Reviewerly/exHarmony huggingface dataset:
A robust knockoff filter for sparse regression analysis of microbiome compositional data

Abstract Microbiome data analysis often relies on the identification of a subset of potential biomarkers associated with a clinical outcome of interest. Robust ZeroSum regression, an elastic-net penalized compositional regression built on the least trimmed squares estimator, is a variable selection procedure capable to cope with the high dimensionality of these data, their compositional nature, and, at the same time, it guarantees robustness against the presence of outliers. The necessity of discovering “true” effects and to improve clinical research quality and reproducibility has motivated us to propose a two-step robust compositional knockoff filter procedure, which allows selecting the set of relevant biomarkers, among the many measured features having a nonzero effect on the response, controlling the expected fraction of false positives. We demonstrate the effectiveness of our proposal in an extensive simulation study, and illustrate its usefulness in an application to intestinal microbiome analysis.
Verification of a static (off-line) signature using a convolutional neural network

This article is devoted to the development of a method for detecting forgery of handwritten signatures. The signature still remains one of the most common methods of identification. The signature on financial and other documents can be forged, so detecting forgery is an urgent task. This is the task of binary classification: to determine whether the signature is genuine or fake. The article describes the results of recognition of handwritten signatures made on paper. A database of handwritten signatures of 10 people was used for experiments. For each person, 10 genuine and 10 forgery signatures made by other people were collected. The signatures were digitized as color images with a resolution of 850×550 pixels. Then a binary representation of each signature was formed. Three variants of reducing signatures to sizes were used for classification: 128×128, 256×256 and 512×512 pixels. These images served as the source data for the convolutional neural network. As a result of testing the proposed approach, the average accuracy of the correct classification was achieved on medium-sized images and is equal to 93.33%.
Image Signal Processing Techniques for Hand-Eye-Foot Coordinated System

In this paper, image signal processing technique is applied to a Hand-Eye-Foot Coordinated System. The considered system integrates IP Camera device, multi-joint robotic arm and wheeled mobile carrier is proposed to simulate the coordination of human hands, eyes and feet, and carry out the behaviour of taking objects in the air during the march. IP Camera performs image capture, cooperates with image processing software, and makes target recognition and target selection similar to the human eye. Multi-joint mechanical arm, simulate the human arm, the target object grab. Wheeled mobile mounts simulate the movement behaviour of human forwards, backs and turns. The rapid image processing and target identification technology related to the system are developed. 2D target tracking technology, including (1) Camera tracking target law and (2) platform tracking camera tracking law are proposed.
The Prediction of a Floating Photovoltaic Generation Utilizing RNN

Renewable energy has advantages in environmental aspects such as greenhouse gas reduction and fine dust reduction, but the stability of the power system is lowered due to the variability & uncertainty of renewable energy sources. Therefore, it is important to predict the amount of generation of renewable energy, which can contribute to system stabilization. In order to predict the power generation of floating photovoltaic(FPV), the generation amount of 500㎾ FPV and meteorological data were used to predict the power generation through the Recurrent Neural Network(RNN). To perform appropriate prediction, identifying the correlation between variables, removing multicollinearity, handling missing values properly are performed and the amount of power generation is predicted through appropriate RNN. In addition, it is analyzed how the influence of wind affects the amount of power generation of FPV
Introduction to the Special Issue on the Federated Learning: Algorithms, Systems, and Applications: Part 1

introduction Introduction to the Special Issue on the Federated Learning: Algorithms, Systems, and Applications: Part 1 Editors: Qiang Yang WeBank, Hong Kong University of Science and Technology, China WeBank, Hong Kong University of Science and Technology, ChinaSearch about this author , Yongxin Tong Beihang University, China Beihang University, ChinaSearch about this author , Yang Liu WeBank, China WeBank, ChinaSearch about this author , Yangqiu Song Hong Kong University of Science and Technology, China Hong Kong University of Science and Technology, ChinaSearch about this author , Hao Peng Beihang University, China Beihang University, ChinaSearch about this author , Boi Faltings Ecole Polytechnique Federale de Lausanne, Switzerland Ecole Polytechnique Federale de Lausanne, SwitzerlandSearch about this author Authors Info & Claims ACM Transactions on Intelligent Systems and TechnologyVolume 13Issue 4August 2022 Article No.: 52pp 1–3https://doi.org/10.1145/3514223Published:25 August 2022Publication History 0citation238DownloadsMetricsTotal Citations0Total Downloads238Last 12 Months238Last 6 weeks63 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Facial Recognition Using Image Processing

