Reviewerly/exHarmony huggingface dataset:
Education on the Utilization of Solar Energy to Realize Mosques with Energy Independence: Application of Renewable Energy in Minhajul Huda Mosque

Minhajul Huda Mosque is a mosque as one of the pilot centers for da'wah in the region. Many mosque activities nowadays commonly require the support of electrical energy infrastructure, especially in lighting systems, sound systems, and other electrical devices. On the other hand, the lack of insight into alternative renewable energy among mosque administrators causes dependence on electricity sources from PLN. In general, the impact is the disruption of the operation of electrical equipment during a PLN outage. All problems related to PLN's electricity dependence should be overcome through the application of alternative and renewable energy. Due to the ease of application, solar power is worth choosing because it is practical and suitable for the conditions of the partner's location. This service activity was carried out with stages of socialization, education, and application of solar power systems (on lighting facilities and mosque loudspeakers) with a power of 672 Wh, followed by monitoring and evaluation. Hopefully, this activity may become a model of an energy-independent mosque in the Muhammadiyah branch area that can educate worshippers about the application of solar energy.
Media Relations for School Promotions in Trensains Muhammadiyah Sragen Senior High School

Abstract. Muhammadiyah Trensains Senior High School Sragen is a leading Muhammadiyah school in Sragen, Central Java Province, Indonesia. The school has excellent achievements in science. The problem encountered is the need for more publication in the mass media. There is a problem in media relations at Muhammadiyah Trensains Senior High School Sragen. For the solution to the problem, this community service was directed at increasing the competency of teachers in the field of media relations. This community service was carried out with workshops involving Muhammadiyah Trensains Senior High School Sragen teachers. Workshop materials included mastery of media relations for school publications and writing news releases. Next, the teacher sent news releases to the mass media. Writing and delivery of news releases have been published in the mass media. The result of this community service is the increased news coverage of Muhammadiyah Trensains Senior High School Sragen in various media.
Analisis Sentimen SEA Games 2023 di Twitter Metode dengan Machine Learning

Sentiment analysis is a method used to analyze and identify the polarity (positive, negative, or neutral) of text or data related to a user's thoughts, opinions, or emotions. This method is widely used in various fields, including sentiment analysis on social media data. One very popular social media platform is Twitter. One of the biggest sporting events in Asia is the SEA Games, which are held every two years. SEA Games, many Indonesians expressed their opinions, support and emotions regarding the 2023 SEA Games on Twitter. Using supervised learning methods can provide valuable insight into how Indonesian society responds and reacts to this important sporting event. The results of this analysis can help related parties, including organizers and sponsors of the 2023 SEA Games, in understanding public sentiment, evaluating performance, and making better decisions in order to organize a successful sporting event. The results of the trials carried out by the SVM method had the best performance with an F-1 score of 61.53%.
Development of E-Book Learning Media in Introducing Science for Early Childhood

The issue of science knowledge comprehension among early childhood children is a complex matter that requires serious attention in many countries, including Indonesia. Several studies indicate that the understanding of science knowledge among early childhood children in Indonesia is still relatively low. One of the efforts to provide a solution to these issues is the development of electronic book (e-book) instructional media. This development research employs the ADDIE model. The ADDIE model's developmental stages include analysis, design, development, implementation, and evaluation. The target audience is Kindergarten Group B children aged 5-6 years from TK Lab.School IKIP PGRI Jember. The instruments used for this research are expert media review, expert design review, expert content review, responses from small and large focus groups. Based on the response results, the understanding of participants reached 90,6%, the appeal was rated at 90,4%, with an overall average of 90,5%. The e-book's development results, based on the participants' responses, indicate that the usage of the e-book is highly suitable and effective in enhancing the understanding of science among children aged 5-6 years.
Learning English Vocabulary with Crossword Media for Early Childhood

This study aims to determine whether Crossword media can improve the mastery of early childhood at an early childhood education institution toward English Vocabulary; and to find out the implementation of Crossword as a media in improving mastery of English Vocabulary at an early childhood education institution. This research is a Classroom Action Research which was conducted from March 2023 to April 2023 by using Crossword as the media. Crossword is able to improve the ability of early childhood in mastering English vocabulary optimally. This can be seen from the pre-test result of 85,77, post-test 1 in cycle one result of 83,85, and post-test 2 result in cycle two of 86,15. Therefore, it can be concluded that Crossword is an effective learning media to be used in improving early childhood English vocabulary mastery.
MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

