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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2301.10523
|
Ilias Zosimadis
|
Ilias Zosimadis and Ioannis Stamelos
|
A Novel IoT-Based System for Ten Pin Bowling
|
20 pages, 6 figures
| null | null | null |
cs.OH
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Bowling is a target sport that is popular among all age groups with
professionals and amateur players. Delivering an accurate and consistent
bowling throw into the lane requires the incorporation of motion techniques.
Consequently, this research presents a novel IoT-Cloud based system for
providing real-time monitoring and coaching services to bowling athletes. The
system includes two inertial measurement units (IMUs) sensors for capturing
motion data, a mobile application and a cloud server for processing the data.
First, the quality of each phase of a throw is assessed using a Dynamic Time
Wrapping (DTW) based algorithm. Second, an on device-level technique is
proposed to identify common bowling errors. Finally, an SVM classification
model is employed for assessing the skill level of bowler athletes. We
recruited nine right-handed bowlers to perform 50 throws wearing the two
sensors and using the proposed system. The results of our experiments suggest
that the proposed system can effectively and efficiently assess the quality of
the throw, detect common bowling errors and classify the skill level of the
bowler.
|
[
{
"version": "v1",
"created": "Wed, 25 Jan 2023 11:04:31 GMT"
}
] | 2023-01-26T00:00:00 |
[
[
"Zosimadis",
"Ilias",
""
],
[
"Stamelos",
"Ioannis",
""
]
] |
new_dataset
| 0.985768 |
2301.10577
|
Sushil Awale
|
Debayan Banerjee, Seid Muhie Yimam, Sushil Awale and Chris Biemann
|
ARDIAS: AI-Enhanced Research Management, Discovery, and Advisory System
| null | null | null | null |
cs.CL cs.AI cs.IR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In this work, we present ARDIAS, a web-based application that aims to provide
researchers with a full suite of discovery and collaboration tools. ARDIAS
currently allows searching for authors and articles by name and gaining
insights into the research topics of a particular researcher. With the aid of
AI-based tools, ARDIAS aims to recommend potential collaborators and topics to
researchers. In the near future, we aim to add tools that allow researchers to
communicate with each other and start new projects.
|
[
{
"version": "v1",
"created": "Wed, 25 Jan 2023 13:30:10 GMT"
}
] | 2023-01-26T00:00:00 |
[
[
"Banerjee",
"Debayan",
""
],
[
"Yimam",
"Seid Muhie",
""
],
[
"Awale",
"Sushil",
""
],
[
"Biemann",
"Chris",
""
]
] |
new_dataset
| 0.992389 |
2301.10604
|
Veronika Solopova
|
Veronika Solopova, Oana-Iuliana Popescu, Christoph Benzm\"uller and
Tim Landgraf
|
Automated multilingual detection of Pro-Kremlin propaganda in newspapers
and Telegram posts
|
9 pages, 3 figures
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The full-scale conflict between the Russian Federation and Ukraine generated
an unprecedented amount of news articles and social media data reflecting
opposing ideologies and narratives. These polarized campaigns have led to
mutual accusations of misinformation and fake news, shaping an atmosphere of
confusion and mistrust for readers worldwide. This study analyses how the media
affected and mirrored public opinion during the first month of the war using
news articles and Telegram news channels in Ukrainian, Russian, Romanian and
English. We propose and compare two methods of multilingual automated
pro-Kremlin propaganda identification, based on Transformers and linguistic
features. We analyse the advantages and disadvantages of both methods, their
adaptability to new genres and languages, and ethical considerations of their
usage for content moderation. With this work, we aim to lay the foundation for
further development of moderation tools tailored to the current conflict.
|
[
{
"version": "v1",
"created": "Wed, 25 Jan 2023 14:25:37 GMT"
}
] | 2023-01-26T00:00:00 |
[
[
"Solopova",
"Veronika",
""
],
[
"Popescu",
"Oana-Iuliana",
""
],
[
"Benzmüller",
"Christoph",
""
],
[
"Landgraf",
"Tim",
""
]
] |
new_dataset
| 0.991323 |
2301.10704
|
Kacper Wardega
|
Kacper Wardega, Max von Hippel, Roberto Tron, Cristina Nita-Rotaru,
Wenchao Li
|
HoLA Robots: Mitigating Plan-Deviation Attacks in Multi-Robot Systems
with Co-Observations and Horizon-Limiting Announcements
|
This is the long version of our paper accepted as an extended
abstract to AAMAS'23
| null | null | null |
cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Emerging multi-robot systems rely on cooperation between humans and robots,
with robots following automatically generated motion plans to service
application-level tasks. Given the safety requirements associated with
operating in proximity to humans and expensive infrastructure, it is important
to understand and mitigate the security vulnerabilities of such systems caused
by compromised robots who diverge from their assigned plans. We focus on
centralized systems, where a *central entity* (CE) is responsible for
determining and transmitting the motion plans to the robots, which report their
location as they move following the plan. The CE checks that robots follow
their assigned plans by comparing their expected location to the location they
self-report. We show that this self-reporting monitoring mechanism is
vulnerable to *plan-deviation attacks* where compromised robots don't follow
their assigned plans while trying to conceal their movement by mis-reporting
their location. We propose a two-pronged mitigation for plan-deviation attacks:
(1) an attack detection technique leveraging both the robots' local sensing
capabilities to report observations of other robots and *co-observation
schedules* generated by the CE, and (2) a prevention technique where the CE
issues *horizon-limiting announcements* to the robots, reducing their
instantaneous knowledge of forward lookahead steps in the global motion plan.
On a large-scale automated warehouse benchmark, we show that our solution
enables attack prevention guarantees from a stealthy attacker that has
compromised multiple robots.
|
[
{
"version": "v1",
"created": "Wed, 25 Jan 2023 17:11:14 GMT"
}
] | 2023-01-26T00:00:00 |
[
[
"Wardega",
"Kacper",
""
],
[
"von Hippel",
"Max",
""
],
[
"Tron",
"Roberto",
""
],
[
"Nita-Rotaru",
"Cristina",
""
],
[
"Li",
"Wenchao",
""
]
] |
new_dataset
| 0.979318 |
2301.10732
|
Pan He
|
Aotian Wu, Pan He, Xiao Li, Ke Chen, Sanjay Ranka, Anand Rangarajan
|
An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation
|
Submitted to IEEE Intelligent Transportation Systems Transactions
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Most existing perception systems rely on sensory data acquired from cameras,
which perform poorly in low light and adverse weather conditions. To resolve
this limitation, we have witnessed advanced LiDAR sensors become popular in
perception tasks in autonomous driving applications. Nevertheless, their usage
in traffic monitoring systems is less ubiquitous. We identify two significant
obstacles in cost-effectively and efficiently developing such a LiDAR-based
traffic monitoring system: (i) public LiDAR datasets are insufficient for
supporting perception tasks in infrastructure systems, and (ii) 3D annotations
on LiDAR point clouds are time-consuming and expensive. To fill this gap, we
present an efficient semi-automated annotation tool that automatically
annotates LiDAR sequences with tracking algorithms while offering a fully
annotated infrastructure LiDAR dataset -- FLORIDA (Florida LiDAR-based Object
Recognition and Intelligent Data Annotation) -- which will be made publicly
available. Our advanced annotation tool seamlessly integrates multi-object
tracking (MOT), single-object tracking (SOT), and suitable trajectory
post-processing techniques. Specifically, we introduce a human-in-the-loop
schema in which annotators recursively fix and refine annotations imperfectly
predicted by our tool and incrementally add them to the training dataset to
obtain better SOT and MOT models. By repeating the process, we significantly
increase the overall annotation speed by three to four times and obtain better
qualitative annotations than a state-of-the-art annotation tool. The human
annotation experiments verify the effectiveness of our annotation tool. In
addition, we provide detailed statistics and object detection evaluation
results for our dataset in serving as a benchmark for perception tasks at
traffic intersections.
|
[
{
"version": "v1",
"created": "Wed, 25 Jan 2023 17:42:15 GMT"
}
] | 2023-01-26T00:00:00 |
[
[
"Wu",
"Aotian",
""
],
[
"He",
"Pan",
""
],
[
"Li",
"Xiao",
""
],
[
"Chen",
"Ke",
""
],
[
"Ranka",
"Sanjay",
""
],
[
"Rangarajan",
"Anand",
""
]
] |
new_dataset
| 0.993061 |
2301.10733
|
Thien-Nam Dinh
|
Thien-Nam Dinh, Nicholas Pattengale, Steven Elliott
|
The Synchronic Web
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
The Synchronic Web is a distributed network for securing data provenance on
the World Wide Web. By enabling clients around the world to freely commit
digital information into a single shared view of history, it provides a
foundational basis of truth on which to build decentralized and scalable trust
across the Internet. Its core cryptographical capability allows mutually
distrusting parties to create and verify statements of the following form: "I
commit to this information--and only this information--at this moment in time."
The backbone of the Synchronic Web infrastructure is a simple, small, and
semantic-free blockchain that is accessible to any Internet-enabled entity. The
infrastructure is maintained by a permissioned network of well-known servers,
called notaries, and accessed by a permissionless group of clients, called
journals. Through an evolving stack of flexible and composable semantic
specifications, the parties cooperate to generate synchronic commitments over
arbitrary data. When integrated with existing infrastructures, adapted to
diverse domains, and scaled across the breadth of cyberspace, the Synchronic
Web provides a ubiquitous mechanism to lock the world's data into unique points
in discrete time and digital space.
|
[
{
"version": "v1",
"created": "Wed, 25 Jan 2023 17:48:37 GMT"
}
] | 2023-01-26T00:00:00 |
[
[
"Dinh",
"Thien-Nam",
""
],
[
"Pattengale",
"Nicholas",
""
],
[
"Elliott",
"Steven",
""
]
] |
new_dataset
| 0.998874 |
1904.07088
|
Frederik Hauser
|
Frederik Hauser and Mark Schmidt and Marco H\"aberle and Michael Menth
|
P4-MACsec: Dynamic Topology Monitoring and Data Layer Protection with
MACsec in P4-SDN
|
Submitted to JSAC "Series on Network Softwarization & Enablers" on
04/15/2019
| null |
10.1109/ACCESS.2020.2982859
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose P4-MACsec to protect network links between P4 switches through
automated deployment of MACsec, a widespread IEEE standard for securing Layer 2
infrastructures. It is supported by switches and routers from major
manufacturers and has only little performance limitations compared to VPN
technologies such as IPsec. P4-MACsec introduces a data plane implementation of
MACsec including AES-GCM encryption and decryption directly on P4 switches.
P4-MACsec features a two-tier control plane structure where local controllers
running on the P4 switches interact with a central controller. We propose a
novel secure link discovery mechanism that leverages protected LLDP frames and
the two-tier control plane structure for secure and efficient management of a
global link map. Automated deployment of MACsec creates secure channel,
generates keying material, and configures the P4 switches for each detected
link between two P4 switches. It detects link changes and performs rekeying to
provide a secure, configuration-free operation of MACsec. In this paper, we
review the technological background of P4-MACsec and explain its architecture.
To demonstrate the feasibility of P4-MACsec, we implement it on the BMv2 P4
software switch and validate the prototype through experiments. We evaluate its
performance through experiments that focus on TCP throughput and round-trip
time. We publish the prototype and experiment setups on Github.
|
[
{
"version": "v1",
"created": "Mon, 15 Apr 2019 14:49:57 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Hauser",
"Frederik",
""
],
[
"Schmidt",
"Mark",
""
],
[
"Häberle",
"Marco",
""
],
[
"Menth",
"Michael",
""
]
] |
new_dataset
| 0.996137 |
1907.03544
|
Frederik Hauser
|
Frederik Hauser, Mark Schmidt, Michael Menth
|
xRAC: Execution and Access Control for Restricted Application Containers
on Managed Hosts
| null | null |
10.1109/NOMS47738.2020.9110380
| null |
cs.NI cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose xRAC to permit users to run special applications on managed hosts
and to grant them access to protected network resources. We use restricted
application containers (RACs) for that purpose. A RAC is a virtualization
container with only a selected set of applications. Authentication verifies the
RAC user's identity and the integrity of the RAC image. If the user is
permitted to use the RAC on a managed host, launching the RAC is authorized and
access to protected network resources may be given, e.g., to internal networks,
servers, or the Internet. xRAC simplifies traffic control as the traffic of a
RAC has a unique IPv6 address so that it can be easily identified in the
network. The architecture of xRAC reuses standard technologies, protocols, and
infrastructure. Those are the Docker virtualization platform and 802.1X
including EAP-over-UDP and RADIUS. Thus, xRAC improves network security without
modifying core parts of applications, hosts, and infrastructure. In this paper,
we review the technological background of xRAC, explain its architecture,
discuss selected use cases, and investigate on the performance. To demonstrate
the feasibility of xRAC, we implement it based on standard components with only
a few modifications. Finally, we validate xRAC through experiments.
|
[
{
"version": "v1",
"created": "Mon, 8 Jul 2019 12:11:26 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Hauser",
"Frederik",
""
],
[
"Schmidt",
"Mark",
""
],
[
"Menth",
"Michael",
""
]
] |
new_dataset
| 0.986794 |
1907.03593
|
Frederik Hauser
|
Frederik Hauser, Marco H\"aberle, Mark Schmidt, Michael Menth
|
P4-IPsec: Site-to-Site and Host-to-Site VPN with IPsec in P4-Based SDN
| null | null |
10.1109/ACCESS.2020.3012738
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we present P4-IPsec, a concept for IPsec in software-defined
networks (SDN) using P4 programmable data planes. The prototype implementation
features ESP in tunnel mode and supports different cipher suites. P4-capable
switches are programmed to serve as IPsec tunnel endpoints. We also provide a
client agent to configure tunnel endpoints on Linux hosts so that site-to-site
and host-to-site application scenarios can be supported which are the base for
virtual private networks (VPNs). While traditional VPNs require complex key
exchange protocols like IKE to set up and renew tunnel endpoints, P4-IPsec
benefits from an SDN controller to accomplish these tasks. One goal of this
experimental work is to investigate how well P4-IPsec can be implemented on
existing P4 switches. We present a prototype for the BMv2 P4 software switch,
evaluate its performance, and publish its source code on GitHub. We explain why
we could not provide a useful implementation with the NetFPGA SUME board. For
the Edgecore Wedge 100BF-32X Tofino-based switch, we presented two prototype
implementations to cope with a missing crypto unit. As another contribution of
this paper, we provide technological background of P4 and IPsec and give a
comprehensive review of security applications in P4, IPsec in SDN, and IPsec
data plane implementations. According to our knowledge, P4-IPsec is the first
implementation of IPsec for P4-based SDN.
|
[
{
"version": "v1",
"created": "Mon, 8 Jul 2019 13:18:46 GMT"
},
{
"version": "v2",
"created": "Sun, 5 Jul 2020 14:18:46 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Hauser",
"Frederik",
""
],
[
"Häberle",
"Marco",
""
],
[
"Schmidt",
"Mark",
""
],
[
"Menth",
"Michael",
""
]
] |
new_dataset
| 0.999593 |
2003.06880
|
Liat Peterfreund
|
Liat Peterfreund
|
Grammars for Document Spanners
| null | null | null | null |
cs.DB
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a new grammar-based language for defining information-extractors
from documents (text) that is built upon the well-studied framework of document
spanners for extracting structured data from text. While previously studied
formalisms for document spanners are mainly based on regular expressions, we
use an extension of context-free grammars, called {extraction grammars}, to
define the new class of context-free spanners. Extraction grammars are simply
context-free grammars extended with variables that capture interval positions
of the document, namely spans. While regular expressions are efficient for
tokenizing and tagging, context-free grammars are also efficient for capturing
structural properties. Indeed, we show that context-free spanners are strictly
more expressive than their regular counterparts. We reason about the expressive
power of our new class and present a pushdown-automata model that captures it.
We show that extraction grammars can be evaluated with polynomial data
complexity. Nevertheless, as the degree of the polynomial depends on the query,
we present an enumeration algorithm for unambiguous extraction grammars that,
after quintic preprocessing, outputs the results sequentially, without
repetitions, with a constant delay between every two consecutive ones.
|
[
{
"version": "v1",
"created": "Sun, 15 Mar 2020 17:50:18 GMT"
},
{
"version": "v2",
"created": "Tue, 24 Mar 2020 11:36:38 GMT"
},
{
"version": "v3",
"created": "Mon, 20 Apr 2020 17:00:06 GMT"
},
{
"version": "v4",
"created": "Thu, 12 Nov 2020 11:10:52 GMT"
},
{
"version": "v5",
"created": "Sat, 13 Mar 2021 10:15:15 GMT"
},
{
"version": "v6",
"created": "Tue, 24 Jan 2023 15:32:26 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Peterfreund",
"Liat",
""
]
] |
new_dataset
| 0.981969 |
2101.07095
|
Andrew Lewis-Pye
|
Andrew Lewis-Pye, Tim Roughgarden
|
Byzantine Generals in the Permissionless Setting
| null | null | null | null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Consensus protocols have traditionally been studied in a setting where all
participants are known to each other from the start of the protocol execution.
In the parlance of the 'blockchain' literature, this is referred to as the
permissioned setting. What differentiates Bitcoin from these previously studied
protocols is that it operates in a permissionless setting, i.e. it is a
protocol for establishing consensus over an unknown network of participants
that anybody can join, with as many identities as they like in any role. The
arrival of this new form of protocol brings with it many questions. Beyond
Bitcoin, what can we prove about permissionless protocols in a general sense?
How does recent work on permissionless protocols in the blockchain literature
relate to the well-developed history of research on permissioned protocols in
distributed computing?
To answer these questions, we describe a formal framework for the analysis of
both permissioned and permissionless systems. Our framework allows for
"apples-to-apples" comparisons between different categories of protocols and,
in turn, the development of theory to formally discuss their relative merits. A
major benefit of the framework is that it facilitates the application of a rich
history of proofs and techniques in distributed computing to problems in
blockchain and the study of permissionless systems. Within our framework, we
then address the questions above. We consider the Byzantine Generals Problem as
a formalisation of the problem of reaching consensus, and address a programme
of research that asks, "Under what adversarial conditions, and for what types
of permissionless protocol, is consensus possible?" We prove a number of
results for this programme, our main result being that deterministic consensus
is not possible for decentralised permissionless protocols. To close, we give a
list of eight open questions.
|
[
{
"version": "v1",
"created": "Mon, 18 Jan 2021 14:36:36 GMT"
},
{
"version": "v2",
"created": "Thu, 4 Feb 2021 12:49:22 GMT"
},
{
"version": "v3",
"created": "Mon, 8 Feb 2021 14:54:43 GMT"
},
{
"version": "v4",
"created": "Thu, 11 Feb 2021 09:09:51 GMT"
},
{
"version": "v5",
"created": "Fri, 12 Feb 2021 09:35:41 GMT"
},
{
"version": "v6",
"created": "Mon, 15 Feb 2021 09:06:37 GMT"
},
{
"version": "v7",
"created": "Sun, 10 Oct 2021 05:32:27 GMT"
},
{
"version": "v8",
"created": "Tue, 8 Feb 2022 15:37:22 GMT"
},
{
"version": "v9",
"created": "Tue, 24 Jan 2023 10:05:30 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Lewis-Pye",
"Andrew",
""
],
[
"Roughgarden",
"Tim",
""
]
] |
new_dataset
| 0.994089 |
2101.07578
|
Quan Quan
|
Quan Quan, Rao Fu, Mengxin Li, Donghui Wei, Yan Gao and Kai-Yuan Cai
|
Practical Distributed Control for VTOL UAVs to Pass a Virtual Tube
| null | null |
10.1109/TIV.2021.3123110
| null |
cs.RO cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unmanned Aerial Vehicles (UAVs) are now becoming increasingly accessible to
amateur and commercial users alike. An air traffic management (ATM) system is
needed to help ensure that this newest entrant into the skies does not collide
with others. In an ATM, airspace can be composed of airways, intersections and
nodes. In this paper, for simplicity, distributed coordinating the motions of
Vertical TakeOff and Landing (VTOL) UAVs to pass an airway is focused. This is
formulated as a virtual tube passing problem, which includes passing a virtual
tube, inter-agent collision avoidance and keeping within the virtual tube.
