Reviewerly/exHarmony huggingface dataset:
Feature Blending Approach for Efficient Categorization of Histopathological Images for Cancer Detection

Advancements in medical imaging has resulted in efficient diagnosis of lethal ailments like cancer by means of histopathological image data. Rich insights about the impact of the disease can be figured out with careful examination of the histopathological images captured using high end cameras. This paper has attempted to investigate the usefulness of pretrained convolutional neural network features (CNN) for automated classification of the histopathological image categories. MobileNetV2 is considered as the pretrained architecture for CNN based feature extraction. The experimentation process has resulted in designing lightweight blended feature vectors using handcrafted techniques which are of significantly smaller dimension compared to bulky CNN features and has disclosed higher classification efficacy compared to CNN features with reduced computational overhead.
Reorganização da atenção aos pacientes que fazem o uso de insulina em um centro de saúde da família de Sobral- CE / Reorganization of the attention to patients who use insulin in a family health center in Sobral- CE

Diabetes mellitus is a chronic disease in which the body does not produce insulin or cannot adequately use the insulin it does produce.In some cases, it is necessary to use exogenous insulin as a drug therapy.However, the use of insulin has peculiar precautions that can interfere with its effectiveness.In the Centro de Saúde da Família (CSF) da Estação (Family Health Center) it was noticed that many insulin-dependent people were not using it correctly.Thus, it was realized the need to reorganize the attention to users who use insulin at the CSF Estação, reorganizing the flow of insulin delivery and conducting continuing education with health professionals.The intervention research was conducted in the period November and December 2017, the participants were the nurses and community health agents and had the Estação Health Center as a setting.
The concept of "self-organization" in the context of modern education

The article focuses on the issue of developing the sixth universal competence proposed in the Federal State Educational Standard 3++, which aims at the development of self-organization skills – the ability to plan one’s activities in the short, medium, and long term and manage time with consideration of one’s capabilities and physical energy. The study offers an analysis of the results of a diagnostic study of the level of learning organization in students at a technical university. The authors propose their perspective on the concept of self-organization of learning. The stages of students’ self-organization of learning activities within the framework of the academic discipline “Fundamentals of self-organization and self-study” are identified as the motivational component, the personality component, the practical component, and the reflection component. The significance of the proposed stages is substantiated, and their content is disclosed. The main methods employed in the study include the theoretical methods of analysis, summarization, classification, and the study of psychological and pedagogical literature, and the empirical methods of observation, recording empirical data, statistical data processing, tests, and the interpretation of a person’s inner world. The results of the study can be of use to teachers and psychologists and be put into practice as part of psychological counseling for students.
Fuzzy Wavelet Neural Network with Social Spider Optimization Algorithm for Pattern Recognition in Medical Domain

Providing accurate and accessible diagnoses is an important challenge for global healthcare system. The recently developed artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for solving medical data classification problems. The current research article presents a new Fuzzy Wavelet Neural Network (FWNN) with Social Spider Optimization (SSO) algorithm to recognize the medical data. The aim of FWNN is to utilize both WNN and Adaptive Fuzzy Neuro Inference System (ANFIS) to increase the convergence rate and achieve better approximation abilities. But a number of parameters should be adjusted in due course. In addition, the fuzzy rules enable the technique to tackle uncertainty, whereas the wavelets contribute to enhancing the accuracy of the input output map. SSO algorithm is applied to optimize the architectural parameters of FWNN model such as learning parameters, weights, dilation, and translation. The application of SSO algorithm helps in determining the hyperparameters of FWNN model during medical diagnosis. This is the novelty of current work and it assists in improving the classification performance. The SSO-FWNN model was experimentally validated using three medical benchmark datasets namely, breast cancer, heart disease, and hepatitis. The outcome of simulations indicates that the SSO-FWNN model achieved maximum outcome on all the applied datasets over existing methods in a significant way.
<b>bbl</b>: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in <i>R</i>

