Reviewerly/exHarmony huggingface dataset:
Conceptual analysis of geothermal neighboring zones characterization with contrasting behavior: applied to a Mexican geothermal field

In Mexico, there are many geothermal fields, which are characterized by high temperature but low permeability.In this work one of these fields is studied, which is a producer with high enthalpy but low mass flow production.The scope of this work is to analyze two neighboring areas of Los Humeros geothermal field (LHGF); whose performance is contrasting.According to productivity behavior, it was found that low permeability of rock formation is related with unfavorable balance between exploitation and water entrance as recharge.Analysis of static temperature profiles of some wells of the field provides temperature range between 300 and 360°C at the bottom.During drilling, low fluid circulation loss (no more than 20m 3 /hr) is observed in wells of this study zone.However, there is a marked difference, in productive characteristics in wells, located in neighboring zone.In this study, the behavior of producer wells located at the western side of the unproductive zone is compared with the unproductive wells.The main conclusion resulting from this study is that the presence of geological structures influences the productive or unproductive behavior of the wells.As a practical application of this study evaluation of stored heat in area of non-producer wells, and its recovery through the use of non-conventional techniques, is proposed.One of these, are related to the methodology of Enhanced Geothermal Systems (EGS).
Super-resolved image perceptual quality improvement via multifeature discriminators

Generative adversarial network (GAN) for image super-resolution (SR) has attracted enormous interests in recent years. However, the GAN-based SR methods only use image discriminator to distinguish SR images and high-resolution (HR) images. Image discriminator fails to discriminate images accurately since image features cannot be fully expressed. We design a GAN-based SR framework GAN-IMC, which includes generator, image discriminator, morphological component discriminator, and color discriminator. The combination of multiple feature discriminators improves the accuracy of image discrimination. Adversarial training between the generator and multifeature discriminators forces SR images to converge with HR images in terms of data and features distribution. Moreover, in some cases, feature enhancement of feature-rich region is also worth considering. GAN-IMC is further optimized by weighted content loss (GAN-IMCW), which effectively restores and enhances feature-rich regions in SR images. The effectiveness and robustness of the proposed method are confirmed by extensive experiments on public datasets. Compared with state-of-the-art methods, the proposed method not only achieves competitive perceptual index and natural image quality evaluator values but also obtains pleasant visual perception in edge, texture, color, and feature-rich regions.
The Effectiveness of the Use of Digital-Based Educational Comic Media in Improving Reading Interest in Elementary School Students

This research aimed to obtain information about the effectiveness of digitalbased educational comic media in improving reading interest of elementary school students This research is a the experimental research with design the one group pretest posttest.The research subjects were the students of SDN Jogorogo 3 and SDN Talang, Ngawi Regency in the 2018/2019 academic year.The data collection techniques using questionnaires and data analysis techniques using t-test.This research discusses the increase in reading interest based on the N-Gain scale which is grouped into several indicators.The results showed effectiveness of the use of digital-based educational comic media in improving reading interest in elementary school students.
GNSS Spoofing Detection and Identification Based on Clock Drift Monitoring Using Only One Signal

In this paper, we propose a GNSS spoofing detection and identification method based on clock drift monitoring using only one signal. At present, there are many anti-spoofing techniques based on time information monitoring, such as clock bias and drift monitoring. However, these techniques estimate the time information using observations of at least four signals. As a result, they can only detect spoofing but cannot identify it. Besides, in order to estimate the time information, they need to solve a group of nonlinear equations iteratively, which requires a heavy computational load. In view of this, we propose a new method to estimate the clock drift using only one signal. Therefore, the proposed method can not only detect the spoofing signal but also identify it, and it can work normally even when authentic signals cannot be received. Furthermore, the method estimates the drift by solving a linear equation without any iteration, which has a lighter computational load compared to the previous methods. Experiments show that the method can detect and identify the spoofing signal when the spoofer moves, manipulates the time information, or is driven by an ordinary clock, such as a crystal clock.
Factors Influencing Mental Toughness

In the life of exercise, there is one factor that can have a significant impact on the success of achieving targets, namely mental toughness.Mentally resilient athletes will be able to maintain their level of performance in difficult times and can view pressure as a challenge to overcome.The purpose of this journal analysis is to conduct a review of the factors that can affect mental toughness in sports.This analysis was conducted on several research journals published in the last ten years.The analysis shows that mental toughness is influenced by two factors, internal factors and external factors.Internal factors include self-concept, self-esteem, self-insight, self-reflection, selfdetermined motivation, characteristic adaptation and competitive trait anxiety.While external factors include psychological skills training, physical training, coaches, coaching, coach behavior and relations with coaches.
An Instance Transfer-Based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition

Recently, neural networks have shown promising results for named entity recognition(NER), which needs a number of labeled data to for model training.When meeting a new domain (target domain) for NER, there is no or a few labeled data, which makes domain NER much more difficult.As NER has been researched for a long time, some similar domain already has well labeled data(source domain).Therefore, in this paper, we focus on domain NER by studying how to utilize the labeled data from such similar source domain for the new target domain.We design a kernel function based instance transfer strategy by getting similar labeled sentences from a source domain.Moreover, we propose an enhanced recurrent neural network (ERNN) by adding an additional layer that combines the source domain labeled data into traditional RNN structure.Comprehensive experiments are conducted on two datasets.The comparison results among HMM, CRF and RNN show that RNN performs better than others.When there is no labeled data in domain target, compared to directly using the source domain labeled data without selecting transferred instances, our enhanced RNN approach gets improvement from 0.8052 to 0.9328 in terms of F1 measure.
Singing Voice Conversion Based on WD-GAN Algorithm

The research of singing voice conversion(SVC) has attracted more and more attention in the field of artificial intelligence. This paper proposes a WD-GAN algorithm for singing voice conversion. WORLD vocoder is used to extract the feature tensor of singing. The feature tensor and the singer's timbre label c are sent into G model for training. Using the differentiability of Wasserstein distance description, the problem of GAN training instability is solved. Through the game process of D model and G model, we can train the timbre characteristics of many singers and realize many-to-many singing voice conversion. Both objective and subjective evaluation experiments show that WD-GAN algorithm has better conversion effect than baseline network without Wasserstein distance in SVC.
Design of a Location-based Word Recommendation System Based on Association Rule Mining Analysis

Association rule mining is the process of discovering new knowledge and non-trivial patterns hidden in textual datasets. This approach is often used by learning analytics researchers and practitioners for exploring unique types of data collected from various educational environments. By leveraging association rule mining, this study aims at providing a solution to a critical challenge that most of the location-based informal learning tools face namely, the limited scopes to learn new words. Hence, the purpose of this work is to design a word recommendation system that can analyze a learner's previously learned words in order to suggest new vocabulary that can be acquired in a particular learning location. We designed a study and explored a dataset for uncovering the insight of language learners' learning behaviors. 20 EFL (English as Foreign Language) learners' data, whose learning location was Tokushima region of Japan, is analyzed by using association rule mining technique. The result indicates that-First, in informal learning context, EFL learners' word learning interests vary much from each other although the learning location is same; and Second, a word recommendation system can be introduced by using association rule mining analysis; however, the possibility of getting highly associated words are low compared with random words.
Using Data Mining Techniques and R Software to Analyze Crime Data in Kenya

Crimes have been the most dangerous threat to peace, development, human right, social, political and economic stability in Kenya.There is a great need to eradicate crime to facilitate development and counter all vices that are caused by crime.Efficient management of crime requires an adequate understanding of the patterns in which crime occur to put the appropriate measures in place for crime prevention.Crime has been in existence since the beginning of time hence will remain, and one of the solutions is to identify the pattern in which it occurs to prevent or counter it effectively as it occurs.The main objective of the study was to find out how different crimes are related.The study considered a number of data mining techniques which included; clustering, specifically k-means algorithm, mapping and APRIORI algorithm to analyze how different crimes are related and how often they occur.Crime cases were found to be decreasing over the years under study and counties with a high population reported higher number of crimes as compared to those with low population.The study suggested that these crimes could be controlled by directing more resources in the highly populated counties.The study leaves a research gap where the same crime data could be analyzed using time series methods since observed crime offenses are recorded alongside the time they occur.
sEMG Gestures Recognition Based on Wavelet Broad Learning System

Gestures recognition plays an important role in the robot systems, which can change existing human-computer interaction. In the existing researches, surface electromyography(sEMG) signals are widely used to classify and recognize gestures. However, the existing classification algorithms of gesture recognition spend a lot of time to train and update the parameters, such as deep learning system. Hence, our work proposes a sEMG gestures recognition algorithm based on wavelet broad learning system(WBLS). Finally, a simulation experiment is carried out and the results verify the effectiveness and efficiency of new method.
A Unified Framework for Depth Prediction from a Single Image and Binocular Stereo Matching

