Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network for Overcrowded Public Transportation Pickup Truck Detection

Thailand has been on the World Health Organization (WHO)'s notorious deadliest road list for several years, currently ranking eighth on the list.Among all types of road fatalities, pickup trucks converted into vehicles for public transportation are found to be the most problematic due to their high occupancy and minimal passenger safety measures, such as safety belts.Passenger overloading is illegal, but it is often overlooked.The country often uses police checkpoints to enforce traffic laws.However, there are few or no highway patrols to apprehend offending drivers.Therefore, in this study, we propose the use of existing closed-circuit television (CCTV) traffic cameras with deep learning techniques to classify overloaded public transport pickup trucks (PTPT) to help reduce accidents.As the said type of vehicle and its passenger occupancy characteristics are unique, a new model is deemed necessary.The contributions of this study are as follows: First, we used various state-of-the-art object detection YOLOv5 (You Only Look Once) models to obtain the optimum overcrowded model pretrained on our manually labeled dataset.Second, we made our custom dataset available.Upon investigation, we compared all the latest YOLOv5 models and discovered that the YOLOv5L yielded the optimal performance with a mean average precision (mAP) of 95.1% and an inference time of 33 frames per second (FPS) on a graphic processing unit (GPU).We aim to deploy the selected model on traffic control computers to alert the police of such passenger-overloading violations.The use of a chosen algorithm is feasible and is expected to help reduce trafficrelated fatalities.
Formation and Development of Children's Periodical Design in Ukraine

The purpose of the paper. The article aims to perform art analysis in order to show the process of formation and development of design of children's periodicals in Ukraine, as well as to determine the prospects of this process. The research methodologyis in the application of a specific historical method to analyse the impact of design development on the promotion of children's periodicals in Ukraine, as well as to use the comparative analysis method and a historical and cultural approach, which help to identify the main trends in the development of children's periodicals in Ukraine. Scientific noveltyis an attempt to understand and identify the main trends of children's periodicals in Ukraine, which can influence the competitiveness of these publications in Ukraine and in the world. These trends are conditioned by historical, social, and cultural processes. Conclusions. The article shows the main characteristics of the concept of creation and development of children's periodicals in Ukraine and their importance for promoting publications. Literature sources are analysed, which confirms the need to develop new trends in the design of aesthetic and functional children's publications and the formation of the child's worldview due to this. The analysis of the essence of formation and development of children's periodical design indicates the interdependence of cultural heritage and achievements of the early twentieth century, which affects the popularization and quality of publications.&#x0D; Key words: design, graphic design, children's periodical, magazine, illustration, font.&#x0D;
CLGA Net: Cross Layer Gated Attention Network for Image Dehazing

In this paper, we propose an end-to-end cross-layer gated attention network (CLGA-Net) to directly restore fog-free images.Compared with the previous dehazing network, the dehazing model presented in this paper uses the smooth cavity convolution and local residual module as the feature extractor, combined with the channel attention mechanism, to better extract the restored features.A large amount of experimental data proves that the defogging model proposed in this paper is superior to previous defogging technologies in terms of structure similarity index (SSIM), peak signal to noise ratio (PSNR) and subjective visual quality.In order to improve the efficiency of decoding and encoding, we also describe a fusion residual module and conduct ablation experiments, which prove that the fusion residual is suitable for the dehazing problem.Therefore, we use fusion residual as a fixed module for encoding and decoding.In addition, we found that the traditional defogging model based on the U-net network may cause some information losses in space.We have achieved effective maintenance of low-level feature information through the cross-layer gating structure that better takes into account global and subtle features.We also present the application of our CLGA-Net in challenging scenarios where the best results in both quantity and quality can be obtained.Experimental results indicate that the present cross-layer gating module can be widely used in the same type of network.
Using Informative Score for Instance Selection Strategy in Semi-Supervised Sentiment Classification

Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service.Existing studies heavily rely on sentiment classification methods that require fully annotated inputs.However, there is limited labelled text available, making the acquirement process of the fully annotated input costly and labour-intensive.Lately, semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods.Nevertheless, some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training.Literature also shows that not all unlabelled instances are equally useful; thus identifying the informative unlabelled instances is beneficial in training a semi-supervised model.To achieve this, an informative score is proposed and incorporated into semisupervised sentiment classification.The evaluation is performed on a semisupervised method without an informative score and with an informative score.By using the informative score in the instance selection strategy to identify informative unlabelled instances, semi-supervised models perform better compared to models that do not incorporate informative scores into their training.Although the performance of semi-supervised models incorporated with an informative score is not able to surpass the supervised models, the results are still found promising as the differences in performance are subtle with a small difference of 2% to 5%, but the number of labelled instances used is greatly reduced from 100% to 40%.The best finding of the proposed instance selection strategy is achieved when incorporating an informative score with a baseline confidence score at a 0.5:0.5 ratio using only 40% labelled data.
Underwater Image Enhancement Using Customized CLAHE and Adaptive Color Correction

