Reviewerly/exHarmony huggingface dataset:
AdaBoost Classifier-Based Binary Age Group Stratification by CASIA Iris Image

Anticipation of age has a significant role in different fields. It has prime importance in determining age in social networking, forensics and in archeology. Machine learning has played a pivotal role in determination of human age groups and reached to an advanced level in the previous years. Categorizers for binary methods are often fairer and more accurate for this sensitive information like age, gender or ethnicity. In this paper, different machine learning methods are used to categorize age group by using iris radius, pupil radius and iris–pupil ratio. Dataset was created by implementing CASIA dataset. CASIA iris twins and CASIA iris intervals were utilized, and radius was calculated by determining diameter of iris. It has provided more accurate information and estimation of age and would prove helpful in future.
Kalman filter and ridge regression backpropagation algorithms

The Kalman filter (KF) compare with the ridge regression backpropagation algorithm (RRBp) by conducting a numerical simulation study that relied on generating random data applicable to the KF and the RRBp in different sample sizes to determine the performance and behavior of the two methods. After implementing the simulation, the mean square error (MSE) value was calculated, which is considered a performance measure, to find out which two methods are better in  making an estimation for random data. After obtaining the results, we find that the Kalman filter has better performance, the higher the randomness and noise in generating the data, while the other  algorithm is suitable for small sample sizes and where the noise ratios are lower.
A Decentralized Privacy Zone for Health Domain

Since lives generate in the world, more and more people have learnt and realized that `There is no secret'. Even though, people best try to keep their personal data for private, secure, and confident. However, the digital era arises, open the gate of connection between people, and object wisely, over high-technology network. Increasingly and rapidly the instances of breaches in the security of large amounts of electronically stored personal data users concern about their privacy issues. For the case, in health domain, all patients (data owners) are pleased to share their data for medical doctor (and hospital), expecting better healthcare treatment. Since people know their data and network behaviors are being observed all the time they shared. Unfortunately, they do not sure and know, how to evaluate the true quality of individual privacy data to sharing zone? In order to cross this obstacle, this paper suggests a model to manage the privacy of data in decentralized concept. This can ensure and more comfort for data owner to share their privacy information. As well as to provide decision makers, system providers, and all relevant parties are using with valuable data and improve their ability to make critical decisions on health related programs.
Adversarial Learning for Image Super Resolution Using Auxiliary Texture Feature Attributes

Recently, image super resolution (SR) has made great progress in computer vision (CV) domain. Due to fine-grained features by convolution neural network (CNN), the mapping function among low resolution (LR) images and high resolution (HR) counterparts could be learned. Textures, which exerts relevant features measured by texture description algorithms, such as gray level co-occurrence matrix (GLCM), among local image patches, are such vital for image SR to recover high frequency details. However, most previous deep learning based methods directly estimate high resolution images ignoring these similar texture attributes. In this paper, we attempt to seek auxiliary texture feature attributes from image patches and cluster them into group indexes so as to improve the reconstruction quality. For the sake of more latent contextual information, a wide residual block is introduced. What's more, we employ 4 loss functions, including adversarial loss, perceptual loss, textural group-conditional loss as well as total variation (TV) loss, tending to estimate high resolution images with realistic textures during the adversarial training. Extensive experimental results on several benchmark datasets demonstrate the effectiveness of proposed method on improving visual quality.
Improved Cuckoo Search Optimization Algorithm based Multi-document Summarization Model

Text summarization is a procedure of extracting the meaning details from source documents. Multi-document summarization is an automated task where the necessary details are extracted using several input documents. Diverse techniques were presented on the generation of the summary from single or many documents. Single and multiple document summarization techniques pose several issues because they might comprise of maximum repetitive information compared to the single document. To resolve this issue, this study develops a novel improved cuckoo search optimization algorithm (ICSA) for multi-document summarization. For improving the optimization abilities of the CS technique, the orthogonal concepts and simulated annealing (SA) are included to raise the exploitation searching capability. The presented ICSA model involves distinct sub-processes namely preprocessing, input, and summary representation. The main aim is to summarize the representation task involves the creation of a document summary including the meaning data. Through the optimal sentence choosing task using the ICSA model, the required sentences defining the summary are selected. An extensive experimental validation process was carried out on the DUC 2006 and 2007 datasets and the efficiency is examined in terms of distinct aspects.
Lessons Learned From Designing a Security Architecture for Real-World Government Agencies

