Reviewerly/exHarmony huggingface dataset:
Preconditions of GPA-ES Algorithm Application to Big Data

Herein presented contribution speaks about preconditions of genetic programming algorithm (GPA) used to work with so-called big data. In the paper, the different ways of decrease of number of (big) training data evaluations as well as parallelization of this process are presented. The paper also discusses using a floating window to provide evolution steps on small data subsets. The presented approach is based on reduction of the number of evaluations to allow fitness function evaluation in acceptable computational time. The original GPA-ES algorithm is described too. This algorithm was developed and tested for many years for application in area of accurate symbolic regression. Now the interest is to apply it to the application domain of big data, especially to data-based modeling and knowledge discovering fields. Then, there are described experiments verifying these hypotheses and the results are discussed. These experiments are based on symbolic regression (discovering of differential equations) describing a training data set representing movement of the Lorenz attractor deterministic chaos system.
A Domain Semantics for Higher-Order Recursive Processes.

The polarized SILL programming language uniformly integrates functional programming and session-typed message-passing concurrency. It supports general recursion, asynchronous and synchronous communication, and higher-order programs that communicate channels and processes. We give polarized SILL a domain-theoretic semantics---the first denotational semantics for a language with this combination of features. Session types in polarized SILL denote pairs of domains of unidirectional communications. Processes denote continuous functions between these domains, and process composition is interpreted by a trace operator. We illustrate our semantics by validating expected program equivalences.
Attentional autoencoder for weighted implicit collaborative filtering

Many recommender systems operate on transactional data, of which the most widely available measure is the level of interaction between users and items. In real-world applications the recommendation process is often further divided into two stages: candidate generation, which selects a number of suitable items from the whole inventory, and ranking, which determines the order of suitability among the candidates. In this paper we define two separate tests to represent the candidate generation and ranking tasks, and evaluate a diverse set of old and new collaborative filtering methods on these tests. The results on three different datasets show that model accuracy on the two tasks do not correlate strongly with each other, thus in practical applications the model choice for each task should be considered independently. Furthermore we introduce an attentional autoencoder for implicit recommendation, based on the Autorec and Transformer architectures, trained with a variational objective. The proposed model yields consistently superior results to the other evaluated methods in both the candidate generation and ranking tasks.
MOGPTK: The Multi-Output Gaussian Process Toolkit

We present MOGPTK, a Python package for multi-channel data modelling using Gaussian processes (GP). The aim of this toolkit is to make multi-output GP (MOGP) models accessible to researchers, data scientists, and practitioners alike. MOGPTK uses a Python front-end, relies on the GPflow suite and is built on a TensorFlow back-end, thus enabling GPU-accelerated training. The toolkit facilitates implementing the entire pipeline of GP modelling, including data loading, parameter initialization, model learning, parameter interpretation, up to data imputation and extrapolation. MOGPTK implements the main multi-output covariance kernels from literature, as well as spectral-based parameter initialization strategies. The source code, tutorials and examples in the form of Jupyter notebooks, together with the API documentation, can be found at this http URL
Implementation Plan on the Implementation Plan of the Linear Algebra Gold Course in Independent Colleges

At June 21st, 2018, the Ministry of Education was held for the first time since the reform and opening up a new era of Undergraduate Education Conference Chinese colleges and universities. Minister Chen Baosheng of the Ministry of Education put forward the slogan of “eliminating water courses and creating gold courses” and quickly formed a wave of education reform in the country. Wuhan Donghu University is not only heart-warming, but also in action. The teachers of the Basic Courses Department of Wuhan Donghu University have developed some effective action plans, action plans and construction measures based on the actual situation of the school and the characteristics of linear algebra. It provides a good direction for the construction of the golden course of the same institution.
An Architecture for e-Health Recommender Systems Based on Similarity of Patients’ Symptoms

Nowadays, data are generated both by users and other systems deriving new data from the previous ones for supporting decision making. The Electronic Health Records contains from structured data (e.g. hospital id, etc.), semi-structured data (e.g. a Health Level Seven-based records), to unstructured data (e.g. patient’s symptoms). The big challenge with health in smart cities is associated with the prevention, both the business and human health point of view. That is to say, avoid the propagation of certain diseases’ patterns is the best option no just for people, but also from the city’s health and the local economy. Thus, an architecture able to integrate into an Organizational Memory the medical data coming from heterogeneous repositories with the aim of gathering different kinds of symptoms is introduced. The query in the architecture is understood such as an unstructured text (i.e. symptoms) or an electronic health record. In this sense, the architecture is able to reach similar cases from the organizational memory based on a textual similarity analysis for limiting the search space. Next, using the International Classification of Diseases is possible to convert a case to a vector model representation in order to compute metric distances and get other cases order by a level of similarity. Each query answer contains a set of recommendations based on the frequency of diagnoses related to similar cases are given in order to share previous experiences. The processes point of view related to architecture is outlined. Finally, some conclusions and future works are outlined.
Classification of Radicalism Content from Twitter Written in Indonesian Language using Long Short Term Memory

