Reviewerly/exHarmony huggingface dataset:
The Comparison of different approaches for solar radiation forecasting using Artificial Neural Network

The paper presents the comparison of different approaches for forecasting of the solar radiation using artificial neural networks (ANN). The estimation of the energy yield of photovoltaic installation is very important for the electrical grid stability. This estimation is only possible if a good solar radiation forecast is available. Different approaches for this forecasting are possible. One of the most used statistical method is the artificial neural network. The main drawback of this method is the need of large amount of collected data for the network learning. The authors' team dispose with large database to avoid this disadvantage. Three approaches for the solar radiation forecasting are considered. The difference is in the input variables. The first approach uses information for the solar radiation for 6 days before the forecasted one. The others two approaches use historical data for the forecasted day - respectively 2 and 4 years before this day. For each approach two different topologies are considered - with one and two hidden layers. The number of neurons in the layers varies. The results show the advantages and the drawbacks of each approach in function of the topology. However, all three approaches are more or less appropriate for the solar radiation forecasting.
Revolutionizing Marketing in Tourism Industry Through Blockchain Technology

The blockchain technology has already started to dramatically disrupt the tourism industry. It has the potential to create a new landscape in the tourism industry generated by increased disintermediation, to generate new markets from several cross-industry applications, to modify the systems of rating, booking, supply chain, transportation, contracts, and payments as we know them. This paper aims to identify the effects that blockchain technology will have on marketing in the tourism industry. Based on the literature review, on the analysis of the success stories and cases, on the observations on the functional models of the actors that already use the blockchain in the tourism industry, the paper highlights the development prospects in the field and builds the basis for shaping the concept of the future marketing in tourism industry.
Ubiquitous Learning: ASystematic Review

This Research Full paper presents a systematic analysis. The objective of this work is to identify the educational context in which u-Learning was applied, as well as to evaluate which technologies are used to analyze the learning process in the period from 2012 to 2019. The quantitative analysis of the publications verified 5347 productions associated to the areas of sustainability, homecare, health, education, among others. The qualitative study of the data revealed: (i) the definition of u-Learning processes, referring to the individual behavioral repertoire, modified by the acquisition of certain knowledge; (ii) evaluation of the learning related to concrete situations in which the individual can manifest the knowledge in the acquired behaviors; and as for the application (iii) it was verified the inexistence of models for analysis of these process of change of behavior. Thus, from our research it was possible to observe, as a priority, the use of technologies as an instrument for the learning process, not being used in the measurement of knowledge. The findings point to the purpose of future studies that may indicate possible behavioral models that allow analyzing the behavior change and consequently the ubiquitous learning process.
Enabling Predictive Number Entry and Editing on Touchscreen-Based Mobile Devices

We propose a method for accommodating predictive number entry and editing on mobile devices via the suggestion bar of a virtual keyboard. We developed a simple predictive system to demonstrate the benefit of this method. It utilizes text-based querying and regular expression to suggest the most probable next numeric actions in the suggestion bar along with word suggestions. We evaluated this method in two user studies. The first explored number entry and the second explored editing. Results revealed that the proposed method significantly increases number entry and editing speed and accuracy. It reduces the number of actions needed per task. It also significantly reduces the time and effort needed to fix errors. Subjective analysis revealed that almost all participants found the method faster, more reliable, and easier to use than the conventional method, thus wanted to keep using it on their mobile devices.
Leakage-Resilient Certificateless Signcryption Scheme

Signcryption has been adopted in several areas because it is more effificient than signature- then-encryption. In the actual application, to keep the original security, a cryptography primitive should withstand various leakage attacks, and the previous leakage- resilient certifificateless signcryption scheme contains true-simulation extractable non- interactive zeroknowledge argument, which makes the computation effificiency of the corresponding construction low. To further solve the above problem, we propose a leakage-resilient certificateless signcryption scheme which need not use any cryptographic primitives, and we can prove the security of proposed scheme based on the traditional complexity assumptions, such as discrete logarithm, decisional Diffifie-Hellman.
Multi-target Tracking Algorithm Based on Multi-source Clustering in Distributed Radar Network (Poster)

In this paper, a multi-source clustering based tracking algorithm is proposed to address the multi-target tracking (MTT) problem with distributed radar network (DRN). Firstly, the multi-source clustering algorithm is proposed to estimate the target locations and differentiate the nearby targets at each scan. Then, a monostatic radar MTT algorithm, leaning against the nearest neighbor cheap joint probabilistic data association (NNCJPDA) approach, is put forward to track multiple targets. Comparing with the conventional MTT method relying on the correlations of tracks, our proposed algorithm considers the correlations within measurements, achieving a reduction in the computation load. Besides, due to the good property of the multi-source clustering algorithm, the nearby targets can also be effectively tracked. Finally, the effectiveness of the proposed algorithm is verified by providing numerical simulations.
Towards Robust Neural Machine Reading Comprehension via Question Paraphrases

