Reviewerly/exHarmony huggingface dataset:
Role-Playing Game as Learning Media To Support Online Learning

Lack of learning media will affect the learning process in the classroom. Various learning media can motivate students in online learning activities. It causes learning media to be essential in the learning process. In the current era of online learning, students are prone to boredom and stress. Therefore this study aims to develop learning media with RPG (Role Playing Games). This research is exploratory because the field of play is vast, with validation using an E-GameFlow survey to assess the level of comfort of players when gaining knowledge. The research will be conducted on 15 junior high school students by distributing online questionnaires. The methods used to collect data are observation and questionnaires. The instrument used to collect data is a questionnaire. The technique used to analyze the data is descriptive qualitative and quantitative analysis. The results of each question on each aspect of E-GameFlow will be analyzed to find conclusions about the games that have been made. The results of the data obtained show that the lowest value is 5.49 in the challenge aspect, which is caused by a static game map and lack of guidance regarding the game. The overall results of E-GameFlow show that the game can provide comfort to players while providing learning materials. This is evident from no aspect with a score below the average.
Application of the Vector Machine Support Method in Twitter Social Media Sentiment Analysis Regarding the Covid-19 Vaccine Issue in Indonesia

According to the Indonesian government, Indonesia has been afflicted by Covid-19 since March 2, 2020. Numerous countries, including Indonesia, have made efforts, but with the spread of perceptions, rumors, and a flood of information into the society regarding vaccines, there are both advantages and disadvantages to vaccines. government-led immunization campaigns. As a result, it is vital to examine public sentiment toward the government's immunization programs. The goal of this study is to ascertain the emotion toward the Covid-19 vaccination in Indonesia based on the classification results. The Support Vector Machine classification technique was employed in this investigation (SVM). The SVM classification method was chosen because it possesses the ability to generalize when it comes to identifying a pattern, excluding the data used in the method's learning phase. Classification with an SVM linear kernel and TF-IDF weighting, as well as data sharing via K-fold cross validation with a value of k=10. Positive and negative classifications are made. Following preprocessing and classification, we determined the f1 values, accuracy, precision, and recall to use as reference values when evaluating the classification. SVM performed well in classifying the data in this investigation, with f1 = 88.7%, accuracy = 84.4%, precision = 86.2%, and recall = 97%. This value is acceptable, and hence SVM is suitable for identifying sentiment data about the Covid-19 vaccine in Indonesia. Additional study can be conducted with richer tweet data, more thorough preprocessing, and comparison to other classification algorithms to obtain a higher categorization evaluation score.
Super-Resolution Generative Adversarial Network Based on the Dual Dimension Attention Mechanism for Biometric Image Super-Resolution

There exist many types of intelligent security sensors in the environment of the Internet of Things (IoT) and cloud computing. Among them, the sensor for biometrics is one of the most important types. Biometric sensors capture the physiological or behavioral features of a person, which can be further processed with cloud computing to verify or identify the user. However, a low-resolution (LR) biometrics image causes the loss of feature details and reduces the recognition rate hugely. Moreover, the lack of resolution negatively affects the performance of image-based biometric technology. From a practical perspective, most of the IoT devices suffer from hardware constraints and the low-cost equipment may not be able to meet various requirements, particularly for image resolution, because it asks for additional storage to store high-resolution (HR) images, and a high bandwidth to transmit the HR image. Therefore, how to achieve high accuracy for the biometric system without using expensive and high-cost image sensors is an interesting and valuable issue in the field of intelligent security sensors. In this paper, we proposed DDA-SRGAN, which is a generative adversarial network (GAN)-based super-resolution (SR) framework using the dual-dimension attention mechanism. The proposed model can be trained to discover the regions of interest (ROI) automatically in the LR images without any given prior knowledge. The experiments were performed on the CASIA-Thousand-v4 and the CelebA datasets. The experimental results show that the proposed method is able to learn the details of features in crucial regions and achieve better performance in most cases.
Free money, but not tax-free: a proposal for the tax treatment of cryptocurrency hard forks