Twitter was one of the most influential social media among users. It might give an either positive or negative impact. One of the negative impacts was the presence of radicalism content. In Indonesia radicalism was often connected to the issue of SARA (ethnicity, religion, race, and intergroup relations). It remained a public issue, requiring an analysis to process information related to radicalism. The research aimed to classify radical contents. The classification based on the types of radicalism and non-radicalism. Data were classified using LSTM. In finding higher accuracy, word2vec was used to transform words into vectors. The accuracy showed using LSTM method was compared with that obtained using SVM and k-NN. The two latest methods were the methods used by previous researchers regarding Indonesian radical contents of Twitter. Referring to the findings, LSTM showed higher accuracy 81.60%.
Comparing Four Important Sorting Algorithms Based on Their Time Complexity

Time complexity and memory complexity are significant for all algorithms, especially sorting algorithms. Using the right sorting algorithm for our data can possibly decrease time and memory usage. The sorting problem has attracted a great deal of attention because efficient sorting is essential to optimize other algorithms as well. Most of the time, a sorting algorithm consists of two nested loops, which can determine the complexity of the algorithm; however, other factors such as the number of data and data types play an important role as well. Thus, by using the right sorting algorithm, we can make more efficient use of time and memory. In this paper, we use different sorting algorithms for different data types in order to determine the optimum use of time and memory for these algorithms. It means that if we know what kind of dataset we have, it can help us to use a more efficient algorithm even close to linear time. One of the interesting results is shell sort. So, by checking each type of data, like primes, Fibonacci, odd, and even datasets, we can know more information about each sorting algorithm. Also, we can determine which algorithm needs additional memory for sorting and which does not. To reduce the time and memory allocation, we use various data, such as numbers in random order or reverse order, as well as a large number of data and a small number of data. By comparing the results, the optimal algorithm in various scenarios may be recognized.
Food Literacy and Dietary Behaviour among Day Students of Senior High Schools in Winneba, Central Region of Ghana

This study investigated the extent to which food literacy competencies of day students in senior high school relates with their dietary behavior in Winneba. The study employed a descriptive survey design where the quantitative approach was followed. Using the purposive and stratified random sampling strategies, a sample size of 404 day students was selected for the study. A structured questionnaire was used for data collection which was analyzed using both descriptive (frequency, percentage, mean, standard deviation) and inferential (t-test, ANOVA, Pearson Moment Correlation) statistics. The study revealed that generally, even though day students had good food literacy skills, they exhibited different competencies in the food literacy constructs, it was discovered that food capacity (eat or intake) was dominant, followed by food selecting skills and preparation and food planning and management. The study further established that generally there was a moderate and statistically significant positive relationship (r=0.660, p<0.05, 2-tailed) between food literacy and students’ dietary behavior. Besides, it was found that the students exhibited inappropriate dietary behavior as greater proportion skipped meals notably breakfast, practiced excessive snacking, low consumption of fruits and vegetables with very few eating three square meals daily. Additionally, it was found that whereas age and form/class/level of students influenced their food literacy levels, gender and type of school attended did not influence their food literacy. Based on these results, it was recommended that school authorities should liaise with the Effutu Municipal Health Directorate to design and implement programmes to conscientize students on the need to be food literate and also to develop and practice effective dietary practices for optimum dietary behavior.
PENGUKUR PARAMETER SEARCH ENGINE OPTIMIZATION (SEO) SECARA ON PAGE PADA TOKO ONLINE UNTUK MENINGKATKAN PENJUALAN

The current search engine is not only for searching the pages of the website you want to visit but also as the main source of visitors on the internet. With the development of technology several ways that can be used to improve website ranking on search engines, one effective way by utilizing Search Engine Optimization (SEO) can help some websites to get SERP rankings (Search Engine Results Pages) on search engines, by a good SERP ranking will then be determined on the number of visitors on the website. An application is needed to calculate SEO applications on a web site, with the application of Online SEO measurement parameters can help business users of online stores to increase visitors and invite visitors who become buyers. In the Complete SEO Parameter Measurement Application on this page has 6 important parameters in SEO on the page provided, Title Tag, Title Tag, Meta Description, Content, Alt Tag and Keyword Density, with an explanation of each - each parameter where The accuracy of the application results in the calculation can be obtained Kappa value of 0.57..
Estimation of Error Constant of an Electromechanical Energy Meter with Machine Vision in a Mobile Application

