Reviewerly/exHarmony huggingface dataset:
Ranks underlie outcome of combining classifiers: Quantitative roles for and

<h2>Summary</h2> Combining classifier systems potentially improves predictive accuracy, but outcomes have proven impossible to predict. Classification most commonly improves when the classifiers are "sufficiently good" (generalized as "accuracy") and "sufficiently different" (generalized as "diversity"), but the individual and joint quantitative influence of these factors on the final outcome remains unknown. We resolve these issues. Beginning with simulated data, we develop the DIRAC framework (DIversity of Ranks and ACcuracy), which accurately predicts outcome of both score-based fusions originating from exponentially modified Gaussian distributions and rank-based fusions, which are inherently distribution independent. DIRAC was validated using biological dual-energy X-ray absorption and magnetic resonance imaging data. The DIRAC framework is domain independent and has expected utility in far-ranging areas such as clinical biomarker development/personalized medicine, clinical trial enrollment, insurance pricing, portfolio management, and sensor optimization.
<scp>CR‐SSL</scp>: A closely related self‐supervised learning based approach for improving breast ultrasound tumor segmentation

Abstract Breast ultrasound (BUS) tumor segmentation can help with the early detection of breast cancer; however, the lack of human‐labeled training data is a big problem in this area. To deal with this, we propose a novel self‐supervised learning approach (CR‐SSL), where several related pretext tasks like unsupervised segmentation and edge detection are firstly learned, followed by the target tumor segmentation based fine‐tuning. Learning such related pretext tasks ensures better representation learning. Experimental study shows that CR‐SSL can improve the mean Dice and Jaccard scores by more than 4–5%, with ≥0.6 scores while having access to only 20–50 human‐labeled training samples. The best Dice scores of 0.7303 and 0.8207, and Jaccard scores of 0.7082 and 0.8015 are obtained by CR‐SSL on the test datasets. Compared with the No‐SSL baseline, CR‐SSL can achieve (10–20)% improvements in segmentation quality while working in small‐sized training dataset scenarios, suggesting its high potential practical utility.
Utilizing Animal Characters of a Mobile Augmented Reality (AR) Reading Kit to Improve Preschoolers’ Reading Skills, Motivation, and Self-Learning: An Initial Study

This study developed and used a mobile Augmented Reality (AR) reading kit to help preschool students recognize alphabets and read simple words more effectively. This study was based on the quantitative approach involving an experimental methodology that used a one-group pretest-posttest design. In this study, the variables of interest to be measured were students’ reading skills, motivation, and self-learning. The learning treatment involved a series of reading sessions using the novel learning application that spanned three weeks, with each session lasting for two hours. The sample of this study comprised 60 preschool students, whose ages ranged from four to six, who were selected from three different preschools. The effectiveness of the novel-reading kit was evaluated in terms of students’ learning performance, learning motivation in reading, and self-learning. The data for the former were gathered from pre-testing and post-testing. At the same time, a survey was administered to the students to elicit their opinions and feedback on the last two factors. Furthermore, all descriptive and inferential statistical procedures have been selected to analyze the data. Specifically, a one-way analysis of variance (ANOVA) was applied to analyze the data, which demonstrated significant differences in the students’ reading skills, motivation, and self-learning before and after the learning interventions. These findings showed the students could recognize alphabets more accurately, read simple words more intelligently, become more motivated to read and be highly engaged in self-learning using the mobile AR reading kit.
A comparative analysis of cryptocurrency wallet management tools

The work is devoted to a comparative analysis of tools for managing a cryptocurrency portfolio. The study aimed to find out which of the tools is currently the best solution for users. This analysis was carried out on the basis of two tools, first: a survey conducted of among 41 responders who are cryptocurrency users, and the second one: cognitive walkthrough. Conducted analysis confirmed the thesis that the Trust Wallet tool is currently the best solution for users.
Theory and experience of digitalization in the system of inclusive education

