Reviewerly/exHarmony huggingface dataset:
The Effectiveness of the Use of Technology in a Primary ESL Classroom: A Systematic Literature Review

This paper presents a systematic literature review of relevant past studies that are related to the effectiveness of the use of technology in a primary ESL classroom.The studies analysed are from 2014 to 2020 with a total number of sixteen past studies.The focus of this paper is to learn the effectiveness of the use of technology in primary ESL classrooms.This paper also includes sub-focused aims which are the types and methods of technology that can be implemented in an ESL classroom and the challenges encountered before, during or after the implementation.Through the analysed articles many relevant information were gathered for this study.The study mostly resulted the engagement and motivation attained by the learners while using technology as a learning tool in an ESL classroom.
RANCANG BANGUN INFRASTRUKTUR CLOUD COMPUTING DENGAN OPENSTACK PADA JARINGAN LOKAL MENGGUNAKAN VIRTUALBOX

The development of cloud computing technology that is mushrooming in the world, as well as in Indonesia, is one of the causes of revolutionary changes in trends that have made many industries / organizations / agencies / institutions / companies adopt and implement them. To face the challenges of these technological developments, the Indonesian Business Polytechnic Murni Sadar Campus Pematangsiantar City is one of the private universities engaged in the world of education ready to face these changing trends to take part in educating the nation's children. The challenge of the development of cloud computing technology is to design a system with openstack as the software platform for the cloud, the ubuntu server as the operating system uses virtualization with virtualbox in order to obtain a capable system before being implemented to a physical server in the laboratory room in order to increase the efficiency of lectures. aimed at meeting the needs for practicum activities for students at the PBI campus so that they can taste and explore cloud computing technology so that they have additional knowledge to face the revolution of industrial 5.0.
SISTEM PAKAR DIAGNOSA GANGGUAN KEJIWAAN MENGGUNAKAN METODE FORWARD CHAINING BERBASIS WEB

According to WHO, mental health is a physical condition of a person's sense of well-being, so that there is the ability to manage the normal stresses of life, to be able to work productively and to complete task detecting a disease with a computer will make it easier for medical personnel and the public to determine conclusions in the form of solutions and handling. One form of technological progress is an expert system which is part of artificial intelligence by utilizing one method, namely forward chaining. This study uses the forward chaining method by providing information to diagnose 9 disease data and 22 symptom data accompanied by disease data, symptoms, explanations of types of diseases, causes, and solutions for patients. A web-based expert system can diagnose mental disorders so that patients can do digital consultations about their illnesses. Based on the results of the analysis on the system, data from the number of visitors showed that the percentage of respondents' answers was 98.99% already user friendly and responsive. And the results of system testing using black boxes show a 100% percentage, until it can be concluded that the expert system application is feasible to use.
FPGA Implementation of a Pseudo-Random Sequence Generators Based on the Modified Lorentz System

The results of the FPGA implementation of an algorithm for generation pseudorandom sequences formed on the basis of a modified dynamic Lorentz system over a finite Galois field have been obtained. This article presents a comparison of implementations of pseudo-random sequences generators based on the quasi-resonance effect and those implemented in arithmetic over a Galois field. The possibility of implementing pseudo-random sequence generators based on the Lorentz system on relatively inexpensive and simple FPGAs is shown. The results of this paper can be used for information masking tools, asynchronous-address information transmission systems, the construction of communication systems with broadband signals, modeling and cryptography.
Deep Learning-Based Automatic Modulation Classification Over MIMO Keyhole Channels

