scholarly journals Generative Facial Prior for Large-Factor Blind Face Super-Resolution

2021 ◽  
Vol 2078 (1) ◽  
pp. 012045
Author(s):  
Xiaomeng Guo ◽  
Li Yi ◽  
Hang Zou ◽  
Yining Gao
Keyword(s):  
Real World ◽  
Prior Information ◽  
Super Resolution ◽  
Feature Transformation ◽  
Magnification Factor ◽  
High Quality ◽  
Large Factor ◽  
Real World Applications ◽  
The Face ◽  
The Rich

Abstract Most existing face super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic down sampling). However, these methods suffer a severe performance drop in various unknown degradations in real-world applications. Previous methods usually rely on facial priors, such as facial geometry prior or reference prior, to restore realistic face details. Nevertheless, low-quality inputs cannot provide accurate geometric priors while high-quality references are often unavailable, which limits the use of face super-resolution in real-world scenes. In this work, we propose GPLSR which used the rich priors encapsulated in the pre-trained face GAN network to perform blind face super-resolution. This generative facial priori is introduced into the face super-resolution process through channel squeeze-and-excitation spatial feature transformation layer (SE-SFT), which makes our method achieve a good balance between realness and fidelity. Moreover, GPLSR can restores facial details with single forward pass because of powerful generative facial prior information. Extensive experiment shows that when the magnification factor is 16, this method achieves better performance than existing techniques in both synthetic and real datasets.

Face Counting and Mask Detection using Deep Learning

2020 ◽  
Vol 11 (12) ◽  
pp. 709-714
Author(s):  
Janani Prabu ◽  
Sai Saranesh ◽  
Dr.S. Ajitha
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Strong Solution ◽  
Real World ◽  
Human Communication ◽  
Human Ability ◽  
Real World Applications ◽  
The Face

Face is one among the foremost important human's biometrics which is used frequently in every day human communication and due to some of its unique characteristics plays a major role in conveying identity and emotion. So far numerous methods have been proposed for face recognition, but it's still remained very challenging in real world applications and up to date; there is no technique which equals human ability to recognize faces despite many variations in appearance that the face can have in a scene and provides a strong solution to all situations.

A Blockchain-based G-code Protection Approach for Cyber-Physical Security in Additive Manufacturing

2020 ◽  
pp. 1-29
Author(s):  
Zhangyue Shi ◽  
Chen Kan ◽  
Wenmeng Tian ◽  
Chenang Liu
Keyword(s):  
Additive Manufacturing ◽  
Data Storage ◽  
Real World ◽  
Physical Security ◽  
Physical Attack ◽  
Real World Applications ◽  
The Face ◽  
G Code ◽  
Code Security ◽  
Intellectual Property Theft

Abstract As an emerging technology, additive manufacturing (AM) is able to fabricate products with complex geometries using various materials. In particular, cyber-enabled AM systems have recently become widely applied in many real-world applications. It significantly improves the flexibility and productivity of AM but poses the system under high risks of cyber-physical attacks. For example, cyber-physical attack could maliciously tamper the product design and process parameters, which, in turn, leads to significant alteration of the desired properties in AM products. Therefore, there is an urgent need in incorporating advanced technologies to improve the cyber-physical security for the cyber-enabled AM systems. In this study, two common types of cyber-physical attacks regarding the G-code security were investigated, namely, unintended design modifications and intellectual property theft. To effectively secure the G-code against these two attacks, a new methodology is developed in this study, which consists of a novel blockchain-based data storage approach and an effective asymmetry encryption technique. The proposed method was also applied to a real-world AM case for ensuring the cyber-physical security of the face shield fabrication, which is critical during the COVID-19 pandemic. Based on the proposed methodology, malicious tampering can be accurately detected in a timely manner and meanwhile the risk of unauthorized access of the G-code file will be greatly eliminated as well.

scholarly journals Planning with Preferences

AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 25 ◽  
Author(s):  
Jorge A, Baier ◽  
Sheila A. McIlraith
Keyword(s):  
Real World ◽  
Automated Planning ◽  
The Other ◽  
Planning Systems ◽  
High Quality ◽  
Planning Techniques ◽  
Preference Representation ◽  
Real World Applications ◽  
Initial States ◽  
Set Of Initial States

Automated Planning is an old area of AI that focuses on the development of techniques for finding a plan that achieves a given goal from a given set of initial states as quickly as possible. In most real-world applications, users of planning systems have preferences over the multitude of plans that achieve a given goal. These preferences allow to distinguish plans that are more desirable from those that are less desirable. Planning systems should therefore be able to construct high-quality plans, or at the very least they should be able to build plans that have a reasonably good quality given the resources available.In the last few years we have seen a significant amount of research that has focused on developing rich and compelling languages for expressing preferences over plans. On the other hand, we have seen the development of planning techniques that aim at finding high-quality plans quickly, exploiting some of the ideas developed for classical planning. In this paper we review the latest developments in automated preference-based planning. We also review various approaches for preference representation, and the main practical approaches developed so far.

scholarly journals Imprecise credibility theory

2021 ◽  
pp. 1-15
Author(s):  
Liang Hong ◽  
Ryan Martin
Keyword(s):  
Real World ◽  
Prior Information ◽  
Model Specification ◽  
Credibility Theory ◽  
Experience Rating ◽  
Credibility Estimator ◽  
Real World Applications ◽  
Property And Casualty Insurance ◽  
Doubly Robust ◽  
Interval Estimator

