Reviewerly/exHarmony huggingface dataset:
Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models

Mixtures-of-Experts models and their maximum likelihood estimation (MLE) via the EM algorithm have been thoroughly studied in the statistics and machine learning literature. They are subject of a growing investigation in the context of modeling with high-dimensional predictors with regularized MLE. We examine MoE with Gaussian gating network, for clustering and regression, and propose an $$\ell _1$$-regularized MLE to encourage sparse models and deal with the high-dimensional setting. We develop an EM-Lasso algorithm to perform parameter estimation and utilize a BIC-like criterion to select the model parameters, including the sparsity tuning hyperparameters. Experiments conducted on simulated data show the good performance of the proposed regularized MLE compared to the standard MLE with the EM algorithm.
Stability and Hopf Bifurcation of a Stochastic Cournot Duopoly Game in a Blockchain Cloud Services Market Driven by Brownian Motion

The blockchain technology has been rapidly popularizing and gaining a quick mileage in many domains owing to its innate nature, so there must be several tremendous market opportunities and uncertainties for the blockchain cloud service. We propose a Cournot duopoly game in a blockchain cloud services market for cyber-physical-social systems (CPSS). Introducing Brownian noise disturbance to the deterministic Cournot duopoly game, we construct a stochastic Cournot duopoly game in a blockchain cloud service market with uncertainty. We analyze the stochastic stability of the Cournot duopoly game by the Lyapunov exponent method, the Lyapunov function, and the singular boundary theory. Then we investigate the stochastic Hopf bifurcation of the Cournot duopoly game by the invariant measure theory. At last, we numerically validate these results by the probability density. These results of stability and Hopf bifurcation illustrate complex behaviors of the stochastic Cournot duopoly game in the blockchain cloud services market.
A Survey of Hyper-heuristics for Dynamic Optimization Problems

Dynamic optimization problems have attracted the attention of researchers due to their wide variety of challenges and their suitability for real-world problems. The application of hyper-heuristics to solve optimization problems is another area that has gained interest recently. These algorithms can apply a search space exploration method at different stages of the execution for finding high quality solutions. However, most of the proposed works using these methodologies do not focus on the development of hyper-heuristics for dynamic optimization problems. Despite that, they arise as very appropriate methods for dynamic problems, being highly responsive and able to quickly adapt to any possible changes in the problem environment. In this paper, we present a brief study of the most salient previously proposed hyper-heuristics to solve dynamic optimization problems, and classify them, taking into consideration the complexity of their low-level heuristics. Then, we identify some the most important research areas that have been vaguely explored in the Literature yet.
Multivariate Long-Term State Forecasting in Cyber-Physical Systems: A Sequence to Sequence Approach

Cyber-physical systems (CPS) are ubiquitous in several critical infrastructure applications. Forecasting the state of CPS, is essential for better planning, resource allocation and minimizing operational costs. It is imperative to forecast the state of a CPS multiple steps into the future to afford enough time for planning of CPS operation to minimize costs and component wear. Forecasting system state also serves as a precursor to detecting process anomalies and faults. Concomitantly, sensors used for data collection are commodity hardware and experience frequent failures resulting in periods with sparse or no data. In such cases, re-construction through imputation of the missing data sequences is imperative to alleviate data sparsity and enable better performance of down-stream analytic models. In this paper, we tackle the problem of CPS state forecasting and data imputation and characterize the performance of a wide array of deep learning architectures - unidirectional gated and non-gated recurrent architectures, sequence to sequence (Seq2Seq) architectures as well as bidirectional architectures - with a specific focus towards applications in CPS. We also study the impact of procedures like scheduled sampling and attention, on model training. Our results indicate that Seq2Seq models are superior to traditional step ahead forecasting models and yield an improvement of at least 28.5% for gated recurrent architectures and about 87.6% for non-gated architectures in terms of forecasting performance. We also notice that bidirectional models learn good representations for forecasting as well as for data imputation. Bidirectional Seq2Seq models show an average improvement of 17.6% in forecasting performance over their unidirectional counterparts. We also demonstrate the effect of employing an attention mechanism in the context of Seq2Seq architectures and find that it provides an average improvement of 57.12% in the case of unidirectional Seq2Seq architectures while causing a performance decline in the case of bidirectional Seq2Seq architectures. Finally, we also find that scheduled sampling helps in training better models that yield significantly lower forecasting error.
Limited Number of Cases May Yield Generalizable Models, a Proof of Concept in Deep Learning for Colon Histology