The growing interest in computer vision of the past decade. Fueled by the steady doubling rate of computing power every 13 months, face detection and recognition has transcended from an esoteric to a popular area of research in computer vision and one of the better and successful applications of image analysis and algorithm based understanding. Because of the intrinsic nature of the problem, computer vision is not only a computer science area of research, but also the object of neuro- scientific and psychological studies, mainly because of the general opinion that advances in computer image processing and understanding research will provide insights into how our brain work and vice versa. Because of general curiosity and interest in the matter, the author has proposed to create an application that would allow user access to a particular machine based on an in-depth analysis of a person’s facial features. This application will be developed using Intel’s open source computer vision project, OpenCV and Microsoft’s .NET framework..
Detection of communicable and non-communicable diseases using hyperparameter optimization with Bi-LSTM model in pathology images

Purpose The present research work is carried out for determining haemoprotozoan diseases in cattle and breast cancer diseases in humans at early stage. The combination of LeNet and bidirectional long short-term memory (Bi-LSTM) model is used for the classification of heamoprotazoan samples into three classes such as theileriosis, babesiosis and anaplasmosis. Also, BreaKHis dataset image samples are classified into two major classes as malignant and benign. The hyperparameter optimization is used for selecting the prominent features. The main objective of this approach is to overcome the manual identification and classification of samples into different haemoprotozoan diseases in cattle. The traditional laboratory approach of identification is time-consuming and requires human expertise. The proposed methodology will help to identify and classify the heamoprotozoan disease in early stage without much of human involvement. Design/methodology/approach LeNet-based Bi-LSTM model is used for the classification of pathology images into babesiosis, anaplasmosis, theileriosis and breast images classified into malignant or benign. An optimization-based super pixel clustering algorithm is used for segmentation once the normalization of histopathology images is conducted. The edge information in the normalized images is considered for identifying the irregular shape regions of images, which are structurally meaningful. Also, it is compared with another segmentation approach circular Hough Transform (CHT). The CHT is used to separate the nuclei from non-nuclei. The Canny edge detection and gaussian filter is used for extracting the edges before sending to CHT. Findings The existing methods such as artificial neural network (ANN), convolution neural network (CNN), recurrent neural network (RNN), LSTM and Bi-LSTM model have been compared with the proposed hyperparameter optimization approach with LeNET and Bi-LSTM. The results obtained by the proposed hyperparameter optimization-Bi-LSTM model showed the accuracy of 98.99% when compared to existing models like Ensemble of Deep Learning Models of 95.29% and Modified ReliefF Algorithm of 95.94%. Originality/value In contrast to earlier research done using Modified ReliefF, the suggested LeNet with Bi-LSTM model, there is an improvement in accuracy, precision and F-score significantly. The real time data set is used for the heamoprotozoan disease samples. Also, for anaplasmosis and babesiosis, the second set of datasets were used which are coloured datasets obtained by adding a chemical acetone and stain.
Blockchain-based supply chain architecture adapted to digital business ecosystems

In this chapter, the framework and structure of the traditional supply chain is briefly reviewed. From the perspective of strengthening supply chain resilience, a new architecture of supply chain which incorporates blockchain mechanisms is proposed. This allows supply chains to strengthen their own resilience without weakening the existing efficiency, so as to fully utilize today's digital technology and achieve rapid innovation and strong integration when faced with crises and challenges in new situations. A particular focus is on extending the application of the blockchain technology to the entire digital business ecosystem. Two Japanese industry examples are used to demonstrate how the new architectural model of supply chain with blockchain mechanism can minimize the losses caused by the stagnation of the supply chain. From these examples, the benefits and risks of the blockchain technology based management to individual enterprises, supply chain and whole digital business ecosystem are identified.
INSTRUCTOR TEACHING EXPERIENCE WITH ONLINE DISTANCE LEARNING FOR PRACTICAL DESIGN COURSES AFTER COVID-19

Due to COVID-19, all classes in schools have been converted to distance learning using online platforms.However, various problems were raised when converting face-to-face classes to online distance learning.In particular, the difficulties were more pronounced for practical design courses because they include practical experience.In this context, this study focused on actual online distance learning classroom cases in design education.Based on the qualitative case study methodology, four instructors were interviewed about their experience with distance learning courses that actively utilized online platforms for college design courses as research cases.In design education, which aims at interdisciplinary and convergence thinking, online distance learning is meaningful in that it can expand experience and opportunities for the overall formative design through the development of technology.On the other hand, there are clear limitations in terms of practical production activities for existing online distance learning methods.Therefore, it is necessary to explore whether to develop classes based on the recently used effective activities or whether to find new strategies in the case that these limitations are fundamental to online distance learning.
Calculation of Torque and Development of Smart Agri Scarecrow