Lyapunov-like functions are designed elaborately, and formal analysis based on
invariant set theorem is made to show that all UAVs can pass the virtual tube
without getting trapped, avoid collision and keep within the virtual tube. What
is more, by the proposed distributed control, a VTOL UAV can keep away from
another VTOL UAV or return back to the virtual tube as soon as possible, once
it enters into the safety area of another or has a collision with the virtual
tube during it is passing the virtual tube. Simulations and experiments are
carried out to show the effectiveness of the proposed method and the comparison
with other methods.
|
[
{
"version": "v1",
"created": "Tue, 19 Jan 2021 11:52:30 GMT"
},
{
"version": "v2",
"created": "Fri, 30 Jul 2021 18:37:56 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Quan",
"Quan",
""
],
[
"Fu",
"Rao",
""
],
[
"Li",
"Mengxin",
""
],
[
"Wei",
"Donghui",
""
],
[
"Gao",
"Yan",
""
],
[
"Cai",
"Kai-Yuan",
""
]
] |
new_dataset
| 0.999546 |
2108.03990
|
Zhengyi Liu
|
Zhengyi Liu, Yuan Wang, Zhengzheng Tu, Yun Xiao, Bin Tang
|
TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer
Embedding Network
| null | null |
10.1145/3474085.3475601
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Salient object detection is the pixel-level dense prediction task which can
highlight the prominent object in the scene. Recently U-Net framework is widely
used, and continuous convolution and pooling operations generate multi-level
features which are complementary with each other. In view of the more
contribution of high-level features for the performance, we propose a triplet
transformer embedding module to enhance them by learning long-range
dependencies across layers. It is the first to use three transformer encoders
with shared weights to enhance multi-level features. By further designing scale
adjustment module to process the input, devising three-stream decoder to
process the output and attaching depth features to color features for the
multi-modal fusion, the proposed triplet transformer embedding network
(TriTransNet) achieves the state-of-the-art performance in RGB-D salient object
detection, and pushes the performance to a new level. Experimental results
demonstrate the effectiveness of the proposed modules and the competition of
TriTransNet.
|
[
{
"version": "v1",
"created": "Mon, 9 Aug 2021 12:42:56 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Liu",
"Zhengyi",
""
],
[
"Wang",
"Yuan",
""
],
[
"Tu",
"Zhengzheng",
""
],
[
"Xiao",
"Yun",
""
],
[
"Tang",
"Bin",
""
]
] |
new_dataset
| 0.999451 |
2112.01006
|
Yan Gao
|
Quan Quan, Yan Gao, Chenggang Bai
|
Distributed Control for a Robotic Swarm to Pass through a Curve Virtual
Tube
|
18 pages, 21 figures
| null |
10.1016/j.robot.2023.104368
| null |
cs.RO cs.MA cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Robotic swarm systems are now becoming increasingly attractive for many
challenging applications. The main task for any robot is to reach the
destination while keeping a safe separation from other robots and obstacles. In
many scenarios, robots need to move within a narrow corridor, through a window
or a doorframe. In order to guide all robots to move in a cluttered
environment, a curve virtual tube with no obstacle inside is carefully designed
in this paper. There is no obstacle inside the tube, namely the area inside the
tube can be seen as a safety zone. Then, a distributed swarm controller is
proposed with three elaborate control terms: a line approaching term, a robot
avoidance term and a tube keeping term. Formal analysis and proofs are made to
show that the curve virtual tube passing problem can be solved in a finite
time. For the convenience in practical use, a modified controller with an
approximate control performance is put forward. Finally, the effectiveness of
the proposed method is validated by numerical simulations and real experiments.
To show the advantages of the proposed method, the comparison between our
method and the control barrier function method is also presented in terms of
calculation speed.
|
[
{
"version": "v1",
"created": "Thu, 2 Dec 2021 06:33:36 GMT"
},
{
"version": "v2",
"created": "Tue, 17 May 2022 12:39:06 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Quan",
"Quan",
""
],
[
"Gao",
"Yan",
""
],
[
"Bai",
"Chenggang",
""
]
] |
new_dataset
| 0.999022 |
2207.06666
|
Yan Gao
|
Yan Gao, Chenggang Bai, Quan Quan
|
Distributed Control for a Multi-Agent System to Pass through a Connected
Quadrangle Virtual Tube
|
12 pages,14 figures. arXiv admin note: substantial text overlap with
arXiv:2112.01006
| null |
10.1109/TCNS.2022.3203936
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In order to guide the multi-agent system in a cluttered environment, a
connected quadrangle virtual tube is designed for all agents to keep moving
within it, whose basis is called the single trapezoid virtual tube. There is no
obstacle inside the tube, namely the area inside the tube can be seen as a
safety zone. Then, a distributed swarm controller is proposed for the single
trapezoid virtual tube passing problem. This issue is resolved by a gradient
vector field method with no local minima. Formal analyses and proofs are made
to show that all agents are able to pass the single trapezoid virtual tube.
Finally, a modified controller is put forward for convenience in practical use.
For the connected quadrangle virtual tube, a modified switching logic is
proposed to avoid the deadlock and prevent agents from moving outside the
virtual tube. Finally, the effectiveness of the proposed method is validated by
numerical simulations and real experiments.
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 05:35:17 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Gao",
"Yan",
""
],
[
"Bai",
"Chenggang",
""
],
[
"Quan",
"Quan",
""
]
] |
new_dataset
| 0.996924 |
2301.00328
|
Rajarshi Roy Chowdhury
|
Rajarshi Roy Chowdhury, Azam Che Idris and Pg Emeroylariffion Abas
|
Internet of Things: Digital Footprints Carry A Device Identity
|
8th Brunei International Conference on Engineering and Technology
(BICET 2021), Universiti Teknologi Brunei
| null |
10.1063/5.0111335
| null |
cs.LG cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
The usage of technologically advanced devices has seen a boom in many
domains, including education, automation, and healthcare; with most of the
services requiring Internet connectivity. To secure a network, device
identification plays key role. In this paper, a device fingerprinting (DFP)
model, which is able to distinguish between Internet of Things (IoT) and
non-IoT devices, as well as uniquely identify individual devices, has been
proposed. Four statistical features have been extracted from the consecutive
five device-originated packets, to generate individual device fingerprints. The
method has been evaluated using the Random Forest (RF) classifier and different
datasets. Experimental results have shown that the proposed method achieves up
to 99.8% accuracy in distinguishing between IoT and non-IoT devices and over
97.6% in classifying individual devices. These signify that the proposed method
is useful in assisting operators in making their networks more secure and
robust to security breaches and unauthorized access.
|
[
{
"version": "v1",
"created": "Sun, 1 Jan 2023 02:18:02 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Chowdhury",
"Rajarshi Roy",
""
],
[
"Idris",
"Azam Che",
""
],
[
"Abas",
"Pg Emeroylariffion",
""
]
] |
new_dataset
| 0.964855 |
2301.03036
|
Zhengyi Liu
|
Bin Tang, Zhengyi Liu, Yacheng Tan, and Qian He
|
HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection
| null |
TCSVT2022
|
10.1109/TCSVT.2022.3202563
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The High-Resolution Transformer (HRFormer) can maintain high-resolution
representation and share global receptive fields. It is friendly towards
salient object detection (SOD) in which the input and output have the same
resolution. However, two critical problems need to be solved for two-modality
SOD. One problem is two-modality fusion. The other problem is the HRFormer
output's fusion. To address the first problem, a supplementary modality is
injected into the primary modality by using global optimization and an
attention mechanism to select and purify the modality at the input level. To
solve the second problem, a dual-direction short connection fusion module is
used to optimize the output features of HRFormer, thereby enhancing the
detailed representation of objects at the output level. The proposed model,
named HRTransNet, first introduces an auxiliary stream for feature extraction
of supplementary modality. Then, features are injected into the primary
modality at the beginning of each multi-resolution branch. Next, HRFormer is
applied to achieve forwarding propagation. Finally, all the output features
with different resolutions are aggregated by intra-feature and inter-feature
interactive transformers. Application of the proposed model results in
impressive improvement for driving two-modality SOD tasks, e.g., RGB-D, RGB-T,
and light field SOD.https://github.com/liuzywen/HRTransNet
|
[
{
"version": "v1",
"created": "Sun, 8 Jan 2023 13:09:01 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Tang",
"Bin",
""
],
[
"Liu",
"Zhengyi",
""
],
[
"Tan",
"Yacheng",
""
],
[
"He",
"Qian",
""
]
] |
new_dataset
| 0.998112 |
2301.09680
|
Yulian Wu
|
Yulian Wu, Chaowen Guan, Vaneet Aggarwal and Di Wang
|
Quantum Heavy-tailed Bandits
|
Online learning; Quantum machine learning
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we study multi-armed bandits (MAB) and stochastic linear
bandits (SLB) with heavy-tailed rewards and quantum reward oracle. Unlike the
previous work on quantum bandits that assumes bounded/sub-Gaussian
distributions for rewards, here we investigate the quantum bandits problem
under a weaker assumption that the distributions of rewards only have bounded
$(1+v)$-th moment for some $v\in (0,1]$. In order to achieve regret
improvements for heavy-tailed bandits, we first propose a new quantum mean
estimator for heavy-tailed distributions, which is based on the Quantum Monte
Carlo Mean Estimator and achieves a quadratic improvement of estimation error
compared to the classical one. Based on our quantum mean estimator, we focus on
quantum heavy-tailed MAB and SLB and propose quantum algorithms based on the
Upper Confidence Bound (UCB) framework for both problems with
$\Tilde{O}(T^{\frac{1-v}{1+v}})$ regrets, polynomially improving the dependence
in terms of $T$ as compared to classical (near) optimal regrets of
$\Tilde{O}(T^{\frac{1}{1+v}})$, where $T$ is the number of rounds. Finally,
experiments also support our theoretical results and show the effectiveness of
our proposed methods.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 19:23:10 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Wu",
"Yulian",
""
],
[
"Guan",
"Chaowen",
""
],
[
"Aggarwal",
"Vaneet",
""
],
[
"Wang",
"Di",
""
]
] |
new_dataset
| 0.986377 |
2301.09717
|
Qingchao Li
|
Qingchao Li, Mohammed El-Hajjar, Ibrahim Hemadeh, Arman Shojaeifard,
Alain A. M. Mourad, Lajos Hanzo
|
Reconfigurable Intelligent Surface Aided Amplitude- and Phase-Modulated
Downlink Transmission
| null | null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
New reconfigurable intelligent surface (RIS) based amplitude and phase
modulation schemes are proposed as an evolution how the phase-only modulation
schemes available in the literature. Explicitly, both the amplitude-phase shift
keying (A-PSK) and quadrature amplitude-phase shift keying (QA-PSK) are
conceived, where the RIS is assumed to be part of a transmitter to deliver
information to the multi-antenna aided downlink receiver. In the proposed
design, the RIS is partitioned into multiple blocks, and the information bits
are conveyed by controlling both the ON-OFF state and the phase shift of the
RIS elements in each block. Since the propagation paths spanning from each RIS
block to the receiver can be coherently combined as a benefit of appropriately
configuring the phase of the RIS elements, the received signal constellations
can be designed by controlling both the ON-OFF pattern of the RIS blocks as
well as the phase shift of the RIS elements. Both the theoretical analysis and
the simulation results show that our proposed RIS-aided modulation schemes
outperform the state-of-the-art RIS-based PSK modulation both in terms of its
discrete-input-continuous-output memoryless channel (DCMC) capacity and its
symbol error probability, especially in the high signal-to-noise-ratio (SNR)
region, when considering realistic finite resolution RIS phase shifts.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 20:47:06 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Li",
"Qingchao",
""
],
[
"El-Hajjar",
"Mohammed",
""
],
[
"Hemadeh",
"Ibrahim",
""
],
[
"Shojaeifard",
"Arman",
""
],
[
"Mourad",
"Alain A. M.",
""
],
[
"Hanzo",
"Lajos",
""
]
] |
new_dataset
| 0.999389 |
2301.09757
|
Bernardo Anibal Subercaseaux Roa
|
Bernardo Subercaseaux and Marijn J. H. Heule
|
The Packing Chromatic Number of the Infinite Square Grid is 15
| null | null | null | null |
cs.DM cs.AI math.CO
|
http://creativecommons.org/licenses/by/4.0/
|
A packing $k$-coloring is a natural variation on the standard notion of graph
$k$-coloring, where vertices are assigned numbers from $\{1, \ldots, k\}$, and
any two vertices assigned a common color $c \in \{1, \ldots, k\}$ need to be at
a distance greater than $c$ (as opposed to $1$, in standard graph colorings).
Despite a sequence of incremental work, determining the packing chromatic
number of the infinite square grid has remained an open problem since its
introduction in 2002. We culminate the search by proving this number to be 15.
We achieve this result by improving the best-known method for this problem by
roughly two orders of magnitude. The most important technique to boost
performance is a novel, surprisingly effective propositional encoding for
packing colorings. Additionally, we developed an alternative symmetry-breaking
method. Since both new techniques are more complex than existing techniques for
this problem, a verified approach is required to trust them. We include both
techniques in a proof of unsatisfiability, reducing the trusted core to the
correctness of the direct encoding.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 23:27:41 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Subercaseaux",
"Bernardo",
""
],
[
"Heule",
"Marijn J. H.",
""
]
] |
new_dataset
| 0.983529 |
2301.09878
|
Mathias Zinnen
|
Mathias Zinnen, Prathmesh Madhu, Ronak Kosti, Peter Bell, Andreas
Maier, Vincent Christlein
|
ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition
|
6 pages, 6 figures
|
2022 26th International Conference on Pattern Recognition (ICPR),
Montreal, QC, Canada, 2022, pp. 4989-4994
|
10.1109/ICPR56361.2022.9956542
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The Odeuropa Challenge on Olfactory Object Recognition aims to foster the
development of object detection in the visual arts and to promote an olfactory
perspective on digital heritage. Object detection in historical artworks is
particularly challenging due to varying styles and artistic periods. Moreover,
the task is complicated due to the particularity and historical variance of
predefined target objects, which exhibit a large intra-class variance, and the
long tail distribution of the dataset labels, with some objects having only
very few training examples. These challenges should encourage participants to
create innovative approaches using domain adaptation or few-shot learning. We
provide a dataset of 2647 artworks annotated with 20 120 tightly fit bounding
boxes that are split into a training and validation set (public). A test set
containing 1140 artworks and 15 480 annotations is kept private for the
challenge evaluation.
|
[
{
"version": "v1",
"created": "Tue, 24 Jan 2023 09:35:43 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Zinnen",
"Mathias",
""
],
[
"Madhu",
"Prathmesh",
""
],
[
"Kosti",
"Ronak",
""
],
[
"Bell",
"Peter",
""
],
[
"Maier",
"Andreas",
""
],
[
"Christlein",
"Vincent",
""
]
] |
new_dataset
| 0.997056 |
2301.09957
|
Alessandro Traspadini
|
Alessandro Traspadini, Marco Giordani, Giovanni Giambene and Michele
Zorzi
|
Real-Time HAP-Assisted Vehicular Edge Computing for Rural Areas
|
6 pages, 2 figures. This paper has been accepted for publication at
IEEE Wireless Communications Letters (WCL). Copyright IEEE 2023. Please cite
it as: A. Traspadini, M. Giordani, G. Giambene and M. Zorzi, "Real-Time
HAP-Assistes Vehicular Edge Computing for Rural Areas," in IEEE Wireless
Communications Letters, doi: 10.1109/LWC.2023.3238851
| null |
10.1109/LWC.2023.3238851
| null |
cs.NI eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Non-Terrestrial Networks (NTNs) are expected to be a key component of 6th
generation (6G) networks to support broadband seamless Internet connectivity
and expand the coverage even in rural and remote areas. In this context, High
Altitude Platforms (HAPs) can act as edge servers to process computational
tasks offloaded by energy-constrained terrestrial devices such as Internet of
Things (IoT) sensors and ground vehicles (GVs). In this paper, we analyze the
opportunity to support Vehicular Edge Computing (VEC) via HAP in a rural
scenario where GVs can decide whether to process data onboard or offload them
to a HAP. We characterize the system as a set of queues in which computational
tasks arrive according to a Poisson arrival process. Then, we assess the
optimal VEC offloading factor to maximize the probability of real-time service,
given latency and computational capacity constraints.
|
[
{
"version": "v1",
"created": "Tue, 24 Jan 2023 12:39:25 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Traspadini",
"Alessandro",
""
],
[
"Giordani",
"Marco",
""
],
[
"Giambene",
"Giovanni",
""
],
[
"Zorzi",
"Michele",
""
]
] |
new_dataset
| 0.997694 |
2301.09992
|
Tariq Alhindi
|
Tariq Alhindi, Tuhin Chakrabarty, Elena Musi and Smaranda Muresan
|
Multitask Instruction-based Prompting for Fallacy Recognition
|
In Proceedings of the 2022 Conference on Empirical Methods in Natural
Language Processing, pages 8172 - 8187
|
Proceedings of the 2022 Conference on Empirical Methods in Natural
Language Processing, pages 8172 - 8187
| null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Fallacies are used as seemingly valid arguments to support a position and
persuade the audience about its validity. Recognizing fallacies is an
intrinsically difficult task both for humans and machines. Moreover, a big
challenge for computational models lies in the fact that fallacies are
formulated differently across the datasets with differences in the input format
(e.g., question-answer pair, sentence with fallacy fragment), genre (e.g.,
social media, dialogue, news), as well as types and number of fallacies (from 5
to 18 types per dataset). To move towards solving the fallacy recognition task,
we approach these differences across datasets as multiple tasks and show how
instruction-based prompting in a multitask setup based on the T5 model improves
the results against approaches built for a specific dataset such as T5, BERT or
GPT-3. We show the ability of this multitask prompting approach to recognize 28
unique fallacies across domains and genres and study the effect of model size
and prompt choice by analyzing the per-class (i.e., fallacy type) results.
Finally, we analyze the effect of annotation quality on model performance, and
the feasibility of complementing this approach with external knowledge.
|
[
{
"version": "v1",
"created": "Tue, 24 Jan 2023 13:39:23 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Alhindi",
"Tariq",
""
],
[
"Chakrabarty",
"Tuhin",
""
],
[
"Musi",
"Elena",
""
],
[
"Muresan",
"Smaranda",
""
]
] |
new_dataset
| 0.993796 |
2301.10001
|
Eric Weber
|
Shannon B. Harper and Eric S. Weber
|
Fiduciary Responsibility: Facilitating Public Trust in Automated
Decision Making
| null | null | null | null |
cs.CY cs.AI cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Automated decision-making systems are being increasingly deployed and affect
the public in a multitude of positive and negative ways. Governmental and
private institutions use these systems to process information according to
certain human-devised rules in order to address social problems or
organizational challenges. Both research and real-world experience indicate
that the public lacks trust in automated decision-making systems and the
institutions that deploy them. The recreancy theorem argues that the public is
more likely to trust and support decisions made or influenced by automated
decision-making systems if the institutions that administer them meet their
fiduciary responsibility. However, often the public is never informed of how
these systems operate and resultant institutional decisions are made. A ``black
box'' effect of automated decision-making systems reduces the public's
perceptions of integrity and trustworthiness. The result is that the public
loses the capacity to identify, challenge, and rectify unfairness or the costs
associated with the loss of public goods or benefits.
The current position paper defines and explains the role of fiduciary
responsibility within an automated decision-making system. We formulate an
automated decision-making system as a data science lifecycle (DSL) and examine
the implications of fiduciary responsibility within the context of the DSL.
Fiduciary responsibility within DSLs provides a methodology for addressing the
public's lack of trust in automated decision-making systems and the
institutions that employ them to make decisions affecting the public. We posit
that fiduciary responsibility manifests in several contexts of a DSL, each of
which requires its own mitigation of sources of mistrust. To instantiate
fiduciary responsibility, a Los Angeles Police Department (LAPD) predictive
policing case study is examined.
|
[
{
"version": "v1",
"created": "Fri, 6 Jan 2023 18:19:01 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Harper",
"Shannon B.",
""
],
[
"Weber",
"Eric S.",
""
]
] |
new_dataset
| 0.962339 |
2301.10165
|
Oriana Riva
|
Pratyay Banerjee, Shweti Mahajan, Kushal Arora, Chitta Baral, Oriana
Riva
|
Lexi: Self-Supervised Learning of the UI Language
|
EMNLP (Findings) 2022
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Humans can learn to operate the user interface (UI) of an application by
reading an instruction manual or how-to guide. Along with text, these resources
include visual content such as UI screenshots and images of application icons
referenced in the text. We explore how to leverage this data to learn generic
visio-linguistic representations of UI screens and their components. These
representations are useful in many real applications, such as accessibility,
voice navigation, and task automation. Prior UI representation models rely on
UI metadata (UI trees and accessibility labels), which is often missing,
incompletely defined, or not accessible. We avoid such a dependency, and
propose Lexi, a pre-trained vision and language model designed to handle the
unique features of UI screens, including their text richness and context
sensitivity. To train Lexi we curate the UICaption dataset consisting of 114k
UI images paired with descriptions of their functionality. We evaluate Lexi on
four tasks: UI action entailment, instruction-based UI image retrieval,
grounding referring expressions, and UI entity recognition.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 09:05:49 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Banerjee",
"Pratyay",
""
],
[
"Mahajan",
"Shweti",
""
],
[
"Arora",
"Kushal",
""
],
[
"Baral",
"Chitta",
""
],
[
"Riva",
"Oriana",
""
]
] |
new_dataset
| 0.975878 |
2301.10180
|
Javad Peymanfard
|
Javad Peymanfard, Samin Heydarian, Ali Lashini, Hossein Zeinali,
Mohammad Reza Mohammadi, Nasser Mozayani
|
A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech
Recognition: the Arman-AV Dataset
| null | null | null | null |
cs.CL cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
In recent years, significant progress has been made in automatic lip reading.