Background: Little is known about the effect of a minimum number of slides required in generating image datasets used to build generalizable machine-learning (ML) models. In addition, the assumption within deep learning is that the increased number of training images will always enhance accuracy and that the initial validation accuracy of the models correlates well with their generalizability. In this pilot study, we have been able to test the above assumptions to gain a better understanding of such platforms, especially when data resources are limited. Methods: Using 10 colon histology slides (5 carcinoma and 5 benign), we were able to acquire 1000 partially overlapping images (Dataset A) that were then trained and tested on three convolutional neural networks (CNNs), ResNet50, AlexNet, and SqueezeNet, to build a large number of unique models for a simple task of classifying colon histopathology into benign and malignant. Different quantities of images (10–1000) from Dataset A were used to construct >200 unique CNN models whose performances were individually assessed. The performance of these models was initially assessed using 20% of Dataset A’s images (not included in the training phase) to acquire their initial validation accuracy (internal accuracy) followed by their generalization accuracy on Dataset B (a very distinct secondary test set acquired from public domain online sources). Results: All CNNs showed similar peak internal accuracies (>97%) from the Dataset A test set. Peak accuracies for the external novel test set (Dataset B), an assessment of the ability to generalize, showed marked variation (ResNet50: 98%; AlexNet: 92%; and SqueezeNet: 80%). The models with the highest accuracy were not generated using the largest training sets. Further, a model’s internal accuracy did not always correlate with its generalization accuracy. The results were obtained using an optimized number of cases and controls. Conclusions: Increasing the number of images in a training set does not always improve model accuracy, and significant numbers of cases may not always be needed for generalization, especially for simple tasks. Different CNNs reach peak accuracy with different training set sizes. Further studies are required to evaluate the above findings in more complex ML models prior to using such ancillary tools in clinical settings.
Teaching English using a genre pedagogy to Islamic junior high school students

This descriptive qualitative study aims to find out how a genre pedagogy is used to teaching the English language. A class consisted of thirty-six students of a public Islamic junior high school was selected purposefully as participants. The data collection involved participant observation, field notes, and documents, in this case, the students' works. The participant observations were conducted in four meetings. During the observations, this study collected the students’ works purposefully. The data analysis used two techniques, namely the thematic analysis to analyze the field notes and functional grammar framework to analyze the students’ independent construction texts. The findings of the study reveal that the students could practice daily life texts in pairs and write a daily life text independently, critically, and creatively as they managed to work on their own and to use specific information, expressions, and contexts in their texts as demanded by the government and the 21st century. This could be achieved as the researcher applied genre pedagogy teaching procedures, explicit teaching, and scaffolding to ensure the students could do the work effectively. This study suggests teachers apply genre-pedagogy teaching procedures and principles to ensure the students could learn English better.
Semi-Supervised Selective Affinity Propagation Ensemble Clustering With Active Constraints

The semi-supervised selective affinity propagation ensemble clustering with active constraints (SSAPEC) method combines affinity propagation (AP) clustering algorithm with ensemble clustering and incorporates prior knowledge (almost a pairwise constraint) to improve the clustering performance. In this paper, the evaluation criterion coverage ratio, which is based on the structure of clustering result, is employed to judge the homogeneity of the component clusters without the assistance of ground-truth during the component cluster selection. And to solve the problem of random selection of pairwise constraints, an active learning strategy is applied to find most informative constraints based on the ensemble clustering result; these pairwise constraints selected by active learning strategy lead to a similarity matrix adjustment. Then, the final result is obtained by running the Average-link algorithm on the adjusted similarity matrix. The experimental results on fourteen datasets demonstrate that the SSAPEC method proposed in this paper outperforms other state-of-the-art ensemble clustering schemes.
Improvement of genetic algorithm using artificial bee colony

Genetic algorithm (GA) is a part of evolutionary computing that simulates the theory of evolution and natural selection, where this technique depends on a heuristic random search. This algorithm reflects the operation of natural selection, where the fittest individuals are chosen for reproduction so that they produce offspring of the next generation. This paper proposes a method to improve GA using artificial bee colony (GABC). This proposed algorithm was applied to random number generation (RNG), and travelling salesman problem (TSP). The proposed method used to generate initial populations for GA rather than the random generation that used in traditional GA. The results of testing on RNG show that the proposed GABC was better than traditional GA in the mean iteration and the execution time. The results of testing TSP show the superiority of GABC on the traditional GA. The superiority of the GABC is clear in terms of the percentage of error rate, the average length route, and obtaining the shortest route. The programming language Python3 was used in programming the proposed methods.
Lexical Variation and Sentiment Analysis of Roman Urdu Sentences with Deep Neural Networks

