Reviewerly/exHarmony huggingface dataset:
Evolutionary Model Validation—An Adversarial Robustness Perspective

WhenAdversarial robustness building Machine Learning models, either manually or automatically, we need to make sure that they are able to solve the task at hand and generalize, i.e., perform well on unseen data. By properly validating a model and estimating its generalization performance, not only do we get a clearer idea of how it behaves but we might also identify problems (e.g., overfitting) before they lead to significant losses in a production environment. Model validation is usually focused on predictive performancePredictive performance, but with models being applied in safety-critical areas, robustness should also be taken into consideration. In this context, a robust model produces correct outputs even when presented with data that somehow deviates from the one used for training, including adversarial examplesAdversarial examples. These are samples to which small perturbations are added in order to purposely fool the model. There are, however, limited studies on the robustness of models designed by evolution. In this chapter, we address this gap in the literature by performing adversarial attacks and evaluating the models created by two prominent NeuroEvolutionNeuroevolution methods (DENSER and NSGA-Net). The results confirm that, despite achieving competitive results in standard settings where only predictive accuracyPredictive accuracy is analyzed, the evolved models are vulnerable to adversarial examplesAdversarial examples. This highlights the need to also address model validation from an adversarial robustnessAdversarial robustness perspective.
Pengaruh Komunikasi Efektif terhadap Kemampuan Bahasa Anak Usia 4-5 Tahun di RA Al Falah Simpang Kabupaten Tasikmalaya

Communication skills are a form of language development possessed by early childhood. Through communication, children can express their feelings and opinions. The language skills of early childhood communication at RA Al Falah Simpang have only reached 20% based on developmental achievements in STPPA so it is necessary to provide direction and habituation to using effective communication that is directed and consistent. This study aims to determine the effect of effective communication on the language development of children aged 4-5 years at RA Al Falah Simpang. The research method used is the experimental method using two groups, namely the experimental class and the control class. Treatment using effective communication was carried out to the experimental class in 12 meetings while the control class used conventional learning as usual. The findings obtained during the 12 meetings based on posttest data from the experimental class and the control class showed a significance value of 0.000 &lt;0.05 that there was a significant difference between the two groups. The results of the comparison of the average values of the experimental class and the control class show that the value of the experimental class is greater than the control class, where the experimental class has an average value of 55.17 while the control class is 34.50. So it can be concluded that effective communication has a positive effect on the language skills of children aged 4-5 years at RA Al Falah Simpang.
PENINGKATAN KETERAMPILAN MEMBACA PUISI DENGAN MENGGUNAKAN MEDIA AUDIO VISUAL

Poetry is a literary work that has specificity, both in terms of language, word choice, and readers. Indonesian language learning in poetry reading material at SMP Negeri 3 Kubu Babussalam One Roof, Rokan Hilir Regency, Riau has so far been less favored, because students are less motivated and lack self-confidence when reading poetry. The author seeks to improve these conditions by conducting learning using audio-visual media in learning to read poetry. The problem discussed in this scientific work is how the process of improving poetry reading by using audio-visual media in class VIII of SMP Negeri 3 Kubu Babussalam One Roof. The aim is to find out the process of improving poetry reading skills using audio-visual media in class VIII of SMP Negeri 3 Kubu Babussalam One Roof, Rokan Hilir Regency, Riau.
Examining Virtual Reality as an Alternative to In-Person and Online Learning Environments

The continuous advances in virtual reality (VR) technology have increased its potential for tasks that require hands-on learning. One potential target for VR is its use as an alternative to online learning when a physical lab cannot be accessed due to learner’s financial, travel, or pandemic-related constraints. We enrolled 15 participants to assess VR’s feasibility as a modality to teach A + certification skills. Each participant was randomly assigned to three groups (in-person lab, online lab, virtual reality lab). Participants completed System Usability Scale (SUS) and User Burden Scale (UBS) surveys and interviewed about their lab experience. In addition, VR participants completed a Technology Acceptance Model (TAM) survey. Participants reported the VR experience with lower system usability and higher user burden than other learning modalities. Virtual Reality participants in the study scored VR highly in its perceived usefulness, average for its perceived use, and high in perceived usefulness for learning and engagement. Learner adoption and onboarding of VR is a challenge worth surmounting. VR retains attention, increases learner retention, and incentivizes failure recovery. This study provides preliminary evidence supporting that further development of VR for training in spatial tasks holds promise over traditional learning modalities.
Transformer-based End-to-End Object Detection in Aerial Images

