Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition Using Ambient Sensors

Ambient sensors positioned throughout a residential residence can assist in detecting human activity without the need for wearables or direct communication with the people being watched. Elderly or disabled folks who have trouble utilizing conventional monitoring equipment will mostly benefit from this. The sensors are simple to deploy in various interior settings, making it possible to monitor huge populations or many sites simultaneously. This methodology applies to smart homes, and we suggest gathering ambient data from participants' houses while they go about their regular lives. Based on the activity in the residents' smart homes, the sensors can identify their activities. To group the movements detected by the sensors, we will utilize a variety of classification algorithms based on the UCI dataset.
An overview of XAI Algorithms

In the field of artificial intelligence (AI) and machine learning, which have already found extensive applications in various domains, the challenges posed by their complexity and opacity cannot be disregarded. This paper delves into Explainable Artificial Intelligence (XAI), a technology aimed at addressing the ‘black box’ problem of AI and enhancing decision transparency. We will discuss two primary approaches to XAI: interpretability within models and post-training interpretability. We will analyze machine learning models inherently possessing interpretability, such as decision trees. Additionally, we will provide a detailed introduction to prominent XAI algorithms like LIME, LRP, and SHAP, among others. This paper will explore the current state of XAI, its challenges, and its future directions and potential. We hope this research contributes to the comprehension and enhancement of AI interpretability.
Arabic Sign Language Datasets: Review and Improvements

The research on Arabic sign language recognition has increased dramatically in the last decade, which required the availability of high-quality datasets. This paper reviews and analyzes the available Arabic sign language datasets according to seven quality metrics where possible. Furthermore, this paper improves the best dataset according to the quality metrics by enhancing its labeling information and expanding the dataset using the augmentation method. The review results show that there are twelve datasets with only one available dataset for public research which is called ArASl2018. Further, ArASl2018 is improved in terms of attributes and expansion. The enhanced labeling gives the ability to apply the statistical analysis while expansion increases the accuracy as verified by the results.
Designing an educational project with a social impact

The designed project aims to absorb the finest experience of cooperation between IT and the education of children with special needs. This can be achieved by teaching students to create assets, valuable for the education of children with special needs, using various technologies, such as gamification, and virtual and augmented reality. And this is how the students will be involved in socially responsible activity, i.e., they will create a free access library of assets of virtual and augmented reality scenes, compliant with the education of children with special needs demands, and open for any participant of inclusive education. The project was designed keeping in mind the means of the Information Systems and Networks Department at the Institute of Computer Science and Information Technologies of Lviv Polytechnic National University, Lviv, Ukraine.
Multithreshold Neural Units and Networks

We deal with theoretical issues concerning the application of the multithreshold architecture in the theory of neural computation. The way of representing a multithreshold function by 2-layer neural network consisting of single-threshold neural units with equal weights is established in the paper. We also study the complexity of the problem of the learning k-threshold neurons and prove that this problem is NP-hard if the number of thresholds is greater than one.
Development of Adaptive Control System Using Fuzzy – Genetic Approach

In this study, the structure of adaptive fuzzy control system based on genetic algorithm (GA) is proposed. This system provides the desired unit step response characteristic of the control system when the coefficients of the mathematical model of the control object vary in a wide range. An adaptation algorithm has been developed for fuzzy controllers using genetic algorithm as learning mechanism. GA corrects the knowledge base of fuzzy controllers and with it, it adapts the technological process to the changing dynamic conditions.A computer simulation of the developed adaptive fuzzy controls for the temperature control of the rectification column of the atmosphere block of the primary oil processing equipment was performed. The simulation results showed that the proposed adaptive system based on the fuzzy-genetic approach provided good enough control quality when the model coefficients changed three/four times.
A Model for Optimization of Feature Extraction in Recognition Systems Based on Parallel Algorithms

Research in improving image recognition systems is essential due to the unreliability of existing systems. The key idea in this paper is to use human-based features for classifying different objects. The paper proposed a two-stage approach for reducing the time and resources required for training and recognition. Firstly, were extracted more informative soft features based on human thinking. The parallel evolutionary algorithm in high performance computing systems has been applied to optimize the features subset selection process.
Context-Aware Security in the Internet of Things: What We Know and Where We are Going

