scholarly journals Extrinsic Camera Calibration with Line-Laser Projection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1091
Author(s):  
Izaak Van Crombrugge ◽  
Rudi Penne ◽  
Steve Vanlanduit
Keyword(s):  
Camera Calibration ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Bundle Adjustment ◽  
Field Of View ◽  
Extrinsic Calibration ◽  
Practical Procedure ◽  
Partial Overlap

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.

scholarly journals Legal Judgment Prediction Based on Multiclass Information Fusion

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kongfan Zhu ◽  
Rundong Guo ◽  
Weifeng Hu ◽  
Zeqiang Li ◽  
Yujun Li
Keyword(s):  
Information Fusion ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
External Information ◽  
Criminal Cases ◽  
Law System ◽  
Large Scale Dataset ◽  
Assistant Systems ◽  
Civil Law System

Legal judgment prediction (LJP), as an effective and critical application in legal assistant systems, aims to determine the judgment results according to the information based on the fact determination. In real-world scenarios, to deal with the criminal cases, judges not only take advantage of the fact description, but also consider the external information, such as the basic information of defendant and the court view. However, most existing works take the fact description as the sole input for LJP and ignore the external information. We propose a Transformer-Hierarchical-Attention-Multi-Extra (THME) Network to make full use of the information based on the fact determination. We conduct experiments on a real-world large-scale dataset of criminal cases in the civil law system. Experimental results show that our method outperforms state-of-the-art LJP methods on all judgment prediction tasks.

scholarly journals Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 19-26 ◽  
Author(s):  
Chong Chen ◽  
Min Zhang ◽  
Yongfeng Zhang ◽  
Weizhi Ma ◽  
Yiqun Liu ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Heterogeneous Data ◽  
Model Parameters ◽  
Online Systems ◽  
Practical Applications ◽  
Real World Datasets ◽  
Primary Type

Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks to improve the expressiveness of models, while typically apply the Negative Sampling (NS) strategy for efficient learning. Despite effectiveness, two important issues have not been well-considered in existing methods: 1) NS suffers from dramatic fluctuation, making sampling-based methods difficult to achieve the optimal ranking performance in practical applications; 2) although heterogeneous feedback (e.g., view, click, and purchase) is widespread in many online systems, most existing methods leverage only one primary type of user feedback such as purchase. In this work, we propose a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation. It can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data (including all unlabeled data) with a rather low time complexity. Extensive experiments on three real-world datasets show that EHCF significantly outperforms state-of-the-art recommendation methods in both traditional (single-behavior) and heterogeneous scenarios. Moreover, EHCF shows significant improvements in training efficiency, making it more applicable to real-world large-scale systems. Our implementation has been released 1 to facilitate further developments on efficient whole-data based neural methods.

scholarly journals Scene text removal via cascaded text stroke detection and erasing

2021 ◽  
Vol 8 (2) ◽  
pp. 273-287
Author(s):  
Xuewei Bian ◽  
Chaoqun Wang ◽  
Weize Quan ◽  
Juntao Ye ◽  
Xiaopeng Zhang ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Processing Unit ◽  
Final Model ◽  
Scene Text ◽  
End To End

AbstractRecent learning-based approaches show promising performance improvement for the scene text removal task but usually leave several remnants of text and provide visually unpleasant results. In this work, a novel end-to-end framework is proposed based on accurate text stroke detection. Specifically, the text removal problem is decoupled into text stroke detection and stroke removal; we design separate networks to solve these two subproblems, the latter being a generative network. These two networks are combined as a processing unit, which is cascaded to obtain our final model for text removal. Experimental results demonstrate that the proposed method substantially outperforms the state-of-the-art for locating and erasing scene text. A new large-scale real-world dataset with 12,120 images has been constructed and is being made available to facilitate research, as current publicly available datasets are mainly synthetic so cannot properly measure the performance of different methods.

scholarly journals MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning

2021 ◽  
Author(s):  
Chen Liu ◽  
Bo Li ◽  
Jun Zhao ◽  
Ming Su ◽  
Xu-Dong Liu
Keyword(s):  
Real Time ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Evolutionary Patterns ◽  
Graph Learning ◽  
Fine Grained ◽  
Variant Detection ◽  
Effectiveness And Efficiency ◽  
The Cost

Detecting the newly emerging malware variants in real time is crucial for mitigating cyber risks and proactively blocking intrusions. In this paper, we propose MG-DVD, a novel detection framework based on dynamic heterogeneous graph learning, to detect malware variants in real time. Particularly, MG-DVD first models the fine-grained execution event streams of malware variants into dynamic heterogeneous graphs and investigates real-world meta-graphs between malware objects, which can effectively characterize more discriminative malicious evolutionary patterns between malware and their variants. Then, MG-DVD presents two dynamic walk-based heterogeneous graph learning methods to learn more comprehensive representations of malware variants, which significantly reduces the cost of the entire graph retraining. As a result, MG-DVD is equipped with the ability to detect malware variants in real time, and it presents better interpretability by introducing meaningful meta-graphs. Comprehensive experiments on large-scale samples prove that our proposed MG-DVD outperforms state-of-the-art methods in detecting malware variants in terms of effectiveness and efficiency.

