Reviewerly/exHarmony huggingface dataset:
ForAlexa, an online tool for the rapid development of artificial intelligence skills for the teaching of evolutionary biology using Amazon’s Alexa

Abstract Intelligent Personal Assistants (IPAs), such as Amazon’s Alexa, are now widely used for an ample variety of tasks, ranging from personal management to education. These tools have shown considerable promise for student-educator interactions, especially at a distance, a potential that has come to the forefront during the ongoing COVID-19 pandemic. Even so, this potential is still underexploited, even in the current scenario. Alexa’s apps are known as skills, which include all the different commands that Alexa is capable of executing. It is important to note, however, that the use of such technology is work-intensive and can be relatively complex. Given this, to facilitate the development of new skills in Alexa, we have developed an online tool that permits the creation of questions and answers, as well as honing the interaction between Alexa and the user. We have named this tool ForAlexa , which has two types of forms, Question-And-Answer (Q&amp;A) and Random-Quote. Both these forms allow the user to build intents (an activity that is invoked by a spoken request from the user), but with slightly different functions. The Q&amp;A form is used to compile answers that Alexa will offer in response to an utterance (question), while the Random-Quote extends the interaction between Alexa and the user, based on the questions asked in the first form. ForAlexa also has a help assistant, as well as a manual, which explains all the steps necessary for the design of an intent. This tool allows educators to develop apps quickly and easily for their classes and this type of app could be an alternative to be used for students with special needs, such as the visually-impaired.
Improving ASI-based solar radiation nowcasting by using&amp;#160;automatic cloud type recognition methods

&amp;lt;p&amp;gt;In the last years there is an increasing interest for developing enhanced solar nowcasting methods, fostered by the massive deployment of small-scale / residential PV systems. Reference nowcasting methods are based on the use of All-Sky-Imagers (ASI). Although ASI-based nowcasting methods has received a notable attention in the last decade, their reliability is relatively low, because the processes involved in deriving the nowcasts are prone to many uncertainties. In this work, we propose and evaluate new methods aimed at increasing this reliability. Notably, we propose the use of an automatic cloud-type recognition system in the nowcasting procedure, allowing the use of cloud-specific transmittance values for the different cloud types. The study was carried out at a location of southern Spain, using a total of 1901 samples representing all the cloud types. Each sample is composed by one-minute time resolution ASI images, GHI and DNI measurements as well as cloud base height values derived from a ceilometer. Up to 30 minutes ahead one-minute time resolution forecasts were obtained and benchmarked against reference methods that not uses cloud-specific transmittances.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In the first part of the study, a statistical analysis was conducted to determine the GHI and DNI transmittance of 11 different cloud types; to this end, a Gaussian Mixture Model (GMM) was used. For some of the cloud types, the cloud base height was also used as a parameter of the model. In a second part of the study, these cloud-specific transmittances were incorporated in the nowcasting procedure using an operational automatic cloud type recognition method. This new nowcasting method was evaluated as an operational forecasting procedure.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Results of the first study reveals, firstly, that DNI total transmittances are almost negligible for cumuliform clouds. In contrast, stratiform clouds show a wide range of transmittances. Notably, while stratus and altostratus clouds behaves as cumuliform clouds, transmittances for cirrostratus and cirrus are, respectively, 30% and 80%, approximately. For the GHI, the total transmittance values ranges from 80% (cirrus) to 30% (stratus).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Results from the second study show that the proposed method provide slightly enhanced GHI nowcasts associated with cirrus, altocumulus and cirrocumulus clouds skies. For these cloud-types skies, the reduction achieved in the rRMSE values ranges from 2% to 6%. On the other hand, the proposed method provide a clear superior performance for the DNI nowcasting, with an overall reduction of rRMSE values of around 10% for the entire dataset. The reduction in rRMSE values also show a dependence of the cloud types, ranging from 6% (cirrostratus/cirrocumulus) to 20% (cirrus clouds).&amp;lt;/p&amp;gt;
Hour-scale cloud cover forecasting using WRF and an infrared all-sky imager

