Reviewerly/exHarmony huggingface dataset:
Identifying Language Learning Strategies for Children with Communication Disorder in New Normal Era

Practicing language skills for children with communication disorders is in dire need of optimally tested strategies. This study aims to explore the perceptions of inclusive primary school teachers on the implementation of language learning strategies for children with communication disorders in the New Normal era. Data was collected through a survey of 137 teachers and semi-structured interviews with 24 inclusive primary school teachers. Data analysis used quantitative surveys and thematic analysis of qualitative data. The results of the analysis found four main themes namely, listening skills learning strategies, speaking skills learning strategies, reading skills learning strategies, and writing skills learning strategies. The results of this study indicate that language learning strategies for children with communication disorders vary widely from listening to voices, listening news, writing by dictation, reading pictures, appreciating drama, telling stories, singing, and many more. However, all language skills learning strategies for children with communication disorders will not succeed without the involvement of teachers, parents, and the surrounding community. We all have to realize that following children's development is the most important thing to realize optimal child development. Parents must have quality time and the ability to detect children's growth and development, especially children's language and communication development. This research can also be a reference for future researchers to develop a learning model that is appropriate for children with communication disorders.
ANALYSIS OF INTERACTIVE VOICE SERVERS AND PARAPHRASES: APPLICATION TO THE CONSULTATION OF RESULTS

The introduction of voice as an additional form of communication to deploy applications has certainly revolutionized the world of computing and this constitutes a means of accessing information without a computer.This article deals with the problem of the oral modality as a means of interaction for a priori increased usability of the interactive system. We will present a typology of systems where the use of speech technologies could represent a real contribution for users in the communication between man and machine, and we will discuss the issue of voice interaction. Then, we will describe an application case based on the study of the implementation of an IVS for the consultation of the results of the examinations
Students’ Creative Writings Mirroring the Social Reality

This article analyses a total of forty-six students’ writings from Grade 11 to master’s level published in one of the college’s annual magazines. Among them 24 are females and 22 are males. This research uses a qualitative content analysis method to analyze the students’ writings under various topics and genres to unearth their concerns. Although there are multiple studies on students’ creative writings focusing on the technical and gender aspects, only a few studies focus on the issues reflected in their contents. This research believes that students’ creative writings are significant as they give information about the issues, interests, and concerns from students’ perspectives. The students’ writings also reflect their concerns and awareness of students regarding various social issues in their writings. The findings from this study revealed that students are concerned about multiple social and political issues in Nepal such as poverty, political corruption, cultural issues, gender discrimination, caste discrimination, and women issues. Moreover, life is a dominant theme in both male and female writings and they are concerned about their life, future goals, and responsibility toward their parents. Some shared themes are the challenges of staying away from parents for study purposes, caste discrimination, gender discrimination, poverty, child labor, and women-related issues. Females have written personalized writings with varieties of issues and dark themes than males and the themes in their writings suggest their struggle in a patriarchal society, social restrictions, gender discrimination, and women violence against males’ writings on general themes such as loss of human values, loss of culture, and struggle of orphaned children.
USE OF INFORMATION TECHNOLOGIES IN EDUCATIONAL ACTIVITIES OF PRIMARY CLASS TEACHER OF THE NEW UKRAINIAN SCHOOL

The article substantiates the need to update the content of the educational activity of the primary school teacher, while emphasizing the need to take into account the realities of today and the need to use information technologies as a necessary component of the educational process. Based on the definitions of the phenomena of «education», «forms of education», «methods of education», «means of education» and the analysis of the situation in the state and the requirements for the personality of its citizens, the purpose and content of the educational activity of the primary school teacher have been defined as an organization of purposeful influence on cognitive, reflective and affective spheres of personality. Network software tools that can be used for educational (in particular, educational) purposes in the conditions of mixed and distance learning have been monitored, since their use allows organizing, conducting, monitoring and adjusting the educational process in general and educational activities in particular. As a result, distance learning educational platforms, means for conducting video conferences, means for creating effective visualization and virtual didactic materials were selected as educational IT tools: virtual test resources, programs for creating word clouds, mental maps, crosswords, puzzles, comics, network puzzles, virtual tours museums, as well as videos from YouTube etc. The principles of using IT tools in the education of students of primary education have been defended: visibility, individualization and differentiation, interactivity, connection with life, citizenship and national-cultural consciousness. The conditions for the selection of means and network educational IT resources for the education of students of primary education in the conditions of NUSh (their compliance with educational goals, the age of the students of education and, of course, technical and sanitary-hygienic and valeological norms) and the integration of the selected means have been specified. Prospective directions of research in the context of the topic of the publication were also outlined.
Stock Price Prediction using Dynamic Neural Networks