According to the systematic approach, the paper reveals the theory and experience of digitalization of the inclusive education system. Research methods: retrospective analysis of the development of digitalization of the educational process of inclusive education in Russia and abroad, conversations, analysis of portfolios, situations, experience of the Pedagogical College named after N.K. Kalugin (Orenburg), assessment and self-assessment of bachelors, teachers, generalization and systematization of the data obtained; statistical methods, cluster analysis, questionnaires, analysis of parents opinions on the conditions and quality of inclusive education, analysis of students opinions on the conditions and quality of vocational training. Research results: the paper provides a theoretical analysis and analysis of research results, which made it possible to: clarify the essence of digital technologies as a set of digital devices and information and communication technologies; identify assistive means for solving corrective and compensatory problems; categories of digital devices for solving the problems of inclusive education; identify the principles, methods and technologies of digital (e-learning) in the context of inclusive, advantages and disadvantages of e-learning; models of organizing inclusive education using e-learning; free services, paid educational platforms, their opportunities, advantages and disadvantages in inclusive education; to identify the means of intellectual information support in the digital educational environment, the didactic potential of the educational platform; virtual fund of scientific effects, providing educational and methodological support for the inclusive educational process, its architectural components; to determine the directions of the virtual fund of scientific effects. Effectiveness and quality of inclusive education is provided by individual educational trajectories, the methodological resource integrates methods of educational activities in an electronic environment, online learning, and access to digital technologies. The experience of organizing distance inclusive education and inclusive group virtual learning has been clarified. Conclusions and recommendations: it has been proven that digitalization is the goal and means of ensuring the effectiveness of inclusive education; revealed the difficulties of students and the role of teachers as professional partners in their elimination; identified digital devices for solving the problems of inclusive education. The principles, methods and technologies of digital (e-learning) are presented in the context of the interaction of students and teachers.
Natural language query formalization to SPARQL for querying knowledge bases using Rasa

Abstract The idea of data to be semantically linked and the subsequent usage of this linked data with modern computer applications has been one of the most important aspects of Web 3.0. However, the actualization of this aspect has been challenging due to the difficulties associated with building knowledge bases and using formal languages to query them. In this regard, SPARQL, a recursive acronym for standard query language and protocol for Linked Open Data and Resource Description Framework databases, is a most popular formal querying language. Nonetheless, writing SPARQL queries is known to be difficult, even for experts. Natural language query formalization, which involves semantically parsing natural language queries to their formal language equivalents, has been an essential step in overcoming this steep learning curve. Recent work in the field has seen the usage of artificial intelligence (AI) techniques for language modelling with adequate accuracy. This paper discusses a design for creating a closed domain ontology, which is then used by an AI-powered chat-bot that incorporates natural language query formalization for querying linked data using Rasa for entity extraction after intent recognition. A precision–recall analysis is performed using in-built Rasa tools in conjunction with our own testing parameters, and it is found that our system achieves a precision of 0.78, recall of 0.79 and F1-score of 0.79, which are better than the current state of the art.
New Science Literacy: Problems and Difficulties of Formation

In a pandemic, science literacy is transformed from a subject of sociological research into a question of the survival of society itself. attitude of various groups and individuals to vaccination shows the real state of science literacy and reveals significant problems in its formation. The article examines the main stages in the development of the concept of natural science (science) literacy and the importance of critical science literacy in a stream containing conflicting scientific information and expert opinions. The article demonstrates the ineffectiveness of formation strategies without specially organized work on the development of initial pre-science ideas in children and adults.
The Influence of Tilt Angle on Output for a Residential 4 kW Solar PV System

The tilt angle of a photovoltaic (PV) module has a significant impact on its energy generation capabilities. This study looks at the influence of monthly tilting versus fixed, one-axis, and two-axis tracking systems on the energy production of a small, residential 4 kW PV system in Texas. System Advisor Model (SAM) was used to simulate and compare each scenario. Just using a manual angle adjustable solar panel mount bracket (ASPVB) to adjust tilt can boost generated electricity by 6% when compared to a fixed rack system with a tilt equal to that of the local latitude.
Knowledge graph technology application in Chinese SSCI: An example of short videos research

Thousands of new publications appear every day in bibliometric databases, so the demand for document retrieval technology is growing. Bibliometrics makes it possible to perform a quantitative analysis of text publications; however, the problem of classifying complex videos with a high level of semantics remains unsolved. Meanwhile, short-form videos gain popularity and attract more researchers. Knowledge Graph seems to be a promising technology in this area. This technology makes it possible to modernize the information search infrastructure. The experiment involved 461 short-video studies. The material for the experiment was collected from the Chinese Social Sciences Citation Index (CSSCI) database. The bibliometric method was recognized as expedient for the analysis. The keyword mapping and clustering operations were performed using the CiteSpace software. The results demonstrate that short-form video research has been popular among Chinese scientists since 2017. Short-form video research focuses on five major topics, that is, development trends, modern media convergence, video production, visual content management, and short-form videos in the public sector. The present findings may be employed in future research to collect relevant samples with exact semantic relationships. The technology is not limited to specific applications and, therefore, may be useful in any field of research.
Equal Bits: Enforcing Equally Distributed Binary Network Weights