Using Ghost Fronts to Predict the Arrival Time of CMEs during 13-17 June 2012

2020 ◽  
Author(s):  
Yutian Chi ◽  
Chris Scott ◽  
Chenglong Shen ◽  
Yuming Wang
Keyword(s):  
Coronal Mass Ejections ◽  
Large Scale ◽  
Arrival Time ◽  
Interplanetary Space ◽  
In Situ Measurements ◽  
Magnetized Plasma ◽  
Field Of View ◽  
Physical Boundary

<div> <div> <div>Coronal mass ejections (CME) are large-scale eruptions of magnetized plasma and huge energy through the corona and out into interplanetary space. <div>A mount of CMEs observed by HI-1 cameras present two fronts that are similar in shape but separated by a few degrees in elongation. Scott et al. (2019) interpret the ghost fronts as projections of separate discrete sections of the physical boundary of the  CME. Ghost fronts could provide information about the longitudinal shape of CME in the field of view of Hi- 1, which can be used to improve the forecast of the arrival time of ICME. During 13-15 June 2012, STEREO/SECCHI recorded two successive launched Earth-directed CMEs. Both of the two CMEs show clearly two fronts in HI-1 images. We use the ghost fronts to predict the arrival time of the two CMEs and utility the in-situ measurements from VEX and Wind to verify the accuracy of the prediction of ghost fronts model. </div> </div> </div> </div>

Geo-Engineered Completion Optimization: An Integrated, Multi-Disciplinary Approach to Improve Stimulation Efficiency in Unconventional Shale Reservoirs

2015 ◽  
Author(s):  
David R. Spain ◽  
Ivan Gil ◽  
Herb Sebastian ◽  
Phil S. Smith ◽  
Jeff Wampler ◽  
...  
Keyword(s):  
Large Scale ◽  
Horizontal Well ◽  
State Of The Art ◽  
Economic Benefits ◽  
Production Performance ◽  
No Production ◽  
Natural Fractures ◽  
Well Performance ◽  
Shale Reservoirs

Abstract Large, high density fracture networks are necessary to deliver commercial production rates from sub-microdarcy permeability organic-rich shale reservoirs. Operators have increased lateral length and fracture stages as the primary means to improve well performance and, more recently, are tailoring completion techniques to local experience and reservoir-specific learning. In particular, closer fracture stage spacing or increased number of stages per well have driven improvements in well performance. Large scale adoption occurs when the change in performance is clearly linked to the reservoir-specific completion design. Horizontal well fracturing efficiency in unconventional reservoirs is notoriously poor. Numerous authors report that 40 to 60 per cent of frac stages or individual perforation clusters have been shown (albeit with highly uncertain surveillance methods) to contribute little or no production. The fracture initiation and propagation process is very complex in shale; it is affected by in-situ stress, geomechanical heterogeneity, presence of natural fractures, and completion parameters. Close cluster spacing can provide enhanced well production; however, if the spacing is too close, stress shadowing among these clusters can actually induce higher stresses, creating fracture competition. This paper presents an approach to the integration of these parameters through both state-of-the-art geological characterization and unconventional 3D hydraulic fracture modeling. We couple stochastic discrete fracture network (DFN) models of in-situ natural fractures with a state-of-the art 3D unconventional fracture simulator. The modeled fracture geometry and associated conductivity is exported into a dynamic reservoir flow model, for production performance prediction. Calibrated toolkits and workflows, underpinned by integrated surveillance including distributed temperature and acoustic fiber optic sensing (DTS/DAS), are used to optimize horizontal well completions. A case study is presented which demonstrates the technical merits and economic benefits of using this multidisciplinary approach to completion optimization.

