Reviewerly/exHarmony huggingface dataset:
THE EFFECT OF PUZZLE GAME TOWARDS STUDENT'S VOCABULARY MASTERY AT THE EIGHTH GRADE STUDENTS OF MTS TANWIRUL KUTUB BAYAH LEBAK – BANTEN IN ACADEMIC YEAR 2021/2022

In learning English, one of the language components that should be acquired by students is vocabulary. It is the element that connects the four English skills such as listening, speaking, reading, and writing. The objective of the research is to find out is there any significant of Puzzle Game towards student's vocabulary mastery at the eighth grade students of MTs Tanwirul Kutub Bayah. In this method, the researcher uses a quasi-experimental method with two groups the experimental class and the control class.The population of the research is all the eighth grade students of MTS Tanwirul Kutub Bayah which consisted of 2 classes, with a total of 66 with a sample of 33 students in class VIII A and class VIII B as many as 33 students. The instrument for collecting quantitative data was a pre-test and a post-test with a total of 25 questions. The Researcher calculated data by using SPSS 2.2. Based on the analysis using paired samples test, it can be concluded that t-count= -7.430 and signification value (p-value) = 0.000 &lt; α = 0.05, so H1 is accepted and H0 is rejected. On the other word, there is a significant effect of puzzle game towards Student's vocabulary mastery.
READING COMPREHENSION DIFFICULTIES IN INDONESIAN EFL STUDENTS

Getting knowledge by reading is one of the prominent ways, and reading comprehension is a skill to help students in learning process. In fact, there are many EFL students have some difficulties in reading comprehension. The objective of this study is to analyze the students’ difficulties in reading comprehension skills. Descriptive qualitative method is used in this study, and by using purposive sampling, 98 students of Basic Reading Class at English Education Program in University of Indraprasta PGRI were engaged. The data collected by administering the questionnaires, then it was analyzed to find out what kind of the difficulties faced by EFL students in reading comprehension. The questionnaires were administered in collecting the data, and it was analyzed descriptively to know what kind of reading comprehension difficulties faced by EFL students. According to the results, the most reading difficulty faced by the students are distinguishing between main ideas and supporting details, getting main idea between paragraphs, the topic of the texts from other culture, relating topic and background knowledge, understanding vocabulary and inferring the text. Based on these findings, some reading strategies should be applied to figure out these barriers in reading comprehension skills.
MODERN EDUCATIONAL TECHNOLOGIES AND ITS PROBLEMS

Modern educational technology in higher education is about problem-based learning, research and problem-solving. The purpose of problem-based learning is to find answers to educational problems, problems and questions in the process of working with students, to acquire new knowledge by solving them, to create and solve problem situations in the educational activities of students. With the help of problem-based learning, students develop a research approach to solving learning problems and specialization issues, the formation of independent learning skills.
Studies on migration in Europe: paths to think about languages, media, and journalism

From the point of view of researcher and journalist Tom Moring, Emeritus Professor at the Swedish School of Social Sciences at the University of Helsinki, this interview addresses how language and the social sharing of information impact in the integration process of refugees and migrants. Considering the researcher’s experience and his studies, the interview problematizes the media coverage and the work of journalists around these themes, highlights the connectivity of the variables migration, integration and information, and discusses how the results of European research in the area can be applied in diverse contexts, such as Latin America.
Improving Imbalanced Data Classification Accuracy by using Fuzzy Similarity Measure and Subtractive Clustering

Improving Imbalanced Data Classification Accuracy by using Fuzzy Similarity Measure and Subtractive Clustering
Overview of the new technologies impacting higher education

Universities, and the role they play in society, are under threat from the impact of the ongoing pandemic.To improve the teaching and learning process and to deliver quality education, there are numerous promising technologies for higher education.As these are complex and expensive technologies, a decision for their use must be based not on technological hype but scientifically validated outcomes.One of the biggest benefits of integrating technologies in education has been the increase in accessibility for students.Today, more people than ever before have access to learning material and the latest curriculum due to the advent of cheap internet and smartphones.Technologies made learning available to everyone, everywhere, and all the time and enabled teaching to occur in a variety of formats, including massive open online course, webinars, and standard online degree programs.This paper provides an overview of these new technologies that have considerable developments and noticeable impact on higher education moving forward.
Zero Student Lite: Model Inovasi dan Kreativitas Kepala Sekolah Di SMA Negeri 3 Jember

