Reviewerly/exHarmony huggingface dataset:
Computer System of Analysis of the Mass Exam Results

Pattern recognition is becoming increasingly utilized within extensive information systems. The combination of advancements in image processing theory and the availability of open source libraries allows for the application of innovative solutions to various practical problems. One such problem involves automatically processing the answers from large-scale exams. This paper presents a developed system designed specifically for handling the results of these exams, demonstrating its ability to provide reliable, efficient, and unbiased assessments.
Decision-Making Based on Information in Medical Media Resources

The article proposes an approach for decision-making based on the information gathered from the medical social media environment. It develops an algorithm based on sentiment analysis and machine learning methods through the Valence Aware Dictionary and Sentiment Reasoner approach to select the best clinic based on patient reviews collected in medical media resources. The results of the experiment on patient reviews are presented in the cms_hospital_satisfaction_2020 database by the Kaggle company.
Fusion Ensemble Modeling Method Using Bayesian Inference and Binary Tree Algorithm

The traditional ensemble modeling method has problems such as complex calculation, low precision, and insufficient generalization performance. Therefore, this article proposes three modeling methods based on Bayesian inference and binary tree fusion ensembled (BTFE). Among them, Bayesian inference are used as the selection rule and to distinguish the adaptability of sub-model. Three different modeling methods based on Bagging and binary tree are proposed to replace the traditional complex ensemble model, which reduces the training difficulty of the model and improves the interpretability of the ensemble model. The binary tree and Bayesian inference fusion method is proposed to construct the selective ensemble model. Experimental results on benchmark data sets show the effectiveness of the proposed method.
Non-Functional Requirement Extracting Methods for AI-based Systems: A Survey

Artificial intelligence (AI) systems are growing rapidly in a variety of fields and have become a part of everyday human life. Traditional software development methods, especially in the requirements engineering domain, are not very efficient in the development of AI-based Systems. Given that the elicitation and analysis of software project requirements is the key to producing a high-quality and efficient product, there must be new approaches to identifying and extracting requirements in the field of AI-based systems. This is done in the research area of Requirements Engineering for Artificial Intelligence (RE4AI). Non-functional requirements are defined in accordance with the system's objectives and operational requirements. They specify the behaviors, limitations, and overall quality of the system; for this reason, they are very important for the success of the system, and lack of attention or neglect to accurately identify it will increase the probability of project failure. In this Paper, we aim to provide a comprehensive overview of the approaches and techniques for extracting non-functional requirements for AI-based systems by reviewing recent papers in this field. The review of these studies shows that there are currently no accurate methods for identifying and extracting non-functional requirements of AI-based Systems, but by relying on traditional methods and redefining their concepts, most of these methods can also be used in this domain. Additionally, new requirements for the field of AI have been introduced that are of high importance.
Performance Evaluation of Algorithms for Anomaly Detection Using Apache Spark

Detecting outliers gain increasing attention as they have various application fields like fraud detection, medical analysis, intrusion detection and so on. There have been established different techniques and methods such as distance-based, density-based, statistical-based, ensemble-based, learning-based methods and this process is still ongoing to find out the more effective algorithms as some of them give the desired result on a certain data set but not effective for the different data set and vise versa. This paper compares the experimental result of the most popular methods on the publicly available datasets using Apache Spark and helps the researchers to shape the future of investigation in regarding to the calculation of outliers.
Social Engineering in Social Network: A Systematic Literature Review

Social engineering is hacking and manipulating people's minds to obtain access to networks and systems in order to acquire sensitive data. A social engineering attack happens when victims are unaware of the strategies utilised and how to avoid them. Although rapid developments in communication technology made communication between individuals easier and faster, on the other hand, individuals' personal and private information is likely to be available online via social networking or other services without adequate security measures to protect such sensitive data. Hackers can use social engineering to target them no matter the technology they use to protect themselves. The methods differ, and the goal is the same, which is to manipulate and deceive organisations, companies, and individuals to obtain sensitive and private in-formation that attackers can benefit from, perhaps to sell it on the dark web or steal the payment card information of victims. The current research presents the attack techniques used in social engineering, as well as ways for pre-venting social engineering assaults. The major purpose of this study is to systematically and impartially conduct a systematic review of previous research on current social engineering attacks and the methods used to reduce these attacks.
Arabic Sentiment Analysis of Food Delivery Services Reviews

