Reviewerly/exHarmony huggingface dataset:
A Novel Generative Model to Synthesize Face Images for Pose-invariant Face Recognition

Face recognition is an active research area in computer vision, which has been widely used in various applications such as security, video surveillance and personal identification. Although recent studies in this field have achieved great successes, they usually require an enormous amount of data for training and yet still have difficulties in in-the-wild dataset due to large variation of pose, illumination, expression. Among these unconstrained conditions, pose variation is thought to be the factor that harms face recognition accuracy the most. In order to deal with pose variation, one can fulfill the incomplete UV map extracted from in-the-wild faces, then attach the completed UV map to a fitted 3D mesh and finally generate different 2D faces of arbitrary poses, which then can be used for training or testing face recognition models. In this paper, we propose a novel generative model called ResCUNet-GAN to improve UV map completion. Particularly, we improve the original UV-GAN by stacking two U-Nets and enhancing it with multiple-level residual connections and feature fusion. The experiments on the popular Multi-PIE dataset shows that our model outperforms the original UV-GAN model.
Collaborative management and control of blockchain in cloud computing environment

Cloud computing, as a product of the fusion and development of computer technology and Internet technology, not only realized the innovation of IT technology but also A major revolution in the IT business model will bring unprecedented and profound changes to the information industry. The main purpose of this article is to study the collaborative management and control method of blockchain in a cloud computing environment. This article mainly uses the blockchain consensus algorithm to analyze and research the blocking technology in the logistics supply chain, and solves the supplier’s benefit formula step by step; also uses the CloudBTF algorithm, Max-min algorithm, FCFS of cloud computing Algorithm, and compare the efficiency and security of the three methods to get the most conducive to the collaborative management and control of the blockchain. The experimental results of this paper show that blockchain collaborative management in a cloud computing environment can greatly improve the security of massive data storage and the collaborative distribution of data. Among them, the use of cloud computing platform priority algorithms can improve system load balancing by up to 12%, while Using the cloud computing platform FCFS algorithm can improve system load balancing by up to 15%.
WITHDRAWN: Modern accounting data analysis platform based on 5G network and FPGA

The assembly of 5G cell, and Advanced Data Analytics will disturb the Information Communications Technology (ICT) organization. The consolidated impact of these advancements will clear the way for new plans of action, innovation development and horde open doors for applications over all industry verticals that depend on Telecom and IT are Bank administrations. 5G promises to empower smart organization and application administrations with network to distant sensors, enormous measures of information and low-dormancy information transmissions. To improve the capacity of bookkeeping information examination and measurements, a strategy for developing current accounting information investigation stage dependent on modern distributed computing is proposed. The time arrangement model of present day accounting information examination is developed by utilizing block bit succession investigation technique, and the affiliation rule trademark amount of accounting information is extricated. Large Data examination will at this point be a precocious idea, and it will assume a noteworthy part in the advancement of 5G norms empowering the knowledge across organization, applications and business. This work investigates the part of examination with regards to 5G, key innovation patterns and business drivers that shape the way to 5G applications and administrations, specialized obstacles in that way lastly a proposed development model of 5G investigation and organizations changing from being setting mindful to being psychological and natural Method propose procedures to accomplish a high-throughput, Field Programmable Gate Arrays (FPGA) engineering for semi cyclic low-thickness equality check codes dependent on grid development. By parting the hub preparing activity in the min-total estimate calculation, goal accomplish pipelining in the layered interpreting plan using extra equipment assets. Elevated level combination accumulation is utilized to plan and build up the engineering on the FPGA equipment stage.
Diffraction Line Imaging

We present a novel computational imaging principle that combines diffractive optics with line (1D) sensing. When light passes through a diffraction grating, it disperses as a function of wavelength. We exploit this principle to recover 2D and even 3D positions from only line images. We derive a detailed image formation model and a learning-based algorithm for 2D position estimation. We show several extensions of our system to improve the accuracy of the 2D positioning and expand the effective field of view. We demonstrate our approach in two applications: (a) fast passive imaging of sparse light sources like street lamps, headlights at night and LED-based motion capture, and (b) structured light 3D scanning with line illumination and line sensing. Line imaging has several advantages over 2D sensors: high frame rate, high dynamic range, high fill-factor with additional on-chip computation, low cost beyond the visible spectrum, and high energy efficiency when used with line illumination. Thus, our system is able to achieve high-speed and high-accuracy 2D positioning of light sources and 3D scanning of scenes.
Lasso Regression with Quantum Whale Optimization Algorithm