Abstract One of the efforts to improve Character Education is discipline. The discipline of entering the classroom, the discipline of carrying out the school discipline, and the discipline of improving learning achievement. One of the educational institutions in Jember district with low student discipline level is SMAN 3 Jember. The lack of discipline in school in SMAN 3 Jember school has been running for many years. The community knows the school with its low-level high discipline, lack of discipline in dealing with troubled students, and often empty hours. The focus of this article is on the innovation and creativity efforts of the principal of SMAN 3 Jember in suppressing the low rate of discipline of students. The method used is qualitative with the type of research library. The findings of this article are that SMAN 3 Jember school successfully innovates and creativity in improving the quality of educational institutions in student discipline. The success of SMAN 3 Jember reduces the low level of discipline of students entering the school through the Zero Student Lite Program. Zero Student Lite program is a program resulting from the innovation and creativity of the Principal to minimize the level of lack of discipline of students who have been rooted for years. Mechanism of implementation of Zero Student Lite Program: first, coordinating and cooperating with stake holders. Second, give punishment or coaching to students late to school by conducting observation (outdoor learning). Third, students are assigned to record and describe the findings of observations. Fourth, collect the results of observation records the next day as evidence of attendance filling.
USED EIGENVALUES AND EIGENVECTORS TO COMPRESS THE IMAGE

The article’s main aim is to point out the significant applications of the linear algebra in the medical engineering field. Hence, the eigenvectors and eigenvalues which represent the core of linear algebra are discussed in details in order to show how they can be used in many engineering applications. The principal components analysis is one of the most important compression and feature extraction algorithms used in the engineering field (1). It mainly depends on the calculation and extraction of eigenvalues and eigenvectors that then used to represent an input; whether it's the image or a simple matrix. In this article, the use of principal component analysis for medical image compression is an important and novel application of linear algebra (2).
A Review on Generative Adversarial Networks for Data Augmentation in Person Re-Identification Systems

Interest in automatic people re-identification systems has significantly grown in recent years, mainly for developing surveillance and smart shops software. Due to the variability in person posture, different lighting conditions, and occluded scenarios, together with the poor quality of the images obtained by different cameras, it is currently an unsolved problem. In machine learning-based computer vision applications with reduced data sets, one possibility to improve the performance of re-identification system is through the augmentation of the set of images or videos available for training the neural models. Currently, one of the most robust ways to generate synthetic information for data augmentation, whether it is video, images or text, are the generative adversarial networks. This article reviews the most relevant recent approaches to improve the performance of person re-identification models through data augmentation, using generative adversarial networks. We focus on three categories of data augmentation approaches: style transfer, pose transfer, and random generation.
Balanced Audiovisual Dataset for Imbalance Analysis

The imbalance problem is widespread in the field of machine learning, which also exists in multimodal learning areas caused by the intrinsic discrepancy between modalities of samples. Recent works have attempted to solve the modality imbalance problem from algorithm perspective, however, they do not fully analyze the influence of modality bias in datasets. Concretely, existing multimodal datasets are usually collected under specific tasks, where one modality tends to perform better than other ones in most conditions. In this work, to comprehensively explore the influence of modality bias, we first split existing datasets into different subsets by estimating sample-wise modality discrepancy. We surprisingly find that: the multimodal models with existing imbalance algorithms consistently perform worse than the unimodal one on specific subsets, in accordance with the modality bias. To further explore the influence of modality bias and analyze the effectiveness of existing imbalance algorithms, we build a balanced audiovisual dataset, with uniformly distributed modality discrepancy over the whole dataset. We then conduct extensive experiments to re-evaluate existing imbalance algorithms and draw some interesting findings: existing algorithms only provide a compromise between modalities and suffer from the large modality discrepancy of samples. We hope that these findings could facilitate future research on the modality imbalance problem.
Deep Neural Networks for Encrypted Inference with TFHE

Fully homomorphic encryption (FHE) is an encryption method that allows to perform computation on encrypted data, without decryption. FHE preserves the privacy of the users of online services that handle sensitive data, such as health data, biometrics, credit scores and other personal information. A common way to provide a valuable service on such data is through machine learning and, at this time, Neural Networks are the dominant machine learning model for unstructured data. In this work we show how to construct Deep Neural Networks (DNN) that are compatible with the constraints of TFHE, an FHE scheme that allows arbitrary depth computation circuits. We discuss the constraints and show the architecture of DNNs for two computer vision tasks. We benchmark the architectures using the Concrete stack, an open-source implementation of TFHE.
Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking

The imputation of missing values in multivariate time series (MTS) data is critical in ensuring data quality and producing reliable data-driven predictive models. Apart from many statistical approaches, a few recent studies have proposed state-of-the-art deep learning methods to impute missing values in MTS data. However, the evaluation of these deep methods is limited to one or two data sets, low missing rates, and completely random missing value types. This survey performs six data-centric experiments to benchmark state-of-the-art deep imputation methods on five time series health data sets. Our extensive analysis reveals that no single imputation method outperforms the others on all five data sets. The imputation performance depends on data types, individual variable statistics, missing value rates, and types. Deep learning methods that jointly perform cross-sectional (across variables) and longitudinal (across time) imputations of missing values in time series data yield statistically better data quality than traditional imputation methods. Although computationally expensive, deep learning methods are practical given the current availability of high-performance computing resources, especially when data quality and sample size are highly important in healthcare informatics. Our findings highlight the importance of data-centric selection of imputation methods to optimize data-driven predictive models.
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks

The bulk of existing research in defending against adversarial examples focuses on defending against a single (typically bounded Lp-norm) attack, but for a practical setting, machine learning (ML) models should be robust to a wide variety of attacks. In this paper, we present the first unified framework for considering multiple attacks against ML models. Our framework is able to model different levels of learner's knowledge about the test-time adversary, allowing us to model robustness against unforeseen attacks and robustness against unions of attacks. Using our framework, we present the first leaderboard, MultiRobustBench, for benchmarking multiattack evaluation which captures performance across attack types and attack strengths. We evaluate the performance of 16 defended models for robustness against a set of 9 different attack types, including Lp-based threat models, spatial transformations, and color changes, at 20 different attack strengths (180 attacks total). Additionally, we analyze the state of current defenses against multiple attacks. Our analysis shows that while existing defenses have made progress in terms of average robustness across the set of attacks used, robustness against the worst-case attack is still a big open problem as all existing models perform worse than random guessing.
Multi-Head Feature Pyramid Networks for Breast Mass Detection

Analysis of X-ray images is one of the main tools to diagnose breast cancer. The ability to quickly and accurately detect the location of masses from the huge amount of image data is the key to reducing the morbidity and mortality of breast cancer. Currently, the main factor limiting the accuracy of breast mass detection is the unequal focus on the mass boxes, leading the network to focus too much on larger masses at the expense of smaller ones. In the paper, we propose the multi-head feature pyramid module (MHFPN) to solve the problem of unbalanced focus of target boxes during feature map fusion and design a multi-head breast mass detection network (MBMDnet). Experimental studies show that, comparing to the SOTA detection baselines, our method improves by 6.58% (in AP@50) and 5.4% (in TPR@50) on the commonly used INbreast dataset, while about 6-8% improvements (in AP@20) are also observed on the public MIAS and BCS-DBT datasets.
Non-Uniform Interpolation in Integrated Gradients for Low-Latency Explainable-AI

There has been a surge in Explainable-AI (XAI) methods that provide insights into the workings of Deep Neural Network (DNN) models. Integrated Gradients (IG) is a popular XAI algorithm that attributes relevance scores to input features commensurate with their contribution to the model's output. However, it requires multiple forward \& backward passes through the model. Thus, compared to a single forward-pass inference, there is a significant computational overhead to generate the explanation which hinders real-time XAI. This work addresses the aforementioned issue by accelerating IG with a hardware-aware algorithm optimization. We propose a novel non-uniform interpolation scheme to compute the IG attribution scores which replaces the baseline uniform interpolation. Our algorithm significantly reduces the total interpolation steps required without adversely impacting convergence. Experiments on the ImageNet dataset using a pre-trained InceptionV3 model demonstrate \textit{2.6-3.6}$\times$ performance speedup on GPU systems for iso-convergence. This includes the minimal \textit{0.2-3.2}\% latency overhead introduced by the pre-processing stage of computing the non-uniform interpolation step-sizes.
A Survey on User Behavior Modeling in Recommender Systems

User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and items have been exploited, which brings compelling improvements in many recommendation tasks. In this paper, we attempt to provide a thorough survey of this research topic. We start by reviewing the research background of UBM. Then, we provide a systematic taxonomy of existing UBM research works, which can be categorized into four different directions including Conventional UBM, Long-Sequence UBM, Multi-Type UBM, and UBM with Side Information. Within each direction, representative models and their strengths and weaknesses are comprehensively discussed. Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions. Finally, we summarize the survey and discuss the future prospects of this field.
Multi-Message Shuffled Privacy in Federated Learning

