Reviewerly/exHarmony huggingface dataset:
Spatial Ability and Digital Literacy Profiles: Preceding Survey on the Need of Augmented Reality Media in Chemistry Instruction

Students are demanded to be equipped with a sufficient level of multiliteracy to perform 21st Century Skills which include spatial ability and digital literacy. Spatial ability is needed to solve problems in chemistry lessons. In addition to spatial ability, digital literacy is also required to face industry 4.0. This research is aimed to analyze both spatial ability and digital literacy skills along with the media that could help develop the two skills. The research method used is descriptive qualitative method uses a survey to collect the data. This study was carried out on 922 senior high school students from four schools and 35 teachers. Purdue Spatial Visualization Test (PSVT), Likert-scale questionnaire, and closed-ended questionnaire were used as the instruments. The results indicate that 98% of the students are of low category in spatial ability while those showing mid and high category each only consists of 1%. However, in terms of the digital literacy index, the students generally score mediocre. The results from the media need a questionnaire on chemistry instruction to indicate that Augmented Reality-based media is needed both by teachers and students. Therefore, to help improve the students’ spatial ability and digital literacy, a teaching media that visualizes both abstract and concrete objects is needed.
The Implementation of Process Based Approach in Teaching Writing on The Tenth Grade Students in Senior High School

In the learning process, language is one of the most important communication tools for humans in conveying messages, feelings, and ideas from every human being (Dutta, 2020). Language also helps a person to form a word, clause, and sentence as the main communication tool. In addition, language can also be divided into four parts which are the basic skills of language, especially English, namely listening, speaking, reading, and writing. This study aims to analyze the implementation of the Process-Based Approach and the problems of it faced by English teachers in teaching writing to tenth grade students. The method used is descriptive research method using observation and interview techniques. The result of this study is that an English teacher uses a five-step process-based approach in teaching writing namely: pre-writing, compiling, revising, editing, and publishing. Then, there are three indicators, namely: lack of a process-based approach, teaching ability, and teaching habits. First, is the implementation of the revision and editing stages are still lacking. Second, time constraints make most students experience obstacles. Third, the teacher's habits can affect the character and abilities of students. That is the result of the research that there are still many techniques and ways to apply the five stages and also there are still some obstacles in the application of the approach.
Gender Influence on Students Attitude Towards the Use of Scramble Game for Learning in Secondary School English Language Concepts in Ilorin Metropolis

Scramble games offer intense mental activities that hone cognitive skills, apply economic principles, and enable language learning in the classroom. Despite the many benefits, the scramble game is challenging and time-consuming. Therefore, this study analyzes the effect of gender on students' attitudes towards scramble games. This study uses a descriptive research design using a quantitative survey method. Two research questions and one answer have been answered and tested, respectively. A total of three hundred and eighty-six (386) respondents were sampled randomly; the number of frequencies, percentages, and averages answered the quick questions tested using the t-test. This finding concludes that gender affects students' attitudes towards using games for learning, among many others. Academic performance in English vocabulary can be improved by using the Scrabble game through students' mental activities while playing games. Likewise, the use of the game Scrabble in teaching English is learner-friendly and gender-friendly. The study recommends that students be encouraged to explore the full benefits of scramble games to enhance active classroom teaching.
Comic Media in Learning Fable Stories for Second Grade Elementary School Students

The students' low interest in reading and the lack of students' understanding of reading without pictures led to the need for developing comics as a medium of learning. The purpose of this research is to create comic media in the learning of fable stories for second-grade elementary school students. The type of research used is development research. This development study utilizes the ADDIE model, which consists of Analysis, Design. Development, Implementation, and Evaluation. The research subjects consisted of 1 learning media expert and 1 learning design expert. The test subjects included 2 teachers and 1 student. The methods used in collecting data are observation and questionnaires. The instrument used in collecting data is a questionnaire. The technique used to analyze the data is descriptive quantitative. The result of the research is that the material expert test got a percentage of 86.00% in the very good category with very decent information. The trial of learning media design experts at a percentage of 90.00% with a very decent description and very good category. The teacher's response to the use of media is 89.00%, and the students are 90.40%, with both categories very good. It is concluded that the comics developed are suitable for use in second-grade elementary school students and can increase students' reading interest.
Pre-Service Teachers’ Integration of Mobile Learning Technologies for Instruction