This paper will analyze and implement a time series dynamic neural network to predict daily closing stock prices. Neural networks possess unsurpassed abilities in identifying underlying patterns in chaotic, non-linear, and seemingly random data, thus providing a mechanism to predict stock price movements much more precisely than many current techniques. Contemporary methods for stock analysis, including fundamental, technical, and regression techniques, are conversed and paralleled with the performance of neural networks. Also, the Efficient Market Hypothesis (EMH) is presented and contrasted with Chaos theory using neural networks. This paper will refute the EMH and support Chaos theory. Finally, recommendations for using neural networks in stock price prediction will be presented.
Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping

Reinforcement learning often needs to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces (often known as the curse of dimensionality). In this work, we address this issue by learning the inherent structure of action-wise similar MDP to appropriately balance the performance degradation versus sample/computational complexity. In particular, we partition the action spaces into multiple groups based on the similarity in transition distribution and reward function, and build a linear decomposition model to capture the difference between the intra-group transition kernel and the intra-group rewards. Both our theoretical analysis and experiments reveal a \emph{surprising and counter-intuitive result}: while a more refined grouping strategy can reduce the approximation error caused by treating actions in the same group as identical, it also leads to increased estimation error when the size of samples or the computation resources is limited. This finding highlights the grouping strategy as a new degree of freedom that can be optimized to minimize the overall performance loss. To address this issue, we formulate a general optimization problem for determining the optimal grouping strategy, which strikes a balance between performance loss and sample/computational complexity. We further propose a computationally efficient method for selecting a nearly-optimal grouping strategy, which maintains its computational complexity independent of the size of the action space.
Adaptive Bernstein Change Detector for High-Dimensional Data Streams

Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detecting changes is challenging when observations are high-dimensional. In high-dimensional data, change detectors should not only be able to identify when changes happen, but also in which subspace they occur. Ideally, one should also quantify how severe they are. Our approach, ABCD, has these properties. ABCD learns an encoder-decoder model and monitors its accuracy over a window of adaptive size. ABCD derives a change score based on Bernstein's inequality to detect deviations in terms of accuracy, which indicate changes. Our experiments demonstrate that ABCD outperforms its best competitor by up to 20% in F1-score on average. It can also accurately estimate changes' subspace, together with a severity measure that correlates with the ground truth.
Stabilization of symmetry-protected long-range entanglement in stochastic quantum circuits

Long-range entangled states are vital for quantum information processing and quantum metrology. Preparing such entangled states by combining measurements with unitary gates has opened new possibilities for efficient protocols with finite-depth quantum circuits. The complexity of these algorithms is crucial for the resource requirements on a quantum device. The stability of the preparation protocols to perturbations decides the fate of their implementation in large-scale noisy quantum devices. In this work, we consider stochastic quantum circuits in one and two dimensions consisting of randomly applied unitary gates and local measurements. These quantum operations preserve a class of discrete local symmetries, which can be broken due to the stochasticity arising from timing and gate imperfections. In the absence of randomness, the protocol is known to generate a symmetry-protected long-range entangled state in a finite-depth circuit. In the general case, by studying the time evolution under this hybrid quantum circuit, we analyze the time to reach the target entangled state. We find two important time scales which we associate with the emergence of certain symmetry generators. The quantum trajectories embody the local symmetry with a time that scales logarithmically with system size, whereas global symmetries require exponentially long times to appear. We devise error-mitigation protocols that provide significant improvement on both time scales and investigate the stability of the algorithm to perturbations that naturally arise in experiments. We also generalize the protocol to realize the toric code and Xu-Moore states in two dimensions, and open avenues for future studies of anyonic excitations present in those systems. Our work paves the way for efficient error correction for quantum state preparation.
Transferable Curricula through Difficulty Conditioned Generators