We study differentially private distributed optimization under communication constraints. A server using SGD for optimization aggregates the client-side local gradients for model updates using distributed mean estimation (DME). We develop a communication-efficient private DME, using the recently developed multi-message shuffled (MMS) privacy framework. We analyze our proposed DME scheme to show that it achieves the order-optimal privacy-communication-performance tradeoff resolving an open question in [1], whether the shuffled models can improve the tradeoff obtained in Secure Aggregation. This also resolves an open question on the optimal trade-off for private vector sum in the MMS model. We achieve it through a novel privacy mechanism that non-uniformly allocates privacy at different resolutions of the local gradient vectors. These results are directly applied to give guarantees on private distributed learning algorithms using this for private gradient aggregation iteratively. We also numerically evaluate the private DME algorithms.
FiNER: Financial Named Entity Recognition Dataset and Weak-Supervision Model

The development of annotated datasets over the 21st century has helped us truly realize the power of deep learning. Most of the datasets created for the named-entity-recognition (NER) task are not domain specific. Finance domain presents specific challenges to the NER task and a domain specific dataset would help push the boundaries of finance research. In our work, we develop the first high-quality NER dataset for the finance domain. To set the benchmark for the dataset, we develop and test a weak-supervision-based framework for the NER task. We extend the current weak-supervision framework to make it employable for span-level classification. Our weak-ner framework and the dataset are publicly available on GitHub and Hugging Face.
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel

A wide breadth of research has devised data augmentation approaches that can improve both accuracy and generalization performance for neural networks. However, augmented data can end up being far from the clean training data and what is the appropriate label is less clear. Despite this, most existing work simply uses one-hot labels for augmented data. In this paper, we show re-using one-hot labels for highly distorted data might run the risk of adding noise and degrading accuracy and calibration. To mitigate this, we propose a generic method AutoLabel to automatically learn the confidence in the labels for augmented data, based on the transformation distance between the clean distribution and augmented distribution. AutoLabel is built on label smoothing and is guided by the calibration-performance over a hold-out validation set. We successfully apply AutoLabel to three different data augmentation techniques: the state-of-the-art RandAug, AugMix, and adversarial training. Experiments on CIFAR-10, CIFAR-100 and ImageNet show that AutoLabel significantly improves existing data augmentation techniques over models' calibration and accuracy, especially under distributional shift.
An EEG-based Experiment on VR Sickness and Postural Instability While Walking in Virtual Environments

Previous studies showed that natural walking reduces the susceptibility to VR sickness. However, many users still experience VR sickness when wearing VR headsets that allow free walking in room-scale spaces. This paper studies VR sickness and postural instability while the user walks in an immersive virtual environment using an electroencephalogram (EEG) headset and a full-body motion capture system. The experiment induced VR sickness by gradually increasing the translation gain beyond the user's detection threshold. A between-group comparison between participants with and without VR sickness symptoms found some significant differences in postural stability but found none on gait patterns during the walking. In the EEG analysis, the group with VR sickness showed a reduction of alpha power, a phenomenon previously linked to a higher workload and efforts to maintain postural control. In contrast, the group without VR sickness exhibited brain activities linked to fine cognitive-motor control. The EEG result provides new insights into the postural instability theory: participants with VR sickness could maintain their postural stability at the cost of a higher cognitive workload. Our result also indicates that the analysis of lower-frequency power could complement behavioural data for continuous VR sickness detection in both stationary and mobile VR setups.
Focusing On Targets For Improving Weakly Supervised Visual Grounding

Weakly supervised visual grounding aims to predict the region in an image that corresponds to a specific linguistic query, where the mapping between the target object and query is unknown in the training stage. The state-of-the-art method uses a vision language pre-training model to acquire heatmaps from Grad-CAM, which matches every query word with an image region, and uses the combined heatmap to rank the region proposals. In this paper, we propose two simple but efficient methods for improving this approach. First, we propose a target-aware cropping approach to encourage the model to learn both object and scene level semantic representations. Second, we apply dependency parsing to extract words related to the target object, and then put emphasis on these words in the heatmap combination. Our method surpasses the previous SOTA methods on RefCOCO, RefCOCO+, and RefCOCOg by a notable margin.