But these methods require large-scale datasets that do not exist for many
low-resource languages. In this paper, we have presented a new multipurpose
audio-visual dataset for Persian. This dataset consists of almost 220 hours of
videos with 1760 corresponding speakers. In addition to lip reading, the
dataset is suitable for automatic speech recognition, audio-visual speech
recognition, and speaker recognition. Also, it is the first large-scale lip
reading dataset in Persian. A baseline method was provided for each mentioned
task. In addition, we have proposed a technique to detect visemes (a visual
equivalent of a phoneme) in Persian. The visemes obtained by this method
increase the accuracy of the lip reading task by 7% relatively compared to the
previously proposed visemes, which can be applied to other languages as well.
|
[
{
"version": "v1",
"created": "Sat, 21 Jan 2023 05:13:30 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Peymanfard",
"Javad",
""
],
[
"Heydarian",
"Samin",
""
],
[
"Lashini",
"Ali",
""
],
[
"Zeinali",
"Hossein",
""
],
[
"Mohammadi",
"Mohammad Reza",
""
],
[
"Mozayani",
"Nasser",
""
]
] |
new_dataset
| 0.999828 |
2301.10235
|
Qiangyu Pei
|
Fangming Liu, Qiangyu Pei, Shutong Chen, Yongjie Yuan, Lin Wang, Max
Muhlhauser
|
When the Metaverse Meets Carbon Neutrality: Ongoing Efforts and
Directions
|
24 pages
| null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The metaverse has recently gained increasing attention from the public. It
builds up a virtual world where we can live as a new role regardless of the
role we play in the physical world. However, building and operating this
virtual world will generate an extraordinary amount of carbon emissions for
computing, communicating, displaying, and so on. This inevitably hinders the
realization of carbon neutrality as a priority of our society, adding heavy
burden to our earth. In this survey, we first present a green viewpoint of the
metaverse by investigating the carbon issues in its three core layers, namely
the infrastructure layer, the interaction layer, and the economy layer, and
estimate their carbon footprints in the near future. Next, we analyze a range
of current and emerging applicable green techniques for the purpose of reducing
energy usage and carbon emissions of the metaverse, and discuss their
limitations in supporting metaverse workloads. Then, in view of these
limitations, we discuss important implications and bring forth several insights
and future directions to make each metaverse layer greener. After that, we
investigate green solutions from the governance perspective, including both
public policies in the physical world and regulation of users in the virtual
world, and propose an indicator Carbon Utility (CU) to quantify the service
quality brought by an user activity per unit of carbon emissions. Finally, we
identify an issue for the metaverse as a whole and summarize three directions:
(1) a comprehensive consideration of necessary performance metrics, (2) a
comprehensive consideration of involved layers and multiple internal
components, and (3) a new assessing, recording, and regulating mechanism on
carbon footprints of user activities. Our proposed quantitative indicator CU
would be helpful in regulating user activities in the metaverse world.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 16:25:18 GMT"
}
] | 2023-01-25T00:00:00 |
[
[
"Liu",
"Fangming",
""
],
[
"Pei",
"Qiangyu",
""
],
[
"Chen",
"Shutong",
""
],
[
"Yuan",
"Yongjie",
""
],
[
"Wang",
"Lin",
""
],
[
"Muhlhauser",
"Max",
""
]
] |
new_dataset
| 0.997395 |
2105.08123
|
Ataberk Olgun
|
Nandita Vijaykumar, Ataberk Olgun, Konstantinos Kanellopoulos, Nisa
Bostanc{\i}, Hasan Hassan, Mehrshad Lotfi, Phillip B. Gibbons, Onur Mutlu
|
MetaSys: A Practical Open-Source Metadata Management System to Implement
and Evaluate Cross-Layer Optimizations
|
A shorter version of this work is to appear at the ACM Transactions
on Architecture and Code Optimization (TACO). 27 pages, 15 figures
| null | null | null |
cs.AR
|
http://creativecommons.org/licenses/by/4.0/
|
This paper introduces the first open-source FPGA-based infrastructure,
MetaSys, with a prototype in a RISC-V core, to enable the rapid implementation
and evaluation of a wide range of cross-layer techniques in real hardware.
Hardware-software cooperative techniques are powerful approaches to improve the
performance, quality of service, and security of general-purpose processors.
They are however typically challenging to rapidly implement and evaluate in
real hardware as they require full-stack changes to the hardware, OS, system
software, and instruction-set architecture (ISA).
MetaSys implements a rich hardware-software interface and lightweight
metadata support that can be used as a common basis to rapidly implement and
evaluate new cross-layer techniques. We demonstrate MetaSys's versatility and
ease-of-use by implementing and evaluating three cross-layer techniques for:
(i) prefetching for graph analytics; (ii) bounds checking in memory unsafe
languages, and (iii) return address protection in stack frames; each technique
only requiring ~100 lines of Chisel code over MetaSys.
Using MetaSys, we perform the first detailed experimental study to quantify
the performance overheads of using a single metadata management system to
enable multiple cross-layer optimizations in CPUs. We identify the key sources
of bottlenecks and system inefficiency of a general metadata management system.
We design MetaSys to minimize these inefficiencies and provide increased
versatility compared to previously-proposed metadata systems. Using three use
cases and a detailed characterization, we demonstrate that a common metadata
management system can be used to efficiently support diverse cross-layer
techniques in CPUs.
|
[
{
"version": "v1",
"created": "Mon, 17 May 2021 19:27:48 GMT"
},
{
"version": "v2",
"created": "Wed, 19 May 2021 08:41:33 GMT"
},
{
"version": "v3",
"created": "Sun, 2 Jan 2022 08:09:50 GMT"
},
{
"version": "v4",
"created": "Wed, 18 Jan 2023 07:51:20 GMT"
},
{
"version": "v5",
"created": "Sat, 21 Jan 2023 10:00:56 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Vijaykumar",
"Nandita",
""
],
[
"Olgun",
"Ataberk",
""
],
[
"Kanellopoulos",
"Konstantinos",
""
],
[
"Bostancı",
"Nisa",
""
],
[
"Hassan",
"Hasan",
""
],
[
"Lotfi",
"Mehrshad",
""
],
[
"Gibbons",
"Phillip B.",
""
],
[
"Mutlu",
"Onur",
""
]
] |
new_dataset
| 0.995368 |
2109.13392
|
Volker Tresp
|
Volker Tresp, Sahand Sharifzadeh, Hang Li, Dario Konopatzki, Yunpu Ma
|
The Tensor Brain: A Unified Theory of Perception, Memory and Semantic
Decoding
|
Neural Computation, Volume 35, Issue 2, February 2023
|
Neural Computation, Volume 35, Issue 2, February 2023
| null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We present a unified computational theory of an agent's perception and
memory. In our model, perception, episodic memory, and semantic memory are
realized by different operational modes of the oscillating interactions between
a symbolic index layer and a subsymbolic representation layer. The two layers
form a bilayer tensor network (BTN). Although memory appears to be about the
past, its main purpose is to support the agent in the present and the future.
Recent episodic memory provides the agent with a sense of the here and now.
Remote episodic memory retrieves relevant past experiences to provide
information about possible future scenarios. This aids the agent in
decision-making. "Future" episodic memory, based on expected future events,
guides planning and action. Semantic memory retrieves specific information,
which is not delivered by current perception, and defines priors for future
observations. We argue that it is important for the agent to encode individual
entities, not just classes and attributes. We demonstrate that a form of
self-supervised learning can acquire new concepts and refine existing ones. We
test our model on a standard benchmark data set, which we expanded to contain
richer representations for attributes, classes, and individuals. Our key
hypothesis is that obtaining a better understanding of perception and memory is
a crucial prerequisite to comprehending human-level intelligence.
|
[
{
"version": "v1",
"created": "Mon, 27 Sep 2021 23:32:44 GMT"
},
{
"version": "v2",
"created": "Wed, 6 Oct 2021 17:08:26 GMT"
},
{
"version": "v3",
"created": "Tue, 11 Oct 2022 15:12:00 GMT"
},
{
"version": "v4",
"created": "Wed, 12 Oct 2022 17:26:49 GMT"
},
{
"version": "v5",
"created": "Mon, 17 Oct 2022 20:42:08 GMT"
},
{
"version": "v6",
"created": "Sun, 22 Jan 2023 20:22:16 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Tresp",
"Volker",
""
],
[
"Sharifzadeh",
"Sahand",
""
],
[
"Li",
"Hang",
""
],
[
"Konopatzki",
"Dario",
""
],
[
"Ma",
"Yunpu",
""
]
] |
new_dataset
| 0.989341 |
2112.06560
|
F\'abio Malcher Miranda MSc.
|
F\'abio M. Miranda, Niklas K\"ohnecke and Bernhard Y. Renard
|
HiClass: a Python library for local hierarchical classification
compatible with scikit-learn
|
17 pages, 9 figures, 7 tables
|
Journal of Machine Learning Research 24 (2023) 1-17
| null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
HiClass is an open-source Python library for local hierarchical
classification entirely compatible with scikit-learn. It contains
implementations of the most common design patterns for hierarchical machine
learning models found in the literature, that is, the local classifiers per
node, per parent node and per level. Additionally, the package contains
implementations of hierarchical metrics, which are more appropriate for
evaluating classification performance on hierarchical data. The documentation
includes installation and usage instructions, examples within tutorials and
interactive notebooks, and a complete description of the API. HiClass is
released under the simplified BSD license, encouraging its use in both academic
and commercial environments. Source code and documentation are available at
https://github.com/scikit-learn-contrib/hiclass.
|
[
{
"version": "v1",
"created": "Mon, 13 Dec 2021 11:04:17 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Dec 2021 12:08:16 GMT"
},
{
"version": "v3",
"created": "Wed, 15 Dec 2021 06:08:42 GMT"
},
{
"version": "v4",
"created": "Mon, 20 Dec 2021 12:08:17 GMT"
},
{
"version": "v5",
"created": "Tue, 12 Jul 2022 20:20:19 GMT"
},
{
"version": "v6",
"created": "Mon, 25 Jul 2022 09:55:00 GMT"
},
{
"version": "v7",
"created": "Thu, 1 Dec 2022 23:19:31 GMT"
},
{
"version": "v8",
"created": "Mon, 5 Dec 2022 10:16:08 GMT"
},
{
"version": "v9",
"created": "Tue, 3 Jan 2023 17:51:02 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Miranda",
"Fábio M.",
""
],
[
"Köhnecke",
"Niklas",
""
],
[
"Renard",
"Bernhard Y.",
""
]
] |
new_dataset
| 0.974184 |
2203.04751
|
Kshitij Tiwari
|
Kshitij Tiwari, Basak Sakcak, Prasanna Routray, Manivannan M., and
Steven M. LaValle
|
Visibility-Inspired Models of Touch Sensors for Navigation
|
Accepted at IEEE IROS 2022
| null |
10.1109/IROS47612.2022.9981084
| null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
This paper introduces mathematical models of \sensors\ for mobile robots
based on visibility. Serving a purpose similar to the pinhole camera model for
computer vision, the introduced models are expected to provide a useful,
idealized characterization of task-relevant information that can be inferred
from their outputs or observations. Possible tasks include navigation,
localization and mapping when a mobile robot is deployed in an unknown
environment. These models allow direct comparisons to be made between
traditional depth sensors, highlighting cases in which touch sensing may be
interchangeable with time of flight or vision sensors, and characterizing
unique advantages provided by touch sensing. The models include contact
detection, compression, load bearing, and deflection. The results could serve
as a basic building block for innovative touch sensor designs for mobile robot
sensor fusion systems.
|
[
{
"version": "v1",
"created": "Fri, 4 Mar 2022 08:23:01 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Jul 2022 07:42:32 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Tiwari",
"Kshitij",
""
],
[
"Sakcak",
"Basak",
""
],
[
"Routray",
"Prasanna",
""
],
[
"M.",
"Manivannan",
""
],
[
"LaValle",
"Steven M.",
""
]
] |
new_dataset
| 0.954848 |
2204.10704
|
Runzhe Zhu
|
Runzhe Zhu, Ling Yin, Mingze Yang, Fei Wu, Yuncheng Yang, Wenbo Hu
|
SUES-200: A Multi-height Multi-scene Cross-view Image Benchmark Across
Drone and Satellite
| null | null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cross-view image matching aims to match images of the same target scene
acquired from different platforms. With the rapid development of drone
technology, cross-view matching by neural network models has been a widely
accepted choice for drone position or navigation. However, existing public
datasets do not include images obtained by drones at different heights, and the
types of scenes are relatively homogeneous, which yields issues in assessing a
model's capability to adapt to complex and changing scenes. In this end, we
present a new cross-view dataset called SUES-200 to address these issues.
SUES-200 contains 24120 images acquired by the drone at four different heights
and corresponding satellite view images of the same target scene. To the best
of our knowledge, SUES-200 is the first public dataset that considers the
differences generated in aerial photography captured by drones flying at
different heights. In addition, we developed an evaluation for efficient
training, testing and evaluation of cross-view matching models, under which we
comprehensively analyze the performance of nine architectures. Then, we propose
a robust baseline model for use with SUES-200. Experimental results show that
SUES-200 can help the model to learn highly discriminative features of the
height of the drone.
|
[
{
"version": "v1",
"created": "Fri, 22 Apr 2022 13:49:52 GMT"
},
{
"version": "v2",
"created": "Sun, 22 Jan 2023 01:49:00 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Zhu",
"Runzhe",
""
],
[
"Yin",
"Ling",
""
],
[
"Yang",
"Mingze",
""
],
[
"Wu",
"Fei",
""
],
[
"Yang",
"Yuncheng",
""
],
[
"Hu",
"Wenbo",
""
]
] |
new_dataset
| 0.998007 |
2206.09372
|
Lalith Sharan
|
Lalith Sharan, Halvar Kelm, Gabriele Romano, Matthias Karck, Raffaele
De Simone, Sandy Engelhardt
|
mvHOTA: A multi-view higher order tracking accuracy metric to measure
spatial and temporal associations in multi-point detection
|
16 pages, 9 figures
|
Computer Methods in Biomechanics and Biomedical Engineering:
Imaging & Visualization (2022) 1-9
|
10.1080/21681163.2022.2159535
| null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Multi-point tracking is a challenging task that involves detecting points in
the scene and tracking them across a sequence of frames. Computing
detection-based measures like the F-measure on a frame-by-frame basis is not
sufficient to assess the overall performance, as it does not interpret
performance in the temporal domain. The main evaluation metric available comes
from Multi-object tracking (MOT) methods to benchmark performance on datasets
such as KITTI with the recently proposed higher order tracking accuracy (HOTA)
metric, which is capable of providing a better description of the performance
over metrics such as MOTA, DetA, and IDF1. While the HOTA metric takes into
account temporal associations, it does not provide a tailored means to analyse
the spatial associations of a dataset in a multi-camera setup. Moreover, there
are differences in evaluating the detection task for points when compared to
objects (point distances vs. bounding box overlap). Therefore in this work, we
propose a multi-view higher order tracking metric (mvHOTA) to determine the
accuracy of multi-point (multi-instance and multi-class) tracking methods,
while taking into account temporal and spatial associations.mvHOTA can be
interpreted as the geometric mean of detection, temporal, and spatial
associations, thereby providing equal weighting to each of the factors. We
demonstrate the use of this metric to evaluate the tracking performance on an
endoscopic point detection dataset from a previously organised surgical data
science challenge. Furthermore, we compare with other adjusted MOT metrics for
this use-case, discuss the properties of mvHOTA, and show how the proposed
multi-view Association and the Occlusion index (OI) facilitate analysis of
methods with respect to handling of occlusions. The code is available at
https://github.com/Cardio-AI/mvhota.
|
[
{
"version": "v1",
"created": "Sun, 19 Jun 2022 10:31:53 GMT"
},
{
"version": "v2",
"created": "Mon, 23 Jan 2023 10:44:12 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Sharan",
"Lalith",
""
],
[
"Kelm",
"Halvar",
""
],
[
"Romano",
"Gabriele",
""
],
[
"Karck",
"Matthias",
""
],
[
"De Simone",
"Raffaele",
""
],
[
"Engelhardt",
"Sandy",
""
]
] |
new_dataset
| 0.998241 |
2208.08100
|
Shangqing Liu
|
Shangqing Liu, Yanzhou Li, Xiaofei Xie, Yang Liu
|
CommitBART: A Large Pre-trained Model for GitHub Commits
| null | null | null | null |
cs.SE cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
GitHub commits, which record the code changes with natural language messages
for description, play a critical role for software developers to comprehend the
software evolution. To promote the development of the open-source software
community, we collect a commit benchmark including over 7.99 million commits
across 7 programming languages. Based on this benchmark, we present CommitBART,
a large pre-trained encoder-decoder Transformer model for GitHub commits. The
model is pre-trained by three categories (i.e., denoising objectives,
cross-modal generation and contrastive learning) for six pre-training tasks to
learn commit fragment representations. Furthermore, we unify a ``commit
intelligence'' framework with one understanding task and three generation tasks
for commits. The comprehensive experiments on these tasks demonstrate that
CommitBARTsignificantly outperforms previous pre-trained works for code.
Further analysis also reveals each pre-training task enhances the model
performance.
|
[
{
"version": "v1",
"created": "Wed, 17 Aug 2022 06:35:57 GMT"
},
{
"version": "v2",
"created": "Sun, 22 Jan 2023 07:14:03 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Liu",
"Shangqing",
""
],
[
"Li",
"Yanzhou",
""
],
[
"Xie",
"Xiaofei",
""
],
[
"Liu",
"Yang",
""
]
] |
new_dataset
| 0.9996 |
2209.00398
|
Mouna Dhaouadi
|
Mouna Dhaouadi, Bentley James Oakes, Michalis Famelis
|
End-to-End Rationale Reconstruction
| null |
ASE '22: Proceedings of the 37th IEEE/ACM International Conference
on Automated Software Engineering 2022
|
10.1145/3551349.3559547
| null |
cs.SE
|
http://creativecommons.org/licenses/by-sa/4.0/
|
The logic behind design decisions, called design rationale, is very valuable.
