Reviewerly/exHarmony huggingface dataset:
The Effect of Using Flipbook Learning Media by Paying Attention to Learning Motivation on Students’ Social Studies Learning Outcomes Class VII at SMPN 13 Bandar Lampung

The progress of a nation can be judged by the quality of its human resources, in order to create and improve a quality nation, educational progress is needed such as the use of learning media which is expected to be able to improve the education system that has been going on so far.One of the successful learning media has a positive influence on student learning outcomes such as the use of flipbook media.The purpose of this study was to determine the effect of using flipbook learning media by paying attention to learning motivation on student learning outcomes in class VII SMPN 13 Bandar Lampung.The method used in this study is a correlational method with a quantitative approach.This study uses a questionnaire technique and is strengthened by interview data, observation, and documentation.This research was conducted at SMPN 13 Bandar Lampung for the academic year 2021/2022 with a sample of 64 students in class VII.The results showed: (1) there was an effect of using flipbook media on students' learning motivation in class VII SMPN 13 Bandar Lampung with t-count of 91.22 with a significance value of 0.000 <0.05.( 2) there is an effect of using flipbook media on social studies learning outcomes at SMPN 13 Bandar Lampung with a tcount value of 16.31 with a significance value of 0.000 <0.5.(3) there is an effect of the use of learning motivation on social studies learning outcomes at SMPN 13 Bandar Lampung with a tcount value of 19.368 with a significance value of 0.000 <0.05.(4) there is an effect of using flipbook media and learning motivation on social studies learning outcomes at SMPN 13 Bandar Lampung with a calculated Fvalue of 29,967 > Ftable of 2.77 with a significance level of 0.000 <0.05.
A Generative Adversarial Network-based Selective Ensemble Characteristic-to-Expression Synthesis (SE-CTES) Approach and Its Applications in Healthcare

Investigating the causal relationships between characteristics and expressions plays a critical role in healthcare analytics. Effective synthesis for expressions using given characteristics can make great contributions to health risk management and medical decision-making. For example, predicting the resulting physiological symptoms on patients from given treatment characteristics is helpful for the disease prevention and personalized treatment strategy design. Therefore, the objective of this study is to effectively synthesize the expressions based on given characteristics. However, the mapping from characteristics to expressions is usually from a relatively low dimension space to a high dimension space, but most of the existing methods such as regression models could not effectively handle such mapping. Besides, the relationship between characteristics and expressions may contain not only deterministic patterns, but also stochastic patterns. To address these challenges, this paper proposed a novel selective ensemble characteristic-to-expression synthesis (SE-CTES) approach inspired by generative adversarial network (GAN). The novelty of the proposed method can be summarized into three aspects: (1) GAN-based architecture for deep neural networks are incorporated to learn the relatively low dimensional mapping to high dimensional mapping containing both deterministic and stochastic patterns; (2) the weights of the two mismatching errors in the GAN-based architecture are proposed to be different to reduce the learning bias in the training process; and (3) a selective ensemble learning framework is proposed to reduce the prediction bias and improve the synthesis stability. To validate the effectiveness of the proposed approach, extensive numerical simulation studies and a real-world healthcare case study were applied and the results demonstrated that the proposed method is very promising.
Algoritma Backpropagation Menggunakan PSO Prediksi Penerimaan Retribusi Peminjaman Rumah Adat Dulohupa

Regional retribution as payment for services or granting certain permits specifically granted and/or issued by local governments for personal or business interests. Gorontalo City Government has several public facilities that are used as a source of regional income in the form of taxes or levies. The Dulohupa traditional house levy carried out by the Gorontalo City Youth and Sports Tourism Office often experiences ups and downs because it is caused by uncertainty about rentals or competition. The purpose of this research is to overcome the existing problems by predicting retribution receipts using the backpropagation method, the use of particle swarm optimization (PSO) to increase the accurate value in predicting. The data collected is daily quantitative univariate time series data. This type of data is the Dulohupa Traditional House Retribution Receipt Data. The dataset taken from the levy receipt variable has 211 records. The best model is generated on the backpropagation algorithm using the particle swarm optimization (PSO) selection feature, which can be seen from the smallest error rate of 0.122. Thus the addition of a selection feature can improve the performance of an algorithm. The results of the predictions for the next four months from January to April which have been denormalized with an average number of predictions of Rp. 1,806,789 with an error value of 0.112.
Language teachers’ perceptions of using Google keyboard in L2 writing

