Reviewerly/exHarmony huggingface dataset:
Machine Learning Efforts on Solar Flare Predictions by UoM Team

Earth and Space Science Open Archive posterOpen AccessYou are viewing the latest version by default [v1]Machine Learning Efforts on Solar Flare Predictions by UoM TeamAuthorsYangCheniDSee all authors Yang CheniDCorresponding Author• Submitting AuthorUniversity of MichiganiDhttps://orcid.org/0000-0002-9516-8134view email addressThe email was not providedcopy email address
A hybrid-based clustering algorithm for targeting porphyry copper mineralization at Chahargonbad district in SE Iran

This work presents a hybrid-based clustering approach for mineral potential mapping (MPM) of porphyry-type Cu mineralization at Kerman province in the SE of Iran. Whereby a multidisciplinary geospatial data set was processed and integrated in the Chahargonbad district. Data-driven prediction-area (P-A) plots were drawn for each evidence layer derived from geological, geochemical, geophysical and satellite imagery data. The P-A plots provide insight into the weight of evidence for synthesizing all geospatial layers. Out of many knowledge-driven methods which biasing from experts' opinions, index overlay and fuzzy operators were employed to find out an optimum Cu favorability map through calculating an efficiency index representing the performance of each MPM. A concentration-area (C-A) fractal model was implemented to separate the mineral favorability map into some populations to ensure correct determining the cluster numbers. Clusters number is a prerequisite which must be defined correctly to increase the performance of clustering analysis for generating reliable results in MPM. Such an appropriate number of clusters can be incorporated in running three prevalent groups of clustering methodologies as data-driven approaches in MPM. They are self-organizing map, fuzzy c-means, and k-means algorithms. One of the reasons for this tendency to consider a hybrid-based method is that it overcomes the shortcomings of the both methods (bias of experts’ opinions and unknown clusters number) in mineral favorability mapping. The unknown number of clusters was determined through a knowledge-driven method, and then it was passed to an unsupervised data-driven method, i.e. clustering algorithm. This hybrid method produces synthesized maps in close association with known porphyry-Cu mineralization in the Chahargonbad area.
Multimodal Dependent Type Theory

We introduce MTT, a dependent type theory which supports multiple modalities. MTT is parametrized by a mode theory which specifies a collection of modes, modalities, and transformations between them. We show that different choices of mode theory allow us to use the same type theory to compute and reason in many modal situations, including guarded recursion, axiomatic cohesion, and parametric quantification. We reproduce examples from prior work in guarded recursion and axiomatic cohesion -- demonstrating that MTT constitutes a simple and usable syntax whose instantiations intuitively correspond to previous handcrafted modal type theories. In some cases, instantiating MTT to a particular situation unearths a previously unknown type theory that improves upon prior systems. Finally, we investigate the metatheory of MTT. We prove the consistency of MTT and establish canonicity through an extension of recent type-theoretic gluing techniques. These results hold irrespective of the choice of mode theory, and thus apply to a wide variety of modal situations.
Discussion on the application of Lagrange mean value theorem

Abstract The Lagrange mean value theorem has been widely used in the following aspects; ( 1 )Prove equation; ( 2 )Proof inequality; ( 3 ) Study the properties of derivatives and functions; (4) Prove the conclusion of the mean value theorem; (5) Determine the existence and uniqueness of the roots of the equation; (6 ) Use the mean value theorem to find the limit.
FACTORS ASSOCIATED WITH THE ATTITUDES OF UNIVERSITY STUDENTS OF HEALTH SCIENCES TOWARDS E-LEARNING