In this paper, we focus on addressing the over-sensitivity issue of neural machine reading comprehension (MRC) models. By oversensitivity, we mean that the neural MRC models give different answers to question paraphrases that are semantically equivalent. To address this issue, we first create a large-scale Chinese MRC dataset with high-quality question paraphrases generated by a toolkit used in Baidu Search. Then, we quantitively analyze the oversensitivity issue of the neural MRC models on the dataset. Intuitively, if two questions are paraphrases of each other, a robust model should give the same predictions. Based on this intuition, we propose a regularized BERT-based model to encourage the model give the same predictions to similar inputs by leveraging high-quality question paraphrases. The experimental results show that our approaches can significantly improve the robustness of a strong BERT-based MRC model and achieve improvements over the BERT-based model in terms of held-out accuracy. Specifically, the different prediction ratio (DPR) for question paraphrases of the proposed model decreases more than 10%.
Intelligent Ticket with Augmented Reality Applications for Archaeological Sites

Tourism and archaeological sites can benefit from the science of Cultural Informatics combined with the intelligent ticket. This paper presents the transformation of an ordinary ticket into an intelligent one with the incorporation of NFC (Near Field Communication) technology to communicate with user’s mobile devices through the use of AR (Augmented Reality) for audiovisual information with correspondence from a specific geographic position. The research approaches and adapts the technological evolution, communication, and dissemination of cultural information to benefit populations and special groups. The importance of receiving the necessary information to gain knowledge from the intelligent ticket is stated. A theoretical model of combining the intelligent ticket, audiovisual science and archeology with user interactions for cultural heritage is developed. Managerial implications for future practical implementation are discussed.
Arched foot based on conformal complex neural network testing

Complex-Valued Neural Networks (CNNs) are the most successful extension of the real-valued neural networks. They play a significant role in deep learning. Following this great fact, CNNs are utilized in computer vision and its critical field Image analysis (IA). Symmetry vision is a theory closely related to assembly and uniformity. More accurately, symmetrical rallies can be categorized as covering self-similarities. In 2D images, the self-similarity comes from rigid transformations (such as operators, polynomials and differential and integral equations) that map one part onto another (in calculus is called an injective map and in the conformal analysis is called a univalent map). In this paper, we define a new style of CNNs called Conformal Neural Networks (CoNNs) based on the concept of conformal theory in the open unit disk. Moreover, we introduce a general symmetric 2D-parameter Beltrami equation (SBE) in a complex domain. The dominant idea is to map the external and inner of the domain conformally to unit disks, using a symmetric differential operator (which is suggested to be the solution of SBE). As an application, we present a new algorithm to obtain the arches of the foot from their marks by training CoNNs. The results prove that the effectiveness of our suggested procedure is a stable form arrangement.
Markov chain analysis of evolutionary algorithms on OneMax function – From coupon collector's problem to (1 + 1) EA

The theoretical investigation of Evolutionary Algorithms (EAs) has increased our understanding of the computational mechanism of algorithms. OneMax is a test function most frequently and deeply studied in the field of EAs. In this work, a method is presented for describing the runtime properties of (1+1) EA on OneMax. This method is motivated by the work of Erdös and Rényi treating the coupon collector's problem. They showed that the success probability of the coupon collector's problem is given by a function of double exponential form, and that the number of uncollected coupons follows the Poisson distribution. Today, the double exponential function is called Gumbel function, which is one of three fundamental functions in extreme value statistics. We introduce an algorithm that is a variant of the (1+1) EA, First Order Evolutionary Algorithm (FO-EA). FO-EA takes into account only the effect of single-bit mutations in the (1+1) EA, which in general includes multiple-bit mutations. We modified the method of Erdös and Rényi to apply FO-EA. We apply the Gumbel distribution for calculating the success probability of the (1+1) EA on OneMax. This method turns out to give a sufficiently reliable estimation for success probabilities, even in the tail region.
The effect of light on Leap Motion Controller in the classification of Sign Language Translator System

