Reviewerly/exHarmony huggingface dataset:
PENERAPAN METODE ROLE PLAYING UNTUK MENINGKATKAN HASIL BELAJAR BAHASA INGGRIS MATERI ANNOUNCEMENT PADA SISWA KELAS X MIPA 1 SMA NEGERI 1 GIRIMARTO SEMESTER 2 TAHUN PELAJARAN 2021/2022

The research is done to know the increasing of studying result of students class X MIPA 1 SMA Negeri 1 Girimarto, Wonogiri, Central Java, By Implementation Role Playing Method of English lesson Material of Announcement. This Research is Class Action research that done at least 2 cycles. Subject of research are 36 students of class X MIPA 1 SMA N 1 Girimarto. Technic of submitting data of students studying result by studying result test. Technic of data analysis is kuantitatif data analysis, It is the average of test value of student studying. The result of this research increase studying result of students class X MIPA 1 SMA Negeri 1 Girimarto at Semester 2 2021/2022 by implementation of Role playing method that proved by increasing the average value of the students studying result and the completeness of students learning. The increasing of students results at pre cycle showed the student average value is 56,26 or 13,89 % students had completed. Cycle 1 of studying Result of students have not reached minimum completed criteria, 41,67 %, with the class average value 66,67 % that still under KKM value &gt;_ 70 and completeness prosentase KKM &gt;_ 70 %. At cycle 2 the result of students studying had reached KKM ( Minimum Completeness Criteria ) 86,11 % with the class average value 83,61 and already beyond KKM value. From this research data can be concluded that the implementation of Role Playing Method had success increased result studying of English the material of Announcement so worthy to applied at SMA Negeri 1 GirimartoWonogiri, Central Java.
Estimation of Road Surface Plane and Object Height Focusing on the Division Scale in Disparity Image Using Fisheye Stereo Camera

In this paper, we propose a novel algorithm for estimating road surface shapes and object heights using a fisheye stereo camera. Environmental recognition is an important task for advanced driver-assistance systems. However, previous studies have only achieved narrow measurement ranges owing to sensor restrictions. Moreover, the previous approaches cannot be used in environments where the slope changes because they assume inflexible constraints on the road surfaces. We use a fisheye stereo camera capable of measuring wide and dense 3D information and design a novel algorithm by focusing on the degree of division in a disparity image to overcome these defects. Experiments show that our method can detect an object in various environments, including those with inclined road surfaces.
Advanced quantum mechanics

Subject Astronomy and Astrophysics Computational Physics Collection: Oxford Scholarship Online
Parsimonious Bayesian Model-Based Clustering with Dissimilarities

Clustering techniques are used to group observations and discover interesting patterns within data. Model-based clustering is one such method that is often an attractive choice due to the specification of a generative model for the given data and the ability to calculate model-selection criteria, which is in turn used to select the number of clusters. However, when only distances between observations are available, model-based clustering can no longer be used, and heuristic algorithms without the aforementioned advantages are usually used instead. As a solution, Oh and Raftery (2007) suggest a Bayesian model-based clustering method (named BMCD) that only requires a dissimilarity matrix as input, while also accounting for the measurement error that may be present within the observed data. In this paper, we extend the BMCD framework by proposing several additional models, alternative model selection criteria, and strategies for reducing computing time of the algorithm. These extensions ensure that the algorithm is effective even in high-dimensional spaces and provides a wide range of choices to the practitioner that can be used with a variety of data. Additionally, a publicly available software implementation of the algorithm is provided as a package in the R programming language.
SODA: Robust Training of Test-Time Data Adaptors

Adapting models deployed to test distributions can mitigate the performance degradation caused by distribution shifts. However, privacy concerns may render model parameters inaccessible. One promising approach involves utilizing zeroth-order optimization (ZOO) to train a data adaptor to adapt the test data to fit the deployed models. Nevertheless, the data adaptor trained with ZOO typically brings restricted improvements due to the potential corruption of data features caused by the data adaptor. To address this issue, we revisit ZOO in the context of test-time data adaptation. We find that the issue directly stems from the unreliable estimation of the gradients used to optimize the data adaptor, which is inherently due to the unreliable nature of the pseudo-labels assigned to the test data. Based on this observation, we propose pseudo-label-robust data adaptation (SODA) to improve the performance of data adaptation. Specifically, SODA leverages high-confidence predicted labels as reliable labels to optimize the data adaptor with ZOO for label prediction. For data with low-confidence predictions, SODA encourages the adaptor to preserve data information to mitigate data corruption. Empirical results indicate that SODA can significantly enhance the performance of deployed models in the presence of distribution shifts without requiring access to model parameters.
Optimising Distributions with Natural Gradient Surrogates

