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2212.05914
Jiale Cheng
Jiale Cheng, Nan Liu, and Wei Kang
On the Asymptotic Capacity of Information Theoretical Privacy-preserving Epidemiological Data Collection
null
null
10.3390/e25040625
null
cs.IT math.IT
http://creativecommons.org/licenses/by/4.0/
We formulate a new secure distributed computation problem, where a simulation center can require any linear combination of $ K $ users' data through a caching layer consisting of $ N $ servers. The users, servers, and data collector do not trust each other. For users, any data is required to be protected from up to $ E $ servers; for servers, any more information than the desired linear combination cannot be leaked to the data collector; and for the data collector, any single server knows nothing about the coefficients of the linear combination. Our goal is to find the optimal download cost, which is defined as the size of message uploaded to the simulation center by the servers, to the size of desired linear combination. We proposed a scheme with the optimal download cost when $E < N-1$. We also prove that when $E\geq N-1$, the scheme is not feasible.
[ { "version": "v1", "created": "Mon, 12 Dec 2022 14:24:26 GMT" } ]
2023-04-19T00:00:00
[ [ "Cheng", "Jiale", "" ], [ "Liu", "Nan", "" ], [ "Kang", "Wei", "" ] ]
new_dataset
0.993112
2301.07405
Zongwei Wu
Zongwei Wu, Guillaume Allibert, Fabrice Meriaudeau, Chao Ma, and C\'edric Demonceaux
HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness
null
null
10.1109/TIP.2023.3263111
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods explicitly leveraging fine-grained details to merge with semantic cues. Thus, despite the auxiliary depth information, it is still challenging for existing models to distinguish objects with similar appearances but at distinct camera distances. In this paper, from a new perspective, we propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection. Our motivation comes from the observation that the multi-granularity properties of geometric priors correlate well with the neural network hierarchies. To realize multi-modal and multi-level fusion, we first use a granularity-based attention scheme to strengthen the discriminatory power of RGB and depth features separately. Then we introduce a unified cross dual-attention module for multi-modal and multi-level fusion in a coarse-to-fine manner. The encoded multi-modal features are gradually aggregated into a shared decoder. Further, we exploit a multi-scale loss to take full advantage of the hierarchical information. Extensive experiments on challenging benchmark datasets demonstrate that our HiDAnet performs favorably over the state-of-the-art methods by large margins.
[ { "version": "v1", "created": "Wed, 18 Jan 2023 10:00:59 GMT" } ]
2023-04-19T00:00:00
[ [ "Wu", "Zongwei", "" ], [ "Allibert", "Guillaume", "" ], [ "Meriaudeau", "Fabrice", "" ], [ "Ma", "Chao", "" ], [ "Demonceaux", "Cédric", "" ] ]
new_dataset
0.99863
2302.01452
M. Hammad Mazhar
M. Hammad Mazhar, Li Li, Endadul Hoque, Omar Chowdhury
MAVERICK: An App-independent and Platform-agnostic Approach to Enforce Policies in IoT Systems at Runtime
13 pages, full version with material cut from version accepted at ACM WiSec 2023
null
null
null
cs.CR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Many solutions have been proposed to curb unexpected behavior of automation apps installed on programmable IoT platforms by enforcing safety policies at runtime. However, all prior work addresses a weaker version of the actual problem due to a simpler, unrealistic threat model. These solutions are not general enough as they are heavily dependent on the installed apps and catered to specific IoT platforms. Here, we address a stronger version of the problem via a realistic threat model, where (i) undesired cyber actions can come from not only automation platform backends (e.g., SmartThings) but also close-sourced third-party services (e.g., IFTTT), and (ii) physical actions (e.g., user interactions) on devices can move the IoT system to an undesirable state. We propose a runtime mechanism, dubbed Maverick, which employs an app-independent, platform-agnostic mediator to enforce policies against all undesired cyber actions and applies corrective-actions to bring the IoT system back to a safe state from an unsafe state transition. Maverick is equipped with a policy language capable of expressing rich temporal invariants and an automated toolchain that includes a policy synthesizer and a policy analyzer for user assistance. We implemented Maverick in a prototype and showed its efficacy in both physical and virtual testbeds, incurring minimal overhead.
[ { "version": "v1", "created": "Thu, 2 Feb 2023 22:39:48 GMT" }, { "version": "v2", "created": "Tue, 18 Apr 2023 16:45:46 GMT" } ]
2023-04-19T00:00:00
[ [ "Mazhar", "M. Hammad", "" ], [ "Li", "Li", "" ], [ "Hoque", "Endadul", "" ], [ "Chowdhury", "Omar", "" ] ]
new_dataset
0.983106
2302.02997
Yftah Ziser
Shun Shao, Yftah Ziser and Shay Cohen
Erasure of Unaligned Attributes from Neural Representations
Accepted to Transactions of the Association for Computational Linguistics, 22 pages (pre-MIT Press publication version)
null
null
null
cs.CL
http://creativecommons.org/licenses/by/4.0/
We present the Assignment-Maximization Spectral Attribute removaL (AMSAL) algorithm, which erases information from neural representations when the information to be erased is implicit rather than directly being aligned to each input example. Our algorithm works by alternating between two steps. In one, it finds an assignment of the input representations to the information to be erased, and in the other, it creates projections of both the input representations and the information to be erased into a joint latent space. We test our algorithm on an extensive array of datasets, including a Twitter dataset with multiple guarded attributes, the BiasBios dataset and the BiasBench benchmark. The last benchmark includes four datasets with various types of protected attributes. Our results demonstrate that bias can often be removed in our setup. We also discuss the limitations of our approach when there is a strong entanglement between the main task and the information to be erased.
[ { "version": "v1", "created": "Mon, 6 Feb 2023 18:32:17 GMT" }, { "version": "v2", "created": "Tue, 18 Apr 2023 10:34:01 GMT" } ]
2023-04-19T00:00:00
[ [ "Shao", "Shun", "" ], [ "Ziser", "Yftah", "" ], [ "Cohen", "Shay", "" ] ]
new_dataset
0.95818
2303.16727
Limin Wang
Limin Wang, Bingkun Huang, Zhiyu Zhao, Zhan Tong, Yinan He, Yi Wang, Yali Wang, Yu Qiao
VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
CVPR 2023 camera-ready version
null
null
null
cs.CV cs.LG
http://creativecommons.org/licenses/by/4.0/
Scale is the primary factor for building a powerful foundation model that could well generalize to a variety of downstream tasks. However, it is still challenging to train video foundation models with billions of parameters. This paper shows that video masked autoencoder (VideoMAE) is a scalable and general self-supervised pre-trainer for building video foundation models. We scale the VideoMAE in both model and data with a core design. Specifically, we present a dual masking strategy for efficient pre-training, with an encoder operating on a subset of video tokens and a decoder processing another subset of video tokens. Although VideoMAE is very efficient due to high masking ratio in encoder, masking decoder can still further reduce the overall computational cost. This enables the efficient pre-training of billion-level models in video. We also use a progressive training paradigm that involves an initial pre-training on a diverse multi-sourced unlabeled dataset, followed by a post-pre-training on a mixed labeled dataset. Finally, we successfully train a video ViT model with a billion parameters, which achieves a new state-of-the-art performance on the datasets of Kinetics (90.0% on K400 and 89.9% on K600) and Something-Something (68.7% on V1 and 77.0% on V2). In addition, we extensively verify the pre-trained video ViT models on a variety of downstream tasks, demonstrating its effectiveness as a general video representation learner. The code and model is available at \url{https://github.com/OpenGVLab/VideoMAEv2}.
[ { "version": "v1", "created": "Wed, 29 Mar 2023 14:28:41 GMT" }, { "version": "v2", "created": "Tue, 18 Apr 2023 11:46:41 GMT" } ]
2023-04-19T00:00:00
[ [ "Wang", "Limin", "" ], [ "Huang", "Bingkun", "" ], [ "Zhao", "Zhiyu", "" ], [ "Tong", "Zhan", "" ], [ "He", "Yinan", "" ], [ "Wang", "Yi", "" ], [ "Wang", "Yali", "" ], [ "Qiao", "Yu", "" ] ]
new_dataset
0.96477
2304.06970
Qijie Bai
Qijie Bai, Jiawen Guo, Haiwei Zhang, Changli Nie, Lin Zhang, Xiaojie Yuan
H2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces
null
The Semantic Web-ISWC 2022: 21st International Semantic Web Conference, Virtual Event, October 23-27, 2022, Proceedings (pp. 179-195)
10.1007/978-3-031-19433-7_11
null
cs.SI cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Temporal heterogeneous information network (temporal HIN) embedding, aiming to represent various types of nodes of different timestamps into low dimensional spaces while preserving structural and semantic information, is of vital importance in diverse real-life tasks. Researchers have made great efforts on temporal HIN embedding in Euclidean spaces and got some considerable achievements. However, there is always a fundamental conflict that many real-world networks show hierarchical property and power-law distribution, and are not isometric of Euclidean spaces. Recently, representation learning in hyperbolic spaces has been proved to be valid for data with hierarchical and power-law structure. Inspired by this character, we propose a hyperbolic heterogeneous temporal network embedding (H2TNE) model for temporal HINs. Specifically, we leverage a temporally and heterogeneously double-constrained random walk strategy to capture the structural and semantic information, and then calculate the embedding by exploiting hyperbolic distance in proximity measurement. Experimental results show that our method has superior performance on temporal link prediction and node classification compared with SOTA models.
[ { "version": "v1", "created": "Fri, 14 Apr 2023 07:39:52 GMT" }, { "version": "v2", "created": "Tue, 18 Apr 2023 06:12:02 GMT" } ]
2023-04-19T00:00:00
[ [ "Bai", "Qijie", "" ], [ "Guo", "Jiawen", "" ], [ "Zhang", "Haiwei", "" ], [ "Nie", "Changli", "" ], [ "Zhang", "Lin", "" ], [ "Yuan", "Xiaojie", "" ] ]
new_dataset
0.980458
2304.08494
Iuliana Marin
Mohammad Rasras, Iuliana Marin, Serban Radu
Smart Home Environment Modelled with a Multi-Agent System
12 pages, 8 figures, journal article
U.P.B. Sci. Bull., Series C, Vol. 85, Iss. 1, 2023, ISSN 2286-3540
null
null
cs.MA cs.AI cs.CY cs.SE
http://creativecommons.org/licenses/by/4.0/
A smart home can be considered a place of residence that enables the management of appliances and systems to help with day-to-day life by automated technology. In the current paper is described a prototype that simulates a context-aware environment, developed in a designed smart home. The smart home environment has been simulated using three agents and five locations in a house. The context-aware agents behave based on predefined rules designed for daily activities. Our proposal aims to reduce operational cost of running devices. In the future, monitors of health aspects belonging to home residents will sustain their healthy life daily.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 08:09:08 GMT" } ]
2023-04-19T00:00:00
[ [ "Rasras", "Mohammad", "" ], [ "Marin", "Iuliana", "" ], [ "Radu", "Serban", "" ] ]
new_dataset
0.99665
2304.08504
Shubham Patil
Shubham Patil, Jayatika Sakhuja, Ajay Kumar Singh, Anmol Biswas, Vivek Saraswat, Sandeep Kumar, Sandip Lashkare, Udayan Ganguly
Schottky Barrier MOSFET Enabled Ultra-Low Power Real-Time Neuron for Neuromorphic Computing
null
null
null
null
cs.ET physics.app-ph
http://creativecommons.org/licenses/by/4.0/
Energy-efficient real-time synapses and neurons are essential to enable large-scale neuromorphic computing. In this paper, we propose and demonstrate the Schottky-Barrier MOSFET-based ultra-low power voltage-controlled current source to enable real-time neurons for neuromorphic computing. Schottky-Barrier MOSFET is fabricated on a Silicon-on-insulator platform with polycrystalline Silicon as the channel and Nickel/Platinum as the source/drain. The Poly-Si and Nickel make the back-to-back Schottky junction enabling ultra-low ON current required for energy-efficient neurons.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 12:39:21 GMT" } ]
2023-04-19T00:00:00
[ [ "Patil", "Shubham", "" ], [ "Sakhuja", "Jayatika", "" ], [ "Singh", "Ajay Kumar", "" ], [ "Biswas", "Anmol", "" ], [ "Saraswat", "Vivek", "" ], [ "Kumar", "Sandeep", "" ], [ "Lashkare", "Sandip", "" ], [ "Ganguly", "Udayan", "" ] ]
new_dataset
0.999496
2304.08580
Pooya Fayyazsanavi
Pooya Fayyazsanavi, Zhiqiang Wan, Will Hutchcroft, Ivaylo Boyadzhiev, Yuguang Li, Jana Kosecka, Sing Bing Kang
U2RLE: Uncertainty-Guided 2-Stage Room Layout Estimation
To be Appear on CVPR 2023
null
null
null
cs.CV cs.RO
http://creativecommons.org/licenses/by/4.0/
While the existing deep learning-based room layout estimation techniques demonstrate good overall accuracy, they are less effective for distant floor-wall boundary. To tackle this problem, we propose a novel uncertainty-guided approach for layout boundary estimation introducing new two-stage CNN architecture termed U2RLE. The initial stage predicts both floor-wall boundary and its uncertainty and is followed by the refinement of boundaries with high positional uncertainty using a different, distance-aware loss. Finally, outputs from the two stages are merged to produce the room layout. Experiments using ZInD and Structure3D datasets show that U2RLE improves over current state-of-the-art, being able to handle both near and far walls better. In particular, U2RLE outperforms current state-of-the-art techniques for the most distant walls.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 19:43:08 GMT" } ]
2023-04-19T00:00:00
[ [ "Fayyazsanavi", "Pooya", "" ], [ "Wan", "Zhiqiang", "" ], [ "Hutchcroft", "Will", "" ], [ "Boyadzhiev", "Ivaylo", "" ], [ "Li", "Yuguang", "" ], [ "Kosecka", "Jana", "" ], [ "Kang", "Sing Bing", "" ] ]
new_dataset
0.977282
2304.08595
Zicong Hong
Zicong Hong and Song Guo and Enyuan Zhou and Jianting Zhang and Wuhui Chen and Jinwen Liang and Jie Zhang and Albert Zomaya
Prophet: Conflict-Free Sharding Blockchain via Byzantine-Tolerant Deterministic Ordering
null
null
null
null
cs.CR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards' independent and random scheduling for cross-shard transactions results in numerous conflicts and aborts, since cross-shard transactions from different shards may access the same account. A deterministic ordering can eliminate conflicts by determining a global order for transactions before processing, as proved in the database field. Unfortunately, due to the intertwining of the Byzantine environment and information isolation among shards, there is no trusted party able to predetermine such an order for cross-shard transactions. To tackle this challenge, this paper proposes Prophet, a conflict-free sharding blockchain based on Byzantine-tolerant deterministic ordering. It first depends on untrusted self-organizing coalitions of nodes from different shards to pre-execute cross-shard transactions for prerequisite information about ordering. It then determines a trusted global order based on stateless ordering and post-verification for pre-executed results, through shard cooperation. Following the order, the shards thus orderly execute and commit transactions without conflicts. Prophet orchestrates the pre-execution, ordering, and execution processes in the sharding consensus for minimal overhead. We rigorously prove the determinism and serializability of transactions under the Byzantine and sharded environment. An evaluation of our prototype shows that Prophet improves the throughput by $3.11\times$ and achieves nearly no aborts on 1 million Ethereum transactions compared with state-of-the-art sharding.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 20:20:44 GMT" } ]
2023-04-19T00:00:00
[ [ "Hong", "Zicong", "" ], [ "Guo", "Song", "" ], [ "Zhou", "Enyuan", "" ], [ "Zhang", "Jianting", "" ], [ "Chen", "Wuhui", "" ], [ "Liang", "Jinwen", "" ], [ "Zhang", "Jie", "" ], [ "Zomaya", "Albert", "" ] ]
new_dataset
0.995301
2304.08630
Anran Hu
Xin Guo, Anran Hu, Matteo Santamaria, Mahan Tajrobehkar, Junzi Zhang
MFGLib: A Library for Mean-Field Games
null
null
null
null
cs.GT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Mean-field games (MFGs) are limiting models to approximate $N$-player games, with a number of applications. Despite the ever-growing numerical literature on computation of MFGs, there is no library that allows researchers and practitioners to easily create and solve their own MFG problems. The purpose of this document is to introduce MFGLib, an open-source Python library for solving general MFGs with a user-friendly and customizable interface. It serves as a handy tool for creating and analyzing generic MFG environments, along with embedded auto-tuners for all implemented algorithms. The package is distributed under the MIT license and the source code and documentation can be found at https://github.com/radar-research-lab/MFGLib/.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 21:54:22 GMT" } ]
2023-04-19T00:00:00
[ [ "Guo", "Xin", "" ], [ "Hu", "Anran", "" ], [ "Santamaria", "Matteo", "" ], [ "Tajrobehkar", "Mahan", "" ], [ "Zhang", "Junzi", "" ] ]
new_dataset
0.99903
2304.08639
Ankur Ankan
Ankur Ankan and Johannes Textor
pgmpy: A Python Toolkit for Bayesian Networks
null
null
null
null
cs.LG stat.ME
http://creativecommons.org/licenses/by/4.0/
Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to work with BNs and related models. It implements algorithms for structure learning, parameter estimation, approximate and exact inference, causal inference, and simulations. These implementations focus on modularity and easy extensibility to allow users to quickly modify/add to existing algorithms, or to implement new algorithms for different use cases. pgmpy is released under the MIT License; the source code is available at: https://github.com/pgmpy/pgmpy, and the documentation at: https://pgmpy.org.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 22:17:53 GMT" } ]
2023-04-19T00:00:00
[ [ "Ankan", "Ankur", "" ], [ "Textor", "Johannes", "" ] ]
new_dataset
0.997456
2304.08640
Baixiang Huang
Baixiang Huang, Bryan Hooi, Kai Shu
TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks
10 pages, 5 figures
null
null
null
cs.LG cs.SI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Road safety is a major global public health concern. Effective traffic crash prediction can play a critical role in reducing road traffic accidents. However, Existing machine learning approaches tend to focus on predicting traffic accidents in isolation, without considering the potential relationships between different accident locations within road networks. To incorporate graph structure information, graph-based approaches such as Graph Neural Networks (GNNs) can be naturally applied. However, applying GNNs to the accident prediction problem faces challenges due to the lack of suitable graph-structured traffic accident datasets. To bridge this gap, we have constructed a real-world graph-based Traffic Accident Prediction (TAP) data repository, along with two representative tasks: accident occurrence prediction and accident severity prediction. With nationwide coverage, real-world network topology, and rich geospatial features, this data repository can be used for a variety of traffic-related tasks. We further comprehensively evaluate eleven state-of-the-art GNN variants and two non-graph-based machine learning methods using the created datasets. Significantly facilitated by the proposed data, we develop a novel Traffic Accident Vulnerability Estimation via Linkage (TRAVEL) model, which is designed to capture angular and directional information from road networks. We demonstrate that the proposed model consistently outperforms the baselines. The data and code are available on GitHub (https://github.com/baixianghuang/travel).