In the past, researchers have tried to automatically extract and exploit this
information, but prior techniques are only applicable to specific contexts and
there is insufficient progress on an end-to-end rationale information
extraction pipeline. Here we outline a path towards such a pipeline that
leverages several Machine Learning (ML) and Natural Language Processing (NLP)
techniques. Our proposed context-independent approach, called Kantara, produces
a knowledge graph representation of decisions and of their rationales, which
considers their historical evolution and traceability. We also propose
validation mechanisms to ensure the correctness of the extracted information
and the coherence of the development process. We conducted a preliminary
evaluation of our proposed approach on a small example sourced from the Linux
Kernel, which shows promising results.
|
[
{
"version": "v1",
"created": "Wed, 31 Aug 2022 13:19:30 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Dhaouadi",
"Mouna",
""
],
[
"Oakes",
"Bentley James",
""
],
[
"Famelis",
"Michalis",
""
]
] |
new_dataset
| 0.987824 |
2301.01586
|
Pedro Hecht
|
Hugo Daniel Scolnik and Juan Pedro Hecht
|
Post-Quantum Key Agreement Protocol based on Non-Square Integer Matrices
|
12 pages, 2 tables, 29 references
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We present in this paper an algorithm for exchanging session keys, coupled
with a hashing encryption module. We show schemes designed for their potential
invulnerability to classical and quantum attacks. In turn, if the parameters
included were appropriate, brute-force attacks exceed the (five) security
levels used in the NIST competition of new post-quantum standards. The original
idea consists of products of rectangular matrices in Zp as public values and
whose factorization is proved to be an NP-complete problem. We present running
times as a function of the explored parameters and their link with operational
safety. To our knowledge there are no classical and quantum attacks of
polynomial complexity available at hand, remaining only the systematic
exploration of the private-key space.
|
[
{
"version": "v1",
"created": "Wed, 4 Jan 2023 13:03:15 GMT"
},
{
"version": "v2",
"created": "Thu, 5 Jan 2023 13:13:34 GMT"
},
{
"version": "v3",
"created": "Sun, 22 Jan 2023 14:56:44 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Scolnik",
"Hugo Daniel",
""
],
[
"Hecht",
"Juan Pedro",
""
]
] |
new_dataset
| 0.953551 |
2301.08714
|
Yangge Li
|
Yangge Li, Haoqing Zhu, Katherine Braught, Keyi Shen, Sayan Mitra
|
Verse: A Python library for reasoning about multi-agent hybrid system
scenarios
|
26 pages, 16 figures
| null | null | null |
cs.SE cs.FL cs.MA
|
http://creativecommons.org/licenses/by/4.0/
|
We present the Verse library with the aim of making hybrid system
verification more usable for multi-agent scenarios. In Verse, decision making
agents move in a map and interact with each other through sensors. The decision
logic for each agent is written in a subset of Python and the continuous
dynamics is given by a black-box simulator. Multiple agents can be instantiated
and they can be ported to different maps for creating scenarios. Verse provides
functions for simulating and verifying such scenarios using existing
reachability analysis algorithms. We illustrate several capabilities and use
cases of the library with heterogeneous agents, incremental verification,
different sensor models, and the flexibility of plugging in different
subroutines for post computations.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 18:18:09 GMT"
},
{
"version": "v2",
"created": "Mon, 23 Jan 2023 04:49:26 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Li",
"Yangge",
""
],
[
"Zhu",
"Haoqing",
""
],
[
"Braught",
"Katherine",
""
],
[
"Shen",
"Keyi",
""
],
[
"Mitra",
"Sayan",
""
]
] |
new_dataset
| 0.981332 |
2301.08730
|
Changan Chen
|
Changan Chen, Alexander Richard, Roman Shapovalov, Vamsi Krishna
Ithapu, Natalia Neverova, Kristen Grauman, Andrea Vedaldi
|
Novel-View Acoustic Synthesis
|
Project page: https://vision.cs.utexas.edu/projects/nvas
| null | null | null |
cs.CV cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce the novel-view acoustic synthesis (NVAS) task: given the sight
and sound observed at a source viewpoint, can we synthesize the sound of that
scene from an unseen target viewpoint? We propose a neural rendering approach:
Visually-Guided Acoustic Synthesis (ViGAS) network that learns to synthesize
the sound of an arbitrary point in space by analyzing the input audio-visual
cues. To benchmark this task, we collect two first-of-their-kind large-scale
multi-view audio-visual datasets, one synthetic and one real. We show that our
model successfully reasons about the spatial cues and synthesizes faithful
audio on both datasets. To our knowledge, this work represents the very first
formulation, dataset, and approach to solve the novel-view acoustic synthesis
task, which has exciting potential applications ranging from AR/VR to art and
design. Unlocked by this work, we believe that the future of novel-view
synthesis is in multi-modal learning from videos.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 18:49:58 GMT"
},
{
"version": "v2",
"created": "Mon, 23 Jan 2023 17:11:30 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Chen",
"Changan",
""
],
[
"Richard",
"Alexander",
""
],
[
"Shapovalov",
"Roman",
""
],
[
"Ithapu",
"Vamsi Krishna",
""
],
[
"Neverova",
"Natalia",
""
],
[
"Grauman",
"Kristen",
""
],
[
"Vedaldi",
"Andrea",
""
]
] |
new_dataset
| 0.999297 |
2301.08774
|
Zhenkun Zhou
|
Chong Zhang, Zhenkun Zhou, Xingyu Peng, Ke Xu
|
DoubleH: Twitter User Stance Detection via Bipartite Graph Neural
Networks
| null | null | null | null |
cs.SI cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Given the development and abundance of social media, studying the stance of
social media users is a challenging and pressing issue. Social media users
express their stance by posting tweets and retweeting. Therefore, the
homogeneous relationship between users and the heterogeneous relationship
between users and tweets are relevant for the stance detection task. Recently,
graph neural networks (GNNs) have developed rapidly and have been applied to
social media research. In this paper, we crawl a large-scale dataset of the
2020 US presidential election and automatically label all users by manually
tagged hashtags. Subsequently, we propose a bipartite graph neural network
model, DoubleH, which aims to better utilize homogeneous and heterogeneous
information in user stance detection tasks. Specifically, we first construct a
bipartite graph based on posting and retweeting relations for two kinds of
nodes, including users and tweets. We then iteratively update the node's
representation by extracting and separately processing heterogeneous and
homogeneous information in the node's neighbors. Finally, the representations
of user nodes are used for user stance classification. Experimental results
show that DoubleH outperforms the state-of-the-art methods on popular
benchmarks. Further analysis illustrates the model's utilization of information
and demonstrates stability and efficiency at different numbers of layers.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 19:20:10 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Zhang",
"Chong",
""
],
[
"Zhou",
"Zhenkun",
""
],
[
"Peng",
"Xingyu",
""
],
[
"Xu",
"Ke",
""
]
] |
new_dataset
| 0.999233 |
2301.08783
|
Andrew Freeman
|
Andrew C. Freeman, Montek Singh, Ketan Mayer-Patel
|
An Asynchronous Intensity Representation for Framed and Event Video
Sources
|
10 pages
| null | null | null |
cs.CV cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Neuromorphic "event" cameras, designed to mimic the human vision system with
asynchronous sensing, unlock a new realm of high-speed and high dynamic range
applications. However, researchers often either revert to a framed
representation of event data for applications, or build bespoke applications
for a particular camera's event data type. To usher in the next era of video
systems, accommodate new event camera designs, and explore the benefits to
asynchronous video in classical applications, we argue that there is a need for
an asynchronous, source-agnostic video representation. In this paper, we
introduce a novel, asynchronous intensity representation for both framed and
non-framed data sources. We show that our representation can increase intensity
precision and greatly reduce the number of samples per pixel compared to
grid-based representations. With framed sources, we demonstrate that by
permitting a small amount of loss through the temporal averaging of similar
pixel values, we can reduce our representational sample rate by more than half,
while incurring a drop in VMAF quality score of only 4.5. We also demonstrate
lower latency than the state-of-the-art method for fusing and transcoding
framed and event camera data to an intensity representation, while maintaining
$2000\times$ the temporal resolution. We argue that our method provides the
computational efficiency and temporal granularity necessary to build real-time
intensity-based applications for event cameras.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 19:46:23 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Freeman",
"Andrew C.",
""
],
[
"Singh",
"Montek",
""
],
[
"Mayer-Patel",
"Ketan",
""
]
] |
new_dataset
| 0.99713 |
2301.08806
|
Nikolay Ivanov
|
Nikolay Ivanov, Qiben Yan and Anurag Kompalli
|
TxT: Real-time Transaction Encapsulation for Ethereum Smart Contracts
|
To appear in IEEE Transactions on Information Forensics and Security
| null |
10.1109/TIFS.2023.3234895
| null |
cs.CR cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Ethereum is a permissionless blockchain ecosystem that supports execution of
smart contracts, the key enablers of decentralized finance (DeFi) and
non-fungible tokens (NFT). However, the expressiveness of Ethereum smart
contracts is a double-edged sword: while it enables blockchain programmability,
it also introduces security vulnerabilities, i.e., the exploitable
discrepancies between expected and actual behaviors of the contract code. To
address these discrepancies and increase the vulnerability coverage, we propose
a new smart contract security testing approach called transaction
encapsulation. The core idea lies in the local execution of transactions on a
fully-synchronized yet isolated Ethereum node, which creates a preview of
outcomes of transaction sequences on the current state of blockchain. This
approach poses a critical technical challenge -- the well-known
time-of-check/time-of-use (TOCTOU) problem, i.e., the assurance that the final
transactions will exhibit the same execution paths as the encapsulated test
transactions. In this work, we determine the exact conditions for guaranteed
execution path replicability of the tested transactions, and implement a
transaction testing tool, TxT, which reveals the actual outcomes of Ethereum
transactions. To ensure the correctness of testing, TxT deterministically
verifies whether a given sequence of transactions ensues an identical execution
path on the current state of blockchain. We analyze over 1.3 billion Ethereum
transactions and determine that 96.5% of them can be verified by TxT. We
further show that TxT successfully reveals the suspicious behaviors associated
with 31 out of 37 vulnerabilities (83.8% coverage) in the smart contract
weakness classification (SWC) registry. In comparison, the vulnerability
coverage of all the existing defense approaches combined only reaches 40.5%.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 21:14:15 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Ivanov",
"Nikolay",
""
],
[
"Yan",
"Qiben",
""
],
[
"Kompalli",
"Anurag",
""
]
] |
new_dataset
| 0.959419 |
2301.08810
|
Yinghao Aaron Li
|
Yinghao Aaron Li, Cong Han, Xilin Jiang, Nima Mesgarani
|
Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme
Predictions
| null | null | null | null |
cs.CL cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large-scale pre-trained language models have been shown to be helpful in
improving the naturalness of text-to-speech (TTS) models by enabling them to
produce more naturalistic prosodic patterns. However, these models are usually
word-level or sup-phoneme-level and jointly trained with phonemes, making them
inefficient for the downstream TTS task where only phonemes are needed. In this
work, we propose a phoneme-level BERT (PL-BERT) with a pretext task of
predicting the corresponding graphemes along with the regular masked phoneme
predictions. Subjective evaluations show that our phoneme-level BERT encoder
has significantly improved the mean opinion scores (MOS) of rated naturalness
of synthesized speech compared with the state-of-the-art (SOTA) StyleTTS
baseline on out-of-distribution (OOD) texts.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 21:36:16 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Li",
"Yinghao Aaron",
""
],
[
"Han",
"Cong",
""
],
[
"Jiang",
"Xilin",
""
],
[
"Mesgarani",
"Nima",
""
]
] |
new_dataset
| 0.986248 |
2301.08828
|
Thanveer Shaik Mr
|
Thanveer Shaik, Xiaohui Tao, Niall Higgins, Haoran Xie, Raj Gururajan,
Xujuan Zhou
|
AI enabled RPM for Mental Health Facility
| null |
In 1st ACM Workshop on Mobile and Wireless Sensing for Smart
Healthcare (MWMSSH 2022), October 21, 2022, Sydney, NSW, Australia. ACM, New
York, NY, USA, 7 pages
|
10.1145/3556551.3561191
| null |
cs.HC cs.AI cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Mental healthcare is one of the prominent parts of the healthcare industry
with alarming concerns related to patients depression, stress leading to
self-harm and threat to fellow patients and medical staff. To provide a
therapeutic environment for both patients and staff, aggressive or agitated
patients need to be monitored remotely and track their vital signs and physical
activities continuously. Remote patient monitoring (RPM) using non-invasive
technology could enable contactless monitoring of acutely ill patients in a
mental health facility. Enabling the RPM system with AI unlocks a predictive
environment in which future vital signs of the patients can be forecasted. This
paper discusses an AI-enabled RPM system framework with a non-invasive digital
technology RFID using its in-built NCS mechanism to retrieve vital signs and
physical actions of patients. Based on the retrieved time series data, future
vital signs of patients for the upcoming 3 hours and classify their physical
actions into 10 labelled physical activities. This framework assists to avoid
any unforeseen clinical disasters and take precautionary measures with medical
intervention at right time. A case study of a middle-aged PTSD patient treated
with the AI-enabled RPM system is demonstrated in this study.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 23:47:16 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Shaik",
"Thanveer",
""
],
[
"Tao",
"Xiaohui",
""
],
[
"Higgins",
"Niall",
""
],
[
"Xie",
"Haoran",
""
],
[
"Gururajan",
"Raj",
""
],
[
"Zhou",
"Xujuan",
""
]
] |
new_dataset
| 0.999407 |
2301.08838
|
Michael Alcorn
|
Michael A. Alcorn
|
AQuaMaM: An Autoregressive, Quaternion Manifold Model for Rapidly
Estimating Complex SO(3) Distributions
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accurately modeling complex, multimodal distributions is necessary for
optimal decision-making, but doing so for rotations in three-dimensions, i.e.,
the SO(3) group, is challenging due to the curvature of the rotation manifold.
The recently described implicit-PDF (IPDF) is a simple, elegant, and effective
approach for learning arbitrary distributions on SO(3) up to a given precision.
However, inference with IPDF requires $N$ forward passes through the network's
final multilayer perceptron (where $N$ places an upper bound on the likelihood
that can be calculated by the model), which is prohibitively slow for those
without the computational resources necessary to parallelize the queries. In
this paper, I introduce AQuaMaM, a neural network capable of both learning
complex distributions on the rotation manifold and calculating exact
likelihoods for query rotations in a single forward pass. Specifically, AQuaMaM
autoregressively models the projected components of unit quaternions as
mixtures of uniform distributions that partition their geometrically-restricted
domain of values. When trained on an "infinite" toy dataset with ambiguous
viewpoints, AQuaMaM rapidly converges to a sampling distribution closely
matching the true data distribution. In contrast, the sampling distribution for
IPDF dramatically diverges from the true data distribution, despite IPDF
approaching its theoretical minimum evaluation loss during training. When
trained on a constructed dataset of 500,000 renders of a die in different
rotations, AQuaMaM reaches a test log-likelihood 14% higher than IPDF. Further,
compared to IPDF, AQuaMaM uses 24% fewer parameters, has a prediction
throughput 52$\times$ faster on a single GPU, and converges in a similar amount
of time during training.
|
[
{
"version": "v1",
"created": "Sat, 21 Jan 2023 00:40:21 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Alcorn",
"Michael A.",
""
]
] |
new_dataset
| 0.984381 |
2301.08969
|
Mario Grobler
|
Mario Grobler, Leif Sabellek, Sebastian Siebertz
|
Parikh Automata on Infinite Words
| null | null | null | null |
cs.FL
|
http://creativecommons.org/licenses/by/4.0/
|
Parikh automata on finite words were first introduced by Klaedtke and
Rue{\ss} [Automata, Languages and Programming, 2003]. In this paper, we
introduce several variants of Parikh automata on infinite words and study their
expressiveness. We show that one of our new models is equivalent to synchronous
blind counter machines introduced by Fernau and Stiebe [Fundamenta
Informaticae, 2008]. All our models admit {\epsilon}-elimination, which to the
best of our knowledge is an open question for blind counter automata. We then
study the classical decision problems of the new automata models.
|
[
{
"version": "v1",
"created": "Sat, 21 Jan 2023 16:32:01 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Grobler",
"Mario",
""
],
[
"Sabellek",
"Leif",
""
],
[
"Siebertz",
"Sebastian",
""
]
] |
new_dataset
| 0.968035 |
2301.09007
|
Hosein Barzekar
|
Hosein Barzekar, Yash Patel, Ling Tong, Zeyun Yu
|
MultiNet with Transformers: A Model for Cancer Diagnosis Using Images
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Cancer is a leading cause of death in many countries. An early diagnosis of
cancer based on biomedical imaging ensures effective treatment and a better
prognosis. However, biomedical imaging presents challenges to both clinical
institutions and researchers. Physiological anomalies are often characterized
by slight abnormalities in individual cells or tissues, making them difficult
to detect visually. Traditionally, anomalies are diagnosed by radiologists and
pathologists with extensive training. This procedure, however, demands the
participation of professionals and incurs a substantial cost. The cost makes
large-scale biological image classification impractical. In this study, we
provide unique deep neural network designs for multiclass classification of
medical images, in particular cancer images. We incorporated transformers into
a multiclass framework to take advantage of data-gathering capability and
perform more accurate classifications. We evaluated models on publicly
accessible datasets using various measures to ensure the reliability of the
models. Extensive assessment metrics suggest this method can be used for a
multitude of classification tasks.
|
[
{
"version": "v1",
"created": "Sat, 21 Jan 2023 20:53:57 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Barzekar",
"Hosein",
""
],
[
"Patel",
"Yash",
""
],
[
"Tong",
"Ling",
""
],
[
"Yu",
"Zeyun",
""
]
] |
new_dataset
| 0.995423 |
2301.09015
|
Letian Zhang
|
Letian Zhang, Jie Xu
|
E$^3$Pose: Energy-Efficient Edge-assisted Multi-camera System for
Multi-human 3D Pose Estimation
| null | null | null | null |
cs.CV cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
Multi-human 3D pose estimation plays a key role in establishing a seamless
connection between the real world and the virtual world. Recent efforts adopted
a two-stage framework that first builds 2D pose estimations in multiple camera
views from different perspectives and then synthesizes them into 3D poses.
However, the focus has largely been on developing new computer vision
algorithms on the offline video datasets without much consideration on the
energy constraints in real-world systems with flexibly-deployed and
battery-powered cameras. In this paper, we propose an energy-efficient
edge-assisted multiple-camera system, dubbed E$^3$Pose, for real-time
multi-human 3D pose estimation, based on the key idea of adaptive camera
selection. Instead of always employing all available cameras to perform 2D pose
estimations as in the existing works, E$^3$Pose selects only a subset of
cameras depending on their camera view qualities in terms of occlusion and
energy states in an adaptive manner, thereby reducing the energy consumption
(which translates to extended battery lifetime) and improving the estimation
accuracy. To achieve this goal, E$^3$Pose incorporates an attention-based LSTM
to predict the occlusion information of each camera view and guide camera
selection before cameras are selected to process the images of a scene, and
runs a camera selection algorithm based on the Lyapunov optimization framework
to make long-term adaptive selection decisions. We build a prototype of
E$^3$Pose on a 5-camera testbed, demonstrate its feasibility and evaluate its
performance. Our results show that a significant energy saving (up to 31.21%)
can be achieved while maintaining a high 3D pose estimation accuracy comparable
to state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Sat, 21 Jan 2023 21:53:33 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Zhang",
"Letian",
""
],
[
"Xu",
"Jie",
""
]
] |
new_dataset
| 0.957725 |
2301.09025
|
Elisabeth Andre
|
Kathrin Janowski, Elisabeth Andr\'e
|
Nichtverbales Verhalten sozialer Roboter: Bewegungen, deren Bedeutung
und die Technik dahinter
|
12 pages, in German language, 4 figures
| null |
10.1007/978-3-658-31114-8_15
| null |
cs.RO cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Nichtverbale Signale sind ein elementarer Bestandteil der menschlichen
Kommunikation. Sie erf\"ullen eine Vielzahl von Funktionen bei der Kl\"arung
von Mehrdeutigkeiten, der subtilen Aushandlung von Rollen oder dem Ausdruck
dessen, was im Inneren der Gespr\"achspartner vorgeht. Viele Studien mit
sozial-interaktiven Robotern zeigen, dass vom Menschen inspirierte
Bewegungsmuster \"ahnlich interpretiert werden wie die von realen Personen.
Dieses Kapitel erl\"autert daher die wichtigsten Funktionen, welche die
jeweiligen Bewegungsmuster in der Kommunikation erf\"ullen, und gibt einen
\"Uberblick dar\"uber, wie sie auf Roboter \"ubertragen werden k\"onnen.