Automatic modulation classification (AMC) is a significant part of cognitive communication systems. In early researches, likelihood-based (LB) and feature-based (FB) solutions were proposed for the AMC problem. With the developments in the data-driven approaches, a third method based on deep learning (DL) has recently gained prominence among AMC researchers. It is shown that convolutional neural network based classifiers are very efficient in the AMC for both single input single output (SISO) and multiple-input multiple-output (MIMO) systems. However, for most of the works in MIMO-AMC, the channel considered is full rank. This work addresses the problem of AMC over rank deficient channels such as a keyhole channel using a DL-based classifier. The proposed DL-classifier utilizes a CNN, which does not employ pooling layers or dropouts in the convolutional layers. To further improve the classification accuracy, decision cooperation as well as feature fusion is employed. In addition to the keyhole effect, this work investigates the effect of antenna correlation on DL-based AMC. A comparative study of the proposed method and the existing FB AMC method for the MIMO keyhole channel is also presented.
THE IMPACT OF PHET INTERACTIVE SIMULATIONS AND POWERPOINT PRESENTATION SLIDE SHOW VISUALISATION ON LEARNING BALANCING OF CHEMICAL EQUATIONS AMONG GRADE 10 PUPILS

This study investigated the impact of PhET interactive simulation and PowerPoint presentation slide show visualisation on learning the balancing of chemical equations to grade 10 learners. A pre-test post-test quasi-experimental research design was used. Students in the experimental group were instructed using simulation games while PowerPoint presentation slides with visualisation were used in the comparison group. The post-test results revealed that the PhET Interactive Simulation was more effective compared to the PowerPoint Presentation Slide Show Visualisation. The PhET interactive simulation software application appeared to work better because of the equations that are embedded in it. The group taught using the PhET interactive simulation had attained better attitudes and high achievement scores towards learning of balancing of chemical equations as compared to the group which was taught using PowerPoint presentation slide show visualisation.
Firefly Algorithm with Mini Batch K-Means Entropy Measure for Clustering Heterogeneous Categorical Timber Data

Clustering analysis is the process of identifying similar patterns in various types of data.Heterogeneous categorical data consists of data on ordinal, nominal, binary, and Likert scales.The clustering solution for heterogeneous data clustering remains difficult due to partitioning complex and dissimilarity features.It is necessary to find a solution to highquality clustering techniques to efficiently determine the significant features of the data.This paper emphasizes using the firefly algorithm to reduce the distance gap between features and improve clustering performance.To obtain an optimal global solution for clustering, we proposed a hybrid of mini-batch kmeans (MBK) clustering-based entropy distance measures (EM) with a firefly optimization algorithm (FA).This study compares the performance of hybrid K-Means, Agglomerative, DBSCAN, and Affinity clustering models with EM and FA.The evaluation uses a variety of data from the timber perception survey dataset.In terms of performance, the proposed MBK+EM+FA has superior and most effective clustering.It achieves a higher accuracy of 96.3 percent, a 97 percent F-measure, a 98 percent precision, and a 97 percent recall.Other external assessments revealed that the Homogeneity (HOMO) is 79.14 percent, the Fowlkes-Mallows Index (FMI) is 93.07 percent, the Completeness (COMP) is 78.04 percent, and the V-Measure (VM) is 78.58 percent.Both proposed MBK+EM+FA and MBK+EM took about 0.45s and 0.35s to compute, respectively.The excellent quality of the clustering results does not justify such time constraints.Surprisingly, the proposed model reduced the distance measure of all heterogeneous features.The future model could put heterogeneous categorical data from a different domain to the test.
Enhancement of the Tracking Performance for Robot Manipulator by Using the Feed-forward Scheme and Reasonable Switching Mechanism