This article describes an algorithm in a smartphone designed to support the accuracy test, periodically done by network operators in energy meters. The algorithm is implemented in a mobile application with the IDE Android Studio. The applied methodology implies in the first stage video stabilization, in the second stage a novel method for the rejection of noise by illumination changes, and in the third stage the estimation of the percentual error of an electromechanical meter. The results of the application show a standard deviation of 0.1354 s, lower than the standard deviation of the manual method = 0.1966 s, which indicates a good performance of the mobile application. The experiment performed show 95% of the accuracy tests performed match the result with those performed manually
Identifying Upflow Zone Based on Thermal Infrared (TIR) Sensor and Field Measurements at Volcanic Field

Abstract Upflow zone identification at volcanic fields is crucial for geothermal resource exploration. The common problem to identify the upflow zone using conventional mapping method is time-consuming and the limitation of access to the area. The application of satellite imaging as ground-truthing is aimed to increase the effectiveness of upflow zone detection at geothermal fields. This study selected the volcanic field around the Bandung Basin for a model case. The data used in this study were thermal images of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Thermal Infrared Radiometer (TIR) by the night observations. The TIR data were corrected and calibrated by Visible Near Infrared Radiometer (VNIR) to measure Land Surface Temperature (LST). We then focused our analysis around a volcanic area that showed high LST at the Papandayan crater and other manifestations. Validations were carried out by measuring surface temperature and gas concentrations including SO 2 and CO 2 . The reading value of the gases was different on each location, but the pattern of the gases was relatively similar especially the SO 2 gas pattern. The SO 2 gas showed a relatively constant trend of gas concentration over time in the upflow zone, but in the outflow zone showed an increase pattern with the time whose reading values were lower than those on the upflow. On the contrary, the non-geothermal features showed that the SO 2 concentration decreased with the time towards almost 0. According to the retrieved LST, the surface manifestations were located not only at the high anomaly but also at medium anomaly depending on the manifestation dimension. The gas and temperature measurements proved that LST could be used to enhance the effectiveness of upflow zone identification.
Cultivation of Innovative Talents in Higher Education Based on Big Data

With the continuous improvement of the informatization level of higher education in China, higher education with the core goal of cultivating innovative talents has become a data-intensive research and practice field, and has begun to show the typical characteristics of big data, such as large amount of data, diverse types and low value density. Starting from the data background, this paper analyzes the necessity and advantages of cultivating innovative talents in higher education with the help of big data, and puts forward four training measures including data analysis, innovative research and development, policy integration and practice expansion. The research results are as follows.
Maximum Diversity in Web Application User Action Privacy

Popular web application features, like autosuggestion and auto-correction, are well-known to amply reveal input actions entered by their users to any eavesdroppers, by generating sufficiently unique network activity bursts on actions. Some techniques, like k-anonymity and l-diversity, already exist in the literature for achieving different levels of privacy of user actions by making the generated network bursts for different actions indistinguishable from each other. We present a method that blends network bursts of not entire actions, but their probabilistic portions, with each other. Our method thus results in the maximum possible amount of l-diversity, namely k-diversity, and is a sianificant improvement over the existing ones.
Effect of using an Educational Software "Pequemath móvil 5" on Mobile Devices in the Learning Process of the Mathematical Area in Children of Five Years

Education is a very important factor for the process of life, especially in the first five years, that is why we are paying attention to initial education, because in our country is common for children to have difficulties in developing topics in the area of mathematics. As a consequence they are presenting low learning rates, especially in our locality Andahuaylas, Apurimac; for this reason, strenuous efforts are made to support and improve the process of their learning. So the present research work has the purpose of determining the effect of the use of the educational software PequeMath Movil for mobile devices in the learning process of the area of mathematics in children of 5 years. The analysis, design, development and implementation of educational software in mobile devices has been carried out, and to measure the effect were formed control and experimental groups, corresponding to the results of the conceptual and procedural aspects of the fourth bimester, which they were compared to observe the effect. When analyzing the data of the evaluations, the use of educational software has a positive effect on the educational process of the children of the Santa Rosa Educational Institution from Andahuaylas, Apurimac.
Security Analysis of Group Action Inverse Problem with Auxiliary Inputs with Application to CSIDH Parameters