--
Non-verbal signals are a fundamental part of human communication. They serve
a variety of functions in clarifying ambiguities, subtly negotiating roles, or
expressing what is going on inside the interlocutors. Many studies with
socially-interactive robots show that human-inspired movement patterns are
interpreted similarly to those of real people. This chapter therefore explains
the most important functions that the respective movement patterns fulfill in
communication and gives an overview of how they can be transferred to robots.
|
[
{
"version": "v1",
"created": "Sat, 21 Jan 2023 23:31:36 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Janowski",
"Kathrin",
""
],
[
"André",
"Elisabeth",
""
]
] |
new_dataset
| 0.995998 |
2301.09055
|
Ryan White
|
Andrew Ekblad and Trupti Mahendrakar and Ryan T. White and Markus
Wilde and Isaac Silver and Brooke Wheeler
|
Resource-constrained FPGA Design for Satellite Component Feature
Extraction
|
9 pages, 7 figures, 4 tables, Accepted at IEEE Aerospace Conference
2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The effective use of computer vision and machine learning for on-orbit
applications has been hampered by limited computing capabilities, and therefore
limited performance. While embedded systems utilizing ARM processors have been
shown to meet acceptable but low performance standards, the recent availability
of larger space-grade field programmable gate arrays (FPGAs) show potential to
exceed the performance of microcomputer systems. This work proposes use of
neural network-based object detection algorithm that can be deployed on a
comparably resource-constrained FPGA to automatically detect components of
non-cooperative, satellites on orbit. Hardware-in-the-loop experiments were
performed on the ORION Maneuver Kinematics Simulator at Florida Tech to compare
the performance of the new model deployed on a small, resource-constrained FPGA
to an equivalent algorithm on a microcomputer system. Results show the FPGA
implementation increases the throughput and decreases latency while maintaining
comparable accuracy. These findings suggest future missions should consider
deploying computer vision algorithms on space-grade FPGAs.
|
[
{
"version": "v1",
"created": "Sun, 22 Jan 2023 04:49:04 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Ekblad",
"Andrew",
""
],
[
"Mahendrakar",
"Trupti",
""
],
[
"White",
"Ryan T.",
""
],
[
"Wilde",
"Markus",
""
],
[
"Silver",
"Isaac",
""
],
[
"Wheeler",
"Brooke",
""
]
] |
new_dataset
| 0.954094 |
2301.09059
|
Ryan White
|
Trupti Mahendrakar and Steven Holmberg and Andrew Ekblad and Emma
Conti and Ryan T. White and Markus Wilde and Isaac Silver
|
Autonomous Rendezvous with Non-cooperative Target Objects with Swarm
Chasers and Observers
|
Presented at AAS/AIAA Spaceflight Mechanics Meeting 2023, 17 pages, 9
figures, 3 tables
| null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Space debris is on the rise due to the increasing demand for spacecraft for
com-munication, navigation, and other applications. The Space Surveillance
Network (SSN) tracks over 27,000 large pieces of debris and estimates the
number of small, un-trackable fragments at over 1,00,000. To control the growth
of debris, the for-mation of further debris must be reduced. Some solutions
include deorbiting larger non-cooperative resident space objects (RSOs) or
servicing satellites in or-bit. Both require rendezvous with RSOs, and the
scale of the problem calls for autonomous missions. This paper introduces the
Multipurpose Autonomous Ren-dezvous Vision-Integrated Navigation system
(MARVIN) developed and tested at the ORION Facility at Florida Institution of
Technology. MARVIN consists of two sub-systems: a machine vision-aided
navigation system and an artificial po-tential field (APF) guidance algorithm
which work together to command a swarm of chasers to safely rendezvous with the
RSO. We present the MARVIN architec-ture and hardware-in-the-loop experiments
demonstrating autonomous, collabo-rative swarm satellite operations
successfully guiding three drones to rendezvous with a physical mockup of a
non-cooperative satellite in motion.
|
[
{
"version": "v1",
"created": "Sun, 22 Jan 2023 05:22:11 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Mahendrakar",
"Trupti",
""
],
[
"Holmberg",
"Steven",
""
],
[
"Ekblad",
"Andrew",
""
],
[
"Conti",
"Emma",
""
],
[
"White",
"Ryan T.",
""
],
[
"Wilde",
"Markus",
""
],
[
"Silver",
"Isaac",
""
]
] |
new_dataset
| 0.999131 |
2301.09093
|
Mohamad Assaad
|
Charbel Bou Chaaya, Mohamad Assaad, Tijani Chahed
|
RIS-assisted Cell-Free MIMO with Dynamic Arrivals and Departures of
Users: A Novel Network Stability Approach
| null | null | null | null |
cs.IT cs.NI math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Reconfigurable Intelligent Surfaces (RIS) have recently emerged as a hot
research topic, being widely advocated as a candidate technology for next
generation wireless communications. These surfaces passively alter the behavior
of propagation environments enhancing the performance of wireless communication
systems. In this paper, we study the use of RIS in cell-free multiple-input
multiple-output (MIMO) setting where distributed service antennas, called
Access Points (APs), simultaneously serve the users in the network. While most
existing works focus on the physical layer improvements RIS carry, less
attention has been paid to the impact of dynamic arrivals and departures of the
users. In such a case, ensuring the stability of the network is the main goal.
For that, we propose an optimization framework of the phase shifts, for which
we derived a low-complexity solution. We then provide a theoretical analysis of
the network stability and show that our framework stabilizes the network
whenever it is possible. We also prove that a low complexity solution of our
framework stabilizes a guaranteed fraction (higher than 78.5%) of the stability
region. We provide also numerical results that corroborate the theoretical
claims.
|
[
{
"version": "v1",
"created": "Sun, 22 Jan 2023 10:21:44 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Chaaya",
"Charbel Bou",
""
],
[
"Assaad",
"Mohamad",
""
],
[
"Chahed",
"Tijani",
""
]
] |
new_dataset
| 0.992971 |
2301.09251
|
Kush Bhatia
|
Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias
|
Congested Bandits: Optimal Routing via Short-term Resets
|
Published at ICML 2022
| null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For traffic routing platforms, the choice of which route to recommend to a
user depends on the congestion on these routes -- indeed, an individual's
utility depends on the number of people using the recommended route at that
instance. Motivated by this, we introduce the problem of Congested Bandits
where each arm's reward is allowed to depend on the number of times it was
played in the past $\Delta$ timesteps. This dependence on past history of
actions leads to a dynamical system where an algorithm's present choices also
affect its future pay-offs, and requires an algorithm to plan for this. We
study the congestion aware formulation in the multi-armed bandit (MAB) setup
and in the contextual bandit setup with linear rewards. For the multi-armed
setup, we propose a UCB style algorithm and show that its policy regret scales
as $\tilde{O}(\sqrt{K \Delta T})$. For the linear contextual bandit setup, our
algorithm, based on an iterative least squares planner, achieves policy regret
$\tilde{O}(\sqrt{dT} + \Delta)$. From an experimental standpoint, we
corroborate the no-regret properties of our algorithms via a simulation study.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 03:11:06 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Awasthi",
"Pranjal",
""
],
[
"Bhatia",
"Kush",
""
],
[
"Gollapudi",
"Sreenivas",
""
],
[
"Kollias",
"Kostas",
""
]
] |
new_dataset
| 0.999197 |
2301.09310
|
Jinho Lee
|
Seongyeon Park, Hajin Kim, Tanveer Ahmad, Nauman Ahmed, Zaid Al-Ars,
H. Peter Hofstee, Youngsok Kim, and Jinho Lee
|
SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence
Alignment on GPUs
|
Published at IPDPS'22
| null | null | null |
cs.DB cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Sequence alignment forms an important backbone in many sequencing
applications. A commonly used strategy for sequence alignment is an approximate
string matching with a two-dimensional dynamic programming approach. Although
some prior work has been conducted on GPU acceleration of a sequence alignment,
we identify several shortcomings that limit exploiting the full computational
capability of modern GPUs. This paper presents SaLoBa, a GPU-accelerated
sequence alignment library focused on seed extension. Based on the analysis of
previous work with real-world sequencing data, we propose techniques to exploit
the data locality and improve workload balancing. The experimental results
reveal that SaLoBa significantly improves the seed extension kernel compared to
state-of-the-art GPU-based methods.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 08:14:40 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Park",
"Seongyeon",
""
],
[
"Kim",
"Hajin",
""
],
[
"Ahmad",
"Tanveer",
""
],
[
"Ahmed",
"Nauman",
""
],
[
"Al-Ars",
"Zaid",
""
],
[
"Hofstee",
"H. Peter",
""
],
[
"Kim",
"Youngsok",
""
],
[
"Lee",
"Jinho",
""
]
] |
new_dataset
| 0.999656 |
2301.09339
|
Khalid Alnujaidi
|
Khalid Alnujaidi and Ghadah Alhabib
|
Computer Vision for a Camel-Vehicle Collision Mitigation System
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
As the population grows and more land is being used for urbanization,
ecosystems are disrupted by our roads and cars. This expansion of
infrastructure cuts through wildlife territories, leading to many instances of
Wildlife-Vehicle Collision (WVC). These instances of WVC are a global issue
that is having a global socio-economic impact, resulting in billions of dollars
in property damage and, at times, fatalities for vehicle occupants. In Saudi
Arabia, this issue is similar, with instances of Camel-Vehicle Collision (CVC)
being particularly deadly due to the large size of camels, which results in a
25% fatality rate [4]. The focus of this work is to test different object
detection models on the task of detecting camels on the road. The Deep Learning
(DL) object detection models used in the experiments are: CenterNet,
EfficientDet, Faster R-CNN, and SSD. Results of the experiments show that
CenterNet performed the best in terms of accuracy and was the most efficient in
training. In the future, the plan is to expand on this work by developing a
system to make countryside roads safer.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 09:45:31 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Alnujaidi",
"Khalid",
""
],
[
"Alhabib",
"Ghadah",
""
]
] |
new_dataset
| 0.998667 |
2301.09378
|
Xavier Salleras
|
Xavier Salleras
|
Citadel: Self-Sovereign Identities on Dusk Network
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
The amount of sensitive information that service providers handle about their
users has become a concerning fact in many use cases, where users have no other
option but to trust that those companies will not misuse their personal
information. To solve that, Self-Sovereign Identity (SSI) systems have become a
hot topic of research in recent years: SSI systems allow users to manage their
identities transparently. Recent solutions represent the rights of users to use
services as Non-Fungible Tokens (NFTs) stored on Blockchains, and users prove
possession of these rights using Zero-Knowledge Proofs (ZKPs). However, even
when ZKPs do not leak any information about the rights, the NFTs are stored as
public values linked to known accounts, and thus, they can be traced. In this
paper, we design a native privacy-preserving NFT model for the Dusk Network
Blockchain, and on top of it, we deploy Citadel: our novel
full-privacy-preserving SSI system, where the rights of the users are privately
stored on the Dusk Network Blockchain, and users can prove their ownership in a
fully private manner.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 11:47:38 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Salleras",
"Xavier",
""
]
] |
new_dataset
| 0.997028 |
2301.09396
|
Diego Silva
|
Josue Rivera, Julio Garrido, Enrique Riveiro, Diego Silva
|
Environment for the Design and Automation of New CDPR Architectures
|
8 pages, 7 figures, preprint, FAIM 2023 conference
| null | null | null |
cs.RO cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper presents a design and automation environment to study the control
trajectory for new CDPR architectures, for instance CDPRs with an unusual
number of cables or different motor location in the robot frame. In order to
test the environment capabilities, an architecture of a planar
under-constrained CDPR was designed, simulated, and implemented using standard
industrial hardware. Both the simulated model and industrial prototype were
running the same trajectories to determine the time delay and the error
position between them. The tests have demonstrated that the simulated model of
the CDPR reproduces the trajectories of the equivalent industrial prototype
with a maximum deviation of 0.35% under loading and different speed conditions,
despite the time delays produced by the data transmission and the
non-deterministic communication protocols used to connect the industrial
automation controller with the simulated model. The results have shown that the
environment is suitable for trajectory control and workspace analysis of new
CDPR architectures under different dynamic conditions.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 12:32:42 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Rivera",
"Josue",
""
],
[
"Garrido",
"Julio",
""
],
[
"Riveiro",
"Enrique",
""
],
[
"Silva",
"Diego",
""
]
] |
new_dataset
| 0.99276 |
2301.09404
|
Dipak Kumar Bhunia
|
Dipak K. Bhunia, Cristina Fern\'andez-C\'ordoba, Merc\`e Villanueva
|
$\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-Additive Hadamard Codes
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
The $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-additive codes are subgroups of
$\mathbb{Z}_2^{\alpha_1} \times \mathbb{Z}_4^{\alpha_2} \times
\mathbb{Z}_8^{\alpha_3}$, and can be seen as linear codes over $\mathbb{Z}_2$
when $\alpha_2=\alpha_3=0$, $\mathbb{Z}_4$-additive or $\mathbb{Z}_8$-additive
codes when $\alpha_1=\alpha_3=0$ or $\alpha_1=\alpha_2=0$, respectively, or
$\mathbb{Z}_2\mathbb{Z}_4$-additive codes when $\alpha_3=0$. A
$\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-linear Hadamard code is a Hadamard code
which is the Gray map image of a
$\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-additive code. In this paper, we
generalize some known results for $\mathbb{Z}_2\mathbb{Z}_4$-linear Hadamard
codes to $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-linear Hadamard codes with
$\alpha_1 \neq 0$, $\alpha_2 \neq 0$, and $\alpha_3 \neq 0$. First, we give a
recursive construction of $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-additive
Hadamard codes of type $(\alpha_1,\alpha_2, \alpha_3;t_1,t_2, t_3)$ with
$t_1\geq 1$, $t_2 \geq 0$, and $t_3\geq 1$. Then, we show that in general the
$\mathbb{Z}_4$-linear, $\mathbb{Z}_8$-linear and
$\mathbb{Z}_2\mathbb{Z}_4$-linear Hadamard codes are not included in the family
of $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-linear Hadamard codes with $\alpha_1
\neq 0$, $\alpha_2 \neq 0$, and $\alpha_3 \neq 0$. Actually, we point out that
none of these nonlinear $\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-linear Hadamard
codes of length $2^{11}$ is equivalent to a
$\mathbb{Z}_2\mathbb{Z}_4\mathbb{Z}_8$-linear Hadamard code of any other type,
a $\mathbb{Z}_2\mathbb{Z}_4$-linear Hadamard code, or a
$\mathbb{Z}_{2^s}$-linear Hadamard code, with $s\geq 2$, of the same length
$2^{11}$.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 12:56:26 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Bhunia",
"Dipak K.",
""
],
[
"Fernández-Córdoba",
"Cristina",
""
],
[
"Villanueva",
"Mercè",
""
]
] |
new_dataset
| 0.997666 |
2301.09440
|
Ana\"is Villedieu
|
Martin Gronemann and Martin N\"ollenburg and Ana\"is Villedieu
|
Splitting Plane Graphs to Outerplanarity
|
12 pages, 4 figures, appears in the proceedings of WALCOM 2023
| null | null | null |
cs.CG
|
http://creativecommons.org/licenses/by/4.0/
|
Vertex splitting replaces a vertex by two copies and partitions its incident
edges amongst the copies. This problem has been studied as a graph editing
operation to achieve desired properties with as few splits as possible, most
often planarity, for which the problem is NP-hard. Here we study how to
minimize the number of splits to turn a plane graph into an outerplane one. We
tackle this problem by establishing a direct connection between splitting a
plane graph to outerplanarity, finding a connected face cover, and finding a
feedback vertex set in its dual. We prove NP-completeness for plane biconnected
graphs, while we show that a polynomial-time algorithm exists for maximal
planar graphs. Finally, we provide upper and lower bounds for certain families
of maximal planar graphs.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 14:02:10 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Gronemann",
"Martin",
""
],
[
"Nöllenburg",
"Martin",
""
],
[
"Villedieu",
"Anaïs",
""
]
] |
new_dataset
| 0.996265 |
2301.09460
|
Michael Ying Yang
|
Kun Li, George Vosselman, Michael Ying Yang
|
HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial
Images
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Visual question answering (VQA) is an important and challenging multimodal
task in computer vision. Recently, a few efforts have been made to bring VQA
task to aerial images, due to its potential real-world applications in disaster
monitoring, urban planning, and digital earth product generation. However, not
only the huge variation in the appearance, scale and orientation of the
concepts in aerial images, but also the scarcity of the well-annotated datasets
restricts the development of VQA in this domain. In this paper, we introduce a
new dataset, HRVQA, which provides collected 53512 aerial images of 1024*1024
pixels and semi-automatically generated 1070240 QA pairs. To benchmark the
understanding capability of VQA models for aerial images, we evaluate the
relevant methods on HRVQA. Moreover, we propose a novel model, GFTransformer,
with gated attention modules and a mutual fusion module. The experiments show
that the proposed dataset is quite challenging, especially the specific
attribute related questions. Our method achieves superior performance in
comparison to the previous state-of-the-art approaches. The dataset and the
source code will be released at https://hrvqa.nl/.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 14:36:38 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Li",
"Kun",
""
],
[
"Vosselman",
"George",
""
],
[
"Yang",
"Michael Ying",
""
]
] |
new_dataset
| 0.999561 |
2301.09545
|
Jesse Josua Benjamin
|
Jesse Josua Benjamin, Heidi Biggs, Arne Berger, Julija Rukanskait\.e,
Michael Heidt, Nick Merrill, James Pierce, Joseph Lindley
|
The Entoptic Field Camera as Metaphor-Driven Research-through-Design
with AI Technologies
|
To be published in Proceedings of the 2023 CHI Conference on Human
Factors in Computing Systems (CHI '23), April 23--28, 2023, Hamburg, Germany
| null |
10.1145/3544548.3581175
| null |
cs.HC cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Artificial intelligence (AI) technologies are widely deployed in smartphone
photography; and prompt-based image synthesis models have rapidly become
commonplace. In this paper, we describe a Research-through-Design (RtD) project
which explores this shift in the means and modes of image production via the
creation and use of the Entoptic Field Camera. Entoptic phenomena usually refer
to perceptions of floaters or bright blue dots stemming from the physiological
interplay of the eye and brain. We use the term entoptic as a metaphor to
investigate how the material interplay of data and models in AI technologies
shapes human experiences of reality. Through our case study using first-person
design and a field study, we offer implications for critical, reflective,
more-than-human and ludic design to engage AI technologies; the
conceptualisation of an RtD research space which contributes to AI literacy
discourses; and outline a research trajectory concerning materiality and design
affordances of AI technologies.
|
[
{
"version": "v1",
"created": "Mon, 23 Jan 2023 17:03:54 GMT"
}
] | 2023-01-24T00:00:00 |
[
[
"Benjamin",
"Jesse Josua",
""
],
[
"Biggs",
"Heidi",
""
],
[
"Berger",
"Arne",
""
],
[
"Rukanskaitė",
"Julija",
""
],
[
"Heidt",
"Michael",
""
],
[
"Merrill",
"Nick",
""
],
[
"Pierce",
"James",
""
],
[
"Lindley",
"Joseph",
""
]
] |
new_dataset
| 0.998632 |
2110.05668
|
Renbo Tu
|
Renbo Tu, Nicholas Roberts, Mikhail Khodak, Junhong Shen, Frederic
Sala, Ameet Talwalkar
|
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
|
NeurIPS 2022 Datasets and Benchmarks Track
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Most existing neural architecture search (NAS) benchmarks and algorithms
prioritize well-studied tasks, e.g. image classification on CIFAR or ImageNet.
This makes the performance of NAS approaches in more diverse areas poorly
understood. In this paper, we present NAS-Bench-360, a benchmark suite to
evaluate methods on domains beyond those traditionally studied in architecture
search, and use it to address the following question: do state-of-the-art NAS
methods perform well on diverse tasks? To construct the benchmark, we curate
ten tasks spanning a diverse array of application domains, dataset sizes,
problem dimensionalities, and learning objectives. Each task is carefully
chosen to interoperate with modern CNN-based search methods while possibly
being far-afield from its original development domain. To speed up and reduce
the cost of NAS research, for two of the tasks we release the precomputed
performance of 15,625 architectures comprising a standard CNN search space.
Experimentally, we show the need for more robust NAS evaluation of the kind
NAS-Bench-360 enables by showing that several modern NAS procedures perform
inconsistently across the ten tasks, with many catastrophically poor results.