Three months after 2020, the world is facing an unprecedented crisis: the outbreak of a new respiratory disease caused by a coronavirus, which has had an impact on different spheres of society, one of them being education. In this context, countries have taken extraordinary measures, being a common trend in education systems to respond to the pandemic with "e-learning" protocols, which mark the rapid transition from face-to-face classes to e-learning systems. Background studies have revealed various factors that influence students' attitudes towards e-learning. An e-learning platform is a web application that integrates a set of tools for online teaching-learning. In this scenario, teachers have had to reinvent their skills and many students react with distrust to the new models that are being implemented, for this reason the objective of the research was to determine the factors associated with the attitudes of university students towards e-learning. The approach was quantitative, cross-correlational design. The sample was made up of 126 health science students from a private university located in Lima-Peru, who participated in the "Research Update Workshop" under the e-learning modality. The instrument used was the scale of attitudes towards e-learning, which consists of 61 items. Data collection was carried out by sending electronic forms of the instrument to the students. The results show that the average age of the students was 30.9 years old, 82.5% (n = 104) were female. 69% (n = 87) had single status, 13.5% (n = 17) married and 9.5% (n = 12) separated. 62.7% (n = 79) work. 63.5% (n = 80) have no previous e-learning experience. 91.3% (n = 115) have only one electronic device to connect, while 8.7% (n = 11) have more than 1 electronic device. Regarding attitudes towards e-learning, in the perception of usefulness dimension, 67.5% (n = 85) were regular and 17.5% (n = 22) were good. In the intention to adopt e-learning dimension, 70.6% (n = 89) had regular attitudes and 15.1% (n = 19) had good attitudes. In ease of use, 64.3% (n = 81) had regular attitudes and 15.1% (n = 19) had good attitudes. In the correlational analysis, only sex was significantly associated with student attitudes towards e-learning (p <0.05). The results reveal that the attitudes of students towards e-learning are mostly regular, requiring the university to develop e-learning strategies that are more attractive to the student and promote active participation, guaranteeing student-teacher-material interaction.
STL_Siam: Real-time Visual Tracking based on reinforcement guided network

Abstract In recent years, deep visual tracking algorithms based on Siamese has made great breakthrough in both speed and accuracy. However, due to the dependence of Siamese network on the target template, these trackers are prone to drift or even fail to track in a complex tracking environment. In this work, we navigate the Siamese network with a STLNet model and propose the STL_Siam method. The STLNet, trained offline with the dataset enhanced by ROMIX method, is introduced into the SiamGrad algorithm to infer the movement of the target during online tracking, and guides the SiamGrad network to track. In order to evaluate the reliability of the proposed algorithm, we conducted experiments on the OTB2013 and VOT2016 two benchmarks. The algorithm achieved excellent performance, and the performance of the sequences with complex obstruction interference has been slightly improved. Experiments suggests that the proposed architecture reaches 40+fps and gets 0.865 precision on OTB2013, which is higher than results of the ACT. Meanwhile, compared with the baseline algorithm ACT, the A, R, and EAO of the proposed approach is increased by 5.6%, 1%, and 0.3% respectively on VOT2016 respectively.
An Automated Method for Bacterial Flora Counting Based on Image Analysis

Abstract How to quickly detect the characteristics of water quality flora, such as bacterial flora number, is a key problem of water quality safety, the traditional manual counting method has some serious defects such as low efficiency, low accuracy, easy to cause visual fatigue and hard to deal with large samples. This paper designs an automated method for bacterial flora counting based on image analysis, including such modules as pretreatment of bacterial image classification and colony counting of culture medium images by filter method (using Hough circle detection algorithm and a circle deduplication algorithm). The accuracy of experimental results is above 90%, which is in line with international standards, and the time complexity of the algorithms is up to the order of square. This method can solve the problems of heavy workload and low efficiency in traditional manual counting method.
A silicon photonics processor for error-protected measurement-based quantum computing

The development of a silicon photonic chip for the generation and measurement of eight-qubit reconfigurable graph and hypergraph states is reported. Its performance is demonstrated through several measurement-based quantum computing protocols enhanced by error-correction encodings.
PENDUGAAN CURAH HUJAN DENGAN TEKNIK STATISTICAL DOWNSCALING MENGGUNAKAN CLUSTERWISE REGRESSION SEBARAN TWEEDIE

Rainfall prediction is one of the most challenging problems of the last century. Statistical Downscaling Technique is one of the rainfall estimation techniques that are often used. The goal of this paper is to develop the modeling of cluster-wise regression with rainfall data set that has Tweedie distribution. The data used in this paper were the precipitation from Climate Forecast System Reanalysis (CFSR) version 2 as the predictor variables and rainfall from BMKG as the response variable. Data were collected from January 2010 to December 2019 on the Bogor, Citeko, Jatiwangi, and Bandung rain posts. The best result of this study is a Cluster-wise Regression model with 4 clusters and using Tweedie distribution in each rain post. The best model was evaluated by the Root Mean Square Error Prediction. RMSEP value on Bogor rain post is 17.11 (three clusters), Citeko rain post 14.85 (two clusters), Jatiwangi rain post 15.26 (three clusters), and Bandung rain post 14.33 (two clusters). This model was able to make models and clusters well on daily rainfall application.
Adaptive STDM-Based PDF Documents Watermarking Algorithm Robust to Fixed Gain Attack

Abstract Spread Transform Dither Modulation (STDM) has good performance in robustness against Additive White Gaussian Noise (AWGN) and re-quantization, so it has been widely used in image watermarking algorithms. However, STDM cannot resist Fixed Gain Attack (FGA). In the paper, we introduce an adaptive function into the traditional STDM algorithm, so that it can automatically adjust the amount of distortion according to the text line spacing in the document. Then we give an improved adaptive function to make our algorithm robust to FGA. Analysis and experimental results show that the algorithm proposed in this paper has good invisibility and high robustness against AWGN and FGA.
Virtual Reality Classroom (VRC): a proposed solution for classroom shortage and an alternative learning environment to the “New Normal” type of learning.

