Reviewerly/exHarmony huggingface dataset:
Automatic classification of nuclear physics data via a Constrained Evolutionary Clustering approach

This paper presents an automatic method for data classification in nuclear physics experiments based on evolutionary computing and vector quantization. The major novelties of our approach are the fully automatic mechanism and the use of analytical models to provide physics constraints, yielding to a fast and physically reliable classification with nearly-zero human supervision. Our method is successfully validated using experimental data produced by stacks of semiconducting detectors. The resulting classification is highly satisfactory for all explored cases and is particularly robust to noise. The algorithm is suitable to be integrated in the online and offline analysis software of existing large complexity detection arrays for the study of nucleus–nucleus collisions at low and intermediate energies.
Hybrid Path Planning of Robots Through Optimal Control and PSO Algorithm

In this paper, a hybrid algorithm for robot's path planning is proposed. This algorithm consists of the optimal control algorithm and PSO meta-heuristic algorithm. In this method, a two-point boundary value equation is derived through optimal control method and the initial solution is needed to solve it. To solve these equations, the PSO provides preliminary solutions and optimizing the solution of equations (robot path) with optimizing initial solution concerning cost function results in global optimization. So the formulation of proposed method is presented and it is used for path planning of a 4-DOF surgical robotic system. The results show that in this algorithm, the cost function does not depend on initial guess in comparison with optimal control and PSO methods.
Consensus of Whom? A Spectrum of Blockchain Consensus Protocols and New Directions

Blockchain technology aims to maintain a consistent ledger which is kept by the nodes of the system in a distributed manner. To achieve this goal, a consensus protocol needs to be employed for the nodes to process the block data and exchange their validation opinions. A variety of consensus protocols have been proposed for different application scenarios. In this paper, we propose a framework of categorizing consensus protocols based on the number, role and selection of nodes to be involved in the validation and ordering procedure. We also identify several possible new directions of consensus protocol design for future blockchain applications.
Deep Fusion Feature Extraction and Classification of Pellet Phase

Pellet quality including chemical composition, physical properties and metallurgical performance of three parts, its quality and mineral composition, properties and structure of the pellets has the close relation, studies show that the mineralogical micro structure and distribution of pellets had significant effects on the metallurgical properties, so the analysis and determination of pellets of mineral composition and micro structure is very important to improve the quality of pellets.Paper to pellets micro mineral as the research object, mainly studies the CNN and PCA two kinds of image processing algorithm, in the heart of the traditional model of CNN structure characteristics obtained by convolution PCA dimension reduction at a time, will be the main features of PCA to extract into the depth of the CNN learning, realize the mineral phase of shallow and deep information of the image to do effective fusion, in a larger extent, reflects the mineralogical characteristics, thus more intuitive response metallurgical properties.The location and alkalinity of the ore phase were identified by the extracted deep fusion feature, and the results were compared with those of the traditional CNN algorithm.It was found that the accuracy of location and alkalinity recognition of PCA and CNN coupling algorithm was 93.82% and 91.26%, respectively, which were higher than the accuracy of traditional CNN algorithm 92.73% and 88.93%, which verified the accuracy of PCA and CNN coupling model and its applicability in mineral phase recognition.
A Gamma-Poisson Mixture Topic Model for Short Text

Most topic models are constructed under the assumption that documents follow a multinomial distribution. The Poisson distribution is an alternative distribution to describe the probability of count data. For topic modelling, the Poisson distribution describes the number of occurrences of a word in documents of fixed length. The Poisson distribution has been successfully applied in text classification, but its application to topic modelling is not well documented, specifically in the context of a generative probabilistic model. Furthermore, the few Poisson topic models in the literature are admixture models, making the assumption that a document is generated from a mixture of topics. In this study, we focus on short text. Many studies have shown that the simpler assumption of a mixture model fits short text better. With mixture models, as opposed to admixture models, the generative assumption is that a document is generated from a single topic. One topic model, which makes this one-topic-per-document assumption, is the Dirichlet-multinomial mixture model. The main contributions of this work are a new Gamma-Poisson mixture model, as well as a collapsed Gibbs sampler for the model. The benefit of the collapsed Gibbs sampler derivation is that the model is able to automatically select the number of topics contained in the corpus. The results show that the Gamma-Poisson mixture model performs better than the Dirichlet-multinomial mixture model at selecting the number of topics in labelled corpora. Furthermore, the Gamma-Poisson mixture produces better topic coherence scores than the Dirichlet-multinomial mixture model, thus making it a viable option for the challenging task of topic modelling of short text.
ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning

Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life machine learning applications. In LDL problems, instance-based algorithms and particularly the adapted version of the k-nearest neighbors method for LDL (AA-kNN) has proven to be very competitive, achieving acceptable results and allowing an explainable model. However, it suffers from several handicaps: it needs large storage requirements, it is not efficient predicting and presents a low tolerance to noise. The purpose of this paper is to mitigate these effects by adding a data reduction stage. The technique devised, called Prototype selection and Label-Specific Feature Evolutionary Optimization for LDL (ProLSFEO-LDL), is a novel method to simultaneously address the prototype selection and the label-specific feature selection pre-processing techniques. Both techniques pose a complex optimization problem with a huge search space. Therefore, we have proposed a search method based on evolutionary algorithms that allows us to obtain a solution to both problems in a reasonable time. The effectiveness of the proposed ProLSFEO-LDL method is verified on several real-world LDL datasets, showing significant improvements in comparison with using raw datasets.
Using emojis in mobile web surveys for Millennials? A study in Spain and Mexico

Abstract To involve Millennials in survey participation, and obtain high-quality answers from them, survey designers may require new tools that better catch Millennials' interest and attention. One key new tool that could improve the communication and make the survey participation more attractive to young respondents are the emojis. We used data from a survey conducted among Millennials by the online fieldwork company Netquest in Spain and Mexico (n = 1614) to determine how emojis can be used in mobile web surveys, in particular in open-ended questions, and how their use can affect data quality, completion time, and survey evaluation. Overall, results show a high willingness of Millennials to use emojis in surveys (both stated and actual use) and a positive impact of encouraging Millennials to use emojis in open-ended questions on the amount of information conveyed, the completion time and the survey enjoyment.
Prototype Similarity Learning for Activity Recognition

Human Activity Recognition (HAR) plays an irreplaceable role in various applications such as security, gaming, and assisted living. Recent studies introduce deep learning to mitigate the manual feature extraction (i.e., data representation) efforts and achieve high accuracy. However, there are still challenges in learning accurate representations for sensory data due to the weakness of representation modules and the subject variances. We propose a scheme called Distance-based HAR from Ensembled spatial-temporal Representations (DHARER) to address above challenges. The idea behind DHARER is straightforward—the same activities should have similar representations. We first learn representations of the input sensory segments and latent prototype representations of each class, using a Convolution Neural Network (CNN)-based dual-stream representation module; then the learned representations are projected to activity types by measuring their similarity to the learned prototypes. We have conducted extensive experiments under a strict subject-independent setting on three large-scale datasets to evaluate the proposed scheme, and our experimental results demonstrate superior performance of DHARER to several state-of-the-art methods.
A Double-Density Clustering Method Based on “Nearest to First in” Strategy

The existing density clustering algorithms have high error rates on processing data sets with mixed density clusters. For overcoming shortcomings of these algorithms, a double-density clustering method based on Nearest-to-First-in strategy, DDNFC, is proposed, which calculates two densities for each point by using its reverse k nearest neighborhood and local spatial position deviation, respectively. Points whose densities are both greater than respective average densities of all points are core. By searching the strongly connected subgraph in the graph constructed by the core objects, the data set is clustered initially. Then each non-core object is classified to its nearest cluster by using a strategy dubbed as ‘Nearest-to-First-in’: the distance of each unclassified point to its nearest cluster calculated firstly; only the points with the minimum distance are placed to their nearest cluster; this procedure is repeated until all unclassified points are clustered or the minimum distance is infinite. To test the proposed method, experiments on several artificial and real-world data sets are carried out. The results show that DDNFC is superior to the state-of-art methods like DBSCAN, DPC, RNN-DBSCAN, and so on.
Automated Deployment of a Spark Cluster with Machine Learning Algorithm Integration

The vast amount of data stored nowadays has turned big data analytics into a very trendy research field. The Spark distributed computing platform has emerged as a dominant and widely used paradigm for cluster deployment and big data analytics. However, to get started up is still a task that may take much time when manually done, due to the requisites that all nodes must fulfill. This work introduces LadonSpark, an open-source and non-commercial solution to configure and deploy a Spark cluster automatically. It has been specially designed for easy and efficient management of a Spark cluster with a friendly graphical user interface to automate the deployment of a cluster and to start up the distributed file system of Hadoop quickly. Moreover, LadonSpark includes the functionality of integrating any algorithm into the system. That is, the user only needs to provide the executable file and the number of required inputs for proper parametrization. Source codes developed in Scala, R, Python, or Java can be supported on LadonSpark. Besides, clustering, regression, classification, and association rules algorithms are already integrated so that users can test its usability from its initial installation.
Hybrid Conditional Random Fields and K-Means for Named Entity Recognition on Indonesian News Documents