Despite the growth of research on mobile technologies in educational contexts, research on language teachers’ perceptions of mobile technologies – particularly in the English as a Foreign Language (EFL) – remains sparse. Hence, the present study explored EFL teachers’ perceptions of using Google keyboard (Gboard) for L2 writing instruction. The participants were two teachers who taught 47 intermediate Turkish EFL learners. Data were collected from the teachers. They were asked to keep a teaching journal and report their perceptions of Gboard implementation as well as the most distinctive lexical errors they deemed to emerge in the learners’ writing. Data analyses indicated that the teachers perceived the integration of Gboard into instruction as an effective intervention that assisted with enhancing the spelling accuracy of the learners. The implications of the study have been discussed.
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction

In recent years, neural implicit surface reconstruction methods have become popular for multi-view 3D reconstruction. In contrast to traditional multi-view stereo methods, these approaches tend to produce smoother and more complete reconstructions due to the inductive smoothness bias of neural networks. State-of-the-art neural implicit methods allow for high-quality reconstructions of simple scenes from many input views. Yet, their performance drops significantly for larger and more complex scenes and scenes captured from sparse viewpoints. This is caused primarily by the inherent ambiguity in the RGB reconstruction loss that does not provide enough constraints, in particular in less-observed and textureless areas. Motivated by recent advances in the area of monocular geometry prediction, we systematically explore the utility these cues provide for improving neural implicit surface reconstruction. We demonstrate that depth and normal cues, predicted by general-purpose monocular estimators, significantly improve reconstruction quality and optimization time. Further, we analyse and investigate multiple design choices for representing neural implicit surfaces, ranging from monolithic MLP models over single-grid to multi-resolution grid representations. We observe that geometric monocular priors improve performance both for small-scale single-object as well as large-scale multi-object scenes, independent of the choice of representation.
A Category Theoretic View of Contextual Types: from Simple Types to Dependent Types

We describe the categorical semantics for a simply typed variant and a simplified dependently typed variant of Cocon, a contextual modal type theory where the box modality mediates between the weak function space that is used to represent higher-order abstract syntax (HOAS) trees and the strong function space that describes (recursive) computations about them. What makes Cocon different from standard type theories is the presence of first-class contexts and contextual objects to describe syntax trees that are closed with respect to a given context of assumptions. Following M. Hofmann's work, we use a presheaf model to characterise HOAS trees. Surprisingly, this model already provides the necessary structure to also model Cocon. In particular, we can capture the contextual objects of Cocon using a comonad $\flat$ that restricts presheaves to their closed elements. This gives a simple semantic characterisation of the invariants of contextual types (e.g. substitution invariance) and identifies Cocon as a type-theoretic syntax of presheaf models. We further extend this characterisation to dependent types using categories with families and show that we can model a fragment of Cocon without recursor in the Fitch-style dependent modal type theory presented by Birkedal et. al..
Adversarial Text Normalization

Text-based adversarial attacks are becoming more commonplace and accessible to general internet users. As these attacks proliferate, the need to address the gap in model robustness becomes imminent. While retraining on adversarial data may increase performance, there remains an additional class of character-level attacks on which these models falter. Additionally, the process to retrain a model is time and resource intensive, creating a need for a lightweight, reusable defense. In this work, we propose the Adversarial Text Normalizer, a novel method that restores baseline performance on attacked content with low computational overhead. We evaluate the efficacy of the normalizer on two problem areas prone to adversarial attacks, i.e. Hate Speech and Natural Language Inference. We find that text normalization provides a task-agnostic defense against character-level attacks that can be implemented supplementary to adversarial retraining solutions, which are more suited for semantic alterations.
Edukasi: Optimasi Menggunakan Metode Simpleks Pada Usaha Bahan Bakar Minyak Berskala Kecil

The COVID-19 pandemic has forced small-scale businesses to innovate. One of them is a business owned by Mr. Ruddy which sells gasoline and pertalite types of fuel oil. Before the pandemic hit, this business could be said to be running smoothly, but during the pandemic this business began to be deserted from consumers. During the pandemic, the Sorong city government implemented regulations related to PPKM. This regulation also contributes to the reduction of consumers for Mr. Ruddy's business, because people are limited in their mobility. Seeing the situation experienced by Mr. Ruddy's business, the PkM team of the Information Systems Study Program at Victory Sorong University offers an educational program on the use of Linear Programs by utilizing the Simplex method and information technology in the form of POM-QM software for windows version 5.3. The purpose of this educational program is to share knowledge to help small-scale businesses, especially Mr. Ruddy's businesses, to continue to innovate and survive during the pandemic. Where Mr. Ruddy can gain knowledge that can be used for decision making in business operations.
Determining Cyber Security-Related Behaviors of Internet Users: Example of the Faculty of Sport Sciences Students