We also demonstrate how NAS-Bench-360 and its associated precomputed results
will enable future scientific discoveries by testing whether several recent
hypotheses promoted in the NAS literature hold on diverse tasks. NAS-Bench-360
is hosted at https://nb360.ml.cmu.edu.
|
[
{
"version": "v1",
"created": "Tue, 12 Oct 2021 01:13:18 GMT"
},
{
"version": "v2",
"created": "Sat, 16 Oct 2021 00:52:02 GMT"
},
{
"version": "v3",
"created": "Tue, 26 Oct 2021 19:37:48 GMT"
},
{
"version": "v4",
"created": "Wed, 6 Jul 2022 01:30:47 GMT"
},
{
"version": "v5",
"created": "Wed, 26 Oct 2022 21:15:13 GMT"
},
{
"version": "v6",
"created": "Thu, 19 Jan 2023 23:17:16 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Tu",
"Renbo",
""
],
[
"Roberts",
"Nicholas",
""
],
[
"Khodak",
"Mikhail",
""
],
[
"Shen",
"Junhong",
""
],
[
"Sala",
"Frederic",
""
],
[
"Talwalkar",
"Ameet",
""
]
] |
new_dataset
| 0.970355 |
2204.01485
|
Edward Boyda
|
Caleb Kruse, Edward Boyda, Sully Chen, Krishna Karra, Tristan
Bou-Nahra, Dan Hammer, Jennifer Mathis, Taylor Maddalene, Jenna Jambeck,
Fabien Laurier
|
Satellite Monitoring of Terrestrial Plastic Waste
|
14 pages, 14 figures
|
PLoS ONE 18(1): e0278997 (2023)
|
10.1371/journal.pone.0278997
| null |
cs.CY cs.CV cs.LG eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Plastic waste is a significant environmental pollutant that is difficult to
monitor. We created a system of neural networks to analyze spectral, spatial,
and temporal components of Sentinel-2 satellite data to identify terrestrial
aggregations of waste. The system works at continental scale. We evaluated
performance in Indonesia and detected 374 waste aggregations, more than double
the number of sites found in public databases. The same system deployed across
twelve countries in Southeast Asia identifies 996 subsequently confirmed waste
sites. For each detected site, we algorithmically monitor waste site footprints
through time and cross-reference other datasets to generate physical and social
metadata. 19% of detected waste sites are located within 200 m of a waterway.
Numerous sites sit directly on riverbanks, with high risk of ocean leakage.
|
[
{
"version": "v1",
"created": "Thu, 24 Mar 2022 22:17:11 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Kruse",
"Caleb",
""
],
[
"Boyda",
"Edward",
""
],
[
"Chen",
"Sully",
""
],
[
"Karra",
"Krishna",
""
],
[
"Bou-Nahra",
"Tristan",
""
],
[
"Hammer",
"Dan",
""
],
[
"Mathis",
"Jennifer",
""
],
[
"Maddalene",
"Taylor",
""
],
[
"Jambeck",
"Jenna",
""
],
[
"Laurier",
"Fabien",
""
]
] |
new_dataset
| 0.994431 |
2207.00658
|
Zhiwu Zheng
|
Zhiwu Zheng, Hsin Cheng, Prakhar Kumar, Sigurd Wagner, Minjie Chen,
Naveen Verma and James C. Sturm
|
Wirelessly-Controlled Untethered Piezoelectric Planar Soft Robot Capable
of Bidirectional Crawling and Rotation
|
Accepted to the 2023 IEEE International Conference on Robotics and
Automation (ICRA)
| null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Electrostatic actuators provide a promising approach to creating soft robotic
sheets, due to their flexible form factor, modular integration, and fast
response speed. However, their control requires kilo-Volt signals and
understanding of complex dynamics resulting from force interactions by on-board
and environmental effects. In this work, we demonstrate an untethered planar
five-actuator piezoelectric robot powered by batteries and on-board
high-voltage circuitry, and controlled through a wireless link. The scalable
fabrication approach is based on bonding different functional layers on top of
each other (steel foil substrate, actuators, flexible electronics). The robot
exhibits a range of controllable motions, including bidirectional crawling (up
to ~0.6 cm/s), turning, and in-place rotation (at ~1 degree/s). High-speed
videos and control experiments show that the richness of the motion results
from the interaction of an asymmetric mass distribution in the robot and the
associated dependence of the dynamics on the driving frequency of the
piezoelectrics. The robot's speed can reach 6 cm/s with specific payload
distribution.
|
[
{
"version": "v1",
"created": "Fri, 1 Jul 2022 20:55:01 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Jan 2023 21:46:27 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Zheng",
"Zhiwu",
""
],
[
"Cheng",
"Hsin",
""
],
[
"Kumar",
"Prakhar",
""
],
[
"Wagner",
"Sigurd",
""
],
[
"Chen",
"Minjie",
""
],
[
"Verma",
"Naveen",
""
],
[
"Sturm",
"James C.",
""
]
] |
new_dataset
| 0.999536 |
2207.01393
|
Hampus Gummesson Svensson
|
Hampus Gummesson Svensson, Esben Jannik Bjerrum, Christian Tyrchan,
Ola Engkvist and Morteza Haghir Chehreghani
|
Autonomous Drug Design with Multi-Armed Bandits
| null | null | null | null |
cs.LG q-bio.BM
|
http://creativecommons.org/licenses/by/4.0/
|
Recent developments in artificial intelligence and automation support a new
drug design paradigm: autonomous drug design. Under this paradigm, generative
models can provide suggestions on thousands of molecules with specific
properties, and automated laboratories can potentially make, test and analyze
molecules with minimal human supervision. However, since still only a limited
number of molecules can be synthesized and tested, an obvious challenge is how
to efficiently select among provided suggestions in a closed-loop system. We
formulate this task as a stochastic multi-armed bandit problem with multiple
plays, volatile arms and similarity information. To solve this task, we adapt
previous work on multi-armed bandits to this setting, and compare our solution
with random sampling, greedy selection and decaying-epsilon-greedy selection
strategies. According to our simulation results, our approach has the potential
to perform better exploration and exploitation of the chemical space for
autonomous drug design.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 13:21:31 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Jan 2023 13:33:13 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Svensson",
"Hampus Gummesson",
""
],
[
"Bjerrum",
"Esben Jannik",
""
],
[
"Tyrchan",
"Christian",
""
],
[
"Engkvist",
"Ola",
""
],
[
"Chehreghani",
"Morteza Haghir",
""
]
] |
new_dataset
| 0.961153 |
2207.01814
|
Jeiyoon Park
|
Jeiyoon Park, Kiho Kwoun, Chanhee Lee, Heuiseok Lim
|
Multimodal Frame-Scoring Transformer for Video Summarization
|
preprint
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As the number of video content has mushroomed in recent years, automatic
video summarization has come useful when we want to just peek at the content of
the video. However, there are two underlying limitations in generic video
summarization task. First, most previous approaches read in just visual
features as input, leaving other modality features behind. Second, existing
datasets for generic video summarization are relatively insufficient to train a
caption generator used for extracting text information from a video and to
train the multimodal feature extractors. To address these two problems, this
paper proposes the Multimodal Frame-Scoring Transformer (MFST), a framework
exploiting visual, text, and audio features and scoring a video with respect to
frames. Our MFST framework first extracts each modality features
(audio-visual-text) using pretrained encoders. Then, MFST trains the multimodal
frame-scoring transformer that uses multimodal representation based on
extracted features as inputs and predicts frame-level scores. Our extensive
experiments with previous models and ablation studies on TVSum and SumMe
datasets demonstrate the effectiveness and superiority of our proposed method
by a large margin in both F1 score and Rank-based evaluation.
|
[
{
"version": "v1",
"created": "Tue, 5 Jul 2022 05:14:15 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Nov 2022 06:34:54 GMT"
},
{
"version": "v3",
"created": "Fri, 20 Jan 2023 00:18:49 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Park",
"Jeiyoon",
""
],
[
"Kwoun",
"Kiho",
""
],
[
"Lee",
"Chanhee",
""
],
[
"Lim",
"Heuiseok",
""
]
] |
new_dataset
| 0.950264 |
2207.13398
|
Manuel Guimar\~aes
|
Manuel Guimar\~aes, Pedro A. Santos, Arnav Jhala
|
Emergent social NPC interactions in the Social NPCs Skyrim mod and
beyond
|
Originally a chapter for Game AI Pro, contains 14 pages, 3 figures
| null | null | null |
cs.AI cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
This work presents an implementation of a social architecture model for
authoring Non-Player Character (NPC) in open world games inspired in academic
research on agentbased modeling. Believable NPC authoring is burdensome in
terms of rich dialogue and responsive behaviors.
We briefly present the characteristics and advantages of using a social agent
architecture for this task and describe an implementation of a social agent
architecture CiF-CK released as a mod Social NPCs for The Elder Scrolls V:
Skyrim
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 09:30:23 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Jan 2023 16:15:12 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Guimarães",
"Manuel",
""
],
[
"Santos",
"Pedro A.",
""
],
[
"Jhala",
"Arnav",
""
]
] |
new_dataset
| 0.95537 |
2209.06545
|
Junyuan Lu
|
Junyuan Lu, Zeyu Wan and Yu Zhang
|
Tac2Structure: Object Surface Reconstruction Only through Multi Times
Touch
|
Accepted for publication in IEEE Robotics And Automation Letters
| null |
10.1109/LRA.2023.3238190
| null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Inspired by humans' ability to perceive the surface texture of unfamiliar
objects without relying on vision, the sense of touch can play a crucial role
in robots exploring the environment, particularly in scenes where vision is
difficult to apply, or occlusion is inevitable. Existing tactile surface
reconstruction methods rely on external sensors or have strong prior
assumptions, making the operation complex and limiting their application
scenarios. This paper presents a framework for low-drift surface reconstruction
through multiple tactile measurements, Tac2Structure. Compared with existing
algorithms, the proposed method uses only a new vision-based tactile sensor
without relying on external devices. Aiming at the difficulty that
reconstruction accuracy is easily affected by the pressure at contact, we
propose a correction algorithm to adapt it. The proposed method also reduces
the accumulative errors that occur easily during global object surface
reconstruction. Multi-frame tactile measurements can accurately reconstruct
object surfaces by jointly using the point cloud registration algorithm,
loop-closure detection algorithm based on deep learning, and pose graph
optimization algorithm. Experiments verify that Tac2Structure can achieve
millimeter-level accuracy in reconstructing the surface of objects, providing
accurate tactile information for the robot to perceive the surrounding
environment.
|
[
{
"version": "v1",
"created": "Wed, 14 Sep 2022 10:48:30 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Dec 2022 06:22:16 GMT"
},
{
"version": "v3",
"created": "Thu, 12 Jan 2023 15:20:40 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Lu",
"Junyuan",
""
],
[
"Wan",
"Zeyu",
""
],
[
"Zhang",
"Yu",
""
]
] |
new_dataset
| 0.99936 |
2301.07853
|
Adam Norton
|
Adam Norton, Reza Ahmadzadeh, Kshitij Jerath, Paul Robinette, Jay
Weitzen, Thanuka Wickramarathne, Holly Yanco, Minseop Choi, Ryan Donald,
Brendan Donoghue, Christian Dumas, Peter Gavriel, Alden Giedraitis, Brendan
Hertel, Jack Houle, Nathan Letteri, Edwin Meriaux, Zahra Rezaei Khavas,
Rakshith Singh, Gregg Willcox, Naye Yoni (University of Massachusetts Lowell)
|
DECISIVE Benchmarking Data Report: sUAS Performance Results from Phase I
|
Approved for public release: PAO #PR2023_74172; arXiv admin note:
substantial text overlap with arXiv:2211.01801
| null | null | null |
cs.RO cs.HC cs.SY eess.SY
|
http://creativecommons.org/publicdomain/zero/1.0/
|
This report reviews all results derived from performance benchmarking
conducted during Phase I of the Development and Execution of Comprehensive and
Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) project by
the University of Massachusetts Lowell, using the test methods specified in the
DECISIVE Test Methods Handbook v1.1 for evaluating small unmanned aerial
systems (sUAS) performance in subterranean and constrained indoor environments,
spanning communications, field readiness, interface, obstacle avoidance,
navigation, mapping, autonomy, trust, and situation awareness. Using those 20
test methods, over 230 tests were conducted across 8 sUAS platforms: Cleo
Robotics Dronut X1P (P = prototype), FLIR Black Hornet PRS, Flyability Elios 2
GOV, Lumenier Nighthawk V3, Parrot ANAFI USA GOV, Skydio X2D, Teal Golden
Eagle, and Vantage Robotics Vesper. Best in class criteria is specified for
each applicable test method and the sUAS that match this criteria are named for
each test method, including a high-level executive summary of their
performance.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 02:50:40 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Jan 2023 14:05:23 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Norton",
"Adam",
"",
"University of Massachusetts Lowell"
],
[
"Ahmadzadeh",
"Reza",
"",
"University of Massachusetts Lowell"
],
[
"Jerath",
"Kshitij",
"",
"University of Massachusetts Lowell"
],
[
"Robinette",
"Paul",
"",
"University of Massachusetts Lowell"
],
[
"Weitzen",
"Jay",
"",
"University of Massachusetts Lowell"
],
[
"Wickramarathne",
"Thanuka",
"",
"University of Massachusetts Lowell"
],
[
"Yanco",
"Holly",
"",
"University of Massachusetts Lowell"
],
[
"Choi",
"Minseop",
"",
"University of Massachusetts Lowell"
],
[
"Donald",
"Ryan",
"",
"University of Massachusetts Lowell"
],
[
"Donoghue",
"Brendan",
"",
"University of Massachusetts Lowell"
],
[
"Dumas",
"Christian",
"",
"University of Massachusetts Lowell"
],
[
"Gavriel",
"Peter",
"",
"University of Massachusetts Lowell"
],
[
"Giedraitis",
"Alden",
"",
"University of Massachusetts Lowell"
],
[
"Hertel",
"Brendan",
"",
"University of Massachusetts Lowell"
],
[
"Houle",
"Jack",
"",
"University of Massachusetts Lowell"
],
[
"Letteri",
"Nathan",
"",
"University of Massachusetts Lowell"
],
[
"Meriaux",
"Edwin",
"",
"University of Massachusetts Lowell"
],
[
"Khavas",
"Zahra Rezaei",
"",
"University of Massachusetts Lowell"
],
[
"Singh",
"Rakshith",
"",
"University of Massachusetts Lowell"
],
[
"Willcox",
"Gregg",
"",
"University of Massachusetts Lowell"
],
[
"Yoni",
"Naye",
"",
"University of Massachusetts Lowell"
]
] |
new_dataset
| 0.996572 |
2301.08295
|
Debarnab Mitra
|
Debarnab Mitra, Lev Tauz, and Lara Dolecek
|
Polar Coded Merkle Tree: Mitigating Data Availability Attacks in
Blockchain Systems Using Informed Polar Code Design
|
36 pages, 10 figures, 2 tables, submitted to IEEE Journal on Selected
Areas in Information Theory
| null | null | null |
cs.IT cs.CR math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Data availability (DA) attack is a well-known problem in certain blockchains
where users accept an invalid block with unavailable portions. Previous works
have used LDPC and 2-D Reed Solomon (2DRS) codes with Merkle trees to mitigate
DA attacks. These codes perform well across various metrics such as DA
detection probability and communication cost. However, these codes are
difficult to apply to blockchains with large blocks due to large decoding
complexity and coding fraud proof size (2D-RS codes), and intractable code
guarantees for large code lengths (LDPC codes). In this paper, we focus on
large block size applications and address the above challenges by proposing the
novel Polar Coded Merkle Tree (PCMT): a Merkle tree encoded using the encoding
graph of polar codes. We provide a specialized polar code design algorithm
called Sampling Efficient Freezing and an algorithm to prune the polar encoding
graph. We demonstrate that the PCMT built using the above techniques results in
a better DA detection probability and communication cost compared to LDPC
codes, has a lower coding fraud proof size compared to LDPC and 2D-RS codes,
provides tractable code guarantees at large code lengths (similar to 2D-RS
codes), and has comparable decoding complexity to 2D-RS and LDPC codes.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 20:12:28 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Mitra",
"Debarnab",
""
],
[
"Tauz",
"Lev",
""
],
[
"Dolecek",
"Lara",
""
]
] |
new_dataset
| 0.997432 |
2301.08327
|
Frederike D\"umbgen
|
Frederike D\"umbgen, Adrien Hoffet, Mihailo Kolund\v{z}ija, Adam
Scholefield, Martin Vetterli
|
Blind as a bat: audible echolocation on small robots
|
8 pages, 10 figures, published in IEEE Robotics and Automation
Letters
| null |
10.1109/LRA.2022.3194669
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For safe and efficient operation, mobile robots need to perceive their
environment, and in particular, perform tasks such as obstacle detection,
localization, and mapping. Although robots are often equipped with microphones
and speakers, the audio modality is rarely used for these tasks. Compared to
the localization of sound sources, for which many practical solutions exist,
algorithms for active echolocation are less developed and often rely on
hardware requirements that are out of reach for small robots. We propose an
end-to-end pipeline for sound-based localization and mapping that is targeted
at, but not limited to, robots equipped with only simple buzzers and low-end
microphones. The method is model-based, runs in real time, and requires no
prior calibration or training. We successfully test the algorithm on the e-puck
robot with its integrated audio hardware, and on the Crazyflie drone, for which
we design a reproducible audio extension deck. We achieve centimeter-level wall
localization on both platforms when the robots are static during the
measurement process. Even in the more challenging setting of a flying drone, we
can successfully localize walls, which we demonstrate in a proof-of-concept
multi-wall localization and mapping demo.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 21:35:13 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Dümbgen",
"Frederike",
""
],
[
"Hoffet",
"Adrien",
""
],
[
"Kolundžija",
"Mihailo",
""
],
[
"Scholefield",
"Adam",
""
],
[
"Vetterli",
"Martin",
""
]
] |
new_dataset
| 0.99828 |
2301.08343
|
Zixi Chen
|
Zixi Chen, Shixin Zhang, Shan Luo, Fuchun Sun, Bin Fang
|
Tacchi: A Pluggable and Low Computational Cost Elastomer Deformation
Simulator for Optical Tactile Sensors
|
8 pages, 6 figures, accepted by IEEE Robotics and Automation Letters
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Simulation is widely applied in robotics research to save time and resources.
There have been several works to simulate optical tactile sensors that leverage
either a smoothing method or Finite Element Method (FEM). However, elastomer
deformation physics is not considered in the former method, whereas the latter
requires a massive amount of computational resources like a computer cluster.
In this work, we propose a pluggable and low computational cost simulator using
the Taichi programming language for simulating optical tactile sensors, named
as Tacchi . It reconstructs elastomer deformation using particles, which allows
deformed elastomer surfaces to be rendered into tactile images and reveals
contact information without suffering from high computational costs. Tacchi
facilitates creating realistic tactile images in simulation, e.g., ones that
capture wear-and-tear defects on object surfaces. In addition, the proposed
Tacchi can be integrated with robotics simulators for a robot system
simulation. Experiment results showed that Tacchi can produce images with
better similarity to real images and achieved higher Sim2Real accuracy compared
to the existing methods. Moreover, it can be connected with MuJoCo and Gazebo
with only the requirement of 1G memory space in GPU compared to a computer
cluster applied for FEM. With Tacchi, physical robot simulation with optical
tactile sensors becomes possible. All the materials in this paper are available
at https://github.com/zixichen007115/Tacchi .