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier.
A Federated Learning Approach to Banking Loan Decisions

Machine Learning (ML) techniques for decision-making have become a topic of popular interest for many organizations. Such models, however, depend on data availability which is not always possible. Banks, for example, often have limited data about their customers. While this data could be supplemented with information from other banks, data sharing leads to privacy concerns due to regulations such as the General Data Protection Regulation (GDPR) and the California Customer Privacy Act (CCPA). An alternative method proposed to solve such a problem is Federated Learning (FL), where the data is not shared, but the models created by each entity are shared to keep privacy. FL has been applied across various sectors and industries, but not all. In this paper, we implement an FL algorithm in a new sector, namely, banking and loans. We used an architecture where multiple banks within the same country, each possessing different customer information, want to evaluate eligibility for loan provision. Our objective is to design an FL model where the banks share the data without exposing any customer hidden information. We implemented our FL model using Flower on the publicly available Loan dataset on Kaggle. Our model achieved an accuracy of 81%.
Interpreting Live Video Sign Language and Translate into Text

American Sign Language (ASL) is a method of communication developed for individuals who are deaf. While there are over 4 million individuals in the deaf community, around 500,000 individuals understand ASL. This research will explore machine learning techniques and a camera to interpret live video sign language and translate the ASL lexicon into text. This is performed through MediaPipe object detection and Open Computer Vision (Open CV) to track a user's hand and body movements and convert the footage into numerical values. These values are then compared to the dataset of the ASL alphabet that we have prepared, through 4 machine learning techniques. These techniques include Sequential Model, Random Forest, Naive Bayes and K-Nearest Neighbors (KNN) algorithms and are used to create machine learning models. The results suggest that KNN is the most accurate model (reporting accuracy up to 94%), where the optimal K value was 1. Random Forest tests accurately (up to 87%) when implemented and tested.
Exploring Classification Models for Video Source Device Identification: A Study of CNN-SVM and Softmax Classifier

Video Source device identification plays a crucial role in video forensics as the proliferation of video capturing devices has given rise to crimes with videos that are challenging to trace. Reliance on metadata extraction is insufficient as it can be corrupted or manipulated to conceal the source of the crime. Another technique employed for source identification is noise pattern extraction, which generates a unique identification for the video camera. However, this method is susceptible to capture faults and can produce diverse noise patterns for each video. In addressing these challenges, there is a need to identify distinctive features that are consistent across all videos captured by the same camera. This has led to the adoption of computer vision techniques utilizing machine learning and deep learning. Classifiers play a crucial role in machine learning and data analysis, as they are responsible for categorizing or predicting results based on input data. Our experiments show that the subject is sensitive to classifiers and developing a good classifier or classifier-level fusions can improve results in practice for all datasets.
Merging Decoded Results of Block Projection with Continuous Degree in Structured Light

The proposed method in this study aims to address the issue of interreflection in structured light scanning. Interreflection occurs when the fringes projected onto the object interfere with each other, leading to inaccurate 3D reconstructions. To alleviate this problem, the researchers employ block projection. This involves projecting fringes onto the object in a block-wise manner. By doing so, the interference between fringes is reduced, minimizing the impact of interreflection on the reconstruction process. Once the block projection images are captured, several filters are applied to remove any erroneous decoding regions. The median filter and standard deviation filter are used to identify and eliminate incorrect decoding values. Additionally, the Epipolar line is utilized to further refine the decoding results. To merge the different decoding blocks, a novel method based on the continuity of decoding values is proposed. This method ensures that the merging process is smooth and accurate, resulting in a more reliable 3D reconstruction. The experimental results demonstrate the effectiveness of the proposed method in merging different decoding blocks. The method successfully mitigates the impact of interreflection, leading to improved accuracy in the reconstructed 3D model.
Efficient Vision Transformer for Accurate Traffic Sign Detection

