Reviewerly/exHarmony huggingface dataset:
MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection

Intrusion detection systems examine the computer or network for potential security vulnerabilities. Time series data is real-valued. The nature of the data influences the type of anomaly detection. As a result, network anomalies are operations that deviate from the norm. These anomalies can cause a wide range of device malfunctions, overloads, and network intrusions. As a result of this, the network's normal operation and services will be disrupted. The paper proposes a new multi-variant time series-based encoder-decoder system for dealing with anomalies in time series data with multiple variables. As a result, to update network weights via backpropagation, a radical loss function is defined. Anomaly scores are used to evaluate performance. The anomaly score, according to the findings, is more stable and traceable, with fewer false positives and negatives. The proposed system's efficiency is compared to three existing approaches: Multiscaling Convolutional Recurrent Encoder-Decoder, Autoregressive Moving Average, and Long Short Term Medium-Encoder-Decoder. The results show that the proposed technique has the highest precision of 1 for a noise level of 0.2. Thus, it demonstrates greater precision for noise factors of 0.25, 0.3, 0.35, and 0.4, and its effectiveness.
Outside-In: Entangled Openness as Subversion Influencing Emergent Change

Those of us in the open education field often talk about how open education expands access, supports knowledge sharing, and potentially enhances the quality of education. We also critique open education for sometimes reproducing inequalities despite promising to promote social justice. But what about the ways in which openness removes/destroys barriers within us? In what ways does openness empower us from the outside-in? When does openness influence critical change and when might it fail to do so? In this opinion piece, I will explore some of the things openness makes possible that are often not possible within the walls of institutions, and which can end up challenging and subverting injustice by focusing on some key principles within the practice of “Virtually Connecting,” and I will explore a notion of entangled openness whereby open practices require a complex interplay between open attitudes, social justice praxis, and digital literacies within a team.
Self-Regulated Learning dan Self-Control Pada Mahasiswa Pendatang

Educational isn't always between teacher and student, even each individual can arrange their learning or what is called learning with their arrangements. This of course requires good self-control from every learner. This study aims to determine the relationship between independent learning and self-control in students through a cross-sectional approach. The population and sample of this study were West Sumatra 2016-2018 students studying at Malikussaleh University using quota sampling. Primary data collection is done by utilizing self-regulated learning scales and self-control scales. Hypothesis test results indicate the value of the correlation coefficient (r = 0.553) at a significance level of 0,000 (p &lt;0.001). The state of this statistical value can be interpreted that the working hypothesis has a significant relationship between the two variables studied.
Artistic and aesthetic senior preschool age сhild development: Organizational and management block

The timeliness of the problem is caused by the contradiction between need for artistic and aesthetic development of the child, who has a desire to learn, be creatively realized and insufficient organizational and methodological support in art schools. The purpose of research work is determining the stages of artistic and aesthetic development of older preschool children in primary art school. The research methodology consisted of the research works by Greene S., Hill M., Green B., Hall E., Turner C., Zyazyun I., Zlatyeva A., Otich O., Padalka H., Rudnytska O., McLeod N., Wright D., McCall K., Fujii M., Muzyka O., Lopatiuk Y., Belinska T., Belozerskaya A., Shvets I., Legkostup P., Kopylkovska I., Tikhomirova T. Results. The following actions are necessary for the successful implementation of the basic provisions of the modern art school: content and methodological blocks substantiation; logical and consistent definition and implementation of artistic and aesthetic development of senior preschool age child complex stages; providing organizational and managerial, control and evaluation units.
Instructor Exchange as a Continuous Professional Development Activity: Are we Ready to Change?

Continuous Professional Development (CPD) has been conducted through different activities for more than three decades in the field of education. However, due to various reasons, institutions have encountered major issues in the application processes of commonly accepted CPD activities, leading to modification of existing and creation of new forms of activities in line with contextual
Progressive Hard Negative Masking: From Global Uniformity to Local Tolerance

&lt;p&gt;Unsupervised contrastive learning has recently become increasingly popular due to its amazing performance without the need for costly annotations. However, indiscriminate sampling of negative pairs is accompanied by the uniformity-tolerance dilemma, which is especially serious in node-level graph contrastive learning due to the smoothing property of graph convolutional operators. Previous negative mining strategies that either overly emphasize hard negatives or rely on precise distribution estimation can make minor improvements or even degrade the performance in such a case. In this paper, we investigate the role of hard negatives in the uniformity-tolerance dilemma and propose a novel contrastive objective with a progressive hard negative masking scheme. The proposed objective, as an asymptotically-tightened lower bound of mutual information, is theoretically and empirically demonstrated to be capable of allowing higher local tolerance and stronger contrastive effects, thus leading to higher-quality embedding distributions and considerable performance improvement in downstream node classification tasks.&lt;/p&gt;
TrCoin: A Blockchain-based Robust Trust Management System for VANET