Hard forks. The first tax is levied on the profit made from the receipt of forked coins, and the second tax is levied on the profit made from the disposition of forked coins. At the same time, a concrete proposal is provided for the applicable coin valuation, tax basis, holding period, and tax rate for the two prongs. The proposed framework is closest to the nature of cryptocurrency hard forks and considers various practical concerns, such as the inefficiency of the cryptocurrency market, the indirect possession of forked coins through third-party exchanges, and the fluctuating trading prices of forked coins when determining the valuation, tax basis, and holding period. This proposal not only provides clarity for taxpayers in filing tax returns and fulfilling tax obligations, but it also relieves the potential tax deferral and tax evasion problems that arise after a cryptocurrency hard fork. Practical significance: the main provisions and conclusions of the article can be used in scientific, pedagogical and law enforcement activities when considering the issues related to taxation regime of cryptocurrency hard forks.
Enhanced Potential Field-Based Collision Avoidance in Cluttered Three-Dimensional Urban Environments

With the various applications of unmanned aerial vehicles (UAVs), the number of UAVs will increase in limited airspace, leading to an increased risk collision. To reduce such potential risk, this work proposes a collision avoidance strategy for UAVs using an enhanced potential field (EPF) approach in cluttered three-dimensional urban environments. Using the EPF formulated in a two-dimensional environment, the avoidance maneuvers for both horizontal and vertical planes are generated by introducing rotation matrices, and these maneuvers are combined by applying a weighting factor. The numerical simulations with various meaningful scenarios are conducted to validate the performance of the proposed approach. To mimic practical situations, UAV dynamics and sensor limitations were considered. The simulation results show that the proposed approach provides an efficient, reliable, and collision-free path without local minima and unreachable goal issues.
Improved Verifier-Based Three-Party Password-Authenticated Key Exchange Protocol from Ideal Lattices

With the advent of large-scale social networks, two communication users need to generate session keys with the help of a remote server to communicate securely. In the existing three-party authenticated key exchange (3PAKE) protocols, users’ passwords need to be stored on the server; it cannot resist the server disclosure attack. To solve this security problem, we propose a more efficient 3PAKE protocol based on the verification element by adopting a public-key cryptosystem and approximate smooth projection hash (ASPH) function on an ideal lattice. Using the structure of separating authentication from the server, the user can negotiate the session key only after two rounds of communication. The analysis results show that it can improve the efficiency of computation and communication and resist the server disclosure attack, quantum algorithm attack, and replay attack; moreover, it has session key privacy to the server. This protocol can meet the performance requirement of the current communication network.
LatteGAN: Visually Guided Language Attention for Multi-Turn Text-Conditioned Image Manipulation

Text-guided image manipulation tasks have recently gained attention in the vision-andlanguage community.While most of the prior studies focused on single-turn manipulation, our goal in this paper is to address the more challenging multi-turn image manipulation (MTIM) task.Previous models for this task successfully generate images iteratively, given a sequence of instructions and a previously generated image.However, this approach suffers from under-generation and a lack of generated quality of the objects that are described in the instructions, which consequently degrades the overall performance.To overcome these problems, we present a novel architecture called a Visually Guided Language Attention GAN (LatteGAN).Here, we address the limitations of the previous approaches by introducing a Visually Guided Language Attention (Latte) module, which extracts fine-grained text representations for the generator, and a Text-Conditioned U-Net discriminator architecture, which discriminates both the global and local representations of fake or real images.Extensive experiments on two distinct MTIM datasets, CoDraw and i-CLEVR, demonstrate the state-of-the-art performance of the proposed model.The code is available online (https://github.com/smatsumori/LatteGAN).
Development of an ultracompact endoscopic three-dimensional scanner with flexible imaging fiber optics

We developed an ultracompact three-dimensional (3D) endoscopic scanner that utilizes an electromagnetic-based stereo imaging method (EBSIM) for shape measurements. The method was used to investigate the applicability of imaging fiber optics to featureless surface profiles in confined spaces. The EBSIM is combined with a coherent flexible fiber bundle for structured light pattern projection to ensure that the scanner head can achieve a final diameter of merely 3.4 mm and a baseline length of 1.4 mm. Compared with the existing methods, the scanner is designed to have a minimum focal distance of 2 mm to address the intraoperative evaluation problem. System registration and complex calibration are unnecessary before measurements, and the scanner can be operated from any position and orientation. The scanner is capable of acquiring 3D videos at 30 frames per second. The experimental results demonstrate the feasibility of using the EBSIM for 3D surface profile measurements of featureless cylindrical surfaces. A prototype scanner is presented in extensor, and the average error of a series of known depth distances is found to be equal to 0.15 mm. The implementation shows that the 3D scanner can be potentially applied for endoscopic inspections and intraoperative evaluations in minimally invasive surgeries.
Towards Knowledge Graphs Validation Through Weighted Knowledge Sources