The hybrid approach has been widely used in several Natural Language Processing, including Named Entity Recognition (NER).This research proposes a NER system for Indonesian News Documents using Hybrid Conditional Random Fields (CRF) and K-Means.The hybrid approach is to try incorporating word embedding as a cluster from K-Means and take as a feature in CRF.Word embedding is a word representation technique, and it can capture the semantic meaning of the words.The clustering result from K-Means shows that similar meaning word is grouped in the cluster.We believe this feature can improve the performance of the baseline model by adding the semantic relatedness of the word from the cluster features.Word embedding in this research uses Indonesian Word2Vec.The dataset is consisting of 51,241 entities from Indonesian Online News.We conducted some experiments by dividing the corpus into training and testing dataset using percentage splitting.We used 4 scenarios for our experiments, which are 60-40, 70-30, 80-20, and 90-10.The best performance for our model was achieved in 60-40 scenario with F1-Score around 87.18% and also improves about 5.01% compared to the baseline models.We also compare our proposed methods with several models, which are BILSTM and BILSTM-CRF, from previous research.The experiments show that our model can achieve better performance by giving the best improvement of around 4.3%.
THE EFFICIENCY OF QUIZLET-BASED EFL VOCABULARY LEARNING IN PREPARING UNDERGRADUATES FOR STATE ENGLISH EXAM

Students’ EFL learning experience in the rural context is poorer in comparison to urban quality of education. More than 70% of students at the State Agrarian and Engineering University in Podillia are from rural areas. To reduce rural-urban disparities in academic learning the university teachers should use high-quality instruction techniques that can make the process of studying both effective and motivating. The traditional methods of EFL teaching no longer satisfy the needs of modern society. Hence, technology applications need a theoretical foundation based on experimental surveys to determine the strategies for effective EFL learning. This research paper was conducted to examine the possibility of utilising such MALL technology like Quizlet in improving EFL learners’ vocabulary acquisition. A brief review of scholarly literature published from 2005-2019 on Quizlet implementation in foreign language learning showed that there are many studies on the benefits of using Quizlet in EFL vocabulary acquisition. However, the research papers on the principles and strategies for adequate Quizlet-based learning in Ukrainian higher educational institutions are still lacking. Pre- and post-experimental questionnaires, pre- and post-experimental tests were adopted as tools for data collection. The results of the experiment have proven the proposed hypothesis. After utilising Quizlet and using spaced repetition technique for vocabulary learning to prepare undergraduates to state English exam, the results proved that the learners of the experimental group improved their level of EFL vocabulary at the post-test.
A Discrete Event Formulation for Multi-Robot Collision Avoidance on Pre-planned Trajectories

In this paper we consider the problem of collision avoidance among robots that follow pre-planned trajectories in a structured environment while minimizing the maximum traveling time among them. More precisely, we consider a discrete event formulation of this problem. Robots are modeled by automata, the environment is partitioned into a square grid where cells represent free space, obstacles and walls, which are modeled as shared resources among robots. The main contribution of this paper is twofold. First, we propose a problem formulation based on mixed integer linear programming to compute an optimal schedule for the pre-planned trajectories. Second, we propose a heuristic method to compute a sub-optimal schedule: the computational complexity of this approach is shown to be polynomial with the number of robots and the dimension of the environment. Finally, simulations are provided to validate performance and scalability of the proposed approach.
Reversible Integer-to-Integer Wavelet Filter Design for Lossless Image Compression

In this paper, factors that influence the lossless compression efficiency of reversible integer-tointeger (ITI) wavelet filters are presented. It is observed that there is a converged region for the bitrate of the compressed subbands. Reaching the converged region, the bitrate increment is equal to log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> K bps, where K is the increment in ratio of the coefficient magnitude mean. The advantages of the 5/3 filters for lossless compression are then clearly explained. Anew quantity, R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mn</sub> , which measures the efficiency of reversible ITI transforms on flat spectrum inputs, is defined and is used in the new reversible filter design. First, keeping the distinctive merits of the 5/3 filters for lossless compression, the half-band product filter series of the Daubechies wavelet series is selected as the analysis high-pass filters. Next, the analysis low-pass filter is found by experimentally minimizing the Rmn value. Finally, it is determined that the 17/11 filters, which employ the half-band product filter with 6 vanishing moments, have the highest compression efficiency. Coding results show that the new 17/11 filters lead to a bitrate reduction of 1.7% over the 5/3 filters for lossless image compression. With further enhancements on the transform part and combining the enhanced transform with more efficient entropy coding, bitrate reductions of about 3.1% and 4% respectively over the state-of-the-art lossless compression algorithms JPEG-LS and JPEG2000 lossless mode are achieved.
Cross-Domain Polarity Models to Evaluate User eXperience in E-learning