This research paper addresses the challenges associated with traffic sign detection in self-driving vehicles and driver assistance systems. The development of reliable and highly accurate algorithms is crucial for the widespread adoption of traffic sign recognition and detection (TSRD) in diverse real-life scenarios. However, this task is complicated by suboptimal traffic images affected by factors such as camera movement, adverse weather conditions, and inadequate lighting. This study specifically focuses on traffic sign detection methods and introduces the application of the Transformer model, particularly the Vision Transformer variants, to tackle this task. The Transformer’s attention mechanism, originally designed for natural language processing, offers improved parallel efficiency. Vision Transformers have demonstrated success in various domains, including autonomous driving, object detection, healthcare, and defense-related applications. To enhance the efficiency of the Transformer model, the research proposes a novel strategy that integrates a locality inductive bias and a transformer module. This includes the introduction of the Efficient Convolution Block and the Local Transformer Block, which effectively capture short-term and long-term dependency information, thereby improving both detection speed and accuracy. Experimental evaluations demonstrate the significant advancements achieved by this approach, particularly when applied to the GTSDB dataset.
Toward Detection of Arabic Cyberbullying on Online Social Networks using Arabic BERT Models

Cyberbullying is one of the serious threats on social networks particularly toward children and teenagers. Cyberbullying can cause many harmful consequences towards victims such as anxiety, depression and even suicide. This research studies cyberbullying detection in Arabic language on online social networks using deep learning techniques to automatically detect and quarantine the cyberbullying messages to safe children from getting exposed to harmful cyberbullying content. In this paper, real Arabic dataset is collected from YouTube and Twitter that is annotated manually to improve the quality of the data. All experiments went through three rounds of trials to make sure that the results are consistent. Many evaluation metrics were used to evaluate the performance of the classifiers such as macro F1 score and AUC. The best model achieves 84.58% and 85.94% in F1 score and AUC respectively.
Exploring the Learning Potential of ELM from Finite Difference Solutions for Heat Equation

The study compares two methods, the finite difference and extreme learning machine (ELM), for solving the one-dimensional heat equation. The finite difference is a classical numerical method, while ELM is a machine learning-based approach that does not use a trial function to represent the solution. The results show that ELM can learns the finite difference. However, it should be noted that ELM faces challenges when directly solve the one-dimensional heat equation itself, as shown in the results. Despite the limitations observed in directly solving the one-dimensional heat equation with ELM, the study suggests that ELM still holds promise as a potentially viable alternative to classical numerical methods for solving partial differential equations (PDEs). Further research could explore incorporating optimization methods or employing a two-phase neural network, as proposed in our future work, to improve the accuracy of ELM's predictions for PDEs.
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique

Identifying human emotions using AI-based computer vision systems, when individuals wear face masks, presents a new challenge in the current Covid-19 pandemic. In this study, we propose a facial emotion recognition system capable of recognizing emotions from individuals wearing different face masks. A novel data augmentation technique was utilized to improve the performance of our model using four mask types for each face image. We evaluated the effectiveness of four convolutional neural networks, Alexnet, Squeezenet, Resnet50 and VGGFace2 that were trained using transfer learning. The experimental findings revealed that our model works effectively in multi-mask mode compared to single-mask mode. The VGGFace2 network achieved the highest accuracy rate, with 97.82% for the person-dependent mode and 74.21% for the person-independent mode using the JAFFE dataset. However, we evaluated our proposed model using the UIBVFED dataset. The Resnet50 has demonstrated superior performance, with accuracies of 73.68% for the person-dependent mode and 59.57% for the person-independent mode. Moreover, we employed metrics such as precision, sensitivity, specificity, AUC, F1 score, and confusion matrix to measure our system's efficiency in detail. Additionally, the LIME algorithm was used to visualize CNN's decision-making strategy.
DI-ESN: Dual Input-Echo State Network for Time Series Forecasting

Echo State Network (ESN) is a typical version of the Recurrent Neural Network model (RNN) which is characterized by sparse reservoir and simple linear output. It has been utilized in several applications, especially for time series forecasting. Nonetheless, the ESN has some drawbacks that are mainly related to the reservoir properties and initialization (weights and connection). Thus, creating an efficient ESN model represents a challenging task. Relying on the initial structure of ESN, we propose an improved version called Dual Input-ESN (DI-ESN). This work aims to optimize the prediction error. Experimental results demonstrate that the DI-ESN model outperforms existing improved ESN models in terms of prediction accuracy.
CTI Challenges and Perspectives as a Comprehensive Approach to Cyber Resilience

