Reviewerly/exHarmony huggingface dataset:
PARENTAL ENGAGEMENT IN ACCOMPANYING KIDS LEARNING ENGLISH THROUGH MONKEY STORIES APPLICATION DURING COVID-19 LOCKDOWN

&#x0D; &#x0D; &#x0D; Coronavirus disease 19 which has changed every aspect in the world, especially in education aspect brought a significant impact. One of them is changing the learning system at schools. Teaching and learning process turn to use online platforms as a form of learning method online way. Therefore parents and teachers are passionately looking for best ways to facilitate online learning, one of them is by aplying monkey stories to introduce English for the kids. This study aimed at determining the parental engagement in accompanying kids learning English through monkey stories application during covid-19 lockdown. This study was a qualitative research by using observation, interview, and documentation as data collection techniques. This study found four themes including: 1) activities followed by the kids using monkey stories application, 2) description of parenting patterns applied at home, 3) the role of parents in assisting the use of monkey stories application, and 4) the effect of monkey stories application on kids’ ability in comprehending English. This study is expected had a fruitful meaning for parents in assist their kids during study at home therefore the kids still feel the school circumstances at home and parents can be active and creative learning partners for their kids at home&#x0D; &#x0D; &#x0D;
Improved Emergency First Aid Knowledge Level Through Training With Algorithm-Based Accessibility Simulation Model

Background: Emergency situations can arise from various diverse conditions, can come from natural conditions, humans and technological advances that have an impact on the emergence of disasters, accidents and diseases that threaten life or cause disability. Every individual in the community needs to know the flow of accessibility in an effort to seek help and the steps to provide first aid, so it is very necessary to attend training. This can be done by participating in an algorithm-based accessibility simulation model training.&#x0D; Methods: This type of research is a comparative experimental study with a pretest-post test design with a control group with a cross sectional approach. The respondents involved are special lay people (members of linmas in the city of Surakarta). The sampling technique used was random sampling by spreading invitations to respondents, so that 110 respondents agreed. This research instrument was tested for expert validity, namely practitioners and academics of emergency nursing, totaling 3 people. Data were analyzed by t-test with the help of a computer&#x0D; Results: The results showed an increase in the value of 4.4 from the pretest (Mean = 73.5) and posttest (Mean = 77.9) and the t test results showed the value of Sig. (2-tailed) is 0.00 so it can be concluded that there is a significant difference between the pretest and posttest values.&#x0D; Conclusion: Training with an algorithm-based accessibility simulation model can significantly increase knowledge related to the emergency field. So it needs to be recommended to be carried out gradually, continuously and thoroughly to community members.
Blackbox Attacks via Surrogate Ensemble Search

Blackbox adversarial attacks can be categorized into transfer- and query-based attacks. Transfer methods do not require any feedback from the victim model, but provide lower success rates compared to query-based methods. Query attacks often require a large number of queries for success. To achieve the best of both approaches, recent efforts have tried to combine them, but still require hundreds of queries to achieve high success rates (especially for targeted attacks). In this paper, we propose a novel method for Blackbox Attacks via Surrogate Ensemble Search (BASES) that can generate highly successful blackbox attacks using an extremely small number of queries. We first define a perturbation machine that generates a perturbed image by minimizing a weighted loss function over a fixed set of surrogate models. To generate an attack for a given victim model, we search over the weights in the loss function using queries generated by the perturbation machine. Since the dimension of the search space is small (same as the number of surrogate models), the search requires a small number of queries. We demonstrate that our proposed method achieves better success rate with at least 30x fewer queries compared to state-of-the-art methods on different image classifiers trained with ImageNet. In particular, our method requires as few as 3 queries per image (on average) to achieve more than a 90% success rate for targeted attacks and 1-2 queries per image for over a 99% success rate for untargeted attacks. Our method is also effective on Google Cloud Vision API and achieved a 91% untargeted attack success rate with 2.9 queries per image. We also show that the perturbations generated by our proposed method are highly transferable and can be adopted for hard-label blackbox attacks. We also show effectiveness of BASES for hiding attacks on object detectors.
Object as Distribution