The authors developed a Virtual Reality Classroom Environment (VRCE) as a proposed solutionfor classroom shortage and served as a tool for blended learning setup. The authors used the Agile ScrumProcess to develop the software, where the designed virtual reality (VR) application served as a learningplatform for the students to receive real time lectures and instructions from their teachers. And the teachersable to give real-time reinforcement and assessment. The developed application able to achieve its goal, interms of creating a virtual learning environment similar to the real and physical classroom setup whileproviding a multi-sensory learning experience to the students. The proposed framework and the VRapplication was evaluated using a Defect Detection Percentage (DDP). Based from the results of DDP itshows a positive result where the total weighted score is 46.66 percent Finally, the scores from usability testshows a positive feedback were 76.5 percent of the participants agreed that this technology is very timely andefficient to be used in this time of pandemic
Rasgos docentes a reflexionar y reconsiderar para el nuevo modelo de la cuarta transformación

espanolEl proposito de este trabajo consiste en analizar los rasgos que han tenido los modelos de docentes en Mexico a fin de obtener algunos elementos que permitan reflexionar y aportar aquellos atributos que se consideren pertinentes en la construccion del nuevo modelo que se pretende lograr para desarrollarse de manera etica, comprometida, con saberes y conocimientos en la cuarta transformacion. La metodologia utilizada fue la cualitativa, con la intencion de comprender lo que se dice, hace y piensa en las instituciones formadoras de docentes, por lo que se utilizo la observacion, la entrevista y el grupo focal, a fin de conocer de viva voz la opinion de: alumnos, maestros y egresados, la cual fue fundamentada con base a resultados del analisis de diversos documentos escritos tales como: libros, articulos, ponencias, etc. Entre los principales resultados encontrados se destaca que en los ultimos planes y programas de estudio no solo se encamino la formacion inicial para el medio urbano, sino tambien se modificaron terminos pedagogicos por conceptos relacionados con el discurso empresarial. Se concluye que la escuela no es como una empresa, que en dichas instituciones educativas se debe promover una formacion academica, integrada por: investigacion educativa, teoria pedagogica, filosofia, historia, etica y civismo de manera conjunta para encaminarse a lograr un perfil mas integral, por lo que se deben de retomar las practicas pedagogicas en los medios: rural, urbano e indigena. EnglishThe purpose of this work is to analyze the features that the models of teachers have had in Mexico in order to obtain some elements that allow us to reflect and contribute those attributes that are considered pertinent in the construction of the new model that is intended to be developed in order to develop Ethical, committed, with knowledge and knowledge in the fourth transformation. The methodology used was qualitative, with the intention of understanding what is said, done and thought in teacher training institutions, so observation, interview and focus group were used, in order to know the voice opinion of: students, teachers and graduates, which was based on the results of the analysis of various written documents such as: books, articles, presentations, etc. Among the main results found, it is highlighted that in the latest plans and study programs, not only was initial training for the urban environment aimed, but also pedagogical terms were modified for concepts related to business discourse. It is concluded that the school is not like a company, that in these educational institutions an academic formation, made up of: educational research, pedagogical theory, philosophy, history, ethics and civility should be promoted jointly to go towards achieving a more comprehensive profile Therefore, pedagogical practices must be resumed in the media: rural, urban and indigenous.
A new correntropy-based level set algorithm using local robust statistics information

Intensity inhomogeneity or noise usually appear in various kinds of images, which cause a challenging task in image segmentation. To solve these issues, a novel correntropy-based level set algorithm utilising local robust statistics information is introduced. In the proposed method, the modified local image fitting (MLIF) equation is built by describing the difference between the images of fitted and local robust statistics. Then, by using the correntropy criterion, the MLIF model can automatically emphasise the weight coefficient of the samples that are approximately to the grey means. In this case, the new guided energy term can accurately process images with weak edge and more adaptive to noise. Finally, we introduce a level set regularisation terms to remove re-initialisation process. Experiments on a lot of images demonstrate our method has good segmentation ability on the part of visual perception and robustness, as compared with traditional algorithms.
Empirical Evaluation of Novel Change Recommender System for Dependency Detection in Object Oriented Environment