The significant growth in the number, variety, and sophistication of cyber threats in recent years makes traditional approaches ineffective against new generation cyber threats. More effective mechanisms against cyber threats and attacks require intelligent analysis of the threats themselves. Cyber Threat Intelligence (CTI) is a new approach based on artificial intelligence technologies aimed at collecting, analyzing and resolving current and potential attacks that threaten the security of an organization or its assets. This thesis explores CTI technologies and considers problems and advantages in this area.
Project solutions and communication strategies for the successful implementation of the grant project “Digital collection of book heritage of the Shevchenko Scientific Society (1874-1913)”

Grant projects play a pivotal role in augmenting the financial resources available to address pressing needs within Ukrainian libraries during periods of martial law. For institutions like the Vasyl Stefanyk National Scientific Library of Ukraine in Lviv, these grants hold the key to safeguarding invaluable literary artifacts and facilitating remote accessibility to a wider audience, particularly researchers who have relocated abroad or those facing financial constraints impeding scholarly travels. Many of these initiatives receive support from both foreign and domestic establishments backed by Ukrainian nonprofit cultural entities. Notably, the Ukrainian Cultural Fund (UCF) stands as a prominent benefactor, dedicated to financing cultural and artistic endeavors meticulously selected through expert evaluation. This study delves into the UCF’s core directive of endorsing cultural heritage preservation, restoration, protection, and promotion as a means to reinforce contemporary Ukrainian identity and forge shared civilizational values. To this end, the “Cultural Heritage” grant initiative has been launched, focusing on projects encompassing digitization, restoration, presentation, and dissemination of printed materials, archival records, audiovisual content, museum artifacts, and more. This year, the Vasyl Stefanyk National Scientific Library of Ukraine in Lviv participated in the UCF grant competition with the project “The Book Heritage of the Shevchenko Scientific Society (1874-1913): digital collection”.
Lempel-Ziv-based Hyper-Heuristic Solution for Longest Common Subsequence Problem

The longest common subsequence of a given set of strings is a string whose characters are common among all of them with two properties of being ordered but not consecutive. The general form of LCS problem belongs to the NP-hard problems, therefore, finding the exact LCS cannot be done in a reasonable time. Thus, heuristic methods have been proposed to find the best possible LCS. However, each heuristic is useful for a specific type of set. Some of them are useful for highly correlated strings and others are useful for less-correlated sets. Therefore, there is no single heuristic for any type of the set. This work introduces a hyper-heuristic that computes the correlation among a given set and accordingly assigns the given set to the proper heuristic. To compute a correlation measure, it uses the concepts of the Lempel-Ziv complexity and the longest common substring. The results show that the proposed hyper-heuristic outperforms the state-of-the-art heuristics and hyper-heuristics.
Differentially Private All-Pairs Shortest Distances for Low Tree-Width Graphs

In this paper, we present a polynomial time algorithm for the problem of differentially private all pair shortest distances over the class of low tree-width graphs. Our result generalizes the result of Sealfon [10] for the case of trees to a much larger family of graphs. Furthermore, if we restrict to the class of low tree-width graphs, the additive error of our algorithm is significantly smaller than that of the best known algorithm for this problem, proposed by Chen et. al. in [3].
Analysis of Semantic Relationships in Ukrainian Text Content Based on Word2Vec and Machine Learning

The study used a corpus of Ukrainian texts from books by different authors to train the Word2Vec model to identify semantic relationships between words. The project aims to analyse semantic relations between words and to construct vector representations of words using the word2vec method. The tasks of the research include data preparation, training of the model, analysis of the obtained vectors and comparison of the results with existing methods. Large amounts of high-quality Ukrainian language datasets for training the model, creating an effective word2vec learning algorithm and developing the user interface are the main challenges of the project. Using Python and TensorFlow, an effective application of the word2vec method is achieved, showing high performance. It runs for about 2.8 seconds and uses a limited amount of memory, ranging from 140 to 200 megabytes.