The use of mobile devices for learning can broaden the scope of higher education and enable it to reach students better. This study aimed to analyze the integration of pre-service teachers in mobile learning technology for teaching. The population for this study consisted of all pre-service teachers in the metropolis. The Proportional Sampling technique selected 150 respondents based on their estimated population using the Israeli Model. The data collection instrument was an adapted questionnaire. Descriptive and inferential statistics were used to answer research questions and test the proposed hypothesis with product and service solution statistics (SPSS) version 20.0 at a significance level of 0.05. The findings show that pre-service teachers integrate mobile technology for learning at Ilorin university. There are no significant differences in pre-service teacher technology integration for mobile learning by gender. The study concludes that pre-service teachers can improve learning if appropriate mobile technologies are used for learning. Therefore, it is suggested that Pre-service teachers apply mobile technology for learning regardless of their gender.
Edmodo Based Blended Learning Model Employed In Comprehending Reading Text

The use of Edmodo-based blended learning in reading comprehension at Surabaya Merchant Marine Polytechnic. This study used both online and offline lectures. Edmodo was used by students and lecturers in online lectures. Students and lecturers discussed the topics they have seen and studied at homes. This learning model is preferred by students. This study used three instruments: participant observation, in-depth interview, and questionnaire. The study's subjects were lecturers and students at Merchant Marine Polytechnic of Surabaya. The results showed an intensity and quality of student to student and student-lecturer interactions. The students preferred the Edmodo-based blended learning model. The workload for students is moderate. Thus, the Blended Learning model operates well in reading comprehension classes. Keyword; Edmodo, Blended Learning, Reading Comprehension
ANALYSIS OF THE TIME FUNCTION IN THE SIMPLE PREPOSITIONS IN THE ITALIAN LANGUAGE AND THEIR EQUIVALENTS IN MACEDONIAN LANGUAGE

Луси Караниколова-
FRACTAL MODELING OF THE CONCEPT HAPPINESS IN THE DISCOURSE OF DYSTOPIA

Д.Д. Старикова (асп. УУНиТ, г. Уфа
GENDER IDENTIFICATION OF THE AUTHOR BY TEXT (BASED ON SOFTWARE TOOLS)

Р.Г. Мифтахова (доц. УУНиТ, г. Уфа
PENGEMBANGAN APLIKASI POWTOON DALAM PEMBELAJARAN HYBRID LEARNING

In the era of digitalization 5.0, learning can not only be done face-to-face, the hybrid learning method allows learning to be carried out without knowing distance and time. Hybrid combines or mixes face to face learning and computer-based learning (online and offline). The learning process can be designed by adjusting to the needs. Learning design aims to increase the efficiency and effectiveness of a learning system. One of the web-based learning models is to use powtoon as a medium. Powtoon is one of the freeware or programs that are used for free by internet users and not many have developed learning media with Powtoon. Several studies have shown that Powtoon can improve the quality of the teaching and learning process, the material presented using the Powtoon application becomes clearer and real and students become easier to understand the material. The purpose of this study is to develop the Powtoon application in hybrid learning learning. This research is a development research using a mixed method The design and approach used in this research is the development of Research &amp; Development (R&amp;D). In the development of this learning video, a model is used. 4D (Four D) development. This research was carried out at SMK Muhammadiyah 3 Pekanbaru, in the even semester of the 2021/2022 academic year. The research population is productive teachers of TKJ SMK Muhammadiyah 3 Pekanbaru and students of class X TKJ 2 SMK Muhammadiyah 3 Pekanbaru. With a sample of 2 productive TKJ teachers and 15 students of class X TKJ 2, which were taken by purposive sampling techniques. The products and instruments developed are: Powtoon animation learning videos and student ability test questions. The results showed that experimental classes that had used Powtoon learning videos had better learning abilities than t test control classes (0.02 &lt; 0.05). It was concluded that the Powtoon learning videos developed were effective in improving students' learning abilities.
Development of Augmented Reality Applications in Teaching and Learning for topic of Current and Voltage Division for Technical and Vocational Education