&lt;p&gt;In VANET, although data sharing among vehicles improves safety and trafﬁc, malicious data providers might share false information to compromise its effectiveness. One typical solution to address this issue is to manage the trustworthiness of data providers by using feedback received from data users quantifying the veracity of the shared data. However, data users might behave maliciously and share untruthful feedback. This paper introduces TrCoin, a blockchain-based trust management system, for VANET which tackles both fake data and untruthful feedback. TrCoin uses an honesty value calculation algorithm to distinguish honest from malicious data users and ﬁlters out the feedback shared by the malicious data users. TrCoin then runs a trust value calculation algorithm to compute the trust values of data providers based on the truthful feedback shared by honest data users. Extensive simulations are conducted to demonstrate the effectiveness of TrCoin under various attack scenarios with false data injection and untruthful feedback.&lt;/p&gt;
DocXClassifier: Towards an Interpretable Deep Convolutional Neural Network for Document Image Classification

&lt;p&gt; Convolutional Neural Networks (ConvNets) have been thoroughly researched for document image classification and are known for their exceptional performance in unimodal image-based document classification. Recently, however, there has been a sudden shift in the field towards multimodal approaches that simultaneously learn from the visual and textual features of the documents. While this has led to significant advances in the field, it has also led to a waning interest in improving pure ConvNets-based approaches. This is not desirable, as many of the multimodal approaches still use ConvNets as their visual backbone, and thus improving ConvNets is essential to improving these approaches. In this paper, we present DocXClassifier, a ConvNet-based approach that, using state-of-the-art model design patterns together with modern data augmentation and training strategies, not only achieves significant performance improvements in image-based document classification, but also outperforms some of the recently proposed multimodal approaches. Moreover, DocXClassifier is capable of generating transformer-like attention maps, which makes it inherently interpretable, a property not found in previous image-based classification models. Our approach achieves a new peak performance in image-based classification on two popular document datasets, namely RVL-CDIP and Tobacco3482, with a top-1 classification accuracy of 94.17% and 95.57% on the two datasets, respectively. Moreover, it sets a new record for the highest image-based classification accuracy of 90.14% on Tobacco3482 without transfer learning from RVL-CDIP. Finally, our proposed model may serve as a powerful visual backbone for future multimodal approaches, by providing much richer visual features than existing counterparts. &lt;/p&gt;
Investigation of Floss Preference by Types and Future Use Intention according to Floss Experience

Other SectionsAbstractIntroductionMaterials and MethodsResultsDiscussionAcknowledgmentsConflict of InterestEthical ApprovalAuthor contributionsData availabilityReference
Systematic Review: Effectiveness of Social Media in Improving Speaking Skills of ESL Learners

The Ministry of Education (MOE) has traditionally placed a strong emphasis on both the teaching and learning of English as a second language by its learners.It is crucial for the Malaysians as a second language users of English to improve their language skills especially speaking skills as it enables language users to be very much involved in verbal communication for the motive of providing and exchanging information as well as establishing long term bonding.Acquiring better speaking knowledge presents a severe challenge for ESL learners who struggle with their speaking, therefore, educators moved beyond traditional teaching approaches to modern ones that involve various tools such as social media which is a new technique for improving their learners' speaking skills.This systematic review focuses on the effectiveness of social media in improving speaking skills of ESL learners.This paper provides a systematic review of relevant published studies on the effectiveness of social media in improving the speaking skill of ESL learners from the year 2017 to 2021.This systematic review tries to answer a research question, i.e., what are the benefits of using social media in enhancing the speaking skills of ESL learners?For this research, fifteen studies were chosen to be reviewed in detail.The findings show that social media is an excellent resource that can be utilized by the educators during teaching and learning of ESL classroom since it improves their speaking skill as well as boosts ESL learners' motivation level and gives suggestions and information on content on how educators can integrate social media into their own teaching methods.As a suggestion for further study in the future, more attention could be paid to investigating the usefulness of social media in teaching and learning of English Language skills including listening, reading and writing.
Turkish Japanese learners’ L3 Japanese oral proficiency

