Reviewerly/exHarmony huggingface dataset:
Pendekatan Algoritma Tree dalam Prediksi Populasi pada Smart Poultry

Intelligent systems for monitoring poultry in kennels are experiencing an increasing trend in several studies. Monitoring poultry is very important in the cage so that you can find out the chickens' condition and environment in the cage. Conditions that can be monitored include the weight of the chickens, whether or not there is enough water in a day, CO2 levels in the cages, air temperature, and humidity in the cages. Several studies have been conducted studies on monitoring poultry cages using IoT-based sensors. However, people have yet to predict the poultry population for tomorrow. So this study aims to predict the number of poultry populations in kennels based on related parameters. The prediction method used in this research is a decision tree and Support Vector Machine (SVM) to see which prediction method is better. The results evaluation techniques used in this study are Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2. The experimental results show that using the decision tree method, and the results are MSE 61987.202, RMSE 248.972, MAE 85.086, and R2 0.969. Overall the results of the decision tree method are superior to SVM.
Sistem Pakar Mendiagnosa Gangguan Koneksi Internet Berbasis Web Menggunakan Algoritma Rete

Expert System to Diagnose Internet Connection Disorders is a series of systems that utilize digital technology to do special analysis. At PT.Telkom the Sumbagut Infratel Division already has its own website, but in providing expert system information about connection disruptions is still via the telephone, therefore a web-based expert system is designed, to facilitate the community in finding solutions to internet connection disruption. With the creation of this expert system, it is expected to provide solutions to current problems. In other words, this expert system can be said as a digital technician. The expert system to diagnose internet connection problems has been designed and implemented using the Visual Basic 2008 program and Microsoft Access as the database while the algorithm used is the Rete algorithm.This application is only admin that can process data input, edit data, and delete data. While the user can only consult and find information about internet connection disruption and the solution, with the program design that the authors make this hopefully can be used by all parties.
Design of Fault Tolerant Array Multiplier Using Parity Preserving Reversible Gate

The digital designs with Reversible logic are more popular now days because of the increased demand of low power usage. Some of the Reversible logic gates are discussed in this paper. This paper also introduces a Parity Preserving Reversible Gate (PPRG) which is used in the fault tolerant devices design such as adders, subtractors and multipliers. The design of an array multiplier by using fault tolerant parallel adders is discussed in this paper. The fault tolerant parallel adders are designed using fault tolerant full adders and half adders and these adders are designed through Parity Preserving Reversible Gate. These adders and multipliers are useful in designing complex circuits such as ALU.
Pengaruh Data Preprocessing terhadap Imbalanced Dataset pada Klasifikasi Citra Sampah menggunakan Algoritma Convolutional Neural Network

Garbage is one of Indonesia's most significant problems with an increase in waste each year reaching 187.2 million tonnes/year. Various efforts to reduce the amount of waste such as Garbage Banks have been encouraged. However, this program has not run well, because some people have difficulty distinguishing the type of waste. One solution to overcome this problem is that need a system that can classify the type of waste. The deep learning approach with the CNN algorithm is currently widely used to solve classification problems. This method requires a large number of datasets to increase the level of accuracy. Getting a garbage dataset is a particular problem in the training process because the dataset is unbalanced. The dataset used amounted to 2527 data consisting of 6 classes. Several treatments such as undersampling and image augmentation are applied to overcome imbalanced datasets. Other treatments such as the type of input image channel and the use of filters are combined into 24 experimental scenarios to achieve the highest accuracy. The results of the experiment get the best scenario, namely, the dataset is undersampling and then augmented with 5 geometric transformation parameters with the input image being RGB and applying a sharpening filter to get an accuracy value of 0.9919 with 20 epochs.
Pengenalan Tulisan Tangan Angka menggunakan Self Organizing Maps (SOM)