&amp;lt;p&amp;gt;Numerical weather prediction (NWP) models such as the Weather Research and Forecasting (WRF) model allow to forecast cloud processes at high spatio-temporal resolutions of a few kilometres and minutes. Nevertheless, WRF is known to underestimate the presence of clouds, which has negative impacts on solar irradiance forecasts for example. Therefore, drawing the forecasts closer to observations of clouds can be an efficient means for improving hour-scale cloudiness forecasts. Determining precise initial conditions from both observations and model output in terms of clouds is a challenge. This study explores the use of four-dimensional data assimilation (FDDA) nudging to take into account cloud cover observations from an infrared all-sky imager. Experiments with two WRF domains (9 km and 3 km grid spacing), driven by Global Forecasting System (GFS) forecasts, are performed over Central Europe. The cloud cover observations are determined by the Sky InSight, an all-sky infrared imager, installed at the Lindenberg Meteorological Observatory &amp;amp;#8211; Richard-Assmann-Observatory (MOL-RAO) in eastern Germany. Compared to observations in the visible range, this instrument has the advantage to deliver cloud cover observations of the same quality at day and night time. The observations are compared to cloud cover values from the WRF background. Depending on the difference between the two, the WRF background humidity profile at the location of the imager is either increased, decreased or unchanged to obtain a new profile for the nudging. Cloud base height values are determined by a ceilometer and empirical values are used for the cloud top height to limit the vertical extent of the derived humidity profiles to be nudged. Reuniwatt&amp;amp;#8217;s custom Meteosat Second Generation (MSG) Satellite Application Facility NoWCasting (SAFNWC) cloud products are used to evaluate the impact of the nudging on WRF forecasts of cloud cover. The fast and cost-effective nudging method leads to an improvement of WRF cloud cover forecasts. It could be easily upscaled to a large amount of ground-based camera observations.&amp;lt;/p&amp;gt;
Reconnaissance Robot for Rescue Services

The spherical robots are becoming very popular in the field for many scientists and researchers working with surveillance, military, or transportation systems. This paper deals with comparing of each driving mechanism of the spherical robot. The vast majority of spherical robots only have inertial sensors, which they use for movement, positioning, travel balancing, etc. Few spherical robots can interact with the environment. Those who can do this have sensors that can measure the properties of the environment or can influence the environment in some way. The article also deals with the possibility to use sensors like cameras, gas sensors, thermocouples, and LiDARs. Data from these sensors will be processed using artificial intelligence techniques to detect anomalies in the environments. Chosen sensors can expand a robot's sensing capabilities which are usually very narrow. The aim of the article is to design s special type of spherical robot. If we join all these components together we can create a reconnaissance spherical robot for rescue services special designated for tunnels or closed spaces.The prototype is a financially inexpensive robot, which is also suitable for the teaching process, as an element that collects data from the environment (sensors, video, etc.), which will be used for working with AI tools.
Stereo-phase rectification for metric profilometry with two calibrated cameras and one uncalibrated projector

Fringe projection profilometry requires calibrating both cameras and projectors for metric measurements. Cameras are relatively simple to calibrate, but projectors require more sophisticated procedures. In this paper, a fringe projection profilometer with two calibrated cameras and one uncalibrated projector is developed for metric measurements. A phase rectification method, which is crucial for stereo matching, is designed by minimizing the perspective distortion. Also, a simple method for point matching using stereo rectified phase maps is proposed. The principles of metric profilometry using the proposed rectification method are introduced. The developed system is evaluated experimentally by the metric measurement of three-dimensional objects. The obtained results confirm a high accuracy of metric measurement and versatility in the design of fringe projection profilometers with uncalibrated projectors.
A Comprehensive Spark-Based Layer for Converting Relational Databases to NoSQL

Currently, the continuous massive growth in the size, variety, and velocity of data is defined as big data. Relational databases have a limited ability to work with big data. Consequently, not only structured query language (NoSQL) databases were utilized to handle big data because NoSQL represents data in diverse models and uses a variety of query languages, unlike traditional relational databases. Therefore, using NoSQL has become essential, and many studies have attempted to propose different layers to convert relational databases to NoSQL; however, most of them targeted only one or two models of NoSQL, and evaluated their layers on a single node, not in a distributed environment. This study proposes a Spark-based layer for mapping relational databases to NoSQL models, focusing on the document, column, and key–value databases of NoSQL models. The proposed Spark-based layer comprises of two parts. The first part is concerned with converting relational databases to document, column, and key–value databases, and encompasses two phases: a metadata analyzer of relational databases and Spark-based transformation and migration. The second part focuses on executing a structured query language (SQL) on the NoSQL. The suggested layer was applied and compared with Unity, as it has similar components and features and supports sub-queries and join operations in a single-node environment. The experimental results show that the proposed layer outperformed Unity in terms of the query execution time by a factor of three. In addition, the proposed layer was applied to multi-node clusters using different scenarios, and the results show that the integration between the Spark cluster and NoSQL databases on multi-node clusters provided better performance in reading and writing while increasing the dataset size than using a single node.
Degraded character recognition from old Kannada documents