Robot manipulator has become an exciting topic for many researchers during several decades. They have investigated the advanced algorithms such as sliding mode control, neural network, or genetic scheme to implement these developments. However, they lacked the integration of these algorithms to explore many potential expansions. Simultaneously, the complicated system requires a lot of computational costs, which is not always supported. Therefore, this paper presents a novel design of switching mechanisms to control the robot manipulator. This investigation is expected to achieve superior performance by flexibly adjusting various strategies for better selection. The Proportional-Integral-Derivative (PID) scheme is well-known, easy to implement, and ensures rapid computation while it might not have much control effect. The advanced interval type-2 fuzzy sliding mode control properly deals with nonlinear factors and disturbances. Consequently, the PID scheme is switched when the tracking error is less than the threshold or is far from the target. Otherwise, the interval type-2 fuzzy sliding mode control scheme is activated to cope with unknown factors. The main contributions of this paper are (i) the recommendation of a suitable switching mechanism to drive the robot manipulator, (ii) the successful integration of the interval type-2 fuzzy sliding mode control to track the desired trajectory, and (iii) the launching of several tests to validate the proposed controller with robot model. From these achievements, it would be stated that the proposed approach is effective in tracking performance, robust in disturbance-rejection, and feasible in practical implementation.
Nonconvex model for mixing noise with fractional-order regularization

In this paper, we propose a restoration model for image degraded by different kinds of blur and mixing Gaussian-impulse noise. Our model consist of a nonconvex Exponential-Type (ET) function, a $ L_{2} $-norm data-fitting term and a fractional-order Besov norm as the regularization term. We employ the proximal linearized minimization (PLM) algorithm and alternating direction method of multipliers (ADMM) algorithm to solve our proposed minimization model, convergence analysis is carried out. The experimental outcomes demonstrate that the restored images by our proposed method are better than those existing relative methods in terms of PSNR, SSIM values and visual quality.
GRAN: graph recurrent attention network for pedestrian orientation classification

Abstract In complex traffic scenes, accurate identification of pedestrian orientations can help drivers determine pedestrian trajectories and help reduce traffic accidents. However, there are still many challenges in pedestrian orientation recognition. First, due to the irregular appearance of pedestrians, it is difficult for general Convolutional Neural Networks (CNNs) to extract discriminative features. In addition, more features of body parts help to judge the orientation of pedestrians. For example, head, arms and legs. However, they are usually small and not conducive to feature extraction. Therefore, in this work, we use several discrete values to define the orientation of pedestrians, and propose a Gated Graph Neural Network (GGNN)-based Graph Recurrent Attention Network (GRAN) to classify the orientation of pedestrians. The contributions are as follows: (1) We construct a body parts graph consisting of head, arms and legs on the feature maps output by the CNN backbone. (2) Mining the dependencies between body parts on the graph via the proposed GRAN, and utilizing the encoder–decoder to propagate features among graph nodes. (3) In this process, we propose an adjacency matrix with attention edge weights to dynamically represent graph node relationships, and the edge weights are learned during network training. To evaluate the proposed method, we conduct experiments on three different benchmarks (PDC, PDRD, and Cityscapes) with 8, 3, and 4 orientations, respectively. Note that the orientation labels for PDRD and Cityscapes are annotated by our hand. The proposed method achieves 97%, 91% and 90% classification accuracy on the three data sets, respectively. The results are all higher than current state-of-the-art methods, which demonstrate the effectiveness of the proposed method.
Flood Disaster Relief Operation: A Systematic Literature Review

A flood is natural disaster that often occurs in many regions. Flood has a significant impact on the nature conditions, local communities, and regional economic losses. The flood can happen due to a damaged environmental system; therefore, it needs deeper study and extra effort to prevent it. Thus, an appropriate and right Disaster Relief Operation (DRO) is needed in responding to flood disaster. In this research, 50 articles categorized in "flood disaster relief operation" published in the range 2012 to 2022 have been reviewed. This review is conducted by using the Systematic Literature Review (SLR) method. This study aims to explore and analyze flood DRO. The findings reveal that the flood DRO still has several weaknesses in the current system that should be improved: the lack of an integrated information system, not enough collaboration of the stakeholders, the lateness of information exchange, and unplanned relief operations through the preparation. For further research, it is recommended to implement the proposed system in the relief operations execution.
Synchronizing tasks for distributed learning in connected and autonomous vehicles

