Reviewerly/exHarmony huggingface dataset:
Development of The "Kisah Si Jojo" Android Application Based on Augmented Reality with Unity 3D

Oriented towards the PISA analysis report, the results of the literacy and numeracy abilities of Indonesian students are low and very concerning. Apart from that, there are other problems regarding the ability and interest in reading of Indonesian students which are still below average. The purpose of this research is to motivate and increase students' interest in reading. Therefore, we need a learning media that can encourage students' interest and reading skills with the help of technology, namely through the development and creation of an android literacy application "Kisah Si Jojo" based on Augmented Reality Unity 3D. This android literacy application "Kisah Si Jojo" talks about the importance of tolerance between friends. The research model used in this study is ADDIE with six stages including starting from analysis, design, development, implementation, and evaluation. Based on the research results it is known that the android literacy application "Kisah Si Jojo" is developing well and can be used according to its function.
Computing the Likelihood

Estimation using the likelihood function proceeds by solving for $$\theta $$ the equation $$S(\theta )=0$$ where $$S(\theta )$$ is the score.
Bayesian Methods

In classical likelihood, an important goal is to learn about a parameter $$\theta $$ regarded as a fixed unknown quantity. This is accomplished by collecting data y assumed to be a realisation from a probability model $$p(y|\theta )$$ indexed by $$\theta $$ . This probability model gives rise to the likelihood, a function of $$\theta $$ conditional on the realised y, from which the maximum likelihood estimate $$\hat {\theta }$$ is obtained. The ML estimator is a random variable (a function of y), whose distribution is characterised by conceptual replications of y.
Meta-feature-based Concept Evolution Detection on Feature Streams

Data in the real world is often not static but generated and processed in streams, such as real-time adjustment of device setting parameters and real-time GPS positioning data. Feature streams means the number of samples is fixed, and their features are generated and arrive individually over time. A significant challenge of learning from online streaming data is a phenomenon known as concept evolution, that the concept of the data may change over time. In the streaming feature scenario, we define meta-features as univariate statistics describing data distribution and use meta-features to capture the data distribution and statistical properties of concepts. Therefore, an efficient Meta-Feature-based Concept Evolution Detection framework on Feature Streams (MF-CED-FS) is proposed, which consists of a sliding window, meta-feature vector similarity discrimination, and a concept detection method based on a weighted bipartite graph. Extensive experiments on real-world high-dimensional datasets verify the effectiveness of MF-CED-FS.
Traffic Load Prediction of Power Communication Network Based on Graph Neural Network

With the rapid growth of power communication network traffic, accurate analysis and management of power communication network traffic has become a key factor to ensure the safe, stable and efficient power communication network. However, the traditional forecasting method cannot investigate the traffic of upstream and downstream nodes, which affects the prediction and analysis of the overall network. Therefore, this paper proposes a traffic prediction model based on Graph Neural Network (GNN). We analyzed the topology architecture of Power Communication Network, and designed and optimized the GNN forecast model to predict network traffic information. The network link traffic occupancy is divided into several levels for prediction. After experimental testing, the prediction results of this model have high accuracy and reliability, which is of great significance to the stability research of power communication network.
UAV Image Denoising based on Swin Deformer for Power System Condition Monitoring

In order to improve the quality of UAV transmission line inspection pictures and ensure the effectiveness of the defect recognition network, this paper proposes a model based on Swin Deformer to denoise the pictures. The data set of this method is the original data set collected by UAV, and the test set and training set are obtained by classifying the raw data. At the same time, Gaussian noise is added to generate tags to ensure the effect of supervised learning. Finally, the test set is input into the pre-training model, and the results obtained prove that our noise reduction network can greatly improve the image quality and further improve the effect of line maintenance.
Context-aware Inductive Graph Matrix Completion with Sentence BERT

Existing graph neural network (GNN) based recommendation models depend highly on initial node features and graph structures. Most prior studies do not support the use of additional information on user-item interactions and adopt a transductive method, which has limitations for actual web service. To overcome these problems, we propose Context-aware Inductive Graph Matrix Completion (CGMC), which utilizes context vectors that represent user-item interactions and user-item bipartite graph structures in an inductive manner. Our method constructs context vectors from the review, timestamp, and rating and uses them as edge features of the user-item bipartite graph. Relations between homogeneous nodes are also constructed based on common neighbors. Our model combines context vectors and relational information using Context-aware Graph Attention Networks and Edge Fusion Graph Convolutional Networks. We conducted extensive experiments using six real-world datasets, and the results show that the proposed model achieves superior performances over other competing models. Furthermore, we analyzed the inductive characteristics of CGMC through the cross-domain transferability. The source code is available in the repository at [https://github.com/venzino-han/CGMC_SBERT].
Filtering of Wind-wave Effects in Controlling the Depth of Stabilization of an Autonomous Unmanned Underwater Vehicle