Deploying cybersecurity technologies in large enterprises is a challenging task. This article highlights our lessons learned from developing a proposed security architecture for U.S. federal departments and agencies to help security researchers and practitioners better understand the nuances involved.
Self-Inference Of Others’ Policies For Homogeneous Agents In Cooperative Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) has been widely applied in various cooperative tasks, where multiple agents are trained to collaboratively achieve global goals. During the training stage of MARL, inferring policies of other agents is able to improve the coordination efficiency. However, most of the existing policy inference methods require each agent to model all other agents separately, which results in quadratic growth of resource consumption as the number of agents increases. In addition, inferring the policy of an agent solely from its observations and actions may lead to failure of agent modeling. To address this issue, we propose to let each agent infer the others’ policies with its own model, given that the agents are homogeneous. This self-inference approach significantly reduces the computation and storage consumption, and guarantees the quality of agent modeling. Experimental results demonstrate effectiveness of the proposed approach.
Research of perception of digital world terminology by future teachers of Computer Science and Mathematics

The authors of the article consider the issue of how future teachers of Computer Science and Mathematics are going to perceive the terminology of the digital world. The authors note that the terminological system “digital” arises as a result of digitalisation of all spheres of human activity. The results of the classification of digital terminology into thematic groups on the basis of decoding their lexical and semantic meaning are presented. The authors consider the issue of the most productive understanding of digital terminology based on the allocation of thematic groups "Space", "Time". “Form and Matter”, “Consumption”, “Technological Transformations”, “Human in the Digital World”. The authors review scientific works from different fields of science, they select phrases with the adjective “digital”. The authors create assignments to identify understanding of digital terminology. The authors determine special attention to the most accessible and complicated understanding of the lexical meaning of the thematic group, they compare the volumes of thematic groups. The novelty of the research lies in the definition of the lexical and semantic group of digital terminology. Topicality of the research lies in the need to expand the vocabulary of a teacher of Computer Science and Mathematics of digital terminology, to build a digital terminology system. The authors prove that the most capacious thematic group is the group “People in the Digital World”. The authors conclude that an assessment of the current level of mastering digital terminology by a Computer Science and Mathematics teacher is to reveal problems in the lexical interpretation of new words in schoolchildren.
Discrimination of Malignant and Benign Breast Masses Using Computer-Aided Diagnosis from Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Aim: To reduce operator dependency and achieve greater accuracy, the computer-aided diagnosis (CAD) systems are becoming a useful tool for detecting noninvasively and determining tissue characterization in medical images.We aimed to suggest a CAD system in discriminating between benign and malignant breast masses. Methods:The dataset was composed of 105 randomly breast magnetic resonance imaging (MRI) including biopsy-proven breast lesions (53 malignant, 52 benign).The expectation-maximization (EM) algorithm was used for image segmentation.2D-discrete wavelet transform was applied to each region of interests (ROIs).After that, intensity-based statistical and texture matrix-based features were extracted from each of the 105 ROIs.Random Forest algorithm was used for feature selection.The final set of features, by random selection base, splatted into two sets as 80% training set (84 MRI) and 20% test set (21 MRI).Three classification algorithms are such that decision tree (DT, C4.5), naive bayes (NB), and linear discriminant analysis (LDA) were used.The accuracy rates of algorithms were compared.Results: C4.5 algorithm classified 20 patients correctly with a success rate of 95.24%.Only one patient was misclassified.The NB classified 19 patients correctly with a success rate of 90.48%.The LDA Algorithm classified 18 patients correctly with a success rate of 85.71%. Conclusion:The CAD equipped with the EM segmentation and C4.5 DT classification was successfully distinguished as benign and malignant breast tumor on MRI.
A Survey on Deep Learning-Based Approaches to Estimation of 3D Human Pose and Shape from Images for the Smart Environments