As the result of regularization of the objective function of linear regression, lasso regression is a classical algorithm of supervised learning in machine learning, and it has a wide range of applications. However, its objective function has the defect of poor derivability, so it does not use the coordinate descent method, but the traditional solution method is the coordinate descent method. But even if the poor derivability is avoided, the method of descending along the coordinate axis also has some defects. For example, when the lasso is more complex, it will obviously reduce the speed; it is easy to fall into the local optimization. In order to solve these defects, this paper chooses a non-convex quantum whale optimization algorithm which is processed by quantum algorithm and has good parallelism on the basis of whale optimization algorithm.
Color-Embedded-Grayscale Quality Enhancement Using Deep Convolutional Networks

The color recovery process of color-embedded-grayscale image can be considered as ill-posed inverse imaging problem. In most cases, the quality of recovered color image is less satisfied due to color distortion and checkerboard artifacts. To overcome this aforementioned problem, we propose a deep learning-based method for increasing the quality of recovered color image. This method employs the Convolutional Neural Networks (CNN) as post processing step in the color recovery stage. As reported in the Experimental Section, the proposed method yields the promising results for the color recovery task examined over subjective and objective quality assessments.
An Occlusion Compensation Learning Framework for Improving the Rendering Quality of Light Field

Occlusions are common phenomena in light field rendering (LFR) technology applications. The 3-D spatial structures of some features may be missing or incorrect when capturing some samples due to occlusion discontinuities. Most prior works on LFR, however, have neglected occlusions from other objects in 3-D scenes that do not participate in the capturing and rendering of the light field. To improve rendering quality, this report proposes an occlusion probability learning framework (OPLF) based on a deep Boltzmann machine (DBM) to compensate for the occluded information. In the OPLF, an occlusion probability density model is applied to calculate the visibility scores, which are modeled as hidden variables. Additionally, the probability of occlusion is related to the visibility, the camera configuration (i.e., position and direction), and the relationship between the occlusion object and occluded object. Furthermore, a deep probability model based on the OPLF is used for learning the occlusion relationship between the camera and object in multiple layers. The proposed OPLF can optimize the LFR quality. Finally, to verify the claimed performance, we also compare the OPLF with the most advanced occlusion theory and light field reconstruction algorithms. The experimental results show that the proposed OPLF outperforms other known occlusion quantization schemes.
Generative Model Based Adaptive Importance Sampling for Flux Calculations in Process TCAD

A key part of advanced three-dimensional feature scale etching and deposition simulations is calculating the particle flux distributions. The most commonly applied flux calculation approach is top-down Monte Carlo which, however, introduces numerical noise. In principal, this noise can be reduced by increasing the number of simulated particles but doing so also increases the overall running time. For complex geometries, especially high aspect ratio structures, which are very prominent in state of the art three-dimensional electronic device designs, increasing the number of samples is not a viable approach: Only a very small subset of simulated particles contributes to reducing the noise in remote and obscured surface regions. We thus propose an adaptive importance sampling approach based on a generative model to more efficiently focus the sampling on those surface regions with high noise levels. We show that, for a constant number of simulated particles, our approach reduces the noise levels in the calculated flux by about 33% for a representative high aspect ratio test structure.
Incorporating rivalry in reinforcement learning for a competitive game

Abstract Recent advances in reinforcement learning with social agents have allowed such models to achieve human-level performance on certain interaction tasks. However, most interactive scenarios do not have performance alone as an end-goal; instead, the social impact of these agents when interacting with humans is as important and largely unexplored. In this regard, this work proposes a novel reinforcement learning mechanism based on the social impact of rivalry behavior. Our proposed model aggregates objective and social perception mechanisms to derive a rivalry score that is used to modulate the learning of artificial agents. To investigate our proposed model, we design an interactive game scenario, using the Chef’s Hat Card Game, and examine how the rivalry modulation changes the agent’s playing style, and how this impacts the experience of human players on the game. Our results show that humans can detect specific social characteristics when playing against rival agents when compared to common agents, which affects directly the performance of the human players in subsequent games. We conclude our work by discussing how the different social and objective features that compose the artificial rivalry score contribute to our results.
Joint Entity Summary and Attribute Embeddings for Entity Alignment Between Knowledge Graphs

