Reviewerly/exHarmony huggingface dataset:
IMPROVING THE STUDENTSâ€™ WRITING THROUGH NOTE-TAKING TECHNIQUE TO THE TENTH GRADE STUDENTS OF STATE SENIOR HIGH SCHOOL 7 PALEMBANG

This study aims to improving the studentsâ€™ writing ability through note-taking technique. This study applied the quasi-experiment method. The population of this study was all of the tenth grade students with a total number of 318 students and 80 students were taken as sample in experimental and control group. In collecting the data, a written test of descriptive paragraph was used, and the data were analysed by using SPSS 23. The result of this study showed that experimental group studentsâ€™ average score in pre-test was 65.10 and in post-test it was 72.26 while in control group, studentsâ€™ average score in pre-test 66.03 and in post-test it was 69.88. the value t-obtained was 3.082 which was higher than the value of t-table 2.000 at the significant level 0.05 in two tailed test with 78 (df). It was assumed that the null hypothesis (Ho) was rejected, and the alternative hypothesis (Ha) was accepted. It means that there was significant difference between the students achievement in writing descriptive paragraph.
THE INFLUENCE OF STORYTELLING STRATEGY AND LEARNING MOTIVATION TOWARDS THE ELEVENTH GRADE STUDENTSâ€™ LISTENING ABILITY

The purpose of this study was to find out whether there is an influence of storytelling that can increase student learning motivation toward Eleventh Grade Studentsâ€™ Listening Ability of MAN 1 Musi Banyuasin. This study was conducted using experimental research with a factorial design. The location of the research was MAN 1 Musi Banyuasin. To get the sample of research, the researcher took one class; it was class XI MIA 4. The total was 34 students. The quantitative data used in this study was taken from the students' listening ability test. The researcher used pre-test and post-test as a method to obtain relevant data. The data was analyzed by SPSS with six steps; descriptive statistics, normality test, homogeneity test, independent sample t-test, paired sample t-test, two-way analysis of variance.
IMPROVING READING SKILL BY USING SKETCH TO STRETCH STRATEGY IN OFFLINE LEARNING AND CONVENTIONAL IN ONLINE LEARNING TO LITERAL READING SUBJECT

This study aims to find out: (1) Is there a significant difference in the Sketch to Stretch strategy in improving students' reading skills in offline learning?, (2) Does conventionally have a significant difference in improving students' reading skills through online learning? and (3) Is there any contribution to increasing students' reading ability by increasing motivation to learn offline and online learning?. To determine the effectiveness of the Sketch to Stretch strategy, researchers had taken two research samples. The first group was taught using the Sketch to Stretch strategy in offline learning and the second used conventional strategies in online learning. To collect data, both groups were given a speech test and a learning motivation questionnaire. The results showed that there was a significant difference in learning motivation in the reading ability of students of the 1A semester English study program after being taught using the Sketch to Stretch strategy. So it can be concluded that the Sketch to Stretch strategy learning has an effect on reading ability and increasing student learning motivation.
Application of Convolutional Neural Networks to Classify Ambiguous Categories

Neural networks are often used for classifying images of specific objects, people, animals, and other objects of interest because of their ability to find particular patterns for categories. In this paper, we apply a Convolutional Neural Network (CNN) to classify images from a dataset of 4 semi-ambiguous classes and compare the scores of different architectures of neural networks and how different preprocessing techniques can affect their performance.
GETTING STUDENTS ACTIVELY INVOLVED USING THE â€˜MISTAKE BUSTER TECHNIQUEâ€ TO IMPROVE THEIR NARRATIVE ESSAY

Checking higher Intermediate 2 level studentsâ€™ narrative essay, an idea to get the students actively involved in improving their narrative essay came up. It could not be by just giving the score and gave it back to them. The mistakes can fossilize. So, the â€œMistake Buster Techniqueâ€ was applied. First, the students applied the â€œMistake Buster Techniqueâ€ by doing peer correction to their narrative essay in their first writing. Second, they used the Mistake Buster Techniqueâ€ to find and correct the â€œunidentified mistakesâ€ from their peer correction activity. Their first writing was used as the pretest, while the posttest was taken from their first periodic test. Based on the data analysis, the results of the pretest mean score is 15.48, and the posttest is 17.14.Â The mean difference is 0.004. The result showed that the â€˜Mistake Buster Techniqueâ€ significantly improves studentsâ€™ narrative essay. It also minimized studentsâ€™ grammatical errors. It was indicated by their grammar pretest mean score 3.36 that improved to 5.00 in their grammar posttest mean score. In additional, this technique allows the students to communicate with their peer in identifying and correcting the mistakes. Another benefit is they can collaboratively identify the possible mistakes themselves. It is a creative activity for the students to take over the role of correcting mistakes. Switching the role of the "mistake corrector" can be used to reinforce theirÂ critical thinking and problem solving. Â Â In short, theâ€ Mistake Buster Techniqueâ€ is very effective and useful to correct their mistakes.
IMPROVING STUDENTSâ€™ WRITING ABILITY THROUGH QUICK WRITING STRATEGY TO THE ELEVENTH GRADERS OF SMA MUHAMMADIYAH PAGARALAM