Handwriting is character pattern recognition. Character pattern recognition is exciting to do research. In character pattern recognition, many types of characters can be recognized by computers and solved by various algorithms. Various kinds of pattern recognition have been successfully applied in multiple fields such as voice recognition, face detection, fingerprint recognition, and handwriting recognition. Handwriting recognition is divided into two types, namely online handwriting recognition and offline handwriting recognition. Online handwriting recognition requires special electronic equipment, and handwriting is captured on a pressure-sensitive tablet. Offline handwriting recognition does not need a particular machine because handwriting data is entered from previously written text such as images scanned by a scanner. Several methods have been developed to recognize handwriting with varying degrees of accuracy. This research uses the feature extraction of United Moment Invariant (UMI) and Self Organizing Maps (SOM). Based on the results of the software experiment for the entire test data set, the primary data yielded an accuracy rate of 88% for 50 images, and the first secondary data paid an accuracy rate of 98.2% for 500 images. However, for the second secondary data experiment with 50 test data, the accuracy rate is 90%. The third secondary data experiment was 500 test data. The accuracy rate was 89%. When viewed from the accuracy value, the primary data has a lower level of accuracy when compared to the two secondary data with different amounts. The story of accuracy resulting from experimenting with varying data sets proves that handwritten characters have a high and inconsistent level of variation. This is caused by the thickness and form of writing that is not consistent in each person and habits that affect the character of one's handwriting. Primary data is data that is taken directly and through a scanner process and still has a lot of noise in the handwritten image of numbers. At the same time, the secondary data has undergone a grey image process so that the handwritten image is clean from noise.
Evaluating Translation of Researches Abstracts

This research studied the reality of a significant PhD and MA studies that were approved and published in referral publications in spite of lingual mistakes in drafting and translation, assuming that some mistakes in translations rise from mistakes in the source text. A sample of ten studies of communication discipline have been conducted, the findings of the research are that many Arabic phrases in the abstracts require reviewing drafting and retranslating to avoid ambiguity, the researcher recommends translations of the abstract should turn attentions to the mistake in the source texts. and researchers and reviewers have to give their attention to the abstract in both source and target languages
Prediksi Persediaan Bahan Baku Makanan Menerapkan Algoritma Apriori Data Mining

The company's operational activities are inseparable from the supply of raw materials that must be met every day to meet consumer demand. The restaurant uses raw materials, namely vegetables, raw meat which includes beef and chicken, yellow noodles and soun noodles, and the main seasoning. Sales of food at this restaurant quite a lot in a day. This will produce sales data that will continue to grow every day, but this data is useless if it is not processed again to get the knowledge contained in the data. The Apriori algorithm is a method for finding patterns of relationships between one or more items from a dataset. Thus the pile of data that has been collected can produce a sales pattern, from which the customer's buying interest in food can be identified. From the results of research using a data sample of 18 items with a minimum of 20% Support and 50% Confidence, it produces 5 interesting rules with the highest Support reaching 33.33% and the highest Confidence reaching 100%.
Implementation of Blended Learning Under the New Normal In Selected Elementary Schools in Puerto Princesa District II, Palawan

The study was conducted to assess the implementation of blended learning under the new normal in selected elementary school in Puerto Princesa District II. Descriptive survey, mean, frequency, Slovin Formula, Pearson-r, and T-test were the statistical tools employed. Laptop computers and smartphones were the most accessible devices for the implementation of blended learning of the teachers and school administrators, while basic cellular phone and smartphone were the most accessible for learners and parents. Mobile data was the most accessible internet connection for school administrators, teachers, learners and parents. Face-to-face modality was more preferable compared to blended learning for school administrators, teachers, learners and parents. Moreover, there is no significant relationship between the respondents’ capacity and access and technical readiness. On the other hand, a significant difference existed between the respondents’ capacity and access for blended learning; and also, between the respondents’ technical readiness for blended learning.
Optimized Fuzzy Inference Based Analytical Process to Determine the Trust Value of Cloud Service Providers

Abstract Users can purchase virtualized computer resources using the cloud computing concept, which is a novel and innovative way of computing. It offers numerous advantages for the IT and healthcare industries over traditional methods. However, the widespread adoption of cloud computing across industries is being hampered by a lack of trust between CSUs and CSPs. Since cloud computing offers a wide range of security models and strategies, it is essential to analyse the service using a detailed methodology in order to choose the appropriate cloud service for various user types. Finding a wide variety of comprehensive elements that are both required and sufficient for evaluating any cloud service is vital in order to achieve that. As a result, this study suggested an accurate, optimized fuzzy logic-based trust evaluation model for cloud computing security. Here, we examine how fuzzy logic raises the efficiency of trust evaluation. Trust is assessed using the Quality of Service (QoS) characteristics of Security, Privacy, Dynamicity, Data Integrity, and Performance. The outcomes of a MATLAB simulation demonstrate the viability of the suggested strategy in a cloud setting.
Sistem Pendukung Keputusan Aplikasi Bantu Pembelajaran Matematika Menggunakan Metode EDAS

