
""
]
] |
new_dataset
| 0.996625 |
2201.01275
|
Soumendu Chakraborty
|
Soumendu Chakraborty, Satish Kumar Singh, and Pavan Chakraborty
|
Local Quadruple Pattern: A Novel Descriptor for Facial Image Recognition
and Retrieval
|
arXiv admin note: substantial text overlap with arXiv:2201.00504,
arXiv:2201.00511
|
Computers & Electrical Engineering, vol-62, pp. 92-104, (2017).
(Elsevier) ISSN/ISBN: 0045-7906
| null | null |
cs.CV cs.MM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this paper a novel hand crafted local quadruple pattern (LQPAT) is
proposed for facial image recognition and retrieval. Most of the existing
hand-crafted descriptors encodes only a limited number of pixels in the local
neighbourhood. Under unconstrained environment the performance of these
descriptors tends to degrade drastically. The major problem in increasing the
local neighbourhood is that, it also increases the feature length of the
descriptor. The proposed descriptor try to overcome these problems by defining
an efficient encoding structure with optimal feature length. The proposed
descriptor encodes relations amongst the neighbours in quadruple space. Two
micro patterns are computed from the local relationships to form the
descriptor. The retrieval and recognition accuracies of the proposed descriptor
has been compared with state of the art hand crafted descriptors on bench mark
databases namely; Caltech-face, LFW, Colour-FERET, and CASIA-face-v5. Result
analysis shows that the proposed descriptor performs well under uncontrolled
variations in pose, illumination, background and expressions.
|
[
{
"version": "v1",
"created": "Mon, 3 Jan 2022 08:04:38 GMT"
}
] | 2022-01-05T00:00:00 |
[
[
"Chakraborty",
"Soumendu",
""
],
[
"Singh",
"Satish Kumar",
""
],
[
"Chakraborty",
"Pavan",
""
]
] |
new_dataset
| 0.998387 |
1801.00540
|
Apoorve Mohan
|
Apoorve Mohan, Ata Turk, Ravi S. Gudimetla, Sahil Tikale, Jason
Hennessey, Ugur Kaynar, Gene Cooperman, Peter Desnoyers, and Orran Krieger
|
M2: Malleable Metal as a Service
|
IEEE International Conference on Cloud Engineering 2018
| null | null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Existing bare-metal cloud services that provide users with physical nodes
have a number of serious disadvantage over their virtual alternatives,
including slow provisioning times, difficulty for users to release nodes and
then reuse them to handle changes in demand, and poor tolerance to failures. We
introduce M2, a bare-metal cloud service that uses network-mounted boot drives
to overcome these disadvantages. We describe the architecture and
implementation of M2 and compare its agility, scalability, and performance to
existing systems. We show that M2 can reduce provisioning time by over 50%
while offering richer functionality, and comparable run-time performance with
respect to tools that provision images into local disks. M2 is open source and
available at https://github.com/CCI-MOC/ims.
|
[
{
"version": "v1",
"created": "Tue, 2 Jan 2018 03:25:28 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Mohan",
"Apoorve",
""
],
[
"Turk",
"Ata",
""
],
[
"Gudimetla",
"Ravi S.",
""
],
[
"Tikale",
"Sahil",
""
],
[
"Hennessey",
"Jason",
""
],
[
"Kaynar",
"Ugur",
""
],
[
"Cooperman",
"Gene",
""
],
[
"Desnoyers",
"Peter",
""
],
[
"Krieger",
"Orran",
""
]
] |
new_dataset
| 0.996048 |
1811.04333
|
Ye Zhao
|
Ye Zhao and Yinan Li and Luis Sentis and Ufuk Topcu and Jun Liu
|
Reactive Task and Motion Planning for Robust Whole-Body Dynamic
Locomotion in Constrained Environments
|
49 pages, 23 figures, 1 table
| null | null | null |
cs.RO cs.FL cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Contact-based decision and planning methods are becoming increasingly
important to endow higher levels of autonomy for legged robots. Formal
synthesis methods derived from symbolic systems have great potential for
reasoning about high-level locomotion decisions and achieving complex
maneuvering behaviors with correctness guarantees. This study takes a first
step toward formally devising an architecture composed of task planning and
control of whole-body dynamic locomotion behaviors in constrained and
dynamically changing environments. At the high level, we formulate a two-player
temporal logic game between the multi-limb locomotion planner and its dynamic
environment to synthesize a winning strategy that delivers symbolic locomotion
actions. These locomotion actions satisfy the desired high-level task
specifications expressed in a fragment of temporal logic. Those actions are
sent to a robust finite transition system that synthesizes a locomotion
controller that fulfills state reachability constraints. This controller is
further executed via a low-level motion planner that generates feasible
locomotion trajectories. We construct a set of dynamic locomotion models for
legged robots to serve as a template library for handling diverse environmental
events. We devise a replanning strategy that takes into consideration sudden
environmental changes or large state disturbances to increase the robustness of
the resulting locomotion behaviors. We formally prove the correctness of the
layered locomotion framework guaranteeing a robust implementation by the motion
planning layer. Simulations of reactive locomotion behaviors in diverse
environments indicate that our framework has the potential to serve as a
theoretical foundation for intelligent locomotion behaviors.
|
[
{
"version": "v1",
"created": "Sun, 11 Nov 2018 02:19:27 GMT"
},
{
"version": "v2",
"created": "Sat, 1 Jan 2022 18:34:15 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Zhao",
"Ye",
""
],
[
"Li",
"Yinan",
""
],
[
"Sentis",
"Luis",
""
],
[
"Topcu",
"Ufuk",
""
],
[
"Liu",
"Jun",
""
]
] |
new_dataset
| 0.99356 |
2004.10506
|
Galymzhan Nauryzbayev
|
Leila Tlebaldiyeva, Galymzhan Nauryzbayev, Sultangali Arzykulov,
Yerassyl Akhmetkaziyev, Mohammad S. Hashmi, and Ahmed M. Eltawil
|
A Non-Ideal NOMA-based mmWave D2D Networks with Hardware and CSI
Imperfections
|
4 pages, 3 figures
| null |
10.1109/ICTC52510.2021.9621035
| null |
cs.IT cs.PF math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This letter investigates a non-orthogonal multiple access (NOMA) assisted
millimeter-wave device-to-device (D2D) network practically limited by multiple
interference noises, transceiver hardware impairments, imperfect successive
interference cancellation, and channel state information mismatch. Generalized
outage probability expressions for NOMA-D2D users are deduced and achieved
results, validated by Monte Carlo simulations, are compared with the orthogonal
multiple access to show the superior performance of the proposed network model
|
[
{
"version": "v1",
"created": "Wed, 22 Apr 2020 11:41:33 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Tlebaldiyeva",
"Leila",
""
],
[
"Nauryzbayev",
"Galymzhan",
""
],
[
"Arzykulov",
"Sultangali",
""
],
[
"Akhmetkaziyev",
"Yerassyl",
""
],
[
"Hashmi",
"Mohammad S.",
""
],
[
"Eltawil",
"Ahmed M.",
""
]
] |
new_dataset
| 0.991293 |
2008.11356
|
Sultangali Arzykulov
|
Sultangali Arzykulov, Abdulkadir Celik, Galymzhan Nauryzbayev, Ahmed
M. Eltawil
|
UAV-Assisted Cooperative & Cognitive NOMA: Deployment, Clustering, and
Resource Allocation
| null | null |
10.1109/TCCN.2021.3105133
| null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cooperative and cognitive non-orthogonal multiple access (CCR-NOMA) has been
recognized as a promising technique to overcome issues of spectrum scarcity and
support massive connectivity envisioned in next-generation wireless networks.
In this paper, we investigate the deployment of an unmanned aerial vehicle
(UAV) as a relay that fairly serves a large number of secondary users in a
hot-spot region. The UAV deployment algorithm must jointly account for user
clustering, channel assignment, and resource allocation sub-problems. We
propose a solution methodology that obtains user clustering and channel
assignment based on the optimal resource allocations for a given UAV location.
To this end, we derive closed-form optimal power and time allocations and show
it delivers optimal max-min fair throughput by consuming less energy and time
than geometric programming. Based on optimal resource allocation, the optimal
coverage probability is also provided in closed-form, which takes channel
estimation errors, hardware impairments, and primary network interference into
account. The optimal coverage probabilities are used by the proposed max-min
fair user clustering and channel assignment approaches. The results show that
the proposed method achieves 100% accuracy in more than five orders of
magnitude less time than the optimal benchmark.
|
[
{
"version": "v1",
"created": "Wed, 26 Aug 2020 03:08:28 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Arzykulov",
"Sultangali",
""
],
[
"Celik",
"Abdulkadir",
""
],
[
"Nauryzbayev",
"Galymzhan",
""
],
[
"Eltawil",
"Ahmed M.",
""
]
] |
new_dataset
| 0.984602 |
2101.05324
|
Pratap Tokekar
|
Deniz Ozsoyeller and Pratap Tokekar
|
Multi-robot Symmetric Rendezvous Search on the Line
| null | null |
10.1109/LRA.2021.3126350
| null |
cs.RO cs.DM
|
http://creativecommons.org/licenses/by/4.0/
|
We study the Symmetric Rendezvous Search Problem for a multi-robot system.
There are $n>2$ robots arbitrarily located on a line. Their goal is to meet
somewhere on the line as quickly as possible. The robots do not know the
initial location of any of the other robots or their own positions on the line.
The symmetric version of the problem requires the robots to execute the same
search strategy to achieve rendezvous. Therefore, we solve the problem in an
online fashion with a randomized strategy. In this paper, we present a
symmetric rendezvous algorithm which achieves a constant competitive ratio for
the total distance traveled by the robots. We validate our theoretical results
through simulations.
|
[
{
"version": "v1",
"created": "Wed, 13 Jan 2021 20:00:18 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Jan 2021 12:24:03 GMT"
},
{
"version": "v3",
"created": "Thu, 28 Jan 2021 17:57:01 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Ozsoyeller",
"Deniz",
""
],
[
"Tokekar",
"Pratap",
""
]
] |
new_dataset
| 0.982339 |
2103.01009
|
Charlie Blake
|
Charlie Blake, Vitaly Kurin, Maximilian Igl, Shimon Whiteson
|
Snowflake: Scaling GNNs to High-Dimensional Continuous Control via
Parameter Freezing
|
20 pages, 14 figures, published at NeurIPS 2021
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent research has shown that graph neural networks (GNNs) can learn
policies for locomotion control that are as effective as a typical multi-layer
perceptron (MLP), with superior transfer and multi-task performance (Wang et
al., 2018; Huang et al., 2020). Results have so far been limited to training on
small agents, with the performance of GNNs deteriorating rapidly as the number
of sensors and actuators grows. A key motivation for the use of GNNs in the
supervised learning setting is their applicability to large graphs, but this
benefit has not yet been realised for locomotion control. We identify the
weakness with a common GNN architecture that causes this poor scaling:
overfitting in the MLPs within the network that encode, decode, and propagate
messages. To combat this, we introduce Snowflake, a GNN training method for
high-dimensional continuous control that freezes parameters in parts of the
network that suffer from overfitting. Snowflake significantly boosts the
performance of GNNs for locomotion control on large agents, now matching the
performance of MLPs, and with superior transfer properties.
|
[
{
"version": "v1",
"created": "Mon, 1 Mar 2021 13:56:10 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Oct 2021 09:35:48 GMT"
},
{
"version": "v3",
"created": "Mon, 3 Jan 2022 16:58:14 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Blake",
"Charlie",
""
],
[
"Kurin",
"Vitaly",
""
],
[
"Igl",
"Maximilian",
""
],
[
"Whiteson",
"Shimon",
""
]
] |
new_dataset
| 0.979106 |
2103.04205
|
Shaddin Dughmi
|
Shaddin Dughmi
|
Matroid Secretary Is Equivalent to Contention Resolution
| null | null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We show that the matroid secretary problem is equivalent to correlated
contention resolution in the online random-order model. Specifically, the
matroid secretary conjecture is true if and only if every matroid admits an
online random-order contention resolution scheme which, given an arbitrary
(possibly correlated) prior distribution over subsets of the ground set,
matches the balance ratio of the best offline scheme for that distribution up
to a constant. We refer to such a scheme as universal. Our result indicates
that the core challenge of the matroid secretary problem lies in resolving
contention for positively correlated inputs, in particular when the positive
correlation is benign in as much as offline contention resolution is concerned.
Our result builds on our previous work which establishes one direction of
this equivalence, namely that the secretary conjecture implies universal
random-order contention resolution, as well as a weak converse, which derives a
matroid secretary algorithm from a random-order contention resolution scheme
with only partial knowledge of the distribution. It is this weak converse that
we strengthen in this paper: We show that universal random-order contention
resolution for matroids, in the usual setting of a fully known prior
distribution, suffices to resolve the matroid secretary conjecture in the
affirmative.
Our proof is the composition of three reductions. First, we use duality
arguments to reduce the matroid secretary problem to the matroid prophet
secretary problem with arbitrarily correlated distributions. Second, we
introduce a generalization of contention resolution we term labeled contention
resolution, to which we reduce the correlated matroid prophet secretary
problem. Finally, we combine duplication of elements with limiting arguments to
reduce labeled contention resolution to classical contention resolution.
|
[
{
"version": "v1",
"created": "Sat, 6 Mar 2021 22:46:29 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Jun 2021 00:31:59 GMT"
},
{
"version": "v3",
"created": "Mon, 3 Jan 2022 09:32:03 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Dughmi",
"Shaddin",
""
]
] |
new_dataset
| 0.999588 |
2104.09379
|
Yihang Yin
|
Yihang Yin, Siyu Huang, Xiang Zhang
|
BM-NAS: Bilevel Multimodal Neural Architecture Search
|
Accepted by AAAI 2022
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep neural networks (DNNs) have shown superior performances on various
multimodal learning problems. However, it often requires huge efforts to adapt
DNNs to individual multimodal tasks by manually engineering unimodal features
and designing multimodal feature fusion strategies. This paper proposes Bilevel
Multimodal Neural Architecture Search (BM-NAS) framework, which makes the
architecture of multimodal fusion models fully searchable via a bilevel
searching scheme. At the upper level, BM-NAS selects the inter/intra-modal
feature pairs from the pretrained unimodal backbones. At the lower level,
BM-NAS learns the fusion strategy for each feature pair, which is a combination
of predefined primitive operations. The primitive operations are elaborately
designed and they can be flexibly combined to accommodate various effective
feature fusion modules such as multi-head attention (Transformer) and Attention
on Attention (AoA). Experimental results on three multimodal tasks demonstrate
the effectiveness and efficiency of the proposed BM-NAS framework. BM-NAS
achieves competitive performances with much less search time and fewer model
parameters in comparison with the existing generalized multimodal NAS methods.
|
[
{
"version": "v1",
"created": "Mon, 19 Apr 2021 15:09:49 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Jan 2022 16:06:43 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Yin",
"Yihang",
""
],
[
"Huang",
"Siyu",
""
],
[
"Zhang",
"Xiang",
""
]
] |
new_dataset
| 0.950767 |
2105.06162
|
Lev Tauz
|
Lev Tauz, Lara Dolecek
|
Variable Coded Batch Matrix Multiplication
|
35 pages, 8 figures, a part of this manuscript was published at IEEE
Global Communications Conference (GLOBECOM) 2021
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A majority of coded matrix-matrix computation literature has broadly focused
in two directions: matrix partitioning for computing a single computation task
and batch processing of multiple distinct computation tasks. While these works
provide codes with good straggler resilience and fast decoding for their
problem spaces, these codes would not be able to take advantage of the natural
redundancy of re-using matrices across batch jobs. In this paper, we introduce
the Variable Coded Distributed Batch Matrix Multiplication (VCDBMM) problem
which tasks a distributed system to perform batch matrix multiplication where
matrices are not necessarily distinct among batch jobs. Inspired in part by
Cross-Subspace Alignment codes, we develop Flexible Cross-Subspace Alignments
(FCSA) codes that are flexible enough to utilize this redundancy. We provide a
full characterization of FCSA codes which allow for a wide variety of system
complexities including good straggler resilience and fast decoding. We
theoretically demonstrate that, under certain practical conditions, FCSA codes
are within a factor of 2 of the optimal solution when it comes to straggler
resilience. Furthermore, our simulations demonstrate that our codes can achieve
even better optimality gaps in practice, even going as low as 1.7.
|
[
{
"version": "v1",
"created": "Thu, 13 May 2021 09:39:32 GMT"
},
{
"version": "v2",
"created": "Fri, 28 May 2021 00:20:39 GMT"
},
{
"version": "v3",
"created": "Tue, 14 Sep 2021 21:02:05 GMT"
},
{
"version": "v4",
"created": "Sat, 1 Jan 2022 01:18:33 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Tauz",
"Lev",
""
],
[
"Dolecek",
"Lara",
""
]
] |
new_dataset
| 0.998948 |
2112.12946
|
Qizhen Zhang
|
Qizhen Zhang, Philip A. Bernstein, Daniel S. Berger, Badrish
Chandramouli
|
Redy: Remote Dynamic Memory Cache
|
This is the extended report of Redy (accepted at VLDB 2022)
| null | null | null |
cs.DC cs.DB
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Redy is a cloud service that provides high performance caches using
RDMA-accessible remote memory. An application can customize the performance of
each cache with a service level objective (SLO) for latency and throughput. By
using remote memory, it can leverage stranded memory and spot VM instances to
reduce the cost of its caches and improve data center resource utilization.