Depth information has long been an important issue in computer vision. The methods for this can be categorized into (1) depth prediction from a single image and (2) binocular stereo matching. However, these two methods are generally regarded as separate tasks, which are accomplished in different network architectures when using deep learning-based methods. This study argues that these two tasks can be achieved using only one network with the same weights. We modify existing networks for stereo matching to perform the two tasks. We first enable the network capable of accepting both a single image and an image pair by duplicating the left image when the right image is absent. Then, we introduce a training procedure that alternatively selects training samples of depth prediction from a single image and binocular stereo matching. In this manner, the trained network can perform both tasks and single-image depth prediction even benefits from stereo matching to achieve better performance. Experimental results on KITTI raw dataset show that our model achieves state-of-the-art performances for accomplishing depth prediction from a single image and binocular stereo matching in the same architecture.
The Interface between Interactive Art and Human-Computer Interaction: Exploring Dialogue Genres and Evaluative Practices

The mutual efforts of joining art and science have been an important source of innovation in many fields throughout history. In this article, we investigate the relatively common origins, differences, and similarities between the Interactive Art and Human-Computer Interaction (HCI) areas of knowledge. We also investigate what kind of human-computer interactions are sought by artists or emerge in Interactive Art examples, as well as what kind of frameworks, evaluation criteria or methodologies are reported in the literature to support the design and evaluation of Interactive Art. As a result of our analysis of Interactive Art examples found in the literature and beyond, we derived four genres of dialogue that emerge naturally or are stressed by authors: visual, embodied, tangible and social. These genres, albeit not comprehensive, can inspire the design of novel forms of interaction in computational systems with or without artistic intent. Moreover, frameworks, evaluation criteria, and methodologies may allow a cross-pollination between Interactive Art and HCI. While interactive artists may provide novel ways to look at the design and evaluation of interactive systems, these artists may also benefit from appropriating traditional HCI methods, tools, and technologies for new purposes. Lastly, we draw on our findings and learned lessons to outline a research agenda with the main objectives of 1) encouraging Interactive Art research, 2) studying Interactive Art examples, 3) practicing Interactive Art design and evaluation, and 4) designing Interactive Art for all.
INDReview on Facial Expression Analysis and its Application in Education

In recent years, with the rapid development of computer technology, facial expression recognition technology has gradually been applied to primary and secondary schools. This article first introduces the application of facial expression recognition in education. Then the development of facial expression recognition and the four basic processes of facial expressions are described. After that, the methods and algorithms used in face detection and localization of face expression recognition and classification are summarized, feature extraction and classification are summarized. Finally, the current application of facial expression recognition technology in education and the existing problems and future development are pointed out.
An active reconstruction algorithm based on partial prior information

Active reconstruction is an intelligent perception method that achieves object modeling with few views and short motion paths by systematically adjusting the parameters of the camera while ensuring model integrity. Part of the object information is always known for vision tasks in real scenes, and it provides some guidance for the view planning. A two-step active reconstruction algorithm based on partial prior information is presented, which includes rough shape estimation phase and complete object reconstruction phase, and both of them introduce the concept of active vision. An information expression method is proposed that can be used to manually initialize the repository according to specific visual tasks, and then the prior information and detected information are used to plan the next best view online until the object reconstruction is completed. The method is evaluated with simulated experiments and the result is compared with other methods.
Domain Adaptation Learning Based on Structural Similarity Weighted Mean Discrepancy for Credit Risk Classification

Domain adaptation learning is an effective method for leveraging knowledge from the source domain with labels to a target domain without any labels. However, most prior methods have neglected the contribution of each sample to the integral measure when adaptively matching either marginal distribution, conditional distribution, or both between domains. In this article, an improved algorithm based on mean discrepancy embedding with structural similarity is proposed, which aims at the contribution of each sample to the integral measure on the performance of target domain learning model by using labeled source samples and unlabeled target samples. The discrepancy between both marginal and conditional distribution are minimized with dimensionality reduction procedure to feature extraction with structural similarity weights for all samples from the source and target domains. The results of empirical analysis demonstrate that the proposed method has better performance over several state-of-the-art methods in credit risk classification.`
An Investigation into Intuitionistic Logic with Identity

