Reviewerly/exHarmony huggingface dataset:
Towards Next Generation Video Coding: from Neural Network Based Predictive Coding to In-Loop Filtering

Audio Video Coding Standard (AVS) Intelligent Coding Group mainly studies video coding tools based on neural network technology and its potential benefit for next generation video coding. Extensive efforts have been dedicated to the research on neural network (NN) based coding tools. In this paper, we present a novel NN based video coding framework by leveraging the supervised trained NN models for multiple modules in the hybrid coding framework, from the predictive coding to the in-loop filtering. Specifically, NN based intra prediction models the non-linear mapping from contextual pixels to the predictions. The inter prediction efficiency is enhanced by introducing a virtual reference frame (VRF) network. The convolutional neural network based loop filtering (CNNLF) with discriminative model selection exploits the texture adaptivity. The experimental results show that the CNNLF, NN Intra, and VRF models can bring 8.60%, 1.02%, and 2.26% luma BD-rate reduction under random access (RA) configuration compared with AVS reference software HPM13.0. Additional experiments with the combined three NN coding tools reveal that around 13% YUV BD-rate reduction could be obtained. The proposed framework opens novel sights for next generation video coding from the intelligent coding perspective.
Light Field Image Quality Assessment Based on Disentangling Bayesian Theory

Quality assessment of light field image (LFI) has attracted increasing attention in recent years. In this paper, we propose a novel light field image quality assessment (LFIQA) method based on disentangling Bayesian theory that attempts to disentangle the coupling information from macro pixel image (MacPI) to accurately evaluate the entire LFI quality. Specifically, since the spatial and angular information of LFI are highly inter-twined with varying disparities, three targeted convolutions are designed to disentangle the spatial, angular and epipolar plane image (EPI) information in MacPI mode. Subsequently, the structural similarity (SSIM) maps are calculated between disentangled feature maps of the original and distorted LFIs. Furthermore, local binary patterns (LBP) operator and natural scene statistics (NSS) in two domains are used to extract the features on the SSIM maps as the quality-aware features of LFI. Finally, support vector regression (SVR) is used to simulate the nonlinear relationship between quality-aware feature and objective quality scores. Experiments show that the proposed model achieves high consistency with human's opinions, and outperforms multiple classical and state-of-the-art LFIQ models.
Scaling JavaScript Abstract Interpretation to Detect and Exploit Node.js Taint-style Vulnerability

Taint-style vulnerabilities, such as OS command injection and path traversal, are common and severe software weaknesses. There exists an inherent trade-off between analysis scalability and accuracy in detecting such vulnerabilities. On one hand, existing syntax-directed approaches often make compromises in the analysis accuracy on dynamic features like bracket syntax. On the other hand, existing abstract interpretation often faces the issue of state explosion in the abstract domain, thus leading to a scalability problem.In this paper, we present a novel approach, called FAST, to scale the vulnerability discovery of JavaScript packages via a novel abstract interpretation approach that relies on two new techniques, called bottom-up and top-down abstract interpretation. The former abstractly interprets functions based on scopes instead of call sequences to construct dynamic call edges. Then, the latter follows specific control-flow paths and prunes the program to skip statements unrelated to the sink. If an end-to-end data-flow path is found, FAST queries the satisfiability of constraints along the path and verifies the exploitability to reduce human efforts.We implement a prototype of FAST and evaluate it against real-world Node.js packages. We show that FAST is able to find 242 zero-day vulnerabilities in NPM with 21 CVE identifiers being assigned. Our evaluation also shows that FAST can scale to real-world applications such as NodeBB and popular frameworks such as total.js and strapi in finding legacy vulnerabilities that no prior works can.
Image Generation from Random Noise using Generative Adversarial Networks

Generative Adversarial Networks (GAN) are an exciting and rapidly changing field that promises to generate realistic samples across a range of domains. They generate new and unique images that have never been seen before, opening a wide range of creative applications, such as in art and design, where they can be used to create novel and mind-blowing visual content. In this research paper, we test a GAN model that intakes random noise and creates realistic output images. The generator generates new images from random noise while updating its weights on the basis of whether the discriminator can be fooled to predict the generated images as real ones. We modeled the architectures for the generator and discriminator and evaluated the model on two datasets, the Simpson faces dataset and the Abstract paintings dataset. We study the results generated from the two datasets to understand the type of data suitable for quality image generation. The datasets vary in terms of feature simplicity and size. Learning curves for both datasets showing model losses have been plotted. Batches of images produced by training both datasets have been analyzed to state which one gives the best results. It was found that images with simple features perform better when trained on GANs compared to images containing randomness and difficult-to-learn features. Overall, the research demonstrates the versatility and effectiveness of GANs for image generation from random noise and also provides insights into the current situation of the state-of-the-art in this field.
Overcoming barriers in teaching adult learners: prospects for using the theory of self-determination