Autonomous underwater vehicles (AUVs) and robots play an essential role in underwater technology. The tasks that these machines are required to do often involve submerging to a certain depth, where the object of work or research is located. The success of the mission and the accuracy of control at that depth largely relies upon the precision and immunity to interference of the pressure sensors, which are used to measure the depth of the underwater device. This work presents a method for addressing issues caused by external disturbances, internal interference and inaccuracies in the instrumentation used for measurement. The research was conducted to see if the Kalman filter could be used to make systems more resistant to noise. To do this, the structure and algorithm of Kalman filtering of wind wave impact as a random stationary process was developed. The theories of automatic control, the theory of filtering of probabilistic signals, and the methods of computer modeling were used in the research
A densely connected LDCT image denoising network based on dual-edge extraction and multi-scale attention under compound loss

Low dose computed tomography (LDCT) uses lower radiation dose, but the reconstructed images contain higher noise that can have negative impact in disease diagnosis. Although deep learning with the edge extraction operators reserves edge information well, only applying the edge extraction operators to input LDCT images does not yield overall satisfactory results.To improve LDCT images quality, this study proposes and tests a dual edge extraction multi-scale attention mechanism convolution neural network (DEMACNN) based on a compound loss.The network uses edge extraction operators to extract edge information from both the input images and the feature maps in the network, improving the utilization of the edge operators and retaining the images edge information. The feature enhancement block is constructed by fusing the attention mechanism and multi-scale module, enhancing effective information, while suppressing useless information. The residual learning method is used to learn the network, improving the performance of the network, and solving the problem of gradient disappearance. Except for the network structure, a compound loss function, which consists of the MSE loss, the proposed joint total variation loss, and the edge loss, is proposed to enhance the denoising ability of the network and reserve the edge of images.Compared with other advanced methods (REDCNN, CT-former and EDCNN), the proposed new network achieves the best PSNR and SSIM values in LDCT images of the abdomen, which are 33.3486 and 0.9104, respectively. In addition, the new network also performs well on head and chest image data.The experimental results demonstrate that the proposed new network structure and denoising algorithm not only effectively removes the noise in LDCT images, but also protects the edges and details of the images well.
Jointly sparse fast hashing with orthogonal learning for large-scale image retrieval

Hash learning is a hot topic since it can save storage space and perform fast retrieval. One of the most representative hashing methods is Supervised Discrete Hashing (SDH). However, there exist several problems in SDH. First, the potential of sparse feature extraction has been overlooked in the SDH-based methods. Second, SDH is incapable of preventing large information loss between the binary codes and the features. Third, the discrete cyclic coordinate descent (DCC) method is time-consuming. To address the aforementioned issues, we propose a novel regression model for hash learning named Jointly Sparse Fast Hashing (JSFH). By designing an orthogonal transformation, we introduce the jointly sparse regression into hash learning. The proposed method successfully integrates the feature extraction and hash learning into a unified framework, the original data and the label information can be comprehensively utilized such that the binary code generated by our method can be more discriminative with less information loss. Furthermore, we adopt an iterative algorithm for optimization, the optimization problem has closed-form solutions for each variable such that the designed iterative algorithm is highly efficient compared to the classic supervised hashing methods. The experimental results on four large-scale datasets demonstrate its superior performance.
Predicting software defects with swarm-intelligence-based machine learning algorithm for improved process quality

The quality and effectiveness of software systems may be significantly impacted by Software Defects (SD). Therefore, enhancing process quality is essential for controlling and minimizing the incidence of faults. Implementing reliable Software Development (SDe) processes and best practices is one way to do this information. SD, commonly referred to as software bugs or software mistakes, are defects or errors that occur in computer programs and cause them to act up or create unintended outcomes. These vulnerabilities may appear for several causes, including programming mistakes, poor design choices, or issues with the SDe cycle. The prediction of software problems based on Machine Learning (ML) using an Enhanced Artificial Neural Network (E-ANN) is implemented in this study to increase software quality and testing effectiveness. Particle swarm optimization and grey wolf optimization, these two algorithms named grey wolf swarm optimization algorithm (GWSA), are combined to recognize the corresponding compensation of the methods following the respective benefits and drawbacks. The hybrid algorithm-based model to the conventional hyperparameter optimization strategy and a single swarm intelligence algorithm's investigation of investigational consequences from six data sets shows that the hybrid algorithm-based model has high and enhanced indicators. Processed by the autoencoder, the model's performance has also improved.
Creating a New Dataset for the Classification of Cyber Bullying

