Reviewerly/exHarmony huggingface dataset:
GOBT: A Synergistic Approach to Game AI Using Goal-Oriented and Utility-Based Planning in Behavior Trees

In this paper, we propose a novel game AI framework, the Goal-Oriented Behavior Tree, using Unity game engine for simulations. This framework integrates the advantages of the Goal-Oriented Action Planning architecture and Utility Theory with traditional Behavior Trees, enabling more flexible agent responses to various situations. The simulated environment contains customizable game characters capable of actions like patrol, attack, retreat etc. GOBT allows developers to design agent decision-making processes by applying logic in traditional BTs and using the dynamic planning capabilities of GOAP and utility-based action selection when necessary. The performance of GOBT framework is verified through simulations using a synthetic dataset of agent behaviors in response to changing environmental factors.
The Method of Mathematical Modeling in Teaching Solving Applied Problems to Secondary School Students

The article describes the relevance of using the method of mathematical modeling in teaching students to solve applied problems, explains the concept of mathematical modeling and considers various ways to use this method in solving problems with practical content. In the course of the research work, psychological, pedagogical, methodological literature was analyzed, as well as a review of domestic and foreign research in the field of information technology and their use in the classroom. The necessity of teaching mathematical modeling in the school course of mathematics is theoretically substantiated. It is shown that the rich resource of mathematics, concentrating the knowledge necessary in everyday life, plays an important role in the intellectual development of the individual, as well as in his professional development, for solving practical problems, orientation and communication in society and life, in the surrounding space as a whole. The article also shows the expediency of preparing students for the study of real situations using mathematical modeling. The authors believe that the use of computer mathematical systems is justified, since it allows stimulating the activity of students in a secondary school, improving the quality of education, raising the professional level of a teacher, and diversifying the forms of communication between participants in the educational process. In addition, the authors believe that the use of computer mathematical systems in the classroom, visualization of information is a great advantage for students to receive it.
Experimental program for the formation of transversal competencies of future teachers in the educational environment of the Pedagogical University

The article reveals the theoretical aspect of the problem of the formation of transversal competencies of future teachers. The material contains a presentation of a pilot program for the formation of transversal competencies of future teachers. The purpose of the study was to highlight the category of "transversal competencies" and develop a plan for a pilot (experimental) program for the formation of transversal competencies of future teachers in the educational environment of the pedagogical university. The study was performed using the following methods: synthesis of information contained in the scientific literature; systematization of the author’s positions of scientists who studied the category of "transversal competencies"; represent the plan of the experimental (pilot) program for the formation of transversal competencies of future teachers in the educational environment of the pedagogical university. Analysis of scientific sources allowed to fix that transversal competencies are characterized by the following features: integral interdisciplinary and territorial transferability; be able to form both in a professional and in a personal environment; dependence on social and interpersonal relations; cross-culturality and cross-functionality in the study; the possibility of development in mixed learning; active communication and interactive character; significance for the production of the "Life-long learning" system. The content of the category "transversal competencies" (for Kopus, Babiy) as a system complex of instrumental (practical-realizable), interpersonal (individually generated) and system (integral) competencies that implement the parameter of transfer (transfer) of the system of acquired knowledge, skills and skills of the individual into the plane of solving professional and pedagogical tasks and situations of educational practice on the basis of introduction of individual metacognitive and professional potential of the person.
Practice-oriented training of future Computer Science teachers

The article reveals the purpose of practical work on the methods of teaching Computer Science, investigates their place in the curriculum, the processes of their organization during the professional training of students in the educational and professional program "Secondary Education (Computer Science)". General secondary education institutions need a Computer Science teacher who has active knowledge of computer science and methods of teaching it, the ability to organize and effectively implement the educational process, alongside the willingness to develop as a person and as a specialist. Theoretical, empirical, and statistical research methods are used. Practice-oriented training combines educational and cognitive, professional and practical, research- and development-centred activities, solving pedagogical situations and methodological problems, integrating the knowledge of computer science, pedagogy, psychology, theory and methods of teaching. An important place in solving this problem is occupied by laboratory and practical work within the discipline "Methods of teaching computer science", which are defined as forms of organization of education in higher education institutions and as methods of professional training aimed at future teachers of Computer Science. The purpose of these forms of education is to train teachers who are able to: perform methodological activities in the design, organization and implementation of the educational process in Computer Science; use forms, means, methods and techniques of teaching students appropriately; develop author's educational and didactic materials for curricula and model programs; develop comprehensively. The conditions for effective practice-oriented training are the reproduction of the process of performing professional duties by a teacher, the use of active teaching methods, as well as the cooperation of the subjects of the educational process. The prospect is to find effective methods of involving students in modelling the professional actions of a teacher, developing methodological cases.
Combatting the Threat of Cybercrime in Nigeria: Examining Current Laws and Policies