Redy automatically customizes the resource configuration for the given SLO,
handles the dynamics of remote memory regions, and recovers from failures. The
experimental evaluation shows that Redy can deliver its promised performance
and robustness under remote memory dynamics in the cloud. We augment a
production key-value store, FASTER, with a Redy cache. When the working set
exceeds local memory, using Redy is significantly faster than spilling to SSDs.
|
[
{
"version": "v1",
"created": "Fri, 24 Dec 2021 05:13:18 GMT"
},
{
"version": "v2",
"created": "Sat, 1 Jan 2022 06:21:02 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Zhang",
"Qizhen",
""
],
[
"Bernstein",
"Philip A.",
""
],
[
"Berger",
"Daniel S.",
""
],
[
"Chandramouli",
"Badrish",
""
]
] |
new_dataset
| 0.998994 |
2201.00059
|
Junyi Geng
|
Xinke Deng, Junyi Geng, Timothy Bretl, Yu Xiang and Dieter Fox
|
iCaps: Iterative Category-level Object Pose and Shape Estimation
| null | null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
This paper proposes a category-level 6D object pose and shape estimation
approach iCaps, which allows tracking 6D poses of unseen objects in a category
and estimating their 3D shapes. We develop a category-level auto-encoder
network using depth images as input, where feature embeddings from the
auto-encoder encode poses of objects in a category. The auto-encoder can be
used in a particle filter framework to estimate and track 6D poses of objects
in a category. By exploiting an implicit shape representation based on signed
distance functions, we build a LatentNet to estimate a latent representation of
the 3D shape given the estimated pose of an object. Then the estimated pose and
shape can be used to update each other in an iterative way. Our category-level
6D object pose and shape estimation pipeline only requires 2D detection and
segmentation for initialization. We evaluate our approach on a publicly
available dataset and demonstrate its effectiveness. In particular, our method
achieves comparably high accuracy on shape estimation.
|
[
{
"version": "v1",
"created": "Fri, 31 Dec 2021 21:15:05 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Deng",
"Xinke",
""
],
[
"Geng",
"Junyi",
""
],
[
"Bretl",
"Timothy",
""
],
[
"Xiang",
"Yu",
""
],
[
"Fox",
"Dieter",
""
]
] |
new_dataset
| 0.979306 |
2201.00080
|
Xiaotong Chen
|
Xiaotong Chen, Seyed Mehdi Iranmanesh, Kuo-Chin Lien
|
PatchTrack: Multiple Object Tracking Using Frame Patches
|
11 pages, 4 figures, 2 tables
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Object motion and object appearance are commonly used information in multiple
object tracking (MOT) applications, either for associating detections across
frames in tracking-by-detection methods or direct track predictions for
joint-detection-and-tracking methods. However, not only are these two types of
information often considered separately, but also they do not help optimize the
usage of visual information from the current frame of interest directly. In
this paper, we present PatchTrack, a Transformer-based
joint-detection-and-tracking system that predicts tracks using patches of the
current frame of interest. We use the Kalman filter to predict the locations of
existing tracks in the current frame from the previous frame. Patches cropped
from the predicted bounding boxes are sent to the Transformer decoder to infer
new tracks. By utilizing both object motion and object appearance information
encoded in patches, the proposed method pays more attention to where new tracks
are more likely to occur. We show the effectiveness of PatchTrack on recent MOT
benchmarks, including MOT16 (MOTA 73.71%, IDF1 65.77%) and MOT17 (MOTA 73.59%,
IDF1 65.23%). The results are published on
https://motchallenge.net/method/MOT=4725&chl=10.
|
[
{
"version": "v1",
"created": "Sat, 1 Jan 2022 00:16:45 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Chen",
"Xiaotong",
""
],
[
"Iranmanesh",
"Seyed Mehdi",
""
],
[
"Lien",
"Kuo-Chin",
""
]
] |
new_dataset
| 0.998847 |
2201.00096
|
Mohamed Amine Kerkouri
|
Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani, Mohamed
Sayeh
|
SalyPath360: Saliency and Scanpath Prediction Framework for
Omnidirectional Images
|
Accepted at Electornic Imaging Sympotium 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
This paper introduces a new framework to predict visual attention of
omnidirectional images. The key setup of our architecture is the simultaneous
prediction of the saliency map and a corresponding scanpath for a given
stimulus. The framework implements a fully encoder-decoder convolutional neural
network augmented by an attention module to generate representative saliency
maps. In addition, an auxiliary network is employed to generate probable
viewport center fixation points through the SoftArgMax function. The latter
allows to derive fixation points from feature maps. To take advantage of the
scanpath prediction, an adaptive joint probability distribution model is then
applied to construct the final unbiased saliency map by leveraging the encoder
decoder-based saliency map and the scanpath-based saliency heatmap. The
proposed framework was evaluated in terms of saliency and scanpath prediction,
and the results were compared to state-of-the-art methods on Salient360!
dataset. The results showed the relevance of our framework and the benefits of
such architecture for further omnidirectional visual attention prediction
tasks.
|
[
{
"version": "v1",
"created": "Sat, 1 Jan 2022 02:37:33 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Kerkouri",
"Mohamed Amine",
""
],
[
"Tliba",
"Marouane",
""
],
[
"Chetouani",
"Aladine",
""
],
[
"Sayeh",
"Mohamed",
""
]
] |
new_dataset
| 0.990184 |
2201.00112
|
Andrew Luo
|
Andrew Luo, Tianqin Li, Wen-Hao Zhang, Tai Sing Lee
|
SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface
Discriminators
|
ICCV 2021. Project page: https://github.com/aluo-x/NeuralRaycaster
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent advances in deep generative models have led to immense progress in 3D
shape synthesis. While existing models are able to synthesize shapes
represented as voxels, point-clouds, or implicit functions, these methods only
indirectly enforce the plausibility of the final 3D shape surface. Here we
present a 3D shape synthesis framework (SurfGen) that directly applies
adversarial training to the object surface. Our approach uses a differentiable
spherical projection layer to capture and represent the explicit zero
isosurface of an implicit 3D generator as functions defined on the unit sphere.
By processing the spherical representation of 3D object surfaces with a
spherical CNN in an adversarial setting, our generator can better learn the
statistics of natural shape surfaces. We evaluate our model on large-scale
shape datasets, and demonstrate that the end-to-end trained model is capable of
generating high fidelity 3D shapes with diverse topology.
|
[
{
"version": "v1",
"created": "Sat, 1 Jan 2022 04:44:42 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Luo",
"Andrew",
""
],
[
"Li",
"Tianqin",
""
],
[
"Zhang",
"Wen-Hao",
""
],
[
"Lee",
"Tai Sing",
""
]
] |
new_dataset
| 0.990029 |
2201.00132
|
Thanh Le-Cong Le-Cong Thanh
|
Bao Hieu Tran, Thanh Le-Cong, Huu Manh Nguyen, Duc Anh Le, Thanh Hung
Nguyen, Phi Le Nguyen
|
SAFL: A Self-Attention Scene Text Recognizer with Focal Loss
|
Accepted to ICMLA 2020
|
2020 19th IEEE International Conference on Machine Learning and
Applications (ICMLA)
|
10.1109/ICMLA51294.2020.00223
| null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In the last decades, scene text recognition has gained worldwide attention
from both the academic community and actual users due to its importance in a
wide range of applications. Despite achievements in optical character
recognition, scene text recognition remains challenging due to inherent
problems such as distortions or irregular layout. Most of the existing
approaches mainly leverage recurrence or convolution-based neural networks.
However, while recurrent neural networks (RNNs) usually suffer from slow
training speed due to sequential computation and encounter problems as
vanishing gradient or bottleneck, CNN endures a trade-off between complexity
and performance. In this paper, we introduce SAFL, a self-attention-based
neural network model with the focal loss for scene text recognition, to
overcome the limitation of the existing approaches. The use of focal loss
instead of negative log-likelihood helps the model focus more on low-frequency
samples training. Moreover, to deal with the distortions and irregular texts,
we exploit Spatial TransformerNetwork (STN) to rectify text before passing to
the recognition network. We perform experiments to compare the performance of
the proposed model with seven benchmarks. The numerical results show that our
model achieves the best performance.
|
[
{
"version": "v1",
"created": "Sat, 1 Jan 2022 06:51:03 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Tran",
"Bao Hieu",
""
],
[
"Le-Cong",
"Thanh",
""
],
[
"Nguyen",
"Huu Manh",
""
],
[
"Le",
"Duc Anh",
""
],
[
"Nguyen",
"Thanh Hung",
""
],
[
"Nguyen",
"Phi Le",
""
]
] |
new_dataset
| 0.999482 |
2201.00199
|
Radostin Cholakov
|
Radostin Cholakov and Todor Kolev
|
The GatedTabTransformer. An enhanced deep learning architecture for
tabular modeling
|
10 pages, 6 figures
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
There is an increasing interest in the application of deep learning
architectures to tabular data. One of the state-of-the-art solutions is
TabTransformer which incorporates an attention mechanism to better track
relationships between categorical features and then makes use of a standard MLP
to output its final logits. In this paper we propose multiple modifications to
the original TabTransformer performing better on binary classification tasks
for three separate datasets with more than 1% AUROC gains. Inspired by gated
MLP, linear projections are implemented in the MLP block and multiple
activation functions are tested. We also evaluate the importance of specific
hyper parameters during training.
|
[
{
"version": "v1",
"created": "Sat, 1 Jan 2022 14:52:04 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Cholakov",
"Radostin",
""
],
[
"Kolev",
"Todor",
""
]
] |
new_dataset
| 0.986928 |
2201.00220
|
Dani Kiyasseh
|
Dani Kiyasseh, Rasheed El-Bouri
|
Turath-150K: Image Database of Arab Heritage
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Large-scale image databases remain largely biased towards objects and
activities encountered in a select few cultures. This absence of
culturally-diverse images, which we refer to as the hidden tail, limits the
applicability of pre-trained neural networks and inadvertently excludes
researchers from under-represented regions. To begin remedying this issue, we
curate Turath-150K, a database of images of the Arab world that reflect
objects, activities, and scenarios commonly found there. In the process, we
introduce three benchmark databases, Turath Standard, Art, and UNESCO,
specialised subsets of the Turath dataset. After demonstrating the limitations
of existing networks pre-trained on ImageNet when deployed on such benchmarks,
we train and evaluate several networks on the task of image classification. As
a consequence of Turath, we hope to engage machine learning researchers in
under-represented regions, and to inspire the release of additional
culture-focused databases. The database can be accessed here:
danikiyasseh.github.io/Turath.
|
[
{
"version": "v1",
"created": "Sat, 1 Jan 2022 17:36:25 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Kiyasseh",
"Dani",
""
],
[
"El-Bouri",
"Rasheed",
""
]
] |
new_dataset
| 0.999091 |
2201.00242
|
Martin T\"orngren
|
Martin T\"orngren, Haydn Thompson, Erik Herzog, Rafia Inam, James
Gross and Gy\"orgy D\'an
|
Industrial Edge-based Cyber-Physical Systems -- Application Needs and
Concerns for Realization
|
7 pages, 1 figure
| null |
10.1145/3453142.3493507
| null |
cs.DC cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Industry is moving towards advanced Cyber-Physical Systems (CPS), with trends
in smartness, automation, connectivity and collaboration. We examine the
drivers and requirements for the use of edge computing in critical industrial
applications. Our purpose is to provide a better understanding of industrial
needs and to initiate a discussion on what role edge computing could take,
complementing current industrial and embedded systems, and the cloud. Four
domains are chosen for analysis with representative use-cases; manufacturing,
transportation, the energy sector and networked applications in the defense
domain. We further discuss challenges, open issues and suggested directions
that are needed to pave the way for the use of edge computing in industrial
CPS.
|
[
{
"version": "v1",
"created": "Sat, 1 Jan 2022 20:54:25 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Törngren",
"Martin",
""
],
[
"Thompson",
"Haydn",
""
],
[
"Herzog",
"Erik",
""
],
[
"Inam",
"Rafia",
""
],
[
"Gross",
"James",
""
],
[
"Dán",
"György",
""
]
] |
new_dataset
| 0.973226 |
2201.00277
|
Balsam Alkouz
|
Balsam Alkouz, Babar Shahzaad, Athman Bouguettaya
|
Service-Based Drone Delivery
|
9 pages, 7 figures. This is an accepted paper and it is going to
appear in the Proceedings of the 2021 IEEE International Conference on
Collaboration and Internet Computing (CIC 2021)
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Service delivery is set to experience a major paradigm shift with fast
advances in drone technologies coupled with higher expectations from customers
and increased competition. We propose a novel service-oriented approach to
enable the ubiquitous delivery of packages in a drone-operated skyway network.
We discuss the benefits, framework and architecture, contemporary approaches,
open challenges and future visioned directions of service-based drone
deliveries.
|
[
{
"version": "v1",
"created": "Sun, 2 Jan 2022 02:34:10 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Alkouz",
"Balsam",
""
],
[
"Shahzaad",
"Babar",
""
],
[
"Bouguettaya",
"Athman",
""
]
] |
new_dataset
| 0.994569 |
2201.00290
|
Luis Jose Alarcon Aneiva
|
Luis Alarcon and Job Ledezma
|
Develop of a Pneumatic Force Sensor Prototype
|
in Spanish
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
One of the difficulties of applying a SEA force control is the complexity
that exists when implementing its three main components, which must work
together one after the other. To facilitate the implementation of a force
control by SEA, in this thesis the pneumatic force sensor is developed. A
pneumatic force sensor differs from other force sensors in that it can work as
a force sensor and as an elastic element. These features facilitate the
implementation of force control by SEA, by reducing the number of components
required. On the other hand, the pneumatic force sensor has reduced proportions
to facilitate its installation in manipulator robots and biomechatronic
prostheses. The first step that was made for the development of the pneumatic
force sensor was the construction of the mathematical model of the sensor, to
later use the MATLAB / Simulink software to simulate it. With the data obtained
from the simulation of the mathematical model, the CAD model and the sensor
planes were developed in SolidWorks software. Subsequently, the prototype of
the pneumatic force sensor was built based on the plans made in the SolidWorks
software. Once the stage of construction of the pneumatic force sensor was
completed, the calibration and classification of the force sensor was carried
out based on the UNE-EN ISO 376 standard, and the experimental tests were
carried out to validate the sensor. Once the classification of the pneumatic
force sensor was obtained, the results of the simulation of the mathematical
model were compared with the results of the experimental test. vi In the
comparison, it was possible to show a graphic coherence in the results
obtained, validating the pneumatic force sensor system.
|
[
{
"version": "v1",
"created": "Sun, 2 Jan 2022 05:03:10 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Alarcon",
"Luis",
""
],
[
"Ledezma",
"Job",
""
]
] |
new_dataset
| 0.997222 |
2201.00377
|
Jo\~ao Morais
|
Jo\~ao Morais, Kaushal Rathi, Bhuvaneshwar Mohan, Shantanu Rajesh
|
Parkour Spot ID: Feature Matching in Satellite and Street view images
using Deep Learning
| null | null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
How to find places that are not indexed by Google Maps? We propose an
intuitive method and framework to locate places based on their distinctive
spatial features. The method uses satellite and street view images in machine
vision approaches to classify locations. If we can classify locations, we just
need to repeat for non-overlapping locations in our area of interest. We assess
the proposed system in finding Parkour spots in the campus of Arizona State
University. The results are very satisfactory, having found more than 25 new
Parkour spots, with a rate of true positives above 60%.
|
[
{
"version": "v1",
"created": "Sun, 2 Jan 2022 16:55:00 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Morais",
"João",
""
],
[
"Rathi",
"Kaushal",
""
],
[
"Mohan",
"Bhuvaneshwar",
""
],
[
"Rajesh",
"Shantanu",
""
]
] |
new_dataset
| 0.973201 |
2201.00455
|
Bejan Sadeghian
|
Bejan Sadeghian
|
Actor-Critic Network for Q&A in an Adversarial Environment
|
6 pages, 3 figures, 3 tables
| null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Significant work has been placed in the Q&A NLP space to build models that
are more robust to adversarial attacks. Two key areas of focus are in
generating adversarial data for the purposes of training against these
situations or modifying existing architectures to build robustness within. This
paper introduces an approach that joins these two ideas together to train a
critic model for use in an almost reinforcement learning framework. Using the
Adversarial SQuAD "Add One Sent" dataset we show that there are some promising
signs for this method in protecting against Adversarial attacks.
|
[
{
"version": "v1",
"created": "Mon, 3 Jan 2022 02:35:58 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Sadeghian",
"Bejan",
""
]
] |
new_dataset
| 0.999451 |
2201.00467
|
Constantine Roros
|
Constantine J. Roros, Avinash C. Kak
|
maskGRU: Tracking Small Objects in the Presence of Large Background
Motions
|
12 pages, 3 figures
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a recurrent neural network-based spatio-temporal framework named
maskGRU for the detection and tracking of small objects in videos. While there
have been many developments in the area of object tracking in recent years,
tracking a small moving object amid other moving objects and actors (such as a
ball amid moving players in sports footage) continues to be a difficult task.
Existing spatio-temporal networks, such as convolutional Gated Recurrent Units
(convGRUs), are difficult to train and have trouble accurately tracking small
objects under such conditions. To overcome these difficulties, we developed the
maskGRU framework that uses a weighted sum of the internal hidden state
produced by a convGRU and a 3-channel mask of the tracked object's predicted
bounding box as the hidden state to be used at the next time step of the
underlying convGRU. We believe the technique of incorporating a mask into the
hidden state through a weighted sum has two benefits: controlling the effect of
exploding gradients and introducing an attention-like mechanism into the
network by indicating where in the previous video frame the object is located.