Natural gradient methods have been used to optimise the parameters of probability distributions in a variety of settings, often resulting in fast-converging procedures. Unfortunately, for many distributions of interest, computing the natural gradient has a number of challenges. In this work we propose a novel technique for tackling such issues, which involves reframing the optimisation as one with respect to the parameters of a surrogate distribution, for which computing the natural gradient is easy. We give several examples of existing methods that can be interpreted as applying this technique, and propose a new method for applying it to a wide variety of problems. Our method expands the set of distributions that can be efficiently targeted with natural gradients. Furthermore, it is fast, easy to understand, simple to implement using standard autodiff software, and does not require lengthy model-specific derivations. We demonstrate our method on maximum likelihood estimation and variational inference tasks.
Lightweight SAR Ship Detection

Non-cooperative vessels pose a challenge to traditional maritime surveillance systems. To overcome this challenge, alternative surveillance methods such as space-based monitoring sensors have been employed. However, the time-consuming process of satellite downlink hampers near-real-time applications. To address these issues, the use of onboard Artificial Intelligence for direct data processing has emerged as a key technology. This study explores the implementation of a lightweight Synthetic Aperture Radar ship detection model inspired by YOLOv8. The model achieves promising results on an annotated data-set, demonstrating the effectiveness of the approach for detecting both small and large ships. The study investigates the impact of atrous and depth-wise convolutions on the model’s performance and explores model quantization for further size reduction. Our final model has 0.3 million parameters and reached an average procession of 95.4 %. The results highlight the potential of lightweight models for onboard ship detection, offering comparable accuracy to larger models.
Domain Distribution Alignment for Boosting Multi-Modal Remote Sensing Image Matching

Multi-modal images can obtain complementary and rich information images, which are more widely used in various applications. However, due to the different imaging mechanisms of different sensors, there are significant domain distribution differences between multi-modal images. In multi-modal image matching, existing deep learning methods should deal with the image content difference caused by rotation transformation and the domain distribution difference caused by different sensors, which are very difficult for the deep network. To address this issue, we propose to combine an instance comparison and a batch comparison to deal with image content differences and domain distribution differences, respectively. We design a new domain distribution alignment method to explicitly constrain the sample domain distribution of the multi-modal images are consistent through the domain distribution alignment loss. Extensive multi-modal remote sensing image patch matching experiments have shown the effectiveness of the proposed method. Furthermore, the proposed multi-modal domain distribution alignment method has more obvious advantages when there are significant content differences and distribution differences.
SAR-UT: A Synthetic-to-Measured SAR Image Translation Network Based on Transformer

In recent years, some studies used computer to generate synthetic aperture radar (SAR) images. Although the synthetic SAR image looks realistic, there is a domain gap and distribution differences between synthetic SAR images and measured SAR images which makes it difficult to use synthetic SAR images directly. The image translation network can reduce the gap between different domain images, but existing optical translation networks are hard to correctly learn and translate the background information of SAR images. In this paper, a synthetic-to-measured SAR target image translation network, called SAR-UT network, based on U-NET network and Transformer is proposed to bridge the domain gap between synthetic and measured SAR target images. The proposed method can extract and fuse the texture features of each layer more effectively, so as to translate SAR images with more realistic details and higher image quality. Experiments on Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset show that the proposed translation network can well learn the potential features of SAR image and translate the synthetic SAR image into the image closer to the measured SAR image. Experimental result shows that the performance of SAR-UT is better than the state-of-the-art methods.
Optimizing Kernel-Target Alignment for Cloud Detection in Multispectral Satellite Images

The optimization of Kernel-Target Alignment (TA) has been recently proposed as a way to reduce the number of hardware resources in quantum classifiers. It allows to exchange highly expressive and costly circuits to moderate size, task oriented ones. In this work we propose a simple toy model to study the optimization landscape of the Kernel-Target Alignment. We find that for underparameterized circuits the optimization landscape possess either many local extrema or becomes flat with narrow global extremum. We find the dependence of the width of the global extremum peak on the amount of data introduced to the model. The experimental study was performed using multispectral satellite data, and we targeted the cloud detection task, being one of the most fundamental and important image analysis tasks in remote sensing.
MMD-based Variable Importance for Distributional Random Forest