Context awareness has been on the grow for the last couple of years and part of the reason for the emergence of this technology is the nature and dynamic of the Internet of Things (IoT) itself. Context awareness provides an additional layer of security for the ever-changing IoT entourage by providing an aware approach to security decisions. Traditional security mechanisms might not be always suitable for the context of IoT, thus context-aware security mechanism comes out as a suitable solution. Context awareness is a technique for identifying contextual circumstances using a particular context framework model of awareness, followed by identifying an appropriate solution that is centred on the findings. This paper presents a holistic view of context awareness for IoT security. Specifically, we intend to analyze how context awareness contributes to IoT security, we focus on investigating the context-aware security solutions proposed in the literature and exploring the techniques and approaches used to enhance IoT security based on contextual information, their effectiveness and benefits together with impediments of these solutions. In addition, we discuss the challenges associated with using context-aware security in the IoT and future research directions that need to be addressed in order to make contextual information more effective for security solutions in IoT.
The Development of the Optical-Electronic Images Segmentation Method Based on the Canny Edge Detector

The article deals with the problem associated with the segmentation of color images. The main groups of color image segmentation methods operation, are considered. The main advantages and disadvantages of each group of methods are shown. The paper suggests the use of the Canny edge detector for optical-electronic images obtained from space observation systems. It is proposed to regulate the parameters of the Canny edge detector in order to improve the segmentation result. It’s determined that the value of the lower threshold should be no more than 10-20 unit, and the value of the upper threshold should be within 60-100 unit.
Information Technology of Transcribing Ukrainian-Language Content Based on Deep Learning

Deep learning-based technology for transcribing Ukrainian-language content has been developed. The developed Ukrainian audio transcription technology was tested experimentally by employing Google Speech Recognizer, Python, and Microsoft Azure Speech. Google Speech Recognizer recognizes Ukrainian-language text if it is uttered clearly and distinctly. When the text is pronounced rapidly and less expressively, the transcription quality suffers: the basic sense is obvious, but certain words are incorrectly transcribed. In the presence of background noise or music, the Google Speech Recognizer technology failed miserably and transcribed a set of words that had little to do with what was stated in the audio. Microsoft Azure Speech Recognizer outperformed Google Speech Recognizer by orders of magnitude. Even with a lot of noise and music in the background, the technology transcribed the text qualitatively almost without losing the content of the audio. Concurrently, it is worth mentioning that the transcribing process utilizing Microsoft Azure technology takes longer, and the service itself is not free (unlike Google Speech Recognizer), but students have restricted access to the technology.
Modeling of a Metal Casting Cooling Process Using the GMDH-Based Computer Technology

The developed technology based on the GMDH was applied to solve the problem of choosing the best model that describes the dependence of the cooling temperature. The use of this technology increases the effectiveness of decision support for the caster in the process of manufacturing castings. The selection of the optimal cooling mode plays a significant role in obtaining a high-quality end product. To build models, the technology incorporates a hybrid iterative-genetic GMDH algorithm, the peculiarity of which is that it is a neural network algorithm with active neurons. The article compares two algorithms for constructing a cooling temperature model depending on the modes of the cooling installation: the LASSO algorithm and the hybrid iterative-genetic algorithm of the GMDH.
Resilient Model-Based Mobile Location Estimators Incorporating Measured Target Speed for Maneuver Tracking

This paper proposes two new model-based resilient mobile location estimators incorporating real-time measured target velocities for maneuvering target tracking in a probabilistically mixed line-of-sight (LOS)/non-LOS (NLOS) urban macro-cell with unknown NLOS measurement noise statistics. The proposed algorithms can robustly and stably achieve satisfactory tracking performance. One is derived from an extended Kalman filter, and the other is obtained from robust regression and an M-estimation technique. The novelty of the proposed algorithms hinges on the integration of a proposed state-space input model with measured target speeds, a proposed measurement preprocessing method and adequate process noise variances. Moreover, we derive sufficient conditions for the choice of suitable process noise variances for each proposed algorithm to ensure performance stability. Simulations illustrate robust and stable tracking performance of the proposed algorithms which meets the FCC requirements.
A classifier of the Random Forest type based on GMDH, logistic transformation and positional voting

This study introduces an optimization of the voting function’s structure and parameters within a self-organizing forest of decision trees, based on an enhanced stepwise logistic regression algorithm and a positional voting method. The stepwise regression algorithm is improved through GMDH-based optimization for selecting threshold values for significance levels, both for the inclusion and exclusion of voting function arguments. The voting function’s arguments are formed using the positional method from branches of self-organizing trees and are supplemented by the forest’s input arguments. Classification results obtained from the standard version of Random Forest, the self-organizing forest, and the proposed enhanced version of the decision tree forest are compared.
Using Augmented Reality for Learning Animals for Early Childhood Education