Cybercrime has been a major global criminological challenge in recent years. Regarded as one of the epicenters of international Internet fraud, Nigeria is gaining notoriety as a cybercrime hotbed. With the continuing advancement in technological innovations and the growth of terrorism in Nigeria, cyberattacks are also a continuing threat. Efforts to tackle the threat of cybercrime have mainly been ineffective. Even though the Nigerian government has introduced measures to tackle the danger posed by cybercrime to economic and social growth, the piecemeal approach it has used in the past has yielded no tangible results. In this paper, we argue that cybercrime cannot be confronted by the Nigerian government alone without a comprehensive, holistic approach, including international cooperation. We examined the various strategies employed by the Nigerian government to curb cybercrime and why cyber criminality has been a malignant tumor that has refused to subside. We also outline some policy measures and recommendations
Deep/Federated Learning Algorithms for Ultrasound Breast Cancer Image Enhancement

Breast cancer stands as a leading cause of global cancer-related deaths among women, emphasizing the urgency for advancements in early detection and precise diagnosis to enhance patient outcomes. This study addresses the pressing need for improved breast cancer imaging through the integration of deep learning algorithms.This paper specifically investigates the application of U-Net architecture and Federated Averaging (FedAvg) techniques for enhancing ultrasound breast cancer images thereby assisting radiologists in accurate cancer diagnosis. Leveraging two authentic datasets, we conduct experiments varying the number of epochs (50, 100, and 200) for FedAvg with U-Net. Our findings demonstrate promising accuracy values: 89.4716, 89.48, and 89.48, showcasing the effectiveness of our approach.Additionally, we explore the impact of U-Net alone and in conjunction with FedAvg on the segmentation process of ultrasound breast cancer images. This dual-pronged investigation offers insights into the synergistic potential of these methodologies for image enhancement.In conclusion, our study contributes to the ongoing efforts for improved breast cancer imaging. Through the fusion of U-Net and FedAvg, our approach demonstrates significant promise, providing a valuable avenue for advancing medical image analysis in the context of breast cancer diagnosis and treatment planning.
Hardware Acceleration For Deep Learning Model

In an era defined by fast technological growth, deep learning has sparked a revolution in artificial intelligence. With this incredible breakthrough, the possibility to unlock previously unimaginable possibilities lies within the depths of innovative hardware acceleration approaches. Deep learning models are computationally intensive and require a significant amount of processing power. FPGAs have emerged as a popular choice for accelerating the training and inference of deep learning models due to their highly parallel architecture, customization, and flexibility, while achieving high parallelism, low power consumption, and better programmability. In this study, we propose an approach that combines FPGA-based hardware acceleration with software-based optimization techniques, using the latest design tools to achieve high performance and efficiency in deep learning applications. Through trials on a facial recognition dataset, we show that our technique outperforms existing software-based approaches in terms of speed and accuracy. Our methodology includes Int-8 bit quantization as a critical step in preparing the trained facial recognition network for hardware deployment. This quantization reduces the numerical precision of the weights and activations in a deep learning model to 8 bits, significantly reducing memory usage and computational complexity while maintaining acceptable accuracy. Our approach showed a significant speedup and better utilization of hardware resources, obtaining a 3. 52x increase in FPS, a 2. 83x decrease in latency, a 1. 5x reduction in LUT utilization, and a 1. 25x reduction in BRAM utilization. In conclusion, this study tackles the challenges of enhancing efficiency and performance in deep learning models, with a particular focus on the application of face recognition.
Augmented Reality as an Educational Enrichment Tool: Integrating the Virtual with the Real