Sentiment analysis is the computational study of reviews, emotions, and sentiments expressed in the text.In the past several years, sentimental analysis has attracted many concerns from industry and academia.Deep neural networks have achieved significant results in sentiment analysis.Current methods mainly focus on the English language, but for minority languages, such as Roman Urdu that has more complex syntax and numerous lexical variations, few research is carried out on it.In this paper, for sentiment analysis of Roman Urdu, the novel "Self-attention Bidirectional LSTM (SA-BiLSTM)" network is proposed to deal with the sentence structure and inconsistent manner of text representation.This network addresses the limitation of the unidirectional nature of the conventional architecture.In SA-BiLSTM, Self-Attention takes charge of the complex formation by correlating the whole sentence, and BiLSTM extracts context representations to tackle the lexical variation of attended embedding in preceding and succeeding directions.Besides, to measure and compare the performance of SA-BiLSTM model, we preprocessed and normalized the Roman Urdu sentences.Due to the efficient design of SA-BiLSTM, it can use fewer computation resources and yield a high accuracy of 68.4% and 69.3% on preprocessed and normalized datasets, respectively, which indicate that SA-BiLSTM can achieve better efficiency as compared with other state-of-the-art deep architectures.
Analysis of Blockchain Forking on an Ethereum Network.

Blockchain systems are on the rise, especially after the introduction and popularization of Bitcoin. The potential of the technology has been expanded and rebooted by Ethereum to support a broader range of applications through smart contracts. Transactions and contracts are activated every day in the Ethereum network and blocks are created at a very high rate. Often blocks are mined at conflicting times, which causes a split on the chain or a fork. Forks pose an inconsistency problem on the network until they are solved and the network agrees on the same instance of the blockchain. Transactions included on side forks, once the fork is resolved, are discarded and have to be mined again. Fork rates also impact the overall performance of the blockchain and the security of the system. This work studies how one of the main networking factors, namely communication delay, impacts forking of the blockchain. An analytical model for forking probability is presented. Furthermore, the impact of block distribution delays on the amount of forks is quantified using a private Ethereum blockchain in controlled experiments in a lab.
ESTIMATION OF CONTAMINATION WITH VANADIUM AND ARSENIC IN COASTAL SEDIMENTS OF BANDAR ABBAS (PERSIAN GULF, IRAN) ON THE BASIS OF GEOCHEMICAL-ENVIRONMENTAL FACTORS

In the present research, the intensity of contamination with heavy elements Vanadium (V) and Arsenic (As) in the coastal sediments of Bandar Abbas is gauged and rated using the method of Atomic Absorption Spectroscopy.All processes of sampling and assimilation of sediments are performed according to the guideline of the Manual of Oceanographic Observations and Pollutant Analyses Methods (MOOPAM) Standard issued by the Regional Organization for the Protection of the Marine Environment (RAPMI).The Mean concentrations of V and As in the sediments are of 102.42 ± 15.21 and 2.57 ± 0.09 µg/g dry weight of sediment, respectively.Statistical analysis showed that the mean concentrations of V and As existing in the channel sediments are meaningfully different with each other, with a confidence coefficient of P<0.05.On the basis of the Muller Geochemical Factor and Geoaccumulation Factor, such contamination is classified within The Zero Contamination Class, and on the basis of the standard of contamination factor, such a matter is classified within the low contamination level.Through statistical tests and calculations and considering the coefficient of correlation between V and As, it is distinguished that their source in coastal sediments of Persian Gulf should be similar.
Selected Tools and Services for Analyzing and Managing Open Access Journal Transformative Agreements

Libraries and institutions around the world are working with publishers to transform scholarly publishing into an open access ecosystem. To accomplish this goal, publisher contracts are being renegotiated and modified to include transformative elements which will move in this direction. With costs shifting from traditional subscriptions to fees which incorporate Read and Publish cost elements, tools are needed to provide underlying analytics both at the negotiating level but also for the ongoing implementation of new agreements. The ongoing workflow will be more complicated since institutions will need to more closely track authors and their associated article processing charge payments. A variety of open source and commercial tools and services are reviewed which have been developed to move to a new open access publishing environment.
EEG Signal Processing Model for Eye Blink Detection

