Reviewerly/exHarmony huggingface dataset:
Integrating Android-Based Applications in Teaching Chemistry for Improved Experiential Learning

With the unfavorable learning environment blamed for students’ subpar Science performance, new approaches to education were developed including the advancing technology and its application in the educational process. This study intended to determine the effectiveness of integrating Android-based applications in teaching chemistry for improved experiential learning of students. The study used a descriptive-experimental design using one hundred twenty (120) junior high school students enrolled in one public high school in Alaminos, Laguna, for the school year 2021-2022. The study revealed a significant difference in the pre-test and post-test scores of the students based on experiential learning constructs. Likewise, a significant relationship was found among variables suggesting that student perceived the integration of Android-based application as effective tool in teaching chemistry. The study’s findings indicate that the level of experiential learning of most students in Science improved after integrating the Android-based applications. Results suggest that teachers may consider using Android-based applications in teaching Science that includes learning activities suited to the topic and can give students the opportunities to improve the level of their experiential learning. Lastly, a study using low and high-performing students may be conducted to further assess the strength of the Android Applications as to applicability and effectiveness.
A Systematic Review of CEFR-Related Research of English Education in South Korea

This study aims to analyze the trend of CEFR(Common European Framework of Reference) related research in South Korea using the method of a systematic review and to discuss the research fields required in relation to CEFR. The Council of Europe released the Common European Framework of Reference for Languages (CEFR) in 2001. It acts as a standard for curriculum, teaching, learning, and evaluation. With this, thirty kinds of literature from the years 2000 to 2020 that satisfied the selection criteria were chosen from a search of CEFR-related research on English education. After the 2015 revised national curriculum was implemented, studies related to CEFR increased by 70% from 2018 in terms of publication year, and 60% of those studies used quantitative methodologies. After organizing the subjects of the studies by the Korean academic levels and CEFR levels, the data showed a focus on research for elementary and university while a wide range of CEFR levels from Basic User to Proficient User was represented. Since CEFR builds vocabulary, grammar, and language competence based on corpus data, 80% of the studies were performed in relation to the curriculum and evaluation using the corpus. However, in order to successfully apply CEFR to Korean English education, research on more detailed level settings and the linkage between each level needs to be actively conducted. More studies are necessary to adapt CEFR to the EFL context in Korea since CEFR describes communication skills that L2 learners should have, including pluricultural competence. This means a wide range of studies on CEFR are needed to expand the quality and quantity of English education in Korea.
Proposal of a Design Direction for a Music Curriculum Through Analysis of Korean Elementary School Students' Perceptions of the Goals, Contents, Methods, and Evaluations of Music Classes

The curriculum must be structured at the level and perspective of the students because the it is for students' learning. In this regard, the purpose of this study is to propose a design direction for the elementary school music curriculum by analyzing the perceptions of elementary school students in grades 5-6 toward school music classes. For this purpose, observation, interview, and checklist methods were conducted for 16 elementary school students to analyze students' perceptions of the goals, contents, methods, and evaluations of school music classes. The learning attitude of the students participating in the school music class was observed for one month, and an interview was conducted in groups to collect the students' free thoughts about the music class. Then, a checklist method was implemented to check the contents and methods of things that should be emphasized or preferred in the music class. The recognition results of students are as follows. First, they wanted music classes that were closely related to their lives. Second, listening to music was preferred along with performing music. Third, they wanted to experience a variety of music genres. Fourth, they wanted a fun music class. Fifth, they wanted the class to be more active when using digital devices. In conclusion, it is necessary to organize all music areas to be connected to life, to experience various genres of music content, and to use more active and interesting methods such as digital. In addition, it was confirmed that the curriculum should be composed of music learning linked to life and various fields.
Dynamics of Job Satisfaction and Its Intervening Effect: An Empirical Study of Diversified Company

The purpose of this study is to unearth and bridge a population gap, which is the mediating effect of job satisfaction on the relationship between dynamics of job satisfaction, i.e., work-life balance, leadership and employee retention. This is quantitative study with hypothesis testing through correlational investigation in a non-contrive setting. Conducted with minimum researchers’ interference, the time horizon of this study is cross-sectional with individual unit of analysis, i.e. managerial employees and non-managerial employees in XYZ diversified company using 77 samples selected based on simple random sampling. Data was gathered through a self-directed questionnaire. In order to bridge the identified population gap, a set of hypotheses was developed based on theoretical and empirical justifications. The findings suggest that there is a mediating effect of job satisfaction on the relationship between work-life balance and retention. As employee retention is crucial for any organization’s success, organizations should give more emphasis on the constructs such as work-life balance and job satisfaction in order to enhance retention of employees.
RBP: a website fingerprinting obfuscation method against intelligent fingerprintingattacks