Binary networks are extremely efficient as they use only two symbols to define the network: {+1, −1}. One can make the prior distribution of these symbols a design choice. The recent IR-Net of Qin et al. argues that imposing a Bernoulli distribution with equal priors (equal bit ratios) over the binary weights leads to maximum entropy and thus minimizes information loss. However, prior work cannot precisely control the binary weight distribution during training, and therefore cannot guarantee maximum entropy. Here, we show that quantizing using optimal transport can guarantee any bit ratio, including equal ratios. We investigate experimentally that equal bit ratios are indeed preferable and show that our method leads to optimization benefits. We show that our quantization method is effective when compared to state-of-the-art binarization methods, even when using binary weight pruning. Our code is available at https://github.com/liyunqianggyn/Equal-Bits-BNN.
Analyses of flood peak discharge in Cimadur river basin, Banten Province, Indonesia

Cimadur river basin is one of the most important catchment areas in Lebak District, Banten Province. For the past few years, the catchment has experienced floods during the rainy season. The big issue of flooding has been recorded recently in December 2019 which has caused damage and negative impacts to the local people and surrounding community. This study aims to analyze the possibility of flood peak discharges in the catchment area of the Cimadur river. The flood discharges are calculated for 2, 5, 10, 25, 50, and 100 years return period based on the daily rainfall data from the year 2011 to 2020. The rainfall and land use data are obtained from PT Saeba Consultant. In this study, the hydrological analyses are including 1) analyses of average annual rainfall using the Thiessen method; 2) analyses of rainfall distribution and estimation of design rainfall by considering three methods involving: Log-Normal, Log Pearson Type III, and Gumbel Type 1; and 3) analyses of flood discharges by adopting Nakayasu Synthetic Hydrograph Unit (SHU). The rainfall distribution analyses show that the Log Pearson Type III provided the best fit. Based on the flood peak discharges analyses, the results show that the flood discharges for the 5, 10, 25, and 50 years return period in the Cimadur river basin are 470.71 m 3 /s, 560.16 m 3 /s, 698 m 3 /s, and 820.4 m 3 /s, respectively.
An Early Breast Cancer Detection System Using Stacked Auto Encoder Deep Neural Network with Particle Swarm Optimization Based Classification Method

The demand in breast cancer’s early detection and diagnosis over the last few decade has given a new research avenues. For an individual who is suffered from breast cancer, a successful treatment plan can be specified if early stage diagnosis of non-communicable disease is done as stated by world health organization (WHO). Around the world, mortality can be reduced by cure disease’s early diagnosis. For breast cancer’s early detection and to detect other abnormalities of human breast tissue, digital mammogram is used as a most popular screening method. Early detection is assisted by periodic clinical check-ups and self-tests and survival chance is significantly enhanced by it. For mammograms (MGs), deep learning (DL) methods are investigated by researchers due to traditional computer-aided detection (CAD) systems limitations and breast cancer’s early detection’s extreme importance and patients false diagnosis high impact. So, there is need to have a noninvasive cancer detection system which is efficient, accurate, fast and robust. There are two process in proposed work, Histogram Rehabilitated Local Contrast Enhancement (HRLCE) technique is used in initial process for contrast enhancement with two processing stages. Contrast enhancements potentiality is enhanced while preserving image’s local details by this technique. So, for cancer classification, Particle Swarm Optimization (PSO) and stacked auto encoders (SAE) combined with framework based on DNN called SAE-PSO-DNN Model is used. The SAE-DNN parameters with two hidden layers are tuned using PSO and Limited-memory BFGS (LBFGS) is used as a technique for reducing features. Specificity, sensitivity, normalized root mean square erro (NRMSE), accuracy parameters are used for evaluating SAE-PSO-DNN models results. Around 92% of accurate results are produced by SAE-PSO-DNN model as shown in experimentation results, which is far better than Convolutional Neural Network (CNN) as well as Support Vector Machine (SVM) techniques.
Claim success, but blame the bot? User reactions to service failure and recovery in interactions with humanoid service robots