Regardless of young or old, people have quickly stepped into the world of internet with today's communication technologies such as phones, tablets, computers and smart devices. As the place of the Internet in people's lives increases, social media platforms are diversifying and users want to take part in these platforms. With the increase in the number of social media users, some negativities are encountered. The most important problem encountered in social media platforms is cyber bullying. Although cyber bullying seems to be a daily dialogue between social media users or between groups, the situation of encountering is increasing day by day with the diversity of shared information, content and agenda social media environments. With the development of technology, it is necessary to develop a platform that detects bullying with artificial intelligence technologies. One of the biggest difficulties in text classification problems that we encounter during the development of these platforms is the need to train the artificial intelligence algorithm to be used with labeled data. In this study, 21 different people, including journalists, athletes, scientists, doctors, politicians, comedians, social media phenomena, and artists who actively use social media, were selected in order to create the necessary dataset for training the models to be developed to detect cyber bullying situations. The public messages (mentions) of these 21 people sent via Twitter were compiled. After filtering the repetitive and meaningless messages sent by bot accounts out of 10500 tweets compiled, the number of messages in the dataset decreased to 7706. The labeling process, which is necessary for the dataset to be used for training and testing purposes in classification processes, was carried out by three independent people who were given preliminary information about cyberbullying (1=Includes Cyber bullying, 0=Does not include Cyber bullying). The majority of the tags, which were read and assigned by 3 different people, were accepted as the final class of the relevant message. Afterwards, the dataset was preprocessed in accordance with the principles of natural language processing and made suitable for classification algorithms. The findings obtained after the classification processes performed with the basic classification algorithms are shared. When the findings are examined, it is understood that the data set created has the competence to be used in the detection and prevention of cyber bullying. In this context, it is predicted that training specially developed and optimized artificial intelligence algorithms with the relevant dataset for the detection of cyberbullying will greatly increase the success rate.
Optical filter with on-chip photonic reservoir computing neural network

To meet the flexible and integrated requirements of future optical networks, we propose the optical filter with variable waveform shape using on-chip photonic reservoir computing neural network. The on-chip photonic reservoir computing neural network implements a hardware network directly to mitigate the latency and power-consumption. Combined with linear regression algorithm, infinite (IIR) and finite (FIR) impulse response optical filters are realized. Compared with the traditional filters, this filter obtains more flexible and higher integration. In future, this filter can be widely used in integrated microwave photonics and wavelength division multiplexing systems.
Model Pruning and Deployment Optimization for Ship Detection

For unmanned aerial vehicles (UAVs) or satellites, realizing real-time detection of intelligent maritime targets based on edge devices is an urgent need for high-efficiency information acquisition. However, current artificial intelligence detection models have large number of model parameters and computational requirements, making it difficult to achieve real-time processing on edge devices. To address this problem, this paper proposes a human-in-the-loop(HIL) model pruning method, which improves the sparse training effect of the model by introducing Gaussian penalty terms, and combines automatic and manual method to set pruning threshold to meet the goal of model lightweighting. Based on the pruned and quantized model, the size of the model is reduced by 87.94% compared to the original model (YOLOv5), with only a 1.48% decrease in accuracy. After deployment optimization on GPUs, the real-time performance is improved by 2.28 times.
Meeting the Stars of the R-Universe: Researching Our Brain with the Magic of the R-Universe

A new post of our interview series “Meeting the stars of the R-universe”. We aim to introduce the teams and people behind the development of software and packages many of us use and which are available through the R-Universe. We want to highlight and explore different teams and projects around the world, the work they do, their processes and users.
Android-Based E-Learning Application Design in Schools