A significant danger to website privacy and web security is posed by website fingerprinting (WF), a passive traffic analysis attack. It gathers the network packets created when a user accesses a website and then employs a number of approaches to identify patterns in the network packets that can be used to determine the kind of website the user is accessing. Numerous anonymous networks, like Tor, can satisfy the need to conceal identify from users while participating in network activities, but they are also vulnerable to WF assaults. In this research, we present Random Bidirectional Padding, a website fingerprinting obfuscation technique against sophisticated fingerprinting techniques (RBP). It is a cutting-edge website fingerprinting defence technology built on time sampling and random bidirectional packet padding that can change real packet distribution to destroy Inter-Arrival Time (IAT) features in the traffic sequence and increase the difference between datasets with random bidirectional virtual packet padding. We test the efficiency of thedefence against cutting-edge website fingerprinting attacks in actual circumstances.
Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images

This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and pre-training with synthetic data. Our results build on our winning submission to the recent Helsinki Deblur Challenge 2021, whose goal was to explore the limits of state-of-the-art deblurring algorithms in a real-world data setting. The task of the challenge was to deblur out-of-focus images of random text, thereby in a downstream task, maximizing an optical-character-recognition-based score function. A key step of our solution is the data-driven estimation of the physical forward model describing the blur process. This enables a stream of synthetic data, generating pairs of ground-truth and blurry images on-the-fly, which is used for an extensive augmentation of the small amount of challenge data provided. The actual deblurring pipeline consists of an approximate inversion of the radial lens distortion (determined by the estimated forward model) and a U-Net architecture, which is trained end-to-end. Our algorithm was the only one passing the hardest challenge level, achieving over $70\%$ character recognition accuracy. Our findings are well in line with the paradigm of data-centric machine learning, and we demonstrate its effectiveness in the context of inverse problems. Apart from a detailed presentation of our methodology, we also analyze the importance of several design choices in a series of ablation studies. The code of our challenge submission is available under https://github.com/theophil-trippe/HDC_TUBerlin_version_1.
Asymptotics for The $k$-means

The $k$-means is one of the most important unsupervised learning techniques in statistics and computer science. The goal is to partition a data set into many clusters, such that observations within clusters are the most homogeneous and observations between clusters are the most heterogeneous. Although it is well known, the investigation of the asymptotic properties is far behind, leading to difficulties in developing more precise $k$-means methods in practice. To address this issue, a new concept called clustering consistency is proposed. Fundamentally, the proposed clustering consistency is more appropriate than the previous criterion consistency for the clustering methods. Using this concept, a new $k$-means method is proposed. It is found that the proposed $k$-means method has lower clustering error rates and is more robust to small clusters and outliers than existing $k$-means methods. When $k$ is unknown, using the Gap statistics, the proposed method can also identify the number of clusters. This is rarely achieved by existing $k$-means methods adopted by many software packages.
FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering

Generative models have recently started to outperform extractive models in Open Domain Question Answering, largely by leveraging their decoder to attend over multiple encoded passages and combining their information. However, generative models tend to be larger than extractive models due to the need for a decoder, run slower during inference due to auto-regressive decoder beam search, and their generated output often suffers from hallucinations. We propose to extend transformer encoders with the ability to fuse information from multiple passages, using global representation to provide cross-sample attention over all tokens across samples. Furthermore, we propose an alternative answer span probability calculation to better aggregate answer scores in the global space of all samples. Using our proposed method, we outperform the current state-of-the-art method by $2.5$ Exact Match score on the Natural Question dataset while using only $25\%$ of parameters and $35\%$ of the latency during inference, and $4.4$ Exact Match on WebQuestions dataset. When coupled with synthetic data augmentation, we outperform larger models on the TriviaQA dataset as well. The latency and parameter savings of our method make it particularly attractive for open-domain question answering, as these models are often compute-intensive.
Perancangan Aplikasi Sistem Informasi Keuangan Berbasis Web: Studi Kasus di Lembaga Keuangan Mikro Syariah Al-Falah POLBAN