[ { "version": "v1", "created": "Mon, 17 Apr 2023 22:18:58 GMT" } ]
2023-04-19T00:00:00
[ [ "Huang", "Baixiang", "" ], [ "Hooi", "Bryan", "" ], [ "Shu", "Kai", "" ] ]
new_dataset
0.980707
2304.08650
Berk \c{C}ilo\u{g}lu
Abdullah Taha \c{C}a\u{g}an, G\"orkem Berkay Ko\c{c}, Handan Yak{\i}n, Berk \c{C}ilo\u{g}lu, Muhammad Zeeshan Ashgar, \"Ozg\"un Ersoy, Jyri H\"am\"al\"ainen, Metin \"Ozt\"urk
UAV-based Maritime Communications: Relaying to Enhance the Link Quality
null
null
null
null
cs.NI eess.SP
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Providing a stable connectivity in maritime communications is of utmost importance to unleash the full potential of smart ports. Nonetheless, due to the crowded nature of harbor environments, it is likely that some ships are shadowed by others, resulting in reduced received power that subsequently diminishes their data rates-even threatens basic connectivity requirements. Given that UAVs have been regarded as an integral part of future generations of wireless communication networks, they can be employed in maritime communications as well. In this paper, we investigate the use of UAV-mounted relays in order to help mitigate the reduced data rates of blocked links in maritime communications. Various communication architectures are considered based on the positioning mechanism of the UAV; in this regard, fixed, k-means algorithm-based, and landing spot-based positioning approaches are examined. On the other hand, since UAVs are predominantly battery-operated, the energy consumption performances of these approaches are also measured. Results reveal that the landing spot-based UAV relay positioning approach finds the best trade-off between the data rate and energy consumption.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 22:54:05 GMT" } ]
2023-04-19T00:00:00
[ [ "Çağan", "Abdullah Taha", "" ], [ "Koç", "Görkem Berkay", "" ], [ "Yakın", "Handan", "" ], [ "Çiloğlu", "Berk", "" ], [ "Ashgar", "Muhammad Zeeshan", "" ], [ "Ersoy", "Özgün", "" ], [ "Hämäläinen", "Jyri", "" ], [ "Öztürk", "Metin", "" ] ]
new_dataset
0.978395
2304.08655
Shuo Chen
Nikolaj Bj{\o}rner, Shuo Chen, Yang Chen, Zhongxin Guo, Peng Liu, Nanqing Luo
An Ethereum-compatible blockchain that explicates and ensures design-level safety properties for smart contracts
null
null
null
null
cs.CR cs.PL
http://creativecommons.org/licenses/by/4.0/
Smart contracts are crucial elements of decentralized technologies, but they face significant obstacles to trustworthiness due to security bugs and trapdoors. To address the core issue, we propose a technology that enables programmers to focus on design-level properties rather than specific low-level attack patterns. Our proposed technology, called Theorem-Carrying-Transaction (TCT), combines the benefits of runtime checking and symbolic proof. Under the TCT protocol, every transaction must carry a theorem that proves its adherence to the safety properties in the invoked contracts, and the blockchain checks the proof before executing the transaction. The unique design of TCT ensures that the theorems are provable and checkable in an efficient manner. We believe that TCT holds a great promise for enabling provably secure smart contracts in the future. As such, we call for collaboration toward this vision.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 23:14:45 GMT" } ]
2023-04-19T00:00:00
[ [ "Bjørner", "Nikolaj", "" ], [ "Chen", "Shuo", "" ], [ "Chen", "Yang", "" ], [ "Guo", "Zhongxin", "" ], [ "Liu", "Peng", "" ], [ "Luo", "Nanqing", "" ] ]
new_dataset
0.999029
2304.08660
Alex Junho Lee
Alex Junho Lee, Seungwon Song, Hyungtae Lim, Woojoo Lee and Hyun Myung
(LC)$^2$: LiDAR-Camera Loop Constraints For Cross-Modal Place Recognition
8 pages, 11 figures, Accepted to IEEE Robotics and Automation Letters (RA-L)
null
null
null
cs.RO cs.AI
http://creativecommons.org/licenses/by/4.0/
Localization has been a challenging task for autonomous navigation. A loop detection algorithm must overcome environmental changes for the place recognition and re-localization of robots. Therefore, deep learning has been extensively studied for the consistent transformation of measurements into localization descriptors. Street view images are easily accessible; however, images are vulnerable to appearance changes. LiDAR can robustly provide precise structural information. However, constructing a point cloud database is expensive, and point clouds exist only in limited places. Different from previous works that train networks to produce shared embedding directly between the 2D image and 3D point cloud, we transform both data into 2.5D depth images for matching. In this work, we propose a novel cross-matching method, called (LC)$^2$, for achieving LiDAR localization without a prior point cloud map. To this end, LiDAR measurements are expressed in the form of range images before matching them to reduce the modality discrepancy. Subsequently, the network is trained to extract localization descriptors from disparity and range images. Next, the best matches are employed as a loop factor in a pose graph. Using public datasets that include multiple sessions in significantly different lighting conditions, we demonstrated that LiDAR-based navigation systems could be optimized from image databases and vice versa.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 23:20:16 GMT" } ]
2023-04-19T00:00:00
[ [ "Lee", "Alex Junho", "" ], [ "Song", "Seungwon", "" ], [ "Lim", "Hyungtae", "" ], [ "Lee", "Woojoo", "" ], [ "Myung", "Hyun", "" ] ]
new_dataset
0.952613
2304.08665
Tanish Jain
Tanish Jain
Insta(nt) Pet Therapy: GAN-generated Images for Therapeutic Social Media Content
7 pages, 7 figures
null
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
The positive therapeutic effect of viewing pet images online has been well-studied. However, it is difficult to obtain large-scale production of such content since it relies on pet owners to capture photographs and upload them. I use a Generative Adversarial Network-based framework for the creation of fake pet images at scale. These images are uploaded on an Instagram account where they drive user engagement at levels comparable to those seen with images from accounts with traditional pet photographs, underlining the applicability of the framework to be used for pet-therapy social media content.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 23:43:29 GMT" } ]
2023-04-19T00:00:00
[ [ "Jain", "Tanish", "" ] ]
new_dataset
0.989248
2304.08695
Yifan Wang Mr
Yifan Wang, Meng Yuan, Lei Li, Karen Sui Geok Chua, Seng Kwee Wee, Wei Tech Ang
Graceful User Following for Mobile Balance Assistive Robot in Daily Activities Assistance
null
null
null
null
cs.RO cs.SY eess.SY
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Numerous diseases and aging can cause degeneration of people's balance ability resulting in limited mobility and even high risks of fall. Robotic technologies can provide more intensive rehabilitation exercises or be used as assistive devices to compensate for balance ability. However, With the new healthcare paradigm shifting from hospital care to home care, there is a gap in robotic systems that can provide care at home. This paper introduces Mobile Robotic Balance Assistant (MRBA), a compact and cost-effective balance assistive robot that can provide both rehabilitation training and activities of daily living (ADLs) assistance at home. A three degrees of freedom (3-DoF) robotic arm was designed to mimic the therapist arm function to provide balance assistance to the user. To minimize the interference to users' natural pelvis movements and gait patterns, the robot must have a Human-Robot Interface(HRI) that can detect user intention accurately and follow the user's movement smoothly and timely. Thus, a graceful user following control rule was proposed. The overall control architecture consists of two parts: an observer for human inputs estimation and an LQR-based controller with disturbance rejection. The proposed controller is validated in high-fidelity simulation with actual human trajectories, and the results successfully show the effectiveness of the method in different walking modes.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 02:01:10 GMT" } ]
2023-04-19T00:00:00
[ [ "Wang", "Yifan", "" ], [ "Yuan", "Meng", "" ], [ "Li", "Lei", "" ], [ "Chua", "Karen Sui Geok", "" ], [ "Wee", "Seng Kwee", "" ], [ "Ang", "Wei Tech", "" ] ]
new_dataset
0.997602
2304.08754
Jie Shao
Xin Man, Chenghong Zhang, Changyu Li, Jie Shao
W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting
null
null
null
null
cs.LG cs.AI physics.ao-ph
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Weather forecasting is a long-standing computational challenge with direct societal and economic impacts. This task involves a large amount of continuous data collection and exhibits rich spatiotemporal dependencies over long periods, making it highly suitable for deep learning models. In this paper, we apply pre-training techniques to weather forecasting and propose W-MAE, a Weather model with Masked AutoEncoder pre-training for multi-variable weather forecasting. W-MAE is pre-trained in a self-supervised manner to reconstruct spatial correlations within meteorological variables. On the temporal scale, we fine-tune the pre-trained W-MAE to predict the future states of meteorological variables, thereby modeling the temporal dependencies present in weather data. We pre-train W-MAE using the fifth-generation ECMWF Reanalysis (ERA5) data, with samples selected every six hours and using only two years of data. Under the same training data conditions, we compare W-MAE with FourCastNet, and W-MAE outperforms FourCastNet in precipitation forecasting. In the setting where the training data is far less than that of FourCastNet, our model still performs much better in precipitation prediction (0.80 vs. 0.98). Additionally, experiments show that our model has a stable and significant advantage in short-to-medium-range forecasting (i.e., forecasting time ranges from 6 hours to one week), and the longer the prediction time, the more evident the performance advantage of W-MAE, further proving its robustness.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 06:25:11 GMT" } ]
2023-04-19T00:00:00
[ [ "Man", "Xin", "" ], [ "Zhang", "Chenghong", "" ], [ "Li", "Changyu", "" ], [ "Shao", "Jie", "" ] ]
new_dataset
0.991083
2304.08893
Manoj Kumar Rajagopal
Aswin Iyer, Santosh Narayan, Naren M, Manoj kumar Rajagopal
Autonomous Systems: Autonomous Systems: Indoor Drone Navigation
null
null
null
null
cs.RO cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
Drones are a promising technology for autonomous data collection and indoor sensing. In situations when human-controlled UAVs may not be practical or dependable, such as in uncharted or dangerous locations, the usage of autonomous UAVs offers flexibility, cost savings, and reduced risk. The system creates a simulated quadcopter capable of autonomously travelling in an indoor environment using the gazebo simulation tool and the ros navigation system framework known as Navigaation2. While Nav2 has successfully shown the functioning of autonomous navigation in terrestrial robots and vehicles, the same hasn't been accomplished with unmanned aerial vehicles and still has to be done. The goal is to use the slam toolbox for ROS and the Nav2 navigation system framework to construct a simulated drone that can move autonomously in an indoor (gps-less) environment.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 10:40:00 GMT" } ]
2023-04-19T00:00:00
[ [ "Iyer", "Aswin", "" ], [ "Narayan", "Santosh", "" ], [ "M", "Naren", "" ], [ "Rajagopal", "Manoj kumar", "" ] ]
new_dataset
0.99504
2304.08901
Alpay Sabuncuo\u{g}lu
Alpay Sabuncuoglu, T. Metin Sezgin
Multimodal Group Activity Dataset for Classroom Engagement Level Prediction
null
null
null
null
cs.HC
http://creativecommons.org/licenses/by-nc-sa/4.0/
We collected a new dataset that includes approximately eight hours of audiovisual recordings of a group of students and their self-evaluation scores for classroom engagement. The dataset and data analysis scripts are available on our open-source repository. We developed baseline face-based and group-activity-based image and video recognition models. Our image models yield 45-85% test accuracy with face-area inputs on person-based classification task. Our video models achieved up to 71% test accuracy on group-level prediction using group activity video inputs. In this technical report, we shared the details of our end-to-end human-centered engagement analysis pipeline from data collection to model development.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 11:09:02 GMT" } ]
2023-04-19T00:00:00
[ [ "Sabuncuoglu", "Alpay", "" ], [ "Sezgin", "T. Metin", "" ] ]
new_dataset
0.999529
2304.08908
Mario Saucedo
Mario A.V. Saucedo, Akash Patel, Rucha Sawlekar, Akshit Saradagi, Christoforos Kanellakis, Ali-Akbar Agha-Mohammadi and George Nikolakopoulos
Event Camera and LiDAR based Human Tracking for Adverse Lighting Conditions in Subterranean Environments
Accepted at IFAC World Congress 2023
null
null
null
cs.RO cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this article, we propose a novel LiDAR and event camera fusion modality for subterranean (SubT) environments for fast and precise object and human detection in a wide variety of adverse lighting conditions, such as low or no light, high-contrast zones and in the presence of blinding light sources. In the proposed approach, information from the event camera and LiDAR are fused to localize a human or an object-of-interest in a robot's local frame. The local detection is then transformed into the inertial frame and used to set references for a Nonlinear Model Predictive Controller (NMPC) for reactive tracking of humans or objects in SubT environments. The proposed novel fusion uses intensity filtering and K-means clustering on the LiDAR point cloud and frequency filtering and connectivity clustering on the events induced in an event camera by the returning LiDAR beams. The centroids of the clusters in the event camera and LiDAR streams are then paired to localize reflective markers present on safety vests and signs in SubT environments. The efficacy of the proposed scheme has been experimentally validated in a real SubT environment (a mine) with a Pioneer 3AT mobile robot. The experimental results show real-time performance for human detection and the NMPC-based controller allows for reactive tracking of a human or object of interest, even in complete darkness.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 11:27:41 GMT" } ]
2023-04-19T00:00:00
[ [ "Saucedo", "Mario A. V.", "" ], [ "Patel", "Akash", "" ], [ "Sawlekar", "Rucha", "" ], [ "Saradagi", "Akshit", "" ], [ "Kanellakis", "Christoforos", "" ], [ "Agha-Mohammadi", "Ali-Akbar", "" ], [ "Nikolakopoulos", "George", "" ] ]
new_dataset
0.999119
2304.08940
Isabella Gra{\ss}l
Isabella Gra{\ss}l and Gordon Fraser
The ABC of Pair Programming: Gender-dependent Attitude, Behavior and Code of Young Learners
null
null
null
null
cs.SE
http://creativecommons.org/licenses/by-nc-nd/4.0/
Young learners are increasingly introduced to programming, and one of the main challenges for educators is to achieve learning success while also creating enthusiasm. As it is particularly difficult to achieve this enthusiasm initially in young females, prior work has identified gender-specific differences in the programming behavior of young learners. Since pair programming, which turns programming into a more sociable activity, has been proposed as an approach to support programming education, in this paper we aim to investigate whether similar gender-specific characteristics can also be observed during pair programming. Therefore, we designed a gender-neutral introductory SCRATCH programming course tailored for integrating pair programming principles, and conducted it with a total of 139 students aged between 8 and 14 years. To identify gender-dependent differences and similarities, we measure the attitude towards programming and the course setting, observe the behavior of the students while programming, and analyze the code of the programs for different gender-combinations. Overall, our study demonstrates that pair programming is well suited for young learners and results in a positive attitude. While the resulting programs are similar in quality and complexity independent of gender, differences are evident when it comes to the compliance to pair programming roles, the exploration of code, and the creative customization of programs. These findings contribute to an in-depth understanding of social and technical gender specifics of pair programming, and provide educators with resources and guidance for implementing gender-sensitive pair programming in the classroom.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 12:30:20 GMT" } ]
2023-04-19T00:00:00
[ [ "Graßl", "Isabella", "" ], [ "Fraser", "Gordon", "" ] ]
new_dataset
0.997728
2304.08956
Naiyu Fang
Naiyu Fang, Lemiao Qiu, Shuyou Zhang, Zili Wang, Kerui Hu
PG-VTON: A Novel Image-Based Virtual Try-On Method via Progressive Inference Paradigm
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Virtual try-on is a promising computer vision topic with a high commercial value wherein a new garment is visually worn on a person with a photo-realistic effect. Previous studies conduct their shape and content inference at one stage, employing a single-scale warping mechanism and a relatively unsophisticated content inference mechanism. These approaches have led to suboptimal results in terms of garment warping and skin reservation under challenging try-on scenarios. To address these limitations, we propose a novel virtual try-on method via progressive inference paradigm (PGVTON) that leverages a top-down inference pipeline and a general garment try-on strategy. Specifically, we propose a robust try-on parsing inference method by disentangling semantic categories and introducing consistency. Exploiting the try-on parsing as the shape guidance, we implement the garment try-on via warping-mapping-composition. To facilitate adaptation to a wide range of try-on scenarios, we adopt a covering more and selecting one warping strategy and explicitly distinguish tasks based on alignment. Additionally, we regulate StyleGAN2 to implement re-naked skin inpainting, conditioned on the target skin shape and spatial-agnostic skin features. Experiments demonstrate that our method has state-of-the-art performance under two challenging scenarios. The code will be available at https://github.com/NerdFNY/PGVTON.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 12:47:26 GMT" } ]
2023-04-19T00:00:00
[ [ "Fang", "Naiyu", "" ], [ "Qiu", "Lemiao", "" ], [ "Zhang", "Shuyou", "" ], [ "Wang", "Zili", "" ], [ "Hu", "Kerui", "" ] ]
new_dataset
0.999256
2304.08994
Alexander Naumann
Alexander Naumann, Felix Hertlein, Laura D\"orr, Kai Furmans
Parcel3D: Shape Reconstruction from Single RGB Images for Applications in Transportation Logistics
Accepted at CVPR workshop on Vision-based InduStrial InspectiON (VISION) 2023, see https://vision-based-industrial-inspection.github.io/cvpr-2023/
null
null
null
cs.CV cs.LG cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We focus on enabling damage and tampering detection in logistics and tackle the problem of 3D shape reconstruction of potentially damaged parcels. As input we utilize single RGB images, which corresponds to use-cases where only simple handheld devices are available, e.g. for postmen during delivery or clients on delivery. We present a novel synthetic dataset, named Parcel3D, that is based on the Google Scanned Objects (GSO) dataset and consists of more than 13,000 images of parcels with full 3D annotations. The dataset contains intact, i.e. cuboid-shaped, parcels and damaged parcels, which were generated in simulations. We work towards detecting mishandling of parcels by presenting a novel architecture called CubeRefine R-CNN, which combines estimating a 3D bounding box with an iterative mesh refinement. We benchmark our approach on Parcel3D and an existing dataset of cuboid-shaped parcels in real-world scenarios. Our results show, that while training on Parcel3D enables transfer to the real world, enabling reliable deployment in real-world scenarios is still challenging. CubeRefine R-CNN yields competitive performance in terms of Mesh AP and is the only model that directly enables deformation assessment by 3D mesh comparison and tampering detection by comparing viewpoint invariant parcel side surface representations. Dataset and code are available at https://a-nau.github.io/parcel3d.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 13:55:51 GMT" } ]
2023-04-19T00:00:00
[ [ "Naumann", "Alexander", "" ], [ "Hertlein", "Felix", "" ], [ "Dörr", "Laura", "" ], [ "Furmans", "Kai", "" ] ]
new_dataset
0.999867
2304.09012
Andrey Sobolevsky
Andrey Sobolevsky, Guillaume-Alexandre Bilodeau, Jinghui Cheng, Jin L.C. Guo
GUILGET: GUI Layout GEneration with Transformer
12 pages, 5 figures, Canadian AI Conference 2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Sketching out Graphical User Interface (GUI) layout is part of the pipeline of designing a GUI and a crucial task for the success of a software application. Arranging all components inside a GUI layout manually is a time-consuming task. In order to assist designers, we developed a method named GUILGET to automatically generate GUI layouts from positional constraints represented as GUI arrangement graphs (GUI-AGs). The goal is to support the initial step of GUI design by producing realistic and diverse GUI layouts. The existing image layout generation techniques often cannot incorporate GUI design constraints. Thus, GUILGET needs to adapt existing techniques to generate GUI layouts that obey to constraints specific to GUI designs. GUILGET is based on transformers in order to capture the semantic in relationships between elements from GUI-AG. Moreover, the model learns constraints through the minimization of losses responsible for placing each component inside its parent layout, for not letting components overlap if they are inside the same parent, and for component alignment. Our experiments, which are conducted on the CLAY dataset, reveal that our model has the best understanding of relationships from GUI-AG and has the best performances in most of evaluation metrics. Therefore, our work contributes to improved GUI layout generation by proposing a novel method that effectively accounts for the constraints on GUI elements and paves the road for a more efficient GUI design pipeline.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 14:27:34 GMT" } ]
2023-04-19T00:00:00
[ [ "Sobolevsky", "Andrey", "" ], [ "Bilodeau", "Guillaume-Alexandre", "" ], [ "Cheng", "Jinghui", "" ], [ "Guo", "Jin L. C.", "" ] ]
new_dataset
0.997557
2304.09048
Ningyu Zhang
Zhen Bi, Jing Chen, Yinuo Jiang, Feiyu Xiong, Wei Guo, Huajun Chen, Ningyu Zhang
CodeKGC: Code Language Model for Generative Knowledge Graph Construction
Work in progress
null
null
null
cs.CL cs.AI cs.IR cs.LG cs.SE
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks. Intuitively, we address the task of generative knowledge graph construction with code language model: given a code-format natural language input, the target is to generate triples which can be represented as code completion tasks. Specifically, we develop schema-aware prompts that effectively utilize the semantic structure within the knowledge graph. As code inherently possesses structure, such as class and function definitions, it serves as a useful model for prior semantic structural knowledge. Furthermore, we employ a rationale-enhanced generation method to boost the performance. Rationales provide intermediate steps, thereby improving knowledge extraction abilities. Experimental results indicate that the proposed approach can obtain better performance on benchmark datasets compared with baselines. Code and datasets are available in https://github.com/zjunlp/DeepKE/tree/main/example/llm.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 15:12:34 GMT" } ]
2023-04-19T00:00:00
[ [ "Bi", "Zhen", "" ], [ "Chen", "Jing", "" ], [ "Jiang", "Yinuo", "" ], [ "Xiong", "Feiyu", "" ], [ "Guo", "Wei", "" ], [ "Chen", "Huajun", "" ], [ "Zhang", "Ningyu", "" ] ]
new_dataset
0.998659
2304.09071
Andrea Ferraguti
Andrea Ferraguti and Dorian Goldfeld and Giacomo Micheli
Number Theoretical Locally Recoverable Codes
null
null
null
null
cs.IT math.IT math.NT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper we give constructions for infinite sequences of finite non-linear locally recoverable codes $\mathcal C\subseteq \prod\limits^N_{i=1}\mathbb F_{q_i}$ over a product of finite fields arising from basis expansions in algebraic number fields. The codes in our sequences have increasing length and size, constant rate, fixed locality, and minimum distance going to infinity.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 15:45:12 GMT" } ]
2023-04-19T00:00:00
[ [ "Ferraguti", "Andrea", "" ], [ "Goldfeld", "Dorian", "" ], [ "Micheli", "Giacomo", "" ] ]
new_dataset
0.998888
2101.06549
Jingkang Wang
Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, Raquel Urtasun
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
CVPR 2021. Corrected typos in the adversarial objective
null
null
null
cs.RO cs.AI cs.CV cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
As self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes with respect to the planning module that takes ground-truth actor states as input. This does not scale and cannot identify all possible autonomy failures, such as perception failures due to occlusion. In this paper, we propose AdvSim, an adversarial framework to generate safety-critical scenarios for any LiDAR-based autonomy system. Given an initial traffic scenario, AdvSim modifies the actors' trajectories in a physically plausible manner and updates the LiDAR sensor data to match the perturbed world. Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack. Our experiments show that our approach is general and can identify thousands of semantically meaningful safety-critical scenarios for a wide range of modern self-driving systems. Furthermore, we show that the robustness and safety of these systems can be further improved by training them with scenarios generated by AdvSim.