One of the most well-known and widely implemented data mining methods is Apriori algorithm which is responsible for mining frequent item sets. The effectiveness of the Apriori algorithm has been improved by a number of algorithms that have been introduced on both parallel and distributed platforms in recent years. They are distinct from one another on account of the method of load balancing, memory system, method of data degradation, and data layout that was utilised in their implementation. The majority of the issues that arise with distributed frameworks are associated with the operating costs of handling distributed systems and the absence of high-level parallel programming languages. In addition, when using grid computing, there is constantly a possibility that a node will fail, which will result in the task being re-executed multiple times. The MapReduce approach that was developed by Google can be used to solve these kinds of issues. MapReduce is a programming model that is applied to large-scale distributed processing of data on large clusters of commodity computers. It is effective, scalable, and easy to use. MapReduce is also utilised in cloud computing. This research paper presents an enhanced version of the Apriori algorithm, which is referred to as Improved Parallel and Distributed Apriori (IPDA). It is based on the scalable environment referred as Hadoop MapReduce, which was used to analyse Big Data. Through the generation of split-frequent data regionally and the early elimination of unusual data, the proposed work has its primary objective to reduce the enormous demands placed on available resources as well as the reduction of the overhead communication that occurs whenever frequent data are retrieved. The paper presents the results of tests, which demonstrate that the IPDA performs better than traditional apriori and parallel and distributed apriori in terms of the amount of time required, the number of rules created, and the various minimum support values.
Exploration Based on Global Iterative Search Algorithm Model

This study explores the role of single-objective optimization algorithms, grid methods, and global iterative search algorithms in solving various problems. These algorithms play a key role in the application of different fields and can find the optimal solution to meet the needs of the problem. Single-objective optimization algorithm is used to solve the optimization problem of single objective function, which is suitable for machine learning, scientific research and other fields. The grid method is a powerful optimization method for discrete problems, which is widely used in image processing and parameter adjustment. The global iterative search algorithm is designed to find the global optimal solution, which is suitable for multi-modal and high-dimensional problems, power system planning and machine learning model parameter optimization. The application of these algorithms provides an effective tool for all walks of life to optimize resource utilization, improve efficiency, improve the decision-making process, and have a profound impact on future technological development and problem solving. Their ability to handle complex and high-dimensional problems makes them indispensable tools for better results in areas such as scientific research, business decision-making and engineering design.
End-to-end Mobile Robot Autonomous Navigation via Deep Recurrent Q-network

In this paper, we propose a mobile robot autonomous navigation method based on a deep recurrent Q-network, which realizes the end-to-end mapping of the mobile robot’s perception of its behavior. In this study, we first extract features separately from the mobile robot’s own state information and the sensor’s detection information of the surrounding environment to form the current real-time observation. Then, the concatenated perception information is input into the recurrent neural network, and the perception representation of the current moment is enhanced by combining the historical state perception information to overcome the problem of bias in state evaluation under incomplete observable conditions. Finally, we design a step-wise reward function for the mobile robot’s autonomous navigation task based on the reward reshaping mechanism to guide the optimization direction of the strategy and further improve the convergence speed of the autonomous navigation model, achieving more efficient training. We constructed a 3D experimental environment to verify the effectiveness of the algorithm. The experimental results demonstrate the proposed method can effectively support the mobile robot’s autonomous navigation in complex scenarios, and compared with the contrast method, the advantages are obvious.
Robotic Information Gathering via Deep Generative Inpainting