Maintenance of a software and ensuring its consistency throughout the lifecycle is a challenging as well as a significant task for the maintenance personnel as infinite requests of change keeps on coming from the clients. It takes a vigilant approach from the maintenance staff to carefully analyze each such change request by considering its impact on the existing software functionality. This vigilant approach of analyzing the change request prior to its implementation is called Change Impact Analysis (CIA) and it is the most crucial activity under Software Maintenance. Performing CIA for an Object-Oriented-System becomes more demanding due to the presence of various object-oriented concepts and complex relationships and dependencies among the software artifacts. Detecting these dependencies is a major activity under CIA where several techniques and tools have been proposed in the literature where the focus of researchers has been mostly on JAVA based systems. This paper propose a novel algorithm for software artifact dependency detection which is implemented as a C# based tool which performs the task of detecting and presenting code dependencies and their inter-relationships and present the results in a most intrigued manner. The functionality of proposed approach is unique in a sense that it presents a three way output which provides clarity to the maintenance personnel to understand the code relationships and it further assists them in deciding about the inclusion or exclusion  of  the proposed change request in the software. Also, the paper provides detailed insights about the functionality, performance and effectiveness of the proposed approach by performing an empirical evaluation of the proposed approach on presenting the results of the empirical evaluation.
Investigating the Communicating of Science

This study investigates the challenges and potential pathways for improved science communication, particularly in regards to research on the topic of smallholder food security and for science being generated by universities in sub-Saharan Africa. Questions asked in this study include those that aim to identify the academic substrate from which a university based in Africa must navigate. It also identifies limitations and opportunities for those institutions and for researchers who actively seek to have an impact on smallholder food security. Finally, it seeks to identify where improvements can be made in order to enhance science-based decision-making in this field.Based on this study, we can see that there are a number of focal actors and agendas to which universities in sub-Saharan Africa must work in concert with, and that there are a number of umbrella organizations in Africa with the intention of creating synergies and networks for knowledge exchange. RUFORUM is identified as an umbrella organization that is particularly wellsituated to be of good support for universities and researchers engaged in questions of foodsecurity. Using webometrics I identified top-performing universities in sub-Saharan Africa, which I then overlapped with the member universities of RUFORUM to identify candidates for closer investigation. Communication channels from universities vary greatly, as one would expect, and strategies range from the creation of a separate campus with the mandate of linking science with local society, to embracing the role of social media in targeting a wider audience, to engaging with existent international research systems such as that of the CGIAR where appropriate.There is a range of challenges to science communication. Some are generalizable to academia at large, such as skewed merit-based systems favoring academic journal publications, limited funding, and the tricky business of reaching out from academia or one’s own discipline while still maintaining credibility. Specific to academia in Africa is the historical pendulum swing of the role and priority given to higher education as well as limiting infrastructure and inadequate national commitment to research. We can see that different strategies for overcoming challenges can work and there is no one ‘right’ way. The agendas of international bodies such as the World Bank and other funding agencies do affect the trajectories and priorities of universities, thus creating both a challenge but also a point of entry for the promotion and resource support for articulate scientific communication for multiple audiences, as part of the academic curriculum and measurements of success as an academician. With such a transformation, the communication of science can become not only the means to a goal (that is, to help attain sustainable smallholder food security) but as a goal in itself, giving university researchers the capacities and opportunities to find meaning and satisfaction in their work by contributing to the wider society. (Less)
Discrimination of seismic waves produced by volcanoes using self-organizing maps

Abstract The analysis and classification of seismic records of volcanoes allow us to determine the alert state in which it is. A timely study of these signs can contribute to decision-making to safeguard the integrity of people in the face of a natural disaster. The present work applies a methodology that combines the analysis of linear prediction coefficients and artificial neural networks to classify earthquakes. Two types of earthquakes that come from Galeras Volcano, Colombia, are studied volcano-tectonic and long-period. The classification is made using the clustering technique based on unsupervised learning. The signals are transformed using the linear prediction filter coefficients technique, which has the function of reducing the size of the vector that contains the original data. MATLAB software is used to generate a self-organizing network that handles clustering. The results show that the best alternative in unsupervised learning is to use the linear prediction coefficients of order 5, 6, and 7 to represent a seismic signal. For lower orders, the necessary information is not captured and for higher orders, noise information is shown.