This paper addresses preparation of a dataset of Kannada characters which are degraded and robust recognition of such characters. The proposed recognition algorithm extracts the histogram of oriented gradients (HOG) features of block sizes 4x4 and 8x8 followed by principal component analysis (PCA) feature reduction. Various classifiers are experimented with and fine K-nearest neighbor classifier performs best. The performance of proposed model is evaluated using 5-fold cross validation method and receiver operating characteristic curve. The dataset devised is of size 10440 characters having 156 classes (distinct characters). These characters are from 75 pages of not well preserved old books. A comparison of proposed model with other features like Haar wavelet and Geometrical features suggests that proposed model is superior. It is observed that the PCA reduced features followed by fine K-nearest neighbor classifier resulted in the best accuracy with acceptance rate of 98.6% and 97.9% for block sizes of 4x4 and 8x8 respectively. The experimental results show that HOG feature extraction has a high recognition rate and the system is robust even with extensively degraded characters.
Investigating EFL students’ interest in the use of online discussion technique in asynchronous learning

The development of the era can be inextricably linked to the development of technology as it has entered all aspects of human life, including education. One of the technology utilization in education is e-learning which allows the learning process can be accessed anytime and anywhere. One technique commonly used in asynchronous learning nowadays is online discussion. This research aims at investigating the level of students’ interest and the factors that affect their interest in the implementation of the online discussion techniques. This research was conducted by mix-method, where the data was collected from the questionnaire and interview. The subjects of this research were 40 students in the questionnaire section and 4 students in the interview section. The result of the interview proved that students had a high interest in the implementation of online discussion techniques; while, the result of the interview indicated some factors that affect students’ interest in the implementation of online discussion techniques such as teacher activeness and involvement, internet connection, students’ motivation and concentration. Hopefully, this research can help to maximize the application of online discussion techniques to gain more benefits in the learning process.
Design of a Blockchain-Based Traceability System with a Privacy-Preserving Scheme of Zero-Knowledge Proof

With the fast development of the industrial Internet, its interconnectivity poses new challenges for the cooperation of industrial entities. Cooperation among these entities is built on trust, and trust is based on high-quality industrial products at reasonable prices. A traceability system can play an essential role in objectively reflecting the production process and promoting this trust. However, traditional traceability systems often have data privacy issues. Because traceability data are collected or generated during the production process (namely, production-related data), they could be considered privacy data. Several researchers have introduced privacy protection schemes into the traceability system, such as authentication or encryption. Nevertheless, when a privacy protection scheme is established, the original data are disclosed to the legal user of the system, but the data may still be leaked intentionally or unintentionally. Except for data privacy issues, a traditional traceability system can be vulnerable to network attacks, data unavailability, and reliability issues. The authors conducted a study to overcome these shortcomings, and this paper reports the results. We built a traceability prototype system using a blockchain protocol and a zero-knowledge proof method. First, we built a blockchain to record key production process data, aiming to maintain data reliability and availability. Second, through an analysis of traceability purpose using production knowledge, the traceability purpose could be divided into multiple provable statements. By introducing privacy protection through a zero-knowledge proof, the traceability process was converted to proving relative statements. Finally, the statements were validated by a smart contract that provided openness and reliability during the traceability process. Analysis has shown that our approach could meet the requirements for high security and privacy. In addition, the paper also discusses the calculation cost of the traceability process to show our work’s viability. The traceability system described in this paper creates new possibilities for constructing a healthy and reliable trust relationship between production entities to provide further support in the development of the industrial Internet.
The Impacts of Internet + Rural Financial Industry on County Economy and Industrial Growth Algorithm

In recent years, commercial banks and rural financial institutions in and below the county area have developed rapidly, and the number of outlets has increased rapidly. But for the vast rural areas of the country, the number of outlets is still limited. At present, there are still a large number of areas with zero financial coverage in rural the country. The development of the financial industry in a region not only affects the level of local economic growth but also has a good effect on improving the income level of local residents and changing the way of local economic development. To better develop rural finance and county economy, this paper constructs an artificial neural network model and FA model to predict the rural finance industry, and four indicators are selected to test the performance measurement of the model. The hit rates of the five models are 54.65%, 53.75%, 59.78%, 65.1%, and 73.08%, respectively. The model proposed in this paper has the highest hit rate and can predict rural finance more accurately as well, allowing for clearer changes in county economic and industrial growth.
Design of Proxy Re-encryption Based Market System for Copyright Protection of Digital Holographic Printing Contents