This study aims to translate the Indonesian signaling system (SIBI), which focuses on 24 SIBI alphabets by comparing several lighting conditions of the room lights. The sensor used is the Leap Motion controller (LMC) to capture hand movements, the Leap Application Programming Interface (API) for the extraction of hand gestures, and Support Vector Machine (SVM) to classify sign languages. The data used consisted of 720 training data and 240 test data. Optimal results are obtained by using a 25W white light with LMC position under the light with 96.2% accuracy.
Natural Language Processing Based Part of Speech Tagger using Hidden Markov Model

In various natural language processing applications, PART-OF-SPEECH (POS) tagging is performed as a preprocessing step. For making POS tagging accurate, various techniques have been explored. But in Indian languages, not much work has been done. This paper describes the methods to build a Part of speech tagger by using hidden markov model. Supervised learning approach is implemented in which, already tagged sentences in malayalam is used to build hidden markov model.
The Evolution of Digital Signal Processors

Digital Signal Processors (DSP) are high-speed dedicated processors designed to perform arithmetic operations on their input signal, and then output the processed signals. They are currently used in many applications, such as telecommunications, video and audio signal processing, flight control systems, spacecraft, missiles, etc. DSP processors have evolved through several generations over the last few decades. This paper outlines the evolution of some of the DSP processors from early days of 1970s to the present.
A NEW NON‐TUNED SELF‐ADAPTIVE MACHINE‐LEARNING APPROACH FOR SIMULATING THE DISCHARGE COEFFICIENT OF LABYRINTH WEIRS

Abstract In this study, the labyrinth weir discharge coefficient was simulated using the self‐adaptive extreme learning machine (SAELM) artificial intelligence model in two cases: normal orientation labyrinth weirs (NLWs) and inverted orientation labyrinth weirs (ILWs). First, the most optimized neuron of the hidden layer was computed. The number of hidden layer neurons was calculated as 30. Also, by analysing the results of different activation functions, it was concluded that the sigmoid activation function has higher accuracy than the others. Next, the superior model was identified by conducting a sensitivity analysis. The model approximated the discharge coefficient of labyrinth weirs with reasonable accuracy. For example, the R 2 , scatter index and Nash–Sutcliffe efficiency coefficient for the best model were estimated as 0.966, 0.034 and 0.964, respectively. In addition, the ratio of the total head above the weir to the height of the weir crest ( H T / P ) and the ratio of length of apex geometry to width of a single cycle ( A / w ) were identified as the most effective parameters. Furthermore, the uncertainty analysis results indicated that the superior model had an overestimated performance. Then, a relationship was proposed in terms of all input variables for the superior model. © 2020 John Wiley &amp; Sons, Ltd.
Texture analysis of lung nodules in computerized tomography images using functional diversity

Although lung cancer is one of the leading causes of cancer deaths worldwide, the chances of survival are higher in the early stages. One of the best tools for diagnosis is computerized tomography. The main problem with this method is that it depends directly on the specialist who is analyzing the image, since the process involved is tiring, and can lead to error. Computer-aided detection systems have emerged as a way to help these specialists. This work presents the use of descriptors based on functional diversity indexes to reduce the number of false positives. Our method can reach an accuracy of 97.73%, a sensitivity of 98.4%, Kappa index of 0.941, and a number of false positives per scan of up to three. Based on the results obtained, the use of a functional diversity index is shown to be a robust method that can be used in a real CAD system.
A GAN-based Motion Blurred Image Restoration Algorithm

Motion blurred images have a wide application in film and television and computer visual technology. The paper, aimed at solving the restoration problems of motion blurred images, regards the mapping from dynamic blurred images to sharp images as a kind of mode mapping, and proposes a motion blurred image restoration algorithm based on GAN in combination with convolution. By learning the mode via the training of image sets, the fitting output can achieve that sharp images correspond to the blurred images. As is demonstrated by the experimental results, the image restoration effect by the algorithm is remarkable and the definition is high.
Credit Card Fraud Detection Using Online Boosting with Extremely Fast Decision Tree

Nowadays, data stream mining is a very hot and high attention research field due to the real-time industrial applications from different sources are generating amount of data continuously as the streaming style. To process these growing and large data streams, data stream mining, classification algorithms have been proposed. These algorithms have to deal with high processing time and memory costs, class imbalance, overfitting and concept drift and so on. It is sure that ensembles of classifiers are being effectively used to improve the precision of single classifiers in either data mining or data stream mining. Thus, to get higher performance in prediction with largely no increasing memory and time costs, this paper proposes an Online Boosting(OLBoost) Approach, which is firstly use the Extremely Fast Decision Tree (EFDT) as base (weak) learner, in order to ensemble them into a single online strong learner. The experiments of the proposed method were carried out for credit card fraud detection domain with the sample benchmark datasets.
Characteristics of everyday conversation derived from the analysis of dialog act annotation

