Reviewerly/exHarmony huggingface dataset:
Web-apps as active learning tools in hydrology classrooms

Active learning strategies such as simulations or problem sets have been shown again and again to be critically useful for helping students understand complex concepts. Students develop a more thorough understanding of processes or problem solving strategies when they are able to practice or explore them with hands-on activities. In hydrology, however, there are several concepts taught, even in introductory classes, where developing suitable activities for this type of learning is difficult. For example, even a simple water balance activity often requires a relatively thorough understanding of spreadsheets or a lot of tedious hand-calculations if students are going to explore relationships between multiple inputs and outputs. Similarly, even the most basic discussion of how a simple box model works is difficult to supplement with an activity that doesn&amp;#8217;t involve spreadsheets or writing computer code. Furthermore, it is often beyond the scope of introductory level hydrology classes to teach programming or spreadsheet skills, and hand calculations often take a prohibitive amount of time. Web applications offer a tool to address some of these issues. With the development of tools like Shiny apps for R or Python, instructors with programming experience can relatively easily create interactive learning tools for their classes. Many studies in fields such as statistics and mathematics have shown that these web-apps aid in student learning. Furthermore, hosting and sharing these apps is becoming more accessible, with organizations like CUAHSI running shiny servers. In this presentation, I will show two examples of implementations of a web app to aid in student learning, one on the concept of a water balance, and one on running and parameterizing a basic catchment hydrology model. I will also discuss tools and strategies for building and hosting your own shiny app to address the learning goals for your classes.
Describing the APIs comprehensively: Obtaining the holistic representations from multiple modalities data for different tasks

API (Application Programming Interface) is an important object in software development, and describing them properly is the basis for solving related problems, such as API recommendation. Recently, multimodal data fusing approaches become a hot research topic in different fields, and they can be used to get comprehensive representations of things by describing them from different angles. This provides us with a new useful way for API representation. In this work, we aim at describing APIs comprehensively by fusing information from multimodal data for supporting different API-related tasks. To achieve this goal, we propose a novel approach BDBM (Bimodal Deep Boltzmann Machine) to obtain holistic representations of APIs by fusing the information in text and code modalities, which are the API descriptions and the codes of the products. Then, the BDBM is applied to two typical API tasks (API recommendation and similar API mining) to analyze its performance. The results show that the API recommendation based on BDBM outperforms the ones based on unimodal API information, our method’s precisions can reach 0.67, 0.65, 0.61 at top-3, top-5 and top-10, while MAP and MRR are 0.66 and 0.67. Meanwhile, the close representations give similar APIs with similar functionalities as well as similar usage in codes. Thus, we believe that multimodal data fusion is suitable for describing APIs, and the holistic representations given by BDBM can be used in different API-related tasks.
Semantic-based conditional generative adversarial hashing with pairwise labels

Hashing has been widely exploited in recent years due to the rapid growth of image and video data on the web. Benefiting from recent advances in deep learning, deep hashing methods have achieved promising results with supervised information. However, it is usually expensive to collect the supervised information. In order to utilize both labeled and unlabeled data samples, many semi-supervised hashing methods based on Generative Adversarial Networks (GANs) have been proposed. Most of them still need the conditional information, which is usually generated by the pre-trained neural networks or leveraging random binary vectors. One natural question about these methods is that how can we generate a better conditional information given the semantic similarity information? In this paper, we propose a general two-stage conditional GANs hashing framework based on the pairwise label information. Both the labeled and unlabeled data samples are exploited to learn hash codes under our framework. In the first stage, the conditional information is generated via a general Bayesian approach, which has a much lower dimensional representation and maintains the semantic information of original data samples. In the second stage, a semi-supervised approach is presented to learn hash codes based on the conditional information. Both pairwise based cross entropy loss and adversarial loss are introduced to make full use of labeled and unlabeled data samples. Extensive experiments have shown that the propose algorithm outperforms current state-of-the-art methods on three benchmark image datasets, which demonstrates the effectiveness of our method.
A Model for Teaching Mathematics to Gifted Students Based on an Effective Combination of Various Approaches for their Preparation