Non-regression-based inferences, such as discriminant analysis, can account for the effect of predictor distributions that may be significant in big data modeling. We describe bbl, an R package for Boltzmann Bayes learning, which enables a comprehensive supervised learning of the association between a large number of categorical predictors and multi-level response variables. Its basic underlying statistical model is a collection of (fully visible) Boltzmann machines inferred for each distinct response level. The algorithm reduces to the naive Bayes learner when interaction is ignored. We illustrate example use cases for various scenarios, ranging from modeling of a relatively small set of factors with heterogeneous levels to those with hundreds or more predictors with uniform levels such as image or genomic data. We show how bbl explicitly quantifies the extra power provided by interactions via higher predictive performance of the model. In comparison to deep learning-based methods such as restricted Boltzmann machines, bbl-trained models can be interpreted directly via their bias and interaction parameters.
Instance Optimal Distribution Testing and Learning

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Analisis Kesulitan Pembelajaran Tematik Pada Siswa Kelas IV Sekolah Dasar Negeri

This study aims to describe the thematic learning difficulties in fourth grade students and describe the difficulty factors that affect thematic learning difficulties in fourth grade students at SDN 014 Binjai Island. This type of research is descriptive qualitative research. Sources of data in this study were fourth grade teachers and 6 fourth grade students at SDN 014 Binjai Island. Data collection techniques used are interviews, observation and documentation. The data analysis technique uses the Milles and Huberman model with stages, data reduction, data presentation, and drawing conclusions. The results showed that there were six thematic learning difficulties in grade IV students, namely, 1) students felt confused, 2) students who had low cognitive/thinking power were left behind, 3) were less than optimal in receiving lessons, 4) parents had difficulties when helping. children study at home 5) students get low scores, and 6) students feel bored. While the difficulty factors that affect thematic learning difficulties at SDN 014 Binjai Island are eight, namely 1) lots of and mixed materials, 2) student learning styles, 3) HR (Human Resources), 4) student age, 5) thinking power. or students' cognitive, 6) parents, 7) long learning time, and 8) classroom atmosphere. The conclusion in this study is that there are still difficulties experienced by students related to thematic learning, this is due to a lot of influencing factors.
Examining the Relationship Between Bank Efficiency, Market Power, and Contemporary Technologies – Smart Banks and Cryptocurrency

The aim of this study is to explore the effect of digital transformation on the efficiency of retail banks in India. Digital transformation is represented by two contemporary technologies, namely: smart branches and cryptocurrencies. The paper tests the direct effects of these factors on efficiency and indirect effects mediation by the market power of commercial banks. This aim is pursued and attained using the quantitative research design and methods of Data Envelopment Analysis (DEA) and panel regression analysis. The sample comprises thirty-four commercial banks in India retrieved from the Bureau van Dijk database for the period from 2012 to 2018. The conceptual framework of this study is represented by the extension of the Structure Conduct Performance (SCP) Paradigm. The main dependent variables are represented by Efficiency Scores estimated using DEA with the assumptions of constant returns to scale, variable returns to scale, and decreasing returns to scale. The market power of commercial banks in India is measured by their concentration ratios linked to total assets. The key variables of digital transformation are the support of cryptocurrencies and the availability of Smart Branches. The results reveal that the support of cryptocurrencies is shown to produce no significant effects either on market power or efficiency of banks confirming there is no direct and indirect relationship between digital currencies and the efficiency of banks. Smart branches demonstrate a significant positive effect on market power but no direct effect on efficiency. Therefore, it is evidenced that the technology of smart branches currently has an indirect effect on the efficiency of banks in India, mediated by the market power of banks. The results have implications for banks and bank managers who are recommended to invest in smart branches to maintain and increase their market share and serve more customers. However, the work is limited by the availability of data with higher frequency, the limitation of DEA to study only technical efficiency, and the limitations of regression analysis, which omits qualitative factors and is not able to explain all changes in dependent variables.
Digital educational and educational services in a consumer society

The digital educational process is designed to facilitate communication in society associated with the growth of knowledge. In the context of a pandemic, the processes have transformed, but, despite this, the attitude towards technology has not changed. The present article attempts to analyze the digital educational and educational services in a consumer society. Moreover, the article also examines various proposals of Russian universities regarding the digital educational environment and the assessment of users of educational services. To meet that aim, the methods and measures of active observation, alternatives, active-learning, problem-analysis method, method of annotation, and selective observation are utilized. Based on the results, the human mind requires development constantly - this phenomenon forms the demand for education. Educational services, in this case, are exclusive because being locked up in their apartment, a person finds a way out in the information environment.
High Quality Far Infrared Image Colorization Based on Generative Adversarial Network