Knowledge Graph (KG) is a popular way of storing facts about the real world entities, where nodes represent the entities and edges denote relations. KG is being used in many AI applications, so several large scale Knowledge Graphs (KGs) e.g., DBpedia, Wikidata, YAGO have become extremely popular. Unfortunately, very limited number of the entities stored in different KGs are aligned. This paper presents an embedding-based entity alignment method. Existing methods mainly focus on the relational structures and attributes to align the same entities of two different KGs. Such methods fail when the entities have less number of attributes or when the relational structure may not capture the meaningful representation of the entities. To solve this problem, we propose a Joint Summary and Attribute Embeddings (JSAE) based entity alignment method. We exploit the entity summary information available in KGs for entities’ summary embedding. To learn the semantics of the entity summary we employ Bidirectional Encoder Representations from Transformers (BERT). Our model learns the representations of entities by using relational triples, attribute triples and description as well. We perform experiments on real-world datasets and the results indicate that the proposed approach significantly outperforms the state-of-the-art models for entity alignment.
An Efficient Method for Computation of Entropy and Joint Entropy of Images

This paper proposes an efficient method to compute entropy and joint entropy of images. Entropy of images is used to determine its quality. It is defined as the randomness or uncertainty present in the image. Similarly, joint entropy is a measure of the uncertainty present in the overlapped region of two images. Entropy and joint entropy computations are vital in several image processing applications. Intensity based image registration is done by maximizing the mutual information between two images. Mutual information is nothing but the difference between sum of individual entropies and joint entropy of two images. Image registration has applications, especially in the medical field, e.g. diagnosis and treatment of diseases. The entropy and joint entropy computation methods proposed in this paper are computationally less expensive than the standard methods. Entropy computation takes 78.60% less time than the standard method while computational time of joint entropy is reduced by 83.59%. This increase in efficiency comes at the cost of an error of 1.52% in entropy and 4.54% in joint entropy.
On the Robustness of Kernel-Based Pairwise Learning

It is shown that many results on the statistical robustness of kernel-based pairwise learning can be derived under basically no assumptions on the input and output spaces. In particular, neither moment conditions on the conditional distribution of Y given X = x nor the boundedness of the output space is needed. We obtain results on the existence and boundedness of the influence function and show qualitative robustness of the kernel-based estimator. The present paper generalizes results by Christmann and Zhou [11] by allowing the prediction function to take two arguments and can thus be applied in a variety of situations such as ranking, similarity learning and distance metric learning.
WITHDRAWN: Reversible watermarking using adaptive edge sensing interpolation

Digital watermarking is an important method employed to conserve the copyright ownership of digital images. The watermarked image is transmitted and the owner of image can confirm ownership of the digital image by extracting the embedded watermark. Reversible watermarking provides more protection to the digital images. This is applicable for data authentication, copyright protection and medical field and so on. In this work, reversible watermarking using Adaptive Edge Sensing Interpolation (AESI) is proposed. The proposed method can improve the embedding capacity in digital images to nearly 2bpp (bits per pixel) and extract the cover image without any damage.
Assessing the finance led growth hypothesis: Empirical evidence from sub-Saharan Africa

Financial development and economic growth nexus is still an ongoing debate. Several studies have indicated the positive benefits associated with enhancing financial development. Nevertheless, more work can still be done to reap the full benefits of financial development. With growing social disparities in African countries, identifying the channels through which financial services can be effective is important to maintain stability on the continent. This paper employs a system generalized methods of moments (GMM) technique, to attempt to identify the channels through which financial services can be effective. The paper tests such an effect using a panel of 30 countries in sub-Saharan Africa from 2005 to 2016. The results suggest that credit provided by financial institutions and liquid liabilities affect economic growth negatively. Credit to the private sector, on the other hand, contributed significantly to GDP growth. The study underlines the important role of a vibrant private sector to serve as an engine for growth and reduce poverty.
Writing Robotics Applications with X-Klaim

Developing robotics applications is a demanding software engineering challenge. Such a software has to perform multiple cooperating tasks in a well-coordinated manner in order to avoid unsatisfactory behavior. In this paper, we define an approach for developing robot software based on the integration of the programming language X-Klaim and the popular robotics framework ROS. X-Klaim is a programming language specifically devised to design distributed applications consisting of software components interacting through multiple distributed tuple spaces. Advantages of using X-Klaim in the robotics domain derive from its high abstraction level, that allows developers to focus on robots’ behavior, and from its computation and communication model, which is especially suitable for dealing with the distributed nature of robots’ architecture. We show the feasibility and the effectiveness of the proposed approach by implementing a scenario involving a robot looking for potential victims in a disaster area.
Controlling for Confounding Variables: Accounting for Dataset Bias in Classifying Patient-Provider Interactions