Abstract The most practical and fastest way to access information in today’s world is via the internet. Thanks to the internet, the necessary information can be reached in a short time. Nevertheless, in addition to the benefits of the internet, it can also pose risks for users. For this reason, it is important to increase the level of awareness of individuals against threats that may occur in the cyber network. Sports organizations, like other sectors, process sensitive personal data and may face cyber attacks. It is important to determine the cyber security behaviors of the students of the faculty of sport sciences, who will be taking part in different careers in sports in the future, and so to contribute to the students’ development in this regard. In this study, the cyber security-related behaviors of the faculty of sport sciences students were examined in terms of gender, age, frequency of internet usage, frequency of monthly purchases of products or services over the internet, and level of knowledge about cyber security. The “Personal Cyber Security Provision Scale” developed by Erol and associates (2015) was used as a data collection tool. For this reason, ANOVA and Independent Samples t-Test were used to investigate the significant differences between the scale scores and the variables. According to the findings of the study, students’ behaviors related to cyber security differ according to gender, daily internet usage, monthly product or service purchase frequency, and knowledge level about cyber security. The age variable, on the other hand, does not affect cyber security behaviors. According to the results obtained from the Personal Cyber Security Ensuring Scale, the students of the faculty of sport sciences have high cyber security awareness. However, it is seen that they have lower scores from the factors of “Take Precautions” and “Privacy Protection” compared to other factors on the scale. Therefore, it is important for students to be informed about cyber security practices, what kind of precautions they should take in this regard, and how they can learn about improvements in this field to create cyber security awareness.
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance

Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration such as the additive action noise often used in continuous control domains. Typically, the scaling factor of this action noise is chosen as a hyper-parameter and is kept constant during training. In this paper, we focus on action noise in off-policy deep reinforcement learning for continuous control. We analyze how the learned policy is impacted by the noise type, noise scale, and impact scaling factor reduction schedule. We consider the two most prominent types of action noise, Gaussian and Ornstein-Uhlenbeck noise, and perform a vast experimental campaign by systematically varying the noise type and scale parameter, and by measuring variables of interest like the expected return of the policy and the state-space coverage during exploration. For the latter, we propose a novel state-space coverage measure $\operatorname{X}_{\mathcal{U}\text{rel}}$ that is more robust to estimation artifacts caused by points close to the state-space boundary than previously-proposed measures. Larger noise scales generally increase state-space coverage. However, we found that increasing the space coverage using a larger noise scale is often not beneficial. On the contrary, reducing the noise scale over the training process reduces the variance and generally improves the learning performance. We conclude that the best noise type and scale are environment dependent, and based on our observations derive heuristic rules for guiding the choice of the action noise as a starting point for further optimization.
Formalization of the principles of brain Programming (Brain Principles Programming)

In the monograph "Strong artificial intelligence. On the Approaches to Superintelligence" contains an overview of general artificial intelligence (AGI). As an anthropomorphic research area, it includes Brain Principles Programming (BPP) -- the formalization of universal mechanisms (principles) of the brain work with information, which are implemented at all levels of the organization of nervous tissue. This monograph contains a formalization of these principles in terms of category theory. However, this formalization is not enough to develop algorithms for working with information. In this paper, for the description and modeling of BPP, it is proposed to apply mathematical models and algorithms developed earlier, which modeling cognitive functions and base on well-known physiological, psychological and other natural science theories. The paper uses mathematical models and algorithms of the following theories: P.K.Anokhin Theory of Functional Brain Systems, Eleanor Rosch prototypical categorization theory, Bob Rehder theory of causal models and "natural" classification. As a result, a formalization of BPP is obtained and computer experiments demonstrating the operation of algorithms are presented.
Cloud Computing Security Issues and Solutions

Cloud computing is a recently developed computing model which comes from distributed computing, grid computing, virtualization technology, and many more computer technologies. Cloud computing is a technique to increase the capacity and add ability dynamically without spending on new infrastructure or licensing new software. The security issues of cloud computing and the techniques to overcome the issue have been discussed in this paper. The security issue of cloud computing is a significant problem and it can hamper the fast development of cloud computing. The basic security problem in cloud computing is service availability and data privacy. A single security technique cannot resolve the security problem of cloud computing, various traditional new strategies and technologies need to utilize together to protect the total cloud computing system.
EDUCATIONAL DIALOGUE AS A PREREQUISITE READYNESS OF FUTURE TEACHERS TO DIALOGUE TEACHING OF SCHOOLCHILDREN

Dialogical approach in the professional training of teachers provides leading role of instructor and subordinate position of student into equal interaction. Training dialogue using in educational process of higher pedagogical educational establishment is a prerequisite o f dialogical training of schoolchildren.
Media Pembelajaran IPS yang Inovatif