This study applies mathematic linguistics to explore Turkish Japanese learners’ oral proficiency. Data were drawn from a self-built annotated corpus of 300 hours of recordings of storytelling (individual) and interaction (group). The oral proficiency for lexical sophistication is measured utilising moving-average morphological richness (MAMR) and mean size of paradigm. The syntactic complexity is measured by mean dependency distance (MDD). Self-written computer programme scripts are used to compute the MAMR, moving-average mean size of paradigm (MAMSP), and MDD. The findings indicate that in both storytelling and interaction, Turkish Japanese learners present a close picture regarding the degree of freedom of MAMR, MAMSP, and MDD to the oral data of native Japanese. This study, therefore, contends that L1 mother tongue morpho-syntactically affects later language acquisition. Moreover, the dependency distance (DD) that attributes the most tokens in interaction is nearly half that of the DD in storytelling, confirming the Principle of Least Effort (Zipf 1949) that human action tends to lighten the processing load. Furthermore, the MDD-frequency relationship in the Turkish oral data demonstrates a good fitting result for three models, that is, Harris, Dacey, and Kelly.
THE EFFECTS OF RENT SEEKING ACTIVITIES ON ECONOMIC GROWTH IN MIDDLE-INCOME COUNTRIES

This study investigates the effects of rent seeking activities on economic growth in 53 middle-income countries. Our data spans the period 2011 to 2020. We use the generalized method of moments estimator to examine the effects of rent-seeking activities on economic growth. Our study also includes several control variables, namely democratic accountability, public debt, human capital, foreign direct investment, capital stock, population, research and development expenditure and trade openness. The empirical results suggest that rent-seeking activities impede economic growth in middle-income countries.
Cat Swarm Optimization Algorithm -- A Survey and Performance Evaluation

This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.
PERFORMANCE COMPARISON OF GRADIENT-BASED CONVOLUTIONAL NEURAL NETWORK OPTIMIZERS FOR FACIAL EXPRESSION RECOGNITION

A convolutional neural network (CNN) is one of the machine learning models that achieve excellent success in recognizing human facial expressions. Technological developments have given birth to many optimizers that can be used to train the CNN model. Therefore, this study focuses on implementing and comparing 14 gradient-based CNN optimizers to classify facial expressions in two datasets, namely the Advanced Computing Class 2022 (ACC22) and Extended Cohn-Kanade (CK+) datasets. The 14 optimizers are classical gradient descent, traditional momentum, Nesterov momentum, AdaGrad, AdaDelta, RMSProp, Adam, Radam, AdaMax, AMSGrad, Nadam, AdamW, OAdam, and AdaBelief. This study also provides a review of the mathematical formulas of each optimizer. Using the best default parameters of each optimizer, the CNN model is trained using the training data to minimize the cross-entropy value up to 100 epochs. The trained CNN model is measured for its accuracy performance using training and testing data. The results show that the Adam, Nadam, and AdamW optimizers provide the best performance in model training and testing in terms of minimizing cross-entropy and accuracy of the trained model. The three models produce a cross-entropy of around 0.1 at the 100th epoch with an accuracy of more than 90% on both training and testing data. Furthermore, the Adam optimizer provides the best accuracy on the testing data for the ACC22 and CK+ datasets, which are 100% and 98.64%, respectively. Therefore, the Adam optimizer is the most appropriate optimizer to be used to train the CNN model in the case of facial expression recognition.
COMPARISON OF ARIMA AND GARMA'S PERFORMANCE ON DATA ON POSITIVE COVID-19 CASES IN INDONESIA

The development of methods in statistics, one of which is used for prediction, is overgrowing. So it requires further analysis related to the goodness of the method. One of the comparisons made to the goodness of this model can be seen by applying it to actual cases around us. The real case still being faced by people worldwide, including in Indonesia, is Covid-19. Therefore, research comparing the autoregressive integrated moving average (ARIMA) and the Gegenbauer autoregressive moving average (GARMA) method in positive confirmed cases of Covid-19 in Indonesia is essential. Based on the results of this research analysis, it was found that the best model with the Aikake's Information Criterion measure of goodness that was used to predict positive confirmed cases of Covid-19 in Indonesia was the Gegenbauer autoregressive moving average (GARMA) model.
ON COMPUTATIONAL BAYESIAN ORDINAL LOGISTIC REGRESSION LINK FUNCTION IN CASES OF CERVICAL CANCER IN TUBAN

Cervical cancer is the most common cancer that causes death in women. This cancer is mainly caused by Human Papilloma Virus (HPV). It is estimated that 52 million of Indonesian women are at risk of having cancer, and 36% of female cancer patients suffer from cervical cancer. This type of cancer cannot be diagnosed immediately as there is several years of pre-malignancy phase; thus, early detection or screening is needed to prevent it from turning into malignant. Pap test as a screening program can detect cancer, precancer, and normal condition. To understand the predicting factors of the test results, a comprehensive mathematical modelling was created using the link function of Bayesian Ordinal Logistic Regression. This study observed several possible factors that may affect Pap test results in Tuban regency, namely Age (X1), Education (X2), Childbirth Experience (X3), Use of Contraceptives (X4), Menstrual Cycle (X5), Age of First Menstruation (X6), History of Miscarriage (X7), Anemia (X8) and Number of Sexual Partners (X9) . The outcomes indicated that the predicting factors of Pap cervical cancer results are Age (X1), Education (X2), Childbirth Experience (X3), Use of Contraceptives (X4), Menstrual Cycle (X5), and Anemia (X8). In this model, there is an inexplainable error dependency as indicated by the varied constance values of alpha.
Multi-Agent Learning for the Inverse Kinematics of a Robotic Arm