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 22:35:03 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Chen",
"Zixi",
""
],
[
"Zhang",
"Shixin",
""
],
[
"Luo",
"Shan",
""
],
[
"Sun",
"Fuchun",
""
],
[
"Fang",
"Bin",
""
]
] |
new_dataset
| 0.994891 |
2301.08348
|
Samuel Epstein
|
Samuel Epstein
|
A Quantum EL Theorem
|
arXiv admin note: text overlap with arXiv:2102.03905
| null | null | null |
cs.CC quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we prove a quantum version of the EL Theorem. It states that
non-exotic projections of large rank must have simple quantum states in their
images. A consequence to this is there is no way to communicate a quantum
source with corresponding large enough von Neumann entropy without using simple
quantum states.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 22:44:45 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Epstein",
"Samuel",
""
]
] |
new_dataset
| 0.995941 |
2301.08406
|
Xu Chen
|
Hao Wang and Hao Bao and Liekang Zeng and Ke Luo and Xu Chen
|
Real-Time High-Resolution Pedestrian Detection in Crowded Scenes via
Parallel Edge Offloading
|
Accepted by IEEE ICC 2023
| null | null | null |
cs.NI cs.AI cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To identify dense and small-size pedestrians in surveillance systems,
high-resolution cameras are widely deployed, where high-resolution images are
captured and delivered to off-the-shelf pedestrian detection models. However,
given the highly computation-intensive workload brought by the high resolution,
the resource-constrained cameras fail to afford accurate inference in real
time. To address that, we propose Hode, an offloaded video analytic framework
that utilizes multiple edge nodes in proximity to expedite pedestrian detection
with high-resolution inputs. Specifically, Hode can intelligently split
high-resolution images into respective regions and then offload them to
distributed edge nodes to perform pedestrian detection in parallel. A
spatio-temporal flow filtering method is designed to enable context-aware
region partitioning, as well as a DRL-based scheduling algorithm to allow
accuracy-aware load balance among heterogeneous edge nodes. Extensive
evaluation results using realistic prototypes show that Hode can achieve up to
2.01% speedup with very mild accuracy loss.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 02:51:53 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Wang",
"Hao",
""
],
[
"Bao",
"Hao",
""
],
[
"Zeng",
"Liekang",
""
],
[
"Luo",
"Ke",
""
],
[
"Chen",
"Xu",
""
]
] |
new_dataset
| 0.969861 |
2301.08431
|
Kevin Haninger
|
Richard Matthias Hartisch and Kevin Haninger
|
Compliant finray-effect gripper for high-speed robotic assembly of
electrical components
|
8 pages, 3 figures, video here: https://youtu.be/J7EGXtE54oYz, CAD
here: https://github.com/richardhartisch/compliantfinray
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fine assembly tasks such as electrical connector insertion have tight
tolerances and sensitive components, limiting the speed and robustness of robot
assembly, even when using vision, tactile, or force sensors. Connector
insertion is a common industrial task, requiring horizontal alignment errors to
be compensated with minimal force, then sufficient force to be brought in the
insertion direction. The ability to handle a variety of objects, achieve
high-speeds, and handle a wide range in object position variation are also
desired. Soft grippers can allow the gripping of parts with variation in
surface geometry, but often focus on gripping alone and may not be able to
bring the assembly forces required. To achieve high-speed connector insertion,
this paper proposes monolithic fingers with structured compliance and
form-closure features. A finray-effect gripper is adapted to realize structured
(i.e. directional) stiffness that allows high-speed mechanical search,
self-alignment in insertion, and sufficient assembly force. The design of the
finray ribs and fingertips are investigated, with a final design allowing plug
insertion with a tolerance window of up to 7.5 mm at high speed.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 06:07:30 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Hartisch",
"Richard Matthias",
""
],
[
"Haninger",
"Kevin",
""
]
] |
new_dataset
| 0.986012 |
2301.08517
|
Nicolas K\"uchler
|
Nicolas K\"uchler, Emanuel Opel, Hidde Lycklama, Alexander Viand,
Anwar Hithnawi
|
Cohere: Privacy Management in Large Scale Systems
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The need for a privacy management layer in today's systems started to
manifest with the emergence of new systems for privacy-preserving analytics and
privacy compliance. As a result, we began to see many independent efforts
emerge that try to provide system support for privacy. Recently, the scope of
privacy solutions used in systems has expanded to encompass more complex
techniques such as Differential Privacy (DP). The use of these solutions in
large-scale systems imposes new challenges and requirements. Careful planning
and coordination are necessary to ensure that privacy guarantees are maintained
across a wide range of heterogeneous applications and data systems. This
requires new solutions for managing shared application state and allocating
scarce and non-replenishable privacy resources. In this paper, we introduce
Cohere, a new data management system that simplifies the use of DP in
large-scale systems. Cohere implements a unified interface that allows
heterogeneous applications to operate on a unified view of users' data. Cohere
further extends existing accounting systems with the ability to manage and
optimally allocate shared privacy resources, i.e., budget, under complex
preferences. We show that Cohere can effectively enable advanced privacy
solutions in existing large-scale systems with minimal modifications to
existing data management systems and with moderate overhead.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 11:27:02 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Küchler",
"Nicolas",
""
],
[
"Opel",
"Emanuel",
""
],
[
"Lycklama",
"Hidde",
""
],
[
"Viand",
"Alexander",
""
],
[
"Hithnawi",
"Anwar",
""
]
] |
new_dataset
| 0.999649 |
2301.08571
|
Xudong Hong
|
Xudong Hong, Asad Sayeed, Khushboo Mehra, Vera Demberg, Bernt Schiele
|
Visual Writing Prompts: Character-Grounded Story Generation with Curated
Image Sequences
|
Paper accepted by Transactions of the Association for Computational
Linguistics (TACL). This is a pre-MIT Press publication version. 15 pages, 6
figures
| null | null | null |
cs.CL cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Current work on image-based story generation suffers from the fact that the
existing image sequence collections do not have coherent plots behind them. We
improve visual story generation by producing a new image-grounded dataset,
Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of
movie shots, each including 5-10 images. The image sequences are aligned with a
total of 12K stories which were collected via crowdsourcing given the image
sequences and a set of grounded characters from the corresponding image
sequence. Our new image sequence collection and filtering process has allowed
us to obtain stories that are more coherent and have more narrativity compared
to previous work. We also propose a character-based story generation model
driven by coherence as a strong baseline. Evaluations show that our generated
stories are more coherent, visually grounded, and have more narrativity than
stories generated with the current state-of-the-art model.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 13:38:24 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Hong",
"Xudong",
""
],
[
"Sayeed",
"Asad",
""
],
[
"Mehra",
"Khushboo",
""
],
[
"Demberg",
"Vera",
""
],
[
"Schiele",
"Bernt",
""
]
] |
new_dataset
| 0.999185 |
2301.08604
|
Celine Jost
|
C\'eline Jost (CHART), Justin Debloos (CHART), Agn\`es Piquard-Kipffer
(Grhapes), Caroline Barbot-Bouzit (Grhapes), Brigitte Le Pevedic (Lab-STICC)
|
PRIM project: what contributions for disabilities?
|
in French language, Handicap 2022, Jun 2022, Paris, France
| null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the PRIM project, we aim at giving people the power to create scenagrams
(interaction scenarios between a human and digital devices) without the need to
learn programming or to ask for computer scientists. In this project, software
design follows an unconventional approach, far from classical codes, to embody
human thinking (based on interactions) instead of computer logic (based on
algorithms). The main idea rests on a new time representation using a
PRIM-specific timeline instead of a standardized timeline. We evaluated
acceptability and cognitive compatibility of this new timeline with 50
participants. Results are very promising. In this paper, we detail qualitative
evaluation results about the interest of such software in the field of
disability.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 14:34:00 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Jost",
"Céline",
"",
"CHART"
],
[
"Debloos",
"Justin",
"",
"CHART"
],
[
"Piquard-Kipffer",
"Agnès",
"",
"Grhapes"
],
[
"Barbot-Bouzit",
"Caroline",
"",
"Grhapes"
],
[
"Pevedic",
"Brigitte Le",
"",
"Lab-STICC"
]
] |
new_dataset
| 0.956346 |
2301.08620
|
Lewin Stein
|
Mathias Lemke, Lewin Stein
|
Adjoint-Based Identification of Sound Sources for Sound Reinforcement
and Source Localization
| null |
Notes on Numerical Fluid Mechanics and Multidisciplinary Design,
vol 145. Springer (2021)
|
10.1007/978-3-030-52429-6_17
| null |
cs.SD eess.AS physics.flu-dyn
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The identification of sound sources is a common problem in acoustics.
Different parameters are sought, among these are signal and position of the
sources. We present an adjoint-based approach for sound source identification,
which employs computational aeroacoustic techniques. Two different applications
are presented as a proof-of-concept: optimization of a sound reinforcement
setup and the localization of (moving) sound sources.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 15:01:46 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Lemke",
"Mathias",
""
],
[
"Stein",
"Lewin",
""
]
] |
new_dataset
| 0.996433 |
2301.08669
|
Moritz B\"ohle
|
Moritz B\"ohle, Mario Fritz, Bernt Schiele
|
Holistically Explainable Vision Transformers
| null | null | null | null |
cs.CV stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Transformers increasingly dominate the machine learning landscape across many
tasks and domains, which increases the importance for understanding their
outputs. While their attention modules provide partial insight into their inner
workings, the attention scores have been shown to be insufficient for
explaining the models as a whole. To address this, we propose B-cos
transformers, which inherently provide holistic explanations for their
decisions. Specifically, we formulate each model component - such as the
multi-layer perceptrons, attention layers, and the tokenisation module - to be
dynamic linear, which allows us to faithfully summarise the entire transformer
via a single linear transform. We apply our proposed design to Vision
Transformers (ViTs) and show that the resulting models, dubbed Bcos-ViTs, are
highly interpretable and perform competitively to baseline ViTs on ImageNet.
Code will be made available soon.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 16:45:34 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Böhle",
"Moritz",
""
],
[
"Fritz",
"Mario",
""
],
[
"Schiele",
"Bernt",
""
]
] |
new_dataset
| 0.995165 |
2301.08695
|
Chirag Shetty
|
Beomyeol Jeon, Linda Cai, Chirag Shetty, Pallavi Srivastava, Jintao
Jiang, Xiaolan Ke, Yitao Meng, Cong Xie, Indranil Gupta
|
Baechi: Fast Device Placement of Machine Learning Graphs
|
Extended version of SoCC 2020 paper:
https://dl.acm.org/doi/10.1145/3419111.3421302
| null | null | null |
cs.DC cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Machine Learning graphs (or models) can be challenging or impossible to train
when either devices have limited memory, or models are large. To split the
model across devices, learning-based approaches are still popular. While these
result in model placements that train fast on data (i.e., low step times),
learning-based model-parallelism is time-consuming, taking many hours or days
to create a placement plan of operators on devices. We present the Baechi
system, the first to adopt an algorithmic approach to the placement problem for
running machine learning training graphs on small clusters of
memory-constrained devices. We integrate our implementation of Baechi into two
popular open-source learning frameworks: TensorFlow and PyTorch. Our
experimental results using GPUs show that: (i) Baechi generates placement plans
654 X - 206K X faster than state-of-the-art learning-based approaches, and (ii)
Baechi-placed model's step (training) time is comparable to expert placements
in PyTorch, and only up to 6.2% worse than expert placements in TensorFlow. We
prove mathematically that our two algorithms are within a constant factor of
the optimal. Our work shows that compared to learning-based approaches,
algorithmic approaches can face different challenges for adaptation to Machine
learning systems, but also they offer proven bounds, and significant
performance benefits.
|
[
{
"version": "v1",
"created": "Fri, 20 Jan 2023 17:26:37 GMT"
}
] | 2023-01-23T00:00:00 |
[
[
"Jeon",
"Beomyeol",
""
],
[
"Cai",
"Linda",
""
],
[
"Shetty",
"Chirag",
""
],
[
"Srivastava",
"Pallavi",
""
],
[
"Jiang",
"Jintao",
""
],
[
"Ke",
"Xiaolan",
""
],
[
"Meng",
"Yitao",
""
],
[
"Xie",
"Cong",
""
],
[
"Gupta",
"Indranil",
""
]
] |
new_dataset
| 0.986951 |
2204.11367
|
Bilal Farooq
|
Kimia Kamal and Bilal Farooq and Mahwish Mudassar and Arash Kalatian
|
Ordered-logit pedestrian stress model for traffic flow with automated
vehicles
|
In: IEEE Intelligent Vehicles Symposium Workshops (XXIV Workshops),
2022, Aachen, Germany
| null |
10.1109/IV51971.2022.9827316
| null |
cs.HC stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An ordered-logit model is developed to study the effects of Automated
Vehicles (AVs) in the traffic mix on the average stress level of a pedestrian
when crossing an urban street at mid-block. Information collected from a
galvanic skin resistance sensor and virtual reality experiments are transformed
into a dataset with interpretable average stress levels (low, medium, and high)
and geometric, traffic, and environmental conditions. Modelling results
indicate a decrease in average stress level with the increase in the percentage
of AVs in the traffic mix.
|
[
{
"version": "v1",
"created": "Sun, 24 Apr 2022 21:59:47 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Kamal",
"Kimia",
""
],
[
"Farooq",
"Bilal",
""
],
[
"Mudassar",
"Mahwish",
""
],
[
"Kalatian",
"Arash",
""
]
] |
new_dataset
| 0.997418 |
2210.00714
|
John Ousterhout
|
John Ousterhout
|
It's Time to Replace TCP in the Datacenter
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In spite of its long and successful history, TCP is a poor transport protocol
for modern datacenters. Every significant element of TCP, from its stream
orientation to its expectation of in-order packet delivery, is wrong for the
datacenter. It is time to recognize that TCP's problems are too fundamental and
interrelated to be fixed; the only way to harness the full performance
potential of modern networks is to introduce a new transport protocol into the
datacenter. Homa demonstrates that it is possible to create a transport
protocol that avoids all of TCP's problems. Although Homa is not API-compatible
with TCP, it should be possible to bring it into widespread usage by
integrating it with RPC frameworks.
|
[
{
"version": "v1",
"created": "Mon, 3 Oct 2022 05:00:24 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Jan 2023 00:58:26 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Ousterhout",
"John",
""
]
] |
new_dataset
| 0.984113 |
2210.12197
|
Oren Sultan
|
Oren Sultan, Dafna Shahaf
|
Life is a Circus and We are the Clowns: Automatically Finding Analogies
between Situations and Processes
|
Accepted to EMNLP 2022 main conference (long paper)
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Analogy-making gives rise to reasoning, abstraction, flexible categorization
and counterfactual inference -- abilities lacking in even the best AI systems
today. Much research has suggested that analogies are key to non-brittle
systems that can adapt to new domains. Despite their importance, analogies
received little attention in the NLP community, with most research focusing on
simple word analogies. Work that tackled more complex analogies relied heavily
on manually constructed, hard-to-scale input representations. In this work, we
explore a more realistic, challenging setup: our input is a pair of natural
language procedural texts, describing a situation or a process (e.g., how the
heart works/how a pump works). Our goal is to automatically extract entities
and their relations from the text and find a mapping between the different
domains based on relational similarity (e.g., blood is mapped to water). We
develop an interpretable, scalable algorithm and demonstrate that it identifies
the correct mappings 87% of the time for procedural texts and 94% for stories
from cognitive-psychology literature. We show it can extract analogies from a
large dataset of procedural texts, achieving 79% precision (analogy prevalence
in data: 3%). Lastly, we demonstrate that our algorithm is robust to
paraphrasing the input texts.
|
[
{
"version": "v1",
"created": "Fri, 21 Oct 2022 18:54:17 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Jan 2023 12:09:23 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Sultan",
"Oren",
""
],
[
"Shahaf",
"Dafna",
""
]
] |
new_dataset
| 0.99159 |
2212.03504
|
Xiang Li
|
Xiang Li, Junbo Yin, Botian Shi, Yikang Li, Ruigang Yang, Jianbing
Shen
|
LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for
Autonomous Driving
|
AAAI2023
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Image instance segmentation is a fundamental research topic in autonomous
driving, which is crucial for scene understanding and road safety. Advanced
learning-based approaches often rely on the costly 2D mask annotations for
training. In this paper, we present a more artful framework, LiDAR-guided
Weakly Supervised Instance Segmentation (LWSIS), which leverages the
off-the-shelf 3D data, i.e., Point Cloud, together with the 3D boxes, as
natural weak supervisions for training the 2D image instance segmentation
models. Our LWSIS not only exploits the complementary information in multimodal
data during training, but also significantly reduces the annotation cost of the
dense 2D masks. In detail, LWSIS consists of two crucial modules, Point Label
Assignment (PLA) and Graph-based Consistency Regularization (GCR). The former
module aims to automatically assign the 3D point cloud as 2D point-wise labels,
while the latter further refines the predictions by enforcing geometry and
appearance consistency of the multimodal data. Moreover, we conduct a secondary
instance segmentation annotation on the nuScenes, named nuInsSeg, to encourage
further research on multimodal perception tasks. Extensive experiments on the
nuInsSeg, as well as the large-scale Waymo, show that LWSIS can substantially
improve existing weakly supervised segmentation models by only involving 3D
data during training. Additionally, LWSIS can also be incorporated into 3D
object detectors like PointPainting to boost the 3D detection performance for
free. The code and dataset are available at https://github.com/Serenos/LWSIS.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 08:08:01 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Jan 2023 08:41:18 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Li",
"Xiang",
""
],
[
"Yin",
"Junbo",
""
],
[
"Shi",
"Botian",
""
],
[
"Li",
"Yikang",
""
],
[
"Yang",
"Ruigang",
""
],
[
"Shen",
"Jianbing",
""
]
] |
new_dataset
| 0.979078 |
2212.11459
|
Aldrin Montana
|
Aldrin Montana and Yuanqing Xue and Jeff LeFevre and Carlos Maltzahn
and Josh Stuart and Philip Kufeldt and Peter Alvaro
|
A Moveable Beast: Partitioning Data and Compute for Computational
Storage
|
14 pages, 7 figures, submitted to SIGMOD 2023 updated
acknowledgements
| null | null | null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Over the years, hardware trends have introduced various heterogeneous compute
units while also bringing network and storage bandwidths within an order of
magnitude of memory subsystems. In response, developers have used increasingly
exotic solutions to extract more performance from hardware; typically relying
on static, design-time partitioning of their programs which cannot keep pace
with storage systems that are layering compute units throughout deepening
hierarchies of storage devices.
We argue that dynamic, just-in-time partitioning of computation offers a
solution for emerging data-intensive systems to overcome ever-growing data
sizes in the face of stalled CPU performance and memory bandwidth. In this
paper, we describe our prototype computational storage system (CSS), Skytether,
that adopts a database perspective to utilize computational storage drives
(CSDs). We also present MSG Express, a data management system for single-cell
gene expression data that sits on top of Skytether. We discuss four design
principles that guide the design of our CSS: support scientific applications;
maximize utilization of storage, network, and memory bandwidth; minimize data
movement; and enable flexible program execution on autonomous CSDs. Skytether
is designed for the extra layer of indirection that CSDs introduce to a storage
system, using decomposable queries to take a new approach to computational
storage that has been imagined but not yet explored.
In this paper, we evaluate: partition strategies, the overhead of function
execution, and the performance of selection and projection. We expected ~3-4x
performance slowdown on the CSDs compared to a consumer-grade client CPU but we
observe an unexpected slowdown of ~15x, however, our evaluation results help us
set anchor points in the design space for developing a cost model for
decomposable queries and partitioning data across many CSDs.
|
[
{
"version": "v1",
"created": "Thu, 22 Dec 2022 02:37:21 GMT"
},
{
"version": "v2",
"created": "Wed, 18 Jan 2023 20:18:33 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Montana",
"Aldrin",
""
],
[
"Xue",
"Yuanqing",
""
],
[
"LeFevre",
"Jeff",
""
],
[
"Maltzahn",
"Carlos",
""
],
[
"Stuart",
"Josh",
""
],
[
"Kufeldt",
"Philip",
""
],
[
"Alvaro",
"Peter",
""
]
] |
new_dataset
| 0.981069 |
2301.02749
|
Jihong Zhu
|
Jihong Zhu, Michael Gienger, Giovanni Franzese, and Jens Kober
|
Do You Need a Hand? -- a Bimanual Robotic Dressing Assistance Scheme
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Developing physically assistive robots capable of dressing assistance has the
potential to significantly improve the lives of the elderly and disabled
population. However, most robotics dressing strategies considered a single
robot only, which greatly limited the performance of the dressing assistance.
In fact, healthcare professionals perform the task bimanually. Inspired by
them, we propose a bimanual cooperative scheme for robotic dressing assistance.
In the scheme, an interactive robot joins hands with the human thus
supporting/guiding the human in the dressing process, while the dressing robot
performs the dressing task. We identify a key feature that affects the dressing
action and propose an optimal strategy for the interactive robot using the
feature. A dressing coordinate based on the posture of the arm is defined to
better encode the dressing policy. We validate the interactive dressing scheme
with extensive experiments and also an ablation study. The experiment video is
available on https://sites.google.com/view/bimanualassitdressing/home
|
[
{
"version": "v1",
"created": "Fri, 6 Jan 2023 23:39:54 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Jan 2023 14:53:51 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Zhu",
"Jihong",
""
],
[
"Gienger",
"Michael",
""
],
[
"Franzese",
"Giovanni",
""
],
[
"Kober",
"Jens",
""
]
] |
new_dataset
| 0.968012 |
2301.05739
|
Mingzhou Yang
|
Yan Li (1), Mingzhou Yang (1), Matthew Eagon (1), Majid Farhadloo (1),
Yiqun Xie (2), William F. Northrop (1), Shashi Shekhar (1) ((1) University of
Minnesota, (2) University of Maryland)
|
Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation
|
Full version of the paper accepted for the SDM23 conference; Yan Li
and Mingzhou Yang contributed equally to this paper
| null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The eco-toll estimation problem quantifies the expected environmental cost
(e.g., energy consumption, exhaust emissions) for a vehicle to travel along a
path. This problem is important for societal applications such as eco-routing,
which aims to find paths with the lowest exhaust emissions or energy need. The
challenges of this problem are three-fold: (1) the dependence of a vehicle's
eco-toll on its physical parameters; (2) the lack of access to data with
eco-toll information; and (3) the influence of contextual information (i.e. the
connections of adjacent segments in the path) on the eco-toll of road segments.