Object detection is a critical part of visual scene understanding. The representation of the object in the detection task has important implications on the efficiency and feasibility of annotation, robustness to occlusion, pose, lighting, and other visual sources of semantic uncertainty, and effectiveness in real-world applications (e.g., autonomous driving). Popular object representations include 2D and 3D bounding boxes, polygons, splines, pixels, and voxels. Each have their strengths and weakness. In this work, we propose a new representation of objects based on the bivariate normal distribution. This distribution-based representation has the benefit of robust detection of highly-overlapping objects and the potential for improved downstream tracking and instance segmentation tasks due to the statistical representation of object edges. We provide qualitative evaluation of this representation for the object detection task and quantitative evaluation of its use in a baseline algorithm for the instance segmentation task.
Interval-valued Data Median Clustering (IWPGMC): Step-by-Step Midpoint Update Formula

In the last few years, laws that restrict the use of biometric data have been implemented in many countries, for example, the General Data Protection Act in Brazil.One effect of this restriction is currently all high resolution fin-gerprint databases are no longer publicly available.To deal with this problem we developed in this work a generative algorithm for fingerprint pore synthesis.The experiments showed that our constructed algorithm using Variational Au-toencoder respects the spatial distribution of pores.As far as we know, this is the first work in the literature in this direction.
Good locally repairable codes via propagation rules

In classical coding theory, it is common to construct new codes via propagation rules. There are various propagation rules to construct classical block codes. However, propagation rules have not been extensively explored for constructions of locally repairable codes. In this paper, we introduce a few propagation rules to construct good locally repairable codes. To our surprise, these simple propagation rules produce a few interesting results. Firstly, by concatenating a locally repairable code as an inner code with a classical block code as an outer code, we obtain quite a few dimension-optimal binary locally repairable codes. Secondly, from this concatenation, we explicitly build a family of locally repairable codes that exceeds the Zyablov-type bound. Thirdly, by a lengthening propagation rule that adds some rows and columns from a parity-check matrix of a given linear code, we are able to produce a family of dimension-optimal binary locally repairable codes from the extended Hamming codes, and to convert a classical maximum distance separable (MDS) code into a Singleton-optimal locally repairable code. Furthermore, via the lengthening propagation rule, we greatly simplify the construction of a family of locally repairable codes in \cite[Theorem 5]{MX20} that breaks the asymptotic Gilbert-Varshamov bound. In addition, we make use of three other propagation rules to produce more dimension-optimal binary locally repairable codes. Finally, one of phenomena that we observe in this paper is that some trivial propagation rules in classical block codes do not hold anymore for locally repairable codes.
An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution

We propose an efficient once-for-all budgeted pruning framework (OFARPruning) to find many compact network structures close to winner tickets in the early training stage considering the effect of input resolution during the pruning process. In structure searching stage, we utilize cosine similarity to measure the similarity of the pruning mask to get high-quality network structures with low energy and time consumption. After structure searching stage, our proposed method randomly sample the compact structures with different pruning rates and input resolution to achieve joint optimization. Ultimately, we can obtain a cohort of compact networks adaptive to various resolution to meet dynamic FLOPs constraints on different edge devices with only once training. The experiments based on image classification and object detection show that OFARPruning has a higher accuracy than the once-for-all compression methods such as US-Net and MutualNet (1-2% better with less FLOPs), and achieve the same even higher accuracy as the conventional pruning methods (72.6% vs. 70.5% on MobileNetv2 under 170 MFLOPs) with much higher efficiency.
RELATIONSHIP BETWEEN LEARNING MOTIVATION AND STUDENTS’ ENGLISH LEARNING ACHIEVEMENTS OF STAHN GDE PUDJA MATARAM

This study aims to find a relationship between learning motivation and Students’ English learning achievement of STAHN Gde Pudja Mataram. This research is an ex post facto research using correlation analysis. Ex post facto research is research that examines causal relationships that are not manipulated or treated (designed and implemented) by the researcher. Research data were collected using questionnaires and tests. Data were analyzed using product moment correlation analysis. The conclusion of this study is: there is a positive and significant relationship between learning motivation and students’ English learning achievement of STAHN Gde Pudja Mataram.
HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks

Recently, graph neural networks have shown the superiority of modeling the complex topological structures in heterogeneous network-based recommender systems. Due to the diverse interactions among nodes and abundant semantics emerging from diverse types of nodes and edges, there is a bursting research interest in learning expressive node representations in multiplex heterogeneous networks. One of the most important tasks in recommender systems is to predict the potential connection between two nodes under a specific edge type (i.e., relationship). Although existing studies utilize explicit metapaths to aggregate neighbors, practically they only consider intra-relationship metapaths and thus fail to leverage the potential uplift by inter-relationship information. Moreover, it is not always straightforward to exploit inter-relationship metapaths comprehensively under diverse relationships, especially with the increasing number of node and edge types. In addition, contributions of different relationships between two nodes are difficult to measure. To address the challenges, we propose HybridGNN, an end-to-end GNN model with hybrid aggregation flows and hierarchical attentions to fully utilize the heterogeneity in the multiplex scenarios. Specifically, HybridGNN applies a randomized inter-relationship exploration module to exploit the multiplexity property among different relationships. Then, our model leverages hybrid aggregation flows under intra-relationship metapaths and randomized exploration to learn the rich semantics. To explore the importance of different aggregation flow and take advantage of the multiplexity property, we bring forward a novel hierarchical attention module which leverages both metapath-level attention and relationship-level attention. Extensive experimental results suggest that HybridGNN achieves the best performance compared to several state-of-the-art baselines.
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images

Pathologists need to combine information from differently stained pathology slices for accurate diagnosis. Deformable image registration is a necessary technique for fusing multi-modal pathology slices. This paper proposes a hybrid deep feature-based deformable image registration framework for stained pathology samples. We first extract dense feature points via the detector-based and detector-free deep learning feature networks and perform points matching. Then, to further reduce false matches, an outlier detection method combining the isolation forest statistical model and the local affine correction model is proposed. Finally, the interpolation method generates the deformable vector field for pathology image registration based on the above matching points. We evaluate our method on the dataset of the Non-rigid Histology Image Registration (ANHIR) challenge, which is co-organized with the IEEE ISBI 2019 conference. Our technique outperforms the traditional approaches by 17% with the Average-Average registration target error (rTRE) reaching 0.0034. The proposed method achieved state-of-the-art performance and ranked 1st in evaluating the test dataset. The proposed hybrid deep feature-based registration method can potentially become a reliable method for pathology image registration.
Learning Stochastic Closures Using Ensemble Kalman Inversion

Although the governing equations of many systems, when derived from first principles, may be viewed as known, it is often too expensive to numerically simulate all the interactions they describe. Therefore researchers often seek simpler descriptions that describe complex phenomena without numerically resolving all the interacting components. Stochastic differential equations (SDEs) arise naturally as models in this context. The growth in data acquisition, both through experiment and through simulations, provides an opportunity for the systematic derivation of SDE models in many disciplines. However, inconsistencies between SDEs and real data at short time scales often cause problems, when standard statistical methodology is applied to parameter estimation. The incompatibility between SDEs and real data can be addressed by deriving sufficient statistics from the time-series data and learning parameters of SDEs based on these. Following this approach, we formulate the fitting of SDEs to sufficient statistics from real data as an inverse problem and demonstrate that this inverse problem can be solved by using ensemble Kalman inversion (EKI). Furthermore, we create a framework for non-parametric learning of drift and diffusion terms by introducing hierarchical, refinable parameterizations of unknown functions, using Gaussian process regression. We demonstrate the proposed methodology for the fitting of SDE models, first in a simulation study with a noisy Lorenz '63 model, and then in other applications, including dimension reduction in deterministic chaotic systems arising in the atmospheric sciences, large-scale pattern modeling in climate dynamics, and simplified models for key observables arising in molecular dynamics. The results confirm that the proposed methodology provides a robust and systematic approach to fitting SDE models to real data.
Token-based Insurance Solutions on Blockchain

With the rising demand for protection against new risks such as loss of digital assets, novel insurance services and products emerge. In particular, token-based insurance solutions on blockchain transform the insurance business by providing cover for new risks and streamlined, (semi-)automated underwriting and claim processes. In the chapter, we present a general framework of token-based insurance solutions, delegating their fundamental building blocks that include core roles, main tokens and assets, as well as key processes and operations. We describe three major token-based insurance solutions in the market and compare them in terms of native token functionality, tokenized cover types, claim assessment process and capital model. Based on the discussion on the general framework and concrete examples of token-based insurance solutions, we summarize their advantages and point out their drawbacks. We conclude that despite being at a nascent stage, the token-based insurance space bears the promise to unseat the incumbent players with increasingly more action taking place and more use cases being explored.
Evaluation and Improvement of Narrative Visualizations