Virtual learning environments are growing in importance as fast as e-learning is becoming highly demanded by universities and students all over the world. This paper investigates how to automatically evaluate User eXperience in this domain using sentiment analysis techniques. For this purpose, a corpus with the opinions given by a total of 583 users (107 English speakers and 476 Spanish speakers) about three learning management systems in different courses has been built. All the collected opinions were manually labeled with polarity information (positive, negative or neutral) by three human annotators, both at the whole opinion and sentence levels. We have applied our state-of-the-art sentiment analysis models, trained with a corpus of a different semantic domain (a Twitter corpus), to study the use of cross-domain models for this task. Cross-domain models based on deep neural networks (convolutional neural networks, transformer encoders and attentional BLSTM models) have been tested. In order to contrast our results, three commercial systems for the same task (MeaningCloud, Microsoft Text Analytics and Google Cloud) were also tested. The obtained results are very promising and they give an insight to keep going the research of applying sentiment analysis tools on User eXperience evaluation. This is a pioneering idea to provide a better and accurate understanding on human needs in the interaction with virtual learning environments and a step towards the development of automatic tools that capture the feed-back of user perception for designing virtual learning environments centered in user's emotions, beliefs, preferences, perceptions, responses, behaviors and accomplishments that occur before, during and after the interaction.
Path Planning for Autonomous Articulated Vehicle Based on Improved Goal-Directed Rapid-Exploring Random Tree

The special steering characteristics and task complexity of autonomous articulated vehicle (AAV) make it often require multiple forward and backward movements during autonomous driving. In this paper, we present a simple yet effective method, named head correction with fixed wheel position (HC-FWP), for the demand of multiple forward and backward movements. The goal-directed rapid-exploring random tree (GDRRT) algorithm is first used to search for a feasible path in the obstacle map, and then, the farthest node search (FNS) algorithm is applied to obtain a series of key nodes, on which HC-FWP is used to correct AAV heading angles. Simulation experiments with Dynapac CC6200 articulated road roller parameters show that the proposed improved goal-directed rapid-exploring random tree (IGDRRT), consisting of GDRRT, FNS, and HC-FWP, can search a feasible path on maps that require the AAV to move back and forth.
Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition

&lt;p&gt;&lt;span&gt;This paper covers the work done in handwritten digit recognition and the various classifiers that have been developed. Methods like MLP, SVM, Bayesian networks, and Random forests were discussed with their accuracy and are empirically evaluated. Boosted LetNet 4, an ensemble of various classifiers, has shown maximum efficiency among these methods. &lt;/span&gt;&lt;/p&gt;
Authentication of User in Connected Governance Model

India is striving hard to deliver Citizen centric electronic services within the affordability of populace. For this purpose, service providers have to rely upon internet as public communication medium, which is susceptible to infringement attempts of adversary. To resolve this issue, vital resources like user information should be managed in secure manner. As the state administrator, Government have to play a vital role to gain the trust of user over this electronic service delivery model. To ensure Privacy, Integrity, Non-Repudiation, Authentication (PINA) of electronic transaction, each user (i.e. Citizen) should be uniquely identified from the initial stage of transaction. This approach will prevent unauthorized access of adversary and hence services can be successfully delivered to the intended recipient only. To achieve this objective, in this paper authors have proposed a 3-level user authentication scheme during Citizen to Government to Bank (i.e. C2G2B) type of electronic transaction carried out through a Citizen centric multivariate smart card based connected service delivery model.
Secured cloud data migration technique by competent probabilistic public key encryption

Cloud computing, a recently emerged paradigm faces major challenges in achieving the privacy of migrated data, network security, etc. Too many cryptographic technologies are raised to solve these issues based on identity, attributes and prediction algorithms yet; these techniques are highly prone to attackers. This would raise a need of an effective encryption technique, which would ensure secure data migration. With this scenario, our proposed methodology Efficient Probabilistic Public Key Encryption (EPPKE) is optimized with Covariance Matrix Adaptation Evolution Strategies (CMA-ES). It ensures data integrity through the Luhn algorithm with BLAKE 2b encapsulation. This enables an optimized security to the data which is migrated through cloud. The proposed methodology is implemented in Open Stack with Java Language. It achieves better results by providing security compared to other existing techniques like RSA, IBA, ABE, PBE, etc.