This paper addresses an attempt to find out the characteristics of everyday conversation data through dialog act information. Although several earlier studies have discussed how to annotate DA information, few studies use the result of the annotation as a clue to derive the characteristics of conversation. We report on the work to annotate dialog act information on utterances in Japanese everyday conversation, and the possibility of extracting the interactional characteristics using the annotation. As a result of the analysis, it was found that the annotation reflects differences in behaviour depending on the type of conversation and participants' age. Also, even in conversations with similar settings, differences were found in the distribution of tags about interactional management. It is suggested that the annotation may also reflect information that is difficult to capture objectively such as the conversational atmosphere.
Online Blind Equalization for QAM Signals Based on Prediction Principle via Complex Echo State Network

Given the nonlinear channel, the online blind equalization algorithm for Quadrature Amplitude Modulation (QAM) signals based on the prediction principle via the Complex Echo State Network(C-ESN) is proposed. The traditional linear prediction error filter is replaced by the C-ESN with good nonlinear mapping ability, and the minimum prediction error is attained by the Kalman filter(KF). After then, the amplitude and phase are adjusted in sequence. Simulation results show that the proposed algorithm can effectively reduce the distortion of the transmitted QAM signals caused by nonlinear channels. Compared with other prediction principle-based blind equalization algorithms and the traditional cost function based ESN blind equalization algorithm, it has lower mean square error and faster convergence speed.
Crowdsourcing sensitive data using public displays—opportunities, challenges, and considerations

Abstract Interactive public displays are versatile two-way interfaces between the digital world and passersby. They can convey information and harvest purposeful data from their users. Surprisingly little work has exploited public displays for collecting tagged data that might be useful beyond a single application. In this work, we set to fill this gap and present two studies: (1) a field study where we investigated collecting biometrically tagged video-selfies using public kiosk-sized screens, and (2) an online narrative transportation study that further elicited rich qualitative insights on key emerging aspects from the first study. In the first study, a 61-day deployment resulted in 199 video-selfies with consent to leverage the videos in any non-profit research. The field study indicates that people are willing to donate even highly sensitive data about themselves in public. The subsequent online narrative transportation study provides a deeper understanding of a variety of issues arising from the first study that can be leveraged in the future design of such systems. The two studies combined in this article pave the way forward towards a vision where volunteers can, should they so choose, ethically and serendipitously help unleash advances in data-driven areas such as computer vision and machine learning in health care.
A family of linear mixed-effects models using the generalized Laplace distribution

We propose a new family of linear mixed-effects models based on the generalized Laplace distribution. Special cases include the classical normal mixed-effects model, models with Laplace random effects and errors, and models where Laplace and normal variates interchange their roles as random effects and errors. By using a scale-mixture representation of the generalized Laplace, we develop a maximum likelihood estimation approach based on Gaussian quadrature. For model selection, we propose likelihood ratio testing and we account for the situation in which the null hypothesis is at the boundary of the parameter space. In a simulation study, we investigate the finite sample properties of our proposed estimator and compare its performance to other flexible linear mixed-effects specifications. In two real data examples, we demonstrate the flexibility of our proposed model to solve applied problems commonly encountered in clustered data analysis. The newly proposed methods discussed in this paper are implemented in the R package nlmm.
Optimised multi‐wavelet domain for decomposed electrooculogram‐based eye movement classification

The human eye movement tracking is possible with the assistance of the electrooculography (EOG) signals. The human eye tracking system allows researchers to analyse the participant's eye movements during certain activities. This study offers the EOG signals to control the human–computer interface systems with the help of Empirical Mean Curve Decomposition (EMCD) decomposition model. At first, the input EOG signal is provided as input to the EMCD decomposition model, later the resultant signal is given to principal component analysis for dimensional reduction, and then the dimensional reduced signal is offered to multi-wavelet decomposition model. The resultant dimensionally reduced multi-wavelet decomposed signal is passed to the proposed Feature Mapping (FM) model, using the k-means clustering model. Then, the Grey Wolf Optimization (GWO) algorithm is utilised to tune the margin. Next to mapping, the obtained features are provided to the nearest neighbour classifier, to obtain the eye movement. Next to the implementation, the proposed method is compared with the existing methods, and it is witnessed that the proposed methodology gives the superior performance in correspondence with accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F1 score and Mathews correlation coefficient.