In the field of Information Visualization, storytelling techniques help to communicate facts and enhance comprehension. The use of data storytelling best practices can inform the process of creating narrative visualizations and increase the quality of charts used in software applications by improving aspects such as memorability or engagement, for instance, supporting end users in the decision-making process. The main goal of this doctoral research is to develop a method for assessing and improving the quality of narrative visualizations in software products, like scatter plots, line, bar, or pie charts, among others. This has included a case study and a systematic mapping study.
Lived Experiences of Public Elementary SLM Writers: Basis for the Development of a Training Program

The study aimed to explore the lived experiences of Public Elementary SLM writers in the new normal education who were required to write modules for various subjects in the elementary level and had difficulties in crafting the modules. The researchers made use of a qualitative approach specifically, descriptive phenomenology to understand the personal experiences of the key informants. A semi-structured online interview was conducted with the eleven selected key informants from various schools. The research study used the purposive sampling technique, open-ended questions as instrument, and data collected were analyzed using Colaizzi’s 1978 phenomenological method. Findings show mastery of content, creativity, time management skills, grammar skills, information and communication technology skills, good communication skills and flexibility are the skills needed to possess as a SLM writer. While the challenges and difficulties encountered are lack of training programs for SLM writers, difficulties in making suitable activities for learners, time constraint in writing modules, overlapping responsibilities, lay-outing difficulties, running out of ideas in content delivery, less proficiency in computer literacy. Different coping practices were also identified by collaboration among colleagues, utilizing academic peripheral resources, time management through priorities, devising ways for the improvement of modules content, and SLM writer’s motivation such as children's educational success and professional development success. The evaluators, criteria and common feedback received during evaluation of SLM were identified. SLM Writers addressed the feedback through considering and taking it as constructive ones. Researchers recommended training and webinars about writing modules. Writers should be resourceful to compensate for the shortcomings of their needs and the children.
Business process modeling at steak restaurant using business process model and notation

The complexity of business processes occurring today makes the company try to find ways to describe its business processes. Business processes are not only an operational standard but also become one of the determining factors for the smooth use of time and costs in a business unit to be more efficient. With good business processes, it makes the flow of information faster so that it can help in making the best decisions in the organization. The business process modeling that will be explained further in this study is the order and procurement business process at steak restaurant using the Business Process Model Notation (BPMN) approach. This research was conducted using a qualitative descriptive method with the aim of observing the business unit to help analyze and make improvements to its business processes. Several series of processes were carried out, namely business identification and modeling with bizagi modeler and process reengineering to produce recommended new business process models that could be beneficial for business units, namely the recommended automation in the form of the use of mobile applications, remote, and database systems to support the effectiveness of the order to cash and procure to pay processes.
Upaya Meningkatan Hasil Belajar Bahasa Inggris Siswa dalam Membaca Text Descriptive melalui Model Pembelajaran Role Playing di Kelas X IPS 2 SMA Negeri 1 Candung

This study aims to find out and obtain information about How Efforts to Improve Students' English Learning Outcomes in Reading Descriptive Text Through Role Playing Learning Model in Class X IPS2 SMA Negeri 1 Candung. This type of research is classroom action research. The research subjects were students of class X IPS2 SMA Negeri 1 Candung with a total of 18 students. The time of this research is the odd semester of the 2021/2022 academic year which starts in September. This research procedure consists of two cycles with four stages of research, namely planning, implementing actions, observing and reflecting. Data collection techniques in this study were field notes, student worksheets, written tests, and documents. The collected data were analyzed using the percentage formula. The results of the study can be concluded that the learning process using the Role Playing Learning Model can improve students' English learning outcomes. Improved student learning outcomes can be seen from the pre-cycle, cycle I and cycle II
Analysis of public opinion sentiment against COVID-19 in Indonesia on twitter using the k-nearest neighbor algorithm and decision tree

COVID-19 has become an ongoing disease pandemic across the globe. The need for information makes social media such as twitter a place to exchange information. This tweet can be used to see public sentiment towards COVID-19 in Indonesia. Sentiment analysis classifies opinions from tweets that have been processed and classified into different sentiments, namely negative, neutral, or positive. The aim of this paper is to find the algorithm that has the best accuracy. The researcher proposes to compare the K-Nearest Neighbors (KNN) and decision tree algorithms to be used in the classification of sentiment data from tweets related to COVID-19 that took place in Indonesia. The results of the evaluation of performance metrics concluded that the decision tree algorithm has a higher level of accuracy than KNN. Decision tree produces accuracy = 0.765, error = 0.235, recall = 0.76, and precision = 0.767 which is better when compared to KNN which produces accuracy = 0.69, error = 0.31, recall = 0.66, and precision = 0.702.