Colorization for far infrared image is a very chal-lenging task in which feature detection is difficult because of the lack of details compared with visible image. In this paper, we propose a high quality far infrared image colorization method based on generative adversarial network. An efficient pre-processing module is used to improve the quality of col-orized image quality in low light environment. In addition, since conventional loss function is not sufficient enough for far infrared image colorization, we propose a composite loss function that combines pixel-wise, adversarial and attention losses. Our proposed method is robust to image pair misalignments. Quantitative and qualitative experiments demonstrate that our proposed method significantly outperforms existing approaches on the KAIST multispectral pedestrian dataset, achieving more natural and plausible colorized images especially in low light environment.
Research on a Financial Investment Crisis Early Warning Model of Listed Companies Based on a Random Forest Algorithm

To realize the global scheduling of the financial investment of listed companies, a financial investment crisis early warning model of listed companies based on a random forest algorithm is proposed. Based on the random forest algorithm, the decision tree is selected and the key decision tree training set is extracted. Collect the financial investment crisis early warning data of listed companies, select the characteristic variables of financial investment crisis early warning of listed companies, and construct the financial investment crisis early warning model of listed companies based on random forest algorithm to realize the financial investment crisis early warning. The experimental results show that under the action of the early warning model based on the random forest algorithm, the overall early warning variance of financial investment of listed companies is larger, and the financial investment performance per unit time is relatively stronger, which meets the practical application requirements of global scheduling of financial investment of listed companies.
Background subtraction and yolo algorithm

Introduction: This article is the result of research entitled “Signal processing system for the detection of people in agglomerations in areas of public space in the city of Cúcuta”, developed at the Universidad Francisco de Paula Santander in 2020.Problem: The high percentage of false positives and false negatives in people detection processes makes decision making in video surveillance, tracking and tracing applications complex. Objective: To determine which technique for the detection of people presents better results in terms of respon-se time and detection hits.Methodology: Two techniques for the detection of people in uncontrolled environments are validated in Python with videos taken inside the Universidad Francisco de Paula Santander: Background subtraction and the YOLO algorithm.Results: With the background subtraction technique, we obtained a hit rate of 84.07 % and an average response time of 0.815 seconds. Likewise, with the YOLO algorithm the hit rate and average response time are 90% and 4.59 seconds respectively.Conclusion: It is possible to infer the use of the background subtraction technique in hardware tools such as the Pi 3B+ Raspberry board for processes in which the analysis of information in real time is prioritized, while the YOLO algorithm presents the characteristics required in the processes in which the information is analyzed after the acquisition of the image.Originality: Through this research, aspects required for the real-time analysis of information obtained in pro-cesses of people detection in uncontrolled environments were analyzed. Limitations: The analyzed videos were taken only at the Universidad Francisco de Paula Santander. Also, the Raspberry Pi 3B+ board overheats when processing the video images, due to the full resource requirement of the device.
Real Time Gender and Age Prediction using Deep Learning Techniques

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.
Experimental Investigation on the Kinematics of an Underactuated Mechanical Finger through Vision-Based Technology

Marker-less vision techniques represent a promising route in the development of experimental methods to study the kinematic and the dynamic parameters of mechanical systems. The knowledge of a great number of these parameters is a fundamental issue in the system behaviour analysis and represents an even more crucial aspect for underactuated mechanical systems. In this paper, a technique is proposed to identify the kinematics of the phalanges of an underactuated mechanical finger, starting from the acquisition of the finger point cloud data by means of contactless vision system devices. The analytical model identified allows to determine the underactuated finger configuration as function of the shaft rotation of the single motor of the mechanical system.
Introduction to Blockchain Technology and Its Role in the Healthcare Sector

