Reviewerly/exHarmony huggingface dataset:
The study of performance and methods of storage of JSON objects in PostgreSQL database management system

Currently there are several methods of data storage, such as table and object, as JSON objects. National and foreign scientists conducted studies on the comparison of performance of relational database management systems and NoSQL database management system, but there were no studies that compared the performance in data insertion and storage using ordinary records and JSON objects within the relational database management system. An experiment was carried out in terms of the real examples of corporate use of the database management system; the results show the relevance of the use of JSON objects in storage of large volumes of data.
Voronoi Tessellation for Efficient Sampling in Gaussian Process-Based Robotic Motion Planning

On-line motion planning in dynamically changing environments poses a significant challenge in the design of autonomous robotic system. Conventional methods often require intricate design choices, while modern deep reinforcement learning (DRL) approaches demand vast amounts of robot motion data. Gaussian process (GP) regression-based imitation learning approaches address such issues by harnessing the GP’s data-efficient learning capabilities to infer generalized policies from a limited number of demonstrations, which can intuitively be generated by human operators. GP-based methods, however, are limited in data scalability as computation becomes cubically expensive as the amount of learned data increases. This issue is addressed by proposing Voronoi tessellation sampling, a novel data sampling strategy for learning GP-based robotic motion planning, where spatial correlation between input features and the output of the trajectory prediction model is exploited to select the data to be learned that are informative yet learnable by the model. Where the baseline is set by an imitation learning framework that uses GP regression to infer trajectories that learns policies optimized via a stochastic, reward-based optimization algorithm, experimental results demonstrate that the proposed method can learn optimal policies spanning over all of feature space using fewer data compared to the baseline method.
Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets

This study addresses dynamic task allocation challenges in coordinated surveillance involving multiple unmanned aerial vehicles (UAVs). A significant concern is the increased UAV flight distance resulting from the assignment of new missions, leading to decreased reconnaissance efficiency. To tackle this issue, we introduce a collaborative multi-target and multi-UAV reconnaissance scheme. Initially, the multitasking constrained multi-objective optimization framework (MTCOM) is employed to optimize task allocation and reconnaissance time in static scenarios. Subsequently, in case of emergency, we iteratively refine the outcomes of static task allocation through an enhanced auction-based distributed algorithm, effectively reducing UAV flight costs in response to new missions, UAV withdrawal, or damage. Simulation results demonstrate the efficacy of our proposed multi-UAV and multi-target cooperative reconnaissance scheme in resolving dynamic task allocation issues. Additionally, our approach achieves a 5.4% reduction in UAV flight distance compared to traditional allocation methods. The main contribution of this paper is to consider a dynamic scenario model involving UAV damage and the emergence of new reconnaissance areas. Then we propose an innovative collaborative multi-target and multi-UAV reconnaissance scheme to address this issue and, finally, conduct experimental simulations to verify the effectiveness of the algorithm.
Methodology and Framework for AI-Based Solutions for Special Education

Special education recognizes limitations of traditional teaching methods in meeting diverse student needs. It empowers individuals through tailored support, specialized instruction, and assistive technologies, unlocking their full potential. Promoting inclusivity, social, and academic growth, it enables fulfilling lives. Integration of AI technology holds promise for enhancing special education's effectiveness and inclusiveness. This chapter presents a comprehensive methodology and framework for AI-based solutions, offering stakeholders a systematic approach to address specific challenges faced by individuals with special needs. By combining data-driven insights and human expertise, this approach revolutionizes special education delivery, empowering individuals to maximize their potential. However, further research and implementation are necessary to validate its effectiveness and scalability in real-world educational settings.
Alibaba and forty thieves algorithm and novel Prioritized Prewitt Pattern(PPP)-based convolutional neural network (CNN) using hyperspherically compressed weights for facial emotion recognition

The visual representations created using the self-distillation paradigm of Bootstrap Your Emotion Latent (BYEL) are empirically found to be less evenly distributed than those created using proposed technique. This proposed work promotes the compression of weights on a hypersphere by minimizing the hyperspherical energy of network weights using a novel method of optimizing manifolds through Riemannian metrics and the Conjugate gradient technique. The proposed work demonstrates how regularising the networks of the BYEL architecture reduces the hyperspherical energy of neurons by directly optimising a measure of uniformity alongside the standard loss. This leads to more uniformly distributed representation and better performance for downstream tasks. The Alibaba and Forty Thieves Algorithm-based Optimization (AFTAO) methodology is used to select the most precise collection of hyperparameters for a novel Prioritized Prewitt Pattern (PPP)-based Convolutional Neural Network (CNN) that results in a higher accuracy for all the six datasets used for facial emotion recognition.
Autonomous Earthquake Location via Deep Reinforcement Learning