Our experiments show that maskGRU outperforms convGRU at tracking objects that
are small relative to the video resolution even in the presence of other moving
objects.
|
[
{
"version": "v1",
"created": "Mon, 3 Jan 2022 04:10:02 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Roros",
"Constantine J.",
""
],
[
"Kak",
"Avinash C.",
""
]
] |
new_dataset
| 0.960527 |
2201.00486
|
Kshitija Taywade
|
Kshitija Taywade, Brent Harrison, Judy Goldsmith
|
Using Non-Stationary Bandits for Learning in Repeated Cournot Games with
Non-Stationary Demand
|
13 pages
| null | null | null |
cs.LG cs.GT cs.MA econ.GN q-fin.EC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many past attempts at modeling repeated Cournot games assume that demand is
stationary. This does not align with real-world scenarios in which market
demands can evolve over a product's lifetime for a myriad of reasons. In this
paper, we model repeated Cournot games with non-stationary demand such that
firms/agents face separate instances of non-stationary multi-armed bandit
problem. The set of arms/actions that an agent can choose from represents
discrete production quantities; here, the action space is ordered. Agents are
independent and autonomous, and cannot observe anything from the environment;
they can only see their own rewards after taking an action, and only work
towards maximizing these rewards. We propose a novel algorithm 'Adaptive with
Weighted Exploration (AWE) $\epsilon$-greedy' which is remotely based on the
well-known $\epsilon$-greedy approach. This algorithm detects and quantifies
changes in rewards due to varying market demand and varies learning rate and
exploration rate in proportion to the degree of changes in demand, thus
enabling agents to better identify new optimal actions. For efficient
exploration, it also deploys a mechanism for weighing actions that takes
advantage of the ordered action space. We use simulations to study the
emergence of various equilibria in the market. In addition, we study the
scalability of our approach in terms number of total agents in the system and
the size of action space. We consider both symmetric and asymmetric firms in
our models. We found that using our proposed method, agents are able to swiftly
change their course of action according to the changes in demand, and they also
engage in collusive behavior in many simulations.
|
[
{
"version": "v1",
"created": "Mon, 3 Jan 2022 05:51:47 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Taywade",
"Kshitija",
""
],
[
"Harrison",
"Brent",
""
],
[
"Goldsmith",
"Judy",
""
]
] |
new_dataset
| 0.992002 |
2201.00598
|
Harveen Singh Chadha
|
Manan Jhaveri, Devanshu Ramaiya, Harveen Singh Chadha
|
Toxicity Detection for Indic Multilingual Social Media Content
|
It was meant for IEEE BigM conference
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Toxic content is one of the most critical issues for social media platforms
today. India alone had 518 million social media users in 2020. In order to
provide a good experience to content creators and their audience, it is crucial
to flag toxic comments and the users who post that. But the big challenge is
identifying toxicity in low resource Indic languages because of the presence of
multiple representations of the same text. Moreover, the posts/comments on
social media do not adhere to a particular format, grammar or sentence
structure; this makes the task of abuse detection even more challenging for
multilingual social media platforms. This paper describes the system proposed
by team 'Moj Masti' using the data provided by ShareChat/Moj in \emph{IIIT-D
Multilingual Abusive Comment Identification} challenge. We focus on how we can
leverage multilingual transformer based pre-trained and fine-tuned models to
approach code-mixed/code-switched classification tasks. Our best performing
system was an ensemble of XLM-RoBERTa and MuRIL which achieved a Mean F-1 score
of 0.9 on the test data/leaderboard. We also observed an increase in the
performance by adding transliterated data. Furthermore, using weak metadata,
ensembling and some post-processing techniques boosted the performance of our
system, thereby placing us 1st on the leaderboard.
|
[
{
"version": "v1",
"created": "Mon, 3 Jan 2022 12:01:47 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Jhaveri",
"Manan",
""
],
[
"Ramaiya",
"Devanshu",
""
],
[
"Chadha",
"Harveen Singh",
""
]
] |
new_dataset
| 0.998847 |
2201.00613
|
Cristobal A. Navarro
|
Felipe A. Quezada, Crist\'obal A. Navarro, Nancy Hitschfeld, Benjamin
Bustos
|
Squeeze: Efficient Compact Fractals for Tensor Core GPUs
| null | null | null | null |
cs.DC cs.CG cs.DM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work presents Squeeze, an efficient compact fractal processing scheme
for tensor core GPUs. By combining discrete-space transformations between
compact and expanded forms, one can do data-parallel computation on a fractal
with neighborhood access without needing to expand the fractal in memory. The
space transformations are formulated as two GPU tensor-core accelerated thread
maps, $\lambda(\omega)$ and $\nu(\omega)$, which act as compact-to-expanded and
expanded-to-compact space functions, respectively. The cost of the maps is
$\mathcal{O}(\log_2 \log_s(n))$ time, with $n$ being the side of a $n \times n$
embedding for the fractal in its expanded form, and $s$ the linear scaling
factor. The proposed approach works for any fractal that belongs to the
Non-overlapping-Bounding-Boxes (NBB) class of discrete fractals, and can be
extended to three dimensions as well. Experimental results using a discrete
Sierpinski Triangle as a case study shows up to $\sim12\times$ of speedup and a
memory reduction factor of up to $\sim 315\times$ with respect to a GPU-based
expanded-space bounding box approach. These results show that the proposed
compact approach will allow the scientific community to efficiently tackle
problems that up to now could not fit into GPU memory.
|
[
{
"version": "v1",
"created": "Mon, 3 Jan 2022 13:03:05 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Quezada",
"Felipe A.",
""
],
[
"Navarro",
"Cristóbal A.",
""
],
[
"Hitschfeld",
"Nancy",
""
],
[
"Bustos",
"Benjamin",
""
]
] |
new_dataset
| 0.996341 |
2201.00618
|
Natanael Arndt
|
Natanael Arndt, Sebastian Z\"anker, Gezim Sejdiu, Sebastian Tramp
|
Jekyll RDF: Template-Based Linked Data Publication with Minimized Effort
and Maximum Scalability
|
16 pages, 8 figures, 2 tables, 2 listings, Conference: ICWE 2019,
Daejeon, South Korea
|
Web Engineering. ICWE 2019. Lecture Notes in Computer Science, vol
11496. Springer, Cham
|
10.1007/978-3-030-19274-7_24
| null |
cs.DB cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Over the last decades the Web has evolved from a human-human communication
network to a network of complex human-machine interactions. An increasing
amount of data is available as Linked Data which allows machines to
"understand" the data, but RDF is not meant to be understood by humans. With
Jekyll RDF we present a method to close the gap between structured data and
human accessible exploration interfaces by publishing RDF datasets as
customizable static HTML sites. It consists of an RDF resource mapping system
to serve the resources under their respective IRI, a template mapping based on
schema classes, and a markup language to define templates to render customized
resource pages. Using the template system, it is possible to create domain
specific browsing interfaces for RDF data next to the Linked Data resources.
This enables content management and knowledge management systems to serve
datasets in a highly customizable, low effort, and scalable way to be consumed
by machines as well as humans.
|
[
{
"version": "v1",
"created": "Fri, 10 Dec 2021 14:55:51 GMT"
}
] | 2022-01-04T00:00:00 |
[
[
"Arndt",
"Natanael",
""
],
[
"Zänker",
"Sebastian",
""
],
[
"Sejdiu",
"Gezim",
""
],
[
"Tramp",
"Sebastian",
""
]
] |
new_dataset
| 0.982321 |
1907.00457
|
Julian Salazar
|
Shaoshi Ling, Julian Salazar, Yuzong Liu, Katrin Kirchhoff
|
BERTphone: Phonetically-Aware Encoder Representations for
Utterance-Level Speaker and Language Recognition
|
Odyssey 2020 camera-ready (presented Nov. 2020)
|
Proc. the Speaker and Language Recognition Workshop (Odyssey
2020), 9-16
|
10.21437/Odyssey.2020-2
| null |
cs.CL cs.LG cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce BERTphone, a Transformer encoder trained on large speech corpora
that outputs phonetically-aware contextual representation vectors that can be
used for both speaker and language recognition. This is accomplished by
training on two objectives: the first, inspired by adapting BERT to the
continuous domain, involves masking spans of input frames and reconstructing
the whole sequence for acoustic representation learning; the second, inspired
by the success of bottleneck features from ASR, is a sequence-level CTC loss
applied to phoneme labels for phonetic representation learning. We pretrain two
BERTphone models (one on Fisher and one on TED-LIUM) and use them as feature
extractors into x-vector-style DNNs for both tasks. We attain a
state-of-the-art $C_{\text{avg}}$ of 6.16 on the challenging LRE07 3sec
closed-set language recognition task. On Fisher and VoxCeleb speaker
recognition tasks, we see an 18% relative reduction in speaker EER when
training on BERTphone vectors instead of MFCCs. In general, BERTphone
outperforms previous phonetic pretraining approaches on the same data. We
release our code and models at
https://github.com/awslabs/speech-representations.
|
[
{
"version": "v1",
"created": "Sun, 30 Jun 2019 20:54:21 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Dec 2021 19:30:41 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Ling",
"Shaoshi",
""
],
[
"Salazar",
"Julian",
""
],
[
"Liu",
"Yuzong",
""
],
[
"Kirchhoff",
"Katrin",
""
]
] |
new_dataset
| 0.994076 |
2007.07047
|
Jianjun Zhao
|
Jianjun Zhao
|
Quantum Software Engineering: Landscapes and Horizons
| null | null | null | null |
cs.SE cs.PL quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantum software plays a critical role in exploiting the full potential of
quantum computing systems. As a result, it has been drawing increasing
attention recently. This paper defines the term "quantum software engineering"
and introduces a quantum software life cycle. The paper also gives a generic
view of quantum software engineering and discusses the quantum software
engineering processes, methods, and tools. Based on these, the paper provides a
comprehensive survey of the current state of the art in the field and presents
the challenges and opportunities we face. The survey summarizes the technology
available in the various phases of the quantum software life cycle, including
quantum software requirements analysis, design, implementation, test, and
maintenance. It also covers the crucial issues of quantum software reuse and
measurement.
|
[
{
"version": "v1",
"created": "Tue, 14 Jul 2020 14:13:44 GMT"
},
{
"version": "v2",
"created": "Fri, 31 Dec 2021 15:13:00 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Zhao",
"Jianjun",
""
]
] |
new_dataset
| 0.975393 |
2010.00975
|
Zhizhe Liu
|
Zhizhe Liu, Xingxing Zhang, Zhenfeng Zhu, Shuai Zheng, Yao Zhao and
Jian Cheng
|
Taking Modality-free Human Identification as Zero-shot Learning
|
This manuscript has been accepted by IEEE Transactions on Circuits
and Systems for Video Technology
| null |
10.1109/TCSVT.2021.3137216
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Human identification is an important topic in event detection, person
tracking, and public security. There have been numerous methods proposed for
human identification, such as face identification, person re-identification,
and gait identification. Typically, existing methods predominantly classify a
queried image to a specific identity in an image gallery set (I2I). This is
seriously limited for the scenario where only a textual description of the
query or an attribute gallery set is available in a wide range of video
surveillance applications (A2I or I2A). However, very few efforts have been
devoted towards modality-free identification, i.e., identifying a query in a
gallery set in a scalable way. In this work, we take an initial attempt, and
formulate such a novel Modality-Free Human Identification (named MFHI) task as
a generic zero-shot learning model in a scalable way. Meanwhile, it is capable
of bridging the visual and semantic modalities by learning a discriminative
prototype of each identity. In addition, the semantics-guided spatial attention
is enforced on visual modality to obtain representations with both high global
category-level and local attribute-level discrimination. Finally, we design and
conduct an extensive group of experiments on two common challenging
identification tasks, including face identification and person
re-identification, demonstrating that our method outperforms a wide variety of
state-of-the-art methods on modality-free human identification.
|
[
{
"version": "v1",
"created": "Fri, 2 Oct 2020 13:08:27 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Dec 2021 08:35:12 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Liu",
"Zhizhe",
""
],
[
"Zhang",
"Xingxing",
""
],
[
"Zhu",
"Zhenfeng",
""
],
[
"Zheng",
"Shuai",
""
],
[
"Zhao",
"Yao",
""
],
[
"Cheng",
"Jian",
""
]
] |
new_dataset
| 0.997743 |
2011.10251
|
Dhruval Jain
|
Dhruval Jain, Arun D Prabhu, Gopi Ramena, Manoj Goyal, Debi Prasanna
Mohanty, Sukumar Moharana, Naresh Purre
|
On-Device Text Image Super Resolution
|
Accepted to the International Conference on Pattern
Recognition(ICPR), 2020
| null |
10.1109/ICPR48806.2021.9412222
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Recent research on super-resolution (SR) has witnessed major developments
with the advancements of deep convolutional neural networks. There is a need
for information extraction from scenic text images or even document images on
device, most of which are low-resolution (LR) images. Therefore, SR becomes an
essential pre-processing step as Bicubic Upsampling, which is conventionally
present in smartphones, performs poorly on LR images. To give the user more
control over his privacy, and to reduce the carbon footprint by reducing the
overhead of cloud computing and hours of GPU usage, executing SR models on the
edge is a necessity in the recent times. There are various challenges in
running and optimizing a model on resource-constrained platforms like
smartphones. In this paper, we present a novel deep neural network that
reconstructs sharper character edges and thus boosts OCR confidence. The
proposed architecture not only achieves significant improvement in PSNR over
bicubic upsampling on various benchmark datasets but also runs with an average
inference time of 11.7 ms per image. We have outperformed state-of-the-art on
the Text330 dataset. We also achieve an OCR accuracy of 75.89% on the ICDAR
2015 TextSR dataset, where ground truth has an accuracy of 78.10%.
|
[
{
"version": "v1",
"created": "Fri, 20 Nov 2020 07:49:48 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Jain",
"Dhruval",
""
],
[
"Prabhu",
"Arun D",
""
],
[
"Ramena",
"Gopi",
""
],
[
"Goyal",
"Manoj",
""
],
[
"Mohanty",
"Debi Prasanna",
""
],
[
"Moharana",
"Sukumar",
""
],
[
"Purre",
"Naresh",
""
]
] |
new_dataset
| 0.998455 |
2012.02990
|
Shubham Vatsal
|
Dhruval Jain, Arun D Prabhu, Shubham Vatsal, Gopi Ramena, Naresh Purre
|
Codeswitched Sentence Creation using Dependency Parsing
| null | null |
10.1109/ICSC50631.2021.00030
| null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Codeswitching has become one of the most common occurrences across
multilingual speakers of the world, especially in countries like India which
encompasses around 23 official languages with the number of bilingual speakers
being around 300 million. The scarcity of Codeswitched data becomes a
bottleneck in the exploration of this domain with respect to various Natural
Language Processing (NLP) tasks. We thus present a novel algorithm which
harnesses the syntactic structure of English grammar to develop grammatically
sensible Codeswitched versions of English-Hindi, English-Marathi and
English-Kannada data. Apart from maintaining the grammatical sanity to a great
extent, our methodology also guarantees abundant generation of data from a
minuscule snapshot of given data. We use multiple datasets to showcase the
capabilities of our algorithm while at the same time we assess the quality of
generated Codeswitched data using some qualitative metrics along with providing
baseline results for couple of NLP tasks.
|
[
{
"version": "v1",
"created": "Sat, 5 Dec 2020 10:00:06 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Jain",
"Dhruval",
""
],
[
"Prabhu",
"Arun D",
""
],
[
"Vatsal",
"Shubham",
""
],
[
"Ramena",
"Gopi",
""
],
[
"Purre",
"Naresh",
""
]
] |
new_dataset
| 0.999466 |
2012.03782
|
Fumiyuki Kato
|
Fumiyuki Kato, Yang Cao, and Masatoshi Yoshikawa
|
PCT-TEE: Trajectory-based Private Contact Tracing System with Trusted
Execution Environment
|
Accepted by ACM TSAS
| null | null | null |
cs.CR cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Existing Bluetooth-based Private Contact Tracing (PCT) systems can privately
detect whether people have come into direct contact with COVID-19 patients.
However, we find that the existing systems lack functionality and flexibility,
which may hurt the success of the contact tracing. Specifically, they cannot
detect indirect contact (e.g., people may be exposed to coronavirus because of
used the same elevator even without direct contact); they also cannot flexibly
change the rules of "risky contact", such as how many hours of exposure or how
close to a COVID-19 patient that is considered as risk exposure, which may be
changed with the environmental situation. In this paper, we propose an
efficient and secure contact tracing system that enables both direct contact
and indirect contact. To address the above problems, we need to utilize users'
trajectory data for private contact tracing, which we call trajectory-based
PCT. We formalize this problem as Spatiotemporal Private Set Intersection. By
analyzing different approaches such as homomorphic encryption that could be
extended to solve this problem, we identify that Trusted Execution Environment
(TEE) is a proposing method to achieve our requirements. The major challenge is
how to design algorithms for spatiotemporal private set intersection under
limited secure memory of TEE. To this end, we design a TEE-based system with
flexible trajectory data encoding algorithms. Our experiments on real-world
data show that the proposed system can process thousands of queries on tens of
million records of trajectory data in a few seconds.
|
[
{
"version": "v1",
"created": "Mon, 7 Dec 2020 15:22:19 GMT"
},
{
"version": "v2",
"created": "Mon, 15 Feb 2021 09:03:46 GMT"
},
{
"version": "v3",
"created": "Wed, 24 Feb 2021 11:32:55 GMT"
},
{
"version": "v4",
"created": "Sun, 27 Jun 2021 14:25:28 GMT"
},
{
"version": "v5",
"created": "Fri, 31 Dec 2021 08:10:24 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Kato",
"Fumiyuki",
""
],
[
"Cao",
"Yang",
""
],
[
"Yoshikawa",
"Masatoshi",
""
]
] |
new_dataset
| 0.998908 |
2012.15579
|
Chun Yui Wong
|
Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe, Bryn Noel Ubald,
Andrew B. Duncan, Geoffrey Parks
|
Blade Envelopes Part II: Multiple Objectives and Inverse Design
| null | null | null | null |
cs.CE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Blade envelopes offer a set of data-driven tolerance guidelines for
manufactured components based on aerodynamic analysis. In Part I of this
two-part paper, a workflow for the formulation of blade envelopes is described
and demonstrated. In Part II, this workflow is extended to accommodate multiple
objectives. This allows engineers to prescribe manufacturing guidelines that
take into account multiple performance criteria. The quality of a manufactured
blade can be correlated with features derived from the distribution of primal
flow quantities over the surface. We show that these distributions can be
accounted for in the blade envelope using vector-valued models derived from
discrete surface flow measurements. Our methods result in a set of variables
that allows flexible and independent control over multiple flow characteristics
and performance metrics, similar in spirit to inverse design methods. The
augmentations to the blade envelope workflow presented in this paper are
demonstrated on the LS89 turbine blade, focusing on the control of loss, mass
flow and the isentropic Mach number distribution. Finally, we demonstrate how
blade envelopes can be used to visualize invariant designs by producing a 3D
render of the envelope using 3D modelling software.
|
[
{
"version": "v1",
"created": "Thu, 31 Dec 2020 12:27:15 GMT"
},
{
"version": "v2",
"created": "Fri, 31 Dec 2021 11:20:02 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Wong",
"Chun Yui",
""
],
[
"Seshadri",
"Pranay",
""
],
[
"Scillitoe",
"Ashley",
""
],
[
"Ubald",
"Bryn Noel",
""
],
[
"Duncan",
"Andrew B.",
""
],
[
"Parks",
"Geoffrey",
""
]
] |
new_dataset
| 0.982614 |
2103.09523
|
Keisuke Sugiura
|
Keisuke Sugiura and Hiroki Matsutani
|
A Universal LiDAR SLAM Accelerator System on Low-cost FPGA
| null | null | null | null |
cs.RO cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and
Mapping) serves as a basis for indoor cleaning, navigation, and many other
useful applications in both industry and household. From a series of LiDAR
scans, it constructs an accurate, globally consistent model of the environment
and estimates a robot position inside it. SLAM is inherently computationally
intensive; it is a challenging problem to realize a fast and reliable SLAM
system on mobile robots with a limited processing capability. To overcome such
hurdles, in this paper, we propose a universal, low-power, and
resource-efficient accelerator design for 2D LiDAR SLAM targeting
resource-limited FPGAs. As scan matching is at the heart of SLAM, the proposed
accelerator consists of dedicated scan matching cores on the programmable logic
part, and provides software interfaces to facilitate the use. Our accelerator
can be integrated to various SLAM methods including the ROS (Robot Operating
System)-based ones, and users can switch to a different method without
modifying and re-synthesizing the logic part. We integrate the accelerator into
three widely-used methods, i.e., scan matching, particle filter, and
graph-based SLAM. We evaluate the design in terms of resource utilization,
speed, and quality of output results using real-world datasets. Experiment
results on a Pynq-Z2 board demonstrate that our design accelerates scan
matching and loop-closure detection tasks by up to 14.84x and 18.92x, yielding
4.67x, 4.00x, and 4.06x overall performance improvement in the above methods,
respectively. Our design enables the real-time performance while consuming only
2.4W and maintaining accuracy, which is comparable to the software counterparts
and even the state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Wed, 17 Mar 2021 09:12:31 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Dec 2021 12:55:05 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Sugiura",
"Keisuke",
""
],
[
"Matsutani",
"Hiroki",
""
]
] |
new_dataset
| 0.997525 |
2105.07795
|
Gopi Ramena
|
Rachit S Munjal, Arun D Prabhu, Nikhil Arora, Sukumar Moharana, Gopi
Ramena
|
STRIDE : Scene Text Recognition In-Device
|
accepted in IJCNN 2021
| null |
10.1109/IJCNN52387.2021.9534319
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Optical Character Recognition (OCR) systems have been widely used in various
applications for extracting semantic information from images. To give the user
more control over their privacy, an on-device solution is needed. The current
state-of-the-art models are too heavy and complex to be deployed on-device. We
develop an efficient lightweight scene text recognition (STR) system, which has
only 0.88M parameters and performs real-time text recognition. Attention
modules tend to boost the accuracy of STR networks but are generally slow and
not optimized for device inference. So, we propose the use of convolution
attention modules to the text recognition networks, which aims to provide
channel and spatial attention information to the LSTM module by adding very
minimal computational cost. It boosts our word accuracy on ICDAR 13 dataset by
almost 2\%. We also introduce a novel orientation classifier module, to support
the simultaneous recognition of both horizontal and vertical text. The proposed
model surpasses on-device metrics of inference time and memory footprint and
achieves comparable accuracy when compared to the leading commercial and other
open-source OCR engines. We deploy the system on-device with an inference speed
of 2.44 ms per word on the Exynos 990 chipset device and achieve an accuracy of
88.4\% on ICDAR-13 dataset.