[ { "version": "v1", "created": "Sat, 16 Jan 2021 23:23:12 GMT" }, { "version": "v2", "created": "Sun, 4 Apr 2021 03:42:18 GMT" }, { "version": "v3", "created": "Sat, 8 Jan 2022 21:50:56 GMT" }, { "version": "v4", "created": "Sun, 16 Apr 2023 20:22:41 GMT" } ]
2023-04-18T00:00:00
[ [ "Wang", "Jingkang", "" ], [ "Pun", "Ava", "" ], [ "Tu", "James", "" ], [ "Manivasagam", "Sivabalan", "" ], [ "Sadat", "Abbas", "" ], [ "Casas", "Sergio", "" ], [ "Ren", "Mengye", "" ], [ "Urtasun", "Raquel", "" ] ]
new_dataset
0.999583
2104.02438
Bartek Klin
Miko{\l}aj Boja\'nczyk, Joanna Fijalkow, Bartek Klin and Joshua Moerman
Orbit-Finite-Dimensional Vector Spaces and Weighted Register Automata
null
null
null
null
cs.FL
http://creativecommons.org/licenses/by/4.0/
We develop a theory of vector spaces spanned by orbit-finite sets. Using this theory, we give a decision procedure for equivalence of weighted register automata, which are the common generalization of weighted automata and register automata for infinite alphabets. The algorithm runs in exponential time, and in polynomial time for a fixed number of registers. As a special case, we can decide, with the same complexity, language equivalence for unambiguous register automata, which improves previous results in three ways: (a) we allow for order comparisons on atoms, and not just equality; (b) the complexity is exponentially better; and (c) we allow automata with guessing.
[ { "version": "v1", "created": "Tue, 6 Apr 2021 11:54:51 GMT" }, { "version": "v2", "created": "Fri, 23 Apr 2021 14:45:04 GMT" }, { "version": "v3", "created": "Sat, 15 Apr 2023 19:14:28 GMT" } ]
2023-04-18T00:00:00
[ [ "Bojańczyk", "Mikołaj", "" ], [ "Fijalkow", "Joanna", "" ], [ "Klin", "Bartek", "" ], [ "Moerman", "Joshua", "" ] ]
new_dataset
0.999309
2106.13797
Wenhai Wang
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao
PVT v2: Improved Baselines with Pyramid Vision Transformer
Accepted to CVMJ 2022
Computational Visual Media, 2022, Vol. 8, No. 3, Pages: 415-424
10.1007/s41095-022-0274-8
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Transformer recently has presented encouraging progress in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (PVT v1) by adding three designs, including (1) linear complexity attention layer, (2) overlapping patch embedding, and (3) convolutional feed-forward network. With these modifications, PVT v2 reduces the computational complexity of PVT v1 to linear and achieves significant improvements on fundamental vision tasks such as classification, detection, and segmentation. Notably, the proposed PVT v2 achieves comparable or better performances than recent works such as Swin Transformer. We hope this work will facilitate state-of-the-art Transformer researches in computer vision. Code is available at https://github.com/whai362/PVT.
[ { "version": "v1", "created": "Fri, 25 Jun 2021 17:51:09 GMT" }, { "version": "v2", "created": "Mon, 28 Jun 2021 15:07:07 GMT" }, { "version": "v3", "created": "Mon, 5 Jul 2021 08:04:40 GMT" }, { "version": "v4", "created": "Sat, 17 Jul 2021 15:12:25 GMT" }, { "version": "v5", "created": "Wed, 9 Feb 2022 03:51:39 GMT" }, { "version": "v6", "created": "Thu, 30 Jun 2022 15:31:56 GMT" }, { "version": "v7", "created": "Mon, 17 Apr 2023 12:49:29 GMT" } ]
2023-04-18T00:00:00
[ [ "Wang", "Wenhai", "" ], [ "Xie", "Enze", "" ], [ "Li", "Xiang", "" ], [ "Fan", "Deng-Ping", "" ], [ "Song", "Kaitao", "" ], [ "Liang", "Ding", "" ], [ "Lu", "Tong", "" ], [ "Luo", "Ping", "" ], [ "Shao", "Ling", "" ] ]
new_dataset
0.976022
2108.05271
Georgi Karadzhov
Georgi Karadzhov, Tom Stafford, Andreas Vlachos
DeliData: A dataset for deliberation in multi-party problem solving
null
null
null
null
cs.CL cs.AI cs.CY
http://creativecommons.org/licenses/by/4.0/
Group deliberation enables people to collaborate and solve problems, however, it is understudied due to a lack of resources. To this end, we introduce the first publicly available dataset containing collaborative conversations on solving a well-established cognitive task, consisting of 500 group dialogues and 14k utterances. In 64% of these conversations, the group members are able to find a better solution than they had identified individually, and in 43.8% of the groups who had a correct answer as their final solution, none of the participants had solved the task correctly by themselves. Furthermore, we propose a novel annotation schema that captures deliberation cues and release all 14k utterances annotated with it. Finally, we use the proposed dataset to develop and evaluate two methods for generating deliberation utterances. The data collection platform, dataset and annotated corpus are publicly available at https://delibot.xyz.
[ { "version": "v1", "created": "Wed, 11 Aug 2021 15:13:07 GMT" }, { "version": "v2", "created": "Sat, 7 May 2022 18:18:00 GMT" }, { "version": "v3", "created": "Sun, 16 Apr 2023 13:11:25 GMT" } ]
2023-04-18T00:00:00
[ [ "Karadzhov", "Georgi", "" ], [ "Stafford", "Tom", "" ], [ "Vlachos", "Andreas", "" ] ]
new_dataset
0.999847
2110.11073
Kai Wang
Kai Wang, Zhene Zou, Minghao Zhao, Qilin Deng, Yue Shang, Yile Liang, Runze Wu, Xudong Shen, Tangjie Lyu, Changjie Fan
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System
4-th version, SIGIR2023
null
null
null
cs.IR cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting recommendations to multi-step decision-making tasks. However, current RL-based RS research commonly has a large reality gap. In this paper, we introduce the first open-source real-world dataset, RL4RS, hoping to replace the artificial datasets and semi-simulated RS datasets previous studies used due to the resource limitation of the RL-based RS domain. Unlike academic RL research, RL-based RS suffers from the difficulties of being well-validated before deployment. We attempt to propose a new systematic evaluation framework, including evaluation of environment simulation, evaluation on environments, counterfactual policy evaluation, and evaluation on environments built from test set. In summary, the RL4RS (Reinforcement Learning for Recommender Systems), a new resource with special concerns on the reality gaps, contains two real-world datasets, data understanding tools, tuned simulation environments, related advanced RL baselines, batch RL baselines, and counterfactual policy evaluation algorithms. The RL4RS suite can be found at https://github.com/fuxiAIlab/RL4RS. In addition to the RL-based recommender systems, we expect the resource to contribute to research in applied reinforcement learning.
[ { "version": "v1", "created": "Mon, 18 Oct 2021 12:48:02 GMT" }, { "version": "v2", "created": "Tue, 15 Feb 2022 09:12:53 GMT" }, { "version": "v3", "created": "Wed, 16 Feb 2022 03:31:15 GMT" }, { "version": "v4", "created": "Sun, 20 Feb 2022 13:08:08 GMT" }, { "version": "v5", "created": "Mon, 17 Apr 2023 10:37:38 GMT" } ]
2023-04-18T00:00:00
[ [ "Wang", "Kai", "" ], [ "Zou", "Zhene", "" ], [ "Zhao", "Minghao", "" ], [ "Deng", "Qilin", "" ], [ "Shang", "Yue", "" ], [ "Liang", "Yile", "" ], [ "Wu", "Runze", "" ], [ "Shen", "Xudong", "" ], [ "Lyu", "Tangjie", "" ], [ "Fan", "Changjie", "" ] ]
new_dataset
0.999552
2111.10756
Keng Ji Chow
Keng Ji Chow, Samson Tan, Min-Yen Kan
TraVLR: Now You See It, Now You Don't! A Bimodal Dataset for Evaluating Visio-Linguistic Reasoning
The first two authors contributed equally
null
null
null
cs.CL cs.CV cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Numerous visio-linguistic (V+L) representation learning methods have been developed, yet existing datasets do not adequately evaluate the extent to which they represent visual and linguistic concepts in a unified space. We propose several novel evaluation settings for V+L models, including cross-modal transfer. Furthermore, existing V+L benchmarks often report global accuracy scores on the entire dataset, making it difficult to pinpoint the specific reasoning tasks that models fail and succeed at. We present TraVLR, a synthetic dataset comprising four V+L reasoning tasks. TraVLR's synthetic nature allows us to constrain its training and testing distributions along task-relevant dimensions, enabling the evaluation of out-of-distribution generalisation. Each example in TraVLR redundantly encodes the scene in two modalities, allowing either to be dropped or added during training or testing without losing relevant information. We compare the performance of four state-of-the-art V+L models, finding that while they perform well on test examples from the same modality, they all fail at cross-modal transfer and have limited success accommodating the addition or deletion of one modality. We release TraVLR as an open challenge for the research community.
[ { "version": "v1", "created": "Sun, 21 Nov 2021 07:22:44 GMT" }, { "version": "v2", "created": "Sat, 4 Mar 2023 12:57:41 GMT" }, { "version": "v3", "created": "Sat, 15 Apr 2023 09:48:44 GMT" } ]
2023-04-18T00:00:00
[ [ "Chow", "Keng Ji", "" ], [ "Tan", "Samson", "" ], [ "Kan", "Min-Yen", "" ] ]
new_dataset
0.999682
2111.11969
Qiang Nie
Qiang Nie, Ziwei Liu, Yunhui Liu
Lifting 2D Human Pose to 3D with Domain Adapted 3D Body Concept
15 pages, a paper submitted to IJCV
Int J Comput Vis 131 (2023) 1250 - 1268
10.1007/s11263-023-01749-2
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Lifting the 2D human pose to the 3D pose is an important yet challenging task. Existing 3D pose estimation suffers from 1) the inherent ambiguity between the 2D and 3D data, and 2) the lack of well labeled 2D-3D pose pairs in the wild. Human beings are able to imagine the human 3D pose from a 2D image or a set of 2D body key-points with the least ambiguity, which should be attributed to the prior knowledge of the human body that we have acquired in our mind. Inspired by this, we propose a new framework that leverages the labeled 3D human poses to learn a 3D concept of the human body to reduce the ambiguity. To have consensus on the body concept from 2D pose, our key insight is to treat the 2D human pose and the 3D human pose as two different domains. By adapting the two domains, the body knowledge learned from 3D poses is applied to 2D poses and guides the 2D pose encoder to generate informative 3D "imagination" as embedding in pose lifting. Benefiting from the domain adaptation perspective, the proposed framework unifies the supervised and semi-supervised 3D pose estimation in a principled framework. Extensive experiments demonstrate that the proposed approach can achieve state-of-the-art performance on standard benchmarks. More importantly, it is validated that the explicitly learned 3D body concept effectively alleviates the 2D-3D ambiguity in 2D pose lifting, improves the generalization, and enables the network to exploit the abundant unlabeled 2D data.
[ { "version": "v1", "created": "Tue, 23 Nov 2021 16:02:12 GMT" } ]
2023-04-18T00:00:00
[ [ "Nie", "Qiang", "" ], [ "Liu", "Ziwei", "" ], [ "Liu", "Yunhui", "" ] ]
new_dataset
0.988816
2201.11221
Alejandro D\'iaz-Caro
Alejandro D\'iaz-Caro and Gilles Dowek
Linear lambda-calculus is linear
This is the full revised journal version of the paper accepted at FSCD 2022 and published at LIPIcs 228:21, 2022
null
null
null
cs.LO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We prove a linearity theorem for an extension of linear logic with addition and multiplication by a scalar: the proofs of some propositions in this logic are linear in the algebraic sense. This work is part of a wider research program that aims at defining a logic whose proof language is a quantum programming language.
[ { "version": "v1", "created": "Wed, 26 Jan 2022 22:48:04 GMT" }, { "version": "v2", "created": "Thu, 3 Feb 2022 16:45:51 GMT" }, { "version": "v3", "created": "Wed, 20 Apr 2022 15:19:40 GMT" }, { "version": "v4", "created": "Sat, 15 Apr 2023 16:48:27 GMT" } ]
2023-04-18T00:00:00
[ [ "Díaz-Caro", "Alejandro", "" ], [ "Dowek", "Gilles", "" ] ]
new_dataset
0.987198
2201.11460
Yuren Cong
Yuren Cong, Michael Ying Yang, Bodo Rosenhahn
RelTR: Relation Transformer for Scene Graph Generation
accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Different objects in the same scene are more or less related to each other, but only a limited number of these relationships are noteworthy. Inspired by DETR, which excels in object detection, we view scene graph generation as a set prediction problem and propose an end-to-end scene graph generation model RelTR which has an encoder-decoder architecture. The encoder reasons about the visual feature context while the decoder infers a fixed-size set of triplets subject-predicate-object using different types of attention mechanisms with coupled subject and object queries. We design a set prediction loss performing the matching between the ground truth and predicted triplets for the end-to-end training. In contrast to most existing scene graph generation methods, RelTR is a one-stage method that predicts a set of relationships directly only using visual appearance without combining entities and labeling all possible predicates. Extensive experiments on the Visual Genome and Open Images V6 datasets demonstrate the superior performance and fast inference of our model.
[ { "version": "v1", "created": "Thu, 27 Jan 2022 11:53:41 GMT" }, { "version": "v2", "created": "Thu, 11 Aug 2022 20:17:58 GMT" }, { "version": "v3", "created": "Fri, 14 Apr 2023 21:44:13 GMT" } ]
2023-04-18T00:00:00
[ [ "Cong", "Yuren", "" ], [ "Yang", "Michael Ying", "" ], [ "Rosenhahn", "Bodo", "" ] ]
new_dataset
0.994143
2203.08897
Swathikiran Sudhakaran
Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
Gate-Shift-Fuse for Video Action Recognition
Accepted to TPAMI. arXiv admin note: text overlap with arXiv:1912.00381
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Convolutional Neural Networks are the de facto models for image recognition. However 3D CNNs, the straight forward extension of 2D CNNs for video recognition, have not achieved the same success on standard action recognition benchmarks. One of the main reasons for this reduced performance of 3D CNNs is the increased computational complexity requiring large scale annotated datasets to train them in scale. 3D kernel factorization approaches have been proposed to reduce the complexity of 3D CNNs. Existing kernel factorization approaches follow hand-designed and hard-wired techniques. In this paper we propose Gate-Shift-Fuse (GSF), a novel spatio-temporal feature extraction module which controls interactions in spatio-temporal decomposition and learns to adaptively route features through time and combine them in a data dependent manner. GSF leverages grouped spatial gating to decompose input tensor and channel weighting to fuse the decomposed tensors. GSF can be inserted into existing 2D CNNs to convert them into an efficient and high performing spatio-temporal feature extractor, with negligible parameter and compute overhead. We perform an extensive analysis of GSF using two popular 2D CNN families and achieve state-of-the-art or competitive performance on five standard action recognition benchmarks.
[ { "version": "v1", "created": "Wed, 16 Mar 2022 19:19:04 GMT" }, { "version": "v2", "created": "Thu, 13 Apr 2023 17:27:02 GMT" }, { "version": "v3", "created": "Sat, 15 Apr 2023 13:06:27 GMT" } ]
2023-04-18T00:00:00
[ [ "Sudhakaran", "Swathikiran", "" ], [ "Escalera", "Sergio", "" ], [ "Lanz", "Oswald", "" ] ]
new_dataset
0.99549
2203.10642
Xuanyao Chen
Xuanyao Chen, Tianyuan Zhang, Yue Wang, Yilun Wang, Hang Zhao
FUTR3D: A Unified Sensor Fusion Framework for 3D Detection
null
CVPR 2023 workshop on autonomous driving
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sensor fusion is an essential topic in many perception systems, such as autonomous driving and robotics. Existing multi-modal 3D detection models usually involve customized designs depending on the sensor combinations or setups. In this work, we propose the first unified end-to-end sensor fusion framework for 3D detection, named FUTR3D, which can be used in (almost) any sensor configuration. FUTR3D employs a query-based Modality-Agnostic Feature Sampler (MAFS), together with a transformer decoder with a set-to-set loss for 3D detection, thus avoiding using late fusion heuristics and post-processing tricks. We validate the effectiveness of our framework on various combinations of cameras, low-resolution LiDARs, high-resolution LiDARs, and Radars. On NuScenes dataset, FUTR3D achieves better performance over specifically designed methods across different sensor combinations. Moreover, FUTR3D achieves great flexibility with different sensor configurations and enables low-cost autonomous driving. For example, only using a 4-beam LiDAR with cameras, FUTR3D (58.0 mAP) achieves on par performance with state-of-the-art 3D detection model CenterPoint (56.6 mAP) using a 32-beam LiDAR.