In recent decades, we have witnessed significant advancements in the application of Augmented Reality (AR) in education. This article explores the integration of AR into the educational system and examines its concrete impacts on the learning process. Through the development of a mobile AR application, initially focused on the field of Science, we aim to provide a clearer and more tangible perspective on the improvements that this technology can bring to education. The results of this study reveal that AR has stimulated increased classroom engagement, interaction, and content retention. Experiments were conducted in a local school classroom with the participation of 18 elementary school students. It was observed that the average accuracy rate in identifying planets reached 92.5%. The findings suggest that AR has the potential to significantly enhance the learning process, serving as an incentive for future research and the expansion of this approach to other disciplines, promoting a more interactive and engaging education.
Deep Neural Network Inference Processor

In recent years, neural networks’ architecture has become complicated and it demands special computing requirements. Thus, new specialized microprocessors are proposed for this purpose. In this paper, a new scalable neural network inference processor architecture is proposed and implemented on FPGA. FPGA enables hardware customization to fit the specific computing requirements. Moreover, FPGA-based accelerators demonstrated high energy efficiency compared to GPUs and CPUs. In the proposed FPGA-based architecture, the size of the systolic matrix multiplication array, the size of data flowing inside the processor and all the primitive blocks of the processor are configurable and scalable. Also, in this paper, an illustrative example is introduced to show how a TensorFlow-Based neural network model can be inferred using the proposed architecture. Moreover, synthesis results in terms of area and power are very promising. The design is validated on Virtex UltraScale+ VCUIIS Evaluation platform. It is tested using face recognition algorithm (160×160 face image at 25FPS) where it achieved an accuracy of 95%. Moreover, it shows performance and power efficiency of 55 GOPS and 10 GOPS/W at 100MHz, respectively.
Optimal Brain Dissection in Dense Autoencoders: Towards Determining Feature Importance in -Omics Data

Recently, there has been increased interest in ma-chine learning explainability. Understanding the complex relationship between input features of a model and their respective outputs is of increased relevance, especially in biological science. In this paper, we introduce Optimal Brain Dissection (OBD), an innovative methodology designed to examine the importance of first-layer connections in a biology-inspired autoencoder. We incorporated regulator-target interactions within the first autoencoder layer, representing biological regulatory networks, and identified their importance to the reconstruction error, a critical aspect in navigating the complexity of high-dimensional omics data. Through a combination of pruning techniques and counterfactual reasoning, OBD offers a method to quantify feature importance, factoring in both weight magnitude and time-to-laziness. To implement this method, we propose a Dense Autoencoder (DAE) architecture, aiming for increased efficiency and reduced computation. Tailored for omics data, the DAE employs skip concatenations and circumvents non-existent target-target interactions. Our approach aims to understand the relative importance of connections for autoencoder performance, a critical step towards better counter-factual reasoning for neural networks.
Improving Balanced Accuracy for Minority Plant Species Under Data Imbalance: A Multi-Architectural Ensemble Approach

Imbalanced datasets pose significant challenges for training classifier models. In this paper, we investigate the efficacy of ensemble strategies to enhance classification performance for both majority and minority plant species under data imbalance. Using two annotated datasets, CR Leaves and PlantCLEF 2017, we explore novel ensemble approaches. Our experiments reveal noteworthy improvements over baselines: for the CR Leaves dataset, the ensemble achieved a balanced accuracy for minority species of 0.777 (+16.6%), and for the PlantCLEF 2017 dataset, the ensemble achieved a balanced accuracy for minority species of 0.363 (+5.2%). Our findings highlight the potential of ensem-ble strategies to rectify imbalanced classifications and improve performance for minority species, with implications for various domains beyond plant species identification.
A Method for Selecting a Representative Image of a Dataset Based on the Singular Value Decomposition

In this paper, we present a novel approach for obtaining a representative image from a dataset <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{M}$</tex> based on the singular value decomposition (SVD). The proposed method consists of two phases: The first phase involves calculating a theoretical representative image <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I_{T}$</tex> , which is obtained using some measure of central tendency. This image <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I_{T}$</tex> may not necessarily represent an image from the dataset <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{M}$</tex> . Therefore, in the second phase, we calculate the practical representative image <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I_P\in\mathcal{M}$</tex> by utilizing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I_{T}$</tex> and the image subspace generated by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{M}$</tex> through an orthonormal basis, which spans the entire subspace <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{M}$</tex> . This basis is obtained using the SVD of the matrix formed by vectorizing the images in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{M}$</tex> . Finally, we conduct simulations of the proposed method and compare it with existing methods in the literature. The advantages of our approach are analyzed and demonstrated through numerical experiments.
Automatic Classification of Seismo-Volcanic Signals with Deep Learning: The Case of Turrialba Volcano