We define Kripke semantics for propositional intuitionistic logic with Suszko’s identity (ISCI). We propose sequent calculus for ISCI along with cut-elimination theorem. We sketch a constructive interpretation of Suszko’s propositional identity connective.
Calculation Behavior of 2D Ising Spin Computing with Different Spin Decision Logics

We proposed "prompt decision logic", which has the advantage for accelerating the ground-state searches of Ising spin model. The Ising spin model describes the behavior of magnetic spins. It consists of each spin having four-nearest-neighbor (adjacent) spins. Escaping local minimum is a key for solving combinatorial optimization problems. Actually, many of the combinatorial optimization problems can be mapped onto the Ising spin model. In this report, we observed convergence properties of Ising spin computing with prompt decision logic, without any scheduling during energy convergence. Furthermore, the prompt decision logic showed better success probability than that of majority voting logic, as the problem size grows. Therefore, the prompt decision logic is useful for Ising spin computing and able to solve the combinatorial optimization problems.
Secured e-payment system based on automated authentication data and iterated salted hash algorithm

Electronic payment has been considered as one of the most significant and convenient applications of modern electronic services e-University compared to traditional methods that impose time-consuming, human resources, and inefficiency. Different automatic identification technologies have been widely used, such as radio frequency identification (RFID). Extensive research and several applications are focusing on taking the maximum advantage of RFID technology. Data and information security had considered a crucial role when information concerning e-commerce, e-banking, or e-payments, especially due to it required real data to establish accessed illegally. Hence, data originality and security fall a very significant and critical issue in data communication services in recent years. Applications such as e-banking or e-commerce regularly contain sensitive and personal information that should be managed and controlled by authorized persons. Thus, keeping a secure password is important to prevent unauthorized users from illegal access. The password hashing is one of the safety methods and means of preventing attacks. In this article, focuses on proposing an RFID based electronic payment and also provide multi-level security privileges for an academic domain by using RFID technology besides the programmable logic circuit as well the system used VB.Net C# environment also desktop and web-based application for system working purposes. The proposed system aims to manage student payments in a secure manner and provides the capabilities of getting a bus ticket, copying books, buying food, paying registration fees, and other services. The results have shown the system is secured by using the confirmation code in addition to password encryption.
Path Planning for Mobile Robot using Fuzzy Controllers with Artificial Potential field

Aiming at the problem of mobile robot path planning in complex environment, a method of integrating fuzzy logic with artificial potential field (APF) was presented. To overcome the local minima problem of APF, a heading fuzzy controller was designed. While the robot is falling into the local minima, the fuzzy controller will generate an angle to modify the current heading so that the robot escapes local minima and avoid obstacles. According to the complexity of the working environment, a step fuzzy controller was designed, which enables the robot to adjust the step adaptively and reduces the algorithm loop times and computation time efficiently. Simulation results show that this method is effective and practical for the mobile robot path planning.
Effect of Changes in Land Use in Flood Disasters in Baleendah District, Bandung Regency

Population growth and urbanization in Bandung Regency causes the need to increase.This encourages land conversion in open areas to become a built area to meet the needs of the community.Changes in land use avoid ecological imbalances between catchment areas and built areas.The flood phenomenon occurs in several districts in Bandung Regency every year, one of which is Baleendah District.The purpose of this study is to discuss changes in land use that occur in Baleendah District and discuss it with a flood disaster index.This study uses qualitative analysis of the exploration of secondary data and literature studies.Secondary data consisted of satellite image superimposition in 2003, 2006, 2009, 2013 and 2015.Then, the flood index used quantitative to increase the impact of the flood index that occurred in 2003, 2006, 2009, 2013 and 2015.Based on the results of the study, identified the existence of changes in flow in the river flow which increases the increase in the flood index.
Literation movement for leading schools: Best practice and leadership power

The school literacy movement is the first step of scientific development, aimed at enhancing science education. It has an important role in encouraging every citizen to have basic knowledge of the nature of science through active participation. This study aims to analyze the implementation of the School Literacy Movement program designated by the Ministry of Education and Culture due to the poor reading interest Indonesia students from several international surveys. The study was conducted at Al Mujahidin Muhammadiyah Middle School in Yogyakarta, using the qualitative method of data collection. The results show that the school literacy program is not only specific for reading and writing but for also other academics as activities such as numerals, science, digitalization, finance, culture, and citizenship. In conclusion, the implementation of a structured and systematic program positive impacts on the growth of literacy achievement for all school members from students, teachers, down to employees in Indonesia.