scholarly journals Devil in the Details: Towards Accurate Single and Multiple Human Parsing

2019 ◽  
Vol 33 ◽  
pp. 4814-4821 ◽  
Author(s):  
Tao Ruan ◽  
Ting Liu ◽  
Zilong Huang ◽  
Yunchao Wei ◽  
Shikui Wei ◽  
...  
Keyword(s):  
The State ◽  
Future Research ◽  
Context Information ◽  
Global Context ◽  
The Arts ◽  
End To End ◽  
Feature Resolution

Human parsing has received considerable interest due to its wide application potentials. Nevertheless, it is still unclear how to develop an accurate human parsing system in an efficient and elegant way. In this paper, we identify several useful properties, including feature resolution, global context information and edge details, and perform rigorous analyses to reveal how to leverage them to benefit the human parsing task. The advantages of these useful properties finally result in a simple yet effective Context Embedding with Edge Perceiving (CE2P) framework for single human parsing. Our CE2P is end-to-end trainable and can be easily adopted for conducting multiple human parsing. Benefiting the superiority of CE2P, we won the 1st places on all three human parsing tracks in the 2nd Look into Person (LIP) Challenge. Without any bells and whistles, we achieved 56.50% (mIoU), 45.31% (mean APr) and 33.34% (APp0.5) in Track 1, Track 2 and Track 5, which outperform the state-of-the-arts more than 2.06%, 3.81% and 1.87%, respectively. We hope our CE2P will serve as a solid baseline and help ease future research in single/multiple human parsing. Code has been made available at https://github.com/liutinglt/CE2P.

scholarly journals Copy or Rewrite: Hybrid Summarization with Hierarchical Reinforcement Learning

2020 ◽  
Vol 34 (05) ◽  
pp. 9306-9313
Author(s):  
Liqiang Xiao ◽  
Lu Wang ◽  
Hao He ◽  
Yaohui Jin
Keyword(s):  
Reinforcement Learning ◽  
Information Loss ◽  
The State ◽  
Automatic Evaluation ◽  
Hierarchical Reinforcement Learning ◽  
Human Evaluation ◽  
The Arts ◽  
Hybrid Framework ◽  
End To End ◽  
Abstractive Summarization

Jointly using the extractive and abstractive summarization methods can combine their complementary advantages, generating both informative and concise summary. Existing methods that adopt an extract-then-abstract strategy have achieved impressive results, yet they suffer from the information loss in the abstraction step because they compress all the selected sentences without distinguish. Especially when the whole sentence is summary-worthy, salient content would be lost by compression. To address this problem, we propose HySum, a hybrid framework for summarization that can flexibly switch between copying sentence and rewriting sentence according to the degree of redundancy. In this way, our approach can effectively combine the advantages of two branches of summarization, juggling informativity and conciseness. Moreover, we based on Hierarchical Reinforcement Learning, propose an end-to-end reinforcing method to bridge together the extraction module and rewriting module, which can enhance the cooperation between them. Automatic evaluation shows that our approach significantly outperforms the state-of-the-arts on the CNN/DailyMail corpus. Human evaluation also demonstrates that our generated summaries are more informative and concise than popular models.

Digital economy: conceptual and strategic aspects of development

2020 ◽  
pp. 40-52
Author(s):  
D. Volkov
Keyword(s):  
Public Administration ◽  
Digital Technologies ◽  
Digital Economy ◽  
The State ◽  
Long Wave ◽  
End To End ◽  
Advanced Development ◽  
Digital Sphere

The article proves the need to "return" the state to the economy in order to implement digital mobilization and form a new mechanism of public administration, including the article analyzes the key conditions for Russia’s transition to the path of "advanced development", reveals not only the content of the levels of the digital sphere, but also its end-to-end digital technologies, all the challenges and threats generated by the development of the digital economy, examines the need and possibility of Russia’s movement to the sixth technological order, provides an algorithm for the transition to the phase of a new long wave (the big or Kondratiev cycle).