Advancements in reinforcement learning (RL) have demonstrated superhuman performance in complex tasks such as Starcraft, Go, Chess etc. However, knowledge transfer from Artificial "Experts" to humans remain a significant challenge. A promising avenue for such transfer would be the use of curricula. Recent methods in curricula generation focuses on training RL agents efficiently, yet such methods rely on surrogate measures to track student progress, and are not suited for training robots in the real world (or more ambitiously humans). In this paper, we introduce a method named Parameterized Environment Response Model (PERM) that shows promising results in training RL agents in parameterized environments. Inspired by Item Response Theory, PERM seeks to model difficulty of environments and ability of RL agents directly. Given that RL agents and humans are trained more efficiently under the "zone of proximal development", our method generates a curriculum by matching the difficulty of an environment to the current ability of the student. In addition, PERM can be trained offline and does not employ non-stationary measures of student ability, making it suitable for transfer between students. We demonstrate PERM's ability to represent the environment parameter space, and training with RL agents with PERM produces a strong performance in deterministic environments. Lastly, we show that our method is transferable between students, without any sacrifice in training quality.
Modelling non-Markovian noise in driven superconducting qubits

Non-Markovian noise can be a significant source of errors in superconducting qubits. We develop gate sequences utilising mirrored pseudoidentities that allow us to characterise and model the effects of non-Markovian noise on both idle and driven qubits. We compare three approaches to modelling the observed noise: (i) a Markovian noise model, (ii) a model including interactions with a two-level system (TLS), (iii) a model utilising the post Markovian master equation (PMME), which we show to be equivalent to the qubit-TLS model in certain regimes. When running our noise characterisation circuits on a superconducting qubit device we find that purely Markovian noise models cannot reproduce the experimental data. Our model based on a qubit-TLS interaction, on the other hand, is able to closely capture the observed experimental behaviour for both idle and driven qubits. We investigate the stability of the noise properties of the hardware over time, and find that the parameter governing the qubit-TLS interaction strength fluctuates significantly even over short time-scales of a few minutes. Finally, we evaluate the changes in the noise parameters when increasing the qubit drive pulse amplitude. We find that although the hardware noise parameters fluctuate significantly over different days, their drive pulse induced relative variation is rather well defined within computed uncertainties: both the phase error and the qubit-TLS interaction strength change significantly with the pulse strength, with the phase error changing quadratically with the amplitude of the applied pulse. Since our noise model can closely describe the behaviour of idle and driven qubits, it is ideally suited to be used in the development of quantum error mitigation and correction methods.
Practical limitations of quantum data propagation on noisy quantum processors

The variational quantum imaginary time evolution algorithm is efficient in finding the ground state of a quantum Hamiltonian. This algorithm involves solving a system of linear equations in a classical computer and the solution is then used to propagate a quantum wavefunction. Here, we show that owing to the noisy nature of current quantum processors, such a quantum algorithm or the family of quantum algorithms that require classical computation of inverting a matrix with high condition number will require single- and two-qubit gates with very low error probability. Failure to meet such condition will result in erroneous quantum data propagation even for a relatively small quantum circuit ansatz. Specifically, we find the upper bounds on how the quantum algorithmic error scales with the probability of errors in quantum hardware. Our work challenges the mainstream notion of hybrid quantum-classical quantum algorithms being able to perform under noisy environments while we show such algorithms in fact require very low error quantum gates to get reliable results.
Continuous Layout Editing of Single Images with Diffusion Models

Recent advancements in large-scale text-to-image diffusion models have enabled many applications in image editing. However, none of these methods have been able to edit the layout of single existing images. To address this gap, we propose the first framework for layout editing of a single image while preserving its visual properties, thus allowing for continuous editing on a single image. Our approach is achieved through two key modules. First, to preserve the characteristics of multiple objects within an image, we disentangle the concepts of different objects and embed them into separate textual tokens using a novel method called masked textual inversion. Next, we propose a training-free optimization method to perform layout control for a pre-trained diffusion model, which allows us to regenerate images with learned concepts and align them with user-specified layouts. As the first framework to edit the layout of existing images, we demonstrate that our method is effective and outperforms other baselines that were modified to support this task. Our code will be freely available for public use upon acceptance.
Named entity recognition in resumes