|
[
{
"version": "v1",
"created": "Mon, 17 May 2021 13:06:23 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Munjal",
"Rachit S",
""
],
[
"Prabhu",
"Arun D",
""
],
[
"Arora",
"Nikhil",
""
],
[
"Moharana",
"Sukumar",
""
],
[
"Ramena",
"Gopi",
""
]
] |
new_dataset
| 0.998536 |
2106.05642
|
Binbin Zhang
|
Di Wu, Binbin Zhang, Chao Yang, Zhendong Peng, Wenjing Xia, Xiaoyu
Chen, Xin Lei
|
U2++: Unified Two-pass Bidirectional End-to-end Model for Speech
Recognition
| null | null | null | null |
cs.SD cs.CL eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
The unified streaming and non-streaming two-pass (U2) end-to-end model for
speech recognition has shown great performance in terms of streaming
capability, accuracy, real-time factor (RTF), and latency. In this paper, we
present U2++, an enhanced version of U2 to further improve the accuracy. The
core idea of U2++ is to use the forward and the backward information of the
labeling sequences at the same time at training to learn richer information,
and combine the forward and backward prediction at decoding to give more
accurate recognition results. We also proposed a new data augmentation method
called SpecSub to help the U2++ model to be more accurate and robust. Our
experiments show that, compared with U2, U2++ shows faster convergence at
training, better robustness to the decoding method, as well as consistent 5\% -
8\% word error rate reduction gain over U2. On the experiment of AISHELL-1, we
achieve a 4.63\% character error rate (CER) with a non-streaming setup and
5.05\% with a streaming setup with 320ms latency by U2++. To the best of our
knowledge, 5.05\% is the best-published streaming result on the AISHELL-1 test
set.
|
[
{
"version": "v1",
"created": "Thu, 10 Jun 2021 10:25:15 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Jul 2021 07:38:58 GMT"
},
{
"version": "v3",
"created": "Thu, 30 Dec 2021 00:30:30 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Wu",
"Di",
""
],
[
"Zhang",
"Binbin",
""
],
[
"Yang",
"Chao",
""
],
[
"Peng",
"Zhendong",
""
],
[
"Xia",
"Wenjing",
""
],
[
"Chen",
"Xiaoyu",
""
],
[
"Lei",
"Xin",
""
]
] |
new_dataset
| 0.977836 |
2107.07964
|
Bosubabu Sambana
|
Bosubabu Sambana
|
Blockchain Technology: Bitcoins, Cryptocurrency and Applications
|
7 Pages, 4 Figures
| null | null | null |
cs.CR cs.AI cs.CY
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Blockchain is a decentralized ledger used to securely exchange digital
currency, perform deals and transactions efficient manner, each user of the
network has access to the least copy of the encrypted ledger so that they can
validate a new transaction. The blockchain ledger is a collection of all
Bitcoin transactions executed in the past. Basically, it's distributed database
that maintains continuously growing tamper-proof data structure blocks that
holds batches of individual transactions. The completed blocks are added in a
linear and chronological order. Each block contains a timestamp and information
link which points to a previous block. Bitcoin is a peer-to-peer permissionless
network that allows every user to connect to the network and send new
transactions to verify and create new blocks. Satoshi Nakamoto described the
design of Bitcoin digital currency in his research paper posted to a
cryptography listserv 2008. Nakamoto's suggestion has solved the long-pending
problem of cryptography and laid the foundation stone for digital currency.
This paper explains the concept of bitcoin, its characteristics, the need for
Blockchain, and how Bitcoin works. It attempts to highlight the role of
Blockchain in shaping the future of banking , financial services, and the
adoption of the Internet of Thinks and future Technologies.
|
[
{
"version": "v1",
"created": "Fri, 16 Jul 2021 15:27:04 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Dec 2021 05:09:57 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Sambana",
"Bosubabu",
""
]
] |
new_dataset
| 0.999476 |
2109.01181
|
Jiunn-Kai Huang
|
Jiunn-Kai Huang, William Clark, and Jessy W. Grizzle
|
Optimal Target Shape for LiDAR Pose Estimation
| null | null |
10.1109/LRA.2021.3138779
| null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Targets are essential in problems such as object tracking in cluttered or
textureless environments, camera (and multi-sensor) calibration tasks, and
simultaneous localization and mapping (SLAM). Target shapes for these tasks
typically are symmetric (square, rectangular, or circular) and work well for
structured, dense sensor data such as pixel arrays (i.e., image). However,
symmetric shapes lead to pose ambiguity when using sparse sensor data such as
LiDAR point clouds and suffer from the quantization uncertainty of the LiDAR.
This paper introduces the concept of optimizing target shape to remove pose
ambiguity for LiDAR point clouds. A target is designed to induce large
gradients at edge points under rotation and translation relative to the LiDAR
to ameliorate the quantization uncertainty associated with point cloud
sparseness. Moreover, given a target shape, we present a means that leverages
the target's geometry to estimate the target's vertices while globally
estimating the pose. Both the simulation and the experimental results (verified
by a motion capture system) confirm that by using the optimal shape and the
global solver, we achieve centimeter error in translation and a few degrees in
rotation even when a partially illuminated target is placed 30 meters away. All
the implementations and datasets are available at
https://github.com/UMich-BipedLab/optimal_shape_global_pose_estimation.
|
[
{
"version": "v1",
"created": "Thu, 2 Sep 2021 19:18:24 GMT"
},
{
"version": "v2",
"created": "Mon, 6 Sep 2021 15:16:39 GMT"
},
{
"version": "v3",
"created": "Tue, 21 Dec 2021 13:36:38 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Huang",
"Jiunn-Kai",
""
],
[
"Clark",
"William",
""
],
[
"Grizzle",
"Jessy W.",
""
]
] |
new_dataset
| 0.993663 |
2109.01183
|
Arnav Malawade
|
Arnav Vaibhav Malawade, Shih-Yuan Yu, Brandon Hsu, Harsimrat Kaeley,
Anurag Karra, Mohammad Abdullah Al Faruque
|
roadscene2vec: A Tool for Extracting and Embedding Road Scene-Graphs
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, road scene-graph representations used in conjunction with graph
learning techniques have been shown to outperform state-of-the-art deep
learning techniques in tasks including action classification, risk assessment,
and collision prediction. To enable the exploration of applications of road
scene-graph representations, we introduce roadscene2vec: an open-source tool
for extracting and embedding road scene-graphs. The goal of roadscene2vec is to
enable research into the applications and capabilities of road scene-graphs by
providing tools for generating scene-graphs, graph learning models to generate
spatio-temporal scene-graph embeddings, and tools for visualizing and analyzing
scene-graph-based methodologies. The capabilities of roadscene2vec include (i)
customized scene-graph generation from either video clips or data from the
CARLA simulator, (ii) multiple configurable spatio-temporal graph embedding
models and baseline CNN-based models, (iii) built-in functionality for using
graph and sequence embeddings for risk assessment and collision prediction
applications, (iv) tools for evaluating transfer learning, and (v) utilities
for visualizing scene-graphs and analyzing the explainability of graph learning
models. We demonstrate the utility of roadscene2vec for these use cases with
experimental results and qualitative evaluations for both graph learning models
and CNN-based models. roadscene2vec is available at
https://github.com/AICPS/roadscene2vec.
|
[
{
"version": "v1",
"created": "Thu, 2 Sep 2021 19:21:18 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Dec 2021 09:52:18 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Malawade",
"Arnav Vaibhav",
""
],
[
"Yu",
"Shih-Yuan",
""
],
[
"Hsu",
"Brandon",
""
],
[
"Kaeley",
"Harsimrat",
""
],
[
"Karra",
"Anurag",
""
],
[
"Faruque",
"Mohammad Abdullah Al",
""
]
] |
new_dataset
| 0.993524 |
2112.14789
|
Ashish Salunkhe
|
Ashish Salunkhe
|
Attention-based Bidirectional LSTM for Deceptive Opinion Spam
Classification
|
arXiv admin note: text overlap with arXiv:1909.04826 by other authors
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Online Reviews play a vital role in e commerce for decision making. Much of
the population makes the decision of which places, restaurant to visit, what to
buy and from where to buy based on the reviews posted on the respective
platforms. A fraudulent review or opinion spam is categorized as an untruthful
or deceptive review. Positive reviews of a product or a restaurant helps
attract customers and thereby lead to an increase in sales whereas negative
reviews may hamper the progress of a restaurant or sales of a product and
thereby lead to defamed reputation and loss. Fraudulent reviews are
deliberately posted on various online review platforms to trick customers to
buy, visit or distract against a product or a restaurant. They are also written
to commend or discredit the product's repute. The work aims at detecting and
classifying the reviews as deceptive or truthful. It involves use of various
deep learning techniques for classifying the reviews and an overview of
proposed approach involving Attention based Bidirectional LSTM to tackle issues
related to semantic information in reviews and a comparative study over
baseline machine learning techniques for review classification.
|
[
{
"version": "v1",
"created": "Wed, 29 Dec 2021 19:02:04 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Salunkhe",
"Ashish",
""
]
] |
new_dataset
| 0.999177 |
2112.14885
|
Clautilde Nguiadem
|
Clautilde Nguiadem, Maxime Raison and Sofiane Achiche
|
A Test Bench For Evaluating Exoskeletons For Upper Limb Rehabilitation
|
35 pages and 7 figures
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The potential of wearable robotics technology is undeniable. However,
quantifying its value is difficult. Various types of exoskeleton robots have
already been developed and tested for upper limb rehabilitation but,
evaluations are not standardized, particularly in pediatric rehabilitation.
This paper proposes a methodology for the quantitative evaluation of upper limb
exoskeletons that, like a test bench, would serve for replicable testing. We
determined the range of motion (ROM) and joint torques using both kinematic
modeling and experimental measurements (using sensors integrated into Dynamixel
actuators). The proposed test bench can provide an accurate range of motion
(ROM) and joint torques during the pronation-supination (PS) task. The range of
motion obtained with the physical prototype was approximately 156.26 +-
4.71{\deg} during the PS task, while it was approximately 146.84 +- 14.32{\deg}
for the multibody model. The results show that the average range of
experimental torques (0.28 +- 0.06 N.m) was overestimated by 40% and just 3.4%,
respectively, when compared to the average range of simulated torques (0.2 +-
0.05 N.m) and to the highest range of simulated torques (0.29 N.m). For the
experimental measurements, test-retest reliability was excellent (0.96-0.98)
within sessions and excellent (0.93) or good (0.81-0.86) between sessions.
Finally, the suggested approach provides a ROM close to the normal ROM
necessary during PS tasks. These results validate the measurements' accuracy
and underline the proposed methodology's relevance. The proposed test bench
could become a reference standard for evaluating exoskeletons. This study also
addresses a methodological aspect on the accurate assessment of joint torques
that can serve in applications such as the sizing of actuators in exoskeletons
or the non-invasive evaluation of muscle forces in the human body.
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 01:56:19 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Nguiadem",
"Clautilde",
""
],
[
"Raison",
"Maxime",
""
],
[
"Achiche",
"Sofiane",
""
]
] |
new_dataset
| 0.996895 |
2112.14916
|
Jinyu Yin
|
Jinyu Yin, Li Jiang, Xinggong Zhang, Bin Liu
|
INTCP: Information-centric TCP for Satellite Network
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Satellite networks are booming to provide high-speed and low latency Internet
access, but the transport layer becomes one of the main obstacles. Legacy
end-to-end TCP is designed for terrestrial networks, not suitable for
error-prone, propagation delay varying, and intermittent satellite links. It is
necessary to make a clean-slate design for the satellite transport layer. This
paper introduces a novel Information-centric Hop-by-Hop transport layer design,
INTCP. It carries out hop-by-hop packets retransmission and hop-by-hop
congestion control with the help of cache and request-response model.
Hop-by-hop retransmission recovers lost packets on hop, reduces retransmission
delay. INTCP controls traffic and congestion also by hop. Each hop tries its
best to maximize its bandwidth utilization and improves end-to-end throughput.
The capability of caching enables asynchronous multicast in transport layer.
This would save precious spectrum resources in the satellite network. The
performance of INTCP is evaluated with the simulated Starlink constellation.
Long-distance communication with more than 1000km is carried out. The results
demonstrate that, for the unicast scenario INTCP could reduce 42% one-way
delay, 53% delay jitters, and improve 60% throughput compared with the legacy
TCP. In multicast scenario, INTCP could achieve more than 6X throughput.
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 04:03:48 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Yin",
"Jinyu",
""
],
[
"Jiang",
"Li",
""
],
[
"Zhang",
"Xinggong",
""
],
[
"Liu",
"Bin",
""
]
] |
new_dataset
| 0.999736 |
2112.14933
|
Saurav Ghosh
|
Saurav Ghosh and Philippe Loustaunau
|
RheFrameDetect: A Text Classification System for Automatic Detection of
Rhetorical Frames in AI from Open Sources
| null | null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Rhetorical Frames in AI can be thought of as expressions that describe AI
development as a competition between two or more actors, such as governments or
companies. Examples of such Frames include robotic arms race, AI rivalry,
technological supremacy, cyberwarfare dominance and 5G race. Detection of
Rhetorical Frames from open sources can help us track the attitudes of
governments or companies towards AI, specifically whether attitudes are
becoming more cooperative or competitive over time. Given the rapidly
increasing volumes of open sources (online news media, twitter, blogs), it is
difficult for subject matter experts to identify Rhetorical Frames in (near)
real-time. Moreover, these sources are in general unstructured (noisy) and
therefore, detecting Frames from these sources will require state-of-the-art
text classification techniques. In this paper, we develop RheFrameDetect, a
text classification system for (near) real-time capture of Rhetorical Frames
from open sources. Given an input document, RheFrameDetect employs text
classification techniques at multiple levels (document level and paragraph
level) to identify all occurrences of Frames used in the discussion of AI. We
performed extensive evaluation of the text classification techniques used in
RheFrameDetect against human annotated Frames from multiple news sources. To
further demonstrate the effectiveness of RheFrameDetect, we show multiple case
studies depicting the Frames identified by RheFrameDetect compared against
human annotated Frames.
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 05:39:42 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Ghosh",
"Saurav",
""
],
[
"Loustaunau",
"Philippe",
""
]
] |
new_dataset
| 0.997823 |
2112.14934
|
Ivan Bajic
|
Takehiro Tanaka, Hyomin Choi, Ivan V. Baji\'c
|
SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences
|
4 pages, 3 figures, submitted to Data in Brief
| null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
We present a dataset that contains object annotations with unique object
identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test
Conditions (CTC) sequences. Ground-truth annotations for 13 sequences were
prepared and released as the dataset called SFU-HW-Tracks-v1. For each video
frame, ground truth annotations include object class ID, object ID, and
bounding box location and its dimensions. The dataset can be used to evaluate
object tracking performance on uncompressed video sequences and study the
relationship between video compression and object tracking.
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 05:52:15 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Tanaka",
"Takehiro",
""
],
[
"Choi",
"Hyomin",
""
],
[
"Bajić",
"Ivan V.",
""
]
] |
new_dataset
| 0.999856 |
2112.14957
|
Qing Li
|
Shangguang Wang, Qing Li, Mengwei Xu, Xiao Ma, Ao Zhou, Qibo Sun
|
Tiansuan Constellation: An Open Research Platform
| null | null | null | null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Satellite network is the first step of interstellar voyages. It can provide
global Internet connectivity everywhere on earth, where most areas cannot
access the Internet by the terrestrial infrastructure due to the geographic
accessibility and high cost. The space industry experiences a rise in large
low-earth-orbit satellite constellations to achieve universal connectivity. The
research community is also urgent to do some leading research to bridge the
connectivity divide. Researchers now conduct their work by simulation, which is
far from enough. However, experiments on real satellites are blocked by the
high threshold of space technology, such as deployment cost and unknown risks.
To solve the above dilemma, we are eager to contribute to the universal
connectivity and build an open research platform, Tiansuan constellation to
support experiments on real satellite networks. We discuss the potential
research topics that would benefit from Tiansuan constellation. We provide two
case studies that have already deployed in two experimental satellites of
Tiansuan constellation.
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 08:20:53 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Wang",
"Shangguang",
""
],
[
"Li",
"Qing",
""
],
[
"Xu",
"Mengwei",
""
],
[
"Ma",
"Xiao",
""
],
[
"Zhou",
"Ao",
""
],
[
"Sun",
"Qibo",
""
]
] |
new_dataset
| 0.999447 |
2112.14958
|
Bo Liu
|
Liu Bo
|
A Benchmark Dataset for Micro-video Thumbnail Selection
| null | null | null | null |
cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The thumbnail, as the first sight of a micro-video, plays a pivotal role in
attracting users to click and watch. Although several pioneer efforts have been
dedicated to jointly considering the quality and representativeness for
selecting the thumbnail, they are limited in exploring the influence of users`
interests. While in the real scenario, the more the thumbnails satisfy the
users, the more likely the micro-videos will be clicked. In this paper, we aim
to select the thumbnail of a given micro-video that meets most users`
interests. Towards this end, we construct a large-scale dataset for the
micro-video thumbnails. Ultimately, we conduct several baselines on the dataset
and demonstrate the effectiveness of our dataset.