[ { "version": "v1", "created": "Sun, 20 Mar 2022 20:41:55 GMT" }, { "version": "v2", "created": "Sat, 15 Apr 2023 13:05:44 GMT" } ]
2023-04-18T00:00:00
[ [ "Chen", "Xuanyao", "" ], [ "Zhang", "Tianyuan", "" ], [ "Wang", "Yue", "" ], [ "Wang", "Yilun", "" ], [ "Zhao", "Hang", "" ] ]
new_dataset
0.998944
2203.16828
Sihan Ma
Sihan Ma, Jizhizi Li, Jing Zhang, He Zhang, Dacheng Tao
Rethinking Portrait Matting with Privacy Preserving
Accepted to the International Journal of Computer Vision (IJCV). The code, dataset, and models are available at https://github.com/ViTAE-Transformer/P3M-Net. arXiv admin note: substantial text overlap with arXiv:2104.14222
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recently, there has been an increasing concern about the privacy issue raised by identifiable information in machine learning. However, previous portrait matting methods were all based on identifiable images. To fill the gap, we present P3M-10k, which is the first large-scale anonymized benchmark for Privacy-Preserving Portrait Matting (P3M). P3M-10k consists of 10,421 high resolution face-blurred portrait images along with high-quality alpha mattes, which enables us to systematically evaluate both trimap-free and trimap-based matting methods and obtain some useful findings about model generalization ability under the privacy preserving training (PPT) setting. We also present a unified matting model dubbed P3M-Net that is compatible with both CNN and transformer backbones. To further mitigate the cross-domain performance gap issue under the PPT setting, we devise a simple yet effective Copy and Paste strategy (P3M-CP), which borrows facial information from public celebrity images and directs the network to reacquire the face context at both data and feature level. Extensive experiments on P3M-10k and public benchmarks demonstrate the superiority of P3M-Net over state-of-the-art methods and the effectiveness of P3M-CP in improving the cross-domain generalization ability, implying a great significance of P3M for future research and real-world applications.
[ { "version": "v1", "created": "Thu, 31 Mar 2022 06:26:07 GMT" }, { "version": "v2", "created": "Mon, 17 Apr 2023 00:19:30 GMT" } ]
2023-04-18T00:00:00
[ [ "Ma", "Sihan", "" ], [ "Li", "Jizhizi", "" ], [ "Zhang", "Jing", "" ], [ "Zhang", "He", "" ], [ "Tao", "Dacheng", "" ] ]
new_dataset
0.982907
2204.13686
Zhongang Cai
Zhongang Cai, Daxuan Ren, Ailing Zeng, Zhengyu Lin, Tao Yu, Wenjia Wang, Xiangyu Fan, Yang Gao, Yifan Yu, Liang Pan, Fangzhou Hong, Mingyuan Zhang, Chen Change Loy, Lei Yang, Ziwei Liu
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling
Homepage: https://caizhongang.github.io/projects/HuMMan/
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications. With the advances of new sensors and algorithms, there is an increasing demand for more versatile datasets. In this work, we contribute HuMMan, a large-scale multi-modal 4D human dataset with 1000 human subjects, 400k sequences and 60M frames. HuMMan has several appealing properties: 1) multi-modal data and annotations including color images, point clouds, keypoints, SMPL parameters, and textured meshes; 2) popular mobile device is included in the sensor suite; 3) a set of 500 actions, designed to cover fundamental movements; 4) multiple tasks such as action recognition, pose estimation, parametric human recovery, and textured mesh reconstruction are supported and evaluated. Extensive experiments on HuMMan voice the need for further study on challenges such as fine-grained action recognition, dynamic human mesh reconstruction, point cloud-based parametric human recovery, and cross-device domain gaps.
[ { "version": "v1", "created": "Thu, 28 Apr 2022 17:54:25 GMT" }, { "version": "v2", "created": "Sun, 16 Apr 2023 12:26:14 GMT" } ]
2023-04-18T00:00:00
[ [ "Cai", "Zhongang", "" ], [ "Ren", "Daxuan", "" ], [ "Zeng", "Ailing", "" ], [ "Lin", "Zhengyu", "" ], [ "Yu", "Tao", "" ], [ "Wang", "Wenjia", "" ], [ "Fan", "Xiangyu", "" ], [ "Gao", "Yang", "" ], [ "Yu", "Yifan", "" ], [ "Pan", "Liang", "" ], [ "Hong", "Fangzhou", "" ], [ "Zhang", "Mingyuan", "" ], [ "Loy", "Chen Change", "" ], [ "Yang", "Lei", "" ], [ "Liu", "Ziwei", "" ] ]
new_dataset
0.999466
2205.15360
Stavros Nousias PhD
Nikos D. Fakotakis, Stavros Nousias, Gerasimos Arvanitis, Evangelia I. Zacharaki, Konstantinos Moustakas
AI-enabled Sound Pattern Recognition on Asthma Medication Adherence: Evaluation with the RDA Benchmark Suite
null
null
10.1109/ACCESS.2023.3243547
null
cs.SD cs.CV cs.CY cs.GL eess.AS
http://creativecommons.org/licenses/by-nc-nd/4.0/
Asthma is a common, usually long-term respiratory disease with negative impact on global society and economy. Treatment involves using medical devices (inhalers) that distribute medication to the airways and its efficiency depends on the precision of the inhalation technique. There is a clinical need for objective methods to assess the inhalation technique, during clinical consultation. Integrated health monitoring systems, equipped with sensors, enable the recognition of drug actuation, embedded with sound signal detection, analysis and identification, from intelligent structures, that could provide powerful tools for reliable content management. Health monitoring systems equipped with sensors, embedded with sound signal detection, enable the recognition of drug actuation and could be used for effective audio content analysis. This paper revisits sound pattern recognition with machine learning techniques for asthma medication adherence assessment and presents the Respiratory and Drug Actuation (RDA) Suite (https://gitlab.com/vvr/monitoring-medication-adherence/rda-benchmark) for benchmarking and further research. The RDA Suite includes a set of tools for audio processing, feature extraction and classification procedures and is provided along with a dataset, consisting of respiratory and drug actuation sounds. The classification models in RDA are implemented based on conventional and advanced machine learning and deep networks' architectures. This study provides a comparative evaluation of the implemented approaches, examines potential improvements and discusses on challenges and future tendencies.
[ { "version": "v1", "created": "Mon, 30 May 2022 18:08:28 GMT" }, { "version": "v2", "created": "Wed, 1 Jun 2022 11:46:47 GMT" }, { "version": "v3", "created": "Sun, 16 Apr 2023 17:32:06 GMT" } ]
2023-04-18T00:00:00
[ [ "Fakotakis", "Nikos D.", "" ], [ "Nousias", "Stavros", "" ], [ "Arvanitis", "Gerasimos", "" ], [ "Zacharaki", "Evangelia I.", "" ], [ "Moustakas", "Konstantinos", "" ] ]
new_dataset
0.997869
2206.04218
Orian Leitersdorf
Orian Leitersdorf, Dean Leitersdorf, Jonathan Gal, Mor Dahan, Ronny Ronen, Shahar Kvatinsky
AritPIM: High-Throughput In-Memory Arithmetic
Accepted to IEEE Transactions on Emerging Topics in Computing (TETC)
null
null
null
cs.AR
http://creativecommons.org/licenses/by-nc-nd/4.0/
Digital processing-in-memory (PIM) architectures are rapidly emerging to overcome the memory-wall bottleneck by integrating logic within memory elements. Such architectures provide vast computational power within the memory itself in the form of parallel bitwise logic operations. We develop novel algorithmic techniques for PIM that, combined with new perspectives on computer arithmetic, extend this bitwise parallelism to the four fundamental arithmetic operations (addition, subtraction, multiplication, and division), for both fixed-point and floating-point numbers, and using both bit-serial and bit-parallel approaches. We propose a state-of-the-art suite of arithmetic algorithms, demonstrating the first algorithm in the literature of digital PIM for a majority of cases - including cases previously considered impossible for digital PIM, such as floating-point addition. Through a case study on memristive PIM, we compare the proposed algorithms to an NVIDIA RTX 3070 GPU and demonstrate significant throughput and energy improvements.
[ { "version": "v1", "created": "Thu, 9 Jun 2022 01:49:52 GMT" }, { "version": "v2", "created": "Sat, 15 Apr 2023 19:52:53 GMT" } ]
2023-04-18T00:00:00
[ [ "Leitersdorf", "Orian", "" ], [ "Leitersdorf", "Dean", "" ], [ "Gal", "Jonathan", "" ], [ "Dahan", "Mor", "" ], [ "Ronen", "Ronny", "" ], [ "Kvatinsky", "Shahar", "" ] ]
new_dataset
0.99921
2206.15331
Arghavan Moradi Dakhel
Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, Michel C. Desmarais, Zhen Ming (Jack) Jiang
GitHub Copilot AI pair programmer: Asset or Liability?
27 pages, 8 figures
null
null
null
cs.SE cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called Copilot, has been proposed by OpenAI and Microsoft as an industrial product. Although some studies evaluate the correctness of Copilot solutions and report its issues, more empirical evaluations are necessary to understand how developers can benefit from it effectively. In this paper, we study the capabilities of Copilot in two different programming tasks: (i) generating (and reproducing) correct and efficient solutions for fundamental algorithmic problems, and (ii) comparing Copilot's proposed solutions with those of human programmers on a set of programming tasks. For the former, we assess the performance and functionality of Copilot in solving selected fundamental problems in computer science, like sorting and implementing data structures. In the latter, a dataset of programming problems with human-provided solutions is used. The results show that Copilot is capable of providing solutions for almost all fundamental algorithmic problems, however, some solutions are buggy and non-reproducible. Moreover, Copilot has some difficulties in combining multiple methods to generate a solution. Comparing Copilot to humans, our results show that the correct ratio of humans' solutions is greater than Copilot's suggestions, while the buggy solutions generated by Copilot require less effort to be repaired.
[ { "version": "v1", "created": "Thu, 30 Jun 2022 15:00:03 GMT" }, { "version": "v2", "created": "Fri, 14 Apr 2023 20:52:00 GMT" } ]
2023-04-18T00:00:00
[ [ "Dakhel", "Arghavan Moradi", "", "Jack" ], [ "Majdinasab", "Vahid", "", "Jack" ], [ "Nikanjam", "Amin", "", "Jack" ], [ "Khomh", "Foutse", "", "Jack" ], [ "Desmarais", "Michel C.", "", "Jack" ], [ "Ming", "Zhen", "", "Jack" ], [ "Jiang", "", "" ] ]
new_dataset
0.991632
2210.03112
Aishwarya Kamath
Aishwarya Kamath, Peter Anderson, Su Wang, Jing Yu Koh, Alexander Ku, Austin Waters, Yinfei Yang, Jason Baldridge and Zarana Parekh
A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning
CVPR 2023
null
null
null
cs.LG cs.CL cs.CV cs.RO
http://creativecommons.org/licenses/by/4.0/
Recent studies in Vision-and-Language Navigation (VLN) train RL agents to execute natural-language navigation instructions in photorealistic environments, as a step towards robots that can follow human instructions. However, given the scarcity of human instruction data and limited diversity in the training environments, these agents still struggle with complex language grounding and spatial language understanding. Pretraining on large text and image-text datasets from the web has been extensively explored but the improvements are limited. We investigate large-scale augmentation with synthetic instructions. We take 500+ indoor environments captured in densely-sampled 360 degree panoramas, construct navigation trajectories through these panoramas, and generate a visually-grounded instruction for each trajectory using Marky, a high-quality multilingual navigation instruction generator. We also synthesize image observations from novel viewpoints using an image-to-image GAN. The resulting dataset of 4.2M instruction-trajectory pairs is two orders of magnitude larger than existing human-annotated datasets, and contains a wider variety of environments and viewpoints. To efficiently leverage data at this scale, we train a simple transformer agent with imitation learning. On the challenging RxR dataset, our approach outperforms all existing RL agents, improving the state-of-the-art NDTW from 71.1 to 79.1 in seen environments, and from 64.6 to 66.8 in unseen test environments. Our work points to a new path to improving instruction-following agents, emphasizing large-scale imitation learning and the development of synthetic instruction generation capabilities.
[ { "version": "v1", "created": "Thu, 6 Oct 2022 17:59:08 GMT" }, { "version": "v2", "created": "Wed, 7 Dec 2022 00:57:11 GMT" }, { "version": "v3", "created": "Mon, 17 Apr 2023 11:17:35 GMT" } ]
2023-04-18T00:00:00
[ [ "Kamath", "Aishwarya", "" ], [ "Anderson", "Peter", "" ], [ "Wang", "Su", "" ], [ "Koh", "Jing Yu", "" ], [ "Ku", "Alexander", "" ], [ "Waters", "Austin", "" ], [ "Yang", "Yinfei", "" ], [ "Baldridge", "Jason", "" ], [ "Parekh", "Zarana", "" ] ]
new_dataset
0.973385
2210.12922
Wenhui Chen
Wenhui Chen and Zhijiang Zhang and Liang Yu and Yichun Tai
BARS: A Benchmark for Airport Runway Segmentation
Applied Intelligence 2023
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Airport runway segmentation can effectively reduce the accident rate during the landing phase, which has the largest risk of flight accidents. With the rapid development of deep learning (DL), related methods achieve good performance on segmentation tasks and can be well adapted to complex scenes. However, the lack of large-scale, publicly available datasets in this field makes the development of methods based on DL difficult. Therefore, we propose a benchmark for airport runway segmentation, named BARS. Additionally, a semiautomatic annotation pipeline is designed to reduce the annotation workload. BARS has the largest dataset with the richest categories and the only instance annotation in the field. The dataset, which was collected using the X-Plane simulation platform, contains 10,256 images and 30,201 instances with three categories. We evaluate eleven representative instance segmentation methods on BARS and analyze their performance. Based on the characteristic of an airport runway with a regular shape, we propose a plug-and-play smoothing postprocessing module (SPM) and a contour point constraint loss (CPCL) function to smooth segmentation results for mask-based and contour-based methods, respectively. Furthermore, a novel evaluation metric named average smoothness (AS) is developed to measure smoothness. The experiments show that existing instance segmentation methods can achieve prediction results with good performance on BARS. SPM and CPCL can effectively enhance the AS metric while modestly improving accuracy. Our work will be available at https://github.com/c-wenhui/BARS.
[ { "version": "v1", "created": "Mon, 24 Oct 2022 02:26:05 GMT" }, { "version": "v2", "created": "Fri, 11 Nov 2022 02:19:44 GMT" }, { "version": "v3", "created": "Mon, 17 Apr 2023 16:00:19 GMT" } ]
2023-04-18T00:00:00
[ [ "Chen", "Wenhui", "" ], [ "Zhang", "Zhijiang", "" ], [ "Yu", "Liang", "" ], [ "Tai", "Yichun", "" ] ]
new_dataset
0.999812
2211.06818
Meghana Sistla
Meghana Sistla, Swarat Chaudhuri, Thomas Reps
CFLOBDDs: Context-Free-Language Ordered Binary Decision Diagrams
130 pages
null
null
null
cs.SC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper presents a new compressed representation of Boolean functions, called CFLOBDDs (for Context-Free-Language Ordered Binary Decision Diagrams). They are essentially a plug-compatible alternative to BDDs (Binary Decision Diagrams), and hence useful for representing certain classes of functions, matrices, graphs, relations, etc. in a highly compressed fashion. CFLOBDDs share many of the good properties of BDDs, but--in the best case--the CFLOBDD for a Boolean function can be exponentially smaller than any BDD for that function. Compared with the size of the decision tree for a function, a CFLOBDD--again, in the best case--can give a double-exponential reduction in size. They have the potential to permit applications to (i) execute much faster, and (ii) handle much larger problem instances than has been possible heretofore. CFLOBDDs are a new kind of decision diagram that go beyond BDDs (and their many relatives). The key insight is a new way to reuse sub-decision-diagrams: components of CFLOBDDs are structured hierarchically, so that sub-decision-diagrams can be treated as standalone ''procedures'' and reused. We applied CFLOBDDs to the problem of simulating quantum circuits, and found that for several standard problems the improvement in scalability--compared to simulation using BDDs--is quite dramatic. In particular, the number of qubits that could be handled using CFLOBDDs was larger, compared to BDDs, by a factor of 128x for GHZ; 1,024x for BV; 8,192x for DJ; and 128x for Grover's algorithm. (With a 15-minute timeout, the number of qubits that CFLOBDDs can handle are 65,536 for GHZ, 524,288 for BV; 4,194,304 for DJ; and 4,096 for Grover's Algorithm.)
[ { "version": "v1", "created": "Sun, 13 Nov 2022 04:57:29 GMT" }, { "version": "v2", "created": "Fri, 7 Apr 2023 16:26:32 GMT" }, { "version": "v3", "created": "Mon, 17 Apr 2023 00:31:05 GMT" } ]
2023-04-18T00:00:00
[ [ "Sistla", "Meghana", "" ], [ "Chaudhuri", "Swarat", "" ], [ "Reps", "Thomas", "" ] ]
new_dataset
0.999307
2211.16076
Jan Hubi\v{c}ka
Geoffrey Barker, Jan Hubi\v{c}ka, Mark Jacobs, Linda Kimrov\'a, Kendra Meyer, Doug Peterson
Finlay, Thames, Dufay, and Paget color screen process collections: Using digital registration of viewing screens to reveal original color
8 figures, 9 pages; submitted to the proceedings of Colour Photography and Film: sharing knowledge of analysis, preservation, conservation, migration of analogue and digital materials
In: 2nd Edition of the Conference "Colour Photography and Film: Sharing knowledge of analysis, preservation, and conservation of analogue and digital materials", 2022, 15--23
10.23738/RCASB.008
null
cs.GR cs.MM
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We discuss digitization, subsequent digital analysis and processing of negatives (and diapositives) made by Finlay, Thames, Dufay, Paget, and similar additive color screen processes. These early color processes (introduced in the 1890s and popular until the 1950s) used a special color screen filter and a monochromatic negative. Due to poor stability of dyes used to produce color screens many of the photographs appear faded; others exist only in the form of (monochromatic) negatives. We discuss the possibility of digitally reconstructing the original color from scans of original negatives or by virtue of infrared imaging of original transparencies (which eliminates the physically coupled color filters) and digitally recreating the original color filter pattern using a new open-source software tool. Photographs taken using additive color screen processes are some of the very earliest color images of our shared cultural heritage. They depict people, places, and events for which there are no other surviving color images. We hope that our new software tool can bring these images back to life.
[ { "version": "v1", "created": "Tue, 29 Nov 2022 10:39:28 GMT" } ]
2023-04-18T00:00:00
[ [ "Barker", "Geoffrey", "" ], [ "Hubička", "Jan", "" ], [ "Jacobs", "Mark", "" ], [ "Kimrová", "Linda", "" ], [ "Meyer", "Kendra", "" ], [ "Peterson", "Doug", "" ] ]
new_dataset
0.995827
2212.13326
Andrew Melnik
Federico Malato, Florian Leopold, Amogh Raut, Ville Hautam\"aki, Andrew Melnik
Behavioral Cloning via Search in Video PreTraining Latent Space
null
null
null
null
cs.LG cs.AI cs.CV
http://creativecommons.org/licenses/by-nc-nd/4.0/
Our aim is to build autonomous agents that can solve tasks in environments like Minecraft. To do so, we used an imitation learning-based approach. We formulate our control problem as a search problem over a dataset of experts' demonstrations, where the agent copies actions from a similar demonstration trajectory of image-action pairs. We perform a proximity search over the BASALT MineRL-dataset in the latent representation of a Video PreTraining model. The agent copies the actions from the expert trajectory as long as the distance between the state representations of the agent and the selected expert trajectory from the dataset do not diverge. Then the proximity search is repeated. Our approach can effectively recover meaningful demonstration trajectories and show human-like behavior of an agent in the Minecraft environment.