Currently one of the urgent goals of mathematical education is the organization of effective work with gifted students.Based on the study of various approaches to teaching mathematically gifted students, many years of experience of teachers, students' work, and an analysis of curricula and materials for schools with in-depth study of mathematics, an author's model for the training of gifted students was developed.The novelty of this model is that it ensures a rational combination of various forms of education for gifted children on the basis of differentiation, individualization of the process of teaching mathematics, advanced learning, openness, democracy, reflection, and adequate control.The pedagogical experiment was carried out for two years in the Abulfazl Balami gymnasium for gifted children in the city of Vahdat, Republic of Tajikistan.41 students and 18 teachers took part in the experiment.The data obtained from the experimental and control groups were subjected to qualitative and quantitative analysis.Over the same time interval there were significant changes in the performance of students in the experimental groups, with 40% of the students moving to a higher level.In the control groups, the change was not significant.
Detection and characterization of slow deformation from GNSS data by deep learning in the Cascadia subduction zone

The stress that accumulates on faults due to tectonic plate motion can be released seismically and aseismically. The seismic release of stress takes place during earthquakes at short time scales (seconds to minutes), and can be identified on seismic records. The aseismic part of this stress release occurs during Slow Slip Events (SSEs), that last from days to years and do not radiate energetic seismic waves. SSEs are monitored with dense Global Navigation Satellite System (GNSS) networks that record the deformation induced at the surface. A precise identification of slow slip events is key to better understand the mechanics of active faults and to better describe the role of aseismic slip in the seismic cycle. Yet, the characterization of SSEs of various sizes from existing GNSS networks is challenging, and extensive SSEs catalogs remains sparse and incomplete: for example, 64 events for SSEs in Cascadia (Michel et al., 2018), 24 long-term SSEs (Takagi et al., 2019) and 284 short-term SSEs (Okada et al., 2022) in Nankai, Japan. Traditional SSE characterization either focus on specific events, identified visually with high signal to noise ratio (e.g. Radiguet et al., 2016), use time series decomposition approaches such as ICAIM (independent Component analysis inversion method) (Michel et al., 2018; Radiguet et al., 2020), or, for small events, geodetic template matching (Okada et al., 2022; Rousset et al., 2017). We focus on the Cascadia subduction zone, where a link between slow slip and bursts of tremors has been established (Rogers &amp; Dragert, 2003). In this direction, tremor catalogues can be used to validate potential SSEs detections against the spatiotemporal distribution of tremors. Moreover, a catalogue of SSEs has been recently assessed by (Michel et al., 2019), providing additional benchmark to our analyses. We generate synthetic SSEs from synthetic dislocations (Okada, 1985) using the slab2 model (Hayes et al., 2018). Each SSE template, assumed as a sigmoidal-shaped transient, is further added to a window of noise obtained from real GNSS data (Costantino et al., in prep.). We develop a deep learning-based method for the systematic detection and characterization of SSEs using a Convolutional Neural Network (CNN) in combination with a Graph Neural Network (GNN). We test our method both on synthetic and real position time series. Results on synthetic data are consistent and show a detection trade-off between the SSEs location, magnitude and the density of the GNSS network. Results on real GNSS positional time series show a good agreement with existing catalogues (cf. Michel et al., 2019). Moreover, new detections have been carried out, which correlate well with the temporal distribution of tremors, suggesting that those events could be new SSE detections, which will be further validated by assessing their spatial-temporal consistency through scaling laws output by the deep learning model.
A Soil Data Platform to Inform Soil Health Investment

Healthy soil is critical to the growth of nutritious food and to farmers&amp;#8217; livelihoods. However, one-third of global soil is already affected by moderate to severe degradation by diverse processes. The reduction of soil degradation is one of the most important policy objectives, but policymakers and local planners require guidance in choosing&amp;#160; the most effective interventions, both scientifically and economically, to reduce soil degradation.&amp;#8239;&amp;#160; The complex interconnections between agriculture, the natural environment, and social well-being increase the need of researchers to access and use comprehensive soil data sets. Our ability to manage and support soil depends on the data we collect and the information and knowledge that we generate from them.&amp;#160;Nevertheless, soil data accessibility and sharing are currently low and represent one of the major barriers to making better decisions in agriculture. A critical first step to capitalize on the opportunities offered by agriculture science and technologies is thus the standardization, organization, and the making of the multitude of specialty datasets generated by studies and projects publicly available to the global community.&amp;#160; To address this challenge, Varda is creating an interactive soil digital platform that allows surfacing and comparing existing soil data across the globe in order to facilitate soil data sharing and interoperability. Moreover, the platform highlights soil data gaps across regions through a soil data gap index, and particularly soil health data gap through another ad hoc index developed by Varda and Aberdeen University. Such indexes will allow multilateral organizations, governments, and agribusiness stakeholders to fill some of these data gaps through ad-hoc soil sampling and data collection campaigns.
DDE scholar report: a new open and Ai frontier and innovative reflect for solid earth