Electroencephalography devices such as the OpenBCI Cyton Biosensing board create a noninvasive and inexpensive way of acquiring signals generated by the brain.These signals are influenced by different types of brain stimuli such as eye blinks but they are also includes a large amount of noise, e.g., generated by the board.However, the noise can be removed with the help of proven filters.In this aspect, the intention of this work is to demonstrate how using different type of filters, it is possible to clean the noise from the brain signals acquired using an encephalography devices (such as Cytonbiosensing board) which are generated when a user blinks his/her eyes and classify them in different type of blinks.We have chosen the study of eye blink brain signals, since, they present a wide range of real-life applications.Our model includes a simple algorithm that classifies user-generated eye blinks into short intended blinks and long composed blinks.Experimental results of the proposed model show an accuracy of 96% which enables the development of real-life applications that do not require real-time control such as IoT devices.
Web Scraping with HTML DOM Method for Data Collection of Scientific Articles from Google Scholar

Google Scholar is a web-based service for searching a broad academic literature. Various types of references can be accessed such as: peer-reviewed papers, theses, books, abstracts and articles from academic publishers, professional communities, pre-printed data centers, universities and other academic organizations. Google Scholar provides the profile creation feature of every researcher, expert and lecturer. Quantity of publication from an academic institution along with detailed data on the publication of scientific articles can be accessed through Google Scholar. A recap of the publication of scientific articles of each researcher in an institution or organization is needed to determine the research performance collectively. But the problems that occur, the unavailability of recap services for publishing scientific articles for each researcher in an institution or organization. So that the scientific article publication data can be utilized by academic institutions or organizations, this research will take data from Google Scholar to make a recap of scientific article publication data by applying web scraping technology. Implementation of web scraping can help to take the available resources on the web and the results can be utilized by other applications. By doing web scraping on Google Scholar, collective scientific article publication data can be obtained. So that the process of making scientific publications data recap can be done quickly. Experiments in this study have succeeded in taking 236 researchers data from Google Scholar, with 9 attributes, and 2,420 articles.
Creating a Digital Writing Classroom: A Mixed Methods Study about a First-year Composition Tablet Initiative

Having a higher level of digital literacy can contribute to students' better outcomes in first-year composition.However, some underprepared and financially disenfranchised students may not have digital literacy skills nor own a portable computing device, such as a tablet, to engage their coursework.This three-year, mixed methods study of 292 first-year composition students and 46 instructors at a research university suggests that implementing an institutional initiative to offer students tablets and a digitized curriculum for first-year composition can facilitate many positive outcomes.At the study's university, a "Tablet Initiative" raised some students' digital-literacy proficiency levels, enabled their digital composition practices, and improved their course outcomes as they spent additional time both inside and outside of the classroom in reading course texts; researching their topic; planning, drafting, and revising their work; and conducting peer reviews of one another's essays.Surveys, interviews, and course observations indicate that for many students, the Tablet Initiative generated a writing-intensive, student-centered, classroom experience, with many students from underprepared or lower socioeconomic backgrounds depending on their tablets.The study's results have implications for teaching and learning with mobile digital devices in first-year composition.
Usability Testing for Android Radio Streaming Apps of Songgolangit FM

Abstract Songgolangit FM Radio (SFMR) is one of Ponorogo pride information and solutions. Based on previous research, a few months ago, SFMR has an Android Streaming Apps launched on July 29, 2019, in the Google Play store (https://play.google.com/store/apps/details?id=com.amoled.radiosonggolangit&amp;hl=en) As a newly launched application, usability testing needs to measure how easy to use its user interface. The research aims to measure ease of use using usability testing of SFMR based on five-factor: learnability, efficiency, memorability, error, and satisfaction. The usability testing used a questionnaire by nine people from five division in SFMR. The usability testing gets an average total score with a value of 88% by Likert scale. The error factor receives the lowest rating due to incomplete information about the SFMR. From all overall calculation can be concluded that it can be accepted and appropriately used by the SFMR management. In the next research, it can improve the error factor based on respondents’ comments.
Robust Particle Filtering via Bayesian Nonparametric Outlier Modeling (Poster)

This paper is concerned with the online estimation of a nonlinear dynamic system from a series of noisy measurements. The focus is on cases wherein outliers are present in-between normal noises. We assume that the outliers follow an unknown generating mechanism which deviates from that of normal noises, and then model the outliers using a Bayesian nonparametric model called Dirichlet process mixture (DPM). A sequential particle-based algorithm is derived for posterior inference for the outlier model as well as the state of the system to be estimated. The resulting algorithm is termed DPM based robust PF (DPM-RPF). The nonparametric feature makes this algorithm allow the data to &#x201C;speak for itself&#x201D; to determine the complexity and structure of the outlier model. Simulation results show that it performs remarkably better than two state-of-the-art methods especially when outliers appear frequently along time.