[ { "version": "v1", "created": "Tue, 27 Dec 2022 00:20:37 GMT" }, { "version": "v2", "created": "Mon, 17 Apr 2023 05:38:15 GMT" } ]
2023-04-18T00:00:00
[ [ "Malato", "Federico", "" ], [ "Leopold", "Florian", "" ], [ "Raut", "Amogh", "" ], [ "Hautamäki", "Ville", "" ], [ "Melnik", "Andrew", "" ] ]
new_dataset
0.992093
2301.06660
Yida Mu
Yida Mu, Mali Jin, Charlie Grimshaw, Carolina Scarton, Kalina Bontcheva, Xingyi Song
VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter
Accepted at ICWSM 2023
null
null
null
cs.CL
http://creativecommons.org/publicdomain/zero/1.0/
Vaccine hesitancy has been a common concern, probably since vaccines were created and, with the popularisation of social media, people started to express their concerns about vaccines online alongside those posting pro- and anti-vaccine content. Predictably, since the first mentions of a COVID-19 vaccine, social media users posted about their fears and concerns or about their support and belief into the effectiveness of these rapidly developing vaccines. Identifying and understanding the reasons behind public hesitancy towards COVID-19 vaccines is important for policy markers that need to develop actions to better inform the population with the aim of increasing vaccine take-up. In the case of COVID-19, where the fast development of the vaccines was mirrored closely by growth in anti-vaxx disinformation, automatic means of detecting citizen attitudes towards vaccination became necessary. This is an important computational social sciences task that requires data analysis in order to gain in-depth understanding of the phenomena at hand. Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination. To this end, we created a new collection of over 3,101 tweets annotated with users' attitudes towards COVID-19 vaccination (stance). Besides, we also develop a domain-specific language model (VaxxBERT) that achieves the best predictive performance (73.0 accuracy and 69.3 F1-score) as compared to a robust set of baselines. To the best of our knowledge, these are the first dataset and model that model vaccine hesitancy as a category distinct from pro- and anti-vaccine stance.
[ { "version": "v1", "created": "Tue, 17 Jan 2023 02:00:31 GMT" }, { "version": "v2", "created": "Fri, 3 Feb 2023 03:28:03 GMT" }, { "version": "v3", "created": "Mon, 10 Apr 2023 18:58:33 GMT" }, { "version": "v4", "created": "Sat, 15 Apr 2023 15:32:43 GMT" } ]
2023-04-18T00:00:00
[ [ "Mu", "Yida", "" ], [ "Jin", "Mali", "" ], [ "Grimshaw", "Charlie", "" ], [ "Scarton", "Carolina", "" ], [ "Bontcheva", "Kalina", "" ], [ "Song", "Xingyi", "" ] ]
new_dataset
0.999805
2301.10872
Abu Reyan Ahmed
Reyan Ahmed, Patrizio Angelini, Michael A. Bekos, Giuseppe Di Battista, Michael Kaufmann, Philipp Kindermann, Stephen Kobourov, Martin N\"ollenburg, Antonios Symvonis, Ana\"is Villedieu, Markus Wallinger
Splitting Vertices in 2-Layer Graph Drawings
null
null
null
null
cs.CG
http://creativecommons.org/licenses/by/4.0/
Bipartite graphs model the relationships between two disjoint sets of entities in several applications and are naturally drawn as 2-layer graph drawings. In such drawings, the two sets of entities (vertices) are placed on two parallel lines (layers), and their relationships (edges) are represented by segments connecting vertices. Methods for constructing 2-layer drawings often try to minimize the number of edge crossings. We use vertex splitting to reduce the number of crossings, by replacing selected vertices on one layer by two (or more) copies and suitably distributing their incident edges among these copies. We study several optimization problems related to vertex splitting, either minimizing the number of crossings or removing all crossings with fewest splits. While we prove that some variants are \NP-complete, we obtain polynomial-time algorithms for others. We run our algorithms on a benchmark set of bipartite graphs representing the relationships between human anatomical structures and cell types.
[ { "version": "v1", "created": "Wed, 25 Jan 2023 23:36:28 GMT" }, { "version": "v2", "created": "Sat, 15 Apr 2023 14:26:19 GMT" } ]
2023-04-18T00:00:00
[ [ "Ahmed", "Reyan", "" ], [ "Angelini", "Patrizio", "" ], [ "Bekos", "Michael A.", "" ], [ "Di Battista", "Giuseppe", "" ], [ "Kaufmann", "Michael", "" ], [ "Kindermann", "Philipp", "" ], [ "Kobourov", "Stephen", "" ], [ "Nöllenburg", "Martin", "" ], [ "Symvonis", "Antonios", "" ], [ "Villedieu", "Anaïs", "" ], [ "Wallinger", "Markus", "" ] ]
new_dataset
0.999383
2302.01857
Nan Jiang
Nan Jiang, Thibaud Lutellier, Yiling Lou, Lin Tan, Dan Goldwasser, and Xiangyu Zhang
KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair
This paper is accepted by 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
null
null
null
cs.SE cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Automated Program Repair (APR) improves software reliability by generating patches for a buggy program automatically. Recent APR techniques leverage deep learning (DL) to build models to learn to generate patches from existing patches and code corpora. While promising, DL-based APR techniques suffer from the abundant syntactically or semantically incorrect patches in the patch space. These patches often disobey the syntactic and semantic domain knowledge of source code and thus cannot be the correct patches to fix a bug. We propose a DL-based APR approach KNOD, which incorporates domain knowledge to guide patch generation in a direct and comprehensive way. KNOD has two major novelties, including (1) a novel three-stage tree decoder, which directly generates Abstract Syntax Trees of patched code according to the inherent tree structure, and (2) a novel domain-rule distillation, which leverages syntactic and semantic rules and teacher-student distributions to explicitly inject the domain knowledge into the decoding procedure during both the training and inference phases. We evaluate KNOD on three widely-used benchmarks. KNOD fixes 72 bugs on the Defects4J v1.2, 25 bugs on the QuixBugs, and 50 bugs on the additional Defects4J v2.0 benchmarks, outperforming all existing APR tools.
[ { "version": "v1", "created": "Fri, 3 Feb 2023 17:02:56 GMT" }, { "version": "v2", "created": "Mon, 27 Feb 2023 07:43:51 GMT" }, { "version": "v3", "created": "Sun, 16 Apr 2023 20:29:38 GMT" } ]
2023-04-18T00:00:00
[ [ "Jiang", "Nan", "" ], [ "Lutellier", "Thibaud", "" ], [ "Lou", "Yiling", "" ], [ "Tan", "Lin", "" ], [ "Goldwasser", "Dan", "" ], [ "Zhang", "Xiangyu", "" ] ]
new_dataset
0.998975
2302.11494
J\'er\'emy Anger
Ngoc Long Nguyen, J\'er\'emy Anger, Lara Raad, Bruno Galerne, Gabriele Facciolo
On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution
4 pages, 3 figures
null
null
null
cs.CV eess.IV
http://creativecommons.org/licenses/by/4.0/
In this work, we study the problem of single-image super-resolution (SISR) of Sentinel-2 imagery. We show that thanks to its unique sensor specification, namely the inter-band shift and alias, that deep-learning methods are able to recover fine details. By training a model using a simple $L_1$ loss, results are free of hallucinated details. For this study, we build a dataset of pairs of images Sentinel-2/PlanetScope to train and evaluate our super-resolution (SR) model.
[ { "version": "v1", "created": "Wed, 22 Feb 2023 17:08:45 GMT" }, { "version": "v2", "created": "Mon, 17 Apr 2023 16:24:05 GMT" } ]
2023-04-18T00:00:00
[ [ "Nguyen", "Ngoc Long", "" ], [ "Anger", "Jérémy", "" ], [ "Raad", "Lara", "" ], [ "Galerne", "Bruno", "" ], [ "Facciolo", "Gabriele", "" ] ]
new_dataset
0.999421
2303.00085
Shrey Pareek
Shrey Pareek, Harris Nisar and Thenkurussi Kesavadas
AR3n: A Reinforcement Learning-based Assist-As-Needed Controller for Robotic Rehabilitation
8 pages, 9 figures, IEEE RA-M
IEEE Robotics and Automation Magazine, 2023
null
null
cs.RO cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
In this paper, we present AR3n (pronounced as Aaron), an assist-as-needed (AAN) controller that utilizes reinforcement learning to supply adaptive assistance during a robot assisted handwriting rehabilitation task. Unlike previous AAN controllers, our method does not rely on patient specific controller parameters or physical models. We propose the use of a virtual patient model to generalize AR3n across multiple subjects. The system modulates robotic assistance in realtime based on a subject's tracking error, while minimizing the amount of robotic assistance. The controller is experimentally validated through a set of simulations and human subject experiments. Finally, a comparative study with a traditional rule-based controller is conducted to analyze differences in assistance mechanisms of the two controllers.
[ { "version": "v1", "created": "Tue, 28 Feb 2023 21:04:05 GMT" }, { "version": "v2", "created": "Thu, 16 Mar 2023 16:12:15 GMT" }, { "version": "v3", "created": "Thu, 6 Apr 2023 01:07:13 GMT" }, { "version": "v4", "created": "Mon, 17 Apr 2023 03:11:45 GMT" } ]
2023-04-18T00:00:00
[ [ "Pareek", "Shrey", "" ], [ "Nisar", "Harris", "" ], [ "Kesavadas", "Thenkurussi", "" ] ]
new_dataset
0.998872
2303.03599
Yili Jin
Kaiyuan Hu, Yili Jin, Haowen Yang, Junhua Liu, Fangxin Wang
FSVVD: A Dataset of Full Scene Volumetric Video
Accepted by MMSys'23 Open Dataset and Software Track. The dataset and additional tools can be accessed via https://cuhksz-inml.github.io/full_scene_volumetric_video_dataset/
null
null
null
cs.MM cs.CV eess.IV
http://creativecommons.org/licenses/by/4.0/
Recent years have witnessed a rapid development of immersive multimedia which bridges the gap between the real world and virtual space. Volumetric videos, as an emerging representative 3D video paradigm that empowers extended reality, stand out to provide unprecedented immersive and interactive video watching experience. Despite the tremendous potential, the research towards 3D volumetric video is still in its infancy, relying on sufficient and complete datasets for further exploration. However, existing related volumetric video datasets mostly only include a single object, lacking details about the scene and the interaction between them. In this paper, we focus on the current most widely used data format, point cloud, and for the first time release a full-scene volumetric video dataset that includes multiple people and their daily activities interacting with the external environments. Comprehensive dataset description and analysis are conducted, with potential usage of this dataset. The dataset and additional tools can be accessed via the following website: https://cuhksz-inml.github.io/full_scene_volumetric_video_dataset/.
[ { "version": "v1", "created": "Tue, 7 Mar 2023 02:31:08 GMT" }, { "version": "v2", "created": "Mon, 17 Apr 2023 08:50:55 GMT" } ]
2023-04-18T00:00:00
[ [ "Hu", "Kaiyuan", "" ], [ "Jin", "Yili", "" ], [ "Yang", "Haowen", "" ], [ "Liu", "Junhua", "" ], [ "Wang", "Fangxin", "" ] ]
new_dataset
0.999676
2303.10335
Su Zhang
Su Zhang, Ziyuan Zhao, Cuntai Guan
Multimodal Continuous Emotion Recognition: A Technical Report for ABAW5
6 pages. 1 figure. arXiv admin note: substantial text overlap with arXiv:2203.13031
null
null
null
cs.MM cs.SD eess.AS
http://creativecommons.org/licenses/by-nc-sa/4.0/
We used two multimodal models for continuous valence-arousal recognition using visual, audio, and linguistic information. The first model is the same as we used in ABAW2 and ABAW3, which employs the leader-follower attention. The second model has the same architecture for spatial and temporal encoding. As for the fusion block, it employs a compact and straightforward channel attention, borrowed from the End2You toolkit. Unlike our previous attempts that use Vggish feature directly as the audio feature, this time we feed the pre-trained VGG model using logmel-spectrogram and finetune it during the training. To make full use of the data and alleviate over-fitting, cross-validation is carried out. The code is available at https://github.com/sucv/ABAW3.
[ { "version": "v1", "created": "Sat, 18 Mar 2023 04:50:07 GMT" }, { "version": "v2", "created": "Fri, 14 Apr 2023 02:18:29 GMT" } ]
2023-04-18T00:00:00
[ [ "Zhang", "Su", "" ], [ "Zhao", "Ziyuan", "" ], [ "Guan", "Cuntai", "" ] ]
new_dataset
0.950255
2303.14114
Shay Snyder
Shay Snyder (1), Hunter Thompson (2), Md Abdullah-Al Kaiser (3), Gregory Schwartz (4), Akhilesh Jaiswal (3), and Maryam Parsa (1) ((1) George Mason University, (2) Georgia Institute of Technology, (3) University of Southern California, (4) Northwestern University)
Object Motion Sensitivity: A Bio-inspired Solution to the Ego-motion Problem for Event-based Cameras
This document is 9 pages and has 6 figures, tables, and algorithms
null
null
null
cs.CV cs.NE
http://creativecommons.org/licenses/by/4.0/
Neuromorphic (event-based) image sensors draw inspiration from the human-retina to create an electronic device that can process visual stimuli in a way that closely resembles its biological counterpart. These sensors process information significantly different than the traditional RGB sensors. Specifically, the sensory information generated by event-based image sensors are orders of magnitude sparser compared to that of RGB sensors. The first generation of neuromorphic image sensors, Dynamic Vision Sensor (DVS), are inspired by the computations confined to the photoreceptors and the first retinal synapse. In this work, we highlight the capability of the second generation of neuromorphic image sensors, Integrated Retinal Functionality in CMOS Image Sensors (IRIS), which aims to mimic full retinal computations from photoreceptors to output of the retina (retinal ganglion cells) for targeted feature-extraction. The feature of choice in this work is Object Motion Sensitivity (OMS) that is processed locally in the IRIS sensor. Our results show that OMS can accomplish standard computer vision tasks with similar efficiency to conventional RGB and DVS solutions but offers drastic bandwidth reduction. This cuts the wireless and computing power budgets and opens up vast opportunities in high-speed, robust, energy-efficient, and low-bandwidth real-time decision making.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 16:22:06 GMT" }, { "version": "v2", "created": "Mon, 27 Mar 2023 01:55:42 GMT" }, { "version": "v3", "created": "Fri, 14 Apr 2023 21:43:46 GMT" } ]
2023-04-18T00:00:00
[ [ "Snyder", "Shay", "" ], [ "Thompson", "Hunter", "" ], [ "Kaiser", "Md Abdullah-Al", "" ], [ "Schwartz", "Gregory", "" ], [ "Jaiswal", "Akhilesh", "" ], [ "Parsa", "Maryam", "" ] ]
new_dataset
0.998145
2303.16818
Haimei Zhao
Haimei Zhao, Qiming Zhang, Shanshan Zhao, Jing Zhang, Dacheng Tao
BEVSimDet: Simulated Multi-modal Distillation in Bird's-Eye View for Multi-view 3D Object Detection
15 pages; add link
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Multi-view camera-based 3D object detection has gained popularity due to its low cost. But accurately inferring 3D geometry solely from camera data remains challenging, which impacts model performance. One promising approach to address this issue is to distill precise 3D geometry knowledge from LiDAR data. However, transferring knowledge between different sensor modalities is hindered by the significant modality gap. In this paper, we approach this challenge from the perspective of both architecture design and knowledge distillation and present a new simulated multi-modal 3D object detection method named BEVSimDet. We first introduce a novel framework that includes a LiDAR and camera fusion-based teacher and a simulated multi-modal student, where the student simulates multi-modal features with image-only input. To facilitate effective distillation, we propose a simulated multi-modal distillation scheme that supports intra-modal, cross-modal, and multi-modal distillation simultaneously, in Bird's-eye-view (BEV) space. By combining them together, BEVSimDet can learn better feature representations for 3D object detection while enjoying cost-effective camera-only deployment. Experimental results on the challenging nuScenes benchmark demonstrate the effectiveness and superiority of BEVSimDet over recent representative methods. The source code will be released at \href{https://github.com/ViTAE-Transformer/BEVSimDet}{BEVSimDet}.
[ { "version": "v1", "created": "Wed, 29 Mar 2023 16:08:59 GMT" }, { "version": "v2", "created": "Thu, 30 Mar 2023 14:53:14 GMT" }, { "version": "v3", "created": "Sat, 15 Apr 2023 02:31:44 GMT" } ]
2023-04-18T00:00:00
[ [ "Zhao", "Haimei", "" ], [ "Zhang", "Qiming", "" ], [ "Zhao", "Shanshan", "" ], [ "Zhang", "Jing", "" ], [ "Tao", "Dacheng", "" ] ]
new_dataset
0.995565
2304.00276
Bingxi Liu
Bingxi Liu, Yujie Fu, Feng Lu, Jinqiang Cui, Yihong Wu, Hong Zhang
NPR: Nocturnal Place Recognition in Streets
10 pages, 6 figures
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Visual Place Recognition (VPR) is the task of retrieving database images similar to a query photo by comparing it to a large database of known images. In real-world applications, extreme illumination changes caused by query images taken at night pose a significant obstacle that VPR needs to overcome. However, a training set with day-night correspondence for city-scale, street-level VPR does not exist. To address this challenge, we propose a novel pipeline that divides VPR and conquers Nocturnal Place Recognition (NPR). Specifically, we first established a street-level day-night dataset, NightStreet, and used it to train an unpaired image-to-image translation model. Then we used this model to process existing large-scale VPR datasets to generate the VPR-Night datasets and demonstrated how to combine them with two popular VPR pipelines. Finally, we proposed a divide-and-conquer VPR framework and provided explanations at the theoretical, experimental, and application levels. Under our framework, previous methods can significantly improve performance on two public datasets, including the top-ranked method.
[ { "version": "v1", "created": "Sat, 1 Apr 2023 09:43:58 GMT" }, { "version": "v2", "created": "Mon, 17 Apr 2023 16:28:47 GMT" } ]
2023-04-18T00:00:00
[ [ "Liu", "Bingxi", "" ], [ "Fu", "Yujie", "" ], [ "Lu", "Feng", "" ], [ "Cui", "Jinqiang", "" ], [ "Wu", "Yihong", "" ], [ "Zhang", "Hong", "" ] ]
new_dataset
0.999083
2304.04301
Yasemin Ozkan Aydin
Sean Even, Yasemin Ozkan-Aydin
Locomotion and Obstacle Avoidance of a Worm-like Soft Robot
null
null
null
null
cs.RO
http://creativecommons.org/licenses/by/4.0/
This paper presents a soft earthworm robot that is capable of both efficient locomotion and obstacle avoidance. The robot is designed to replicate the unique locomotion mechanisms of earthworms, which enable them to move through narrow and complex environments with ease. The robot consists of multiple segments, each with its own set of actuators, that are connected through rigid plastic joints, allowing for increased adaptability and flexibility in navigating different environments. The robot utilizes proprioceptive sensing and control algorithms to detect and avoid obstacles in real-time while maintaining efficient locomotion. The robot uses a pneumatic actuation system to mimic the circumnutation behavior exhibited by plant roots in order to navigate through complex environments. The results demonstrate the capabilities of the robot for navigating through cluttered environments, making this development significant for various fields of robotics, including search and rescue, environmental monitoring, and medical procedures.