The concept of E-Learning itself is a guide between learning processes that are packaged in the form of information technology. During the pandemic, schools in Indonesia began to make changes to offline learning to online by utilizing various applications such as the Google Classroom and WhatsApp applications. Based on these conditions, the researcher tried to develop a system for use in schools in the form of Android-based e-learning using the waterfall method. Based on the results of system design and testing it appears that this system can be used at research locations because the system can run well based on the results of black box testing, while the expert judgment of 10 people said it was very good, while the user response was 150 people with a very good response of 70 percent (105 people), good 30 percent (45 people). So that by using an Android-based E-Learning application, of course learning done in schools is better than those that only use conventional models or only use Whatsapp or Google Classroom. In addition, with E-Learning the learning process that is usually carried out in schools can be done without having to go to school again, students and teachers can carry out the learning process from their respective homes using smartphones and the internet.
Meeting the Stars of the R-universe: ThinkR's Approach to Contributing to a Growing and Friendly R Community

This is the second post of our interview series “Meeting the stars of the R-universe”. We aim to introduce the working groups and people behind the development of software and packages many of us use and which are available through the R-universe. We want to highlight and explore different teams and projects around the world, the work they do, their processes and users.
Meeting the stars of the R-universe: R Community, Exchange and Learn

This is the first post of our interview series &lt;strong&gt;“Meeting the stars of the R-universe”&lt;/strong&gt;. We aim to introduce the working groups and people behind the development of software and packages many of us use and which are available through the &lt;em&gt;R-Universe&lt;/em&gt;. We want to highlight and explore different teams and projects around the world, the work they do, their processes and users.
Maintain or Co-Maintain an rOpenSci Package!

The rOpenSci suite of packages is mainly composed of packages contributed by the community through peer-review, but also includes some packages maintained by staff.Over time, the commitments and availability of the original developers of a package can change.
Experimental Evaluation of Proximal Policy Optimization and Advantage Actor-Critic RL Algorithms using MiniGrid Environment

This paper studies the extended experiments of agent-environment interactions of recent reinforcement learning (RL) algorithms, i.e., Proximal Policy Optimization (PPO) and Advantage Actor Critic (A2C).In addition to the evaluation of accumulated rewards in the MiniGrid environment, this work also assesses the learned value tables and others.The experiment platform is expanded with arbitrary shape of mazes, customized training scheme, and the average computation from different initial seeds.The key RL formulas and hyperparameters are explicitly discussed with MiniGrid settings.From the obtained results, the conclusion provides the strengths and weaknesses of neural network implementation for RL methods.This is an undergraduate project from summer 2020.
A teacher’s perception on the use of online assessment for reading comprehension

The purpose of this research is to investigate the data related to teacher’s perception on the use of online assessment for assessing reading comprehension in higher education. In this study, the researcher used Qualitative Approach, which is thematic analysis is the foundation method of qualitative research. To identify the teacher perception on the use online assessment to assess reading comprehension, researcher will used interview to collect the data. The participants in this study were intensive reading lecturers of English language education program in Yogyakarta. The results of this study indicate that reading comprehension assessment using the online platform as a tool can assist teachers in assessing student work. In addition, by not giving restrictions to students in working on the task of making answers to their assignments beyond the teacher's expectations. &#x0D; Keywords: Online Assessmen;, Reading Comprehension; Online Platform; Restriction&#x0D;
Scheduling Mastodon Posts in R with rtoot and GitHub Actions

A lot of what I do as rOpenSci’s community assistant is related to social media.I check for posts about rOpenSci packages, invite people to share usecases,advertise upcoming events, as well as promotingnew packages which completed software peer review.
On the existence of non-catastrophic p-encoders of (2,1) convolutional codes over ℤp2

In this work, we analyze the problem of catastrophicity of encoders of convolutional codes over the Laurent series with coefficients in [Formula: see text], [Formula: see text]. Kuijper and Pinto proved in [M. Kuijper and R. Pinto, On minimality of convolutional ring encoders, IEEE Trans. Autom. Control 55(11) (2009) 4890–4897] that, contrary to what happens for codes over [Formula: see text], where [Formula: see text] is a field, when dealing with [Formula: see text] there are convolutional codes that do not admit non-catastrophic encoders. Nevertheless it was conjectured that any catastrophic convolutional code admits another type of non-catastrophic encoder called [Formula: see text]-encoder. In this paper we solve this conjecture for a class of [Formula: see text] convolutional codes over [Formula: see text] and show that, in fact, these codes always admit a non-catastrophic [Formula: see text]-encoder. We also describe a constructive procedure that allows us to obtain a non-catastrophic [Formula: see text]-encoder.