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 08:22:03 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Bo",
"Liu",
""
]
] |
new_dataset
| 0.999763 |
2112.14961
|
EPTCS
|
Christian Retor\'e (LIRMM, Univ Montpellier & CNRS)
|
Flag: a Self-Dual Modality for Non-Commutative Contraction and
Duplication in the Category of Coherence Spaces
|
In Proceedings Linearity&TLLA 2020, arXiv:2112.14305
|
EPTCS 353, 2021, pp. 157-174
|
10.4204/EPTCS.353.8
| null |
cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
After reminding what coherences spaces are and how they interpret linear
logic, we define a modality "flag" in the category of coherence spaces (or
hypercoherences) with two inverse linear (iso)morphisms: "duplication" from
(flag A) to ((flag A) < (flag A)) and "contraction" in the opposite direction
-- where < is the self dual and non-commutative connective known as "before" in
pomset logic and known as "seq(ential)" in the deep inference system (S)BV. In
addition, as expected, the coherence space A is a retract of its modal image
(flag A). This suggests an intuitive interpretation of (flag A) as "repeatedly
A" or as "A at any instant" when "before" is given a temporal interpretation.
We hope the semantic construction of flag(A) will help to design proof rules
for "flag" and we briefly discuss this at the end of the paper.
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 08:35:30 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Retoré",
"Christian",
"",
"LIRMM, Univ Montpellier & CNRS"
]
] |
new_dataset
| 0.999275 |
2112.15001
|
Josep Domingo-Ferrer
|
Josep Domingo-Ferrer and Jes\'us Manj\'on
|
Circuit-Free General-Purpose Multi-Party Computation via Co-Utile
Unlinkable Outsourcing
|
IEEE Transactions on Dependable and Secure Computing, to appear
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multiparty computation (MPC) consists in several parties engaging in joint
computation in such a way that each party's input and output remain private to
that party. Whereas MPC protocols for specific computations have existed since
the 1980s, only recently general-purpose compilers have been developed to allow
MPC on arbitrary functions. Yet, using today's MPC compilers requires
substantial programming effort and skill on the user's side, among other things
because nearly all compilers translate the code of the computation into a
Boolean or arithmetic circuit. In particular, the circuit representation
requires unrolling loops and recursive calls, which forces programmers to
(often manually) define loop bounds and hardly use recursion. We present an
approach allowing MPC on an arbitrary computation expressed as ordinary code
with all functionalities that does not need to be translated into a circuit.
Our notion of input and output privacy is predicated on unlinkability. Our
method leverages co-utile computation outsourcing using anonymous channels via
decentralized reputation, makes a minimalistic use of cryptography and does not
require participants to be honest-but-curious: it works as long as participants
are rational (self-interested), which may include rationally malicious peers
(who become attackers if this is advantageous to them). We present example
applications, including e-voting. Our empirical work shows that reputation
captures well the behavior of peers and ensures that parties with high
reputation obtain correct results.
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 10:26:13 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Domingo-Ferrer",
"Josep",
""
],
[
"Manjón",
"Jesús",
""
]
] |
new_dataset
| 0.995449 |
2112.15043
|
Cunliang Kong
|
Yingying Wang, Cunliang Kong, Liner Yang, Yijun Wang, Xiaorong Lu,
Renfen Hu, Shan He, Zhenghao Liu, Yun Chen, Erhong Yang, Maosong Sun
|
YACLC: A Chinese Learner Corpus with Multidimensional Annotation
|
4 pages, 3 figures
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Learner corpus collects language data produced by L2 learners, that is second
or foreign-language learners. This resource is of great relevance for second
language acquisition research, foreign-language teaching, and automatic
grammatical error correction. However, there is little focus on learner corpus
for Chinese as Foreign Language (CFL) learners. Therefore, we propose to
construct a large-scale, multidimensional annotated Chinese learner corpus. To
construct the corpus, we first obtain a large number of topic-rich texts
generated by CFL learners. Then we design an annotation scheme including a
sentence acceptability score as well as grammatical error and fluency-based
corrections. We build a crowdsourcing platform to perform the annotation
effectively (https://yaclc.wenmind.net). We name the corpus YACLC (Yet Another
Chinese Learner Corpus) and release it as part of the CUGE benchmark
(http://cuge.baai.ac.cn). By analyzing the original sentences and annotations
in the corpus, we found that YACLC has a considerable size and very high
annotation quality. We hope this corpus can further enhance the studies on
Chinese International Education and Chinese automatic grammatical error
correction.
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 13:07:08 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Wang",
"Yingying",
""
],
[
"Kong",
"Cunliang",
""
],
[
"Yang",
"Liner",
""
],
[
"Wang",
"Yijun",
""
],
[
"Lu",
"Xiaorong",
""
],
[
"Hu",
"Renfen",
""
],
[
"He",
"Shan",
""
],
[
"Liu",
"Zhenghao",
""
],
[
"Chen",
"Yun",
""
],
[
"Yang",
"Erhong",
""
],
[
"Sun",
"Maosong",
""
]
] |
new_dataset
| 0.999637 |
2112.15065
|
Xin Du
|
Junjin He, Yujie Wang, Xin Du, Zhihui Lu
|
V2V-Based Task Offloading and Resource Allocation in Vehicular Edge
Computing Networks
|
IEEE Access
| null | null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the research and application of vehicle ad hoc networks (VANETs), it is
often assumed that vehicles obtain cloud computing services by accessing to
roadside units (RSUs). However, due to the problems of insufficient
construction quantity, limited communication range and overload of calculation
load of roadside units, the calculation mode relying only on vehicle to
roadside units is difficult to deal with complex and changeable calculation
tasks. In this paper, when the roadside unit is missing, the vehicle mobile
unit is regarded as a natural edge computing node to make full use of the
excess computing power of mobile vehicles and perform the offloading task of
surrounding mobile vehicles in time. In this paper, the OPFTO framework is
designed, an improved task allocation algorithm HGSA is proposed, and the
pre-filtering process is designed with full consideration of the moving
characteristics of vehicles. In addition, vehicle simulation experiments show
that the proposed strategy has the advantages of low delay and high accuracy
compared with other task scheduling strategies, which provides a reference
scheme for the construction of Urban Intelligent Transportation in the future.
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 13:59:14 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"He",
"Junjin",
""
],
[
"Wang",
"Yujie",
""
],
[
"Du",
"Xin",
""
],
[
"Lu",
"Zhihui",
""
]
] |
new_dataset
| 0.96253 |
2112.15124
|
Diptesh Kanojia
|
Diptesh Kanojia, Kevin Patel, Pushpak Bhattacharyya, Malhar Kulkarni,
Gholamreza Haffari
|
Utilizing Wordnets for Cognate Detection among Indian Languages
|
Published at GWC 2019
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Automatic Cognate Detection (ACD) is a challenging task which has been
utilized to help NLP applications like Machine Translation, Information
Retrieval and Computational Phylogenetics. Unidentified cognate pairs can pose
a challenge to these applications and result in a degradation of performance.
In this paper, we detect cognate word pairs among ten Indian languages with
Hindi and use deep learning methodologies to predict whether a word pair is
cognate or not. We identify IndoWordnet as a potential resource to detect
cognate word pairs based on orthographic similarity-based methods and train
neural network models using the data obtained from it. We identify parallel
corpora as another potential resource and perform the same experiments for
them. We also validate the contribution of Wordnets through further
experimentation and report improved performance of up to 26%. We discuss the
nuances of cognate detection among closely related Indian languages and release
the lists of detected cognates as a dataset. We also observe the behaviour of,
to an extent, unrelated Indian language pairs and release the lists of detected
cognates among them as well.
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 16:46:28 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Kanojia",
"Diptesh",
""
],
[
"Patel",
"Kevin",
""
],
[
"Bhattacharyya",
"Pushpak",
""
],
[
"Kulkarni",
"Malhar",
""
],
[
"Haffari",
"Gholamreza",
""
]
] |
new_dataset
| 0.986485 |
2112.15167
|
Wei Wang
|
Sai Rugved Lola, Rahul Dhadvai, Wei Wang, Ting Zhu
|
Chatbot for fitness management using IBM Watson
| null | null | null | null |
cs.SE cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Chatbots have revolutionized the way humans interact with computer systems
and they have substituted the use of service agents, call-center
representatives etc. Fitness industry has always been a growing industry
although it has not adapted to the latest technologies like AI, ML and cloud
computing. In this paper, we propose an idea to develop a chatbot for fitness
management using IBM Watson and integrate it with a web application. We
proposed using Natural Language Processing (NLP) and Natural Language
Understanding (NLU) along with frameworks of IBM Cloud Watson provided for the
Chatbot Assistant. This software uses a serverless architecture to combine the
services of a professional by offering diet plans, home exercises, interactive
counseling sessions, fitness recommendations.
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 18:49:19 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Lola",
"Sai Rugved",
""
],
[
"Dhadvai",
"Rahul",
""
],
[
"Wang",
"Wei",
""
],
[
"Zhu",
"Ting",
""
]
] |
new_dataset
| 0.991648 |
2112.15253
|
Sergey A. Slavnov
|
Sergey Slavnov
|
First order linear logic and tensor type calculus for categorial
grammars
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We study relationship between first order multiplicative linear logic (MLL1),
which has been known to provide representations to different categorial
grammars, and the recently introduced extended tensor type calculus (ETTC). We
identify a fragment of MLL1, which seems sufficient for many grammar
representations, and establish a correspondence between ETTC and this fragment.
The system ETTC, thus, can be seen as an alternative syntax and intrinsic
deductive system together with a geometric representation for the latter. We
also give a natural deduction formulation of ETTC, which might be convenient.
|
[
{
"version": "v1",
"created": "Fri, 31 Dec 2021 00:35:48 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Slavnov",
"Sergey",
""
]
] |
new_dataset
| 0.954297 |
2112.15283
|
Weichong Yin
|
Han Zhang, Weichong Yin, Yewei Fang, Lanxin Li, Boqiang Duan, Zhihua
Wu, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
|
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional
Vision-Language Generation
|
15 pages, 7 figures
| null | null | null |
cs.CV cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Conventional methods for the image-text generation tasks mainly tackle the
naturally bidirectional generation tasks separately, focusing on designing
task-specific frameworks to improve the quality and fidelity of the generated
samples. Recently, Vision-Language Pre-training models have greatly improved
the performance of the image-to-text generation tasks, but large-scale
pre-training models for text-to-image synthesis task are still under-developed.
In this paper, we propose ERNIE-ViLG, a unified generative pre-training
framework for bidirectional image-text generation with transformer model. Based
on the image quantization models, we formulate both image generation and text
generation as autoregressive generative tasks conditioned on the text/image
input. The bidirectional image-text generative modeling eases the semantic
alignments across vision and language. For the text-to-image generation
process, we further propose an end-to-end training method to jointly learn the
visual sequence generator and the image reconstructor. To explore the landscape
of large-scale pre-training for bidirectional text-image generation, we train a
10-billion parameter ERNIE-ViLG model on a large-scale dataset of 145 million
(Chinese) image-text pairs which achieves state-of-the-art performance for both
text-to-image and image-to-text tasks, obtaining an FID of 7.9 on MS-COCO for
text-to-image synthesis and best results on COCO-CN and AIC-ICC for image
captioning.
|
[
{
"version": "v1",
"created": "Fri, 31 Dec 2021 03:53:33 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Zhang",
"Han",
""
],
[
"Yin",
"Weichong",
""
],
[
"Fang",
"Yewei",
""
],
[
"Li",
"Lanxin",
""
],
[
"Duan",
"Boqiang",
""
],
[
"Wu",
"Zhihua",
""
],
[
"Sun",
"Yu",
""
],
[
"Tian",
"Hao",
""
],
[
"Wu",
"Hua",
""
],
[
"Wang",
"Haifeng",
""
]
] |
new_dataset
| 0.956831 |
2112.15337
|
Fabrice Rossi
|
Elie Mengin (SAMM), Fabrice Rossi (CEREMADE)
|
Binary Diffing as a Network Alignment Problem via Belief Propagation
| null |
36th IEEE/ACM International Conference on Automated Software
Engineering (ASE 2021), IEEE; ACM, Nov 2021, Melbourne, Australia
| null | null |
cs.LG cs.AI stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we address the problem of finding a correspondence, or
matching, between the functions of two programs in binary form, which is one of
the most common task in binary diffing. We introduce a new formulation of this
problem as a particular instance of a graph edit problem over the call graphs
of the programs. In this formulation, the quality of a mapping is evaluated
simultaneously with respect to both function content and call graph
similarities. We show that this formulation is equivalent to a network
alignment problem. We propose a solving strategy for this problem based on
max-product belief propagation. Finally, we implement a prototype of our
method, called QBinDiff, and propose an extensive evaluation which shows that
our approach outperforms state of the art diffing tools.
|
[
{
"version": "v1",
"created": "Fri, 31 Dec 2021 07:54:11 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Mengin",
"Elie",
"",
"SAMM"
],
[
"Rossi",
"Fabrice",
"",
"CEREMADE"
]
] |
new_dataset
| 0.97724 |
2112.15462
|
Yansheng Wu
|
Yansheng Wu, Chengju Li, Fu Xiao
|
Quaternary linear codes and related binary subfield codes
|
24 pages, to appear in IEEE TIT
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we mainly study quaternary linear codes and their binary
subfield codes. First we obtain a general explicit relationship between
quaternary linear codes and their binary subfield codes in terms of generator
matrices and defining sets. Second, we construct quaternary linear codes via
simplicial complexes and determine the weight distributions of these codes.
Third, the weight distributions of the binary subfield codes of these
quaternary codes are also computed by employing the general characterization.
Furthermore, we present two infinite families of optimal linear codes with
respect to the Griesmer Bound, and a class of binary almost optimal codes with
respect to the Sphere Packing Bound. We also need to emphasize that we obtain
at least 9 new quaternary linear codes.
|
[
{
"version": "v1",
"created": "Fri, 31 Dec 2021 13:52:11 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Wu",
"Yansheng",
""
],
[
"Li",
"Chengju",
""
],
[
"Xiao",
"Fu",
""
]
] |
new_dataset
| 0.999642 |
2112.15475
|
Dmitri Rachkovskij A.
|
Dmitri A. Rachkovskij
|
Shift-Equivariant Similarity-Preserving Hypervector Representations of
Sequences
| null | null | null | null |
cs.AI cs.LG cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architectures
(VSA), is a promising framework for the development of cognitive architectures
and artificial intelligence systems, as well as for technical applications and
emerging neuromorphic and nanoscale hardware. HDC/VSA operate with
hypervectors, i.e., distributed vector representations of large fixed dimension
(usually > 1000). One of the key ingredients of HDC/VSA are the methods for
encoding data of various types (from numeric scalars and vectors to graphs)
into hypervectors. In this paper, we propose an approach for the formation of
hypervectors of sequences that provides both an equivariance with respect to
the shift of sequences and preserves the similarity of sequences with identical
elements at nearby positions. Our methods represent the sequence elements by
compositional hypervectors and exploit permutations of hypervectors for
representing the order of sequence elements. We experimentally explored the
proposed representations using a diverse set of tasks with data in the form of
symbolic strings. Although our approach is feature-free as it forms the
hypervector of a sequence from the hypervectors of its symbols at their
positions, it demonstrated the performance on a par with the methods that apply
various features, such as subsequences. The proposed techniques were designed
for the HDC/VSA model known as Sparse Binary Distributed Representations.
However, they can be adapted to hypervectors in formats of other HDC/VSA
models, as well as for representing sequences of types other than symbolic
strings.
|
[
{
"version": "v1",
"created": "Fri, 31 Dec 2021 14:29:12 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Rachkovskij",
"Dmitri A.",
""
]
] |
new_dataset
| 0.993296 |
2112.15544
|
Luca Buoncompagni
|
Luca Buoncompagni, Syed Yusha Kareem, and Fulvio Mastrogiovanni
|
OWLOOP: A Modular API to Describe OWL Axioms in OOP Objects Hierarchies
|
This version of the manuscript has been published on the SoftwareX
Elsevier journal in January 2022. The manuscript is made of 21 pages, which
include 3 tables, 6 figures, and 4 listings
|
SoftwareX, January 2022, 100952, Vol. 17, Elsevier
|
10.1016/j.softx.2021.100952
| null |
cs.AI cs.LO cs.SE
|
http://creativecommons.org/licenses/by-sa/4.0/
|
OWLOOP is an Application Programming Interface (API) for using the Ontology
Web Language (OWL) by the means of Object-Oriented Programming (OOP). It is
common to design software architectures using the OOP paradigm for increasing
their modularity. If the components of an architecture also exploit OWL
ontologies for knowledge representation and reasoning, they would require to be
interfaced with OWL axioms. Since OWL does not adhere to the OOP paradigm, such
an interface often leads to boilerplate code affecting modularity, and OWLOOP
is designed to address this issue as well as the associated computational
aspects. We present an extension of the OWL-API to provide a general-purpose
interface between OWL axioms subject to reasoning and modular OOP objects
hierarchies.
|
[
{
"version": "v1",
"created": "Fri, 31 Dec 2021 16:46:45 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Buoncompagni",
"Luca",
""
],
[
"Kareem",
"Syed Yusha",
""
],
[
"Mastrogiovanni",
"Fulvio",
""
]
] |
new_dataset
| 0.989699 |
2112.15561
|
Jungwon Lim
|
Jungwon Lim (1), Yonghwi Jin (2), Mansour Alharthi (1), Xiaokuan Zhang
(1), Jinho Jung (1), Rajat Gupta (1), Kuilin Li (1), Daehee Jang (3), Taesoo
Kim (1) ((1) Georgia Institute of Technology, (2) Theori Inc., (3) Sungshin
Women's University)
|
SOK: On the Analysis of Web Browser Security
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Web browsers are integral parts of everyone's daily life. They are commonly
used for security-critical and privacy sensitive tasks, like banking
transactions and checking medical records. Unfortunately, modern web browsers
are too complex to be bug free (e.g., 25 million lines of code in Chrome), and
their role as an interface to the cyberspace makes them an attractive target
for attacks. Accordingly, web browsers naturally become an arena for
demonstrating advanced exploitation techniques by attackers and
state-of-the-art defenses by browser vendors. Web browsers, arguably, are the
most exciting place to learn the latest security issues and techniques, but
remain as a black art to most security researchers because of their
fast-changing characteristics and complex code bases.