Writing is one of the most difficult skills to be mastered by students and most of students did not have enough ability to write, in order to solve this problem the writer used one of strategy which is quick writing. The main objective of this study was to identify whether or not the use of Quick Writing strategy was effective to enhance the student writing ability. The population was Eleventh grade students of SMA Muhammadiyah Pagaralam. In taking the sample, the researcher used purposive sampling. Sixty students, involved as a participants in this study. The research design used quasi experimental. The data were collected by using writing test. The data were analyzed by using paired sample t-test and independent sample t-test. The value t-obtain was (4.765) at the significant level 0.000. Since the value of t-obtained was higher than critical value of t-table (2.001). The result showed that there was significant difference between the students who were taught by using Quick writing strategy and those were not. In short, the students who were taught by using Quick writing strategy had better improvement in their writing ability. It was effective to use quick writing strategy
How do Cognitive Biases affect Strategic Decision Making in SME’s and How can they Avoid their Mistakes made Due to such Biases

The field of behavioural economics is an emerging area of study which has answered various questions that revolve around core economic problems. Therefore, it becomes essential to look into strategic decision making in small and micro enterprises through a behavioural perspective since such enterprises are run by individuals and there is an absence of a decision making body that in turn leads to flawed decision making. This paper examines biases and heuristics that affect entrepreneurial decision making across various lines in the SME sector. Biases such as the status quo bias, the hyperbolic discounting bias and the hindsight bias in particular are discussed in this paper. The paper proposes a specified solution set for every bias to negate their effects, and also specifically talks about the assumption of rationality and the framework of behavioural biases that leads to flawed decisions.
THE EFFECTIVENESS OF THE IMPLEMENTATION OF ONLINE ENGLISH LEARNING DURING THE COVID-19 PANDEMIC ERA OF THE EIGHTH GRADE STUDENTS AT STATE JUNIOR HIGH SCHOOL 2 OF RAMBANG NIRU MUARA ENIM

This study aims to determine the effectiveness of online learning in learning English in the eighth grade students at state Junior High School 2 of Rambang Niru, Muara Enim. The questions are the questions that this research may try to answer: 1)How is the implementation of online Â English learning during the Covid-19 pandemic era atÂ Â theÂ eighth grade?2)How is the effectiveness of online Â English learning perceived by students, teachers and parents in English learning during the Covid-19 pandemic era 3) What are the drawbacks of the effectiveness of online learningÂ Â perceievedÂ by at the eighth grade ?This research using the Qualitatif method. The findings of this study found the ineffectiveness of online English learning.
THE INFLUENCE OF HYPNOTEACHING STRATEGY AND READING MOTIVATION TOWARD STUDENTSâ€™ READING ABILITY

This study concerned with the influence of hypnoteaching strategy and reading motivation towards studentsâ€™ reading ability. This study applied 2x2 (two by two) factorial design method. The sample was 60 students that divided into two groups. There were 30 students as experimental group and 30 students as control group. The data were collected by using a questionnaire and written test with multiple choice questions that analyzed by using t-test and two-ways Anova. The findings showed that (1) there was significant different in reading ability between the students who had high reading motivation and low reading motivation taught by using hypnoteaching strategy and no experimental treatment (2) there are interaction effects of hypnoteaching strategy and no experimental treatment, and (3) hypnoteaching strategy can be utilized to promote and contributes the reading ability of the students. It could be concluded that hypnoteaching strategy and reading motivation showed significant role to improve studentsâ€™ reading ability on narrative text.
An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems

The Segment Anything Model (SAM) has demonstrated exceptional performance and versatility, making it a promising tool for various related tasks. In this report, we explore the application of SAM in Weakly-Supervised Semantic Segmentation (WSSS). Particularly, we adapt SAM as the pseudo-label generation pipeline given only the image-level class labels. While we observed impressive results in most cases, we also identify certain limitations. Our study includes performance evaluations on PASCAL VOC and MS-COCO, where we achieved remarkable improvements over the latest state-of-the-art methods on both datasets. We anticipate that this report encourages further explorations of adopting SAM in WSSS, as well as wider real-world applications.
Learning hard distributions with quantum-enhanced Variational Autoencoders

