Reviewerly/exHarmony huggingface dataset:
Quantum Computers for Weather and Climate Prediction: The Good, the Bad and the Noisy

Over the past few years, quantum computers and quantum algorithms have attracted considerable interest and attention from numerous scientific disciplines. In this article, we aim to provide a non-technical, yet informative introduction to key aspects of quantum computing. We discuss whether quantum computers one day might become useful tools for numerical weather and climate prediction. Using a recently developed quantum algorithm for solving non-linear differential equations, we integrate a simple non-linear model. In addition to considering the advantages that quantum computers have to offer, we shall also discuss the challenges one faces when trying to use quantum computers for real-world problems involving ''big data'', such as weather prediction.
Accelerated Quantum Monte Carlo with Probabilistic Computers

Quantum Monte Carlo (QMC) techniques are widely used in a variety of scientific problems and much work has been dedicated to developing optimized algorithms that can accelerate QMC on standard processors (CPU). With the advent of various special purpose devices and domain specific hardware, it has become increasingly important to establish clear benchmarks of what improvements these technologies offer compared to existing technologies. In this paper, we demonstrate 2 to 3 orders of magnitude acceleration of a standard QMC algorithm using a specially designed digital processor, and a further 2 to 3 orders of magnitude by mapping it to a clockless analog processor. Our demonstration provides a roadmap for 5 to 6 orders of magnitude acceleration for a transverse field Ising model (TFIM) and could possibly be extended to other QMC models as well. The clockless analog hardware can be viewed as the classical counterpart of the quantum annealer and provides performance within a factor of $&lt;10$ of the latter. The convergence time for the clockless analog hardware scales with the number of qubits as $\sim N$, improving the $\sim N^2$ scaling for CPU implementations, but appears worse than that reported for quantum annealers by D-Wave.
Zero Waste Scientific Research Evaluation: The Scientific Research Evaluation System Framework to Stimulate Scholars’ Empathy and Innovation Intention

Based on the two-dimensional University Research Evaluation System (URES), this paper aimed to develop a comprehensive and scientific measurement scale and to empirically verify the impact of the URES on scholars’ empathy and willingness to innovate. Grounded in theory, this study analyzed the personal information and interview data of 26 university scholars publicly available online. First, through qualitative analysis (using Nvivo 12 software), we developed an initial scale for URES. Second, we tested the reliability and validity of the scale by structural equation modeling (SEM) using Mplus 8.0 software. The results show that the URES includes two dimensions: research process evaluation and investment output evaluation. The URES scale showed good reliability and validity and was confirmed to be positively correlated with scholars’ empathy and willingness to innovate. Therefore, the URES constructed in this study not only fully stimulates scholars’ empathy and innovation willingness, but also promotes the optimal use of scholars’ knowledge resources. Finally, this research helps to reduce unnecessary educational and political investment, which has important implications for the sustainable development of society.
Behavior of Glasses Users in Refractive Disorders with Theory of Reasoned Action Approaches

Issues and Problems : Glasses are a safe method for correcting refractive errors of the eye. A person with refractive error makes the decision to buy and use glasses because of various factors. One of them is caused by interests that are influenced by norms or other people's views about wearing glasses. Objective : This study was conducted to analyze the behavior of eyeglass users with refractive errors using the theory of Reasoned Behavior. Method: Qualitative method that utilizes primary and secondary sources in the form of official reference sources, and other printed and unprinted sources, as well as online sources that have been used. Finding: The results show that although glasses are a necessity for people with refractive errors, their behavior is influenced by the intentions of the users of the glasses. And the intention to get a visual aid is also influenced by the subjective attitudes and norms of the glasses user. Attitudes are formed from the beliefs or beliefs held about glasses and the evaluation of their use in helping the user's appearance. Meanwhile, norms are formed from norms that are believed to be related to the use of tools as a tool for refractive errors and motivation from the closest people and medical personnel. Recomendation: people with refractive errors are advised to increase their knowledge and understanding of the use in treating refractive disorders which are the controllers of negative norms owned by users so that the goal of overcoming visual disturbances and reducing eye damage can be carried out properly. Health workers are expected to contribute in providing information for the myths that exist in the community regarding the use of glasses
Pengembangan Pocketbook Sebagai Media Edukasi Dalam Pembentukan First Responder Emergency Response Pada Lingkup Prehospital