The child learns to speak gradually. To interest the child, animals and their sounds are used for clarity. Animal sounds are simpler, often consisting of simple syllables. Therefore, the sounds of animals are quite suitable as a training for the speech apparatus of a child. The paper proposes the development of a mobile application with augmented reality, which generates 3D models of animals with their sound. The study of the operation of the application under different conditions of the angle of inclination of the phone and the distance of the camera to the image-marker, on which the formation of the object of augmented reality depends further, is given. The above experiments confirmed that the parameters of the proposed application allow using the phone together with the child. Using the AR-application increases the child’s attention to learning.
Multimodal Cyber-Harassment Detection

Cyberbullying is a prevalent social issue that can cause significant harm to individuals, particularly young people. Traditional approaches to detecting cyberbullying rely on text-based analysis of online messages, but the use of multiple modes of communication in cyberbullying presents challenges for detection. In this study, we compare the effectiveness of audio-only, visual-only, and text-only approaches for cyberbullying detection. We collected different datasets for the study and used machine learning algorithms, speech emotion recognition, text conversion, middle finger, and landmarks techniques to analyze the data. Our results show that the text-only approach achieved the highest accuracy of 95%. Our study demonstrates that text-based features remain the most informative for cyberbullying detection, while audio and visual features alone are less effective. These findings suggest that the development of more comprehensive cyberbullying detection methods should focus on improving text-based analysis while considering the potential benefits of incorporating audio and visual features.
Detecting Conventional and Adversarial Attacks Using Deep Learning Techniques: A Systematic Review

Significant progress has been made towards developing Deep Learning (DL) in Artificial Intelligence (AI) models that can make independent decisions. However, this progress has also highlighted the emergence of malicious entities that aim to manipulate the outcomes generated by these models. Due to increasing complexity, this is a concerning issue in various fields, such as medical image classification, autonomous vehicle systems, malware detection, and criminal justice. Recent research advancements have highlighted the vulnerability of these classifiers to both conventional and adversarial assaults, which may skew their results in both the training and testing stages. The Systematic Literature Review (SLR) aims to analyse traditional and adversarial attacks comprehensively. It evaluates 45 published works from 2017 to 2023 to better understand adversarial attacks, including their impact, causes, and standard mitigation approaches.
Investigating Availability of Cloud-deployed Systems on Redundancy, Architecture, and Recovery Time

This paper presents a study of the reliability of information systems deployed in the cloud environment. The purpose of this study is to identify the factors that affect the reliability of these systems and to propose strategies to improve their reliability.The study uses a mixed-methods approach that combines both a quantitative assessment method and a study of the impact of system deployment options on reliability.The results of this study highlight several factors that affect the reliability of information systems in a cloud environment, including service level agreements (SLAs), monitoring, speed of failure detection and response, and scalability. The study also suggests several strategies to improve the reliability of these systems, including the use of redundant architectures, load balancing, and proactive monitoring. The results of this study provide valuable information for organizations planning to deploy information systems in the cloud. By paying attention to the factors affecting reliability and implementing the suggested strategies, organizations can improve the reliability of their information systems and ensure their availability and performance in the cloud environment.
Information Technology for Finding Answers to Questions from Open Web Resources

The purpose of this work is to develop a question answering system for the questions related to the highest degree of comparison based on open web resources. Examples of such questions are: "What are the most popular cars 2023?", "What is the best novel ever?". The system is implemented in Python. Additional libraries were used like Selenium for web scraping and NLTK for keywords extraction. The result of the system is ranked list of items with number of occurrences. The ranking is based on the popularity of item, or more specifically on the number of web resources in which the particular item appeared. It differs from the classical QA systems because they usually answering unequivocal questions. In contrast, implemented system aggregates opinions from different resources to answer subjective questions. There are no datasets for our specific task, so the system was validated on the manually created sample questions. Considering the above limitation system accuracy expressed in F1 score is close to 0.8. In future the system can a website that aggregates data from many web resources designed to rank items from different domains.
SmartyBin: A Tangible AI-Based Waste Classification Prototype

