Reviewerly/exHarmony huggingface dataset:
Positional Approach to the Voting Function Formation of Random Forest Trees as an Example of Solving the Differentiating Tuberculosis Forms Problem

The paper proposes a new voting technology for random forest trees – the Positional Approach to the Voting Function Formation (PAVFF). In contrast to existing forms of organizing the voting of random forest trees, the paper proposes to change the subjects of voting and to use as such individual finite elements of the tree, with weights determined in accordance with the competences of new voting units. Each forest tree in the voting function is represented by its individual branch (voting unit) with the corresponding competence level assigned at the stage of tree finite element verification. Furthermore, we propose different mechanisms for organizing the received units in the voting process. The effectiveness of the new mechanism is shown by the example of the differentiation problem of drug-sensitive and drug-resistant forms of tuberculosis. The task feature space is formed by the ROI (regions of interest) textural characteristics on the patient’s lungs CT scan. The initial feature space was composed of the elements of a few textural characteristic matrices. From over half a million input features, a few optimal ensembles were selected to form random forest trees. We used intra- and inter-class variance selection techniques for this purpose, with the final selection made by a genetic algorithm using a combined correlation criterion. After verification of voting units (finite elements of trees) 3 variants of voting by competence were formed: by the most competent unit, weighted average participation of all participants, and group voting with coefficient revaluation by the Group Method of Data Handling. The results were compared with similar voting by random forest trees. A 5% improvement in classification quality is shown.
The effectiveness of organizing robotics circles using virtual didactic tool

Scientific research on the use of robotics elements in the formation of the competencies of elementary school students in programming and electronics, research on their organizational and methodological foundations are conducted on a global scale. In this research paper, distance education platforms that allow creating online circles on robotics are considered, the methodology of organizing circles on robotics is described in detail on the example of primary classes of secondary schools using virtual didactic tools, i.e. LMS systems. The significance of such circles is the organization of employment of students in their free time.
Application of the Ontological Approach to the Iterative GMDH Algorithms Metamodel Construction

Construction of an ontological metamodel of iterative algorithms is proposed to structure knowledge about these algorithms and their implementation. The metamodel will automate the design and use of specialized software for solving specific applied modeling problems. The advantage of iterative GMDH algorithms over combinatorial ones is that they allow the big datasets processing. The known generalized iterative algorithm, allows you to create typical architectures of previously developed modifications of these algorithms when setting up various modes of operation of this algorithm. The authors have developed an ontological metamodel of iterative GMDH algorithms using the Protégé environment.
NLP Tool for Extracting Relevant Information from Criminal Reports or Fakes/Propaganda Content

The goal of paper is to develop a natural language processing system to extract relevant information from criminal reports or fake/propaganda. A detailed description of the developed system was made. The technical requirements were formed. System development is divided into three points: data preparation, word embedding, and classification. Structure of the product under development have been described. It used libraries and ways of implementing the used methods. The structure of the neural network, initial parameters of the network are described in detail based on knowledge rather than machine learning methods: this means that it is necessary to specify patterns, and not to have a purpose to learn and generalize them. The second aim of this project was to apply the text summarization to case reports; that is, the converting text from long, detailed reports into a coherent, natural-language summary. The article describes the implemented training of the model for identifying propaganda using keywords. That is, propaganda carries a potential fake, and since informative noise is generally dangerous and threatens the sober thinking of a person, we suggest analysing the news for propaganda. In general, there is a lot of propaganda in the world, but many more fakes do not even carry propaganda, which improves the accuracy of model training. In this way, it allows you to filter out both fakes and propaganda.
A Technique for Detecting Software Quality Based on the Confidence and Prediction Intervals of Nonlinear Regression for RFC Metric

Nowadays software metrics are used for detecting software quality, including RFC (response for a class) at the app level. However, only their average values (point estimates) are known for detecting the quality of software systems. Also, it is known that RFC depends on the CBO (coupling between object classes) and WMC (weighted methods per class) metrics. That is why we propose a technique to detect the software quality of applications (apps) based on the confidence and prediction intervals of nonlinear regression for RFC depending on CBO and WMC predictors. According to this technique, the RFC value for the app inside the confidence interval indicates its medium quality. The RFC value for the app between the borders of the confidence and prediction intervals indicates its high quality. The RFC value for the app higher than the upper border of the prediction interval indicates its low quality. We give an example of using a proposed technique to detect the software quality of open-source apps developed in Java.
Development of software tools for testing the autonomous navigation system of UAVs