Technological developments have occurred at this time. The rapid development of technology forces students to be wiser in using it. One of the functions of technology is to become a learning medium like a cell phone. HP, which has become an obligation for students, has given rise to ideas for experts to bring up many new applications, especially in the field of mathematics. The emergence of these various applications makes students confused to choose which one is feasible to choose. To solve this problem, a Decision Support System is needed. SPK is a computer-based system whose function is to assist parties or individuals who need it, especially in making a decision. SPK can function properly, if you use the method. The method used in Ono's research is the EDAS method. The EDAS method is a method that uses a formula or formula, where the decision is generated from positive and negative distances. Based on this study, the results were found, namely alternative A1 with the name of the application, namely QANDA with a value of 0.0767 as the highest score
Text Classification of Indonesian Translated Hadith Using XGBoost Model and Chi-Square Feature Selection

Aside from the Holy Qur'an, Hadith is indeed a life guide that every Muslims in this world must follow. The technology for classifying texts and sentences, including categorizing hadiths, is evolving in tandem with the advancement of the times. The model used to perform classification has also been developed and optimized such as the use of the XGBoost algorithm which is more optimized than the previous tree algorithm. This can also make it easier for us as Muslims to study hadiths by categorizing them according to recommendations, prohibitions, and information. This study conducted text classification of Indonesian translations of hadith texts based on recommendations, prohibitions, and information using the XGBoost algorithm, TF-IDF for its feature extraction, and Chi-Square for its feature selection. In this study, experiments were carried out by changing the order of the preprocessing process for the stopword removal and stemming parts, performing the classification process with and without using chi-square as a feature selection, and adding parameter value during the modeling process with XGBoost and the highest final results obtained were 79% for accuracy, 79% for precision, 78% for recall and 78% for F1-score.
Sign Language Translator Based on Raspberry Pi Camera Using The Haar Cascade Classifier Method

Sign language is the main tool of communication for people with hearing impairments. Communication is very limited and difficult to understand between normal people who do not know sign language, so an interpreter is needed. Where not everyone, even a few normal people, learns sign language, especially the Indonesian Sign Language System (SIBI). Motion Detection is an important subject in the field of computer vision, which is used by many systems. Today's Internet of Things is very helpful and facilitates daily human activities. An internet network allows a device to be controlled from a considerable distance. This study described a sign language translator tool for the deaf and speech impaired using a raspberry-pi camera and displayed it on the other device monitor. This system was built using the Python programming language and the OpenCV Library. The system is using Haar Cascade Classifier algorithm, where there will be data on all hand shapes based on the letters to be translated. This application uses the OpenCV library and Visual Studio Code IDE software connected to the Raspberry Pi Camera. The publisher will send data to other devices using the MQTT Broker to connect and display detection results to other device monitors wirelessly using a local network. The research was conducted at various distances between the hand and the webcam, from 30cm to 150cm. The research results using the Haar Cascade Classifier method to detect sign language obtained an accuracy of 82%.
Multi-Objective Hyperparameter Optimization on Gradient Boosting for Breast Cancer Detection

Abstract The most commonly occurring cancer among women, breast cancer, causes lakhs of deaths yearly, which can be prevented by early detection and treatment. Detection can be done by using machine learning models on histopathological images that are affordable, reliable and accurate. Previous works in this regard have focused on transfer learning methods combining feature selection using Convolutional Neural Networks (CNNs) and ensemble of gradient-boosting algorithms. However, none of the state-of-the-art techniques capture the multi-objective nature of breast cancer detection and tend to improve a single performance measure such as Accuracy and F1 score, which fail to capture certain essential aspects of the problem as the cost of misclassification varies greatly depending on its type. In this study, a Multi-Objective Hyperparameter Optimization technique for Breast Cancer Prediction is proposed by comparing Random Search, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Bayesian Optimization. This approach is applied to ensemble of three popular Gradient Boosting techniques: Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) on features obtained from Inception-ResNet-v2 CNN model applied on the benchmark BreakHis dataset to optimize Precision, Recall, Accuracy and AUC simultaneously. The novel NSGA2-IRv2-CXL model proposed in this study achieves maximum Accuracy of 94.40%, AUC of 98.16, Precision of 95.77%, and Recall of 99.29% for 100x magnification. The study also establishes tradeoffs between performance metrics which opens avenues for further research in multi-objective approaches on breast cancer detection which can provide a much larger view of the strengths and weaknesses of the classification model.
The algorithmic collusion debate: a focus on (autonomous) tacit collusion