Distributional Random Forest (DRF) is a flexible forest-based method to estimate the full conditional distribution of a multivariate output of interest given input variables. In this article, we introduce a variable importance algorithm for DRFs, based on the well-established drop and relearn principle and MMD distance. While traditional importance measures only detect variables with an influence on the output mean, our algorithm detects variables impacting the output distribution more generally. We show that the introduced importance measure is consistent, exhibits high empirical performance on both real and simulated data, and outperforms competitors. In particular, our algorithm is highly efficient to select variables through recursive feature elimination, and can therefore provide small sets of variables to build accurate estimates of conditional output distributions.
Bitcoin monthly return forecast: A comparison of ARIMA and multi layer Perceptron Artificial Neural Network

In this paper, we compare the predictive power of Auto Regressive Integrated Moving Averages (ARIMA) and Multi-Layer Perceptron Artificial Neural Networks (MLP ANN) model to short-term forecast the monthly returns of Bitcoin cryptocurrency. We evaluate the performance of two models using time series with monthly data from January 2018 to December 2021. The key parameters for the final assessment of prognostic models are the values of Root Mean Square Error-RMSE and Forecast Error-FE. The results of the short-term BTC return forecast showed better properties of composite compared to univariate time series forecasting models, i.e., higher prognostic power of the MLP ANN model compared to the selected ARIMA (1,1,3) model (lower RMSE and FE). The results point to further comparative research of prognostic models and the possibility of forming more complex and hybrid structures of neural network models in order to predict economic phenomena as accurately as possible.
Text-to-Image Generation Tools: A Survey and NSFW Content Analysis

This study investigates the main tools for generating images through Artificial Intelligence (AI) known as “Text-to-Image”. Free tools available on the Web were collected and evaluated for their ability to generate inappropriate content (i.e., NSFW). The work emphasizes the importance of a solid ethical foundation in implementing these tools, considering the risks of disseminating inappropriate information. The results provide a compilation of the identified tools, along with an analysis of the content generated by them.
Text Representation through Multimodal Variational Autoencoder for One-Class Learning

Multi-class learning (MCL) methods perform Automatic Text Classification (ATC), which requires labeling for all classes. MCL fails when there is no well-defined class information and requires a great eff ort in labeling. One-Class Learning (OCL) can mitigate these limitations since the training only has instances from one class, reducing the labeling eff ort and making the ATC more appropriate for open-domain applications. However, OCL is more challenging due to the lack of counterexamples. Even so, most studies use unimodal representations, even though different domains contain other information (modalities). Thus, this study proposes the Multimodal Variational Autoencoder (MVAE) for OCL. MVAE is a multimodal method that learns a new representation from more than one modality, capturing the characteristics of the interest class in an adequate way. MVAE explores semantic, density, linguistic, and spatial information modalities. The main contribution is a new multimodal method for representation learning on OCL scenarios considering few instances to train with state-of-the-art results in three domains.
Development of Augmented Reality-Based Mobile App Learning Media for Computer Network Hardware Material

The objective of this research is to create a mobile app-based augmented reality learning media for computer network hardware. The specific aims are to describe the application model design, application functionality, feasibility, and effectiveness of using augmented reality-based mobile app learning media on computer network hardware. The research methodology employed in this study is Research and Development (R&amp;D). The development stages include research, data collection, planning, draft product development, product validation, revision based on product validation results, field testing, revision based on field testing results, final product refinement, and dissemination. Furthermore, to assess the improvement in learning outcomes, the Independent Sample T Test statistical method is used. The results obtained in this research show a significant difference in the experimental class compared to the control class, where the average score in the experimental class is higher. Therefore, it can be concluded that learning computer network hardware material using augmented reality has successfully improved student learning outcomes.&#x0D; Keywords: Learning Media, Augmented Reality, Mobile Apps, Computer Network Hardware.
Penguatan Literasi Mangrove di OKL SL bagi Keberlanjutan Ekologis Mangrove di Desa Debunaruk Kabupaten Malaka