The Deep-Time Digital Earth (DDE) program was initiated by the International Union of Geological Sciences (IUGS) and is being developed in cooperation with national geological surveys, professional associations, academic institutions and scientists around the world. Following the FAIR (findable, accessible, interoperable and reusable)&amp;#160;and TRUST (Transparency, Responsibility, User focus, Sustainability and Technology) principles, DDE aims to link and harmonize global deep-time Earth data and share global geoscience knowledge with the goal of stimulating data-driven discoveries in the study of Earth's evolution through deep time.In order to release DDE's expert knowledge bonus, data bonus and platform bonus, and help scientists deal with the challenges of earth science research based on their needs, DDE plans to launch a series of annual thematic, scholarly reports. Through in-depth analysis and interpretation of global research trends, it will help scientists understand major research breakthroughs in the field of solid earth sciences, and understand research methods, solutions to major scientific problems and highlight key topics and themes for future research.The first DDE report is due to be released in 2023. DDE Report will use the theory and methods from bibliometric and altimetric research and release the &amp;#8220;hot&amp;#8221; topics and development of earth science research in solid earth science. The report will explore the deep reason behind trends and become the barometer of earth science research publications and outputs. DDE report aims to help scientists on determining the future research focus and innovation, and finally, to guide future earth science research trends.
EFL Students’ Interest and Attitude in Learning Through WhatsApp Group

Evaluating one of the mostly used online media during this COVID-19 pandemic Indonesia namely WhatsApp, this study investigates EFL students’ interest and attitude toward learning English through WhatsApp Group as well as the influencing factors. Taking 30 university students of English Education Department at IAIN Salatiga as the subjects, this qualitative study makes use of online questionnaire and online interview to collect data. The findings show that students averagely have positive interest and positive attitude toward the use of WhatsApp group as their primary e-learning tool, with the enjoyment aspect of interest and the affective aspect of attitude being most dominant values. Meanwhile, five main influencing factors of students’ interest and attitude have been identified including (1) the teachers’ methods or strategies of online teaching (2) WhatsApp features and affordances, (3) students’ mood, (4) the network accessibility and strength, and (5) students’ personality. Furthermore, in order to enhance students’ interest and attitude, it is suggestible that teachers employ innovative and creative learning strategies, whereas the students be more disciplined in following their online classes.
Teaching Procedure Text by Using Youtube as A Media in English Language Teaching: EFL Students’ Perspectives

This study aims to determine students' perceptions of the use of Youtube application for learning procedure text. The method used was qualitative using a descriptive approach. The population in this study were students of SMPN 06 Lebong and the sample used was purposive sampling for class VII students of SMPN 06 Lebong. The data collection technique in this study was used a questionnaire given to students using a Google form given in the third week after learning the text procedure was completed by using Youtube video as a media during learning, as well as interviews with several selected students which were carried out using Whatsapp to find out their perceptions of the use of Youtube media in learning procedure text. The data is explained in detail according to the statement given by the respondent. The results showed that the use of the Youtube application as a medium for learning procedure texts could make it easier for students to understand the material related to procedure texts, besides that students found it easier to access information related to learning materials so that they were able to explain and make procedural texts properly and correctly. Thus, the use of the Youtube application in the procedural text learning process can help students understand the procedural text material well and can have a positive impact on English learning activities
Attracting Students’ Interest in Learning English Through Quipper School Premium in 12th grade of SMAN 1 Karangkobar

This study aims to determine students' interest in learning English by using Quipper School Premium as Learning Media. There are 132 students at 12 graders from 4 science classes of SMA N 1 Karangkobar. The data analyzed using the qualitative descriptive method followed by a random sampling technique to take the sample. The responses of the interview are concluded to answer the research. The results showed that most of the students are interested and not too difficult in using Quipper School as one of the online learning media even though it is the first time using it. Some of the features in Quipper School include 4 skills, namely speaking, listening, reading, and writing. Quipper School provides complete materials, exercises, and assignments. This makes students feel comfortable in learning English and also motivates them to study and improve their English skills.
Students’ Perception on Learning Reading Comprehension Through Google Meet in English Development Class