In today's era of automation, mobile robots are being used for collecting meaningful information about an ambient phenomenon such as temperature or moisture distribution in an agricultural field. Most of the studies in the literature assume that the underlying information field is Gaussian, and therefore, Gaussian Process (GP)-based models are extremely popular. Furthermore, we have found that due to the inherent computational complexity of such naive GP-based techniques, most studies in the literature do not scale well beyond small-size environments, i.e., where the number of informative points <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n &lt; 1000$</tex> . These render such a predictive model more or less useless in many practical applications. In this paper, we posit that a different technique, Generative Adversarial Network-based inpainting, for robotic information gathering can be useful. The state-of-art inpainting techniques 1) do not assume that the underlying data is Gaussian, and 2) easily scale to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n\gg 1000$</tex> . Thus, they eliminate the two bottlenecks posed by the GP-based solutions. We have tested our hypothesis on a synthetic and a real-world crop dataset. Results show that while the inpainting technique easily scales to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1024\times 1024$</tex> , GP-based predictions cannot. On the other hand, their solution qualities are shown to be comparable.
SubmergeStyleGAN: Synthetic Underwater Data Generation with Style Transfer for Domain Adaptation

Underwater computer vision applications are challenged by limited access to annotated underwater datasets. Additionally, convolutional neural networks (CNNs) trained on in-air datasets do not perform well underwater due to the high domain variance caused by the degradation impact of the water column. This paper proposes an air-to-water dataset generator to create visually plausible underwater scenes out of existing in-air datasets. SubmergeStyleGAN, a generative adversarial network (GAN) designed to model attenuation, backscattering, and absorption, utilizes depth maps to apply range-dependent attenuation style transfer. In this work, the generated attenuated images and their corresponding original pairs are used to train an underwater image enhancement CNN. Real underwater datasets were used to validate the proposed approach by assessing various image quality metrics, including UCIQE, UIQM and CCF, as well as disparity estimation accuracy before and after enhancement. SubmergeStyleGAN exhibits a faster and more robust training procedure compared to existing methods in the literature.
CoRL: A Cost-Responsive Learning Optimizer for Neural Networks

Selection of the optimal learning rate for training neural networks has often been a matter of concern for the machine learning community. The existing learning rates are dependent on multiple scaling factors. This paper proposes Cost-Responsive Learning (CoRL) which does not require manual hyper-parameter tuning. It maintains a linear relationship with the prediction error of the neural network. This is expected to offer the lowest learning rate at the global minima, and higher learning rates elsewhere. Hence, a number proportional to the prediction error is used as a learning rate, subject to the constraint that the number is within an acceptable range (here [0,1]). The derivation of an optimal learning rate from a given cost function is illustrated with the popular binary/categorical cross-entropy cost function(s). Experiments performed under multiple settings demonstrate that, with the CoRL optimizer needs no parameter tuning to obtain state-of-the-art results with significantly lower training time for equivalent performance.
An Approach Toward Multiobjective Optimization Problems in Hysteresis Neural Networks

This paper studies a multiobjective optimization problem in continuous-time recurrent neural networks. For simplicity, we use a simple recurrent neural network: a hysteresis associative memory characterized by a binary hysteresis activation function and ternary cross-connection parameters. The optimization problem is based on two objectives. The first objective evaluates the memory accuracy and the second objective evaluates connection sparsity. In order to analyze the optimization problem, we present a simple evolutionary algorithm with growing connection structure. Applying the algorithm to typical examples, we have obtained a Pareto front that guarantees existence of a trade-off between the two objectives. The trade-off provides basic information for the system dynamics and becomes a criterion to optimize the system parameters.
Tissue Classification Using Data from Vibroacoustic Signals Produced from Needle-Tissue Interaction

In many clinical procedures, precise needle localization is crucial for avoiding organ damage and ensuring accurate target placement. Most imaging systems come with some limitations and produce artifacts in combination with the devices that hinder accurate needle localization, particularly with respect to visualizing the tip of the needle. We propose to use vibroacoustic signals generated during needle movement through human tissue, in combination with advanced data processing and deep learning techniques for precise localization. To validate this concept, we designed a specialized phantom using animal tissues submerged in gelatine to collect the vibroacoustic data. This paper summarizes initial experiments, involving data acquisition and preprocessing, conversion into Mel and continuous wavelet transform spectrograms, as well as their use as inputs for two distinct deep learning models: NeedleNet and ResNet-34. The aim of this research was to prove that vibroacoustic signals can be used to identify tissue types during needle insertion and serve as a baseline for further investigations.Clinical relevance: These are the first phantom studies that show the usefullness of vibroacoustic signals that are generated during device-tissue interaction as a support tool for localization information.
Coding of Multi-Focused Plenoptic Image using Disparity Shift and Sharpness-Aware Constraints