The purpose of this research is to produce an accounting information system application that will provide information related to member data, savings data and recording of savings and loan transactions at the Al-Falah Polban Syariah Microfinance Institution. Other goals include making it easier to record financial transactions, presenting reports on cash deposits, cash withdrawals, member data, savings data, and general journals at the Al-Falah Polban Sharia Microfinance Institution. And find out whether the application of this research can help the company's performance to be better and more efficient or not. This research methodology uses research and development (R&amp;D) methods, namely processes or steps that develop new products or improve existing products that can be accounted for. According to other sources, research and development methods are research methods used to make certain products and test the effectiveness of those products. The product in question can be in the form of software or computer programs, as well as hardware, such as tools, books, modules, or other equipment. The results of the questionnaire distributed to users, this LKMS information system application is included in the very good category, which means that by using this application, recording financial data, making savings and loan transaction reports, member data, savings data, and general journals is faster and more accurate.
Model-based Causal Bayesian Optimization

How should we intervene on an unknown structural equation model to maximize a downstream variable of interest? This setting, also known as causal Bayesian optimization (CBO), has important applications in medicine, ecology, and manufacturing. Standard Bayesian optimization algorithms fail to effectively leverage the underlying causal structure. Existing CBO approaches assume noiseless measurements and do not come with guarantees. We propose the model-based causal Bayesian optimization algorithm (MCBO) that learns a full system model instead of only modeling intervention-reward pairs. MCBO propagates epistemic uncertainty about the causal mechanisms through the graph and trades off exploration and exploitation via the optimism principle. We bound its cumulative regret, and obtain the first non-asymptotic bounds for CBO. Unlike in standard Bayesian optimization, our acquisition function cannot be evaluated in closed form, so we show how the reparameterization trick can be used to apply gradient-based optimizers. The resulting practical implementation of MCBO compares favorably with state-of-the-art approaches empirically.
Pengaruh Stimulasi Bahasa Melalui Metode Flashcard Terhadap Kemampuan Bahasa Anak 4-6 Tahun di Ra Nurrohman Pacitan

The World Health Organization (WHO) in 2018 stated that the prevalence data for children under five with growth and development disorders was 28.7%. Indonesia ranks third with the highest prevalence in the South-East Asia Region (SEAR). Children's growth and development in Indonesia must receive serious attention, about 5-10% experience general developmental delays. One in 100 children have a language delay. The problem of language skills of children aged 4-6 years can be honed by providing language stimulation through the flashcard method. To determine the effect of language stimulation through the flashcard method on the language skills of children 4-6 years old at Raudhotul Athfal Nurrohman. Quasi Experimental Design Research with One Group Pre-test – Post-test Design, non-probability sampling technique with purposive sampling method, population 50 students, sample 33 respondents, research instrument with Denver II test. after analyzing the data using the Wilcoxon test, the p value = 0.000 &lt;0.05. There is an effect of language stimulation through the flashcard method on the language skills of children 4-6 years old at Raudhotul Athfal Nurrohman.
Unsupervised 3D Pose Transfer with Cross Consistency and Dual Reconstruction

The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh while preserving the identity information (e.g., face, body shape) of the target mesh. Deep learning-based methods improved the efficiency and performance of 3D pose transfer. However, most of them are trained under the supervision of the ground truth, whose availability is limited in real-world scenarios. In this work, we present X-DualNet, a simple yet effective approach that enables unsupervised 3D pose transfer. In X-DualNet, we introduce a generator $G$ which contains correspondence learning and pose transfer modules to achieve 3D pose transfer. We learn the shape correspondence by solving an optimal transport problem without any key point annotations and generate high-quality meshes with our elastic instance normalization (ElaIN) in the pose transfer module. With $G$ as the basic component, we propose a cross consistency learning scheme and a dual reconstruction objective to learn the pose transfer without supervision. Besides that, we also adopt an as-rigid-as-possible deformer in the training process to fine-tune the body shape of the generated results. Extensive experiments on human and animal data demonstrate that our framework can successfully achieve comparable performance as the state-of-the-art supervised approaches.
Large Scale Radio Frequency Wideband Signal Detection &amp; Recognition