The health cadres play an essential role, as community health educator. The development of pocketbook emergency safety and first aid as a learning media to improve health cadres’s knowledge and skills as first responder emergency response. The purpose of this study is to determine the effect of pocketbook emergency safety and first aid as a learning media in health cadres ability as a first responder emergency response. This study used a quasi-experimental method with one group pre-post test design, with a purposive sampling technique. In this study, the average pretest value of knowledge was 57,50±4,075 and the posttest value of knowledge was 78,27±2,420, with a p-value of 0.000, which means that there is a significant difference between the knowledge before and after the intervention. The average pretest value of practice was 54,80±2,265 and the posttest value of practice was 73,73±1,999, with a p-value of 0.000, which means that there is a significant difference between the practice before and after the intervention.
AP Association Algorithm Based on VR User Behavior Awareness

With the rapid development of virtual reality (VR) technology, this paper proposes an access point (AP) correlation method based on VR user behavior awareness to address the problem of how current AP correlation methods only focus on the performance improvements of ordinary users and ignore the impact of VR user behavior on service quality. This paper analyzes the AP association method under the coverage scenario of a multi-access point (multi-AP) scenario environment and controls the performance improvement of VR user APs or APs under the access controller (AC) by association. Firstly, the VR network application scenario and system model were constructed, and secondly, the user behavior was sensed by analyzing the viewing habits of users. Then, the VR user association problem based on VR user behavior perception was transformed into a “many-to-many” matching problem between VR user devices and APs, and the generalized multidimensional multiple choice knapsack (GMMKP) model was established to solve the problem using the backpack problem theory; the suboptimal solution algorithm was selected to obtain the best VR user AP association strategy. The experimental results show by simulation that the proposed algorithm in this paper performed better in terms of the AP load balancing and average network download latency compared to the comparison algorithms.
EFL Pre-service Teachers' Online Reading Strategy Use and their Insight into Teaching Reading

The purpose of this study is to investigate the usage of online reading strategies among EFL pre-service teachers in the Education Program and their pedagogical practices in teaching online reading. A total of 107 participants from the English Education program in East Java were involved in this study. A mixed-method design was used as the research approach. For data collection, this present study used the Second Language Online Reading Strategy Inventory (SLORSI) closed-ended questionnaire and semi-structured interviews. Overall, 13 participants were involved in the interview session. Based on the statistical analysis of the questionnaire, it was found that EFL pre-service teachers showed a high level of usage of online reading strategies (M= 3,71 – 4.14). The most frequently used was the Saving strategy (M= 4,14). However, the interview results were inconsistent with the questionnaire; although the participants showed a high level of usage of online reading, they offered different perspectives on teaching online reading. The participants mentioned inadequate reading digital literacy and strategies for future reading classes. Therefore, this study suggests that EFL education programs must highlight the importance of providing explicit practice for pre-service teachers using online reading strategies and model the strategy for future students.
Preface to the Special Issue on “Quantum Computing Algorithms and Computational Complexity”

In 1982, Richard Feynman stated that in order to simulate quantum systems, we would rather go for a sort of brand-new powered quantum processor instead of a classical one [...]
Block-Wisely Supervised Network Pruning with Knowledge Distillation and Markov Chain Monte Carlo

Structural network pruning is an effective way to reduce network size for deploying deep networks to resource-constrained devices. Existing methods mainly employ knowledge distillation from the last layer of network to guide pruning of the whole network, and informative features from intermediate layers are not yet fully exploited to improve pruning efficiency and accuracy. In this paper, we propose a block-wisely supervised network pruning (BNP) approach to find the optimal subnet from a baseline network based on knowledge distillation and Markov Chain Monte Carlo. To achieve this, the baseline network is divided into small blocks, and block shrinkage can be independently applied to each block under a same manner. Specifically, block-wise representations of the baseline network are exploited to supervise subnet search by encouraging each block of student network to imitate the behavior of the corresponding baseline block. A score metric measuring block accuracy and efficiency is assigned to each block, and block search is conducted under a Markov Chain Monte Carlo scheme to sample blocks from the posterior. Knowledge distillation enables effective feature representations of the student network, and Markov Chain Monte Carlo provides a sampling scheme to find the optimal solution. Extensive evaluations on multiple network architectures and datasets show BNP outperforms the state of the art. For instance, with 0.16% accuracy improvement on the CIFAR-10 dataset, it yields a more compact subnet of ResNet-110 than other methods by reducing 61.24% FLOPs.
Perbedaan Warna Cahaya Lampu Terhadap Performa Cacing Tanah (Lumbricus rubellus)