An important task in quantum generative machine learning is to model the probability distribution of measurements of many-body quantum systems. Classical generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), can model the distributions of product states with high fidelity, but fail or require an exponential number of parameters to model entangled states. In this paper, we introduce a quantum-enhanced VAE (QeVAE), a generative quantum-classical hybrid model that uses quantum correlations to improve the fidelity over classical VAEs, while requiring only a linear number of parameters. We provide a closed-form expression for the output distributions of the QeVAE. We also empirically show that the QeVAE outperforms classical models on several classes of quantum states, such as 4-qubit and 8-qubit quantum circuit states, haar random states, and quantum kicked rotor states, with a more than 2x increase in fidelity for some states. Finally, we find that the trained model outperforms the classical model when executed on the IBMq Manila quantum computer. Our work paves the way for new applications of quantum generative learning algorithms and characterizing measurement distributions of high-dimensional quantum states.
ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation

We propose a new dataset and a novel approach to learning hand-object interaction priors for hand and articulated object pose estimation. We first collect a dataset using visual teleoperation, where the human operator can directly play within a physical simulator to manipulate the articulated objects. We record the data and obtain free and accurate annotations on object poses and contact information from the simulator. Our system only requires an iPhone to record human hand motion, which can be easily scaled up and largely lower the costs of data and annotation collection. With this data, we learn 3D interaction priors including a discriminator (in a GAN) capturing the distribution of how object parts are arranged, and a diffusion model which generates the contact regions on articulated objects, guiding the hand pose estimation. Such structural and contact priors can easily transfer to real-world data with barely any domain gap. By using our data and learned priors, our method significantly improves the performance on joint hand and articulated object poses estimation over the existing state-of-the-art methods. The project is available at https://zehaozhu.github.io/ContactArt/ .
Augmented Electronic Ising Machine as an Effective SAT Solver

With the slowdown of improvement in conventional von Neumann systems, increasing attention is paid to novel paradigms such as Ising machines. They have very different approach to NP-complete optimization problems. Ising machines have shown great potential in solving binary optimization problems like MaxCut. In this paper, we present an analysis of these systems in satisfiability (SAT) problems. We demonstrate that, in the case of 3-SAT, a basic architecture fails to produce meaningful acceleration, thanks in no small part to the relentless progress made in conventional SAT solvers. Nevertheless, careful analysis attributes part of the failure to the lack of two important components: cubic interactions and efficient randomization heuristics. To overcome these limitations, we add proper architectural support for cubic interaction on a state-of-the-art Ising machine. More importantly, we propose a novel semantic-aware annealing schedule that makes the search-space navigation much more efficient than existing annealing heuristics. With experimental analyses, we show that such an Augmented Ising Machine for SAT (AIMS), outperforms state-of-the-art software-based, GPU-based and conventional hardware SAT solvers by orders of magnitude. We also demonstrate AIMS to be relatively robust against device variation and noise.
A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge

Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing. In this paper, we describe our proposed spoken semantic parsing system for the quality track (Track 1) in Spoken Language Understanding Grand Challenge which is part of ICASSP Signal Processing Grand Challenge 2023. We experiment with both end-to-end and pipeline systems for this task. Strong automatic speech recognition (ASR) models like Whisper and pretrained Language models (LM) like BART are utilized inside our SLU framework to boost performance. We also investigate the output level combination of various models to get an exact match accuracy of 80.8, which won the 1st place at the challenge.
Privacy-Preserving In-Context Learning for Large Language Models

In-context learning (ICL) is an important capability of Large Language Models (LLMs), enabling these models to dynamically adapt based on specific, in-context exemplars, thereby improving accuracy and relevance. However, LLM's responses may leak the sensitive private information contained in in-context exemplars. To address this challenge, we propose Differentially Private In-context Learning (DP-ICL), a general paradigm for privatizing ICL tasks. The key idea for DP-ICL paradigm is generating differentially private responses through a noisy consensus among an ensemble of LLM's responses based on disjoint exemplar sets. Based on the general paradigm of DP-ICL, we instantiate several techniques showing how to privatize ICL for text classification and language generation. We evaluate DP-ICL on four text classification benchmarks and two language generation tasks, and our empirical results show that DP-ICL achieves a strong utility-privacy tradeoff.
Comparison Analysis of Global Healthcare of virtual reality Systems Market by Geography in Existing Market Scenario