In this paper the testing environment for the artificial intelligence system that operates the unmanned aerial vehicle (UAV) is considered. The approach given in this paper covers developing part of the computer software that will reflect the structure and functional behavior of the UAV. This software implements simulation modeling of processes that occur on different UAV’s subsystems. To implement this kind of software that will model UAV’s actions in real-world environment and time, the hybrid dynamic models will be used. These models will consist of probability component such as weather conditions, availability of cellular data or any other kind of connection, so they could be called stochastic. The main point of this research is to develop a visual, mathematical and analytical representation of the processes that take place in artificial intelligence system.
Performance Investigation of the CPU-based Paralleling the Generalized Relaxational Iterative Algorithm

This paper shows the scalability tests of the parallelized version of Generalized Relaxational Iterative Algorithm (GRIA) based on resolving the regression identification problem. The description of the algorithm, its parallelized stages, parallelization principles, and tools used are given.
Hiding Document Format Files Using Video Steganography Techniques With Least Significant Bit Method

Video Steganography is one type that can use to hide secret messages. Video Steganography is a technique to hide messages in video media by inserting messages into one of the video frames. Cryptography can be combined with the Steganography technique to secure hidden messages in video files. This research was conducted to analyze the LSB (Least Significant Bit) steganography test combined with the Fernet cryptographic process. This study investigates the file insertion process, the test extraction process, the speed of system implementation, the visual attack, the Peak Signal Noise Ratio (PSNR) value, and the audio comparison between original video and video with embedded files. The results of this test indicate that the embedding process in the video is directly proportional to the results received. The larger the original video size, the larger the embedded video size will be.
Efficient energy smart sensor for fall detection based on accelerometer data and CNN model

Fall detection helps to provide medical assistance quickly and to avoid the aggravation of injuries. In this paper, we propose a new noninvasive and energy-efficient smart sensor for fall detection. The sensor is based on accelerometer data and is attached to 20 building workers. To reduce power consumption, a new method of data selection is proposed. This method is based on the use of sensor timers, which allows for the reduction of 91% of the acquired data and 94% of the transmitted data. Regarding the classification, a new classification approach is proposed. Indeed, each data segment is displayed as a graph. Then, a convolution neural network is trained to detect the presence or absence of falls in each graph. An accuracy of 98% was obtained. This result exceeds that obtained in several studies and shows the effectiveness of the proposed approach.
Multi-Population-based Parallelization of Ensemble of Differential Evolution Variants for Constrained Real Parameter Optimization

Differential evolution (DE) algorithms face performance challenges, which lean on improving solutions quality, speed-up, and exploitation of computational resources. Parallelism represents a suitable paradigm for overcoming the DE challenges. The ensemble of differential evolution variants (EDEV) algorithm is a recent DE algorithm. EDEV constitutes three DE variants (JADE, CoDE, and EPSDE), which may decrease its speedup. In this paper, a multi-population parallel ensemble of differential evolution variants (MPPEDEV) is proposed based on the synchronous master/slave parallel model. The performance of the proposed MPPEDEV is tested using a constrained real parameter problem proposed in CEC 2006. Compared to four state-of-the-art DE algorithms, which are JADE, CoDE, EPSDE, and EDEV, the results show that MPPEDEV outperforms EDEV in terms of execution time and solutions quality, depending on the population size as a control parameter. Furthermore, MPPEDEV and EDEV outperform JADE, CoDE, and EPSDE in terms of solutions' quality.
School Zoning System for Student Admission using Constrained K-Means Algorithms

The issuance of the Regulation Minister of Education and Culture number 51 of 2018 regulates new student admission by implementing a zoning system to achieve equal distribution of education quality in every school, especially at the high school level in Makassar city. This study aims to cluster the school zoning area based on the closest distance between the student's domicile and the school location. The dataset used is 22 school locations and 2248 student location data. In this paper, the method used is constrained k-means to cluster the prospective new students to each school. The constrained k-means method works based on the value of K as the closest cluster center to the value of N (cluster members) with a linear programming algorithm (LPA) approach so that each cluster has a balanced N member. The results of this study can overcome the unbalanced data distribution problem with an average cluster member value of 103 and the absence of empty clusters in each school/centroid. Thus, the system can be implemented in the new student admissions process as a reference in determining the optimal and accurate school zoning area based on the cluster center.
FPGA Implementation of Support Vector Machine for Gait Activity Classification