Earthworms are nocturnal animals and have negative phototaxis, therefore the presence and color of light are thought to affect the performance of this Lumbricus rubellus earthworm. This study aims to analyze the effect of different color lights on Lumbricus rubellus performance. This study used acompletely randomized design (RAL), six color treatments (red, orange, yellow, green, blue, and purple, and dark as a control) and three replications with variables observed were weight gain, media weight, number of segments to the clitellum, and the number of cocoons. The results showed that the six types of color had different effects on the performance of the earthworm Lumbricus rubellus. The red color lights had a better effect on the weight of the earthworm, the number of cocoons and the number of segments compared to other colors, while the yellow color had a lower effect.
ANALYSIS OF INDIVIDUAL PROFESSIONAL DEFICITS AND ADAPTATION DIFFICULTIES AMONG TEACHERS OF EDUCATIONAL ORGANIZATIONS AND METHODS TO OVERCOME THEM

Statement of the problem. The article analyzes and formulates the problems of overcoming individual professional deficits and adaptation difficulties in the profession of teachers of educational organizations. The problem is highlighted due to discrepancy between the requirements for results of training young teachers and their ability to overcome professional deficits and adaptation difficulties in the profession. The purpose of the article is to identify and substantiate the content and technological components in the process of preparing students to solve the identified problems in the conditions of existing educational standards. The methodology of the research consists of the analysis and generalization of organizational documents in the field of higher education, research works of international and Russian scientists recognized by the scientific community, the experience of teaching students in various fields of training; the development of diagnostic tools to identify individual professional deficits and adaptive difficulties of teachers in the profession and the definition of ways to overcome them. Research results. On the basis of activity-based and competence-based approaches, the main provisions for solving the problem of overcoming individual professional deficits and adaptation difficulties by teachers in the profession are formulated. Conclusion. The author’s concept of identifying individual professional deficits and finding ways to overcome the adaptive difficulties of teachers in the profession, proposed in the article, can be implemented in the process of professional activity. The result of its implementation may be the use of special diagnostic tools to identify individual professional deficits and adaptive difficulties of teachers, developed taking into account the new requirements for teacher training; and based on the principles of: digitalization of education, description of the results of mastering in the format of competencies, design of educational activities, creative innovation of the educational process, compliance with the requirements and level of professional training of teachers, as well as in determining ways to overcome these professional deficits and difficulties.
Motivation assessment model for intelligent tutoring system based on mamdani inference system

&lt;span lang="EN-US"&gt;Many educators have used the benefit offer by intelligent tutoring system. To become more personalizing and effective tutoring system, student characteristics need to be considered. One of important student characteristic is motivation. Therefore, in this study a motivation assessment model based on self-efficacy theory was proposed. Refer to the theory, effort, choice of activities, performance and persistence were discussed as motivation attributes. Further, time spend, difficulty level, number of correct answers and number of questions skipped are the parameters was defined for each attribute. The model was designed by taking the advantages of Mamdani inference system as fuzzy logic technique to predict students’ motivation level. The model able to inmates like a human tutor does in the traditional classroom to understand students’ motivation level.&lt;/span&gt;
A Conceptual Model of Culture-Based English Learning Materials in Indonesia

The purpose of this research was to create a culture-based English learning model for use in junior high schools. This study was part of a larger research project that used a research and development (R&amp;D) design. In the first year, the project aims to create a conceptual model of culture-based English learning materials. This study was conducted at Muhammadiyah 1 Junior High School in Ponorogo, East Java province, Indonesia. Muhammadiyah is one of the biggest Islamic non-governmental organizations in Indonesia. We chose this school due to its easy access and low motivation levels of its students in learning English. Sample selection was conducted using a multi-stage sampling technique. The data were collected using questionnaires, document analysis, and interviews, and triangulated using expert judgment. The results of the data analysis led to the production of a conceptual model of English learning material which contains core competencies, basic competencies, themes, or materials of English for junior high school students. The result of the interviews with the students and teachers during need analysis indicated that it is necessary to include local culture in English learning materials to increase students’ engagement and knowledge about their culture. The results of the product try-out showed that students are motivated because they are familiar with the culture-based learning materials. As such, this conceptual model will be the basis to develop similar modules of English learning materials for junior high school students.
Information-gathering dialog system using acoustic features and user’s motivation

&lt;p&gt;Recently, as society continues to age, automation of watching elderly people who live apart from their families has been gradually expected. However, we must prevent them from losing their purpose in life, which declines due to lack of communication. Thus, a chat dialog system has attracted widespread attention as a method that achieves both problems: keeping their purpose in life and watching their daily lives. Unlike a task-oriented dialog system, a chat dialog system has explicitly no task to accomplish and makes a conversation to continue communication with the users. Keeping a conversation is essential for elderly people who are mostly unfamiliar with digital devices. Moreover, conversing daily on the chat dialog system provides the opportunity to collect information for their care. This study realizes an information-gathering dialog system, a chat dialog system that collects healthcare information of elderly people. Furthermore, we use the nonverbal acoustic features from their speech, since automatic speech recognition is not necessarily accurate in current systems. This paper illustrates the effectiveness of two important elements, topic change for keeping the talking user motivated with the dialog system and motivation estimation, for attaining an information-gathering dialog system using nonverbal acoustic features.&lt;/p&gt;
Game-based learning: An alternatif learning model in covid-19 distance learning