Applications of deep learning to the radio frequency (RF) domain have largely concentrated on the task of narrowband signal classification after the signals of interest have already been detected and extracted from a wideband capture. To encourage broader research with wideband operations, we introduce the WidebandSig53 (WBSig53) dataset which consists of 550 thousand synthetically-generated samples from 53 different signal classes containing approximately 2 million unique signals. We extend the TorchSig signal processing machine learning toolkit for open-source and customizable generation, augmentation, and processing of the WBSig53 dataset. We conduct experiments using state of the art (SoTA) convolutional neural networks and transformers with the WBSig53 dataset. We investigate the performance of signal detection tasks, i.e. detect the presence, time, and frequency of all signals present in the input data, as well as the performance of signal recognition tasks, where networks detect the presence, time, frequency, and modulation family of all signals present in the input data. Two main approaches to these tasks are evaluated with segmentation networks and object detection networks operating on complex input spectrograms. Finally, we conduct comparative analysis of the various approaches in terms of the networks' mean average precision, mean average recall, and the speed of inference.
Invariant Learning via Diffusion Dreamed Distribution Shifts

Though the background is an important signal for image classification, over reliance on it can lead to incorrect predictions when spurious correlations between foreground and background are broken at test time. Training on a dataset where these correlations are unbiased would lead to more robust models. In this paper, we propose such a dataset called Diffusion Dreamed Distribution Shifts (D3S). D3S consists of synthetic images generated through StableDiffusion using text prompts and image guides obtained by pasting a sample foreground image onto a background template image. Using this scalable approach we generate 120K images of objects from all 1000 ImageNet classes in 10 diverse backgrounds. Due to the incredible photorealism of the diffusion model, our images are much closer to natural images than previous synthetic datasets. D3S contains a validation set of more than 17K images whose labels are human-verified in an MTurk study. Using the validation set, we evaluate several popular DNN image classifiers and find that the classification performance of models generally suffers on our background diverse images. Next, we leverage the foreground & background labels in D3S to learn a foreground (background) representation that is invariant to changes in background (foreground) by penalizing the mutual information between the foreground (background) features and the background (foreground) labels. Linear classifiers trained on these features to predict foreground (background) from foreground (background) have high accuracies at 82.9% (93.8%), while classifiers that predict these labels from background and foreground have a much lower accuracy of 2.4% and 45.6% respectively. This suggests that our foreground and background features are well disentangled. We further test the efficacy of these representations by training classifiers on a task with strong spurious correlations.
Rhodes: A Next-Generation OS based on Resource Governance Model

&lt;p&gt;The kernel is the core part of the operating system. At present, four types of kernels are widely available: monolithic kernels, microkernels, hybrid kernels, and exokernels. These kernel architectures are either too tightly-coupled, like monolithic kernels, put too many modules into the kernel, or too free, like exokernels, allow users to freely develop core features such as IPC, Virtual Memory, etc, or have no rules, like hybrid kernels, kernel modules organization is uncontrolled. In recent years, to solve these problems, resource management concept frequently appears in industry and academia. Although resource management concept makes resources in OSes orderly, no existing OSes or software systems can modify and extend management rules dynamically.&lt;/p&gt; &lt;p&gt;This paper proposes a resource governance model that places resources such as applications and hardware into the model. Through unified rule governance, the organization of resources is more orderly and scalable. At the same time, by separating rules from resources, rules in the model are also scalable and flexible. Based on the model, we built Rhodes OS that allows users to modify and extend resource management rules by governance center which separates rules from resources. In addition, Rhodes also ensures the security of key resources in it and high performance of rule execution. We believe that OSes will enter resource governance age from resource management age in the near future.&lt;/p&gt;
Steganalysis Method for LSB Replacement Based On Local Gradient of Image Histogram

Today's encryption and decryption processes rely heavily on the use of modern stenography techniques, which ultimately lead to steganalysis. Because of its enormous benefits, such as the protection of information or the transport of data, steganalysis is increasingly becoming an interesting research topic. This investigation is primarily concerned with steganalysis techniques, more specifically those that are utilized on photographic images. In this research, the many LSB steganalysis processes that are supported by previous research are investigated further, along with the outcomes of those operations. According to the findings of this research, in order for modern steganography and steganalysis techniques to be more efficient, they need to fundamentally incorporate artificial intelligence and machine learning methodologies. Keywords: Steganalysis, LSB, Replacement, Local Gradient, Image Histogram