Abstract Recent advances in artificial intelligence allow seismologists to upgrade the workflow for locating earthquakes. The standard workflow concatenates a sequence of data processing modules, including event detection, phase picking, association, and event location, with elaborately fine-tuned parameters, lacking automation and convenience. Here, we leverage deep reinforcement learning and develop a state-of-the-art earthquake robot (EQBot) to help advance automated earthquake location. The EQBot learns from tremendous trial-and-error explorations, which aims to best align the observed P and S waves, complying with the geophysical principle of gather alignments in source imaging. After training on earthquakes (M ≥ 2.0) for a decade in the Los Angeles region, it can locate earthquakes directly from waveforms with mean absolute errors of 1.32 km, 1.35 km, and 1.96 km in latitude, longitude, and depth, respectively, closely comparable to the cataloged locations. Moreover, it can automatically implement quality control by examining the alignments of P and S waves. Our study provides a new solution to advance the earthquake location process toward full automation.
Peningkatan Hasil Belajar Bahasa Inggris Siswa Melalui Model Pembelajaran Contextual Teaching Learning (CTL) di SMPN 8 Makassar

This research is a classroom action research that aims to improve English learning outcomes through the Contextual Teaching Learning (CTL) learning model, on the basic competencies of contextually capturing meaning related to social functions, text structure, and linguistic elements of narrative text, oral and written, very short and simple, related to fairytales in the even semester of the 2022/2023 academic year with research subjects as many as 25 students. The technique of collecting observations during the learning process and tests given at the beginning and end of the cycle. Based on the results of data analysis and discussion, the average score for cycle I was 1305 while the classical completeness reached 20 percent. And in cycle II obtained an average score of 1542.5 with classical completeness of 80 percent. The increase in students' English learning outcomes by using the Contextual Teaching Learning (CTL) learning model is characterized by an increasing percentage of categories from the low category in cycle I to the high category in cycle II. Observation data shows an increase in student learning outcomes in learning English during learning. From the results of this study it can be concluded that the Contextual Teaching Learning (CTL) learning model can improve English learning outcomes at SMPN 8 Makassar.
The Association of TNFα -308 Polymorphism and TNFα serum level in a sample of Multiple Sclerosis Iraqi patients

Tumor necrosis factor-alpha TNF-α) is a pro-inflammatory cytokine that is involved in the pathogenesis of Multiple Sclerosis. The current study was designed to examine the association between TNF-α level and TNF-α gene polymorphisms in Multiple Sclerosis of Iraqi patients. Blood samples were collected from fifty Iraqi patients who suffered from Multiple Sclerosis (20 men and 30 women) with ages ranging between 23-54 years, and 50 healthy volunteers as a control group. The serum level of TNF-α was detected by using an Enzyme-Linked Immuno-sorbent assay (ELISA), and TNF-α-308 gene polymorphism was assessed by TaqMan Real-time Polymerase Chain Reaction (Taq-RT-PCR). The results of the estimation of TNF-α level showed high elevation in the patients’ group (4.88 ± 0.17 pg/ml) with a high significance difference (P≤0.01) as compared with the control group (2.96 ± 0.09 pg/ml). While detection of TNF-α-308 polymorphism in MS patients revealed that the wild genotype G/G was 3 (6.00%), heterogeneous genotype GA was 15 (30.00%). Homogeneous genotype AA was 32 (46.00%), while G allele frequency was 0.21 and A allele was 0.79 with significant difference (P≤ 0.005) and even as in control G/G genotype was 47 (94 %), GA genotype was 3 (6.00%), AA genotype was 0 (0.00%), G allele frequency was 0.97. A allele was 0.03 with significant difference (P≤ 0.01). The result revealed a significant difference between TNF-α-308 genotype and TNFα serum level in MS patients and control. Keywords: TNF-α-308, MS, ELISA, Taq-PCR.
Towards a Better Scoring

In this paper, we propose a novel method to calculate trainee performance scores in a flight simulator environment using flight simulator logs. Our approach improves upon the existing scoring system designed in the AFRL by better fitting scores into the existing model of the training process.
Evolving Dilation Functions for Parameter Estimation