Transformer models have achieved significant mile-stones in the field of Artificial Intelligence in recent years, primarily focusing on text processing and natural language processing. However, the application of these models in the domain of image processing, particularly on aerial images data, is actively research. This study concentrates on the experimental evaluation of Transformer-based models such as DETR, DAB-DETR, and DINO on the challenging Visdrone dataset, which is also essential for aerial image data processing. The experimental results indicate that Transformer-based models exhibit substantial potential, especially in object detection on aerial image data. Nevertheless, their application is not without challenges, including low resolution, dense object occurrences, and environmental noise. This work provides an initial glimpse into both the capabilities and limitations of Transformer-based approaches within this domain, with the aim of stimulating further development and optimization for practical applications, including traffic monitoring, environmental protection, and various other domains.
A Small Dummy Disrupting Database Reconstruction in a Cache Side-Channel Attack

This paper demonstrates the feasibility of a database reconstruction attack on open-source database engines and presents a defense method against it. We launch a Flush+Reload attack on SQLite, which returns approximate, noisy volumes returned by range queries for a private database. Given the volumes, our database reconstruction uses two al-gorithms, a Modified Clique-Finding algorithm and Match-Extension algorithm, to recover the database. Experiments show that an attacker can reconstruct the victim’s database with a size of 10,000 and a range of 12 with an error rate of up to 0.07% at most. To mitigate the attack, a small dummy data is added to the result volumes of range queries, which makes the approximation more confused. Experimental results show that by adding about 1% of dummy data, an attack success rate (in terms of the number of reconstructed volumes in the database) is reduced to 60% from 100% and an error rate increases to 15% from 0.07%. It is also observed that by adding about 2%of dummy data, the reconstruction is completely failed.
Exploring the Utilization of Program Semantics in Extreme Code Summarization: An Experimental Study Based on Acceptability Evaluation

With the rise of deep learning methods, neural network architecture adopted from neural machine translation has been widely studied in code summarization by learning the sequential content of code. Given the inherent nature of programming languages, learning the representation of source code from the parsed structural information is also a typical way for constructing code summarization models. Recent studies show that the overall performance of the neural models for code summarization can be improved by utilizing sequential and structural information in a hybrid manner. However, both of these two kinds of information fed to the neural models for code summarization fail to embrace the semantics of source code snippets in an explicit way. Is it really a good way to just leave the semantics as hidden things in the source code and have the neural models capture whatever they can get? To observe the utilization of program semantics in automatic code summarization, we conducted an experimental study by analyzing the acceptability of the extreme code summaries generated from neural models. To make the models aligned in the same context for this experimental study and to focus on the observation of the semantics, we re-implement the neural models from three selected studies as extreme code summarization solutions. After an intuitive observation and exploration of the generated summaries with the models trained from a Java dataset, we identify five acceptability aspects: (1) function name format; (2) function naming style; (3) semantic level similarity; (4) the differences in hitting rate of representative words; and (5) the correlation between extreme code summaries with function body. Based on the false negative and false positive phenomena in the results, ablation experiments have shown that the use of program semantics has a positive effect on generating high-quality abstracts in neural models. Our work proves the potential of utilizing the program semantics explicitly in code summarization, and the possible directions are also indicated.
A Gradient-Based Wind Power Forecasting Attack Method Considering Point and Direction Selection

Machine learning methods have been prevailing in wind power forecasting, while these data-driven based methods are susceptible to cyberattacks. Typical attack methods inject malicious data into influence factors according to the gradient direction of the forecasting model to randomly increase or decrease forecasting results, ignoring the number of attacks and attack effect. In this paper, an attack sample selection model is proposed to select vulnerability sample points for attack in order to reduce the number of attacks. At the same time, an attack direction judgment model is developed to launch the attack in the correct gradient direction to maximize the attack effect. Moreover, the effectiveness of the proposed approach is validated on two public wind power datasets and nine typical machine learning based forecasting models such as ANN, ENN, RNN, LSTM, GRU, BiLSTM, BiGRU, CNN and TCN. Compared with the existing gradient-based attack methods, the proposed attack method increases MAPE values of the nine models by about 9% on average while improving the attack concealment.
The Perception of Teachers and Students on the Development of Code Learning Model in Learning English Productive Skills