Multi-focused plenoptic images possess many special characteristics related to the micro-images (MIs) array, which are expected to be useful in further increasing its compression performance. Those special characteristics come from the much overlap and sharpness variance among its micro-images, and proper handling of such properties can lead to better patch-based prediction. In this paper, for multi-focused plenoptic image data, we design a new prediction model taking into account the disparity shift constraint coming from the overlaps and the sharpness variation. Experiment results show coding gain respectively of 21% over the HEVC Intra and 27% when the proposed method is combined with the Intra Block Copy (IBC) tool which is reported very effective in plenoptic image coding.
Learning Résumé Embeddings with Search Data and Transformers

In typical search engines, users are exposed to an interface that combines free-form textual fields and hard filters. After the initial query, users implicitly express their preferences through clicks, purchases, or connection requests. In this paper, we propose leveraging implicit user feedback to learn similarity metrics between search results that receive attention together. The learned similarity model can notably be used to serve recommendations. It captures a form of latent similarity that is not known to the search engine but learned from implicit user feedback. We introduce a method to create a dataset suited for similarity learning from search data. Using this dataset, we use contrastive learning and train a similarity model that outputs large scores for pairs of results that tend to receive the same attention from users and low scores otherwise. Our experiments show that BERT models and their siamese equivalents (Sentence BERT) produce meaningful similarity metrics when fine-tuned on the dataset built from search data.
Preparing VVC for Streaming: A Fast Multi-Rate Encoding Approach

The integration of advanced video codecs into the streaming pipeline is growing in response to the increasing demand for high quality video content. However, the significant computational demand for advanced codecs like Versatile Video Coding (VVC) poses challenges for service providers, including longer encoding time and higher encoding cost. This challenge becomes even more pronounced in streaming, as the same content needs to be encoded at multiple bitrates (also known as representations) to accommodate different network conditions. To accelerate the encoding process of multiple representations of the same content in VVC, we employ the encoding map of a single representation, known as the reference representation, and utilize its partitioning structure to accelerate the encoding of the remaining representations, referred to as dependent representations. To ensure compatibility with parallel processing, we designate the lowest bitrate representation as the reference representation. The experimental results indicate a substantial improvement in the encoding time for the dependent representations, achieving an average reduction of 40%, while maintaining a minimal average quality drop of only 0.43 in Video Multi-method Assessment Fusion (VMAF). This improvement is observed when utilizing Versatile Video Encoder (VVenC), an open and optimized VVC encoder implementation.
Foreground-Background Disentanglement based on Image and Feature Co-Learning for 3D-Aware Generative Models

Recently, studies on generative models using 3D information are active. GIRAFFE, one of the latest 3D-aware generative models, shows better feature disentanglement than existing generative models because it generates an image through volume rendering of independently formed 3D neural feature fields. However, GIRAFFE still suffers from an issue where foreground and background disentanglement is not smooth. In order to accomplish better disentanglement performance than GIRAFFE, we propose co-adversarial learning of the generative model at both image- and feature-levels. As a result of rich simulation experiments, the proposed generative model can produce photo-realistic images with only fewer parameters than existing 3D-aware generative models, along with excellent foreground-background disentanglement performance.
Cryptanalysis of a Reversible Data Hiding Scheme in Encrypted Images by Improved Redundant Space Transfer

In this paper, we propose a novel attack model called the Got Plaintext Attack (GPA), where the attacker only requires one plaintext and the ciphertext image set stored in the cloud to attack the content of the ciphertext image. Using this model, we examine the security of the Improved Redundant Space Transfer (IRST) encryption method. To this end, we define an ordered characteristic matrix based on the properties of the three keys used in IRST. By comparing the histogram distance of the ordered characteristic matrix, we are able to obtain a plain-ciphertext pair. Furthermore, by leveraging the invariant properties of the ordered characteristic matrix of image blocks in the plain-ciphertext pair, we estimate the block permutation Π <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and the bit-plane permutation sequence Π <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Our experiments show that the accuracy of estimating Π <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is higher than 70% for block sizes of 3x3 pixels or larger. Despite a 40% accuracy in estimating Π <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , the content information of the ciphertext image can still be exposed.