Monitoring and surveillance of volcanic activity is crucial to properly forecast the associated hazards. In this context, the analysis of volcanic seismicity plays a fundamental role. There are different seismic signals associated with volcanic activity, such as: volcano tectonic earthquakes (VT), long period earthquakes (LP) or low frequency earthquakes, earthquakes associated with explosions, hybrid earthquakes, deep LP earth-quakes and tremors. These are originated due to the movement or pumping of magma, fracturing of the rock under the surface, sound vibrations in the emission conduits, magma gasification processes, collapse of the magmatic chamber and explosions originated by the eruption. The identification and classification of these signals is a complex and time-consuming proces due to the lack of applicability of conventional tectonic earthquake location procedures and the difficulty experienced by expert operators during periods of high volcanic activity. In recent years, numer-ous research works have been carried out proposing approaches based on machine learning techniques, specifically in the field of deep learning, for the automatic classification of seismo-volcanic events. However, due to the intrinsic variability of seismo-volcanic signals and the heterogeneity of the characteristics of volcanic buildings, which greatly influence the waveforms of these signals, a concrete and definitive method for their characterization has not yet been established. In this paper we show how a convolutional neural network (CNN) can be used to classify seismic-volcanic signals at Turrialba volcano, located in Costa Rica. To train this CNN we use a transfer learning approach on 3 different pre-trained model architectures to correctly identify 12 event categories. We evaluate the performance of our proposal with 1941 data collected from seismo-volcanic events of Turrialba volcano. The results show that our approach achieves an accuracy of over 80 % in event classification.
Foundations of the Fast Galois Field Arithmetic

The effectiveness of utilizing theoretical- numerical transformations in Galois code systems has been substantiated based on an analysis of known methods of data transformation and digital processing. The drawbacks of dynamic execution of arithmetic operations in binary codes are identified, along with methods for eliminating them during Galois encoding, resulting in increased digital processing speed. The research utilized recursion theory, cyclic coding methods, discrete transformations, and digital automata. A novel method for parallel execution of arithmetic operations in Galois fields, which eliminates inter-digit transitions, has been proposed. Mathematical models for executing arithmetic operations have been developed, accompanied by a structural diagram and an explanation of the principles behind arithmetic operations in Galois codes. A comparison was made between known and proposed methods, revealing that the proposed method reduces conversion time by 6 to 25%. The use of recursive data coding methods with Galois codes has enhanced information exchange security while improving data processing speed and efficiency. The development of data processing methods in Galois field, along with the implementation of Galois arithmetic processors, hinges on ensuring high computational environment homogeneity. This, in turn, simplifies their implementation in microelectronic performance, increases operational reliability, and reduces the cost of implementing and operating information processing systems.
Neural Network Architectures for Assessing the Accuracy of Image Registration

In this paper, we study the problem of assessing the accuracy of image registration. Image registration is considered concerning the problem of remote sensing of unimodal images of the Earth. A simulation model based on twostep registration (SIFT, normalized cross-correlation) has been created to form a dataset for estimating the probability of correct registration and the root mean square error for registering small patches of real remote sensing maps. The paper analyzes the use of well-known approaches for assessing registration accuracy (for example, based on bootstrap), as well as machine learning methods: using handcrafted feature extraction, convolutional neural network, and applying the attention mechanism. It is shown that machine learning solutions (in particular, neural networks) can improve the accuracy of assessing registration quality.
Secured method of providing hierarchical private data via a smartphone

The new method of providing access to the hierarchical medical data by switching the smartphone to the emergency mode is presented. This method provides hierarchical medical data, which is assigned to a user and stored on the smartphone. The medical data is classified in a hierarchical structure, according to the one of three levels of data and is not duplicated. Each data level is encrypted by its own key, according to the access levels, wherein the key is needed to decrypt a low-level data that is contained in a data field of a higher-level. To permit access to the medical data photos from a two-camera device, a face recognition system and notifications to the trusted person are used.
Using phase noise modulation for secure transmission of digital signals

The possibility of using phase noise modulation for covert information transmission is demonstrated. A simple enough hardware implementation of phase noise modulation for transmitting digital information was proposed. The main parameters of the correlation receiver were determined to ensure the optimal level of the output signal. The method proposes high noise immunity for digital signal transmission.