[ { "version": "v1", "created": "Sun, 9 Apr 2023 19:30:49 GMT" }, { "version": "v2", "created": "Mon, 17 Apr 2023 01:33:54 GMT" } ]
2023-04-18T00:00:00
[ [ "Even", "Sean", "" ], [ "Ozkan-Aydin", "Yasemin", "" ] ]
new_dataset
0.992913
2304.05224
Rafiah Patel
Rafiah Patel
A user co-designed digital INtervention for Child LangUage DisordEr: The INCLUDE Project Protocol
9 pages, 1 figure, 1 table. Paper has been selected following peer review for presenting at the "CHI 2023 Workshop on Child-centred AI Design: Definition, Operation and Considerations, April 23, 2023, Hamburg, Germany"
null
null
null
cs.HC
http://creativecommons.org/licenses/by-nc-nd/4.0/
Around ten percent of children may present with a disorder where language does not develop as expected. This often affects vocabulary skills, i.e., finding the words to express wants, needs and ideas, which can influence behaviours linked to wellbeing and daily functioning, such as concentration, independence, social interactions and managing emotions. Without specialist support, needs can increase in severity and continue to adulthood. The type of support, known as interventions, showing strongest evidence for improving vocabulary with some signs of improved behaviour and wellbeing are ones that use word webs. These are diagrams consisting of lines that connect sound and meaning information about a word to strengthen the child's word knowledge and use. The diagrams resemble what is commonly known as mind-maps and are widely used by Speech and Language Therapists in partnership with school educators to help children with language difficulties. In addition, interventions delivered through mobile-devices has led in some cases to increased vocabulary gains with positive influence on wellbeing and academic attainment. With advances in technology and availability of user-friendly mobile devices to capture, combine and replay multimedia, new opportunities for designing bespoke vocabulary instruction have emerged that are without timing and location constraints. This brings the potential to engage and motivate users and harbour independence through functional strategies that support each child's unique language needs. To achieve this, children with language disorder, their parents/carers, support professionals and software development team members must work jointly to create an intervention that is fit for purpose. This is the first research planned to explore the collaborative development and acceptability of a digitally enhanced vocabulary intervention for child language disorder.
[ { "version": "v1", "created": "Tue, 11 Apr 2023 13:51:45 GMT" }, { "version": "v2", "created": "Wed, 12 Apr 2023 11:11:27 GMT" }, { "version": "v3", "created": "Mon, 17 Apr 2023 15:16:41 GMT" } ]
2023-04-18T00:00:00
[ [ "Patel", "Rafiah", "" ] ]
new_dataset
0.983319
2304.06002
Hao Xu
Bo Li, YiHua Chen, Hao Xu and Fei Zhong
Fast vehicle detection algorithm based on lightweight YOLO7-tiny
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The swift and precise detection of vehicles plays a significant role in intelligent transportation systems. Current vehicle detection algorithms encounter challenges of high computational complexity, low detection rate, and limited feasibility on mobile devices. To address these issues, this paper proposes a lightweight vehicle detection algorithm based on YOLOv7-tiny (You Only Look Once version seven) called Ghost-YOLOv7. The width of model is scaled to 0.5 and the standard convolution of the backbone network is replaced with Ghost convolution to achieve a lighter network and improve the detection speed; then a self-designed Ghost bi-directional feature pyramid network (Ghost-BiFPN) is embedded into the neck network to enhance feature extraction capability of the algorithm and enriches semantic information; and a Ghost Decouoled Head (GDH) is employed for accurate prediction of vehicle location and species; finally, a coordinate attention mechanism is introduced into the output layer to suppress environmental interference. The WIoU loss function is employed to further enhance the detection accuracy. Ablation experiments results on the PASCAL VOC dataset demonstrate that Ghost-YOLOv7 outperforms the original YOLOv7-tiny model. It achieving a 29.8% reduction in computation, 37.3% reduction in the number of parameters, 35.1% reduction in model weights, 1.1% higher mean average precision (mAP), the detection speed is higher 27FPS compared with the original algorithm. Ghost-YOLOv7 was also compared on KITTI and BIT-vehicle datasets as well, and the results show that this algorithm has the overall best performance.
[ { "version": "v1", "created": "Wed, 12 Apr 2023 17:28:30 GMT" }, { "version": "v2", "created": "Thu, 13 Apr 2023 03:38:22 GMT" }, { "version": "v3", "created": "Mon, 17 Apr 2023 06:47:01 GMT" } ]
2023-04-18T00:00:00
[ [ "Li", "Bo", "" ], [ "Chen", "YiHua", "" ], [ "Xu", "Hao", "" ], [ "Zhong", "Fei", "" ] ]
new_dataset
0.993734
2304.06943
Qingsen Yan
Qingsen Yan, Weiye Chen, Song Zhang, Yu Zhu, Jinqiu Sun, Yanning Zhang
A Unified HDR Imaging Method with Pixel and Patch Level
accepted by CVPR2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Mapping Low Dynamic Range (LDR) images with different exposures to High Dynamic Range (HDR) remains nontrivial and challenging on dynamic scenes due to ghosting caused by object motion or camera jitting. With the success of Deep Neural Networks (DNNs), several DNNs-based methods have been proposed to alleviate ghosting, they cannot generate approving results when motion and saturation occur. To generate visually pleasing HDR images in various cases, we propose a hybrid HDR deghosting network, called HyHDRNet, to learn the complicated relationship between reference and non-reference images. The proposed HyHDRNet consists of a content alignment subnetwork and a Transformer-based fusion subnetwork. Specifically, to effectively avoid ghosting from the source, the content alignment subnetwork uses patch aggregation and ghost attention to integrate similar content from other non-reference images with patch level and suppress undesired components with pixel level. To achieve mutual guidance between patch-level and pixel-level, we leverage a gating module to sufficiently swap useful information both in ghosted and saturated regions. Furthermore, to obtain a high-quality HDR image, the Transformer-based fusion subnetwork uses a Residual Deformable Transformer Block (RDTB) to adaptively merge information for different exposed regions. We examined the proposed method on four widely used public HDR image deghosting datasets. Experiments demonstrate that HyHDRNet outperforms state-of-the-art methods both quantitatively and qualitatively, achieving appealing HDR visualization with unified textures and colors.
[ { "version": "v1", "created": "Fri, 14 Apr 2023 06:21:57 GMT" }, { "version": "v2", "created": "Mon, 17 Apr 2023 01:38:17 GMT" } ]
2023-04-18T00:00:00
[ [ "Yan", "Qingsen", "" ], [ "Chen", "Weiye", "" ], [ "Zhang", "Song", "" ], [ "Zhu", "Yu", "" ], [ "Sun", "Jinqiu", "" ], [ "Zhang", "Yanning", "" ] ]
new_dataset
0.964106
2304.07274
Simon van Wageningen
Simon van Wageningen, Tamara Mchedlidze, Alexandru Telea
Identifying Cluttering Edges in Near-Planar Graphs
Short paper for proceedings of EuroVis 2023 conference
null
null
null
cs.CG cs.DS
http://creativecommons.org/licenses/by/4.0/
Planar drawings of graphs tend to be favored over non-planar drawings. Testing planarity and creating a planar layout of a planar graph can be done in linear time. However, creating readable drawings of nearly planar graphs remains a challenge. We therefore seek to answer which edges of nearly planar graphs create clutter in their drawings generated by mainstream graph drawing algorithms. We present a heuristic to identify problematic edges in nearly planar graphs and adjust their weights in order to produce higher quality layouts with spring-based drawing algorithms. Our experiments show that our heuristic produces significantly higher quality drawings for augmented grid graphs, augmented triangulations, and deep triangulations.
[ { "version": "v1", "created": "Fri, 14 Apr 2023 17:36:41 GMT" }, { "version": "v2", "created": "Mon, 17 Apr 2023 14:03:43 GMT" } ]
2023-04-18T00:00:00
[ [ "van Wageningen", "Simon", "" ], [ "Mchedlidze", "Tamara", "" ], [ "Telea", "Alexandru", "" ] ]
new_dataset
0.999156
2304.07291
Emilio Mart\'inez-Pa\~neda
K. Au-Yeung, A. Quintanas-Corominas, E. Mart\'inez-Pa\~neda, W. Tan
Hygroscopic phase field fracture modelling of composite materials
null
null
null
null
cs.CE cond-mat.mtrl-sci physics.app-ph
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper investigates the effect of moisture content upon the degradation behaviour of composite materials. A coupled phase field framework considering moisture diffusion, hygroscopic expansion, and fracture behaviour is developed. This multi-physics framework is used to explore the damage evolution of composite materials, spanning the micro-, meso- and macro-scales. The micro-scale unit-cell model shows how the mismatch between the hygroscopic expansion of fibre and matrix leads to interface debonding. From the meso-scale ply-level model, we learn that the distribution of fibres has a minor influence on the material properties, while increasing moisture content facilitates interface debonding. The macro-scale laminate-level model shows that moisture induces a higher degree of damage on the longitudinal ply relative to the transverse ply. This work opens a new avenue to understand and predict environmentally-assisted degradation in composite materials.
[ { "version": "v1", "created": "Thu, 6 Apr 2023 16:22:15 GMT" } ]
2023-04-18T00:00:00
[ [ "Au-Yeung", "K.", "" ], [ "Quintanas-Corominas", "A.", "" ], [ "Martínez-Pañeda", "E.", "" ], [ "Tan", "W.", "" ] ]
new_dataset
0.986432
2304.07303
Gabriel Avelino Sampedro
Jayrald Empino, Jean Allyson Junsay, Mary Grace Verzon, Mideth Abisado, Shekinah Lor Huyo-a, Gabriel Avelino Sampedro
Smart Metro: Deep Learning Approaches to Forecasting the MRT Line 3 Ridership
null
International Journal of Computing Sciences Research (ISSN print: 2546-0552; ISSN online: 2546-115X), Vol. 7, pp. 1923-1936
10.25147/ijcsr.2017.001.1.137
null
cs.LG cs.AI cs.CY
http://creativecommons.org/licenses/by/4.0/
Since its establishment in 1999, the Metro Rail Transit Line 3 (MRT3) has served as a transportation option for numerous passengers in Metro Manila, Philippines. The Philippine government's transportation department records more than a thousand people using the MRT3 daily and forecasting the daily passenger count may be rather challenging. The MRT3's daily ridership fluctuates owing to variables such as holidays, working days, and other unexpected issues. Commuters do not know how many other commuters are on their route on a given day, which may hinder their ability to plan an efficient itinerary. Currently, the DOTr depends on spreadsheets containing historical data, which might be challenging to examine. This study presents a time series prediction of daily traffic to anticipate future attendance at a particular station on specific days.
[ { "version": "v1", "created": "Fri, 14 Apr 2023 07:39:10 GMT" } ]
2023-04-18T00:00:00
[ [ "Empino", "Jayrald", "" ], [ "Junsay", "Jean Allyson", "" ], [ "Verzon", "Mary Grace", "" ], [ "Abisado", "Mideth", "" ], [ "Huyo-a", "Shekinah Lor", "" ], [ "Sampedro", "Gabriel Avelino", "" ] ]
new_dataset
0.951236
2304.07328
Henrik Ejersbo
Henrik Ejersbo, Kenneth Lausdahl, Mirgita Frasheri, Lukas Esterle
fmiSwap: Run-time Swapping of Models for Co-simulation and Digital Twins
null
null
null
null
cs.DC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Digital Twins represent a new and disruptive technology, where digital replicas of (cyber)-physical systems operate for long periods of time alongside their (cyber)-physical counterparts, with enabled bi-directional communication between them. However promising, the development of digital twins is a non-trivial problem, since what can initially be adequate models may become obsolete in time due to wear and tear of the physical components, accumulated errors, or the evolving interaction with the environment. As such, there is a clear need for mechanisms that support swapping in new models, as well changing model structures as a whole when necessary. To address this challenge, we propose in this paper a novel artefact, fmiSwap, that is FMI compliant and allows for run-time swapping in standalone co-simulations, where different strategies can be tested easily, as well in fully deployed DT settings with hardware in the loop. We adopt a water-tank case-study consisting of a tank and its controller to demonstrate how fmiSwap works and how it can support swaps in a safe manner.
[ { "version": "v1", "created": "Mon, 20 Mar 2023 12:12:19 GMT" } ]
2023-04-18T00:00:00
[ [ "Ejersbo", "Henrik", "" ], [ "Lausdahl", "Kenneth", "" ], [ "Frasheri", "Mirgita", "" ], [ "Esterle", "Lukas", "" ] ]
new_dataset
0.999116
2304.07349
Jingrong Chen
Jingrong Chen and Yongji Wu and Shihan Lin and Yechen Xu and Xinhao Kong and Thomas Anderson and Matthew Lentz and Xiaowei Yang and Danyang Zhuo
Remote Procedure Call as a Managed System Service
NSDI 2023
null
null
null
cs.NI cs.OS
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Remote Procedure Call (RPC) is a widely used abstraction for cloud computing. The programmer specifies type information for each remote procedure, and a compiler generates stub code linked into each application to marshal and unmarshal arguments into message buffers. Increasingly, however, application and service operations teams need a high degree of visibility and control over the flow of RPCs between services, leading many installations to use sidecars or service mesh proxies for manageability and policy flexibility. These sidecars typically involve inspection and modification of RPC data that the stub compiler had just carefully assembled, adding needless overhead. Further, upgrading diverse application RPC stubs to use advanced hardware capabilities such as RDMA or DPDK is a long and involved process, and often incompatible with sidecar policy control. In this paper, we propose, implement, and evaluate a novel approach, where RPC marshalling and policy enforcement are done as a system service rather than as a library linked into each application. Applications specify type information to the RPC system as before, while the RPC service executes policy engines and arbitrates resource use, and then marshals data customized to the underlying network hardware capabilities. Our system, mRPC, also supports live upgrades so that both policy and marshalling code can be updated transparently to application code. Compared with using a sidecar, mRPC speeds up a standard microservice benchmark, DeathStarBench, by up to 2.5$\times$ while having a higher level of policy flexibility and availability.
[ { "version": "v1", "created": "Fri, 14 Apr 2023 18:47:55 GMT" } ]
2023-04-18T00:00:00
[ [ "Chen", "Jingrong", "" ], [ "Wu", "Yongji", "" ], [ "Lin", "Shihan", "" ], [ "Xu", "Yechen", "" ], [ "Kong", "Xinhao", "" ], [ "Anderson", "Thomas", "" ], [ "Lentz", "Matthew", "" ], [ "Yang", "Xiaowei", "" ], [ "Zhuo", "Danyang", "" ] ]
new_dataset
0.994775
2304.07411
Emmanouil Panaousis Prof.
Shanto Roy, Emmanouil Panaousis, Cameron Noakes, Aron Laszka, Sakshyam Panda, George Loukas
SoK: The MITRE ATT&CK Framework in Research and Practice
null
null
null
null
cs.CR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The MITRE ATT&CK framework, a comprehensive knowledge base of adversary tactics and techniques, has been widely adopted by the cybersecurity industry as well as by academic researchers. Its broad range of industry applications include threat intelligence, threat detection, and incident response, some of which go beyond what it was originally designed for. Despite its popularity, there is a lack of a systematic review of the applications and the research on ATT&CK. This systematization of work aims to fill this gap. To this end, it introduces the first taxonomic systematization of the research literature on ATT&CK, studies its degree of usefulness in different applications, and identifies important gaps and discrepancies in the literature to identify key directions for future work. The results of this work provide valuable insights for academics and practitioners alike, highlighting the need for more research on the practical implementation and evaluation of ATT&CK.