Global optimization problems are among the most complex and widespread tasks in Computer Science. The capability of finding the global optimum is often hindered by many features-e.g., multi-modality, noisiness and non-differentiability- of the fitness landscape related to the problem. To overcome such issues, Dilation Functions (DFs) can be used to perform problem-dependent manipulations of the fitness landscape to “expand” promising regions and “compress” less promising regions. Since in many real-world scenarios the knowledge of the problem characteristics to handcraft tailored DFs is lacking, two automatic approaches to evolve ad-hoc DFs have been proposed and assessed on benchmark problems. One approach is a two-layered method that leverages an Evolution Strategies (ES) and a self-tuning variant of Particle Swarm optimization to evolve a DF. The other approach uses Genetic Programming (GP) to evolve a set of tailored DFs for each dimension of the search space. In this work, we introduce Evolutionary LBDFs (EvLBDFs), a novel approach based on ES to evolve Local Bubble Dilation Functions, a family of DFs that locally dilate hyper-spherical bounded regions of the search space. Moreover, we compare these approaches to solve the Parameter Estimation (PE) problems of two bio-chemical systems. Our results highlight that all three approaches evolved DFs that simplified the PE landscapes. The GP-based approach outperformed the other approaches on the PE problem with the higher number of kinetic parameters to infer.
An Effective Image Denoising Approach Based on Denoising with Image Interpolation

Image denoising is a task to remove corrupted pixels from the image. The corrupted pixel betides to the image while capturing and transmission from various sources. For image denoising, many filtering approaches are used in literature. However, the resolution of the image deteriorated due to denoising. Therefore, In the proposed work, denoising is unified with upsampling to get a noise-free informative and magnificent image. In the proposed approach first denoising is performed with the Weighted median filter to remove noise from the image. The Weighted median filter has been extensively used and preferred as it minimizes loss of information by filtering out background noise without generating unrealistic pixels and providing more weightage to the central pixel. The denoised image is then upsampled to have good visual perception quality. Considering the PSNR and SSIM, it has been evident that the proposed method effectively outcompetes the simple denoising methods.
Gold Prices Forecasting Using Recurrent Neural Network with Attention Tuned by Metaheuristics

A large number of statistical studies have proven that the value of gold is highly valued nowadays, despite the numerous crises that have hit the global market. Due to the great risks that daily affect sudden changes in gold prices on the global market, it is of great benefit to develop tools that will enable automated monitoring and prediction of gold price changes. This can be quite helpful for those countries and major corporations whose economy depends on this. In this paper recurrent neural network with attention layer was used to forecast the gold price, while the hyperparameters of the network were tuned by the novel variant of the particle swarm optimization algorithm. Obtained results in terms of standard regression metrics were compared to those generated by other cutting-edge methods and proposed method proved to be very promising in this area.
Minimum Selection Feature Importance Guided Attack

Machine learning models are under severe threat by adversarial attacks. Machine learning is swiftly taking on a central role in organizations' value propositions, and as a result, organizations need to safeguard them. Like every machine learning model, adversarial attack algorithms are trained on data. Recently, specific a I gorithms have been developed for tabular datasets such as the Feature Importance Guided Attack (FIGA) [1]. However, FIGA assumes the knowledge of the total available data for training. In a typical case, the attacker will query a target model (an oracle) to label their training data and perform an adversarial attack on the labelled data. Attackers would like to minimize the queries made in order to remain discreet; however, FIGA requires the maximum amount of data. We present a novel methodology to train FIGA with reduced training samples. This method leverages an intuitive approach that utilizes the K-means clustering algorithm to select legitimate samples from the training data for querying. Our results demonstrate a 99 % reduction in the data required for training.
AccMER: Accelerating Multi-Agent Experience Replay with Cache Locality-Aware Prioritization

Multi-Agent Experience Replay (MER) is a key component of off-policy reinforcement learning (RL) algorithms. By remembering and reusing experiences from the past, experience replay significantly improves the stability of RL algorithms and their learning efficiency. In many scenarios, multiple agents interact in a shared environment during online training under centralized training and decentralized execution (CTDE) paradigm. Current multi-agent reinforcement learning (MARL) algorithms consider experience replay with uniform sampling or based on priority weights to improve transition data sample efficiency in the sampling phase. However, moving transition data histories for each agent through the processor memory hierarchy is a performance limiter. Also, as the agents' transitions continuously renew every iteration, the finite cache capacity results in increased cache misses. To this end, we propose AccMER, that repeatedly reuses the transitions (experiences) for a window of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> steps in order to improve the cache locality and minimize the transition data movement, instead of sampling new transitions at each step. Specifically, our optimization uses priority weights to select the transitions so that only high-priority transitions will be reused frequently, thereby improving the cache performance. Our experimental results on the Predator- Prey environment demonstrate the effectiveness of reusing the essential transitions based on the priority weights, where we observe an end-to-end training time reduction of 25.4% (for 32 agents) compared to existing prioritized MER algorithms without notable degradation in the mean reward.
Formation of scientific communication etiquette of beginning scientist