Service robots are changing the nature of service delivery in the digital economy.However, frequently occurring service failures represent a great challenge to achieve service robot acceptance.To understand how different service outcomes in interactions with service robots affect usage intentions, this research investigates (1) how users attribute failures committed by humanoid service robots and (2) whether responsibility attribution varies depending on service robot design.In a 3 (success vs. failure vs. failure with recovery) ✕ 2 (warm vs. competent service robot design) between-subject online experiment, this research finds evidence for the self-serving bias in a service robot context, that is, attributing successes to oneself, but blaming others for failures.This effect emerges independently from service robot design.Furthermore, recovery through human intervention can mitigate consequences of failure only for robots with warm design.The authors discuss consequences for applications of humanoid service robots and implications for further research.
Activation Function: Cell Recognition Based on YoLov5s/m

Activation functions play a critical role in neural networks.The paper mainly studies activation functions with four activation functions that were the selection for reference and comparison.The Mish activation function was expending as the Mish_PLUS activation function, the Sigmoid activation function, and the Tanh were combined to obtain a new Sigmoid_Tanh activation function.We used the recently popular YoLov5s and YoLov5m as the basic structure of the neural network.The function realized in this article was the recognition function of red blood cells, white blood cells, and platelets.Through the role and comparison of different activation functions in the neural network structure, the test results show that, in this paper, the training precision curve under the Sigmoid_Tanh activation function was better than that under the action of other activation functions.That means that the accuracy of cell recognition under the activation function was higher.
Method for Automatic Term Extraction from Scientific Articles Based on Weak Supervision

We propose a method for scientific terms extraction from the texts in Russian based on weakly supervised learning. This approach doesn't require a large amount of hand-labeled data. To implement this method we collected a list of terms in a semi-automatic way and then annotated texts of scientific articles with these terms. These texts we used to train a model. Then we used predictions of this model on another part of the text collection to extend the train set. The second model was trained on both text collections: annotated with a dictionary and by a second model. Obtained results showed that giving additional data, annotated even in an automatic way, improves the quality of scientific terms extraction.
From polarization multipoles to higher-order coherences

We demonstrate that the multipoles associated with the density matrix are truly observable quantities that can be unambiguously determined from intensity moments. Given their correct transformation properties, these multipoles are the natural variables to deal with a number of problems in the quantum domain. In the case of polarization, the moments are measured after the light has passed through two quarter-wave plates, one half-wave plate, and a polarizing beam splitter for specific values of the angles of the wave plates. For more general two-mode problems, equivalent measurements can be performed.
Decision-Making Taxonomy of DevOps Success Factors Using Preference Ranking Organization Method of Enrichment Evaluation

Due to multitudes factors like rapid change in technology, customer needs, and business trends, the software organizations are facing pressure to deliver quality software on time. To address this concern, the software industry is continually looking the solution to improve processing timeline. Thus, the Development and Operations (DevOps) has gained a wide popularity in recent era, and several organizations are adopting it, to leverage its perceived benefits. However, companies are facing several problems while executing the DevOps practices. The objective of this work is to identify the DevOps success factors that will help in DevOps process improvement. To accomplish this research firstly, a systematic literature review is conducted to identify the factors having positive influence on DevOps. Secondly, success factors were mapped with DevOps principles, i.e., culture, automation, measurement, and sharing. Thirdly, the identified success factors and their mapping were further verified with industry experts via questionnaire survey. In the last step, the PROMETHEE-II method has been adopted to prioritize and investigate logical relationship of success factors concerning their criticality for DevOps process. This study’s outcomes portray the taxonomy of the success factors, which help the experts design the new strategies that are effective for DevOps process improvement.
Detection of Improperly Worn Face Masks using Deep Learning – A Preventive Measure Against the Spread of COVID-19

Coronavirus disease 2019 has had a pressing impact on people all around the world.Ceasing the spread of this infectious disease is the urgent need of the hour.A vital method of protection against the virus is wearing masks in public areas.Not merely wearing masks but wearing masks properly can ensure that the respiratory droplets do not get transmitted to other people.In this paper, we have proposed a deep learning-based model, which can be used to detect people who are not wearing their face masks properly.A convolutional neural network model based on the concept of transfer learning is trained on a self-made dataset of images and implemented with light-weighted neural network called MobileNetV2 for mobile architectures.OpenCV is used with Caffe framework to detect faces in an input frame which are further forwarded to our trained convolutional neural network for classification.The method has been implemented on various input images and classification results have been obtained for the same.The experimental results show that the proposed model achieves a testing accuracy and training accuracy of 93.58% and 92.27% respectively.Optimal results with high confidence scores and correct classification have also been achieved when the proposed model was tested on individual input images.