Large Scale Graph Mining with MapReduce

2011 ◽  
pp. 299-314
Author(s):  
Charalampos E. Tsourakakis
Keyword(s):  
Survey Research ◽  
Present State ◽  
Real World ◽  
Graph Mining ◽  
Large Scale ◽  
State Of The Art ◽  
Research Work

In this Chapter, we present state of the art work on large scale graph mining using MapReduce. We survey research work on an important graph mining problem, counting the number of triangles in large-real world networks. We present the most important applications related to the count of triangles and two families of algorithms, a spectral and a combinatorial one, which solve the problem efficiently.

scholarly journals Summarizing Source Code with Transferred API Knowledge

2018 ◽  
Author(s):  
Xing Hu ◽  
Ge Li ◽  
Xin Xia ◽  
David Lo ◽  
Shuai Lu ◽  
...  
Keyword(s):  
Real World ◽  
Software Maintenance ◽  
Large Scale ◽  
State Of The Art ◽  
Source Code ◽  
Code Search ◽  
Novel Approach ◽  
Software Maintenance And Evolution ◽  
World Industry ◽  
Similar Code

Code summarization, aiming to generate succinct natural language description of source code, is extremely useful for code search and code comprehension. It has played an important role in software maintenance and evolution. Previous approaches generate summaries by retrieving summaries from similar code snippets. However, these approaches heavily rely on whether similar code snippets can be retrieved, how similar the snippets are, and fail to capture the API knowledge in the source code, which carries vital information about the functionality of the source code. In this paper, we propose a novel approach, named TL-CodeSum, which successfully uses API knowledge learned in a different but related task to code summarization. Experiments on large-scale real-world industry Java projects indicate that our approach is effective and outperforms the state-of-the-art in code summarization.

scholarly journals Adversarial Feature Disentanglement for Long-Term Person Re-identification

2021 ◽  
Author(s):  
Wanlu Xu ◽  
Hong Liu ◽  
Wei Shi ◽  
Ziling Miao ◽  
Zhisheng Lu ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Feeding Strategy ◽  
Feature Space ◽  
Image Space ◽  
Original Image ◽  
Short Term ◽  
Identification Methods

Most existing person re-identification methods are effective in short-term scenarios because of their appearance dependencies. However, these methods may fail in long-term scenarios where people might change their clothes. To this end, we propose an adversarial feature disentanglement network (AFD-Net) which contains intra-class reconstruction and inter-class adversary to disentangle the identity-related and identity-unrelated (clothing) features. For intra-class reconstruction, the person images with the same identity are represented and disentangled into identity and clothing features by two separate encoders, and further reconstructed into original images to reduce intra-class feature variations. For inter-class adversary, the disentangled features across different identities are exchanged and recombined to generate adversarial clothes-changing images for training, which makes the identity and clothing features more independent. Especially, to supervise these new generated clothes-changing images, a re-feeding strategy is designed to re-disentangle and reconstruct these new images for image-level self-supervision in the original image space and feature-level soft-supervision in the disentangled feature space. Moreover, we collect a challenging Market-Clothes dataset and a real-world PKU-Market-Reid dataset for evaluation. The results on one large-scale short-term dataset (Market-1501) and five long-term datasets (three public and two we proposed) confirm the superiority of our method against other state-of-the-art methods.

scholarly journals Privacy-Preserving Interdomain Routing at Internet Scale

2017 ◽  
Vol 2017 (3) ◽  
pp. 147-167 ◽  
Author(s):  
Gilad Asharov ◽  
Daniel Demmler ◽  
Michael Schapira ◽  
Thomas Schneider ◽  
Gil Segev ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Computation Time ◽  
Interdomain Routing ◽  
Organizational Networks ◽  
Sensitive Information ◽  
Slow Convergence ◽  
Level Scale ◽  
Real World Problems

Abstract The Border Gateway Protocol (BGP) computes routes between the organizational networks that make up today’s Internet. Unfortunately, BGP suffers from deficiencies, including slow convergence, security problems, a lack of innovation, and the leakage of sensitive information about domains’ routing preferences. To overcome some of these problems, we revisit the idea of centralizing and using secure multi-party computation (MPC) for interdomain routing which was proposed by Gupta et al. (ACM HotNets’12). We implement two algorithms for interdomain routing with state-of-the-art MPC protocols. On an empirically derived dataset that approximates the topology of today’s Internet (55 809 nodes), our protocols take as little as 6 s of topology-independent precomputation and only 3 s of online time. We show, moreover, that when our MPC approach is applied at country/region-level scale, runtimes can be as low as 0.17 s online time and 0.20 s pre-computation time. Our results motivate the MPC approach for interdomain routing and furthermore demonstrate that current MPC techniques are capable of efficiently tackling real-world problems at a large scale.

Sign in / Sign up

Export Citation Format

Share Document