Adults are a fast—growing group of students in the Russian higher education system. Socio-demographic status, morphological and personal characteristics, as well as the specifics of adult education create significant obstacles for many of them to successfully study in higher education. In the short term, universities will have to rethink their educational programs, evaluate resources and reorganize the educational process so as to help adult students overcome these obstacles and meet their educational needs. The successful participation of adults in additional and continuing education, the elimination of barriers to learning are the key tasks of a modern university. This study was conducted using a critical analysis of the literature on adult learning to answer three questions: 1) what factors and barriers affecting adult learning are highlighted by modern authors? 2) what motives are inherent in adult students? 3) how to overcome obstacles in adult learning? Based on the results of the study, a classification of barriers in adult learning has been developed. The main motives for obtaining higher education by adult students are highlighted. The conclusion is made about the need for flexible curricula and auxiliary resources that students can adapt to the requirements of their work and life. Based on the theory of self-determination, recommendations have been developed to increase the involvement of adult students in the educational process.
Heuristic Optimization Algorithm Based Watermarking on Content Authentication and Tampering Detection for English Text

Text information is a natural language dependent, which enhances reliability and security of text data exchanged through Internet networks and is becoming main concern for researchers. Content authentication, Tampering detection, and integrity verification of digital content are considered challenges in the information and communication exchange area through the Internet. Watermarking algorithms emerge as a potential method for achieving this with text. In simple words, text watermarking is embedding imperceptible text messages within the content that serve as unique signatures or identifiers. The text message has data regarding the content creator, copyright data, or other metadata utilized for authentication purposes. Therefore, this study presents a new Coyote Optimization Algorithm with Watermarking-based Content Authentication and Tampering Detection (COAW-CATD) technique for English text. The presented COAW-CATD technique aims to secure the English text via content authentication and tampering recognition. To accomplish this, the presented COAW-CATD technique designs a zero-watermarking (ZWM) approach to produce watermarks depending on the textual content. The generated watermark can be extracted to assure the authentication of the text document. Moreover, the COA can be utilized to optimize the placement of the watermarks in the content to ensure that it is imperceptible and robust to tampering. The experimental outcomes of the COAW-CATD algorithm are tested utilizing a series of simulations and the comparative study reported its superior performance with improved tampering detection accuracy of 95.93%.
Use of augmented output membership functions to improve control accuracy of second-order fuzzy system

The efficacious application of fuzzy-based systems largely depends on some parameters of subjective decisions, e.g., fuzzy membership functions (MFs). The second-order fuzzy-based system with decoupling technique, which is generally a multi-inputs single-output (MISO) system executing augmented output MFs, is introduced to enhance the fuzzy system’s precision, heftiness, and recital. The best suitable protocol for input and output MFs is presented to design the input and output MFs in the discourse. The results of the uniformed and augmented designed output MFs are compared in identical circumstances. The experimental results and simulation results are in virtuous agreement. By applying the proposed method to different fuzzy systems, the mean absolute error (MAE) was reduced 82%, and the relative error was decreased to an adequate (≤±10%) range. The control precision is enhanced, and the heftiness is improved by reducing the MAE through the proposed second-order fuzzy model. Furthermore, the energy and cost efficacy of the proposed second-order fuzzy model will be highly enhanced by applying the augmented output MFs.
Using YouTube as EFL/ESL Tertiary Students’ Self-English Language Learning Strategies