The present research aims to examine teachers and students perception in developing Code Learning Model in learning English productive skills, speaking and writing. The researchers utilized a descriptive study conducted in five universities in Bengkulu. Two hundred and sixteen students were randomly selected from sixth semester students studying in the English department participated in the study. Also, seventeen teachers were included in giving their comments on the questionnaire. The results of this study reveal that according to the perceptions of teachers and students, a coding learning model is essential and can be alternative learning in the era of the industrial revolution 4.0 that puts forward the use of technology. The application of this model can train students' 21st-century competencies and computational thinking because they align with today's developments. It can also motivate students to learn productive English skills to be more exciting and accessible.
INCORPORATING CEFR PRINCIPLES IN THE DEVELOPMENT OF A HYPERTEXT POETRY LEARNING PLATFORM FOR HIGH SCHOOL STUDENTS

This study examines the decline of English education in Indonesia and the need for innovative teaching methods to improve students' English proficiency. Despite Indonesia's low ranking on the English Proficiency Index, the traditional teaching approach remains prevalent, which focuses on grammar and vocabulary. The research by Sudimantara (2021) and highlight the benefits of non-traditional approaches such as incorporating nonverbal components. Lian A.-B (2017) also creates an innovative approach called reading for emotion to help students learn English better without worrying about grammar and vocabulary. The study also shows the importance of poetry and literature in English language learning, as proven by Zeman, A., Milton, F., Smith, A., &amp; Rylance, R. (2015) that poetry precedes prose and it is a knowledge management system in the world. The study employed a research and development design to demonstrate the collaboration of using pedagogy, technology, and CEFR to create innovative learning resources that shift students' mindsets and also build the students’ engagement in learning English. This study offers valuable insights into developing innovative learning resources for English language learning in Indonesia, and further research in this area could lead to more effective teaching methods and improve the English proficiency of Indonesian students.
Politeness Strategies used by Students in Communicating Through WhatsApp

Lack of politeness in society has begun to increase irrespective of the interlocutors in the communication process. There have been some concerns pertaining to the way people talk digitally, mainly because in today’s digital world there are massive ways of communication. Hence, certain communicators tend to apply distinct strategies when communicating, especially when using an advanced and instant platform such as WhatsApp. This is due to the differences in the nature of information exchange between the traditional oral and the online written communication. This research was conducted to identify the types of positive and negative politeness strategies used by students in WhatsApp conversations. Positive and negative politeness strategies refer to strategies which are implemented when interlocutors interact with each other regardless of the social distance they share. This research is based on Brown and Levinson’s (1987) theory of politeness. This qualitative research was conducted in several stages. It involved collecting 50 WhatsApp conversations from students. The frequency and percentage of positive and negative politeness strategies were tabulated, and the type of positive and negative politeness strategies used by students in these WhatsApp conversations were identified. The major finding of the study indicates that students demonstrated more positive politeness strategies when sending WhatsApp messages to those they are close to such as family and friends, but used more negative politeness strategies when communicating with strangers. Therefore, this study may impact WhatsApp users particularly students with reference to decency even though this study is limited to only written form of data using WhatsApp application. However, the limitation is not prominent since maintaining politeness or decent manners may come from various platforms regardless written or verbal.
Facial Expression Recognition from Occluded Images Using Deep Convolution Neural Network with Vision Transformer

Facial expression recognition (FER) is a challenging task due to various unrestricted conditions. Normal facial expression algorithms work well on frontal faces. However, detection expression from the occluded faces is still a challenging task. In this paper, we propose a novel deep convolution neural network with self-attention mechanism in order to detect the occlusion regions in the face for efficient recognition of facial expression. Firstly, we use a backbone CNN to extract feature maps of the facial images. Then the global self-attention with relative position encodings is utilized to process and aggregate the information contained in the feature maps. The global self-attention can learn the relationships between the single feature and entire facial information. Inorder to pay attention to the highly relative region and ignore the information-deficient regions. To evaluate the efficiency of the proposed model, we use two wild benchmark datasets RAF and AffectNet for FER. The results show that proposed model is effective to recognize the facial expression from the occluded images.
Digital Mading Training In Supporting Reading and Writing Literacy At SMPN 3 Rambah