The combination of technology Augmented Reality (AR) with educational content aims to create new ideas that can improve the effectiveness and attractiveness of teaching and learning for students in real-life scenarios. The use of AR technology can make theprocess of teaching and learning more enjoyable, in which students' interest in learning can be stimulated. The AR technology has three characteristics. The first feature is that AR technology is a combination of virtual objects with the real world. The second feature is that AR operates in real time and interactively. The last feature is that it has 3 dimensions (3D) for virtual objects. In this study, the ADDIE model includes five phases, namely Analysis, Design, Development, Implementation and Evaluation, which are utilised to develop applications based on AR technology in education for the topic of Current and Voltage Division. To identify the effectiveness of the application of AR technology, a survey using questionnaire was conducted among pre-serviceteachers to know their perceptions of the development of AR applications namely as Voltrent AR. As this is a quantitative study where questionnaires were distributed to 73 students from the class of Bachelor of Technology with Education (Electric and Electronics) with Honours and Bachelor of Technology with Education (Living Skills) with Honours at Universiti Teknologi Malaysia (UTM) Skudai, Johor. Most respondents gave very positive feedback on the use of AR in education, especially for the topic of Current and Voltage Division. AR is viewed as a medium of instruction that is innovative, interesting and effective. Voltrent AR application development is successfully developed based on the ADDIE model, and the application development of the constructivism theory was applied in this AR application. Thus, the AR application development has reached the desired objective of producing application-based Augmented Reality as a teaching aid.
Re-usability Metrics for Black Box Component Based Software Development

Software re-usability is the extent to which a software component can be deployed during component based software development.The choice of component to be selected for reuse requires measuring the software component reusability.This paper proposes a re-usability metrics that can measure black box component re-usability.Complexity, understandability and custom ability are the properties used for measuring component reusability.The goal of this paper is to present a reusability metrics for black box component based software development.
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning

The goal of reinforcement learning (RL) is to find a policy that maximizes the expected cumulative return. It has been shown that this objective can be represented as an optimization problem of state-action visitation distribution under linear constraints. The dual problem of this formulation, which we refer to as dual RL, is unconstrained and easier to optimize. In this work, we first cast several state-of-the-art offline RL and offline imitation learning (IL) algorithms as instances of dual RL approaches with shared structures. Such unification allows us to identify the root cause of the shortcomings of prior methods. For offline IL, our analysis shows that prior methods are based on a restrictive coverage assumption that greatly limits their performance in practice. To fix this limitation, we propose a new discriminator-free method ReCOIL that learns to imitate from arbitrary off-policy data to obtain near-expert performance. For offline RL, our analysis frames a recent offline RL method XQL in the dual framework, and we further propose a new method f-DVL that provides alternative choices to the Gumbel regression loss that fixes the known training instability issue of XQL. The performance improvements by both of our proposed methods, ReCOIL and f-DVL, in IL and RL are validated on an extensive suite of simulated robot locomotion and manipulation tasks. Project code and details can be found at this https://hari-sikchi.github.io/dual-rl.
Automated detection of symmetry-protected subspaces in quantum simulations

The analysis of symmetry in quantum systems is of utmost theoretical importance, useful in a variety of applications and experimental settings, and is difficult to accomplish in general. Symmetries imply conservation laws, which partition Hilbert space into invariant subspaces of the time-evolution operator, each of which is demarcated according to its conserved quantity. We show that, starting from a chosen basis, any invariant, symmetry-protected subspaces which are diagonal in that basis are discoverable using transitive closure on graphs representing state-to-state transitions under $k$-local unitary operations. Importantly, the discovery of these subspaces relies neither upon the explicit identification of a symmetry operator or its eigenvalues nor upon the construction of matrices of the full Hilbert space dimension. We introduce two classical algorithms, which efficiently compute and elucidate features of these subspaces. The first algorithm explores the entire symmetry-protected subspace of an initial state in time complexity linear to the size of the subspace by closing local basis state-to-basis state transitions. The second algorithm determines, with bounded error, if a given measurement outcome of a dynamically-generated state is within the symmetry-protected subspace of the state in which the dynamical system is initialized. We demonstrate the applicability of these algorithms by performing post-selection on data generated from emulated noisy quantum simulations of three different dynamical systems: the Heisenberg-XXX model and the $T_6$ and $F_4$ quantum cellular automata. Due to their efficient computability and indifference to identifying the underlying symmetry, these algorithms lend themselves to the post-selection of quantum computer data, optimized classical simulation of quantum systems, and the discovery of previously hidden symmetries in quantum mechanical systems.
Foundation Models for Natural Language Processing -- Pre-trained Language Models Integrating Media