In the area of virtual reality, there are some critical issues that need to deal with accessing, managing, and sharing the wide amount of information which is increasing day by day. The information produced is not in a form that machines can easily process. So, there is a need to develop and design techniques which can easily process the information and can be embedded with the techniques used for virtual reality. This chapter gives an insight into the various applications of virtual reality, and a comparative analysis of the global healthcare of virtual reality systems market by geography in the existing market scenario.
Benefits of English for Preschool Children. The Case Study of a Bilingual School in Italy

The research aims at investigating the communicative abilities of Italian children in a bilingual (English-Italian) preschool, focusing on the benefits of bilingualism in children’s cognitive, social, and cultural growth. After an introduction on the role of bilingual education in early childhood in promoting a child’s life-long love of language and bilingual proficiency, through the support of some European documents and key studies in the field, the research design is presented. A variety of instruments were used, such as video recorded class observations, field notes and observation sheets, semi-structured interviews with L1 and L2 teachers and structured interviews with children, questionnaires for parents, as well as language knowledge tests for children and teachers. Children’s lexicon development was taken into consideration, especially the frequency of use of English language, thus showing a significant growth of the perceptive and productive lexicon for the investigated age range. This provided evidence that the children in this study were still at the sensitive age for lexicon acquisition. The linguistic phenomena that, among the preschool children, were practised the most were: code-switching and code-mixing. This study provided new findings on the early acquisition of English language in bilingual children with the home language Italian. This language experience usually takes place along five dimensions: the materiality of language and its use, children’s perceptions about them, beliefs about self and others in the speech community, emotional responses about language and about language users.
Image Regeneration for Old Damaged Monument Reel Picture using Deep Learning

We suggest a deep learning approach for restoring severely degraded old photos. However, unlike conventional restoration tasks that can be solved through supervised learning, restoring real photos is challenging due to the complexity of the degradation and the domain gap between synthetic images and real old photos, which makes the network unable to generalize. To overcome this, we propose a novel triplet domain translation network that uses both real photos and a large number of synthetic image pairs.Our approach involves training two variational autoencoders (VAEs) to transform old and clean photos into two separate latent spaces, with synthetic paired data used to learn the translation between these spaces. This translation is effective because the domain gap is closed in the compact latent space, which allows it to generalize well to real photos.To address the challenge of multiple degradations mixed in one old photo, we design a global branch that includes a partial nonlocal block to target structured defects such as scratches and dust spots, and a local branch to address unstructured defects such as noise and blur. The two branches are fused in the latent space, leading to improved capability to restore old photos from multiple defects. Our proposed method outperforms state-of-the-art methods in terms of visual quality for restoring old photos.
The Analysis of Second Semester Students’ Ability in Comprehending Reading Text

This study deals with an analysis of second semester students ability in comprehending reading text. The objective of the study is to investigate the influences of students ability in comprehending text. It used qualitative research. The population of this research is the 2021/2022 second semester students of Indonesia Prima University, Medan. The sample is 32 students. The data of this study are collected by using a test. The test is consisted of 30 questions. The type of the test is an objective test. After collecting the data, then the writer analyzed students’ score. It is found that most of the students unable to comprehend reading text. There are 19 students (59.3%) get score under 65. It makes they really need more attention from the teacher to help them increasing their ability in comprehending the text.
Research Results of Time Management Teaching Methods at the Undergraduate Level at TSU

The paper describes the results of implementing methods of developing time management habits among bachelor’s degree students. The research was carried out under the auspices of the Human Potential Management Laboratory of Ivane Javakhishvili Tbilisi State University, within the framework of the elective course ("Leadership - The Art of Management") of the undergraduate level of the Faculty of Economics and Business of the same university. The paper presents 2 practical methods of teaching time management and its results: 1. Assessment of students' time management habits through Haddon's test and results; 2. A method of developing time budget management habits by evaluating the actual day and week and determining the desired time budget for the week. Teaching students of time management skills are important, especially in countries where the school and university infrastructure does not provide an opportunity to be aware and totally introduce a healthy lifestyle among young people. Making students aware of the importance of a variety of activities in their time and showing them opportunities in this direction will teach them the skills necessary to balance life. Teaching time management helps the student to write down/plan his goals in time and take concrete steps towards success.
Self-Refine Learning For Data-Centric Deep Learning

&lt;p&gt;In industry NLP application, our manually labeled data has a certain number of noise data. We present a simple method to find the noise data and remove them. We select the noise data whose human label is not contained in the top-K model's predictions. The experiment result shows that our method works. For industry deep learning application, our method improve the text classification accuracy from 80.5% to 90.6% in dev dataset, and improve the human-evaluation accuracy from 83.2% to 90.1%.&lt;/p&gt;