Distance learning as Covid-19 pandemic solution has student learning boredom as a negative impact. Game application can increase students' enthusiasm and motivation to learn in distance learning. The game-based learning (GBL) model is potentially applied to create a fun learning atmosphere and overcome learning boredom. This study aims to determine the effect of GBL implementation on cognitive learning outcomes and student responses in the biological interaction chapter in Covid-19 distance learning. This research wasexperimental research (one-group pretest-posttest design). The research sample was 25 students. This research used one treatment group without a control group. The test questions were used as cognitive learning outcomes instruments. The questionnaire was used as a student response instrument. Game application used were classified as mobile games, single-player games, role-playing game (RPG). GBL syntaxes were pre-experimentation, experimentation, reflection, activity, and discussion. The results showed that the implementation of GBL in the biological interaction chapter in Covid-19 distance learning affected students' cognitive learning outcomes. The effect of this model could improve students' cognitive learning outcomes with 28% students of low category N-gain while 72% students of medium and high.
Artificial neural network for cervical abnormalities detection on computed tomography images

&lt;span lang="EN-US"&gt;Cervical cancer is the second deadliest after breast cancer in Indonesia. Sundry diagnostic imaging modalities had been used to decide the location and severity of cervical cancer, one among those is computed tomography (CT) Scan. This study handles a CT image dataset consisting of two categories, abnormal cervical images of cervical cancer patients and normal cervix images of patients with other diseases. It focuses on the ability of segmentation and classification programs to localize cervical cancer areas and classify images into normal and abnormal categories based on the features contained in them. We conferred a novel methodology for the contour detection round the cervical organ classified with artificial neural network (ANN) which was employed to categorize the image data. The segmentation algorithm used was a region-based snake model. The texture features of the cervical image area were arranged in the form of gray level co-occurrence matrix (GLCM). Support vector machine (SVM) had been added to determine which algorithm was better for comparison. Experimental results show that ANN model has better receiver operating characteristic (ROC) parameter values than SVM model’s and existing approach’s regarding 96.2% of sensitivity, 95.32% of specificity, and 95.75% of accuracy. &lt;/span&gt;
A hybrid composite features based sentence level sentiment analyzer

&lt;div align="left"&gt;&lt;span lang="EN-US"&gt;Current lexica and machine learning based sentiment analysis approaches still suffer from a two-fold limitation. First, manual lexicon construction and machine training is time consuming and error-prone. Second, the prediction’s accuracy entails sentences and their corresponding training text should fall under the same domain. In this article, we experimentally evaluate four sentiment classifiers, namely Support Vector Machines, Naive Bayes, Logistic Regression and Random Forest. We quantify the quality of each of these models using three real-world datasets that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic movie reviews. Specifically, we study the impact of a variety of natural language processing (NLP) pipelines on the quality of the predicted sentiment orientations. Additionally, we measure the impact of incorporating lexical semantic knowledge captured by WordNet on expanding original words in sentences. Findings demonstrate that the utilizing different NLP pipelines and semantic relationships impacts the quality of the sentiment analyzers. In particular, results indicate that coupling lemmatization and knowledge-based n-gram features proved to produce higher accuracy results. With this coupling, the accuracy of the support vector machine (SVM) classifier has improved to 90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three other classifiers. &lt;/span&gt;&lt;/div&gt;
Forecasting the number of dengue fever based on weather conditions using ensemble forecasting method

&lt;p&gt;&lt;span lang="EN-US"&gt;Dengue fever is still a crucial public health problem in Indonesia, with the highest fatality rate (CFR) is 1.01% in East Java, Malang Regency. One of the solutions to control the death rate and cases is to forecast the cases number. This study proposed ensemble forecasting that build from several penalized regressions. Penalized regressions are able to overcome linear regression analysis’ shortcomings by using penalty values, that will affect regression’s coefficient, resulting on regression model with a slight bias in order to reduce parameter estimations and prediction values' variances. Penalized regressions are evaluated and built as ensemble forecasting method to minimize the shortcomings of other existing model, so it could produce more accurate values comparing to single penalized regression model. The result showed that the ensemble model `consists of smoothly clipped absolute deviation (SCAD) and Elastic-net is sufficient to capture data patterns with root mean squared error (RMSE) 6.38. &lt;/span&gt;&lt;/p&gt;