Named entity recognition (NER) is used to extract information from various documents and texts such as names and dates. It is important to extract education and work experience information from resumes in order to filter them. Considering the fact that all information in a resume has to be entered to the companys system manually, automatizing this process will save time of the companies. In this study, a deep learning-based semi-automatic named entity recognition system has been implemented with a focus on resumes in the field of IT. Firstly, resumes of employees from five different IT related fields has been annotated. Six transformer based pre-trained models have been adapted to named entity recognition problem using the annotated data. These models have been selected among popular models in the natural language processing field. The obtained system can recognize eight different entity types which are city, date, degree, diploma major, job title, language, country and skill. Models used in the experiments are compared using micro, macro and weighted F1 scores and the performance of the methods was evaluated. Taking these scores into account for test set the best micro and weighted F1 score is obtained by RoBERTa and the best macro F1 score is obtained by Electra model.
Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective

We present a new dataset condensation framework termed Squeeze, Recover and Relabel (SRe$^2$L) that decouples the bilevel optimization of model and synthetic data during training, to handle varying scales of datasets, model architectures and image resolutions for efficient dataset condensation. The proposed method demonstrates flexibility across diverse dataset scales and exhibits multiple advantages in terms of arbitrary resolutions of synthesized images, low training cost and memory consumption with high-resolution synthesis, and the ability to scale up to arbitrary evaluation network architectures. Extensive experiments are conducted on Tiny-ImageNet and full ImageNet-1K datasets. Under 50 IPC, our approach achieves the highest 42.5% and 60.8% validation accuracy on Tiny-ImageNet and ImageNet-1K, outperforming all previous state-of-the-art methods by margins of 14.5% and 32.9%, respectively. Our approach also surpasses MTT in terms of speed by approximately 52$\times$ (ConvNet-4) and 16$\times$ (ResNet-18) faster with less memory consumption of 11.6$\times$ and 6.4$\times$ during data synthesis. Our code and condensed datasets of 50, 200 IPC with 4K recovery budget are available at https://github.com/VILA-Lab/SRe2L.
Employing Nearpod as a Resource to Encourage Active Students in BYOD Mathematics Learning Model

In the period of the last few decades, more complex technology have been integrated into our daily lives. Therefore, teachers are advocating that students BYOD (bring your own device). This aims are investigated the activities of students during the lesson, which enables teachers to evaluate their students' progress in learning. In addition to this, students' learning experiences and raise their levels of satisfaction with their education with Nearpod and the dynamic interaction that occurs between them. Descriptive statistics were used for the analysis. Descriptive data for the questions and items were compiled in Excel, and included frequency, percentage, mean, and standard deviation. The goal of this research is to explore how female students feel about using Nearpod in conjunction with a video-conferencing learning system on a Bring Your Own Device (BYOD) learning platform. This study was conducted at Universitas Muhammadiyah Purwokerto (n = 78 students) and Purwokerto Secondary School in Central Java (n = 83 students and 9 teachers), during the even semester of the 2020/2021 school year. The objectives of this study required the development and administration of a questionnaire to all of the women enrolled in the "Mathematics Education" course. Researchers concluded from their study that students are open to using their own devices in class and are willing to benefit from the affordances provided by the BYOD platform and the Nearpod to engage in active learning during lectures. Therefore, there has to be more work done by universities to figure out which of the growing elements of education should be prioritized in order to create the best possible conditions for instruction and learning.
UTILIZING “WORDWALLS’ AS AN ASSESSMENT TOOL FOR INDONESIAN JUNIOR HIGH SCHOOL STUDENTS

The purpose of this research was to analyze students' perceptions of the use of interactive media Wordwall as an assessment tool at SMP Negeri 1 Kolaka. The design of this study used a study case design. The subjects in this study were students of class VIII G SMP Negeri 1 Kolaka, totaling 26 students in 2022. The collection technique used questionnaires and interviews. Questionnaires were given to determine students' perceptions and interviews were conducted to find out more in-depth answers so that researchers could describe and study optimally students' perceptions of the Wordwall application as an assessment tool. The results of this research indicate that 87,5% of students are interested in using the Wordwall application as an assessment tool. Easy use, attractive appearance, and varied questions make students want to use the Wordwall application as an assessment tool in the future.