Today, the use of Technology in the forms of internet-based video media, YouTube, is considered as the complementary, supporting, and effective learning media in English Language Learning. YouTube as the mostly and broadly-used social media, online video service, present the appropriate solutions dealing with the educational issues in the forms of rich, creative, and ground-breaking language learning strategies. The current study aims at knowing the dominant language skills and language components the tertiary students learn through YouTube and to investigate the why they used YouTube flatform as their Self-Language Learning Strategies. It used the descriptive qualitative study to describe the usages of YouTube as language learning media and to know the dominant types of language skills and language components always fostered through YouTube from the numerical data which is needed to be descriptively-statistical analized. The data was obtained through questionnaire and semi-structured interview by involving 35 the sophomore students of computer science. The results suggest that of the four English skills, the tertiary students mostly used YouTube to individually study or to improve listening (90%), speaking (81%), writing (47%), reading (59%). The receptive skills are more dominant than the productive skills. The tertiary students dominantly used YouTube as self-learning strategy to improve their language components, that is studying pronunciation (79%), Grammars (74%), vocabulary (71%). The EFL/ESL tertiary students used YouTube to individually study English as they believed that YouTube learning media as the complementary language learning sources, YouTube as the mostly-accessable language learning sources, and YouTube as the innovative self strategies to study more about language skills and language componenets.
A Genetic Programming Based Heuristic to Simplify Rugged Landscapes Exploration

Some optimization problems are difficult to solve due to a considerable number of local optima, which may result in premature convergence of the optimization process. To address this problem, we propose a novel heuristic method for constructing a smooth surrogate model of the original function. The surrogate function is easier to optimize but maintains a fundamental property of the original rugged fitness landscape: the location of the global optimum. To create such a surrogate model, we consider a linear genetic programming approach coupled with a self-tuning fitness function. More specifically, to evaluate the fitness of the produced surrogate functions, we employ Fuzzy Self-Tuning Particle Swarm Optimization, a setting-free version of particle swarm optimization. To assess the performance of the proposed method, we considered a set of benchmark functions characterized by high noise and ruggedness. Moreover, the method is evaluated over different problems’ dimensionalities. The proposed approach reveals its suitability for performing the proposed task. In particular, experimental results confirm its capability to find the global argminimum for all the considered benchmark problems and all the domain dimensions taken into account, thus providing an innovative and promising strategy for dealing with challenging optimization problems. Doi: 10.28991/ESJ-2023-07-04-01 Full Text: PDF
PENGARUH LITERASI MEMBACA DAN MENULIS TERHADAP KEMAMPUAN PEMECAHAN MASALAH MATEMATIS MAHASISWA PENDIDIKAN MATEMATIKA UINSU MEDAN DI ERA SOCIETY 5.0

This research was conducted to see the influence of reading and writing literacy on the mathematical problem solving abilities of UINSU Medan mathematics education students. This study used a quasi-experimental design by taking the smallest sampling unit, namely students from two classes, PMM-1 for the reading literacy class group and PMM-2 for the writing literacy class group. The sampling technique for this study was saturated sampling, with a sample of 35 students per class. Withdrawal of decisions from this study was carried out using the t test. The results of this study are: (1) there is an effect of reading literacy on the mathematical problem solving abilities of UINSU Medan mathematics education students; (2) there is an influence of writing literacy on the mathematical problem solving abilities of UINSU Medan mathematics education students; (3) there are differences in reading and writing literacy towards the mathematical problem solving abilities of UINSU Medan mathematics education students.
Applying Read, Cover, Remember, and Retell (RCRR) Strategy to Increase Students’ Reading Comprehension of Recount Text

In 2013 curriculum, reading competence section, students in high school are anticipated to be able comprehend concise functional texts in everyday situations, such as narrative, analytical exposition, recount, and descriptive texts. Meanwhile, a quasi-experimental design was used a test was given. Therefore, as to know students’ reading comprehension with pre-tests and post-tests. In current study, the writer therefore analyzes the data using Independent Sample T-test to find out if there is a significant in the mean, which compared the two distinct groups. This study's population 52 students were divided into two classes for the sample. using the purposive sampling method; X D the RCRR strategy was not applied to the controlled class, while X C was the experimental class. The study's findings demonstrated that students' reading comprehension of recount texts varied significantly between those who used the RCRR strategy and those who did not. It demonstrated that the RCRR method significantly improved the reading comprehension of recount texts for tenth-grade students at SMA Zainul Hasan 1 Genggong.
Out-of-Java Students’ Perceptions of Self-Efficacy in English-Speaking Skills