Nowadays, on the one hand, due to the increase of equipment and smart environments, increasing attention to intelligence and the need to know more about the pose and shape of humans in these smart environments, and on the other hand the increase of being integrated the virtual world with the real world caused that the proper representation of humans in the virtual world has a great importance. Hence, human analysis of images has become very important. However, this work of human analysis not only goes beyond estimating a two-dimensional pose for one or multiple person, but it also goes beyond estimating a simple three-dimensional skeleton.The estimation of 3D human pose and shape of images has received special attention due to various applications in the real world.After studying and reviewing the papers in this field, it can be concluded that the existing approaches to obtain these estimates can be broadly grouped into two main approaches. An optimization-based approach and a deep learning-based approach are presented in which deep learning-based approaches are made in two methods: parametric and non-parametric.Optimization-based approaches provide the most reliable solution for obtaining these three-dimensional estimates. However, optimization-based approaches are slow to implement, sensitive to appropriate initialization, and often fail due to weak local minimums. So, the focus is on deep learning approaches that regress poses and shapes directly from images. But on the drawbacks, can be said of these deep learning-approaches require a lot of training data and time-consuming; in the other words, their execution time is also high and produce low-resolution 3D predictions. So after reviewing both available approaches, it is found that both methods are challenged to obtain acceptable results. In this paper we have mentioned the optimization-based approaches in general and our main focus is on the deep learning-based approaches.
Ontology Modelling for Metallurgy as a Domain and Retrieval Using Particle Swarm Optimization

Ontologies are the entities that are built to gather knowledge from the chosen domain and make it available to both machines and humans. In this chapter, an ontology that structures all the processes involved in the metallurgical extraction of the metals has been modelled. Ontology Development 101 methodology with minor modifications has been adopted to model the ontologies for metallurgy as the principal domain, and the processes involved in the various fields of metallurgy have been modelled into an ontology by defining the classes and the relationship between them. The model is implemented using Protégé and quantitatively evaluated using the semiotics approach. To comprehensively demonstrate the applicability of the developed ontology, an algorithm for retrieval of entities from the web incorporating particle swarm optimization and that makes use of the modelled ontology has been proposed. Finally, the quality of the modelled ontology and as well as the performance efficiency of the entity retrieval has been quantitatively evaluated.
Boundary guided image translation for pose estimation from ultra-low resolution thermal sensor

This work addresses the pose estimation task on low-resolution images captured using thermal sensors which can operate in a no-light environment. Low-resolution thermal sensors have been widely adopted in various applications for cost control and privacy protection purposes. In this paper, targeting the challenging scenario of ultra-low resolution thermal imaging (32×32 pixels), we aim to estimate human poses for the purpose of monitoring health conditions and indoor events. To overcome the challenges in ultra-low resolution thermal imaging such as blurred boundaries and data scarcity, we propose a new Image-to-Image (I2I) translation architecture which can translate the original blurred thermal image into a visible light image with sharper boundaries. Then the generated visible light image can be fed into the off-the-shelf pose estimator which was well-trained in the visible domain. Experimental results suggest that the proposed framework outperforms other state-of-the-art methods in the I2I based pose estimation task for our thermal image dataset. Furthermore, we also demonstrated the merits of the proposed method on the publicly available FLIR dataset by measuring the quality of translated images.
A System Design for Combined Approach of WCID and Wavelet Transformation to Optimize the Underwater Image Enhancement

This research study focuses on system design for combined approach of wavelength compensation and image dehazing (WCID) and wavelet transformation technique to optimize the underwater image-enhancement technique. Image enhancement is an activity of new and improved standards of input image for better understanding of the future viewers. Image enhancement is verbally expressed to upgrade the information content of the image & modifies the visual effect of the image on the visual examiner. The characteristics of the image are observed to be intensified by the process of image enhancement. Image enhancement accentuates the image features like effulgence, contrast and exhibits edges of the photographs more subsidiary for study and examination purposes. Contrast stretching, noise filtering, pseudo-coloring and noise clipping are some of the paramount operations that are included in the image enhancement process. Underwater images are generally affected with poor overtones, poor placidity and poor color contrast. It happens because of nature of water and the particles present in it.
An Image Enhancement Method Based on Partial Differential Equations to Improve Dark Channel Theory

Abstract Image applications appears in all aspects of life, such as medical imaging, electronic monitoring, face recognition, aerial photography remote sensing, fingerprint analysis, robot vision, automatic driving, intelligent car and so on in the civil field, as well as tanks, armored vehicles, artillery, missiles, bat submunitions in the military field. However, in the process of image acquisition or storage and transportation, the image quality will decline due to environmental conditions, equipment conditions or transmission compression, and so on. In order to accurately analyze the image, we need to carry out enhancement processing. In this paper, the current research status of image enhancement methods is analyzed. The traditional dark channel prior algorithm has some problems, such as edge or texture blur and so on. The processing effect is not ideal. Considering the limitations of the traditional dark channel prior algorithm, this paper proposes a improved algorithms based on the dark channel prior by combining partial differential equation. The main work includes the following aspects. First, the partial differential equation algorithm is combined with the dark channel prior method, and the improved algorithm is applied to enhance the image with fog. The contrast experiment of enhancement effect and running time is designed to verify the image enhancement effect of the improved algorithm. Second, the improved algorithms proposed in this paper are compared with traditional algorithms. After making the subjective and objective evaluation, it is found that the improved method in this paper has better image processing effect, stronger fog and noise removal effect, better edge retention effect, more information acquisition, and higher computational efficiency.
Structural analysis of three-dimensional finite element model to design multifunction wheelchair for patients