The mangrove ecosystem is categorized into the coastal ecosystem to be recognized and preserved so that it can be functional for the community and the environment. The community of OKL Street Library is a community group that exists in the area of the Beibleka mangrove ecosystem, Debunaruk Village – Malaka. Unfortunately, this community group is not yet familiar with both this type of mangrove species and the size of the ecosystem. The community service proposed is entitled a mangrove literacy strengthening at OKL Street Library for mangrove ecological sustainability in Debunaruk Village in the District of Malaka. This activity is targeted at the active users of OKL Street Library. The objective of this activity is to increase the knowledge in understanding various species types of mangroves ecosystem. Direct observation using information technology applications, namely Google Earth and the internet. The point is to enhance positive perception and real action in preserving the mangrove ecosystem that provides ecological and economic benefits to the community of Debunaruk-Tahak, the Dsitrict of Malaka. The area size calculation employs Google Earth and the introduction of mangrove species using the internet, through which learners are introduced to technology-based applications. This activity is conducted hand in hand between the OKL Street Library and the community service staff, both in setting the problem focus and activity alternatives. OKL SL got improvement knowledge and skills about the type and usages of mangroves and the implementation of Google Earth. We helped them by sharing some books too for the students.
Development and Effectiveness of Informal Environmental Education Program based on Design Thinking f

Objectives The purposes of this study were to develop and apply an informal environmental education program based on the design thinking process to improve creative problem-solving skills of out-of-school youth.&#x0D; Methods In order to achieve the research objectives, an environmental education program using design thinking process was developed. The developed informal environmental education program was applied to 13 out-of-school teenagers belonging to the G-gun Youth Counseling Support Center. Afterwards, the skills for creative problem solving were investigated by pre- and post-tests accompanying open surveys, and in-depth interviews to judge the achievements of the objectives.&#x0D; Results As represented on previous research and literature analyses, three informal environmental education ex-perts designed a draft of education program. For the completion, the program was evaluated as well as validated by other five experts. Afterwards, the program was applied to the 12th educational program of the Busan National Science Museum &lt;Learning Science 2.0&gt; from May to June 2021, and its effectiveness was validated.&#x0D; Conclusions In this study, an informal environmental education program using the design thinking process was developed and applied to out-of-school youth. The effectiveness of the program was validated in that creative problem-solving skills and a core competency necessary for out-of-school youth were enhanced.
The Playful Nature of Elementary Social Studies Activities

Objectives This study aimed to present the suggestion about the direction of playfulness to be pursued by Elementary social studies classroom activities by investigating the playful nature inherent in it.&#x0D; Methods For this purpose, this study examined the concept and attributes of play through Play Theory, and ob-served and interpreted learners' play patterns in the activities of three elementary school teachers' social studies classes. And indentified the playfulness of social studies activities through classroom observations, interviews with teachers and learners, and analysis of post-classroom reflections.&#x0D; Results From a heortē perspective, play is interpreted as an attribute of activity or intensity of play, and the activ-ities of social studies would be seen as having a playful character or as play itself. The factors that make learners playful in an activity are related to the attributes of play, such as ‘spontaneity’, ‘conversation’, ‘authenticity’, and ‘playfulness, and seriousness’, and when an activity has these playful characteristics, learners feel and engage in the activity itself as play.&#x0D; Conclusions In social studies activies, play should be pursued as purpose, not a means. Social studies activities as play can restore learners' subjectivity and foster practical attitudes by placing learning in the context of real life. The social studies classroom plays a role as a playground, making learning a joyful play of participation and communication.
Query Answering over the Polymorphic Web of Data

Knowledge graphs are a versatile means to gather Semantic Web data and are typically stored and queried with the W3C standards, RDF and SPARQL. Despite the significant progress made in query processing, predicting plausible answers in presence of missing facts remains a challenge. This aspect has been tackled by proposing methods to predict links and solve queries in some reduced fragments of SPARQL. Thus far, I have explored two parallel directions for this thesis. First, I study how to use knowledge graph embedding methods to predict missing facts. In particular, I explore how to combine different knowledge graph embedding methods to improve the quality of the predictions. Second, I study how to connect techniques to query knowledge graphs from these two research areas, namely database query processing, and graph learning. The former techniques provide actual answers of a query, while the latter provide plausible ones. My hypothesis is that I can define a common query interface, based on SPARQL, to provide answers from these polymorphic data sources. To this end, I propose an extension for SPARQL, called polymorphic SPARQL.