Blockchain is a technology used for implementing a decentralized ledger. The data is entered in blocks, which have a header, and cannot enter the next block without gaining access from the previous one. The data is tamper-proof and cryptographic hash is used to secure the data. Blockchain transfers the values between various parties, which are called transactions. The properties of blockchain are atomicity, synchronicity, availability, immutability, and immortality. The blockchain can be used in the sectors of supply chain management, decentralized notary, electoral voting, healthcare, identity management system, access control system, etc. Numerous applications of blockchain 3.0 show that this technology is still on trend after a few years from its birth and indicates that there is potential of it progressing in the coming years. Blockchain is a system for the storage and sharing of information that is secured due to its transparency. Blockchain technology has a lot of uses in the healthcare sector, including enhancing capabilities in various subsectors such as use of wearable health devices and tracking gadgets, keeping health information up-to-date, and storing data digitally. Blockchain research in health care is at the moment inadequate. This chapter incorporates the introduction to blockchain and has briefly discusses various components of blockchain in detail. The emphasis is made on the application of blockchain with respect to the healthcare sector and its implementation in the healthcare sector. Some of the beneficiary points are explained, such as as records of individual or groups of patients, master patient indices, adjudicating claims, supply chain management as well as interoperability. In the end, statistical analysis has been done.
The Impact of Word Segmentation Techniques on Neural and Statistical Machine Translation: English-Arabic Case

This paper deals with Machine Translation between the English and Arabic languages. This task is very tricky given the morphological richness of the Arabic language and the unavailability of large parallel corpora. To overcome those issues, we have examined the impact of word segmentation (sub-word and morphological segmentation) on machine translation performance. We have tested both the statistical approach and the neural approach which is widely employed in recent years owing to its promising results. In our experiments, carried out on English-Arabic direction and based on the United Nations parallel corpus, we show that applying morphological segmentation to the target language proved very beneficial, whereas sub-word segmentation made no significant impact on both neural and statistical models.
Solving the Feature Diversity Problem Based on Multi-Model Scheme

Generally, the performance of deep learning models is related to the captured features of training samples. When the training samples belong to different domains, the diverse features may increase the difficulty of training high performance models. In this paper, we built a new framework that generates multiple models on the organized samples to increase the accuracy of classification. Firstly, our framework selects some existing models and trains each of them on organized training sets to get multiple trained models. Secondly, we select some of them based on a validation set. Finally, we use some fusion method on the outputs of the selected models to get more accurate results. The experimental results show that our framework achieved higher accuracy than the existing methods. Our framework can be an option for the deep learning system to increase the classification accuracy.
Main Structure Extraction Algorithm Based on Relative Total Variation Model

Extracting main structure from texture images is a basic process in computer vision and graphics applications. Aiming at solving the problems that the relative total variation model (RTV) algorithm is not easy to implement on hardware and blurs the edges when filtering out the texture, an improved relative total variation model algorithm is proposed. First, the reciprocal function is used to replace the exponential form of the weight function in the original algorithm, which is convenient for hardware implementation. Then, on the basis of the original spatial relation, the relation between gradient value is added into the weight function, so as to avoid the misjudgment between the main structure pixel and texture pixel and effectively maintain the edge. The experimental results show that the improved RTV model makes the difference in the value of the windowed inherent variation between main structure and texture more obvious and the edge preserving ability is better than the original algorithm. Moreover, the effect of extracting main structure from texture images is better than other similar algorithms.
Inertial Derivative Data Storage System Based on STM32H743

An inertial derivative data storage system based on STM32H743 is designed. MPU6050 gyroscope accelerometer, GPS and other sensors are used to compose the inertial navigation system. After transplantation of FATFS file system, data can be stored through NAND FLASH storage medium, and data can be read through USB interface. This auxiliary debugging system has the advantages of small size, accurate data and strong processing ability. From the principle, design scheme, software implementation and other aspects of the realization of the explanation.
How to Improve Scientific Publication Performance of Private University Lecturers: an Empirical Analysis on Individual and Institutional Factors

Indonesian scientific publications on Scopus show encouraging increase by being listed as number 1 in Southeast Asia. However, fewer literary contribution by private university compared to those made by state university. Thus this study investigates which factors will impact publication performance of private university lecturers. Using multiple linear regression analysis of 226 data, it is found that motivation, self-efficacy, research skills, research time, and training are proven to have positive impact on publication performance. Findings. Teaching workload was found to have significant negative impact, while supporting resources were the only variable that had no impact on publication performance. This research has contribution towards institutional policies in encouraging lecturers to produce scientific publications by strengthening the influencing factors.