This paper presents a solution to the inverse kinematics problem for robotic manipulator based on the Adaptive Multi-Agent System (AMAS) approach. In this research, multi-agent system is in charge of controlling a robot arm with four degrees of freedom (DOF) and two motorized wheels, giving appropriate commands, such as rotation angles and velocities, to reach the desired position and orientation of the end effector. The calculation of commands is directly related to the solving of forward and inverse kinematics. Before the learning process of AMOEBA, the rotational angles, θ values, are encoded into a single number N, this parameter is the desired value that we are going to predict in the predicting stage. During the learning phase, the Agnostic MOdEl Builder by self-Adaptation (AMOEBA) builds context agents, which has local models and is able to self-adapt. After the getting the predicted value, Npred, it will be decoded back to get the set of rotational angles that is given to robot end effector. In addition, the robot with all its physical parameters is modeled and simulated in the Robot Operating System (ROS) environment
CLASSIFICATION OF STUNTING IN CHILDREN UNDER FIVE YEARS IN PADANG CITY USING SUPPORT VECTOR MACHINE

Stunting is a nutritional problem in children characterized by the child’s height that is less than twice the standard deviation of the median standard from children growth that has been determined by the WHO. Stunting is influenced by many factors. If the conditional of these factors are known, it can be expected earlier whether a child is stunted or not. In this study, the prediction of stunting was carried out using the Support Vector Machine (SVM) classification method. SVM is a method to find the best hyperplane that can be used to separate two or more classes. In this study, the parameter of the SVM model that must be determined is the cost value and gamma. Based on the result of research using parameters cost=10 and gamma=5, the estimation result of the classification with 100% accuracy can be obtained.
AN ORDINAL LOGISTIC REGRESSION MODEL FOR ANALYZING RISK ZONE STATUS OF COVID-19 SPREAD

Coronavirus disease 2019 (COVID-19) is a new type of virus that has been found to have infected human since it first appeared in Wuhan, China, in December 2019. This study aims to determine the factors that influence the risk zone status of COVID-19 spread in Indonesia using ordinal logistic regression. The ordinal logistic regression model in this study uses proportional odds model because the researcher assumes probability of predictor variable coefficients is the same for each respond category. The response variable is secondary data from the COVID-19 Handling Task Force, namely the status of the risk zone for the spread of COVID-19 who has 4 categorical levels, namely high, medium, low, and no cases. Predictor variables are elderly population, COVID-19 referral hospital, diabetes mellitus, hypertension, hand washing behavior, male population, and smoking habits. Based on results of the analysis, variables that significantly affect the risk zone status of COVID-19 spread in Indonesia are elderly population and diabetes mellitus. The Odds proportional figure shows that the higher percentage of the elderly population, the higher chance of an area with high-risk zone status (OR=1.171). The higher percentage of comorbidities diabetes mellitus, the higher chance of an area with high-risk zone status (OR=1.569).
Sistem Pakar Diagnosis Hama Penyakit Tanaman Jeruk Keprok Borneo Prima (Citrus Reticulata) Dengan Fuzzy Tsukamoto

Borneo Prima Citrus (Citrus Reticulata) is a monoculture commodity that is susceptible to pests or diseases. The role of an expert is constrained by the fact that expert knowledge is not well documented, the detection process is still manual and expensive, and the distance between agricultural extension workers and plantation sites is far. This problem can be solved by an expert system. The Fuzzy-Tsukamoto method is suitable for solving citrus disease diagnosis problems which have many rules with several symptoms that can represent several diseases. Fuzzy Tsukamoto applies monotonous reasoning to each rule. If in monotonous reasoning, the system only has one rule, in the Tsukamoto method the system consists of several rules. Because it uses the basic concept of monotonous reasoning, in Fuzzy-Tsukamoto's method, every consequence of the IF-THEN rule is represented by a fuzzy set on a monotonic membership function. There were 13 citrus trees indicated to be infected with the disease, which was observed from 500 trees. The accuracy of the results obtained between the expert system and expert knowledge is 84% of 13 diseases and 36 symptoms. The result shows that there are two diseases that are not suitable and 11 diseases according to experts.
Overview of Blockchain Based Electronic Healthcare Solutions and Security

One of the major keywords in the current digital world is the blockchain, which plays a major part in all kinds of advanced service systems, offering more convenience and better efficiency/effectiveness by controlling system hardware intelligently in a way humans have never experienced [...]