The performance of applications, such as personal assistants and search engines, relies on high-quality knowledge bases, a.k.a. Knowledge Graphs (KGs). To ensure their quality one important task is knowledge validation, which measures the degree to which statements or triples of KGs are semantically correct. KGs inevitably contain incorrect and incomplete statements, which may hinder their adoption in business applications as they are not trustworthy. In this paper, we propose and implement a Validator that computes a confidence score for every triple and instance in KGs. The computed score is based on finding the same instances across different weighted knowledge sources and comparing their features. We evaluate our approach by comparing its results against a baseline validation. Our results suggest that we can validate KGs with an f-measure of at least 75%. Time-wise, the Validator, performed a validation of 2530 instances in 15 min approximately. Furthermore, we give insights and directions toward a better architecture to tackle KG validation.
Approximations in Transmon Simulation

Classical simulations of time-dependent quantum systems are widely used in quantum control research. In particular, these simulations are commonly used to host iterative optimal control algorithms. This is convenient for algorithms that are too onerous to run in the loop with current-day quantum hardware, as well as for researchers without consistent access to hardware. However, if the model used to represent the system is not selected carefully, an optimized control protocol may be rendered futile when applied to hardware. We present a series of models, ordered in a hierarchy of progressive approximation, which appear in quantum control literature. The validity of each model is characterized experimentally by designing and benchmarking control protocols for an IBMQ cloud quantum device. This result demonstrates error amplification induced by the application of a first-order perturbative approximation. Furthermore, the emergence of errors that cannot be corrected by simple amplitude scaling of control pulses is demonstrated in simulation, due to an underlying mistreatment of noncomputational dynamics. Finally, an evaluation of simulated control dynamics reveals that despite the substantial variance in numerical predictions across the proposed models, the complexity of discovering local optimal control protocols appears invariant in the simple control scheme setting.
A Patch-Based Low-Rank Minimization Approach for Speckle Noise Reduction in Ultrasound Images

Ultrasound is a low-cost, non-invasive and real-time imaging modality that has proved popular for many medical applications.Unfortunately, the acquired ultrasound images are often corrupted by speckle noise from scatterers smaller than ultrasound beam wavelength.The signal-dependent speckle noise makes visual observation difficult.In this paper, we propose a patch-based low-rank approach for reducing the speckle noise in ultrasound images.After constructing the patch group of the ultrasound images by the block-matching scheme, we establish a variational model using the weighted nuclear norm as a regularizer for the patch group.The alternating direction method of multipliers (ADMM) is applied for solving the established nonconvex model.We return all the approximate patches to their original locations and get the final restored ultrasound images.Experimental results are given to demonstrate that the proposed method outperforms some existing state-of-the-art methods in terms of visual quality and quantitative measures.
Coping with the Document Frequency Bias in Sentiment Classification

In this article, we study the polarity detection problem using linear supervised classifiers. We show the interest of penalizing the document frequencies in the regularization process to increase the accuracy. We propose a systematic comparison of different loss and regularization functions on this particular task using the Amazon dataset. Then, we evaluate our models according to three criteria: accuracy, sparsity and subjectivity. The subjectivity is measured by projecting our dictionary and optimized weight vector on the SentiWordNet lexicon. This original approach highlights a bias in the selection of the relevant terms during the regularization procedure: frequent terms are overweighted compared to their intrinsic subjectivities.We show that this bias appears whatever the chosen loss or regularization and on all datasets: it is closely link to the gradient descent technique. Penalizing the document frequency during the learning step enables us to improve significantly our performances. A lot of sentimental markers appear rarely and thus, are unappreciated by statistical learning algorithms. Explicitly boosting their influences leads to increasing the accuracy in the sentiment classification task.
Quantifying the selective forgetting and integration of ideas in science and technology.