To bridge this gap, this paper attempts to systematize the security landscape
of modern web browsers by studying the popular classes of security bugs, their
exploitation techniques, and deployed defenses. More specifically, we first
introduce a unified architecture that faithfully represents the security design
of four major web browsers. Second, we share insights from a 10-year
longitudinal study on browser bugs. Third, we present a timeline and context of
mitigation schemes and their effectiveness. Fourth, we share our lessons from a
full-chain exploit used in 2020 Pwn2Own competition. and the implication of bug
bounty programs to web browser security. We believe that the key takeaways from
this systematization can shed light on how to advance the status quo of modern
web browsers, and, importantly, how to create secure yet complex software in
the future.
|
[
{
"version": "v1",
"created": "Fri, 31 Dec 2021 17:32:59 GMT"
}
] | 2022-01-03T00:00:00 |
[
[
"Lim",
"Jungwon",
""
],
[
"Jin",
"Yonghwi",
""
],
[
"Alharthi",
"Mansour",
""
],
[
"Zhang",
"Xiaokuan",
""
],
[
"Jung",
"Jinho",
""
],
[
"Gupta",
"Rajat",
""
],
[
"Li",
"Kuilin",
""
],
[
"Jang",
"Daehee",
""
],
[
"Kim",
"Taesoo",
""
]
] |
new_dataset
| 0.989994 |
2111.01193
|
Supriya Nagesh
|
Supriya Nagesh, Alexander Moreno, Stephanie M. Carpenter, Jamie Yap,
Soujanya Chatterjee, Steven Lloyd Lizotte, Neng Wan, Santosh Kumar, Cho Lam,
David W. Wetter, Inbal Nahum-Shani, James M. Rehg
|
Transformers for prompt-level EMA non-response prediction
| null | null | null | null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Ecological Momentary Assessments (EMAs) are an important psychological data
source for measuring current cognitive states, affect, behavior, and
environmental factors from participants in mobile health (mHealth) studies and
treatment programs. Non-response, in which participants fail to respond to EMA
prompts, is an endemic problem. The ability to accurately predict non-response
could be utilized to improve EMA delivery and develop compliance interventions.
Prior work has explored classical machine learning models for predicting
non-response. However, as increasingly large EMA datasets become available,
there is the potential to leverage deep learning models that have been
effective in other fields. Recently, transformer models have shown
state-of-the-art performance in NLP and other domains. This work is the first
to explore the use of transformers for EMA data analysis. We address three key
questions in applying transformers to EMA data: 1. Input representation, 2.
encoding temporal information, 3. utility of pre-training on improving
downstream prediction task performance. The transformer model achieves a
non-response prediction AUC of 0.77 and is significantly better than classical
ML and LSTM-based deep learning models. We will make our a predictive model
trained on a corpus of 40K EMA samples freely-available to the research
community, in order to facilitate the development of future transformer-based
EMA analysis works.
|
[
{
"version": "v1",
"created": "Mon, 1 Nov 2021 18:38:47 GMT"
}
] | 2022-01-02T00:00:00 |
[
[
"Nagesh",
"Supriya",
""
],
[
"Moreno",
"Alexander",
""
],
[
"Carpenter",
"Stephanie M.",
""
],
[
"Yap",
"Jamie",
""
],
[
"Chatterjee",
"Soujanya",
""
],
[
"Lizotte",
"Steven Lloyd",
""
],
[
"Wan",
"Neng",
""
],
[
"Kumar",
"Santosh",
""
],
[
"Lam",
"Cho",
""
],
[
"Wetter",
"David W.",
""
],
[
"Nahum-Shani",
"Inbal",
""
],
[
"Rehg",
"James M.",
""
]
] |
new_dataset
| 0.998965 |
2112.01273
|
David Smith
|
David S. Smith
|
RawArray: A Simple, Fast, and Extensible Archival Format for Numeric
Data
|
8 pages, 3 figures
| null | null | null |
cs.DB cs.LG cs.MS
|
http://creativecommons.org/licenses/by/4.0/
|
Raw data sizes are growing and proliferating in scientific research, driven
by the success of data-hungry computational methods, such as machine learning.
The preponderance of proprietary and shoehorned data formats make computations
slower and make it harder to reproduce research and to port methods to new
platforms. Here we present the RawArray format: a simple, fast, and extensible
format for archival storage of multidimensional numeric arrays on disk.
The RawArray file format is a simple concatenation of a header array and a
data array. The header comprises seven or more 64-bit unsigned integers. The
array data can be anything. Arbitrary user metadata can be appended to an
RawArray file if desired, for example to store measurement details, color
palettes, or geolocation data.
We present benchmarks showing a factor of 2--3$\times$ speedup over HDF5 for
a range of array sizes and a speedup of up to 20$\times$ in reading the common
deep learning datasets MNIST and CIFAR10.
|
[
{
"version": "v1",
"created": "Tue, 30 Nov 2021 03:51:24 GMT"
}
] | 2022-01-02T00:00:00 |
[
[
"Smith",
"David S.",
""
]
] |
new_dataset
| 0.999313 |
2002.00253
|
Karthik Abinav Sankararaman
|
Karthik Abinav Sankararaman and Aleksandrs Slivkins
|
Bandits with Knapsacks beyond the Worst-Case
|
The initial version, titled "Advances in Bandits with Knapsacks", was
published on arxiv.org in Jan'20. The present version improves both upper and
lower bounds, deriving Theorem 3.2(ii) and Theorem 4.2. Moreover, it
simplifies the algorithm and analysis in the main result, and fixes several
issues in the lower bounds
| null | null | null |
cs.LG cs.DS stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Bandits with Knapsacks (BwK) is a general model for multi-armed bandits under
supply/budget constraints. While worst-case regret bounds for BwK are
well-understood, we present three results that go beyond the worst-case
perspective. First, we provide upper and lower bounds which amount to a full
characterization for logarithmic, instance-dependent regret rates. Second, we
consider "simple regret" in BwK, which tracks algorithm's performance in a
given round, and prove that it is small in all but a few rounds. Third, we
provide a general "reduction" from BwK to bandits which takes advantage of some
known helpful structure, and apply this reduction to combinatorial
semi-bandits, linear contextual bandits, and multinomial-logit bandits. Our
results build on the BwK algorithm from \citet{AgrawalDevanur-ec14}, providing
new analyses thereof.
|
[
{
"version": "v1",
"created": "Sat, 1 Feb 2020 18:50:44 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Dec 2020 22:45:16 GMT"
},
{
"version": "v3",
"created": "Mon, 3 May 2021 06:05:07 GMT"
},
{
"version": "v4",
"created": "Fri, 28 May 2021 16:29:16 GMT"
},
{
"version": "v5",
"created": "Mon, 31 May 2021 17:18:54 GMT"
},
{
"version": "v6",
"created": "Tue, 26 Oct 2021 01:46:36 GMT"
},
{
"version": "v7",
"created": "Tue, 28 Dec 2021 17:55:19 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Sankararaman",
"Karthik Abinav",
""
],
[
"Slivkins",
"Aleksandrs",
""
]
] |
new_dataset
| 0.99693 |
2002.01852
|
Biao Yang
|
Biao Yang, Caizhen He, Pin Wang, Ching-yao Chan, Xiaofeng Liu, and
Yang Chen
|
TPPO: A Novel Trajectory Predictor with Pseudo Oracle
|
14 pages, 7 figures, 2 tables. arXiv admin note: substantial text
overlap with arXiv:2002.00391. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Forecasting pedestrian trajectories in dynamic scenes remains a critical
problem in various applications, such as autonomous driving and socially aware
robots. Such forecasting is challenging due to human-human and human-object
interactions and future uncertainties caused by human randomness. Generative
model-based methods handle future uncertainties by sampling a latent variable.
However, few studies explored the generation of the latent variable. In this
work, we propose the Trajectory Predictor with Pseudo Oracle (TPPO), which is a
generative model-based trajectory predictor. The first pseudo oracle is
pedestrians' moving directions, and the second one is the latent variable
estimated from ground truth trajectories. A social attention module is used to
aggregate neighbors' interactions based on the correlation between pedestrians'
moving directions and future trajectories. This correlation is inspired by the
fact that pedestrians' future trajectories are often influenced by pedestrians
in front. A latent variable predictor is proposed to estimate latent variable
distributions from observed and ground-truth trajectories. Moreover, the gap
between these two distributions is minimized during training. Therefore, the
latent variable predictor can estimate the latent variable from observed
trajectories to approximate that estimated from ground-truth trajectories. We
compare the performance of TPPO with related methods on several public
datasets. Results demonstrate that TPPO outperforms state-of-the-art methods
with low average and final displacement errors. The ablation study shows that
the prediction performance will not dramatically decrease as sampling times
decline during tests.
|
[
{
"version": "v1",
"created": "Tue, 4 Feb 2020 03:28:55 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Dec 2021 15:57:33 GMT"
},
{
"version": "v3",
"created": "Wed, 29 Dec 2021 06:28:52 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Yang",
"Biao",
""
],
[
"He",
"Caizhen",
""
],
[
"Wang",
"Pin",
""
],
[
"Chan",
"Ching-yao",
""
],
[
"Liu",
"Xiaofeng",
""
],
[
"Chen",
"Yang",
""
]
] |
new_dataset
| 0.999139 |
2007.04954
|
Chuang Gan
|
Chuang Gan, Jeremy Schwartz, Seth Alter, Damian Mrowca, Martin
Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek
Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Michael
Lingelbach, Aidan Curtis, Kevin Feigelis, Daniel M. Bear, Dan Gutfreund,
David Cox, Antonio Torralba, James J. DiCarlo, Joshua B. Tenenbaum, Josh H.
McDermott, Daniel L.K. Yamins
|
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
|
Oral Presentation at NeurIPS 21 Datasets and Benchmarks Track.
Project page: http://www.threedworld.org
| null | null | null |
cs.CV cs.GR cs.LG cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce ThreeDWorld (TDW), a platform for interactive multi-modal
physical simulation. TDW enables simulation of high-fidelity sensory data and
physical interactions between mobile agents and objects in rich 3D
environments. Unique properties include: real-time near-photo-realistic image
rendering; a library of objects and environments, and routines for their
customization; generative procedures for efficiently building classes of new
environments; high-fidelity audio rendering; realistic physical interactions
for a variety of material types, including cloths, liquid, and deformable
objects; customizable agents that embody AI agents; and support for human
interactions with VR devices. TDW's API enables multiple agents to interact
within a simulation and returns a range of sensor and physics data representing
the state of the world. We present initial experiments enabled by TDW in
emerging research directions in computer vision, machine learning, and
cognitive science, including multi-modal physical scene understanding, physical
dynamics predictions, multi-agent interactions, models that learn like a child,
and attention studies in humans and neural networks.
|
[
{
"version": "v1",
"created": "Thu, 9 Jul 2020 17:33:27 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Dec 2021 17:03:21 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Gan",
"Chuang",
""
],
[
"Schwartz",
"Jeremy",
""
],
[
"Alter",
"Seth",
""
],
[
"Mrowca",
"Damian",
""
],
[
"Schrimpf",
"Martin",
""
],
[
"Traer",
"James",
""
],
[
"De Freitas",
"Julian",
""
],
[
"Kubilius",
"Jonas",
""
],
[
"Bhandwaldar",
"Abhishek",
""
],
[
"Haber",
"Nick",
""
],
[
"Sano",
"Megumi",
""
],
[
"Kim",
"Kuno",
""
],
[
"Wang",
"Elias",
""
],
[
"Lingelbach",
"Michael",
""
],
[
"Curtis",
"Aidan",
""
],
[
"Feigelis",
"Kevin",
""
],
[
"Bear",
"Daniel M.",
""
],
[
"Gutfreund",
"Dan",
""
],
[
"Cox",
"David",
""
],
[
"Torralba",
"Antonio",
""
],
[
"DiCarlo",
"James J.",
""
],
[
"Tenenbaum",
"Joshua B.",
""
],
[
"McDermott",
"Josh H.",
""
],
[
"Yamins",
"Daniel L. K.",
""
]
] |
new_dataset
| 0.999528 |
2008.09870
|
Qiang Fu
|
Qiang Fu, Hongshan Yu, Xiaolong Wang, Zhengeng Yang, Yong He, Hong
Zhang, and Ajmal Mian
|
Fast ORB-SLAM without Keypoint Descriptors
|
This paper has been accepted by Transaction on Image Processing,
DOI:10.1109/TIP.2021.3136710
|
Transaction on image processing, 2022
|
10.1109/TIP.2021.3136710
| null |
cs.RO cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Indirect methods for visual SLAM are gaining popularity due to their
robustness to environmental variations. ORB-SLAM2 \cite{orbslam2} is a
benchmark method in this domain, however, it consumes significant time for
computing descriptors that never get reused unless a frame is selected as a
keyframe. To overcome these problems, we present FastORB-SLAM which is
lightweight and efficient as it tracks keypoints between adjacent frames
without computing descriptors. To achieve this, a two-stage coarse-to-fine
descriptor independent keypoint matching method is proposed based on sparse
optical flow. In the first stage, we predict initial keypoint correspondences
via a simple but effective motion model and then robustly establish the
correspondences via pyramid-based sparse optical flow tracking. In the second
stage, we leverage the constraints of the motion smoothness and epipolar
geometry to refine the correspondences. In particular, our method computes
descriptors only for keyframes. We test FastORB-SLAM on \textit{TUM} and
\textit{ICL-NUIM} RGB-D datasets and compare its accuracy and efficiency to
nine existing RGB-D SLAM methods. Qualitative and quantitative results show
that our method achieves state-of-the-art accuracy and is about twice as fast
as the ORB-SLAM2.
|
[
{
"version": "v1",
"created": "Sat, 22 Aug 2020 16:37:58 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Jun 2021 06:57:53 GMT"
},
{
"version": "v3",
"created": "Wed, 15 Dec 2021 02:16:10 GMT"
},
{
"version": "v4",
"created": "Wed, 29 Dec 2021 01:42:56 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Fu",
"Qiang",
""
],
[
"Yu",
"Hongshan",
""
],
[
"Wang",
"Xiaolong",
""
],
[
"Yang",
"Zhengeng",
""
],
[
"He",
"Yong",
""
],
[
"Zhang",
"Hong",
""
],
[
"Mian",
"Ajmal",
""
]
] |
new_dataset
| 0.997777 |
2103.08983
|
Michel Gokan Khan
|
Michel Gokan Khan, Javid Taheri, Auday Al-Dulaimy, Andreas Kassler
|
PerfSim: A Performance Simulator for Cloud Native Microservice Chains
|
for the dataset used for evaluation, see
https://ieee-dataport.org/documents/experiments-data-used-evaluating-perfsim-simulation-accuracy-based-sfc-stress-workloads
and https://ui.neptune.ai/o/kau/org/PerfSim/experiments. Source code will be
available via perfsim.io in end of January 2022
|
IEEE Transactions on Cloud Computing, 2021
|
10.1109/TCC.2021.3135757
| null |
cs.DC cs.PF
|
http://creativecommons.org/licenses/by/4.0/
|
Cloud native computing paradigm allows microservice-based applications to
take advantage of cloud infrastructure in a scalable, reusable, and
interoperable way. However, in a cloud native system, the vast number of
configuration parameters and highly granular resource allocation policies can
significantly impact the performance and deployment cost. For understanding and
analyzing these implications in an easy, quick, and cost-effective way, we
present PerfSim, a discrete-event simulator for approximating and predicting
the performance of cloud native service chains in user-defined scenarios. To
this end, we proposed a systematic approach for modeling the performance of
microservices endpoint functions by collecting and analyzing their performance
and network traces. With a combination of the extracted models and user-defined
scenarios, PerfSim can then simulate the performance behavior of all services
over a given period and provide an approximation for system KPIs, such as
requests' average response time. Using the processing power of a single laptop,
we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent
scenarios and compared the simulation results with the identical deployment in
a real Kubernetes cluster. We achieved ~81-99% simulation accuracy in
approximating the average response time of incoming requests and ~16-1200 times
speed-up factor for the simulation.
|
[
{
"version": "v1",
"created": "Tue, 16 Mar 2021 11:21:04 GMT"
},
{
"version": "v2",
"created": "Mon, 27 Dec 2021 23:23:32 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Khan",
"Michel Gokan",
""
],
[
"Taheri",
"Javid",
""
],
[
"Al-Dulaimy",
"Auday",
""
],
[
"Kassler",
"Andreas",
""
]
] |
new_dataset
| 0.951032 |
2105.01830
|
Agnieszka Ciborowska
|
Agnieszka Ciborowska, Aleksandar Chakarov, Rahul Pandita
|
Contemporary COBOL: Developers' Perspectives on Defects and Defect
Location
| null | null |
10.1109/ICSME52107.2021.00027
| null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Mainframe systems are facing a critical shortage of developer workforce as
the current generation of COBOL developers retires. Furthermore, due to the
limited availability of public COBOL resources, entry-level developers, who
assume the mantle of legacy COBOL systems maintainers, face significant
difficulties during routine maintenance tasks, such as code comprehension and
defect location. While we made substantial advances in the field of software
maintenance for modern programming languages yearly, mainframe maintenance has
received limited attention. With this study, we aim to direct the attention of
researchers and practitioners towards investigating and addressing challenges
associated with mainframe development. Specifically, we explore the scope of
defects affecting COBOL systems and defect location strategies commonly
followed by COBOL developers and compare them with the modern programming
language counterparts. To this end, we surveyed 30 COBOL and 74 modern
Programming Language (PL) developers to understand the differences in defects
and defect location strategies employed by the two groups. Our preliminary
results show that: (1) major defect categories affecting the COBOL ecosystem
are different than defects encountered in modern PL software projects; (2) the
most challenging defect types in COBOL are also the ones that occur most
frequently; and (3) COBOL and modern PL developers follow similar strategies to
locate defective code.
|
[
{
"version": "v1",
"created": "Wed, 5 May 2021 02:04:29 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Jul 2021 22:14:35 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Ciborowska",
"Agnieszka",
""
],
[
"Chakarov",
"Aleksandar",
""
],
[
"Pandita",
"Rahul",
""
]
] |
new_dataset
| 0.999776 |
2106.15322
|
Abdelrahman Abdallah
|
Abdelrahman Abdallah, Alexander Berendeyev, Islam Nuradin, Daniyar
Nurseitov
|
TNCR: Table Net Detection and Classification Dataset
| null |
Neurocomputing, Volume 473, 7 February 2022, Pages 79-97
|
10.1016/j.neucom.2021.11.101
| null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We present TNCR, a new table dataset with varying image quality collected
from free websites. The TNCR dataset can be used for table detection in scanned
document images and their classification into 5 different classes. TNCR
contains 9428 high-quality labeled images. In this paper, we have implemented
state-of-the-art deep learning-based methods for table detection to create
several strong baselines. Cascade Mask R-CNN with ResNeXt-101-64x4d Backbone
Network achieves the highest performance compared to other methods with a
precision of 79.7%, recall of 89.8%, and f1 score of 84.4% on the TNCR dataset.