Abstract The classical credibility theory is a cornerstone of experience rating, especially in the field of property and casualty insurance. An obstacle to putting the credibility theory into practice is the conversion of available prior information into a precise choice of crucial hyperparameters. In most real-world applications, the information necessary to justify a precise choice is lacking, so we propose an imprecise credibility estimator that honestly acknowledges the imprecision in the hyperparameter specification. This results in an interval estimator that is doubly robust in the sense that it retains the credibility estimator’s freedom from model specification and fast asymptotic concentration, while simultaneously being insensitive to prior hyperparameter specification.

scholarly journals Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender Berdasarkan Citra Wajah

2019 ◽  
Vol 18 (2) ◽  
pp. 211-221
Author(s):  
Bambang Krismono Triwijoyo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Real World ◽  
Face Image ◽  
Gender Classification ◽  
Learning Method ◽  
Image Transfer ◽  
Real World Applications ◽  
The Face

The face is a challenging object to be recognized and analyzed automatically by a computer in many interesting applications such as facial gender classification. The large visual variations of faces, such as occlusions, pose changes, and extreme lightings, impose great challenge for these tasks in real world applications. This paper explained the fast transfer learning representations through use of convolutional neural network (CNN) model for gender classification from face image. Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. The experimental results showed that the transfer learning method have faster and higher accuracy than CNN network without transfer learning.

scholarly journals Information accuracy in legislative oversight: Theoretical implications and experimental evidence

2017 ◽  
Vol 29 (2) ◽  
pp. 226-254 ◽  
Author(s):  
Susumu Shikano ◽  
Michael F Stoffel ◽  
Markus Tepe
Keyword(s):  
Decision Making ◽  
Real World ◽  
Important Determinant ◽  
Random Noise ◽  
High Quality ◽  
Specific Cost ◽  
Legislative Oversight ◽  
Real World Applications ◽  
The Relationship

The relationship between legislatures and bureaucracies is typically modeled as a principal–agent game. Legislators can acquire information about the (non-)compliance of bureaucrats at some specific cost. Previous studies consider the information from oversight to be perfect, which contradicts most real-world applications. We therefore provide a model that includes random noise as part of the information. The quality of provided goods usually increases with information accuracy while simultaneously requiring less oversight. However, bureaucrats never provide high quality if information accuracy is below a specific threshold. We assess the empirical validity of our predictions in a lab experiment. Our data show that information accuracy is indeed an important determinant of both legislator and bureaucrat decision-making.

Semi-supervised community detection using label propagation

2014 ◽  
Vol 28 (29) ◽  
pp. 1450208 ◽  
Author(s):  
Dong Liu ◽  
Hong-Yu Bai ◽  
Hui-Jia Li ◽  
Wen-Jun Wang
Keyword(s):  
Community Detection ◽  
Real World ◽  
Prior Information ◽  
Label Propagation ◽  
Background Information ◽  
Network Nodes ◽  
Detection Approach ◽  
Real World Applications ◽  
Topology Information ◽  
Almost All

Almost all existing approaches for community detection only make use of the network topology information, which completely ignore the background information of the network. However, in many real world applications, we may know some prior information that could be useful in detecting the community structures. Specifically, the true community assignments of certain nodes are known in advance. In this paper, a novel semi-supervised community detection approach is proposed based on label propagation, which can utilize prior information to guide the discovery process of community structure. Our algorithm can propagate the labels from the labeled nodes to the whole network nodes. The algorithm is evaluated on several artificial and real-world networks and shows that it is highly effective in recovering communities.

scholarly journals Deep & Attentional Crossing Network for Click-through Rate Prediction

2020 ◽  
Author(s):  
Youming Zhang ◽  
Ruofei Zhu ◽  
Zhengzhou Zhu ◽  
Qun Guo ◽  
Lei Pang
Keyword(s):  
Real World ◽  
Online Advertising ◽  
Great Success ◽  
Real World Data ◽  
High Quality ◽  
Feature Interactions ◽  
Real World Applications ◽  
Implementation Costs ◽  
Core Issue ◽  
Click Through Rate

The problem of Click-through rate(CTR) prediction is the core issue to many real-world applications such as online advertising and recommendation systems. An effective prediction relies on high-order combinatorial features, which are often hand-crafted by experts. Limited by human experience and high implementation costs, combinatorial features cannot be manually captured thoroughly and comprehensively. There have been efforts in improving hand-crafted features automatically by designing feature-generating models such as FMs, DCN, and so on. Despite the great success of these structures, most of the existing models cannot differentiate the high-quality feature interactions from the huge amount of useless feature interactions, which can easily impair their performance. In this paper, we propose a Higher-Order Attentional Network(HOAN) to select high-quality combinatorial features. HOAN is a hierarchical structure, the multiple crossing layers can learn feature interactions of any order in an end-toend manner. Inside the crossing layer, each interaction item has its unique weight with consideration of global information to eliminate useless features and select high-quality features. Besides, HOAN also maintains the integrity of individual feature embedding and offers interpretive feedback to the calculating process. Furthermore, we combine DNN and HOAN, proposing a Deep & Attentional Crossing Network (DACN) to comprehensively model feature interactions from different perspectives. Experiments on sufficient real-world data show that HOAN and DACN outperform state-of-the-art models.

scholarly journals Tackling the rich vehicle routing problem with nature-inspired algorithms

2022 ◽  
Author(s):  
Veronika Lesch ◽  
Maximilian König ◽  
Samuel Kounev ◽  
Anthony Stein ◽  
Christian Krupitzer
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Routing Problem ◽  
Real World Applications ◽  
Np Complete ◽  
The Rich ◽  
Nature Inspired Algorithms ◽  
Problem Instances

AbstractIn the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.

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