Prior work on eco-toll estimation has mostly relied on pure data-driven
approaches and has high estimation errors given the limited training data. To
address these limitations, we propose a novel Eco-toll estimation
Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas,
namely, (1) a physics-informed decoder that integrates the physical laws of the
vehicle engine into the network, (2) an attention-based contextual information
encoder, and (3) a physics-informed regularization to reduce overfitting.
Experiments on real-world heavy-duty truck data show that the proposed method
can greatly improve the accuracy of eco-toll estimation compared with
state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Fri, 13 Jan 2023 19:34:18 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Jan 2023 03:21:34 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Li",
"Yan",
""
],
[
"Yang",
"Mingzhou",
""
],
[
"Eagon",
"Matthew",
""
],
[
"Farhadloo",
"Majid",
""
],
[
"Xie",
"Yiqun",
""
],
[
"Northrop",
"William F.",
""
],
[
"Shekhar",
"Shashi",
""
]
] |
new_dataset
| 0.998593 |
2301.05804
|
Ross Greer
|
Ross Greer, Akshay Gopalkrishnan, Nachiket Deo, Akshay Rangesh, Mohan
Trivedi
|
Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over
Full Visual Context
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Detecting road traffic signs and accurately determining how they can affect
the driver's future actions is a critical task for safe autonomous driving
systems. However, various traffic signs in a driving scene have an unequal
impact on the driver's decisions, making detecting the salient traffic signs a
more important task. Our research addresses this issue, constructing a traffic
sign detection model which emphasizes performance on salient signs, or signs
that influence the decisions of a driver. We define a traffic sign salience
property and use it to construct the LAVA Salient Signs Dataset, the first
traffic sign dataset that includes an annotated salience property. Next, we use
a custom salience loss function, Salience-Sensitive Focal Loss, to train a
Deformable DETR object detection model in order to emphasize stronger
performance on salient signs. Results show that a model trained with
Salience-Sensitive Focal Loss outperforms a model trained without, with regards
to recall of both salient signs and all signs combined. Further, the
performance margin on salient signs compared to all signs is largest for the
model trained with Salience-Sensitive Focal Loss.
|
[
{
"version": "v1",
"created": "Sat, 14 Jan 2023 01:47:09 GMT"
},
{
"version": "v2",
"created": "Wed, 18 Jan 2023 19:48:16 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Greer",
"Ross",
""
],
[
"Gopalkrishnan",
"Akshay",
""
],
[
"Deo",
"Nachiket",
""
],
[
"Rangesh",
"Akshay",
""
],
[
"Trivedi",
"Mohan",
""
]
] |
new_dataset
| 0.999488 |
2301.07747
|
Ond\v{r}ej Leng\'al
|
Yu-Fang Chen, Kai-Min Chung, Ond\v{r}ej Leng\'al, Jyun-Ao Lin, Wei-Lun
Tsai, Di-De Yen
|
An Automata-based Framework for Verification and Bug Hunting in Quantum
Circuits (Technical Report)
| null | null | null | null |
cs.LO cs.FL
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce a new paradigm for analysing and finding bugs in quantum
circuits. In our approach, the problem is given by a triple $\{P\}\,C\,\{Q\}$
and the question is whether, given a set $P$ of quantum states on the input of
a circuit $C$, the set of quantum states on the output is equal to (or included
in) a set $Q$. While this is not suitable to specify, e.g., functional
correctness of a quantum circuit, it is sufficient to detect many bugs in
quantum circuits. We propose a technique based on tree automata to compactly
represent sets of quantum states and develop transformers to implement the
semantics of quantum gates over this representation. Our technique computes
with an algebraic representation of quantum states, avoiding the inaccuracy of
working with floating-point numbers. We implemented the proposed approach in a
prototype tool and evaluated its performance against various benchmarks from
the literature. The evaluation shows that our approach is quite scalable, e.g.,
we managed to verify a large circuit with 40 qubits and 141,527 gates, or catch
bugs injected into a circuit with 320 qubits and 1,758 gates, where all tools
we compared with failed. In addition, our work establishes a connection between
quantum program verification and automata, opening new possibilities to exploit
the richness of automata theory and automata-based verification in the world of
quantum computing.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 19:26:01 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Chen",
"Yu-Fang",
""
],
[
"Chung",
"Kai-Min",
""
],
[
"Lengál",
"Ondřej",
""
],
[
"Lin",
"Jyun-Ao",
""
],
[
"Tsai",
"Wei-Lun",
""
],
[
"Yen",
"Di-De",
""
]
] |
new_dataset
| 0.983391 |
2301.07757
|
Lucas Silva
|
Lucas de Oliveira Silva
|
Freeze-Tag is NP-Hard in 3D with $L_1$ distance
| null | null | null | null |
cs.CG cs.CC
|
http://creativecommons.org/licenses/by/4.0/
|
Arkin et al. in 2002 introduced a scheduling-like problem called Freeze-Tag
Problem (FTP) motivated by robot swarm activation. The input consists of the
locations of n mobile punctual robots in some metric space or graph. Only one
begins "active", while the others are initially "frozen". All active robots can
move at unit speed and, upon reaching a frozen one's location, activates it.
The goal is to activate all the robots in the minimum amount of time, the
so-called makespan. Until 2017 the hardness of this problem in metric spaces
was still open, but then Yu et al. proved it to be NP-Hard in the Euclidian
plane, and in the same year, Demaine and Roudoy demonstrated that the FTP is
also hard in 3D with any $L_p$ distance (with p > 1). However, we still don't
know whether Demaine's and Roudoy's result could be translated to the plane.
This paper fills the p=1 gap by showing that the FTP is NP-Hard in 3D with
$L_1$ distance.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 19:45:31 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Silva",
"Lucas de Oliveira",
""
]
] |
new_dataset
| 0.998745 |
2301.07775
|
Zhaoxu Zhang
|
Zhaoxu Zhang, Robert Winn, Yu Zhao, Tingting Yu and William G. J.
Halfond
|
Automatically Reproducing Android Bug Reports Using Natural Language
Processing and Reinforcement Learning
|
Accepted to the 32nd ACM SIGSOFT International Symposium on Software
Testing and Analysis (ISSTA 2023)
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As part of the process of resolving issues submitted by users via bug
reports, Android developers attempt to reproduce and observe the failures
described by the bug report. Due to the low-quality of bug reports and the
complexity of modern apps, the reproduction process is non-trivial and
time-consuming. Therefore, automatic approaches that can help reproduce Android
bug reports are in great need. However, current approaches to help developers
automatically reproduce bug reports are only able to handle limited forms of
natural language text and struggle to successfully reproduce failures for which
the initial bug report had missing or imprecise steps. In this paper, we
introduce a new fully automated Android bug report reproduction approach that
addresses these limitations. Our approach accomplishes this by leveraging
natural language process techniques to more holistically and accurately analyze
the natural language in Android bug reports and designing new techniques, based
on reinforcement learning, to guide the search for successful reproducing
steps. We conducted an empirical evaluation of our approach on 77 real world
bug reports. Our approach achieved 67% precision and 77% recall in accurately
extracting reproduction steps from bug reports, and reproduced 74% of the bug
reports, significantly outperforming state of the art techniques.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 20:32:49 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Zhang",
"Zhaoxu",
""
],
[
"Winn",
"Robert",
""
],
[
"Zhao",
"Yu",
""
],
[
"Yu",
"Tingting",
""
],
[
"Halfond",
"William G. J.",
""
]
] |
new_dataset
| 0.998541 |
2301.07889
|
Rizwan Patan
|
Rizwan Patan, Reza M. Parizi, Mohsen Dorodchi, Seyedamin Pouriyeh,
Audrey Rorrer
|
Blockchain Education: Current State, Limitations, Career Scope,
Challenges, and Future Directions
| null | null | null | null |
cs.CY cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Blockchain is a revolutionary technology, and its growth started in various
industries (such as IT, education, business, banking, and many others) to
capitalize on it. Currently, in higher education institutions (HEIs) adoption
of blockchain education needs to be improved in the academic programs and
curriculums. In addition, HEIs must make many intense changes in the teaching
and learning methods to educate learners about blockchain technology and its
applications to meet the current industry workforce demand. Due to a lack of
academic programs and courses, students nowadays rely on online resources and
pay non-academic organizations a high fee. This paper provides a comprehensive
survey of blockchain education's current state of the art by reviewing the
different academic programs and industry workforce demand. In addition,
blockchain application trends which include market growth and demands are
discussed. Moreover, the blockchain career scope for different disciplines of
students is examined.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 05:23:32 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Patan",
"Rizwan",
""
],
[
"Parizi",
"Reza M.",
""
],
[
"Dorodchi",
"Mohsen",
""
],
[
"Pouriyeh",
"Seyedamin",
""
],
[
"Rorrer",
"Audrey",
""
]
] |
new_dataset
| 0.985926 |
2301.07947
|
Boris Mocialov
|
Boris Mocialov, Eirik Eythorsson, Reza Parseh, Hoang Tran, Vegard
Flovik
|
Point Cloud Data Simulation and Modelling with Aize Workspace
|
Extended abstract, Northern Lights Deep Learning Conference, 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
This work takes a look at data models often used in digital twins and
presents preliminary results specifically from surface reconstruction and
semantic segmentation models trained using simulated data. This work is
expected to serve as a ground work for future endeavours in data
contextualisation inside a digital twin.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 08:47:31 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Mocialov",
"Boris",
""
],
[
"Eythorsson",
"Eirik",
""
],
[
"Parseh",
"Reza",
""
],
[
"Tran",
"Hoang",
""
],
[
"Flovik",
"Vegard",
""
]
] |
new_dataset
| 0.976862 |
2301.07967
|
Lara Bargmann
|
Lara Bargmann and Heike Wehrheim
|
View-Based Axiomatic Reasoning for PSO (Extended Version)
| null | null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
Weak memory models describe the semantics of concurrent programs on modern
multi-core architectures. Reasoning techniques for concurrent programs, like
Owicki-Gries-style proof calculi, have to be based on such a semantics, and
hence need to be freshly developed for every new memory model. Recently, a more
uniform approach to reasoning has been proposed which builds correctness proofs
on the basis of a number of core axioms. This allows to prove program
correctness independent of memory models, and transfers proofs to specific
memory models by showing these to instantiate all axioms required in a proof.
The axiomatisation is built on the notion of thread views as first class
elements in the semantics. In this paper, we investigate the applicability of
this form of axiomatic reasoning to the Partial Store Order (PSO) memory model.
As the standard semantics for PSO is not based on views, we first of all
provide a view-based semantics for PSO and prove it to coincide with the
standard semantics. We then show the new view-based semantics to satisfy all
but one axiom. The missing axiom refers to message-passing (MP) abilities of
memory models, which PSO does not guarantee. As a consequence, only proofs
without usage of the MP axiom are transferable to PSO. We illustrate the
reasoning technique by proving correctness of a litmus test employing a fence
to ensure message passing.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 09:44:04 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Bargmann",
"Lara",
""
],
[
"Wehrheim",
"Heike",
""
]
] |
new_dataset
| 0.991327 |
2301.07978
|
Spyridon Kantarelis
|
Ioannis Dimolitsas, Spyridon Kantarelis, Afroditi Fouka
|
SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify
| null | null | null | null |
cs.SD cs.LG eess.AS
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this study, we approached the Hit Song Prediction problem, which aims to
predict which songs will become Billboard hits. We gathered a dataset of nearly
18500 hit and non-hit songs and extracted their audio features using the
Spotify Web API. We test four machine-learning models on our dataset. We were
able to predict the Billboard success of a song with approximately 86\%
accuracy. The most succesful algorithms were Random Forest and Support Vector
Machine.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 10:13:52 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Dimolitsas",
"Ioannis",
""
],
[
"Kantarelis",
"Spyridon",
""
],
[
"Fouka",
"Afroditi",
""
]
] |
new_dataset
| 0.971038 |
2301.07996
|
Warley F. R. Ribeiro
|
Warley F. R. Ribeiro, Kentaro Uno, Masazumi Imai, Koki Murase, Kazuya
Yoshida
|
RAMP: Reaction-Aware Motion Planning of Multi-Legged Robots for
Locomotion in Microgravity
|
Submitted version of paper accepted for presentation at the 2023 IEEE
International Conference on Robotics and Automation (ICRA)
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Robotic mobility in microgravity is necessary to expand human utilization and
exploration of outer space. Bio-inspired multi-legged robots are a possible
solution for safe and precise locomotion. However, a dynamic motion of a robot
in microgravity can lead to failures due to gripper detachment caused by
excessive motion reactions. We propose a novel Reaction-Aware Motion Planning
(RAMP) to improve locomotion safety in microgravity, decreasing the risk of
losing contact with the terrain surface by reducing the robot's momentum
change. RAMP minimizes the swing momentum with a Low-Reaction Swing Trajectory
(LRST) while distributing this momentum to the whole body, ensuring zero
velocity for the supporting grippers and minimizing motion reactions. We verify
the proposed approach with dynamic simulations indicating the capability of
RAMP to generate a safe motion without detachment of the supporting grippers,
resulting in the robot reaching its specified location. We further validate
RAMP in experiments with an air-floating system, demonstrating a significant
reduction in reaction forces and improved mobility in microgravity.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 10:54:49 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Ribeiro",
"Warley F. R.",
""
],
[
"Uno",
"Kentaro",
""
],
[
"Imai",
"Masazumi",
""
],
[
"Murase",
"Koki",
""
],
[
"Yoshida",
"Kazuya",
""
]
] |
new_dataset
| 0.961831 |
2301.08107
|
Ryosei Kojima
|
Ryosei Kojima, Shitara Akihisa, Tatsuki Fushimi, Ryogo Niwa, Atushi
Shinoda, Ryo Iijima, Kengo Tanaka, Sayan Sarcar, and Yoichi Ochiai
|
SHITARA: Sending Haptic Induced Touchable Alarm by Ring-shaped Air
vortex
|
30 pages, 22 figures
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Social interaction begins with the other person's attention, but it is
difficult for a d/Deaf or hard-of-hearing (DHH) person to notice the initial
conversation cues. Wearable or visual devices have been proposed previously.
However, these devices are cumbersome to wear or must stay within the DHH
person's vision. In this study, we have proposed SHITARA, a novel accessibility
method with air vortex rings that provides a non-contact haptic cue for a DHH
person. We have developed a proof-of-concept device and determined the air
vortex ring's accuracy, noticeability and comfortability when it hits a DHH's
hair. Though strength, accuracy, and noticeability of air vortex rings decrease
as the distance between the air vortex ring generator and the user increases,
we have demonstrated that the air vortex ring is noticeable up to 2.5 meters
away. Moreover, the optimum strength is found for each distance from a DHH.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 14:54:55 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Kojima",
"Ryosei",
""
],
[
"Akihisa",
"Shitara",
""
],
[
"Fushimi",
"Tatsuki",
""
],
[
"Niwa",
"Ryogo",
""
],
[
"Shinoda",
"Atushi",
""
],
[
"Iijima",
"Ryo",
""
],
[
"Tanaka",
"Kengo",
""
],
[
"Sarcar",
"Sayan",
""
],
[
"Ochiai",
"Yoichi",
""
]
] |
new_dataset
| 0.999732 |
2301.08193
|
Kimiaki Shirahama
|
Zihao Chen, Hisashi Handa, Kimiaki Shirahama
|
JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its
Applications
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Contrastive learning is widely used for sentence representation learning.
Despite this prevalence, most studies have focused exclusively on English and
few concern domain adaptation for domain-specific downstream tasks, especially
for low-resource languages like Japanese, which are characterized by
insufficient target domain data and the lack of a proper training strategy. To
overcome this, we propose a novel Japanese sentence representation framework,
JCSE (derived from ``Contrastive learning of Sentence Embeddings for
Japanese''), that creates training data by generating sentences and
synthesizing them with sentences available in a target domain. Specifically, a
pre-trained data generator is finetuned to a target domain using our collected
corpus. It is then used to generate contradictory sentence pairs that are used
in contrastive learning for adapting a Japanese language model to a specific
task in the target domain.
Another problem of Japanese sentence representation learning is the
difficulty of evaluating existing embedding methods due to the lack of
benchmark datasets. Thus, we establish a comprehensive Japanese Semantic
Textual Similarity (STS) benchmark on which various embedding models are
evaluated. Based on this benchmark result, multiple embedding methods are
chosen and compared with JCSE on two domain-specific tasks, STS in a clinical
domain and information retrieval in an educational domain. The results show
that JCSE achieves significant performance improvement surpassing direct
transfer and other training strategies. This empirically demonstrates JCSE's
effectiveness and practicability for downstream tasks of a low-resource
language.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 17:41:46 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Chen",
"Zihao",
""
],
[
"Handa",
"Hisashi",
""
],
[
"Shirahama",
"Kimiaki",
""
]
] |
new_dataset
| 0.990398 |
2301.08237
|
Xizi Wang
|
Xizi Wang, Feng Cheng, Gedas Bertasius, David Crandall
|
LoCoNet: Long-Short Context Network for Active Speaker Detection
|
tech report
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Active Speaker Detection (ASD) aims to identify who is speaking in each frame
of a video. ASD reasons from audio and visual information from two contexts:
long-term intra-speaker context and short-term inter-speaker context. Long-term
intra-speaker context models the temporal dependencies of the same speaker,
while short-term inter-speaker context models the interactions of speakers in
the same scene. These two contexts are complementary to each other and can help
infer the active speaker. Motivated by these observations, we propose LoCoNet,
a simple yet effective Long-Short Context Network that models the long-term
intra-speaker context and short-term inter-speaker context. We use
self-attention to model long-term intra-speaker context due to its
effectiveness in modeling long-range dependencies, and convolutional blocks
that capture local patterns to model short-term inter-speaker context.
Extensive experiments show that LoCoNet achieves state-of-the-art performance
on multiple datasets, achieving an mAP of 95.2%(+1.1%) on AVA-ActiveSpeaker,
68.1%(+22%) on Columbia dataset, 97.2%(+2.8%) on Talkies dataset and
59.7%(+8.0%) on Ego4D dataset. Moreover, in challenging cases where multiple
speakers are present, or face of active speaker is much smaller than other
faces in the same scene, LoCoNet outperforms previous state-of-the-art methods
by 3.4% on the AVA-ActiveSpeaker dataset. The code will be released at
https://github.com/SJTUwxz/LoCoNet_ASD.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 18:54:43 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Wang",
"Xizi",
""
],
[
"Cheng",
"Feng",
""
],
[
"Bertasius",
"Gedas",
""
],
[
"Crandall",
"David",
""
]
] |
new_dataset
| 0.996997 |
2301.08245
|
Pierluigi Zama Ramirez
|
Pierluigi Zama Ramirez, Alex Costanzino, Fabio Tosi, Matteo Poggi,
Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
|
Booster: a Benchmark for Depth from Images of Specular and Transparent
Surfaces
|
Extension of the paper "Open Challenges in Deep Stereo: the Booster
Dataset" that was presented at CVPR 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Estimating depth from images nowadays yields outstanding results, both in
terms of in-domain accuracy and generalization. However, we identify two main
challenges that remain open in this field: dealing with non-Lambertian
materials and effectively processing high-resolution images. Purposely, we
propose a novel dataset that includes accurate and dense ground-truth labels at
high resolution, featuring scenes containing several specular and transparent
surfaces. Our acquisition pipeline leverages a novel deep space-time stereo
framework, enabling easy and accurate labeling with sub-pixel precision. The
dataset is composed of 606 samples collected in 85 different scenes, each
sample includes both a high-resolution pair (12 Mpx) as well as an unbalanced
stereo pair (Left: 12 Mpx, Right: 1.1 Mpx). Additionally, we provide manually
annotated material segmentation masks and 15K unlabeled samples. We divide the
dataset into a training set, and two testing sets, the latter devoted to the
evaluation of stereo and monocular depth estimation networks respectively to
highlight the open challenges and future research directions in this field.
|
[
{
"version": "v1",
"created": "Thu, 19 Jan 2023 18:59:28 GMT"
}
] | 2023-01-20T00:00:00 |
[
[
"Ramirez",
"Pierluigi Zama",
""
],
[
"Costanzino",
"Alex",
""
],
[
"Tosi",
"Fabio",
""
],
[
"Poggi",
"Matteo",
""
],
[
"Salti",
"Samuele",
""
],
[
"Mattoccia",
"Stefano",
""
],
[
"Di Stefano",
"Luigi",
""
]
] |
new_dataset
| 0.999548 |
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