Abstract: A Smart scarecrow is used to scare the birds and to the animals to store the crop withinside the fields. A farmer positioned the clever scarecrow withinside the center of the world to store plenty of his crop from the birds and animals. We have visible that clever scarecrow has no motion while the birds are to be had field. A clever scarecrow is a decoy or mannequin, frequently withinside the form of a human. Humanoid scarecrows are normally carrying antique garments and located in open fields to deter birds from worrying and feeding on lately forged seed and developing plants. Scarecrows are used throughout the planet via way of means of farmers, and are a extraordinary image of farms and consequently the geographical region in famous culture. The not unusualplace shape of a clever scarecrow is a humanoid determine wearing antique garments and located in open fields to deter birds along with crows or sparrows from worrying and feeding on lately forged seed and developing plants. Machinery like windmills are employed as scarecrows, but the effectiveness lessens as animals grow to be conversant withinside the structures. Farming contributes a first-rate earnings to the Malaysian economy. It is an huge difficulty to farmers as soon as they're a long way farfar from their plants and exposing it to plants’ chance like crow unfavourable the plants and theft. Farming has contributed to almost as much as 22% of a country’s. Keywords: Sensor, Solar Panel, Buzzer, 360º Wi-Fi Camera.
Deep Reinforcement Learning for 2D Flappy Brid Game

In recent years, the prevailing of application of Deep Reinforcement Learning have granted the traditional game AI training a brand-new perspective. Google’s Alpha Go agent might mark the beginning of the trend. While many 2D games have been researched for effective trained agent to gain extraordinary performance, Flappy Bird is among, perhaps, the most popular one that could demonstrate the effectiveness of trained AI. This research has successfully trained a efficient agent using Deep-Q network that could outperforms its human counterparts. Although previous trainings have also granted successful results, due to the optimization in the memory array to store previous states, the training time has been largely reduced, which is useful for future agent training optimization.
A new camera model combining an analytical model and a discrete correction to overcome refractive index variation challenges

Abstract When performing Tomo-PIV experiments, the calculation of accurate camera models is a key point for successful measurements. In severe configurations, where optical interfaces are involved and refractive index variations occur along the line of sight, analytical models can fail to accurately represent the projection and back-projection functions. This inability can lead to significant inaccuracies in particle location and volume reconstruction, which has a considerable impact on the calculation of velocity fields. In order to overcome these limitations, an innovative camera model based on the combination of an analytical model, such as a pinhole model or a polynomial model, with discrete corrections is proposed. In this method, the analytical projection and back-projection are adjusted with a discrete correction stored in two adaptive grids that save both memory and computation time. These correction grids require calibration which is performed with triangulation procedures similar to those used in misalignment corrections. The calculation and operation of the model are described in this paper. The performance of the camera model is evaluated on simulated and experimental setups based on a large depth-of-field calibration performed in a glass water tank. The presence of multiple optical interfaces and fluids, resulting in large light deviations, makes it difficult to compute a high accuracy camera model. In this configuration, the proposed technique successfully reduces the triangulation error from 1 pixel to less than 0.01 pixel. The usefulness of the model is demonstrated in a Tomo-PIV experiment where the deflection of light through the water tunnel walls prevents classical analytical functions from accurately modelling the projection and back-projection functions. This corrected model can also solve discontinuity problems in the projection functions and can be used when there are interfaces in the measurement volume. It opens new perspectives in the study of fluid-structure interaction when transparent solids are involved.
PROJECT TO FORM TEACHERS’ READINESS TO SUPPORT STUDENTS’ SELF-DETERMINATION

Supporting students’ self-determination is a pedagogical task according to legislative acts, social demand and analysis of scientific literature. However, modern research proves that in practice, teachers are not sufficiently motivated and do not have necessary knowledge to provide this assistance. Generalization and consideration of the literature has revealed that existing projects to support students’ self-determination are mostly aimed at students, and the formation of teachers' readiness to support students remains unexplored. Thus, there is a need to increase the level of teachers’ readiness to support students’ self-determination and develop a project to raise it. The article describes the development and implementation of the project aimed to form teachers’ readiness to support students’ self-determination. The author clarifies the concept of pedagogical project, describes pedagogical models and projects aimed to support students’ self-determination; develops a project to form teachers’ readiness to promote students’ self-determination; describes the components of this project; implements and evaluates its effectiveness. The methods of scientific analysis and synthesis, survey and statistical data processing were used. The novelty of our project is in the fact that we develop teachers’ readiness to assist students’ self-determination. The teacher training program was implemented in person, remotely and in a blended form. Comparison of the effectiveness of the project depending on the form of implementation makes it possible to conclude that the most effective one is a face-to-face form.
Bunching, Unbunching, and Antibunching of Light