This paper introduces a tangible intelligent trash classification bin that leverages Deep Learning (DL) techniques to efficiently identify and accurately classify diverse waste materials, including metal, plastic, paper, and others. To build a robust classification model, a dataset comprising 4,122 colored labeled JPG images of waste materials was collected from Kaggle. The dataset was divided into a training set, which constituted 70% of the images, and a validation set, which comprised the remaining 30%. The proposed system achieves an impressive validation accuracy score of 94.53% for real-time waste classification. To create an affordable solution, we constructed the trash bin from scratch, incorporating an ESP32CAM camera to capture images of the waste materials. By utilizing the trained Convolutional Neural Network (CNN), the developed bin achieves an approximate 98% accuracy in classifying waste materials, thus significantly enhancing the effectiveness of waste management practices. This paper introduces a tangible intelligent trash classification bin that leverages Deep Learning (DL) techniques to efficiently identify and accurately classify diverse waste materials, including metal, plastic, paper, and others. To build a robust classification model, a dataset comprising 4,122 colored labeled JPG images of waste materials was collected from Kaggle. The dataset was divided into a training set, which constituted 70% of the images, and a validation set, which comprised the remaining 30%. The proposed system achieves an impressive validation accuracy score of 94.53% for realtime waste classification. To create an affordable solution, we constructed the trash bin from scratch, incorporating an ESP32CAM camera to capture images of the waste materials. By utilizing the trained Convolutional Neural Network (CNN), the developed bin achieves an approximate 98% accuracy in classifying waste materials, thus significantly enhancing the effectiveness of waste management practices.
IPattern: Islamic-Inspired Pattern Generation Using Generative Adversarial Networks

The prevalence of Islamic patterns design and its strong influence on other societies is undoubted. However, the incorporation of technology into people's daily life resulted in a noticeable shift in their interests to acquiring instant computer-generated content. Not to mention, the nature of producing Islamic art patterns requires dedicating long hours to the craft. Subsequently, these patterns have not caught up with today's latest technological advances, and it is feared that these patterns will potentially be overlooked and lose importance over time. In this paper, we seek to close the gap between technology and Islamic patterns by using one of the most recent deep learning algorithms, Generative Adversarial Networks (GANs). Specifically, we focus on GANs that are used to generate art. To begin, we start by exploring different architectural models of GANs to assist us in choosing a base model. Then, we proceed with testing the chosen model by collecting a significant number of images, feeding these images to the network, and discussing the results obtained. During the first training attempts, we show examples of GANs failure detected by image replication and discuss how the problem was resolved. Additionally, after producing the trained network and resulting images, we apply multiple techniques to improve the variety and quality of the images, such as applying truncations. Finally, the results were analyzed using quantitative methods by producing graphs and metric scores, and qualitative methods by subjecting the results to Islamic design experts.
Robust Object Detection Model for UAV Application

Not only common issues on object detection task need to be deal with, for Unmanned Aerial Vehicle (UAV) applications, small object is one of the critical problems that needs to be solved. YOLOv7 is a powerful network architecture that provides high efficiency and accuracy object detection results. This paper adopts YOLOv7 as an object detection model for two different kinds of targets, one is vehicle, and the other is ocean flotsam. By training the model with open datasets and fine-tuning the model with self-collected datasets, we prove through sequences collected from real-world scenarios that YOLOv7 is able to provide robust and accurate object detection results, including vehicles and ocean flotsam, with real-time efficiency. Based on such experimental result, we confirmed that YOLOv7 can be the baseline for object detection model development.
Pruning and Mixed Precision Techniques for Accelerating Neural Network

This study investigates the use of pruning and mixed precision techniques to enhance neural network performance, focusing on the AlexNet model trained on the MNIST dataset. Pruning removes unnecessary components, while mixed precision optimizes memory and computation efficiency. The study applies structured pruning to create a pruned model, which achieves improved inference time compared to the baseline. Automatic mixed precision is also employed, further enhancing inference speed. Combining pruning and mixed precision in a single model yields superior performance, surpassing the individual approaches. The combined model achieves significantly faster inference time by leveraging both techniques. The research highlights the potential of combining pruning and mixed precision for faster and more efficient neural network computations, reducing network size, optimizing memory utilization, and accelerating computations. The findings provide valuable insights for integrating these techniques into the AlexNet model and lay the groundwork for future exploration in larger and more complex models. This work holds promise for developing faster and more efficient deep learning models to meet the demands of real-world applications, particularly in resource-constrained environments.