Natural Language Processing (NLP) is a key enabling technology for re-use of information in free-text clinical notes. However, a barrier to deployment is the availability of labeled corpora for supervised machine learning, which are expensive to acquire as they must be annotated by experienced clinicians. Where corpora are available, they may be opportunistically collected and thus vulnerable to bias. Here we evaluate an approach for accounting for dataset bias in the context of identifying specific patient-provider interactions. In this context, bias is the result of a phenomenon being over or under-represented in a particular type of clinical note as a result of the way a dataset was curated. Using a clinical dataset which represents a great deal of variation in terms of author and setting, we control for confounding variables using a backdoor adjustment approach [, ], which to our knowledge has not been previously applied the clinical domain. This approach improves precision by up to 5% and the adjusted models’ scores for false positives are generally lower, resulting in a more generalizable model with the potential to enhance the downstream utility of models trained using opportunistically collected clinical corpora.
Assessing the Quality of Swipe Interactions for Mobile Biometric Systems

Quality estimation is a key study in biometrics, allowing optimisation and improvement of existing authentication systems by giving a prediction on the model performance based on the goodness of the sample or the user.In this paper, we propose a quality metric for swipe gestures on mobile devices.We evaluate a quality score for subjects on enrollment and for swipe samples, we estimate three quality groups and explore the correlation between our quality score and a state-of-art biometric authentication classifier performance.A further analysis based on the combined effects of subject quality and the amount of enrollment samples is conducted, investigating if increasing or decreasing enrollment size affects the authentication performance for different quality groups.Results are shown for three different public datasets, highlighting how higher quality users score a lower equal error rate compared to medium and low quality users, while high quality samples get a higher similarity score from the classifier.
A Robust Context-Aware Proposal Refinement Method for Weakly Supervised Object Detection

Supervised object detection models require fully annotated data for training the network. However, labeling large datasets is a very time-consuming task, therefore, weakly supervised object detection (WSOD) is a substitute approach to fully supervised learning for the object detection task. Many methods have been proposed for WSOD to date, their performance is still lower than supervised approaches since WSOD is a very challenging task. The major problem with existing WSOD methods is partial object detection and false detection in an objects cluster with the same category. The majority of the methods on WSOD follow multiple instance learning approaches, which does not guarantee the completeness of detected objects. To address these issues, we propose a three-fold refinement strategy to proposals to learn complete instances. We generate class-specific localization maps by fused class activation maps obtained from fused complementary classification networks. These localization maps are used to amend the detected proposals from the instance classification branch (detection network). Deep reinforcement learning networks are proposed to learn decisive-agent and rectifying-agent based on policy gradient algorithm to further refine the proposals. The refined bounding boxes are then fed to instance classification network. The refinement operations result in learning complete objects and greatly improve detection performance. Experimental results show better detection performance by the proposed WSOD method compared to the state-of-the-art methods on PASCAL VOC2007 and VOC2012 benchmarks.
Exploring the Challenges of Cloud Migrations During a Global Pandemic

There are many benefits of migrating applications to the cloud, including highly available and elastic compute power, unlimited backup and storage, fully managed services, and overall cost savings. However, there are many challenges that software engineers face when migrating applications to the cloud. Even more challenges during the recent COVID-19 pandemic. The focus of this paper is to shed light on the challenges software engineers face performing cloud migrations during a global pandemic. A proposed set of research questions will be used to determine the challenges remote software engineers face, as well as the tools and methodologies used during the cloud migration process. Future work will consist of building a process model by examining the current state of cloud migration approaches as well as new approaches due to a global pandemic.
<i>Erlang</i> mixture distribution with application on COVID-19 cases in Egypt

In this paper, a finite mixture of [Formula: see text]-Erlang distributions is proposed. Different moments, shape characteristics and parameter estimates of the proposed model are also provided. The proposed mixture has the property that it has a bounded hazard function. A special case of the mixed Erlang distribution is introduced and discussed. In addition, a predictive technique is introduced to estimate the needed number of mixture components to fit a certain data. A real data concerning the confirmed COVID-19 cases in Egypt is introduced to utilize the predictive estimation technique. Two more real datasets are used to examine the flexibility of the proposed model.