Light exists in many different forms that exhibit surprising properties. These properties can be harnessed to understand fundamental processes associated with subtle effects of light and help develop new kinds of devices capable of making high-precision measurements. To understand the different forms of light, there are two major theoretical frameworks: (1) semiclassical theory and (2) complete quantum theory. Although semiclassical theory offers simplicity, it does not account for quantum behavior, whereas quantum theory is capable of explaining almost all the existing forms of light including bunching, anti-bunching, and their variants. Such light is known to exhibit distributions that are not Poisson in nature and can exhibit sub- and super-Poisson behavior. In this chapter, we will also briefly discuss how to generate these unusual forms of light.
The photon blockade effect of a complete Buck-Sukumar model

The Buck-Sukumar (BS) model, with a nonlinear coupling between the atom and the light field, is well defined only when its coupling strength is lower than a critical coupling. Its energy collapses at a critical coupling and is unbounded beyond that value. In other words, the BS model is incomplete. We introduce a simple and a complete BS model by adding a nonlinear photon term into the initial BS model. Considering the rotating wave approximation, this complete BS model conserves the excited number and the parity. By expanding it in the subspace of the product state between the atom and the field, we solve the time-independent SchrÖdinger equation to obtain the eigenenergy and eigenstate. Furthermore, we explore the influence of the nonlinear photon term on the energy spectrum and the photon blockade effect for the complete BS model by calculating the excited number and second-order correlation function. Our study shows that, the nonlinear photon term not only eliminates the energy spectral collapse but also makes it well-defined and complete in all the coupling regime. When at the resonance between the atomic and the field frequency, the nonlinear photon term breaks the harmonicity of the energy spectrum and produces a ladder of the excited number in the ground state. Because the larger nonlinear photon term inhibits the photon transition from an energy level to the higher one, it produces the single-photon projection state in the larger coupling region. Accordingly, we find that the nonlinear photon term promotes photon blockade by calculating the second-order correlation function. When at the non-resonant region, the nonlinear photon term enlarges the originally anharmonic energy ladder. For a complete BS model with the fixed nonlinear photon coupling strength and the fixed detuning, the energy level for the positive detuning is lower than that with the negative detuning, and more energy is required to overcome the absorption of a photon. Therefore, the positive detuning promotes the photon blockade. For the negative detuning, the system is more likely to absorb a photon and jump to a higher energy level, and therefore, suppresses the photon blockade.
Pre-analytical phase and causes of variations in the results of hematological laboratory exams: a literature review

Laboratory diagnosis has undergone several changes over time due to automation.In addition to automation, the adoption of a quality assurance program (PGQ), thus resulting in greater accuracy and accuracy in exams.The present study aimed to analyze in the literature the main laboratory errors related to the pre-analytical phase.The same is a literature review, where data collection was composed of scientific articles taken from electronic databases of scientific publications: SciELO, CAPES Journal Portal and MEDLINE, and data analyses were performed between July and August 2021.The main question of the present study was: "What are the main laboratory errors of the pre-analytical phase found in scientific publications?".The search in the database was performed using the combination of the following keywords: Laboratory tests; pre-analytical phase; Laboratory hematology.In view of the findings in the studies, it was possible to notice that pre-analytical variables continue to be an important source of attention for the clinical laboratory.And that it is responsible for most of the errors that occur in the dynamic process of the clinical analysis laboratory, which negatively impacts the laboratory result of the patient.
Information-Intensive Operations-Based Service Process Classification

This paper attempts to extend our understanding of service operations in the advent of virtual services.Through analysis of the central aspects of the service delivery process and extant service classification literature, the paper introduces a service provider-customer input-based service classification.Empirical breakdown of a number of service processes to their constituent parts suggests that for a service classification to be complete it ought to consider the actions and input of the service provider and of the customer.Additionally, it is suggested that there are different variants of information-intensive services.The findings point towards emergent high-potential research areas in service operations management.