Autonomous driving relies greatly on deep learning to comprehend the surroundings and activities of the road systems. The learning models are traditionally trained off-line and used during driving. However, recent research on federated learning has enabled distributed deep learning for model adaptation with new data inputs from end users. Similarly, the recent research on continual learning has enabled the upgrading of the learned model with newer rounds of training, without losing previously acquired knowledge. For autonomous and/or connected vehicles, these mean it is possible to take new, maybe real time, data inputs from various sensors from multiple vehicles in vicinity, to train and update the preloaded models. The updated models can improve the safety and reduce human involvements when driving through unfamiliar situations. This paper tackles one important issue, namely, the model data dissemination for enabling such a distributed learning. The model data dissemination consists of steps of soliciting workers, transmitting model, and collecting the updates. In a mixed autonomous vehicles and connected vehicles network scenario, communication overhead and network dynamics are the major challenges. We present analyses on critical latency issues pertained to the various aspects of the model data dissemination to gain insights on the feasibility of federated learning for such a scenario. Also, we introduce a communication architecture and a publish-subscribe system for the model data dissemination. Our system is built with an information-centric networking paradigm via a tiered edge network architecture. Such a system organizes the steps of the model data dissemination clearly and manages the dynamics of the participating vehicles easily. The results show that the presented system reduces overall communication overhead and delay. It also provides high resiliency to packet losses in the mixed wireless connected and autonomous vehicular network.
Diagnostically Cognitive Assessment of Complex Semantic Explanations and Learning: A Bayesian Network Approach

This study explored a diagnostically cognitive assessment model for the ANOVA score model emphasizing semantic explanations. The study used the mixed methods designs, in which the ANOVA score model was decomposed into measurable components. This consists of the proficiency student model. Such kinds of data were transferred to a quantitative representation via the Bayesian network model of the ANOVA score model and semantic explanation assessment. This diagnostically cognitive assessment consists of 28 variables hierarchically, which are explanatory variables and evidence variables. Nine variables are explanatory variables that are latent. Nineteen variables are evidence variables that collect students’ learning information and propagate the information to the explanatory variables. The data are simulated data; the semantic explanations from twelve students were recorded and input into the nineteen evidence variables. Semantic explanations indicate 3 levels: lower level, medium level and high level. The score should be more than 82 points, which indicates a mastery level. The study also suggests that if a student achieves a high score in a module, the student has a better chance of achieving a high score in the overall assessment model.
Odds Inversion Problem With Replacement

"Odds Inversion Problem With Replacement." The American Mathematical Monthly, ahead-of-print(ahead-of-print), p. 1
Implementasi Algoritma K-Nearest Neighbor (K-NN) dan Single Layer Perceptron (SLP) Dalam Prediksi Penyakit Sirosis Biliari Primer

Primary biliary cirrhosis is a chronic cholestatic liver disease that can lead to liver failure. The majority of individuals who suffer from this disease are women. Primary biliary cirrhosis is recorded as contributing to mortality worldwide with a percentage of 0.6% to 2.0%. However, so far, randomized trials have shown that some immunosuppressant or immunosuppressive drugs do not play a major role in patients with primary biliary cirrhosis. Therefore, early detection is important to start treatment and planning for appropriate medical needs. The results of the processing accuracy with the K-NN algorithm of 76.2% and the SLP algorithm of 63% using the Percentage Split method show that the K-NN algorithm is better for early detection of primary biliary cirrhosis. The K-Nearest Neighbor algorithm is able to perform early detection of primary biliary cirrhosis with a precision of 77% and recall of 75% with the hope that the percentage of mortality worldwide can decrease. However, the K-NN algorithm is not superior in retrieving information in patients with primary biliary cirrhosis. On the other hand, the SLP algorithm is superior in retrieving information in patients with primary biliary cirrhosis with a recall value of 65%.
Toward information equity among academic libraries