We have made TNCR open source in the hope of encouraging more deep learning
approaches to table detection, classification, and structure recognition. The
dataset and trained model checkpoints are available at
https://github.com/abdoelsayed2016/TNCR_Dataset.
|
[
{
"version": "v1",
"created": "Sat, 19 Jun 2021 10:48:58 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Abdallah",
"Abdelrahman",
""
],
[
"Berendeyev",
"Alexander",
""
],
[
"Nuradin",
"Islam",
""
],
[
"Nurseitov",
"Daniyar",
""
]
] |
new_dataset
| 0.999798 |
2108.06296
|
EPTCS
|
Sandra Alves (University of Porto), Miguel Ramos (University of Porto)
|
An ML-style Record Calculus with Extensible Records
|
In Proceedings MFPS 2021, arXiv:2112.13746
|
EPTCS 351, 2021, pp. 1-17
|
10.4204/EPTCS.351.1
|
EPTCS 351-1
|
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
In this work, we develop a polymorphic record calculus with extensible
records. Extensible records are records that can have new fields added to them,
or preexisting fields removed from them. We also develop a static type system
for this calculus and a sound and complete type inference algorithm. Most
ML-style polymorphic record calculi that support extensible records are based
on row variables. We present an alternative construction based on the
polymorphic record calculus developed by Ohori. Ohori based his polymorphic
record calculus on the idea of kind restrictions. This allowed him to express
polymorphic operations on records such as field selection and modification.
With the addition of extensible types, we were able to extend Ohori's original
calculus with other powerful operations on records such as field addition and
removal.
|
[
{
"version": "v1",
"created": "Fri, 13 Aug 2021 16:02:03 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Dec 2021 15:11:07 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Alves",
"Sandra",
"",
"University of Porto"
],
[
"Ramos",
"Miguel",
"",
"University of Porto"
]
] |
new_dataset
| 0.992289 |
2110.00534
|
Aishwarya Padmakumar
|
Aishwarya Padmakumar, Jesse Thomason, Ayush Shrivastava, Patrick
Lange, Anjali Narayan-Chen, Spandana Gella, Robinson Piramuthu, Gokhan Tur,
Dilek Hakkani-Tur
|
TEACh: Task-driven Embodied Agents that Chat
|
Accepted at AAAI 2022; 7 pages main, 28 pages total, 29 figures;
Version 3 uses a new test set for EDH instances that restrict evaluation to
state changes only on task-relevant objects
| null | null | null |
cs.CV cs.AI cs.CL cs.RO
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Robots operating in human spaces must be able to engage in natural language
interaction with people, both understanding and executing instructions, and
using conversation to resolve ambiguity and recover from mistakes. To study
this, we introduce TEACh, a dataset of over 3,000 human--human, interactive
dialogues to complete household tasks in simulation. A Commander with access to
oracle information about a task communicates in natural language with a
Follower. The Follower navigates through and interacts with the environment to
complete tasks varying in complexity from "Make Coffee" to "Prepare Breakfast",
asking questions and getting additional information from the Commander. We
propose three benchmarks using TEACh to study embodied intelligence challenges,
and we evaluate initial models' abilities in dialogue understanding, language
grounding, and task execution.
|
[
{
"version": "v1",
"created": "Fri, 1 Oct 2021 17:00:14 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Oct 2021 17:08:43 GMT"
},
{
"version": "v3",
"created": "Wed, 29 Dec 2021 02:25:05 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Padmakumar",
"Aishwarya",
""
],
[
"Thomason",
"Jesse",
""
],
[
"Shrivastava",
"Ayush",
""
],
[
"Lange",
"Patrick",
""
],
[
"Narayan-Chen",
"Anjali",
""
],
[
"Gella",
"Spandana",
""
],
[
"Piramuthu",
"Robinson",
""
],
[
"Tur",
"Gokhan",
""
],
[
"Hakkani-Tur",
"Dilek",
""
]
] |
new_dataset
| 0.998055 |
2111.03210
|
Ron Roth
|
Ron M. Roth
|
Higher-Order MDS Codes
|
Main changes from v1: replaced Theorem 4 by a stronger result and
added Corollary 5, Lemma 8, and Corollary 9
| null | null | null |
cs.IT cs.DM math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An improved Singleton-type upper bound is presented for the list decoding
radius of linear codes, in terms of the code parameters [n,k,d] and the list
size L. L-MDS codes are then defined as codes that attain this bound (under a
slightly stronger notion of list decodability), with 1-MDS codes corresponding
to ordinary linear MDS codes. Several properties of such codes are presented;
in particular, it is shown that the 2-MDS property is preserved under duality.
Finally, explicit constructions for 2-MDS codes are presented through
generalized Reed-Solomon (GRS) codes.
|
[
{
"version": "v1",
"created": "Fri, 5 Nov 2021 01:31:14 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Dec 2021 17:59:51 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Roth",
"Ron M.",
""
]
] |
new_dataset
| 0.97725 |
2112.06730
|
Jiaolong Yang
|
Yizhong Zhang, Jiaolong Yang, Zhen Liu, Ruicheng Wang, Guojun Chen,
Xin Tong, and Baining Guo
|
VirtualCube: An Immersive 3D Video Communication System
|
Project page:
https://www.microsoft.com/en-us/research/project/virtualcube/
| null | null | null |
cs.CV cs.GR cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The VirtualCube system is a 3D video conference system that attempts to
overcome some limitations of conventional technologies. The key ingredient is
VirtualCube, an abstract representation of a real-world cubicle instrumented
with RGBD cameras for capturing the 3D geometry and texture of a user. We
design VirtualCube so that the task of data capturing is standardized and
significantly simplified, and everything can be built using off-the-shelf
hardware. We use VirtualCubes as the basic building blocks of a virtual
conferencing environment, and we provide each VirtualCube user with a
surrounding display showing life-size videos of remote participants. To achieve
real-time rendering of remote participants, we develop the V-Cube View
algorithm, which uses multi-view stereo for more accurate depth estimation and
Lumi-Net rendering for better rendering quality. The VirtualCube system
correctly preserves the mutual eye gaze between participants, allowing them to
establish eye contact and be aware of who is visually paying attention to them.
The system also allows a participant to have side discussions with remote
participants as if they were in the same room. Finally, the system sheds lights
on how to support the shared space of work items (e.g., documents and
applications) and track the visual attention of participants to work items.
|
[
{
"version": "v1",
"created": "Mon, 13 Dec 2021 15:34:08 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Dec 2021 05:09:37 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Zhang",
"Yizhong",
""
],
[
"Yang",
"Jiaolong",
""
],
[
"Liu",
"Zhen",
""
],
[
"Wang",
"Ruicheng",
""
],
[
"Chen",
"Guojun",
""
],
[
"Tong",
"Xin",
""
],
[
"Guo",
"Baining",
""
]
] |
new_dataset
| 0.999263 |
2112.13488
|
Djoko Suprijanto -
|
Djoko Suprijanto and Hopein Christofen Tang
|
Quantum codes constructed from cyclic codes over the ring
$\mathbb{F}_q+v\mathbb{F}_q+v^2\mathbb{F}_q+v^3\mathbb{F}_q+v^4\mathbb{F}_q$
|
14 pages, submitted to the journal at November 28, 2021
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this article, we investigate properties of cyclic codes over a finite
non-chain ring
$\mathbb{F}_q+v\mathbb{F}_q+v^2\mathbb{F}_q+v^3\mathbb{F}_q+v^4\mathbb{F}_q,$
where $q=p^r,$ $r$ is a positive integer, $p$ is an odd prime, $4 \mid (p-1),$
and $v^5=v.$ As an application, we construct several quantum error correcting
codes over the finite field $\mathbb{F}_q.$
|
[
{
"version": "v1",
"created": "Mon, 27 Dec 2021 02:55:39 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Dec 2021 09:45:37 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Suprijanto",
"Djoko",
""
],
[
"Tang",
"Hopein Christofen",
""
]
] |
new_dataset
| 0.975829 |
2112.13545
|
Bin Wang
|
Xian Wei, Bin Wang, Mingsong Chen, Ji Yuan, Hai Lan, Jiehuang Shi,
Xuan Tang, Bo Jin, Guozhang Chen, Dongping Yang
|
ViR:the Vision Reservoir
|
10 pages, 7 figures
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The most recent year has witnessed the success of applying the Vision
Transformer (ViT) for image classification. However, there are still evidences
indicating that ViT often suffers following two aspects, i) the high
computation and the memory burden from applying the multiple Transformer layers
for pre-training on a large-scale dataset, ii) the over-fitting when training
on small datasets from scratch. To address these problems, a novel method,
namely, Vision Reservoir computing (ViR), is proposed here for image
classification, as a parallel to ViT. By splitting each image into a sequence
of tokens with fixed length, the ViR constructs a pure reservoir with a nearly
fully connected topology to replace the Transformer module in ViT. Two kinds of
deep ViR models are subsequently proposed to enhance the network performance.
Comparative experiments between the ViR and the ViT are carried out on several
image classification benchmarks. Without any pre-training process, the ViR
outperforms the ViT in terms of both model and computational complexity.
Specifically, the number of parameters of the ViR is about 15% even 5% of the
ViT, and the memory footprint is about 20% to 40% of the ViT. The superiority
of the ViR performance is explained by Small-World characteristics, Lyapunov
exponents, and memory capacity.
|
[
{
"version": "v1",
"created": "Mon, 27 Dec 2021 07:07:50 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Dec 2021 06:30:56 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Wei",
"Xian",
""
],
[
"Wang",
"Bin",
""
],
[
"Chen",
"Mingsong",
""
],
[
"Yuan",
"Ji",
""
],
[
"Lan",
"Hai",
""
],
[
"Shi",
"Jiehuang",
""
],
[
"Tang",
"Xuan",
""
],
[
"Jin",
"Bo",
""
],
[
"Chen",
"Guozhang",
""
],
[
"Yang",
"Dongping",
""
]
] |
new_dataset
| 0.980906 |
2112.13572
|
Chandrakant Bothe
|
Chandrakant Bothe
|
Polite Emotional Dialogue Acts for Conversational Analysis in Daily
Dialog Data
| null | null | null | null |
cs.CL cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Many socio-linguistic cues are used in the conversational analysis, such as
emotion, sentiment, and dialogue acts. One of the fundamental social cues is
politeness, which linguistically possesses properties useful in conversational
analysis. This short article presents some of the brief findings of polite
emotional dialogue acts, where we can correlate the relational bonds between
these socio-linguistics cues. We found that the utterances with emotion classes
Anger and Disgust are more likely to be impolite while Happiness and Sadness to
be polite. Similar phenomenon occurs with dialogue acts, Inform and Commissive
contain many polite utterances than Question and Directive. Finally, we will
conclude on the future work of these findings.
|
[
{
"version": "v1",
"created": "Mon, 27 Dec 2021 08:48:57 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Dec 2021 19:44:20 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Bothe",
"Chandrakant",
""
]
] |
new_dataset
| 0.99115 |
2112.13808
|
Jamin Shin
|
Jamin Shin, Juneyoung Park
|
Pedagogical Word Recommendation: A novel task and dataset on
personalized vocabulary acquisition for L2 learners
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When learning a second language (L2), one of the most important but tedious
components that often demoralizes students with its ineffectiveness and
inefficiency is vocabulary acquisition, or more simply put, memorizing words.
In light of such, a personalized and educational vocabulary recommendation
system that traces a learner's vocabulary knowledge state would have an immense
learning impact as it could resolve both issues. Therefore, in this paper, we
propose and release data for a novel task called Pedagogical Word
Recommendation (PWR). The main goal of PWR is to predict whether a given
learner knows a given word based on other words the learner has already seen.
To elaborate, we collect this data via an Intelligent Tutoring System (ITS)
that is serviced to ~1M L2 learners who study for the standardized English
exam, TOEIC. As a feature of this ITS, students can directly indicate words
they do not know from the questions they solved to create wordbooks. Finally,
we report the evaluation results of a Neural Collaborative Filtering approach
along with an exploratory data analysis and discuss the impact and efficacy of
this dataset as a baseline for future studies on this task.
|
[
{
"version": "v1",
"created": "Mon, 27 Dec 2021 17:52:48 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Dec 2021 04:52:26 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Shin",
"Jamin",
""
],
[
"Park",
"Juneyoung",
""
]
] |
new_dataset
| 0.995251 |
2112.13981
|
Xing Wang
|
Xing Wang, Hanwen Kang, Hongyu Zhou, Wesley Au, Chao Chen
|
Soft Robotic Finger with Variable Effective Length enabled by an
Antagonistic Constraint Mechanism
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Compared to traditional rigid robotics, soft robotics has attracted
increasing attention due to its advantages as compliance, safety, and low cost.
As an essential part of soft robotics, the soft robotic gripper also shows its
superior while grasping the objects with irregular shapes. Recent research has
been conducted to improve its grasping performance by adjusting the variable
effective length (VEL). However, the VEL achieved by multi-chamber design or
tunable stiffness shape memory material requires complex pneumatic circuit
design or a time-consuming phase-changing process. This work proposes a
fold-based soft robotic actuator made from 3D printed filament, NinjaFlex. It
is experimentally tested and represented by the hyperelastic model. Mathematic
and finite element modelling is conducted to study the bending behaviour of the
proposed soft actuator. Besides, an antagonistic constraint mechanism is
proposed to achieve the VEL, and the experiments demonstrate that better
conformity is achieved. Finally, a two-mode gripper is designed and evaluated
to demonstrate the advances of VEL on grasping performance.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 03:33:07 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Wang",
"Xing",
""
],
[
"Kang",
"Hanwen",
""
],
[
"Zhou",
"Hongyu",
""
],
[
"Au",
"Wesley",
""
],
[
"Chen",
"Chao",
""
]
] |
new_dataset
| 0.997667 |
2112.14000
|
Sitong Wu
|
Sitong Wu, Tianyi Wu, Haoru Tan, Guodong Guo
|
Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped
Attention
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Recently, Transformers have shown promising performance in various vision
tasks. To reduce the quadratic computation complexity caused by the global
self-attention, various methods constrain the range of attention within a local
region to improve its efficiency. Consequently, their receptive fields in a
single attention layer are not large enough, resulting in insufficient context
modeling. To address this issue, we propose a Pale-Shaped self-Attention
(PS-Attention), which performs self-attention within a pale-shaped region.
Compared to the global self-attention, PS-Attention can reduce the computation
and memory costs significantly. Meanwhile, it can capture richer contextual
information under the similar computation complexity with previous local
self-attention mechanisms. Based on the PS-Attention, we develop a general
Vision Transformer backbone with a hierarchical architecture, named Pale
Transformer, which achieves 83.4%, 84.3%, and 84.9% Top-1 accuracy with the
model size of 22M, 48M, and 85M respectively for 224 ImageNet-1K
classification, outperforming the previous Vision Transformer backbones. For
downstream tasks, our Pale Transformer backbone performs better than the recent
state-of-the-art CSWin Transformer by a large margin on ADE20K semantic
segmentation and COCO object detection & instance segmentation. The code will
be released on https://github.com/BR-IDL/PaddleViT.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 05:37:24 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Wu",
"Sitong",
""
],
[
"Wu",
"Tianyi",
""
],
[
"Tan",
"Haoru",
""
],
[
"Guo",
"Guodong",
""
]
] |
new_dataset
| 0.997902 |
2112.14019
|
Jiawei Liu
|
Jiawei Liu, Jing Zhang, Nick Barnes
|
Semi-supervised Salient Object Detection with Effective Confidence
Estimation
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The success of existing salient object detection models relies on a large
pixel-wise labeled training dataset. How-ever, collecting such a dataset is not
only time-consuming but also very expensive. To reduce the labeling burden, we
study semi-supervised salient object detection, and formulate it as an
unlabeled dataset pixel-level confidence estimation problem by identifying
pixels with less confident predictions. Specifically, we introduce a new latent
variable model with an energy-based prior for effective latent space
exploration, leading to more reliable confidence maps. With the proposed
strategy, the unlabelled images can effectively participate in model training.
Experimental results show that the proposed solution, using only 1/16 of the
annotations from the original training dataset, achieves competitive
performance compared with state-of-the-art fully supervised models.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 07:14:48 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Liu",
"Jiawei",
""
],
[
"Zhang",
"Jing",
""
],
[
"Barnes",
"Nick",
""
]
] |
new_dataset
| 0.997928 |
2112.14044
|
EPTCS
|
Bart Jacobs (Institute for Computing and Information Sciences (iCIS),
Radboud University Nijmegen, The Netherlands)
|
Multinomial and Hypergeometric Distributions in Markov Categories
|
In Proceedings MFPS 2021, arXiv:2112.13746
|
EPTCS 351, 2021, pp. 98-115
|
10.4204/EPTCS.351.7
| null |
cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Markov categories, having tensors with copying and discarding, provide a
setting for categorical probability. This paper uses finite colimits and what
we call uniform states in such Markov categories to define a (fixed size)
multiset functor, with basic operations for sums and zips of multisets, and a
graded monad structure. Multisets can be used to represent both urns filled
with coloured balls and also draws of multiple balls from such urns. The main
contribution of this paper is the abstract definition of multinomial and
hypergeometric distributions on multisets, as draws. It is shown that these
operations interact appropriately with various operations on multisets.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 09:02:45 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Jacobs",
"Bart",
"",
"Institute for Computing and Information Sciences"
]
] |
new_dataset
| 0.999148 |
2112.14049
|
EPTCS
|
Giulio Fellin (Universit\`a di Verona), Peter Schuster (Universit\`a
di Verona)
|
A General Glivenko-G\"odel Theorem for Nuclei
|
In Proceedings MFPS 2021, arXiv:2112.13746
|
EPTCS 351, 2021, pp. 51-66
|
10.4204/EPTCS.351.4
| null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
Glivenko's theorem says that, in propositional logic, classical provability
of a formula entails intuitionistic provability of double negation of that
formula. We generalise Glivenko's theorem from double negation to an arbitrary
nucleus, from provability in a calculus to an inductively generated abstract
consequence relation, and from propositional logic to any set of objects
whatsoever. The resulting conservation theorem comes with precise criteria for
its validity, which allow us to instantly include G\"odel's counterpart for
first-order predicate logic of Glivenko's theorem. The open nucleus gives us a
form of the deduction theorem for positive logic, and the closed nucleus
prompts a variant of the reduction from intuitionistic to minimal logic going
back to Johansson.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 09:06:54 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Fellin",
"Giulio",
"",
"Università di Verona"
],
[
"Schuster",
"Peter",
"",
"Università\n di Verona"
]
] |
new_dataset
| 0.998353 |
2112.14056
|
EPTCS
|
Rasmus Ejlers M{\o}gelberg, Andrea Vezzosi
|
Two Guarded Recursive Powerdomains for Applicative Simulation
|
In Proceedings MFPS 2021, arXiv:2112.13746
|
EPTCS 351, 2021, pp. 200-217
|
10.4204/EPTCS.351.13
| null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
Clocked Cubical Type Theory is a new type theory combining the power of
guarded recursion with univalence and higher inductive types (HITs). This type
theory can be used as a metalanguage for synthetic guarded domain theory in
which one can solve guarded recursive type equations, also with negative
variable occurrences, and use these to construct models for reasoning about
programming languages. Combining this with HITs allows for the use of type
constructors familiar from set-theory based approaches to semantics, such as
quotients and finite powersets in these models.