People with lower limb amputations suffer from mobility limitations that degrade their quality of life. In this paper, we propose a hardware system dedicated to human walking activity recognition for smart prostheses, which uses a support vector machine (SVM) algorithm and time domain features to perform activity classification. To achieve a flexible and efficient hardware design, the architecture is implemented on FPGA Nexys 4 Artix 7 board using the Xilinx System Generator (XSG) for DSP. The performance evaluation of the proposed system has been done through a comparative study, the comparison has been done between floating point MATLAB results and fixed point XSG results.
Improving Generalization of Model-Agnostic Meta-Learning by Channel Exchanging

Among the few-shot learning algorithms, model-agnostic meta-learning (MAML)can quickly learn new tasks with only a small amount of labeled training data, and achieve impressive results. However, due to the small number of samples, the generalization of the model is poor. In this regard, a method called channel exchanging is adopted, which uses the scaling factor of the batch normalization layer to measure the importance of each channel, and replaces the unimportant channels of each class with the feature mean of the channels of other classes. At the same time, contrastive loss is used to perform contrastive learning between the original channel and the exchanged channel, and the obtained loss value is passed into the external circulation as additional priori knowledge for training to obtain a better optimization direction. This method can enhance the ability to learn information interactively between classes in model-agnostic meta-learning, and mine potential differences and connections between classes, thereby improving the ability to generalize to new things. A model-agnostic meta-learning framework based on channel exchanging (EX-MAML) is built. The method fits with the way humans learn new things. Finally, the experimental results show that the performance of EX-MAML is improved on traditional few-shot datasets, and its generalization performance is further verified on other few-shot datasets from different sources.
A Natural Language Processing and Machine Learning-Based Framework to Automatically Identify Cyberbullying and Hate Speech in Real-Time

In the last 15 years, social media has slowly developed into the world’s biggest stage for communication of ideas. It has allowed people from around the world to directly chat without any economic or social barriers. But with widespread usage, it has also brought forth an influx in rates of cyberbullying. This influx has led to millions of deaths and hospitalizations of cyberbullying victims. To solve this problem, this paper proposes, BullyScan, a novel natural language processing and machine learning-based framework that automatically, accurately, and efficiently identifies cyberbullying and hate speech and alerts the user to change their language in real-time. BullyScan uses a Logistic Regression Algorithm that was developed after thorough training, validation, and testing of five different machine learning models using a combination of three different cyberbullying/hate-speech datasets. BullyScan achieved a high accuracy of 92% and F1-Score of.91. It was further validated to be optimal for an industrial setting through multi-level testing and analysis of a number of other evaluation metrics. This framework has the ability to significantly reduce the rate of cyberbullying and hate speech, increase positivity on social media, and save countless lives in the future.
An Analysis of Implementing a GAN to Generate MIDI Music

Music generated with machine learning has the potential to serve as inspiration and accompaniment for all kinds of musicians. Although neural network technology has been applied to music generation before, these applications have often implemented large and complex networks. This research aimed to use a generative adversarial network (GAN) to generate multi-instrumental, complex music while implementing a simpler structure than past models. This required a data pre-processing algorithm to simplify the encoding of MIDI files. A simple GAN was developed in PyTorch, and generator outputs were scaled to values within the bounds of the MIDI Standard. The GAN ultimately produced MIDI data, but the results were not distinctly musical and lacked a clear melody, perhaps because the network lacked sufficient complexity to process or replicate the temporal structure of music.
The k-Centre Problem for Classes of Cyclic Words

The problem of finding k uniformly spaced points (centres) within a metric space is well known as the k-centre selection problem. In this paper, we introduce the challenge of k-centre selection on a class of objects of exponential size and study it for the class of combinatorial necklaces, known as cyclic words. The interest in words under translational symmetry is motivated by various applications in algebra, coding theory, crystal structures and other physical models with periodic boundary conditions. We provide solutions for the centre selection problem for both one-dimensional necklaces and largely unexplored objects in combinatorics on words - multidimensional combinatorial necklaces. The problem is highly non-trivial as even verifying a solution to the k-centre problem for necklaces can not be done in polynomial time relative to the length of the cyclic words and the alphabet size unless $$P = NP$$ . Despite this challenge, we develop a technique of centre selection for a class of necklaces based on de-Bruijn Sequences and provide the first polynomial $$O(k \cdot n)$$ time approximation algorithm for selecting k centres in the set of 1D necklaces of length n over an alphabet of size q with an approximation factor of $$O\left( 1 + \frac{\log _q(k \cdot n)}{n - \log _q(k \cdot n)}\right) $$ . For the set of multidimensional necklaces of size $$n_1 \times n_2 \times \ldots \times n_d$$ we develop an $$O(k \cdot N^2)$$ time algorithm with an approximation factor of $$O\left( 1 + \frac{\log _q(k \cdot N)}{N - \log _q(k \cdot N)}\right) $$ in $$O(k \cdot N^2)$$ time, where $$N = n_1 \cdot n_2 \cdot \ldots \cdot n_d$$ by approximating de Bruijn hypertori technique.
On the 2-Layer Window Width Minimization Problem