Hologram printing technology is a technology that outputs digital holographic printing contents on a flat surface of the real world.Since the printed holographic printing content shows different images depending on the observer's point of view on a flat object, a three-dimensional shape can be displayed without a separate device.However, the source of digital holographic printing content requires about 10 TB of data capacity to output A4 paper size.Therefore, in order to construct a market for trading original digital holographic printing contents, a technology for stably transmitting large amounts of data is required.In addition, if an act beyond the scope and authority of digital holographic printing content is allowed, financial damage may occur due to illegal copying and undue gain.To solve this problem, a market and DRM technology are needed to safely and efficiently transmit large amounts of digital data while stably transmitting it.In this study, proxy re-encryption technology was used to design a system that satisfies these requirements, and in this process, the required information and system model were designed, and a protocol to share data safely was designed.Proxy re-encryption technology is a technology that can safely and efficiently deliver data without exposing data and encryption/decryption keys to unauthorized users, and is suitable for communication and server environments that are not completely trusted.In this study, based on such proxy re-encryption technology, it is expected that the foundation for safe and efficient transaction of digital holographic printing contents will be prepared, and furthermore, it will provide a driving force for the growth of the holographic printing market.
The Competence Approach in Education

In this article, the competence approach is considered fundamental to modern educational philosophy. It is accepted as part of the reforms in education. The concepts of ‘competence’ and ‘competences’ are analysed. Separate opinions of the author are given on these concepts. The types of competences/competencies are defined according to individual views and ideas of the author. Special attention is paid to the professional competence of the teacher.
PENINGKATAN HASIL BELAJAR MATEMATIKA MENGGUNAKAN TEKNIK PROBING PROMPTING DI KELAS XI IPA 1 SEKOLAH MENENGAH ATAS NEGERI 2 PALEMBANG

This research is a Classroom Action Research, which aims to determine the improvement of mathematics learning outcomes using the probing prompting technique with 41 students of class XI IPA 1 students of SMAN 2 Palembang, in the even semester of the 2017/2018 academic year, using function derivative materials. This research consists of two cycles which include the stages of planning, implementation, observation and reflection. Data collection is done by giving a test after the learning process in each cycle takes place. As supporting data, observations were made during the implementation of learning. The results show increase of learning outcomes of mathematics, namely in the first cycle the average class 65.9 increased in the second cycle to 78.8. Classical learning completeness with KKM set at 75%, in cycle 1 it has not been fulfilled, which is only 68.25% but in cycle 2 it becomes 85.0%. In addition, student learning activities during the learning process also increased from cycle 1 to cycle 2. This shows that the probing prompting technique can improve mathematics learning outcomes. This study recommends that the probing prompting technique is effective to be used as an alternative teaching technique in an effort to improve mathematics learning outcomes.
COMPLEX DYNAMIC SYSTEMS THEORY IN LANGUAGE LEARNING: A SCOPING REVIEW OF 25 YEARS OF RESEARCH – CORRIGENDUM

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The Effectiveness of Using E-Learning as an Online Learning Media at Polytechnic Indonusa Surakarta

The Covid-19 outbreak has had a tremendous effect in no time. The Minister of Education and Culture of the Republic of Indonesia has adopted a policy to protect the nation's children from studying at home by means of distance education. Various technology platforms can be used to assist in the learning process. PJJ is e-learning-based learning. Indonusa Surakarta Polytechnic seeks to improve campus e-learning performance. To increase the use of e-learning, it is necessary to evaluate the use of campus E-Learning as an online learning medium at the Polytechnic Indonusa Surakarta. For a good evaluation, researchers conduct research in order to get feedback from users. The intended users are lecturers and students. The researcher used a qualitative descriptive method and the collection was done by means of a literature study and a questionnaire. The questionnaire used a linkert 4 scale, namely a 4 linkert scale questionnaire, namely Strongly Agree (Score 4), Agree (Score 3), Disagree (Score 2) and Disagree (Score 1). The methodology carried out begins with the formulation of the problem, data collection. The results of data collection were carried out by descriptive analysis so as to produce accurate conclusions. The conclusions of this study are: 67.2% of respondents agree during the pandemic and future online learning, 68.6% of respondents agree that lectures use the campus e-learning platform, 71.4% of respondents agree that campus e-learning is easy to use, 65 % of respondents agree that campus e-learning can facilitate learning between lecturers and students, 58.9% of respondents agree that campus e-learning can improve student competence, 90.2% of respondents agree that lectures use books/modules as lecture references, references in the form of printed books (59.4%) and E-Books (56.7%), The use of online learning platforms other than Campus E-Learning include: Zoom (56.3%), Gmeet (86.5%), Google Classroom (82, 5%), WhatsApp Group (71.9%), E-mail (36.5%) and others (9.4%)