In this paper, we consider the security of a problem called Group Action Inverse Problem with Auxiliary Inputs (GAIPwAI). The Group Action Inverse Problem (GAIP) plays an important role in the security of several isogeny-based cryptosystems, such as CSIDH, SeaSign and CSI-FiSh. Briefly speaking, given two isogenous supersingular curves E and $$E'$$ over $$\mathbb F_p$$, where $$E'$$ is defined by an ideal $$\mathfrak a$$ in the $$\mathbb F_p$$-endomorphism ring of E and denoted by $$E' = [\mathfrak a]*E$$, GAIP requires finding $$\mathfrak a \subset {\text {End}}_{\mathbb F_p}(E)$$. Its best classical algorithm is based on the baby-step-giant-step method and it runs in time $$O(p^{1/4})$$. In this paper, we show that if E and $$E'$$ are given together with $$[\mathfrak a^d]*E$$ for a positive divisor d that divides the order of the class group of $${\mathbb Z}[\sqrt{-p}]$$, then $$\mathfrak a$$ can be computed in $$O\big ( ( p^{1/2} /d)^{1/2} + d^{1/2} \big )$$ time complexity. In particular, when $$d \approx p^{1/4}$$, it can be solved in time $$O( p^{1/8} )$$ which is significantly less than $$O( p^{1/4} )$$. Applying the idea to CSIDH-512 parameters, we show that, if an additional isogenous curve $$[\mathfrak a^d] * E$$ is given, the security level of this cryptosystem reduces to 68-bit security instead of 128-bit security as originally believed.
Insight GT: A Public, Fast, Web Image Ground Truth Authoring Tool

This paper proposes the community the development of a public web tool for fast image Ground Truth Authoring Tool (GTAT). Image ground truth authoring tools are key to generate training and validation data for image segmentation and classification systems. The paper does a short review of similar publicly available GTAT’s, its features and short-comings, in order to spot the key features missing for a public GTAT to the community. Based in the concluded wished features, we aim to develop a free and open GTAT in the future.
Application of Kolintang Traditional Music Instrument Using Motion Sensor Detection: Webcam

Kolintang or Indonesian's xylophones are a traditional musical instruments originating from Minahasa (Sulawesi-Utara) which belong to the group of pitched percussion instruments, as an idiophonic musical instrument the source of kolintang sound comes from its vibrating bar when hit. Unfortunately, nowadays this traditional musical instrument is quite difficult to obtain, with a large and heavy size, as well as requires extensive storage space and should not be damp. In addition, the lack of knowledge and interests of the younger generation. In the event of allowed to continue, will be forgotten by the younger generation, even though it is a cultural heritage that must be preserved. Our purpose in this research, to build an application for kolintang musical instruments using a webcam as a PC motion detection sensor, thus providing a new atmosphere in playing the kolintang musical instruments on portable computer. This application has been successfully built, where the actual results obtained are in accordance with the expected results, that is kolintang musical instruments can use motion detection sensors: webcam, and other interactive media such as keyboard, mouse, and webcam in portable computer. Besides that, this application also can be run on smartphones.
AN ADVICE MECHANISM FOR HETEROGENEOUS ROBOT TEAMS

The use of reinforcement learning for robot teams has enabled complex tasks to be performed, but at the cost of requiring a large amount of exploration. Exchanging information between robots in the form of advice is one method to accelerate performance improvements. This thesis presents an advice mechanism for robot teams that utilizes advice from heterogeneous advisers via a method guaranteeing convergence to an optimal policy. The presented mechanism has the capability to use multiple advisers at each time step, and decide when advice should be requested and accepted, such that the use of advice decreases over time. Additionally, collective collaborative, and cooperative behavioural algorithms are integrated into a robot team architecture, to create a new framework that provides fault tolerance and modularity for robot teams.%%%%M.A.S.
The effects of the first reading experience of infancy on reading and academic achievement of elementary first graders

The purpose of this study was to analyze the structural relationship among the age of first reading, child’s reading motivation, reading ability, and academic achievement in elementary first graders. A total of 267 pairs of elementary first graders and their mothers and teachers were sampled in Gyeonggi province, South Korea. First graders were tested with tasks to measure reading ability and mothers’ responses and teachers’ evaluation were collected to measure students’ age of first reading, reading motivation, and academic achievement. The results were as follows. First, first reading, reading motivation, reading ability, and academic achievement were partly different by socio-demographic factors. Second, earlier age of first reading was associated with higher reading motivation, reading ability, and academic achievement in the first grade. Third, SEM analysis showed that reading motivation and reading ability fully mediated the effect of the age of first reading on academic achievement.
Side Channel Attack of Lightweight Block Cipher Simeck Based on Deep Learning

Simeck32/64 algorithm is a lightweight encryption algorithm have no replacement and substitution operation. Therefore, in this paper, under the condition that intermediate value information and plaintext ciphertext are unknown, only get electromagnetic leakage data of the copy of the equipment of, we use these data trained a deep learning model of to attack the last 48bit simeck 32/64, so as to realize a “blind” gray box bypass electromagnetic attack algorithm based on deep learning.