When interacting with a visualization of a bipartite graph, one of the most common tasks requires identifying the neighbors of a given vertex. In interactive visualizations, selecting a vertex of interest usually highlights the edges to its neighbors while hiding/shading the rest of the graph. If the graph is large, the highlighted subgraph may not fit in the display window. This motivates a natural optimization task: find an arrangement of the vertices along two layers that reduces the size of the window needed to see a selected vertex and all its neighbors. We consider two variants of the problem; for one we present an efficient algorithm, while for the other we show NP-hardness and give a 2-approximation.
Augmenting Historical Alphabet Datasets Using Generative Adversarial Networks

In this paper, we present a method for expanding small classification datasets. Every research project is based on data and methods, including text analysis. When analyzing historical texts in different alphabets, there are not always Optical Character Recognition algorithms available and, in many cases, such texts need to be transliterated and translated manually, or alternatively, an OCR algorithm can be developed. In order to create such an algorithm, a large volume of input data is needed - each alphabet consists of elementary data - either letters, vowels, or in some cases ideograms. The texts need to be segmented into such elements, and then, the elements are classified. In many cases, it is very difficult and time-costly to get a sufficient amount of data, and it is advisable to use augmentation methods. In our research, we propose using Generative Adversarial Network to expand a relatively small dataset of Palmyrene letters and prove that even by adding generated data equal to the third of size of the original dataset, the classification results are improved by 120%.
Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi-label text classification on big social network graph data

Determining labels of textual content is a kind of challenging task. Classification systems are helping to identify different aspects of life especially in prediction of personality, elections, and healthcare related insights, etc. Our work presented a unique modified multi-label k nearest algorithm (Modified-MLKNN) in the direction to provide multiple labels of textual content that can help in early alarming systems and especially in case of monitoring infectious disease propagation. The study in this direction showed that text related pre-processing configurations play an important role and largely impact the classification task. The overlapping of terms during the learning phase leads to incorrect classification and requires more powerful ways to correctly classify text documents into multiple labels. The proposed Modified MLKNN algorithm which gave a minimum of 5% improved result over traditional ML-KNN. The algorithm is experimented and validated over two different multi-label text corpus Disease and Seattle.
An empirical investigation on BigGraph using deep learning

The sparse nature of BigGraphs makes tasks like Graph Classification, Node Classification, and Link Prediction very challenging. Furthermore, homogeneous or heterogeneous characteristics of data in BigGraph make it challenging to do real-time operations. This paper examines some approaches for the mentioned tasks on BigGraph that outperform the previous state-of-the-art approaches. We also empirically investigate these algorithms on various datasets. These comparisons of the various Graph-based deep neural network architectures such as Graph Convolutional Network (GCN), Deep Graph Convolutional Neural Network (DGCNN), etc., on datasets like Mutag, Proteins, etc., shows that deep architecture learns enough patterns. We also show the experimental results that reveal which architecture performs best on a dataset for a given graph-based task.
Exploring Indonesian Companies’ Chatbots to Gather Customer Experience

We aim to explore whether recipients of the Indonesia Customer Experience Champions 2022 as Best of The Best Customer Experience Team prize use chatbots effectively to improve their customer experience. Using a qualitative method, we explore their programs related to customer experience, along with the information regarding their chatbots. Then, we directly use two chatbots to gather our personal experience. We found that only four of eight winners use chatbots. Next, from our personal experience, the two chatbots are not yet functioning optimally because of some inaccurate or unclear information. This can lower the level of customer experience. Therefore, companies need to improve the quality of their chatbots.
Evaluation of the reverse transformation methods complexity of the residual number system for secure data storage

The methods of conversion from the residual number system to the decimal number system based on the classical Chinese remainder theorem (CRT) and its improvements CRT I, CRT II are considered in this paper. Analytical dependences of the time complexity of the specified methods are analyzed and constructed. As the result of carried out investigation, it is established that CRT II is characterized by greater efficiency compared to the other methods mentioned above. Examples of the implementation of direct and reverse conversion of RNS based on the application of CRT , CRT I, CRT II are given.