Media Canva is a web-based online graphic design and visual communication platform that allows users to easily create various types of visual materials, posters, presentations, social media, and instant animations or templates. This Canva-based digital media training aims to train teachers and students in creating attractive and well-managed digital media to support students' reading and writing literacy in schools. SMPN 3 Rambah has conventional madding. These conventional media have weaknesses that cause problems for the school, such as, the media are less attractive, the condition of the media is inadequate and there is no system for managing the media. It is hoped that the results of this training will produce digital media as an information medium as well as a place for students' creativity and as a forum for improving reading and writing literacy skills for teachers and students that are easily accessible anywhere and anytime. The result of this training is an understanding of designing digital media, the existence of digital media websites and managing digital media
Online Education after the Pandemic: Student Problems and Opportunities Research Using Big Data Tools

This paper presents a scientifically based approach to analyzing large volumes of data from digital traces of students on social networks, which allows you to effectively identify emerging and most discussed problems among students, as well as highlight pain points that provide opportunities for growth, development of universities and improvement of the characteristics of the educational process, support for students etc. The study is based on a thematic analysis of messages published in university communities on the VKontakte social network using big data tools. The study results showed that Russian university students still face a number of challenges, including weak technical infrastructure at universities, a digital divide in access to online education, and negative attitudes towards distance learning. The scientific problem of the study is the contradiction between the existing volume of unstructured data of students’ digital traces in social networks and the lack of a scientifically-based and proven methodological approach to the analysis and evaluation of this voluminous data, which creates obstacles to fundamental research into the relationship between students’ activity in social networks and their satisfaction quality of the educational process. The practical focus is determined in conducting data analysis using big data tools. The findings and evidence-based implications are useful for developing innovative strategies and tools for assessing and supporting students. The results show that the use of big data tools for tracking trends based on digital traces of students on social networks provides highly accurate analytical data and can become the basis for identifying problematic situations in individual universities and the industry as a whole, for data-driven decision-making and management .
SAGAN: Self-attention Generative Adversarial Network for RGB-D Saliency Prediction

A key problem in RGB-D saliency prediction is how to effectively exploit the multi-model cues. In the paper, we propose a self-attention generative adversarial network (SAGAN) for RGB-D saliency prediction, which can extract heterogeneous features from RGB and depth by driving long-range dependency modeling and adversarial training. Specifically, we explore selective fusion based on channel attention (SFA) and prior initialization approach to efficiently learn the salient cues of RGB-D images. SFA is designed to adaptively select and fuse features in different level between RGB and depth, and prior initialization is introduced to reduce the demand for annotated RGB-D datasets and accelerate the convergence of model training by reusing RGB prior weights. Extensive experiments on two publicly available datasets demonstrate the superiority of our approach over other state-of-the-art methods.
Analysis of the Spatial and Temporal Variation Characteristics of the Atmospheric Electric Field on Fair-weather

At present, research on the fair-weather atmospheric electric field are mostly carried out on a single region or on a 2～3 years time scale, while the analysis and comparison on multiple observation stations with different longitude and latitude and large time scale are few. In this study, the observational data of near-surface fair-weather atmospheric electric field from five stations of the Meridian Project in recent ten years were compared and analyzed on three different time scales: annual variation, seasonal variation and diurnal variation. The results show that the peak and valley types of the average daily fair-weather atmospheric electric field are different at different latitudes, and the wave peaks at Zhaoqing Station in Guangzhou, Jiufeng Station in Wuhan and Mohe Station in Heilongjiang tend to shift to the left or the right year by year. Located in the low latitudes in Zhaoqing, Guangzhou, Wuhan, Chengdu PI county stand the Jiufeng station sites, such as nearly a decade of sunny overall atmospheric electric field amplitude shows the characteristics of the change of the reduced year by year, while the electric field amplitude of Changchun Nong'an Station and Heilongjiang Mohe Station in the middle and high latitudes showed an annual increase in the overall trend, and the annual changes were linear. The occurrence time of maximum crest is negatively correlated with geographical longitude, but positively correlated with geographical latitude. There was no obvious latitude effect at each station in the last ten years. The daily average level of the fair-weather atmospheric electric field is higher in winter and lower in summer, and the minimum and maximum values of the daily average fair-weather electric field are approximately normally distributed in all seasons; the variation of the minimum and maximum values is generally consistent on both annual and seasonal time scales. The results reveal the variation characteristics of fair-weather atmospheric electric fields on different time scales.