This chapter examines the algorithmic collusion debate. It observes that four models of algorithmic collusion have been developed (‘Messenger, Hub and Spoke, Predictable Agent and Digital Eye’), which are widely adopted within the academic debate without many critical voices. The actual debate centred on the last two scenarios, which assume the algorithm's ability to automatically reach a colluded outcome. The centre of the debate was on the question of whether algorithms can collude autonomously and how competition law can or should respond. The former question tells the technological debate, whereas the latter recounts the legal debate. This chapter elaborates on the details of both debates. The technological debate focuses on the following issues: what types of algorithms can collude under what conditions; can algorithms end in pricing strategies other than algorithmic collusion; can it be possible that algorithms will never collaborate in a real-world scenario; can audit existing algorithms or testing algorithms prevent anti-competitive conduct. The legal discussion has been marked from the start by a significant divide between those who believe competition law is enough for dealing with algorithms and others who believe competition law must be modified. The call for change can be divided between re-interpreting existing concepts and introducing new concepts. A more drastic option that has been suggested is the creation of regulatory instruments outside the traditional competition law framework. Finally, the chapter introduces the book against the backdrop of the debate.
Breathero: Not Another Slow Breathing Game — Exploring Faster-Paced VR Breathing Exercise Games

BreatHero is a faster-paced Virtual Reality (VR) action game that explores the potential of combining breathing techniques learning with the combat elements of action games. BreatHero uses breathing techniques as various skills for attacking and defending against the enemies, so players can do breathing exercises while enjoying defeating the in-game enemies. We choose three breathing techniques: Kapalabhati, Box Breathing, and Full Yogic Breathing, which are all beneficial for physical and mental health. To enhance breathing techniques learning and memorization in action games, we present two design concepts: Imitative Breathing Feedback and Associate Learning on Breathing Techniques. Results from our study indicated that players found the game to be more engaging and were willing to play it repeatedly, suggesting that breathing exercises can be effectively integrated into faster-paced action games.
A Framework for Analyzing Cross-correlators using Price's Theorem and Piecewise-Linear Decomposition

Precise estimation of cross-correlation or similarity between two random variables lies at the heart of signal detection, hyperdimensional computing, associative memories, and neural networks. Although a vast literature exists on different methods for estimating cross-correlations, the question what is the best and simplest method to estimate cross-correlations using finite samples ? is still unclear. In this paper, we first argue that the standard empirical approach might not be the optimal method even though the estimator exhibits uniform convergence to the true cross-correlation. Instead, we show that there exists a large class of simple non-linear functions that can be used to construct cross-correlators with a higher signal-to-noise ratio (SNR). To demonstrate this, we first present a general mathematical framework using Price's Theorem that allows us to analyze cross-correlators constructed using a mixture of piece-wise linear functions. Using this framework and high-dimensional embedding, we show that some of the most promising cross-correlators are based on Huber's loss functions, margin-propagation (MP) functions, and the log-sum-exp (LSE) functions.
Application of genetic algorithm to load balancing in networks with a homogeneous traffic flow

The concept of extended cloud requires efficient network infrastructure to support ecosystems reaching form the edge to the cloud(s). Standard approaches to network load balancing deliver static solutions that are insufficient for the extended clouds, where network loads change often. To address this issue, a genetic algorithm based load optimizer is proposed and implemented. Next, its performance is experimentally evaluated and it is shown that it outperforms other existing solutions.
Sliding Block Hashing (Slick) -- Basic Algorithmic Ideas

We present {\bf Sli}ding Blo{\bf ck} Hashing (Slick), a simple hash table data structure that combines high performance with very good space efficiency. This preliminary report outlines avenues for analysis and implementation that we intend to pursue.
SP-BatikGAN: An Efficient Generative Adversarial Network for Symmetric Pattern Generation

Following the contention of AI arts, our research focuses on bringing AI for all, particularly for artists, to create AI arts with limited data and settings. We are interested in geometrically symmetric pattern generation, which appears on many artworks such as Portuguese, Moroccan tiles, and Batik, a cultural heritage in Southeast Asia. Symmetric pattern generation is a complex problem, with prior research creating too-specific models for certain patterns only. We provide publicly, the first-ever 1,216 high-quality symmetric patterns straight from design files for this task. We then formulate symmetric pattern enforcement (SPE) loss to leverage underlying symmetric-based structures that exist on current image distributions. Our SPE improves and accelerates training on any GAN configuration, and, with efficient attention, SP-BatikGAN compared to FastGAN, the state-of-the-art GAN for limited setting, improves the FID score from 110.11 to 90.76, an 18% decrease, and model diversity recall score from 0.047 to 0.204, a 334% increase.