In this paper we show how to reason about the combination of finite
non-determinism and recursion in this type theory. Unlike traditional domain
theory which takes an ordering of programs as primitive, synthetic guarded
domain theory takes the notion of computation step as primitive in the form of
a modal operator. We use this extra intensional information to define two
guarded recursive (finite) powerdomain constructions differing in the way
non-determinism interacts with the computation steps. As an example application
of these we show how to prove applicative similarity a congruence in the cases
of may- and must-convergence for the untyped lambda calculus with finite
non-determinism. Such results are usually proved using operational reasoning
and Howe's method. Here we use an adaptation of a denotational method developed
by Pitts in the context of domain theory.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 09:09:25 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Møgelberg",
"Rasmus Ejlers",
""
],
[
"Vezzosi",
"Andrea",
""
]
] |
new_dataset
| 0.981528 |
2112.14059
|
Zhi Chen
|
Zhi Chen, Fan Yang, Wenbing Tao
|
DetarNet: Decoupling Translation and Rotation by Siamese Network for
Point Cloud Registration
|
Accepted by AAAI-2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Point cloud registration is a fundamental step for many tasks. In this paper,
we propose a neural network named DetarNet to decouple the translation $t$ and
rotation $R$, so as to overcome the performance degradation due to their mutual
interference in point cloud registration. First, a Siamese Network based
Progressive and Coherent Feature Drift (PCFD) module is proposed to align the
source and target points in high-dimensional feature space, and accurately
recover translation from the alignment process. Then we propose a Consensus
Encoding Unit (CEU) to construct more distinguishable features for a set of
putative correspondences. After that, a Spatial and Channel Attention (SCA)
block is adopted to build a classification network for finding good
correspondences. Finally, the rotation is obtained by Singular Value
Decomposition (SVD). In this way, the proposed network decouples the estimation
of translation and rotation, resulting in better performance for both of them.
Experimental results demonstrate that the proposed DetarNet improves
registration performance on both indoor and outdoor scenes. Our code will be
available in \url{https://github.com/ZhiChen902/DetarNet}.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 09:12:26 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Chen",
"Zhi",
""
],
[
"Yang",
"Fan",
""
],
[
"Tao",
"Wenbing",
""
]
] |
new_dataset
| 0.950554 |
2112.14067
|
Canze Zhu
|
Canze Zhu and Qunying Liao
|
The complete weight enumerator of the Reed-Solomon code with dimension
two or three
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by-sa/4.0/
|
It is well-known that Reed-Solomon codes and extended Reed-Solomon codes are
two special classes of MDS codes with wide applications in practice. The
complete weight enumerators of these codes are very important for determining
the capability of both error-detection and error-correction. In this paper, for
any positive integer $m$ and prime $p$, basing on the character sums, we
determine the complete weight enumerators of the Reed-Solomon code and the
extended Reed-Solomon code with dimension $k$ $(k=2,3)$ over
$\mathbb{F}_{p^m}$, explictly, which are generalizations of the corresponding
results in \cite{BK91,K04}.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 09:31:57 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Zhu",
"Canze",
""
],
[
"Liao",
"Qunying",
""
]
] |
new_dataset
| 0.999333 |
2112.14109
|
Beat Signer
|
Ahmed A.O. Tayeh, Bruno Dumas, Beat Signer
|
A Metamodel and Prototype for Fluid Document Formats
| null | null | null | null |
cs.DL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the transformation of computing from personal computers to the Internet,
document formats have also seen some changes over the years. Future document
formats are likely going to adapt to the emerging needs of ubiquitous
computing, where information processing is embedded in everyday activities and
objects. While most existing document formats have originally been a digital
emulation of paper documents, over the years they have been enriched with
additional digital features. These features were mainly incorporated to take
advantage of the new functionality offered by the devices on which the
documents are accessed. With the advent of ubiquitous computing, document
formats seem to be facing the next evolutionary step. They will have to adapt
to novel mobile devices, innovative interaction modalities, the distribution
over multiple devices as well as heterogeneous input sources. This adaptation
to the age of ubiquitous computing asks for several new document features. We
outline a roadmap towards future fluid document representations for ubiquitous
information environments. Based on the resource-selector-link (RSL) hypermedia
metamodel - a general hypermedia metamodel supporting distribution, user rights
management and content adaptation - we developed a metamodel for fluid document
formats and the corresponding online text editor for fluid documents.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 12:23:01 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Tayeh",
"Ahmed A. O.",
""
],
[
"Dumas",
"Bruno",
""
],
[
"Signer",
"Beat",
""
]
] |
new_dataset
| 0.978525 |
2112.14119
|
Shan Luo Dr
|
Jiaqi Jiang and Shan Luo
|
Robotic Perception of Object Properties using Tactile Sensing
|
19 pages, 6 figures
| null | null | null |
cs.RO
|
http://creativecommons.org/publicdomain/zero/1.0/
|
The sense of touch plays a key role in enabling humans to understand and
interact with surrounding environments. For robots, tactile sensing is also
irreplaceable. While interacting with objects, tactile sensing provides useful
information for the robot to understand the object, such as distributed
pressure, temperature, vibrations and texture. During robot grasping, vision is
often occluded by its end-effectors, whereas tactile sensing can measure areas
that are not accessible by vision. In the past decades, a number of tactile
sensors have been developed for robots and used for different robotic tasks. In
this chapter, we focus on the use of tactile sensing for robotic grasping and
investigate the recent trends in tactile perception of object properties. We
first discuss works on tactile perception of three important object properties
in grasping, i.e., shape, pose and material properties. We then review the
recent development in grasping stability prediction with tactile sensing. Among
these works, we identify the requirement for coordinating vision and tactile
sensing in the robotic grasping. To demonstrate the use of tactile sensing to
improve the visual perception, our recent development of vision-guided tactile
perception for crack reconstruction is presented. In the proposed framework,
the large receptive field of camera vision is first leveraged to achieve a
quick search of candidate regions containing cracks, a high-resolution optical
tactile sensor is then used to examine these candidate regions and reconstruct
a refined crack shape. The experiments show that our proposed method can
achieve a significant reduction of mean distance error from 0.82 mm to 0.24 mm
for crack reconstruction. Finally, we conclude this chapter with a discussion
of open issues and future directions for applying tactile sensing in robotic
tasks.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 12:37:29 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Jiang",
"Jiaqi",
""
],
[
"Luo",
"Shan",
""
]
] |
new_dataset
| 0.983864 |
2112.14125
|
Jagadeesh Harshan
|
Rusni Kima Mangang and J. Harshan
|
Do Not Forget the Past: A Buffer-Aided Framework for Relay Based Key
Generation
|
15 pages, 6 figures
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We address relay-assisted key generation wherein two wireless nodes, that
have no direct channel between them, seek the assistance of an intermediate
relay to generate secret keys. In a celebrated version of the relay-assisted
protocol, as applied by Lai et al., Zhou et al., Wang et al. and Waqas et al.,
the relay node generates pair-wise keys with the two nodes, and then broadcasts
an XOR version of the two keys. Although such protocols are simple and
effective, we observe that they face reduction in key rates due to two
problems. First, for confidentiality, the relay broadcasts an XOR function of
the pair-wise keys thereby pruning the length of the shared key to be the
minimum of the key lengths of the pair-wise keys. Secondly, the broadcast phase
may also experience outages thereby not being able to share the generated key
in every round of the protocol. Identifying these issues, we propose a
buffer-aided relaying protocol wherein buffer is used at the relay to store
unused secret bits generated in the previous rounds of the protocol so as to
provide confidentiality in the subsequent rounds of broadcast. On this
buffer-aided protocol, we propose a power-allocation strategy between the
phases of key generation and broadcast so as to maximize the throughput and key
rate. Rigorous analyses show that buffer-aided relay when implemented along
with the proposed power-allocation strategy offer remarkable advantages over
existing baselines.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 13:08:11 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Mangang",
"Rusni Kima",
""
],
[
"Harshan",
"J.",
""
]
] |
new_dataset
| 0.976968 |
2112.14225
|
Binoy Nair
|
Dineshkumar, N R Sakthivel, Binoy B Nair
|
Robotic Hand Rehabilitation System
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Rehabilitation exercises are essential to ensure speedy recovery of stroke
patients. An automated system to assist the patient in performing a
rehabilitation exercise repeatedly is designed. The design process is presented
in this report.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 17:19:10 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Dineshkumar",
"",
""
],
[
"Sakthivel",
"N R",
""
],
[
"Nair",
"Binoy B",
""
]
] |
new_dataset
| 0.98594 |
2112.14267
|
Matthew Fickus
|
Matthew Fickus and Joseph W. Iverson and John Jasper and Dustin G.
Mixon
|
Harmonic Grassmannian codes
| null | null | null | null |
cs.IT math.IT math.MG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An equi-isoclinic tight fusion frame (EITFF) is a type of Grassmannian code,
being a sequence of subspaces of a finite-dimensional Hilbert space of a given
dimension with the property that the smallest spectral distance between any
pair of them is as large as possible. EITFFs arise in compressed sensing,
yielding dictionaries with minimal block coherence. Their existence remains
poorly characterized. Most known EITFFs have parameters that match those of one
that arose from an equiangular tight frame (ETF) in a rudimentary,
direct-sum-based way. In this paper, we construct new infinite families of
non-"tensor-sized" EITFFs in a way that generalizes the one previously known
infinite family of them as well as the celebrated equivalence between harmonic
ETFs and difference sets for finite abelian groups. In particular, we construct
EITFFs consisting of $Q$ planes in $\mathbb{C}^Q$ for each prime power $Q\geq
4$, of $Q-1$ planes in $\mathbb{C}^Q$ for each odd prime power $Q$, and of $11$
three-dimensional subspaces in $\mathbb{R}^{11}$. The key idea is that every
harmonic EITFF -- one that is the orbit of a single subspace under the action
of a unitary representation of a finite abelian group -- arises from a smaller
tight fusion frame with a nicely behaved "Fourier transform." Our particular
constructions of harmonic EITFFs exploit the properties of Gauss sums over
finite fields.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 19:06:03 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Fickus",
"Matthew",
""
],
[
"Iverson",
"Joseph W.",
""
],
[
"Jasper",
"John",
""
],
[
"Mixon",
"Dustin G.",
""
]
] |
new_dataset
| 0.998135 |
2112.14298
|
Shan Luo Dr
|
Guanqun Cao and Shan Luo
|
Multimodal perception for dexterous manipulation
|
19 pages, 10 figures
| null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Humans usually perceive the world in a multimodal way that vision, touch,
sound are utilised to understand surroundings from various dimensions. These
senses are combined together to achieve a synergistic effect where the learning
is more effectively than using each sense separately. For robotics, vision and
touch are two key senses for the dexterous manipulation. Vision usually gives
us apparent features like shape, color, and the touch provides local
information such as friction, texture, etc. Due to the complementary properties
between visual and tactile senses, it is desirable for us to combine vision and
touch for a synergistic perception and manipulation. Many researches have been
investigated about multimodal perception such as cross-modal learning, 3D
reconstruction, multimodal translation with vision and touch. Specifically, we
propose a cross-modal sensory data generation framework for the translation
between vision and touch, which is able to generate realistic pseudo data. By
using this cross-modal translation method, it is desirable for us to make up
inaccessible data, helping us to learn the object's properties from different
views. Recently, the attention mechanism becomes a popular method either in
visual perception or in tactile perception. We propose a spatio-temporal
attention model for tactile texture recognition, which takes both spatial
features and time dimension into consideration. Our proposed method not only
pays attention to the salient features in each spatial feature, but also models
the temporal correlation in the through the time. The obvious improvement
proves the efficiency of our selective attention mechanism. The spatio-temporal
attention method has potential in many applications such as grasping,
recognition, and multimodal perception.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 21:20:26 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Cao",
"Guanqun",
""
],
[
"Luo",
"Shan",
""
]
] |
new_dataset
| 0.998077 |
2112.14680
|
Sebastian Drost
|
Sebastian Drost, Arne Vogt, Christian Danowski-Buhren, Simon Jirka,
Verena Kirstein, Kian Pakzad, and Matthes Rieke
|
WaCoDiS: Automated Earth Observation Data Processing within an
Event-Driven Architecture for Water Monitoring
| null | null |
10.1016/j.cageo.2021.105003
| null |
cs.DC cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
To ensure an efficient and environmentally friendly water resource
management, water management associations need means for efficient water
monitoring as well as novel strategies to reduce the pollution of surface and
ground water. Traditionally, water management associations operate large sensor
networks to suffice their needs for hydrological and meteorological measurement
data to monitor and model physical processes within catchments. Implementing a
comprehensive monitoring system often suffers from sparse coverage of in-situ
data. Due to the evolvement of the Copernicus satellite platforms, the broader
availability of satellite data provides a great potential for deriving
complementary information from Earth Observation data. Although the number of
satellite data platforms that provide online processing environments is
growing, it is still a big challenge to integrate those platforms into
traditional workflows of users from environmental domains such as hydrology.
Thus, in this paper, we introduce a software architecture to facilitate the
generation of Earth Observation information targeted towards hydrology. The
presented WaCoDiS System comprises several microservices as well standardized
interfaces that enable a platform-independent processing of satellite data.
First, we discuss the contribution of Earth Observation data to water
monitoring and derive several challenges regarding the facilitation of
satellite data processing. We then describe our system design with a brief
overview about the different system components which form an automated
processing pipeline. The suitability of our system is proven as part of a
pre-operational deployment for a German water management association. In
addition, we demonstrate how our system is capable of integrating satellite
data platforms, using the Copernicus Data and Exploitation Platform -
Deutschland (CODE-DE) as a reference example.
|
[
{
"version": "v1",
"created": "Thu, 23 Dec 2021 15:37:10 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Drost",
"Sebastian",
""
],
[
"Vogt",
"Arne",
""
],
[
"Danowski-Buhren",
"Christian",
""
],
[
"Jirka",
"Simon",
""
],
[
"Kirstein",
"Verena",
""
],
[
"Pakzad",
"Kian",
""
],
[
"Rieke",
"Matthes",
""
]
] |
new_dataset
| 0.999432 |
2112.14719
|
Daniel Katz
|
Jonathan M. Castello, Daniel J. Katz, Jacob M. King, and Alain
Olavarrieta
|
Sets of Low Correlation Sequences from Cyclotomy
|
52 pages
| null | null | null |
cs.IT cs.DM eess.SP math.CO math.IT math.NT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Low correlation (finite length) sequences are used in communications and
remote sensing. One seeks codebooks of sequences in which each sequence has low
aperiodic autocorrelation at all nonzero shifts, and each pair of distinct
sequences has low aperiodic crosscorrelation at all shifts. An overall
criterion of codebook quality is the demerit factor, which normalizes all
sequences to unit Euclidean norm, sums the squared magnitudes of all the
correlations between every pair of sequences in the codebook (including
sequences with themselves to cover autocorrelations), and divides by the square
of the number of sequences in the codebook. This demerit factor is expected to
be $1+1/N-1/(\ell N)$ for a codebook of $N$ randomly selected binary sequences
of length $\ell$, but we want demerit factors much closer to the absolute
minimum value of $1$. For each $N$ such that there is an $N\times N$ Hadamard
matrix, we use cyclotomy to construct an infinite family of codebooks of binary
sequences, in which each codebook has $N-1$ sequences of length $p$, where $p$
runs through the primes with $N\mid p-1$. As $p$ tends to infinity, the demerit
factor of the codebooks tends to $1+1/(6(N-1))$, and the maximum magnitude of
the undesirable correlations (crosscorrelations between distinct sequences and
off-peak autocorrelations) is less than a small constant times
$\sqrt{p}\log(p)$. This construction also generalizes to nonbinary sequences.
|
[
{
"version": "v1",
"created": "Wed, 29 Dec 2021 18:22:46 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Castello",
"Jonathan M.",
""
],
[
"Katz",
"Daniel J.",
""
],
[
"King",
"Jacob M.",
""
],
[
"Olavarrieta",
"Alain",
""
]
] |
new_dataset
| 0.994736 |
2112.14731
|
Shounak Paul
|
Shounak Paul, Pawan Goyal and Saptarshi Ghosh
|
LeSICiN: A Heterogeneous Graph-based Approach for Automatic Legal
Statute Identification from Indian Legal Documents
|
This paper has been accepted at the Main Track of the AAAI Conference
on Artificial Intelligence (AAAI) 2022. Dataset and codes are available at
https://github.com/Law-AI/LeSICiN
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The task of Legal Statute Identification (LSI) aims to identify the legal
statutes that are relevant to a given description of Facts or evidence of a
legal case. Existing methods only utilize the textual content of Facts and
legal articles to guide such a task. However, the citation network among case
documents and legal statutes is a rich source of additional information, which
is not considered by existing models. In this work, we take the first step
towards utilising both the text and the legal citation network for the LSI
task. We curate a large novel dataset for this task, including Facts of cases
from several major Indian Courts of Law, and statutes from the Indian Penal
Code (IPC). Modeling the statutes and training documents as a heterogeneous
graph, our proposed model LeSICiN can learn rich textual and graphical
features, and can also tune itself to correlate these features. Thereafter, the
model can be used to inductively predict links between test documents (new
nodes whose graphical features are not available to the model) and statutes
(existing nodes). Extensive experiments on the dataset show that our model
comfortably outperforms several state-of-the-art baselines, by exploiting the
graphical structure along with textual features. The dataset and our codes are
available at https://github.com/Law-AI/LeSICiN.
|
[
{
"version": "v1",
"created": "Wed, 29 Dec 2021 18:39:35 GMT"
}
] | 2021-12-30T00:00:00 |
[
[
"Paul",
"Shounak",
""
],
[
"Goyal",
"Pawan",
""
],
[
"Ghosh",
"Saptarshi",
""
]
] |
new_dataset
| 0.957975 |
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