[ { "version": "v1", "created": "Fri, 14 Apr 2023 22:10:38 GMT" } ]
2023-04-18T00:00:00
[ [ "Roy", "Shanto", "" ], [ "Panaousis", "Emmanouil", "" ], [ "Noakes", "Cameron", "" ], [ "Laszka", "Aron", "" ], [ "Panda", "Sakshyam", "" ], [ "Loukas", "George", "" ] ]
new_dataset
0.99616
2304.07444
Thanh-Danh Nguyen
Thanh-Danh Nguyen, Anh-Khoa Nguyen Vu, Nhat-Duy Nguyen, Vinh-Tiep Nguyen, Thanh Duc Ngo, Thanh-Toan Do, Minh-Triet Tran, and Tam V. Nguyen
Few-shot Camouflaged Animal Detection and Segmentation
Under-review Journal
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Camouflaged object detection and segmentation is a new and challenging research topic in computer vision. There is a serious issue of lacking data of camouflaged objects such as camouflaged animals in natural scenes. In this paper, we address the problem of few-shot learning for camouflaged object detection and segmentation. To this end, we first collect a new dataset, CAMO-FS, for the benchmark. We then propose a novel method to efficiently detect and segment the camouflaged objects in the images. In particular, we introduce the instance triplet loss and the instance memory storage. The extensive experiments demonstrated that our proposed method achieves state-of-the-art performance on the newly collected dataset.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 01:33:14 GMT" } ]
2023-04-18T00:00:00
[ [ "Nguyen", "Thanh-Danh", "" ], [ "Vu", "Anh-Khoa Nguyen", "" ], [ "Nguyen", "Nhat-Duy", "" ], [ "Nguyen", "Vinh-Tiep", "" ], [ "Ngo", "Thanh Duc", "" ], [ "Do", "Thanh-Toan", "" ], [ "Tran", "Minh-Triet", "" ], [ "Nguyen", "Tam V.", "" ] ]
new_dataset
0.999656
2304.07491
Akira Terui
Ayane Ito, Takefumi Kasai, Akira Terui
Computer-assisted proofs of "Kariya's theorem" with computer algebra
null
null
null
null
cs.SC math.AC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We demonstrate computer-assisted proofs of "Kariya's theorem," a theorem in elementary geometry, with computer algebra. In the proof of geometry theorem with computer algebra, vertices of geometric figures that are subjects for the proof are expressed as variables. The variables are classified into two classes: arbitrarily given points and the points defined from the former points by constraints. We show proofs of Kariya's theorem with two formulations according to two ways for giving the arbitrary points: one is called "vertex formulation," and the other is called "incenter formulation," with two methods: one is Gr\"obner basis computation, and the other is Wu's method. Furthermore, we show computer-assisted proofs of the property that the point so-called "Kariya point" is located on the hyperbola so-called "Feuerbach's hyperbola", with two formulations and two methods.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 06:56:55 GMT" } ]
2023-04-18T00:00:00
[ [ "Ito", "Ayane", "" ], [ "Kasai", "Takefumi", "" ], [ "Terui", "Akira", "" ] ]
new_dataset
0.991107
2304.07500
Zheng Tang
Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Meenakshi S. Arya, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff, Pranamesh Chakraborty, Sanjita Prajapati, Alice Li, Shangru Li, Krishna Kunadharaju, Shenxin Jiang and Rama Chellappa
The 7th AI City Challenge
Summary of the 7th AI City Challenge Workshop in conjunction with CVPR 2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by-nc-sa/4.0/
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential. The 2023 challenge had five tracks, which drew a record-breaking number of participation requests from 508 teams across 46 countries. Track 1 was a brand new track that focused on multi-target multi-camera (MTMC) people tracking, where teams trained and evaluated using both real and highly realistic synthetic data. Track 2 centered around natural-language-based vehicle track retrieval. Track 3 required teams to classify driver actions in naturalistic driving analysis. Track 4 aimed to develop an automated checkout system for retail stores using a single view camera. Track 5, another new addition, tasked teams with detecting violations of the helmet rule for motorcyclists. Two leader boards were released for submissions based on different methods: a public leader board for the contest where external private data wasn't allowed and a general leader board for all results submitted. The participating teams' top performances established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 08:02:16 GMT" } ]
2023-04-18T00:00:00
[ [ "Naphade", "Milind", "" ], [ "Wang", "Shuo", "" ], [ "Anastasiu", "David C.", "" ], [ "Tang", "Zheng", "" ], [ "Chang", "Ming-Ching", "" ], [ "Yao", "Yue", "" ], [ "Zheng", "Liang", "" ], [ "Rahman", "Mohammed Shaiqur", "" ], [ "Arya", "Meenakshi S.", "" ], [ "Sharma", "Anuj", "" ], [ "Feng", "Qi", "" ], [ "Ablavsky", "Vitaly", "" ], [ "Sclaroff", "Stan", "" ], [ "Chakraborty", "Pranamesh", "" ], [ "Prajapati", "Sanjita", "" ], [ "Li", "Alice", "" ], [ "Li", "Shangru", "" ], [ "Kunadharaju", "Krishna", "" ], [ "Jiang", "Shenxin", "" ], [ "Chellappa", "Rama", "" ] ]
new_dataset
0.974158
2304.07511
Rongxuan Mu
Rongxuan Mu, Yuhe Nie, Kent Cao, Ruoxin You, Yinzong Wei, Xin Tong
Pilgrimage to Pureland: Art, Perception and the Wutai Mural VR Reconstruction
null
null
null
null
cs.HC
http://creativecommons.org/licenses/by-nc-sa/4.0/
Virtual reality (VR) supports audiences to engage with cultural heritage proactively. We designed an easy-to-access and guided Pilgrimage To Pureland VR reconstruction of Dunhuang Mogao Grottoes to offer the general public an accessible and engaging way to explore the Dunhuang murals. We put forward an immersive VR reconstruction paradigm that can efficiently convert complex 2D artwork into a VR environment. We reconstructed the Mt. Wutai pilgrimage mural in Cave 61, Mogao Grottoes, Dunhuang, into an immersive VR environment and created a plot-based and interactive experience that offers users a more accessible solution to visit, understand and appreciate the complex religious, historical, and artistic value of Dunhuang murals. \textcolor{black}{Our system remarkably smoothed users' approaches to those elusive cultural heritages. Appropriate adaptation of plots and 3D VR transfer consistent with the original art style could enhance the accessibility of cultural heritages.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 08:42:51 GMT" } ]
2023-04-18T00:00:00
[ [ "Mu", "Rongxuan", "" ], [ "Nie", "Yuhe", "" ], [ "Cao", "Kent", "" ], [ "You", "Ruoxin", "" ], [ "Wei", "Yinzong", "" ], [ "Tong", "Xin", "" ] ]
new_dataset
0.997768
2304.07529
Astha Agrawal
Astha Agrawal and R. K. Sharma
ACD codes over non-symmetric dualities
null
null
null
null
cs.IT math.IT
http://creativecommons.org/licenses/by/4.0/
The applications of additive codes mainly lie in quantum error correction and quantum computing. Due to their applications in quantum codes, additive codes have grown in importance. In addition to this, additive codes allow the implementation of a variety of dualities. The article begins by developing the properties of Additive Complementary Dual (ACD) codes with respect to arbitrary dualities over finite abelian groups. Further, we calculate precisely the total number of dualities over finite fields and introduce a new class of non-symmetric dualities, denoted as class A. Two conditions have been obtained, one is necessary and sufficient condition and other is a necessary condition. The necessary and sufficient condition is for an additive code to be an ACD code over arbitrary dualities, along with an algorithm for determining whether an additive code is an ACD code or not. The necessary condition is on the generator matrix of an ACD code for any duality belonging to the class A. We provide bounds for the highest possible distance of ACD codes over finite fields. Finally, we examine non-symmetric dualities over F4.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 10:36:30 GMT" } ]
2023-04-18T00:00:00
[ [ "Agrawal", "Astha", "" ], [ "Sharma", "R. K.", "" ] ]
new_dataset
0.999138
2304.07547
Jingyao Li
Jingyao Li, Pengguang Chen, Shengju Qian, Jiaya Jia
TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recent success of Contrastive Language-Image Pre-training~(CLIP) has shown great promise in pixel-level open-vocabulary learning tasks. A general paradigm utilizes CLIP's text and patch embeddings to generate semantic masks. However, existing models easily misidentify input pixels from unseen classes, thus confusing novel classes with semantically-similar ones. In our work, we disentangle the ill-posed optimization problem into two parallel processes: one performs semantic matching individually, and the other judges reliability for improving discrimination ability. Motivated by special tokens in language modeling that represents sentence-level embeddings, we design a trusty token that decouples the known and novel category prediction tendency. With almost no extra overhead, we upgrade the pixel-level generalization capacity of existing models effectively. Our TagCLIP (CLIP adapting with Trusty-guidance) boosts the IoU of unseen classes by 7.4% and 1.7% on PASCAL VOC 2012 and COCO-Stuff 164K.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 12:52:23 GMT" } ]
2023-04-18T00:00:00
[ [ "Li", "Jingyao", "" ], [ "Chen", "Pengguang", "" ], [ "Qian", "Shengju", "" ], [ "Jia", "Jiaya", "" ] ]
new_dataset
0.987091
2304.07549
Ajian Liu
Ajian Liu and Yanyan Liang
MA-ViT: Modality-Agnostic Vision Transformers for Face Anti-Spoofing
7 pages, 4 figures, conference
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The existing multi-modal face anti-spoofing (FAS) frameworks are designed based on two strategies: halfway and late fusion. However, the former requires test modalities consistent with the training input, which seriously limits its deployment scenarios. And the latter is built on multiple branches to process different modalities independently, which limits their use in applications with low memory or fast execution requirements. In this work, we present a single branch based Transformer framework, namely Modality-Agnostic Vision Transformer (MA-ViT), which aims to improve the performance of arbitrary modal attacks with the help of multi-modal data. Specifically, MA-ViT adopts the early fusion to aggregate all the available training modalities data and enables flexible testing of any given modal samples. Further, we develop the Modality-Agnostic Transformer Block (MATB) in MA-ViT, which consists of two stacked attentions named Modal-Disentangle Attention (MDA) and Cross-Modal Attention (CMA), to eliminate modality-related information for each modal sequences and supplement modality-agnostic liveness features from another modal sequences, respectively. Experiments demonstrate that the single model trained based on MA-ViT can not only flexibly evaluate different modal samples, but also outperforms existing single-modal frameworks by a large margin, and approaches the multi-modal frameworks introduced with smaller FLOPs and model parameters.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 13:03:44 GMT" } ]
2023-04-18T00:00:00
[ [ "Liu", "Ajian", "" ], [ "Liang", "Yanyan", "" ] ]
new_dataset
0.985004
2304.07554
Ella Gale
Ella Gale
Shape is (almost) all!: Persistent homology features (PHFs) are an information rich input for efficient molecular machine learning
18 pages, 15 figures
null
null
null
cs.LG cond-mat.dis-nn math.GN
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
3-D shape is important to chemistry, but how important? Machine learning works best when the inputs are simple and match the problem well. Chemistry datasets tend to be very small compared to those generally used in machine learning so we need to get the most from each datapoint. Persistent homology measures the topological shape properties of point clouds at different scales and is used in topological data analysis. Here we investigate what persistent homology captures about molecular structure and create persistent homology features (PHFs) that encode a molecule's shape whilst losing most of the symbolic detail like atom labels, valence, charge, bonds etc. We demonstrate the usefulness of PHFs on a series of chemical datasets: QM7, lipophilicity, Delaney and Tox21. PHFs work as well as the best benchmarks. PHFs are very information dense and much smaller than other encoding methods yet found, meaning ML algorithms are much more energy efficient. PHFs success despite losing a large amount of chemical detail highlights how much of chemistry can be simplified to topological shape.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 13:24:35 GMT" } ]
2023-04-18T00:00:00
[ [ "Gale", "Ella", "" ] ]
new_dataset
0.95284
2304.07555
Anuran Roy
Anuran Roy, Sridhar Raj S
SerPyTor: A distributed context-aware computational graph execution framework for durable execution
5 pages, 2 figures
null
null
null
cs.DC
http://creativecommons.org/licenses/by/4.0/
Distributed computation is always a tricky topic to deal with, especially in context of various requirements in various scenarios. A popular solution is to use Apache Spark with a setup of multiple systems forming a cluster. However, the prerequisite setup for a Spark cluster often induces an additional overhead, often limiting usage in constrained scenarios, especially in scenarios requiring context propagation. In this paper, we explore a relatively lightweight computational graph execution framework requiring little setup and fast speeds, coupled with context awareness.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 13:25:42 GMT" } ]
2023-04-18T00:00:00
[ [ "Roy", "Anuran", "" ], [ "S", "Sridhar Raj", "" ] ]
new_dataset
0.991987
2304.07572
Huixin Dong
Huixin Dong, Yirong Xie, Xianan Zhang, Wei Wang, Xinyu Zhang, Jianhua He
GPSMirror: Expanding Accurate GPS Positioning to Shadowed and Indoor Regions with Backscatter
13 pages, 26 figures, to appear in MobiCom 2023
null
10.1145/3570361.3592511
null
cs.NI eess.SP
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Despite the prevalence of GPS services, they still suffer from intermittent positioning with poor accuracy in partially shadowed regions like urban canyons, flyover shadows, and factories' indoor areas. Existing wisdom relies on hardware modifications of GPS receivers or power-hungry infrastructures requiring continuous plug-in power supply which is hard to provide in outdoor regions and some factories. This paper fills the gap with GPSMirror, the first GPS-strengthening system that works for unmodified smartphones with the assistance of newly-designed GPS backscatter tags. The key enabling techniques in GPSMirror include: (i) a careful hardware design with microwatt-level power consumption that pushes the limit of backscatter sensitivity to re-radiate extremely weak GPS signals with enough coverage approaching the regulation limit; and (ii) a novel GPS positioning algorithm achieving meter-level accuracy in shadowed regions as well as expanding locatable regions under inadequate satellites where conventional algorithms fail. We build a prototype of the GPSMirror tags and conduct comprehensive experiments to evaluate them. Our results show that a GPSMirror tag can provide coverage up to 27.7 m. GPSMirror achieves median positioning accuracy of 3.7 m indoors and 4.6 m in urban canyon environments, respectively.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 14:54:17 GMT" } ]
2023-04-18T00:00:00
[ [ "Dong", "Huixin", "" ], [ "Xie", "Yirong", "" ], [ "Zhang", "Xianan", "" ], [ "Wang", "Wei", "" ], [ "Zhang", "Xinyu", "" ], [ "He", "Jianhua", "" ] ]
new_dataset
0.998305
2304.07583
Tao Zhou
Tao Zhou, Yizhe Zhang, Yi Zhou, Ye Wu, Chen Gong
Can SAM Segment Polyps?
Technical Report
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks. As we know, polyp segmentation is a fundamental task in the medical imaging field, which plays a critical role in the diagnosis and cure of colorectal cancer. In particular, applying SAM to the polyp segmentation task is interesting. In this report, we evaluate the performance of SAM in segmenting polyps, in which SAM is under unprompted settings. We hope this report will provide insights to advance this polyp segmentation field and promote more interesting works in the future. This project is publicly at https://github.com/taozh2017/SAMPolyp.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 15:41:10 GMT" } ]
2023-04-18T00:00:00
[ [ "Zhou", "Tao", "" ], [ "Zhang", "Yizhe", "" ], [ "Zhou", "Yi", "" ], [ "Wu", "Ye", "" ], [ "Gong", "Chen", "" ] ]
new_dataset
0.99044
2304.07584
Wenxian Wu Ncu
Li Zhu, Jiahui Xiong, Wenxian Wu, Hongyu Yu
FSDNet-An efficient fire detection network for complex scenarios based on YOLOv3 and DenseNet
null
null
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by/4.0/
Fire is one of the common disasters in daily life. To achieve fast and accurate detection of fires, this paper proposes a detection network called FSDNet (Fire Smoke Detection Network), which consists of a feature extraction module, a fire classification module, and a fire detection module. Firstly, a dense connection structure is introduced in the basic feature extraction module to enhance the feature extraction ability of the backbone network and alleviate the gradient disappearance problem. Secondly, a spatial pyramid pooling structure is introduced in the fire detection module, and the Mosaic data augmentation method and CIoU loss function are used in the training process to comprehensively improve the flame feature extraction ability. Finally, in view of the shortcomings of public fire datasets, a fire dataset called MS-FS (Multi-scene Fire And Smoke) containing 11938 fire images was created through data collection, screening, and object annotation. To prove the effectiveness of the proposed method, the accuracy of the method was evaluated on two benchmark fire datasets and MS-FS. The experimental results show that the accuracy of FSDNet on the two benchmark datasets is 99.82% and 91.15%, respectively, and the average precision on MS-FS is 86.80%, which is better than the mainstream fire detection methods.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 15:46:08 GMT" } ]
2023-04-18T00:00:00
[ [ "Zhu", "Li", "" ], [ "Xiong", "Jiahui", "" ], [ "Wu", "Wenxian", "" ], [ "Yu", "Hongyu", "" ] ]
new_dataset
0.991748
2304.07596
Alisha Sharma
Alisha Sharma, Jason Geder, Joseph Lingevitch, Theodore Martin, Daniel Lofaro, Donald Sofge
Acoustic Beamforming for Object-relative Distance Estimation and Control in Unmanned Air Vehicles using Propulsion System Noise
7 pages, 12 figures
null
null
null
cs.RO cs.SD eess.AS
http://creativecommons.org/licenses/by/4.0/
Unmanned air vehicles often produce significant noise from their propulsion systems. Using this broadband signal as "acoustic illumination" for an auxiliary sensing system could make vehicles more robust at a minimal cost. We present an acoustic beamforming-based algorithm that estimates object-relative distance with a small two-microphone array using the generated propulsion system noise of a vehicle. We demonstrate this approach in several closed-loop distance feedback control tests with a mounted quad-rotor vehicle in a noisy environment and show accurate object-relative distance estimates more than 2x further than the baseline channel-based approach. We conclude that this approach is robust to several practical vehicle and noise situations and shows promise for use in more complex operating environments.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 17:03:21 GMT" } ]
2023-04-18T00:00:00
[ [ "Sharma", "Alisha", "" ], [ "Geder", "Jason", "" ], [ "Lingevitch", "Joseph", "" ], [ "Martin", "Theodore", "" ], [ "Lofaro", "Daniel", "" ], [ "Sofge", "Donald", "" ] ]
new_dataset
0.983809
2304.07609
Chul Gwon
Chul Gwon and Steven C. Howell
ODSmoothGrad: Generating Saliency Maps for Object Detectors
To be published in XAI4CV Workshop Proceedings at CVPR 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Techniques for generating saliency maps continue to be used for explainability of deep learning models, with efforts primarily applied to the image classification task. Such techniques, however, can also be applied to object detectors, not only with the classification scores, but also for the bounding box parameters, which are regressed values for which the relevant pixels contributing to these parameters can be identified. In this paper, we present ODSmoothGrad, a tool for generating saliency maps for the classification and the bounding box parameters in object detectors. Given the noisiness of saliency maps, we also apply the SmoothGrad algorithm to visually enhance the pixels of interest. We demonstrate these capabilities on one-stage and two-stage object detectors, with comparisons using classifier-based techniques.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 18:21:56 GMT" } ]
2023-04-18T00:00:00
[ [ "Gwon", "Chul", "" ], [ "Howell", "Steven C.", "" ] ]
new_dataset
0.997448
2304.07637
Abhishek Bamotra
Abhishek Bamotra, Phani Krishna Uppala
TransDocs: Optical Character Recognition with word to word translation
null
null
null
null
cs.CV cs.CL cs.LG
http://creativecommons.org/licenses/by/4.0/
While OCR has been used in various applications, its output is not always accurate, leading to misfit words. This research work focuses on improving the optical character recognition (OCR) with ML techniques with integration of OCR with long short-term memory (LSTM) based sequence to sequence deep learning models to perform document translation. This work is based on ANKI dataset for English to Spanish translation. In this work, I have shown comparative study for pre-trained OCR while using deep learning model using LSTM-based seq2seq architecture with attention for machine translation. End-to-end performance of the model has been expressed in BLEU-4 score. This research paper is aimed at researchers and practitioners interested in OCR and its applications in document translation.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 21:40:14 GMT" } ]
2023-04-18T00:00:00
[ [ "Bamotra", "Abhishek", "" ], [ "Uppala", "Phani Krishna", "" ] ]
new_dataset
0.999277
2304.07646
Jonas Skackauskas Mr
Jonas Skackauskas, Tatiana Kalganova
Herder Ants: Ant Colony Optimization with Aphids for Discrete Event-Triggered Dynamic Optimization Problems
null
null
null
null
cs.NE
http://creativecommons.org/licenses/by-nc-sa/4.0/
Currently available dynamic optimization strategies for Ant Colony Optimization (ACO) algorithm offer a trade-off of slower algorithm convergence or significant penalty to solution quality after each dynamic change occurs. This paper proposes a discrete dynamic optimization strategy called Ant Colony Optimization (ACO) with Aphids, modelled after a real-world symbiotic relationship between ants and aphids. ACO with Aphids strategy is designed to improve solution quality of discrete domain Dynamic Optimization Problems (DOPs) with event-triggered discrete dynamism. The proposed strategy aims to improve the inter-state convergence rate throughout the entire dynamic optimization. It does so by minimizing the fitness penalty and maximizing the convergence speed that occurs after the dynamic change. This strategy is tested against Full-Restart and Pheromone-Sharing strategies implemented on the same ACO core algorithm solving Dynamic Multidimensional Knapsack Problem (DMKP) benchmarks. ACO with Aphids has demonstrated superior performance over the Pheromone-Sharing strategy in every test on average gap reduced by 29.2%. Also, ACO with Aphids has outperformed the Full-Restart strategy for large datasets groups, and the overall average gap is reduced by 52.5%.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 22:21:41 GMT" } ]
2023-04-18T00:00:00
[ [ "Skackauskas", "Jonas", "" ], [ "Kalganova", "Tatiana", "" ] ]
new_dataset
0.997982
2304.07651
Anthony Goeckner
Eugene M. Taranta II, Adam Seiwert, Anthony Goeckner, Khiem Nguyen, Erin Cherry
From Warfighting Needs to Robot Actuation: A Complete Rapid Integration Swarming Solution
58 pages, 29 figures. Published in Field Robotics
Field Robotics, 3, 460-515 (2023)
10.55417/fr.2023015
null
cs.RO cs.HC cs.MA
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Swarm robotics systems have the potential to transform warfighting in urban environments, but until now have not seen large-scale field testing. We present the Rapid Integration Swarming Ecosystem (RISE), a platform for future multi-agent research and deployment. RISE enables rapid integration of third-party swarm tactics and behaviors, which was demonstrated using both physical and simulated swarms. Our physical testbed is composed of more than 250 networked heterogeneous agents and has been extensively tested in mock warfare scenarios at five urban combat training ranges. RISE implements live, virtual, constructive simulation capabilities to allow the use of both virtual and physical agents simultaneously, while our "fluid fidelity" simulation enables adaptive scaling between low and high fidelity simulation levels based on dynamic runtime requirements. Both virtual and physical agents are controlled with a unified gesture-based interface that enables a greater than 150:1 agent-to-operator ratio. Through this interface, we enable efficient swarm-based mission execution. RISE translates mission needs to robot actuation with rapid tactic integration, a reliable testbed, and efficient operation.
[ { "version": "v1", "created": "Sat, 15 Apr 2023 22:40:00 GMT" } ]
2023-04-18T00:00:00
[ [ "Taranta", "Eugene M.", "II" ], [ "Seiwert", "Adam", "" ], [ "Goeckner", "Anthony", "" ], [ "Nguyen", "Khiem", "" ], [ "Cherry", "Erin", "" ] ]
new_dataset
0.957947
2304.07687
Sam Van Der Poel
Sam van der Poel, Dakotah Lambert, Kalina Kostyszyn, Tiantian Gao, Rahul Verma, Derek Andersen, Joanne Chau, Emily Peterson, Cody St. Clair, Paul Fodor, Chihiro Shibata, Jeffrey Heinz
MLRegTest: A Benchmark for the Machine Learning of Regular Languages
38 pages, MLRegTest benchmark available at the OSF at https://osf.io/ksdnm , associated code at https://github.com/heinz-jeffrey/subregular-learning
null
null
null
cs.LG cs.CL cs.FL
http://creativecommons.org/licenses/by/4.0/
Evaluating machine learning (ML) systems on their ability to learn known classifiers allows fine-grained examination of the patterns they can learn, which builds confidence when they are applied to the learning of unknown classifiers. This article presents a new benchmark for ML systems on sequence classification called MLRegTest, which contains training, development, and test sets from 1,800 regular languages. Different kinds of formal languages represent different kinds of long-distance dependencies, and correctly identifying long-distance dependencies in sequences is a known challenge for ML systems to generalize successfully. MLRegTest organizes its languages according to their logical complexity (monadic second order, first order, propositional, or monomial expressions) and the kind of logical literals (string, tier-string, subsequence, or combinations thereof). The logical complexity and choice of literal provides a systematic way to understand different kinds of long-distance dependencies in regular languages, and therefore to understand the capacities of different ML systems to learn such long-distance dependencies. Finally, the performance of different neural networks (simple RNN, LSTM, GRU, transformer) on MLRegTest is examined. The main conclusion is that their performance depends significantly on the kind of test set, the class of language, and the neural network architecture.