The Corona Virus Disease 2019 (COVID-19) pandemic is a global problem that has an impact throughout the world, one of which is Indonesia. This is in line with the English Development Class 29 at IAIN Purwokerto which applies the Google Meet application in reading comprehension lessons. The aim of this research is to investigate students’ perception of using Google Meet in reading comprehension class. The method of this research used is a collaboration between quantitative and qualitative. The researchers used interview 4 students as a sample via chat Whatsapp and survey applied respondents answer sheets of any statement submitted via Google Form. The results are Google Meet is an online learning media that is easily accessible by students and students are greatly helped by the existence of Google Meet during the online learning. Some of students (8,9%) strongly agree, most students (80%) agree that the used of Google Meet caused material understanding easier and some students are disagree (11,1%) because of some obstacles they have. The conclusion of this research is students perception in the use of the Google Meet especially in learning reading comprehension proved to be effective.
A Systematic Review: Telegram As Learning Media in Teaching English

Social media as a medium for learning English is rapidly increasing during unusual conditions like in pandemics nowadays. Telegram, well-known as a communicative tool used by people worldwide, then transformed to use as learning media. This article contains Telegram as a medium that is packaging with a systematic review method. In short, a systematic review is a method that is used by research through analysis of some source that has a related topic, such as articles. This article aims to explore how telegrams as a learning medium are performing in teaching English. Besides, researchers tried to look for the effect of using telegrams as learning media. Researchers searched for articles on electronic search engines, namely Google Scholar, related to several articles; Telegram, MALL, English Learning, Learning Media. Moreover, for the characteristic researcher focus on Telegram as an English language learning medium, publications around 2017-2020, quantitative, and field research. Finally, 27 articles are relating to the keywords. Furthermore, researchers reduce eight articles because some aspects are not related, and the total from the articles include in this systematic review are 19 articles. Telegram performers were used as teaching assistants, assessment language, and learning media in English learning for the result. Additionally, in brief, researchers found that 18 articles positively affect learning English, yet 1 article is damaging. In its application of learning on Telegram, the teacher should apply a different strategy instead of offline because the students need explicit instructions about what the teacher wants. However, Telegram can be used as an English learning media assistant. Fundamental skills such as vocab, writing, and reading. The limitation of the research was the insufficient article resources related to language learning topics.
The Use of Whatsapp Group Discussion to Improve Grammar Competence in MAN 1 Banyumas

Since the pandemic disease of coronavirus or colloquially known as COVID-19 in 2020 most of educational activities are constrained. This leads to the problem in the learning process at schools. Due to the event, schools are temporarily could not implemented face-to-face learning, yet the education process must go on, however. Thus, the Minister of Education and Culture of the Republic of Indonesia recommend a series of distance learning in this situation. WhatsApp is used as the distance learning media to teach English in MAN 1 Banyumas. WhatsApp is an application provided by now well-known company Facebook. WhatsApp was intended on conducting people in easier and convenient instant messaging which uses end to end encryption and requires internet data network to operate it. The English teacher in MAN 1 Banyumas uses WhatsApp as the media. Thus, it become phenomenon following the situation which was able to lead the curiosity of the researchers to conduct the study based on what is occurring in the current time. The study uses a qualitative method to find out the effectiveness of WhatsApp media group discussion to improve grammar competence in MAN 1 Banyumas, to discover the grammar competence of the students, and the advantage of WhatsApp group discussion to improve grammar competence according to the teacher. Otherwise, the teacher also stated that online learning through WhatsApp group discussion is easier to be done even though there are issues in the internet signal and data plan.
I-vectors and Deep Convolutional Neural Networks for Language Identification in Clean and Reverberant Environments

In the current study, a method for automatic language identification based on deep convolutional neural networks (DCNN) and the i-vector paradigm is proposed. Convolutional neural networks (CNN) have been successfully applied to image classification, speech emotion recognition, and facial expression recognition. In the current study, a variant of typical CNN is being applied and experimentally investigated in spoken language identification. When the proposed method was evaluated on the NIST 2015 i-vector Machine Learning Challenge task for the recognition of 50 in-set languages, a 3.9% equal error rate (EER) was achieved. The proposed method was compared to two baseline methods showing superior performance. The results obtained are very promising and show the effectiveness of using DCNN in spoken language identification. Furthermore, in the current study, a front-end feature enhancement and dereverberation approach based on a deep convolutional autoencoder is also reported.