Towards Interconnected Blockchains

2021 ◽  
Vol 54 (7) ◽  
pp. 1-39
Author(s):  
Ankur Lohachab ◽  
Saurabh Garg ◽  
Byeong Kang ◽  
Muhammad Bilal Amin ◽  
Junmin Lee ◽  
...  
Keyword(s):  
State Of The Art ◽  
The State ◽  
Future Research ◽  
Missing Link ◽  
Layered Architecture ◽  
Blockchain Technology ◽  
Wide Scale ◽  
Effective Development ◽  
Insight Into ◽  
Game Changer

Unprecedented attention towards blockchain technology is serving as a game-changer in fostering the development of blockchain-enabled distinctive frameworks. However, fragmentation unleashed by its underlying concepts hinders different stakeholders from effectively utilizing blockchain-supported services, resulting in the obstruction of its wide-scale adoption. To explore synergies among the isolated frameworks requires comprehensively studying inter-blockchain communication approaches. These approaches broadly come under the umbrella of Blockchain Interoperability (BI) notion, as it can facilitate a novel paradigm of an integrated blockchain ecosystem that connects state-of-the-art disparate blockchains. Currently, there is a lack of studies that comprehensively review BI, which works as a stumbling block in its development. Therefore, this article aims to articulate potential of BI by reviewing it from diverse perspectives. Beginning with a glance of blockchain architecture fundamentals, this article discusses its associated platforms, taxonomy, and consensus mechanisms. Subsequently, it argues about BI’s requirement by exemplifying its potential opportunities and application areas. Concerning BI, an architecture seems to be a missing link. Hence, this article introduces a layered architecture for the effective development of protocols and methods for interoperable blockchains. Furthermore, this article proposes an in-depth BI research taxonomy and provides an insight into the state-of-the-art projects. Finally, it determines possible open challenges and future research in the domain.

scholarly journals HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

2020 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Zhiyong Xu ◽  
Weicun Zhang ◽  
Tianxiang Zhang ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Context Information ◽  
Remote Sensing Images ◽  
Global Context ◽  
Boundary Information ◽  
Extraction Stage

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

scholarly journals Methods for image denoising using convolutional neural network: a review

2021 ◽  
Author(s):  
Ademola E. Ilesanmi ◽  
Taiwo O. Ilesanmi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Denoising ◽  
Speckle Noise ◽  
Future Research ◽  
The Arts

AbstractImage denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated.

Funding and Directing Research or Rewarding Scientific Achievements?

Nuncius ◽  
2019 ◽  
Vol 34 (2) ◽  
pp. 317-355 ◽  
Author(s):  
Patrice Bret
Keyword(s):  
19Th Century ◽  
Royal Academy ◽  
The State ◽  
Future Research ◽  
Main Question ◽  
Technical Problems ◽  
Académie Des Sciences ◽  
Academy Of Sciences ◽  
The 19Th Century ◽  
Scientific Achievements

Abstract This study examines the science and technology prize system of the Académie des Sciences through a first survey of the prizes granted over the period extending from the 1720s to the end of the 19th century. No reward policy was envisaged by the Royal Academy of Sciences in the Réglement (statute) promulgated by King Louis XIV in 1699. Prizes were proposed later, first by private donors and then by the state, and awarded in international contests setting out specific scientific or technical problems for savants, inventors and artists to solve. Using cash prizes, under the Ancien Régime the Academy effectively directed and funded research for specific purposes set by donors. By providing it with significant extra funding, the donor-sponsored prizes progressively gave the Academy relative autonomy from the political power of the state. In the 19th century, with the growing awareness of the importance of scientific research, the main question became whether to use the prizes to reward past achievements or to incentivize future research, and the scale and nature of the prizes changed.

scholarly journals EXPRESS: Power resources and successful trade union actions that address precarity in adverse contexts: The case of Central and Eastern Europe

2021 ◽  
pp. 001872672110201
Author(s):  
Aurora Trif ◽  
Valentina Paolucci ◽  
Marta Kahancova ◽  
Aristea Koukiadaki
Keyword(s):  
Trade Unions ◽  
Future Research ◽  
Eastern European ◽  
External Resources ◽  
Internal Resources ◽  
Global Context ◽  
Deregulated Markets ◽  
Power Resources ◽  
Eastern European Countries ◽  
Depth Interviews

Is it possible for trade unions to fight precarity in an adverse global context? Although existing research suggests this is possible, there is limited understanding of the interplay of resources that enable unions to address precarity in deregulated markets. This study employs a power resource approach to investigate how unions overcome their external constraints. It draws upon 130 in-depth interviews with key informants across nine Central and Eastern European countries to investigate successful and unsuccessful union actions in sectors with differing external resources. In each sector, unions that mobilise their internal resources have been able to reduce various precarity dimensions, such as low wages, lack of voice, and irregular working time. The results reveal that unions whose objectives are based on convincing win–win discourses can make strides, acting as drivers of change in precarity patterns even in unfavourable conditions. Moreover, the study introduces a multi-dimensional conceptualisation of union success, identifying union actions that result in measurable improvements in precarity dimensions for all worker types. To deepen understanding of the role unions play in fighting precarity in adverse contexts, future research could investigate union actions that improve a wider range of precarity dimensions for all workers.

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