In this all-digital era, education in Indonesia is increasingly facing massive challenges in achieving educational goals. Among the many options for developing existing learning components, the learning media has a high creative power. Because the learning media contains elements to facilitate/help the learning process, the results of the application of the media can be seen directly. The purpose of this research is to find out the innovative social studies learning media. From the results of the study, there are five steps to determine learning media, namely designing learning media, determining the media to be selected, designing learning media innovations, starting to make learning media, applying media in the learning process.
A Human-Centric Assessment Framework for AI

With the rise of AI systems in real-world applications comes the need for reliable and trustworthy AI. An essential aspect of this are explainable AI systems. However, there is no agreed standard on how explainable AI systems should be assessed. Inspired by the Turing test, we introduce a human-centric assessment framework where a leading domain expert accepts or rejects the solutions of an AI system and another domain expert. By comparing the acceptance rates of provided solutions, we can assess how the AI system performs compared to the domain expert, and whether the AI system's explanations (if provided) are human-understandable. This setup -- comparable to the Turing test -- can serve as a framework for a wide range of human-centric AI system assessments. We demonstrate this by presenting two instantiations: (1) an assessment that measures the classification accuracy of a system with the option to incorporate label uncertainties; (2) an assessment where the usefulness of provided explanations is determined in a human-centric manner.
Global Capital Markets and Cryptocurrency: Exploring the International Political Economy of Blockchain Ecosystems and Metaverse Development

&lt;p&gt;Key contributions include 1) analytical dollar-denominated comparisons between blockchain/metaverse assets and fiat-denominated traditional markets, 2) comparative analysis of blockchain ecosystems both theoretically and empirically, and 3) demonstration of topic relevance to international relations, international political economy, and international development. The paper exploits public deep-order-book auction markets, allowing tick-to-tick observation of values of underlying assets (reminiscent of the early CRSP papers at Chicago), tethering the analysis to real prices paid in real markets.&lt;/p&gt;
Use of Machine Learning for Active Public Debt Collection with Recommendation for the Method of Collection Via Protest

This work consists of applying supervised Machine Learning techniques to identify which types of active debts are appropriate for the collection method called protest, one of the means of collection used by the Attorney General of the State of Pernambuco. For research, the following techniques were applied, Neural Network (NN), Logistic Regression (LR), and Support Vector Machine (SVM). The NN model obtained more satisfactory results among the other classification techniques, achieving better values in the following metrics: Accuracy (AC), FMeasure (F1), Precision (PR), and Recall (RC) with indexes above 97% in the evaluation with these metrics. The results showed that the construction of an Artificial Intelligence/Machine Learning model to choose which debts can succeed in the collection process via protest could bring benefits to the government of Pernambuco increasing its efficiency and effectiveness.
Low-dose CT I mage D enoising M ethod B ased on Generative Adversarial Network

Abstract In order to solve the problems of artifacts and noise in low-dose computed tomography ( CT ) images in clinical medical diagnosis, an improved image denoising algorithm under the architecture of generative adversarial network ( GAN ) is proposed. First, a noise model based on Style GAN2 is constructed to estimate the real noise distribution, and the noise information similar to the real noise distribution is generated by as the experimental noise data set. Then, a network model with encoder-decoder architecture as the core based on GAN idea is constructed, and the network model is trained with the generated noise data set until it reaches the optimal value. Finally, the noise and artifacts in low-dose CT images can be removed by inputting low-dose CT images into the denoising network. The experimental results show that the constructed network model based on GAN architecture can improve the utilization rate of noise feature information and the stability of network training, remove image noise and artifacts, and the reconstructed image has rich texture and realistic visual effect.
Tangible and embedded objects and practices

The chairs' editorial introduction for the joint theme track of the DRS Special Interest Groups tentSiG (Design for Tangible, Embedded and Networked Technologies) and openSIG (Objects, Practices, Experiences, Networks).
Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI

Evaluating an explanation's faithfulness is desired for many reasons such as trust, interpretability and diagnosing the sources of model's errors. In this work, which focuses on the NLI task, we introduce the methodology of Faithfulness-through-Counterfactuals, which first generates a counterfactual hypothesis based on the logical predicates expressed in the explanation, and then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic (i.e. if the new formula is \textit{logically satisfiable}). In contrast to existing approaches, this does not require any explanations for training a separate verification model. We first validate the efficacy of automatic counterfactual hypothesis generation, leveraging on the few-shot priming paradigm. Next, we show that our proposed metric distinguishes between human-model agreement and disagreement on new counterfactual input. In addition, we conduct a sensitivity analysis to validate that our metric is sensitive to unfaithful explanations.