How long will this article be remembered? How long will people reference it in their conversations, and for how many years will other authors cite its findings in their own works? A community's attention to a cultural object decays as time passes, a process known as collective forgetting. Recent work models this decay as the result of two different processes. One linked to communicative memory-memories sustained by human communication-and the other linked to cultural memory-memories sustained by the physical recording of content. Collective forgetting has significant impacts on communities, yet little is known about how the collective forgetting dynamic changes over time. Here, we study the temporal changes of collective memory and attention by focusing on two knowledge communities: inventors and physicists. We use data on patents from the United States Patent and Trademark Office (USPTO) and physics papers published by the American Physical Society (APS) to quantify those changes over time. The model enables us to distinguish between two branches of forgetting. One branch is short-lived, going directly from communicative memory to oblivion. The other branch is long-lived, going from communicative memory to cultural memory before going on to oblivion. The data analysis shows an increase in the forgetting rate for both communities as the amount of information in each of them grows. That growth of information forces knowledge communities to increase their selectivity regarding what is stored in their cultural memory. These findings confirm the forgetting as annulment hypothesis and show that knowledge communities can slow down collective forgetting and improve selectivity processes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Data-Centric Machine Learning: Improving Model Performance and Understanding Through Dataset Analysis

Machine learning research typically starts with a fixed data set created early in the process. The focus of the experiments is finding a model and training procedure that result in the best possible performance in terms of some selected evaluation metric. This paper explores how changes in a data set influence the measured performance of a model. Using three publicly available data sets from the legal domain, we investigate how changes to their size, the train/test splits, and the human labelling accuracy impact the performance of a trained deep learning classifier. Our experiments suggest that analyzing how data set properties affect performance can be an important step in improving the results of trained classifiers, and leads to better understanding of the obtained results.
Optimization of Truss Structures with Sizing of Bars by Using Hybrid Algorithms

Structural engineering is one of the fields where optimization approaches are widely used. In this regard, the major aim is to generate the lightest, cheapest or sustainable structural design for any model such as beam, retaining wall, truss, etc. Therefore, in the present study, some stages as optimization analyses were presented for independent structural models including 10 and 25-bar truss designs. Also, the smallest structural weights were found for both structures. As to mention optimization approaches, these are related to metaheuristic algorithms, which were developed and expressed by inspiring from various features of nature, abilities of lives and also mechanisms of chemical or physical processes. In this respect, Jaya algorithm (JA) is hybridized with different techniques as flower pollination algorithm (FPA) and teaching-learning-based optimization (TLBO). Approaches were compared to each other through some parameters by calculating different statistical expressions containing the best objective function, average together standard deviation of this value.
The Effect of Instructional Leadership and Creative Teaching on Student Actualization: Student Satisfaction as a Mediator Variable

This research was carried out to determine the effect of instructional leadership and creative teaching as the main aspects in maximizing student input and potential, in order to achieve learning satisfaction and self-actualization for the educational success of students.The research examines the effect of principal instructional leadership and teaching creativity on self-actualization and its impact on student satisfaction.Across sectional study was used to determine the relationship between exogenous and endogenous variables.Respondents were 307 students on Madrasah Aliyah and Al-Kamal Kebumen Islamic Vocational High Schools, Indonesia.Data was collected using a questionnaire consisting of 4 indicators representing each variable.The research data was analyzed using structural equation modeling (SEM) with the AMOS application.Out of 5 proposed hypotheses, 1 was rejected because of not fulfilling the Critical Ratio (CR) value > 1.96 and the p-value <0.05 criteria.The results showed that this model fulfilled the Goodness of Fit requirements in SEM analysis.It was evident that principal's instructional leadership and creative teaching affects student learning satisfaction.The results also showed that the principal's instructional leadership affects student self-actualization, while creative teaching has no direct effect unless it is mediated by learning satisfaction.
Pedagogical Senses of Digital Learning ion the Context of the Covid‑19 Pandemic: Case Studies of Several Eastern European Countries

In the context of the Covid‑19 pandemic, teacher education at universities has faced great problems concerning the teaching-learning practice. Educational processes have become possible only with the help of digital technologies. The development of digital technologies and the new level of accessibility to educational resources have opened up new ways for teaching and learning and the creation of new didactic scenarios. The reopening of educational institutions after the Covid‑19 is followed by many tasks that challenge the main actors in the teaching-learning process in a new way. The authors reflect on whether it makes sense for teachers and future teachers to continue learning how to work with digital technologies and create new learning formats for their students. In order to develop teacher education at universities and serious attention to digital didactics, not only the challenges but also the pedagogical senses of the Covid‑19 pandemic need to be identified. The unconscious and «detached» perception of current digital transformations in educational policy, higher teacher education and school education hinders the development of teacher education at the university and decreases the role of the teacher in society. A comparative analysis of cases of teacher education from five Eastern European universities is used to address these goals. The analysis aims to examine the hypotheses stated with inductive reasoning