This study investigates out-of-Java students’ perceptions of their self-efficacy in English-speaking skills. To figure out their perceptions, the researchers formulated two research questions: (1) To what extent do out-of-Java students’ educational backgrounds impact their perceptions of self-efficacy in English speaking skills? (2) How do students cope with the challenges of speaking English? Participants in this study were the third-semester students from English Language Education Study Program. Written reflections and semi-structured interviews were chosen as the primary data collection methods. In analyzing the data, Thematic Analysis was employed in this study. The researchers described the participants’ self-reflections and transcribed their interviews. This study revealed two important results. First, there was an influence of participants' educational backgrounds on their perception of self-efficacy in English-speaking skills. Second, the participants had their strategies in overcoming the difficulties that they experienced in speaking. The results of the study showed that out-of-Java students have high self-efficacy beliefs which increase with their time at university and can improve their English-speaking skills by applying some learning strategies.
The Increasing Student Motivation for Speaking Skills with MALL (Mobile-Assisted Language Learning)

MALL (mobile-assisted language learning) was implemented in order to raise pupils' grades, especially in English-speaking classes. Microsite was used to assist students in accessing learning materials. The study was carried out at one of Batam's secondary schools. Students in grade 10 of the second semester at one public high school were the subject of this study. The researchers as teachers would observe 50 science students through the evaluation in English class. To acquire data, the researchers compared pre-test and post-test results. and found out whether the strategy was successful in enhancing students' speaking abilities. Classroom action research (CAR) was applied to solve the issue in the class. The information was obtained in four steps: planning, acting, observing, and reflecting. The results revealed the speaking abilities of students have improved between pre-tests and post-tests. Students received a score of 59.56. for the average score of pre-tests and were categorized poorly. After implementing MALL (mobile-assisted language learning) activities, the average grades of the students increased. Post-tests gained 65.47 with a great improvement in several aspects such as pronunciation was 10.14% and followed by fluency was 6,3% improvement. The evidence indicates that students responded positively to the MALL (mobile-assisted language learning) method particularly when using Microsite. The findings concluded that students' enthusiasm to learn English has been enhanced by MALL (mobile-assisted language learning), which has also boosted their speaking abilities.
Towards an ontology of virtual environments: A critical account

The growing critical and economic success of Virtual Reality technologies is generating renewed scholarly interest in virtual environments. One of the most long-lasting and influential perspectives on the topic has been labelled «virtual realism» (Heim [1998]), and it has passed throughout the entire history of virtual environments studies up to recent days (Chalmers [2022]). Virtual Realism frames virtual environments in terms of realism, and precisely of perceptive soundness and isomorphism between physical environments and virtual ones, producing a convincing illusion of being physically present in the digital space. This article develops a critical counter-argument to this account. By employing James J. Gibson’s ecology of perception and Deleuze’s and Lévy’s philosophy of the virtual, the article aims at demonstrating that the ontology of virtual environments is rooted in a domain of predetermined possibilities, and that the resulting aesthetics can not be fully immersive. Instead, the latter should embrace the «emersive» and anti-realistic qualities of the medium as an expressive device.
INFORMATION TECHNOLOGIES AT THE CLASSES OF UKRAINIAN AS A FOREIGN LANGUAGE IN THE CONDITIONS OF DISTANCE EDUCATION

Тhe role of the information technologies in the conditions of distance learning at the classes of the Ukrainian as a foreign language is analyzed in the article. The advantages of using of the innovative technologies in the modern educational process, in particular, during the study of the Ukrainian language as a foreign language, have been determined. Ways to overcome the communication barriers in the process of distance learning are identified, one of which is seemed to be in mastering of the information technologies. The important online tools for organizing a high-quality and interesting educational process are characterized. Available information means (interactive whiteboards, platforms, and other online resources) are described. For example, such information tools that contribute to the activation of students, diversify the means of communication between students and teachers, and allow creating a database of educational materials that is accessible and convenient for viewing at any time. Emphasis is placed on the problem of adaptation of foreign students in the conditions of distance learning. It could be noted that such adaptation is primarily facilitated by the selection of information resources for the organization of a high-quality educational process with the provision of feedback. Based on this criterion, a classification of online education tools at the classes of the Ukrainian as the foreign language is proposed. Therefore, information technologies are divided into several categories (which contribute to the activation of students at practical classes; which diversify the means of communication between students and teachers; which contribute to the qualitative assessment of students’ knowledge (multilevel testing system); which provide feedback; which allow creating a base of educational materials, accessible and convenient for viewing at any time. The advantages of working with a cloud environment for saving and reproducing educational materials are defined. For example, creating cases, sites, online textbooks with Book Creator. Ways to improve the informational competences of teachers of the Ukrainian as a foreign language are analyzed. Key words: information technologies, Ukrainian language as a foreign language, didactic process, distance learning, educational platforms.