The article highlights the problem of the formation of a scientistʼs etiquette, in particular the etiquette of scientific communication, of beginning scientists who pursue higher education at the second (masterʼs) and third (educational and scientific) levels. The author emphasizes that it is extremely important for novice scientists to possess the competencies of scientific speech, scientific etiquette, corporate culture, etc. since during scientific research activities these skills will help effective communication in scientific circles and allow the beginners to present their work and research results in the best way. The purpose of the article is to analyze the problem of forming a culture of scientific speech of novice scientists for the successful presentation of the results of research activities and the development of scientific etiquette for the successful cooperation in scientific circles of the chosen field. The author used the following research methods: theoretical (analysis and synthesis, generalization and systematization of scientific sources of information) – to identify the state of the raised problemʼs development in pedagogical theory and practice); empirical (interviews, surveys, observations, and pedagogical experience needed to reveal the peculiarities of the formation of the etiquette of scientific communication in students of higher education (beginning scientists)). The disciplines studied by students of the second (masterʼs) and third (educational and scientific) levels of higher education at Kherson State Agrarian and Economic University, namely: “Philosophy of science” and “Philosophy of science and academic ethics”, are aimed at mastering the necessary competencies for the implementation of high-quality and effective scientific activity, application of philosophical approaches, methods of scientific research, and formation of the relevant qualities of a scientist in students of higher education. The author enlists qualities of a scientist as a creative personality; scientific traditions; requirements of professional ethics; general ethical principles of a scientist, etc. Entry into the scientific community for beginning scientists is a difficult stage of professional life, so they should be ready for the challenges that they are going to meet in their research work. It is the teachers of higher education institutions who are responsible for forming the etiquette of scientific communication in students of higher education.
Initial Intrusion Detection in Advanced Persistent Threats (APT’s) Using Machine Learning

Cyber-assaults have resulted in significant financial losses and the disruption of essential governmental services in recent years. Among these cyberattacks, the Advanced Persistent Threat (APT) has become a major security issue to cybersecurity, affecting a number of businesses and organizations. The APT attacks are targeted and have a specific objective behind the attacks. An attacker builds an entry point to the target network as part of the APT attack. This is commonly accomplished by infecting the targeted system with malware that leaves a backdoor open for future access. The spear-phishing email is a prevalent method for breaking into a network for establishing the initial foothold into a target network. The phishing mails may contain malware attachment or link which leads to website injected with malware code. This research offered an authentication-based detection approach for spear-phishing emails. The proposed technique takes the trust email dataset’s stylometric, gender, and personality traits and extracts them.
Software Defects Prediction Using Machine Learning Algorithms

The most significant feature of a software is its quality. Software defect prediction has grown significantly in popularity over the past several years and may directly impact quality. Defective software modules significantly lower software quality, which results in cost overruns, missed deadlines, and far higher maintenance expenses. One of the best strategies in this direction is to predict software problems using machine learning (ML) methods. In this study, ML algorithms used in this inquiry include artificial neural networks (ANNs), random forest (RF), random tree (RT), decision table (DT), linear regression (LR), Gaussian processes (GP), SMOreg, and M5P. For the purpose of predicting future software defects, a novel software defect prediction model is put forth. The defect prediction is grounded in the past. The outcomes demonstrated that a combination of ML techniques might be used to anticipate software problems in an accurate manner over other algorithms in terms of defect prediction accuracy.
A Survey on Smart Contract Vulnerabilities Including Auditing Tools

Ethereum is an open-source global computing peer-to-peer network that lets users code and executes Smart Contracts. Security incidents involving smart contracts have caused significant monetary losses. Attacks have increased since Cryptocurrencies have become accessible to the general public. In this chapter, we present a comprehensive report on the kind of attacks possible in Smart Contracts. Also, we give best practices while coding Smart Contracts and discuss possible solutions to these vulnerabilities. The rise of Smart Contract coding exploits has been the motivation behind this survey. The development of Auditing Tools for Smart Contracts has attracted the attention of many researchers. We have discussed several of these auditing tools which successfully detect and prevent vulnerabilities in Smart Contracts.
Performance Analysis of Automatic Modulation Classification Method for Different Modulation Techniques Using CNN Algorithm

Automatic Modulation Classification is a technique for determining the modulation of a receiving signal. IoT devices receive signals from numerous resources using modern methods such as multiple input multiple output (MIMO). Modulation recognition is therefore essential. This chapter develops a Convolutional Neural Networks-based model for automatic modulation classification. The classification accuracies of the proposed scheme for 11 modulation schemes are evaluated. The effect of Signal to Noise Ration on classification accuracy is also studied.