[ { "version": "v1", "created": "Sun, 16 Apr 2023 03:49:50 GMT" } ]
2023-04-18T00:00:00
[ [ "van der Poel", "Sam", "" ], [ "Lambert", "Dakotah", "" ], [ "Kostyszyn", "Kalina", "" ], [ "Gao", "Tiantian", "" ], [ "Verma", "Rahul", "" ], [ "Andersen", "Derek", "" ], [ "Chau", "Joanne", "" ], [ "Peterson", "Emily", "" ], [ "Clair", "Cody St.", "" ], [ "Fodor", "Paul", "" ], [ "Shibata", "Chihiro", "" ], [ "Heinz", "Jeffrey", "" ] ]
new_dataset
0.999708
2304.07743
Simon Korman
Deborah Levy, Amit Peleg, Naama Pearl, Dan Rosenbaum, Derya Akkaynak, Simon Korman, Tali Treibitz
SeaThru-NeRF: Neural Radiance Fields in Scattering Media
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Research on neural radiance fields (NeRFs) for novel view generation is exploding with new models and extensions. However, a question that remains unanswered is what happens in underwater or foggy scenes where the medium strongly influences the appearance of objects. Thus far, NeRF and its variants have ignored these cases. However, since the NeRF framework is based on volumetric rendering, it has inherent capability to account for the medium's effects, once modeled appropriately. We develop a new rendering model for NeRFs in scattering media, which is based on the SeaThru image formation model, and suggest a suitable architecture for learning both scene information and medium parameters. We demonstrate the strength of our method using simulated and real-world scenes, correctly rendering novel photorealistic views underwater. Even more excitingly, we can render clear views of these scenes, removing the medium between the camera and the scene and reconstructing the appearance and depth of far objects, which are severely occluded by the medium. Our code and unique datasets are available on the project's website.
[ { "version": "v1", "created": "Sun, 16 Apr 2023 10:17:26 GMT" } ]
2023-04-18T00:00:00
[ [ "Levy", "Deborah", "" ], [ "Peleg", "Amit", "" ], [ "Pearl", "Naama", "" ], [ "Rosenbaum", "Dan", "" ], [ "Akkaynak", "Derya", "" ], [ "Korman", "Simon", "" ], [ "Treibitz", "Tali", "" ] ]
new_dataset
0.995223
2304.07750
Valerio Marsocci
Valerio Marsocci, Nicolas Gonthier, Anatol Garioud, Simone Scardapane, Cl\'ement Mallet
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (RS) semantic segmentation is costly and time-consuming. Unsupervised Domain Adaption (UDA) could tackle these issues by adapting a model trained on a source domain, where labels are available, to a target domain, without annotations. UDA, while gaining importance in computer vision, is still under-investigated in RS. Thus, we propose a new lightweight model, GeoMultiTaskNet, based on two contributions: a GeoMultiTask module (GeoMT), which utilizes geographical coordinates to align the source and target domains, and a Dynamic Class Sampling (DCS) strategy, to adapt the semantic segmentation loss to the frequency of classes. This approach is the first to use geographical metadata for UDA in semantic segmentation. It reaches state-of-the-art performances (47,22% mIoU), reducing at the same time the number of parameters (33M), on a subset of the FLAIR dataset, a recently proposed dataset properly shaped for RS UDA, used for the first time ever for research scopes here.
[ { "version": "v1", "created": "Sun, 16 Apr 2023 11:00:43 GMT" } ]
2023-04-18T00:00:00
[ [ "Marsocci", "Valerio", "" ], [ "Gonthier", "Nicolas", "" ], [ "Garioud", "Anatol", "" ], [ "Scardapane", "Simone", "" ], [ "Mallet", "Clément", "" ] ]
new_dataset
0.999303
2304.07822
JiaHao Xie
JiaHao Xie, Ye Luo, Jianwei Lu
A Random-patch based Defense Strategy Against Physical Attacks for Face Recognition Systems
null
null
null
null
cs.CV cs.AI
http://creativecommons.org/publicdomain/zero/1.0/
The physical attack has been regarded as a kind of threat against real-world computer vision systems. Still, many existing defense methods are only useful for small perturbations attacks and can't detect physical attacks effectively. In this paper, we propose a random-patch based defense strategy to robustly detect physical attacks for Face Recognition System (FRS). Different from mainstream defense methods which focus on building complex deep neural networks (DNN) to achieve high recognition rate on attacks, we introduce a patch based defense strategy to a standard DNN aiming to obtain robust detection models. Extensive experimental results on the employed datasets show the superiority of the proposed defense method on detecting white-box attacks and adaptive attacks which attack both FRS and the defense method. Additionally, due to the simpleness yet robustness of our method, it can be easily applied to the real world face recognition system and extended to other defense methods to boost the detection performance.
[ { "version": "v1", "created": "Sun, 16 Apr 2023 16:11:56 GMT" } ]
2023-04-18T00:00:00
[ [ "Xie", "JiaHao", "" ], [ "Luo", "Ye", "" ], [ "Lu", "Jianwei", "" ] ]
new_dataset
0.997806
2304.07862
Xinyi Li
Xinyi Li, Yongfeng Zhang, Edward C. Malthouse
PBNR: Prompt-based News Recommender System
null
null
null
null
cs.IR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Online news platforms often use personalized news recommendation methods to help users discover articles that align with their interests. These methods typically predict a matching score between a user and a candidate article to reflect the user's preference for the article. Some previous works have used language model techniques, such as the attention mechanism, to capture users' interests based on their past behaviors, and to understand the content of articles. However, these existing model architectures require adjustments if additional information is taken into account. Pre-trained large language models, which can better capture word relationships and comprehend contexts, have seen a significant development in recent years, and these pre-trained models have the advantages of transfer learning and reducing the training time for downstream tasks. Meanwhile, prompt learning is a newly developed technique that leverages pre-trained language models by building task-specific guidance for output generations. To leverage textual information in news articles, this paper introduces the pre-trained large language model and prompt-learning to the community of news recommendation. The proposed model "prompt-based news recommendation" (PBNR) treats the personalized news recommendation as a text-to-text language task and designs personalized prompts to adapt to the pre-trained language model -- text-to-text transfer transformer (T5). Experimental studies using the Microsoft News dataset show that PBNR is capable of making accurate recommendations by taking into account various lengths of past behaviors of different users. PBNR can also easily adapt to new information without changing the model architecture and the training objective. Additionally, PBNR can make recommendations based on users' specific requirements, allowing human-computer interaction in the news recommendation field.
[ { "version": "v1", "created": "Sun, 16 Apr 2023 19:03:01 GMT" } ]
2023-04-18T00:00:00
[ [ "Li", "Xinyi", "" ], [ "Zhang", "Yongfeng", "" ], [ "Malthouse", "Edward C.", "" ] ]
new_dataset
0.984458
2304.07883
Lia Morra
Luca Piano, Filippo Gabriele Prattic\`o, Alessandro Sebastian Russo, Lorenzo Lanari, Lia Morra, Fabrizio Lamberti
Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification
null
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023, pp. 4881-4891
10.1109/WACV56688.2023.00486
null
cs.CV cs.GR cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Instance-level object re-identification is a fundamental computer vision task, with applications from image retrieval to intelligent monitoring and fraud detection. In this work, we propose the novel task of damaged object re-identification, which aims at distinguishing changes in visual appearance due to deformations or missing parts from subtle intra-class variations. To explore this task, we leverage the power of computer-generated imagery to create, in a semi-automatic fashion, high-quality synthetic images of the same bike before and after a damage occurs. The resulting dataset, Bent & Broken Bicycles (BBBicycles), contains 39,200 images and 2,800 unique bike instances spanning 20 different bike models. As a baseline for this task, we propose TransReI3D, a multi-task, transformer-based deep network unifying damage detection (framed as a multi-label classification task) with object re-identification. The BBBicycles dataset is available at https://huggingface.co/datasets/GrainsPolito/BBBicycles
[ { "version": "v1", "created": "Sun, 16 Apr 2023 20:23:58 GMT" } ]
2023-04-18T00:00:00
[ [ "Piano", "Luca", "" ], [ "Pratticò", "Filippo Gabriele", "" ], [ "Russo", "Alessandro Sebastian", "" ], [ "Lanari", "Lorenzo", "" ], [ "Morra", "Lia", "" ], [ "Lamberti", "Fabrizio", "" ] ]
new_dataset
0.97741
2304.07909
Muriel Franco Dr.
Muriel Figueredo Franco, Christian Omlin, Oliver Kamer, Eder John Scheid, Burkhard Stiller
SECAdvisor: a Tool for Cybersecurity Planning using Economic Models
12 pages, 7 figures, 2 tables, 9 equations
null
null
null
cs.CR cs.CY
http://creativecommons.org/licenses/by/4.0/
Cybersecurity planning is challenging for digitized companies that want adequate protection without overspending money. Currently, the lack of investments and perverse economic incentives are the root cause of cyberattacks, which results in several economic impacts on companies worldwide. Therefore, cybersecurity planning has to consider technical and economic dimensions to help companies achieve a better cybersecurity strategy. This article introduces SECAdvisor, a tool to support cybersecurity planning using economic models. SECAdvisor allows to (a) understand the risks and valuation of different businesses' information, (b) calculate the optimal investment in cybersecurity for a company, (c) receive a recommendation of protections based on the budget available and demands, and (d) compare protection solutions in terms of cost-efficiency. Furthermore, evaluations on usability and real-world training activities performed using SECAdvisor are discussed.
[ { "version": "v1", "created": "Sun, 16 Apr 2023 22:31:50 GMT" } ]
2023-04-18T00:00:00
[ [ "Franco", "Muriel Figueredo", "" ], [ "Omlin", "Christian", "" ], [ "Kamer", "Oliver", "" ], [ "Scheid", "Eder John", "" ], [ "Stiller", "Burkhard", "" ] ]
new_dataset
0.992492
2304.07911
Zepeng Huai
Zepeng Huai and Yuji Yang and Mengdi Zhang and Zhongyi Zhang and Yichun Li and Wei Wu
M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation
null
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023)
10.1145/3539618.3591720
null
cs.IR
http://creativecommons.org/licenses/by/4.0/
Cross-domain recommendation (CDR) is an effective way to alleviate the data sparsity problem. Content-based CDR is one of the most promising branches since most kinds of products can be described by a piece of text, especially when cold-start users or items have few interactions. However, two vital issues are still under-explored: (1) From the content modeling perspective, sufficient long-text descriptions are usually scarce in a real recommender system, more often the light-weight textual features, such as a few keywords or tags, are more accessible, which is improperly modeled by existing methods. (2) From the CDR perspective, not all inter-domain interests are helpful to infer intra-domain interests. Caused by domain-specific features, there are part of signals benefiting for recommendation in the source domain but harmful for that in the target domain. Therefore, how to distill useful interests is crucial. To tackle the above two problems, we propose a metapath and multi-interest aggregated graph neural network (M2GNN). Specifically, to model the tag-based contents, we construct a heterogeneous information network to hold the semantic relatedness between users, items, and tags in all domains. The metapath schema is predefined according to domain-specific knowledge, with one metapath for one domain. User representations are learned by GNN with a hierarchical aggregation framework, where the intra-metapath aggregation firstly filters out trivial tags and the inter-metapath aggregation further filters out useless interests. Offline experiments and online A/B tests demonstrate that M2GNN achieves significant improvements over the state-of-the-art methods and current industrial recommender system in Dianping, respectively. Further analysis shows that M2GNN offers an interpretable recommendation.
[ { "version": "v1", "created": "Sun, 16 Apr 2023 22:47:53 GMT" } ]
2023-04-18T00:00:00
[ [ "Huai", "Zepeng", "" ], [ "Yang", "Yuji", "" ], [ "Zhang", "Mengdi", "" ], [ "Zhang", "Zhongyi", "" ], [ "Li", "Yichun", "" ], [ "Wu", "Wei", "" ] ]
new_dataset
0.999241
2304.07940
Hyunwoo Choi
Hyunwoo Choi, Suryeon Kim, Seungwon Shin
AVX Timing Side-Channel Attacks against Address Space Layout Randomization
Accepted to Design Automation Conference (DAC) 2023
The 60th Annual Design Automation Conference (DAC), 2023
null
null
cs.CR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Modern x86 processors support an AVX instruction set to boost performance. However, this extension may cause security issues. We discovered that there are vulnerable properties in implementing masked load/store instructions. Based on this, we present a novel AVX timing side-channel attack that can defeat address space layout randomization. We demonstrate the significance of our attack by showing User and Kernel ASLR breaks on the recent Intel and AMD processors in various environments, including cloud computing systems, an SGX enclave (a fine-grained ASLR break), and major operating systems. We further demonstrate that our attack can be used to infer user behavior, such as Bluetooth events and mouse movements. We highlight that stronger isolation or more fine-grained randomization should be adopted to successfully mitigate our presented attacks.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 01:38:18 GMT" } ]
2023-04-18T00:00:00
[ [ "Choi", "Hyunwoo", "" ], [ "Kim", "Suryeon", "" ], [ "Shin", "Seungwon", "" ] ]
new_dataset
0.998573
2304.07983
Sofiane Tanji
Sofiane Tanji and Andrea Della Vecchia and Fran\c{c}ois Glineur and Silvia Villa
Snacks: a fast large-scale kernel SVM solver
6 pages
null
null
null
cs.LG math.OC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Kernel methods provide a powerful framework for non parametric learning. They are based on kernel functions and allow learning in a rich functional space while applying linear statistical learning tools, such as Ridge Regression or Support Vector Machines. However, standard kernel methods suffer from a quadratic time and memory complexity in the number of data points and thus have limited applications in large-scale learning. In this paper, we propose Snacks, a new large-scale solver for Kernel Support Vector Machines. Specifically, Snacks relies on a Nystr\"om approximation of the kernel matrix and an accelerated variant of the stochastic subgradient method. We demonstrate formally through a detailed empirical evaluation, that it competes with other SVM solvers on a variety of benchmark datasets.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 04:19:20 GMT" } ]
2023-04-18T00:00:00
[ [ "Tanji", "Sofiane", "" ], [ "Della Vecchia", "Andrea", "" ], [ "Glineur", "François", "" ], [ "Villa", "Silvia", "" ] ]
new_dataset
0.998539
2304.07984
Nan Li
Nan Li, Yutong Li, Ilya Kolmanovsky
A Unified Safety Protection and Extension Governor
8 pages, 4 figures
null
null
null
cs.SY math.OC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we propose a supervisory control scheme that unifies the abilities of safety protection and safety extension. It produces a control that is able to keep the system safe indefinitely when such a control exists. When such a control does not exist due to abnormal system states, it optimizes the control to maximize the time before any safety violation, which translates into more time to seek recovery and/or mitigate any harm. We describe the scheme and develop an approach that integrates the two capabilities into a single constrained optimization problem with only continuous variables. For linear systems with convex constraints, the problem reduces to a convex quadratic program and is easy to solve. We illustrate the proposed safety supervisor with an automotive example.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 04:20:04 GMT" } ]
2023-04-18T00:00:00
[ [ "Li", "Nan", "" ], [ "Li", "Yutong", "" ], [ "Kolmanovsky", "Ilya", "" ] ]
new_dataset
0.993686
2304.08077
Doratossadat Dastgheib
Doratossadat Dastgheib, Hadi Farahani
Doxastic Lukasiewicz Logic with Public Announcement
null
null
null
null
cs.LO math.LO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we propose a doxastic extension $BL^+$ of Lukasiewicz logic which is sound and complete relative to the introduced corresponding semantics. Also, we equip our doxastic Lukasiewicz logic $BL^+$ with public announcement and propose the logic $DL$. As an application, we model a fuzzy version of muddy children puzzle with public announcement using $DL$. Finally, we define a translation between $DL$ and $BL^+$, and prove the soundness and completeness theorems for D L
[ { "version": "v1", "created": "Mon, 17 Apr 2023 08:41:48 GMT" } ]
2023-04-18T00:00:00
[ [ "Dastgheib", "Doratossadat", "" ], [ "Farahani", "Hadi", "" ] ]
new_dataset
0.999443
2304.08085
Xiao Wang
Xiao Wang, Weikang Zhou, Can Zu, Han Xia, Tianze Chen, Yuansen Zhang, Rui Zheng, Junjie Ye, Qi Zhang, Tao Gui, Jihua Kang, Jingsheng Yang, Siyuan Li, Chunsai Du
InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction
null
null
null
null
cs.CL cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Large language models have unlocked strong multi-task capabilities from reading instructive prompts. However, recent studies have shown that existing large models still have difficulty with information extraction tasks. For example, gpt-3.5-turbo achieved an F1 score of 18.22 on the Ontonotes dataset, which is significantly lower than the state-of-the-art performance. In this paper, we propose InstructUIE, a unified information extraction framework based on instruction tuning, which can uniformly model various information extraction tasks and capture the inter-task dependency. To validate the proposed method, we introduce IE INSTRUCTIONS, a benchmark of 32 diverse information extraction datasets in a unified text-to-text format with expert-written instructions. Experimental results demonstrate that our method achieves comparable performance to Bert in supervised settings and significantly outperforms the state-of-the-art and gpt3.5 in zero-shot settings.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 09:00:50 GMT" } ]
2023-04-18T00:00:00
[ [ "Wang", "Xiao", "" ], [ "Zhou", "Weikang", "" ], [ "Zu", "Can", "" ], [ "Xia", "Han", "" ], [ "Chen", "Tianze", "" ], [ "Zhang", "Yuansen", "" ], [ "Zheng", "Rui", "" ], [ "Ye", "Junjie", "" ], [ "Zhang", "Qi", "" ], [ "Gui", "Tao", "" ], [ "Kang", "Jihua", "" ], [ "Yang", "Jingsheng", "" ], [ "Li", "Siyuan", "" ], [ "Du", "Chunsai", "" ] ]
new_dataset
0.988988
2304.08095
Maxime Guillaud
Paul Ferrand, Maxime Guillaud, Christoph Studer, Olav Tirkkonen
Wireless Channel Charting: Theory, Practice, and Applications
Accepted for publication in the IEEE Communication Magazine
null
null
null
cs.IT cs.AI math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Channel charting is a recently proposed framework that applies dimensionality reduction to channel state information (CSI) in wireless systems with the goal of associating a pseudo-position to each mobile user in a low-dimensional space: the channel chart. Channel charting summarizes the entire CSI dataset in a self-supervised manner, which opens up a range of applications that are tied to user location. In this article, we introduce the theoretical underpinnings of channel charting and present an overview of recent algorithmic developments and experimental results obtained in the field. We furthermore discuss concrete application examples of channel charting to network- and user-related applications, and we provide a perspective on future developments and challenges as well as the role of channel charting in next-generation wireless networks.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 09:10:46 GMT" } ]
2023-04-18T00:00:00
[ [ "Ferrand", "Paul", "" ], [ "Guillaud", "Maxime", "" ], [ "Studer", "Christoph", "" ], [ "Tirkkonen", "Olav", "" ] ]
new_dataset
0.999369