For two decades, publishers and vendors have used e-book licenses to back academic libraries into a corner. These rightsholders and intermediaries lease rather than sell content, and they dictate what constitutes permitted downstream usages. Libraries have historically used interlibrary loans to fill gaps in collections, but publishers and vendors unilaterally claim that interlibrary loans of entire e-books infringe on their exclusive rights. As a result, libraries at small and mid-sized colleges and universities are constrained to providing patrons access only to e-books that fall within the limits of modest collections budgets. Grounded on the premise that e-book interlibrary loans are needed to advance and protect information equity in higher education, this presentation invites interdisciplinary discussions and collaboration with respect to the future of resource sharing in academic libraries.
Assessment of Association Rule Mining Using Interest Measures on the Gene Data

Aim: Data mining is the discovery process of beneficial information, not revealed from large-scale data beforehand. One of the fields in which data mining is widely used is health. With data mining, the diagnosis and treatment of the disease and the risk factors affecting the disease can be determined quickly. Association rules are one of the data mining techniques. The aim of this study is to determine patient profiles by obtaining strong association rules with the apriori algorithm, which is one of the association rule algorithms.&#x0D; Material and Method: The data set used in the study consists of 205 acute myocardial infarction (AMI) patients. The patients have also carried the genotype of the FNDC5 (rs3480, rs726344, rs16835198) polymorphisms. Support and confidence measures are used to evaluate the rules obtained in the Apriori algorithm. The rules obtained by these measures are correct but not strong. Therefore, interest measures are used, besides two basic measures, with the aim of obtaining stronger rules. In this study For reaching stronger rules, interest measures lift, conviction, certainty factor, cosine, phi and mutual information are applied.&#x0D; Results: In this study, 108 rules were obtained. The proposed interest measures were implemented to reach stronger rules and as a result 29 of the rules were qualified as strong.&#x0D; Conclusion: As a result, stronger rules have been obtained with the use of interest measures in the clinical decision making process. Thanks to the strong rules obtained, it will facilitate the patient profile determination and clinical decision-making process of AMI patients.
Optimizing the Quantum Circuit for Solving Boolean Equations Based on Grover Search Algorithm

The solution of nonlinear Boolean equations in a binary field plays a crucial part in cryptanalysis and computational mathematics. To speed up the process of solving Boolean equations is an urgent task that needs to be addressed. In this paper, we propose a method for solving Boolean equations based on the Grover algorithm combined with preprocessing using classical algorithms, optimizing the quantum circuit for solving the equations, and implementing the automatic generation of quantum circuits. The method first converted Boolean equations into Boolean expressions to construct the oracle in the Grover algorithm. The quantum circuit was emulated based on the IBM Qiskit framework and then simulated the Grover algorithm on this basis. Finally, the solution of the Boolean equation was implemented. The experimental results proved the feasibility of using the Grover algorithm to solve nonlinear Boolean equations in a binary field, and the correct answer was successfully found under the conditions that the search space was 221 and three G iterations were used. The method in this paper increases the solving scale and solving speed of Boolean equations and enlarges the application area of the Grover algorithm.
Implementation of NLP based automatic text summarization using spacy

The amount of data on the Internet has increased exponentially over the past decade. Therefore, we need a solution that converts this massive amount of raw information into useful information that the human brain can understand. One such common technique in research that helps when dealing with large amounts of data is text summarization. Automatic summarization is a well-known approach to reduce documents to key ideas. This works by storing important information by creating a shortened version of the text. Text summaries are divided into extraction and abstraction methods. The extraction summary method minimizes the summarization burden by selecting a subset of relevant sentences from the actual text. There are many methods, but researchers specializing in natural language processing (NLP) are particularly attracted to the extraction method. The meaning of the sentence is calculated using linguistic and statistical features. In this work, extractive and abstract methods for summarizing texts were examined. This white paper uses a spacey algorithm to analyze the above methods, resulting in fewer iterations and a more focused summary.