Abstract The disabled and patients, especially hemiplegia and paraplegia cases and elder need to use a wheelchair for movement and admitted to the hospital or stay at home. The problem of using wheelchairs for patients and the elder is due to their different shapes. There is only a standard type of wheelchair in a hospital, therefore could not respond to differences in shape and usability. The ability of a wheelchair that does not provide enough facilities will result in the service as being ineffective in achieving and will make patients feel uncomfortable. The basis of the strength analysis, as well as stress distribution of the device structure design, is still minimal due to computer program limitations. A comprehensive understanding of the fundamentals of strength analysis will support the recommendation to design of wheelchair to handle patient for appropriate use in each situation. This research aim provides strength analysis to design the multifunction wheelchair. The multifunction wheelchair is designed to be turned into a bed, height-adjustable to use in a situation where a standard wheelchair could not be achieved due to the height is not enough and arm rest’s width adjustable for the convenience of patients with different body shapes. The detail regarding concept design and strength analysis of main components are solved by the finite element method (FEM) with a three-dimensional model. The results obtained from the simulation solution are examined and compared with the analytical solution. It is found that the highest maximum stress value does not exceed the yield strength of the material. The obtained results can be helpful in determining the basis for the guideline of wheelchair design and develop medical instruments situation by using principles of engineering.
An efficient bioinformatics algorithm for healthcare: Detection and counting of leukocytes, blasts, and erythrocytes

Interdisciplinarity has been responsible for solving several problems in medical science and its areas, which is the purpose of translational medicine. A large part of the population of the world is harmed by the difficulty of access to health, commonly generated by the lack of financial resources. The diagnosis of leukemia is given to 257,000 people in the world every year, where this disease is classified as a disorder in the generation of blood cells within the bone marrow. So, with a focus on interdisciplinarity, bioinformatics is a multidisciplinary science that arose from the need to understand biologic functions, and it includes the use areas of exact and biologic sciences together. And from this science, it is possible that its main laboratory finding is the presence of blasts in the bloodstream, which can be easily detected by a hemogram, since early diagnosis is of great importance for cure rate. Thus, echoing the issue of access to health, and given the foregoing, reducing the costs of the hemogram without the loss of the reliability of the examination is the prime necessity. In this sense, this research has the objective of developing an algorithm that performs the detection through the segmentation of digital images directed to leukocytes, platelets, and blasts, red blood cells (erythrocytes), since the use of this technology can replace by a simple computer the employment of high-cost hematologic equipment. For this, an algorithm based on image processing techniques was developed, showing high accuracy (90% and 100%), with an average execution time of 7.32 s. Therefore, the WHT-MO methodology can be seen as the first step to perform the hemogram test with low-cost and without losing quality, speed, or credibility.
Automated Planning With Invalid States Prediction

The increase of automated systems in space missions raises concerns about safety and reliability in operations carried out by satellites due to performance degradation. There have been several studies about the automatic planning process, but many approaches are generated with invalid states. The invalid state can be understood as a prohibited, degraded or risky scenario for the domain. This paper proposes an automated planning process with restrictions that enables automatic planners to not generate plans with invalid states. We implement a validator method for the planner software which proves that plan generation matches the restrictions imposed on the domain. In the experiments, we test an automatic planning process that is specific to the aerospace area, where a knowledge base with invalid states is available in the context of the operation of a satellite. Our proposal to carry out the verification of invalid states in automatic planning, can contribute to plans being generated with higher quality, ensuring that the goal of a plan is only achieved through valid intermediate states. It is also expected that the generated plans will be executed with better performance and will require less computational resources, since the search space is reduced.
Reducing the Impact of Outliers on the One-Class Classification Decision Rule

A modified version of one-class classification criterion reducing the impact of outliers on the one-class classification decision rule is proposed based on support vector data description (SVDD) by D. Tax. The optimization method utilizes the substitution of nondifferentiable objective function by the smooth one. A comparative experimental study of existing one-class methods shows the superiority of the proposed criterion in anomaly detection.