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.
Low Latency Video Denoising for Online Conferencing Using CNN Architectures

In this paper, we propose a pipeline for real-time video denoising with low runtime cost and high perceptual quality. The vast majority of denoising studies focus on image denoising. However, a minority of research works focusing on video denoising do so with higher performance costs to obtain higher quality while maintaining temporal coherence. The approach we introduce in this paper leverages the advantages of both image and video-denoising architectures. Our pipeline first denoises the keyframes or one-fifth of the frames using HI-GAN blind image denoising architecture. Then, the remaining four-fifths of the noisy frames and the denoised keyframe data are fed into the FastDVDnet video denoising model. The final output is rendered in the user's display in real-time. The combination of these low-latency neural network architectures produces real-time denoising with high perceptual quality with applications in video conferencing and other real-time media streaming systems. A custom noise detector analyzer provides real-time feedback to adapt the weights and improve the models' output.
Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling

Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filling, etc. However, the satisfying results achieved by traditional supervised-based approaches heavily depend on the large amounts of human annotation data, which may not be feasible in real-world scenarios due to data privacy and computation efficiency issues. This paper presents SeqUST, a novel uncertain-aware self-training framework for NSL to address the labeled data scarcity issue and to effectively utilize unlabeled data. Specifically, we incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to perform uncertainty estimation at the token level and then select reliable language tokens from unlabeled data based on the model confidence and certainty. A well-designed masked sequence labeling task with a noise-robust loss supports robust training, which aims to suppress the problem of noisy pseudo labels. In addition, we develop a Gaussian-based consistency regularization technique to further improve the model robustness on Gaussian-distributed perturbed representations. This effectively alleviates the over-fitting dilemma originating from pseudo-labeled augmented data. Extensive experiments over six benchmarks demonstrate that our SeqUST framework effectively improves the performance of self-training, and consistently outperforms strong baselines by a large margin in low-resource scenarios
Performance comparison of the two reconstruction methods for stabilizer-based quantum secret sharing

Stabilizer-based quantum secret sharing has two methods to reconstruct a quantum secret: The erasure correcting procedure and the unitary procedure. It is known that the unitary procedure has a smaller circuit width. On the other hand, it is unknown which method has smaller depth and fewer circuit gates. In this paper, it is shown that the unitary procedure has smaller depth and fewer circuit gates when the circuits are designed for quantum secret sharing using $[[5, 1, 3]]$ binary stabilizer codes.
PAC Prediction Sets for Large Language Models of Code

Prediction sets have recently been shown to be a promising strategy for quantifying the uncertainty of deep neural networks in a way that provides theoretical guarantees. However, existing techniques have largely targeted settings where the space of labels is simple, so prediction sets can be arbitrary subsets of labels. For structured prediction problems where the space of labels is exponential in size, even prediction sets containing a small fraction of all labels can be exponentially large. In the context of code generation, we propose a solution that considers a restricted set of prediction sets that can compactly be represented as partial programs, which are programs with portions replaced with holes. Given a trained code generation model, our algorithm leverages a programming language's abstract syntax tree to generate a set of programs such that the correct program is in the set with high-confidence. Valuable applications of our algorithm include a Codex-style code generator with holes in uncertain parts of the generated code, which provides a partial program with theoretical guarantees. We evaluate our approach on PICARD (a T5 model for SQL semantic parsing) and Codex (a GPT model for over a dozen programming languages, including Python), demonstrating that our approach generates compact PAC prediction sets. This is the first research contribution that generates PAC prediction sets for generative code models.