Underwater images degraded due to low contrast and visibility issues.Therefore, it is important to enhance the images and videos taken in the underwater environment before processing.Enhancement is a way to improve or increase image quality and to improve the contrast of degraded images.The original image or video which is captured through image processing devices needs to improve as there are various issues such as less light available, low resolution, and blurriness in underwater images caused by the normal camera.Various researchers have proposed different solutions to overcome these problems.Dark channel prior (DCP) is one of the most used techniques which produced a better Peak Signal to Noise Ratio (PSNR) value.However, DCP has some issues such as it tends to darken images, reduce contrast, and produce halo effects.The proposed method solves these issues with the help of contrast-limited adaptive histogram equalization (CLAHE) and the Adaptive Color Correction Method.The proposed method was assessed using Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and some images were collected from the internet.The measure of entropy (MOE), Measure of Enhancement (EME), Mean Square Error (MSE), and PSNR opted as performance measures during experiments.The values of MSE and PSNR achieved by the proposed framework are 0.26 and 32 respectively which shows better results.
Deep Transfer Learning Driven Automated Fall Detection for Quality of燣iving of Disabled Persons

Mobile communication and the Internet of Things (IoT) technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities, a fall event is an important cause of mortality and post-traumatic complications.Therefore, detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-Driven Automated Fall Detection (WOADTL-AFD) technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this, the proposed WOADTL-AFD model initially uses a modified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition, the WOADTL-AFD technique classifies the fall events using an extreme Gradient Boosting (XGBoost) classifier.In the presented WOADTL-AFD technique, the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposed WOADTL-AFD technique was experimentally validated using the benchmark datasets, and the results confirmed the superior performance of the proposed WOADTL-AFD method compared to other recent approaches.6720
A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency.Recently, researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it; this is called the stego image.This paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response (QR) code and maze game image generation.This system consists of two components.The first component contains two processes, encryption process, and hiding process.The encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game image.On the other hand, the second component contains two processes, extraction and decryption, which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image, respectively.The results were obtained using the bit error rate (BER) metric.These results confirmed that the system achieved high hiding capacity, good performance, and a high level of robustness against attackers compared with other coverless steganography methods.
Interaction of the National Library of Ukraine Named After V. I. Vernadskyi with Higher Education Institutions in the Context of Information Technology

One of the most important areas of modernization of the entire library and information sphere of the country is the establishment of close interaction of domestic libraries with higher education institutions. The aim of the article is to disclose the main trends and directions of fruitful cooperation of V. I. Vernadskyi National Library of Ukraine with domestic and foreign institutions of higher education. The methodological basis of the article was formed by a complex of general scientific and special methods of scientific research. The principle of unity of interconnection with modern conditions is used; normative and alternative approaches; methods of analysis, source search, cognitive, structural-functional, descriptive, synthesis. The scientific novelty is to argue the diversification interaction of the V. I. Vernadskyi National Library of Ukraine with domestic and foreign institutions of higher education as an invasive process in the near future by the following vectors: change of practice strategies and internship programs; V. I. Vernadskyi National Library of Ukraine participation in competitions of student scientific works; Qualitative modification in the field of advanced training (V. I. Vernadskyi National Library of Ukraine as a base for teachers of profile higher education institutions and vice versa profile higher education institutions – base for V. I. Vernadskyi National Library of Ukraine specialists) and others. Conclusions. The impact of globalization and dynamic information technology of society requires acceleration of integration processes, modernization of structure and qualitative strategy of interaction between the educational services market and the labor market, so 1) it is necessary to develop optimal forms of interaction on partnerships between profile higher education institutions and V. I. Vernadskyi National Library of Ukraine at a rapid rate Communications through the enrichment of interaction methods, diversification of platforms (forums, meetings, round tables, etc.), 3) rationally spread the best experience of both domestic interaction and foreign. The interaction of V. I. Vernadskyi National Library of Ukraine with higher education institutions is constructively developed in such tested areas as international cooperation, cooperation, partnership, integration, coordination and cooperation. International cooperation of V. I Vernadskyi National Library of Ukraine with foreign higher education institutions is implemented through traditional document exchange, joint organization and active participation in scientific conferences, membership in the editorial boards of periods of professional publications. The promising direction of development of information service is the V. I. Vernadskyi National Library of Ukraine partner interaction with the libraries of leading domestic higher education institutions.
A Novel Efficient Patient Monitoring FER System Using Optimal DL-Features

Automated Facial Expression Recognition (FER) serves as the backbone of patient monitoring systems, security, and surveillance systems.Real-time FER is a challenging task, due to the uncontrolled nature of the environment and poor quality of input frames.In this paper, a novel FER framework has been proposed for patient monitoring.Preprocessing is performed using contrast-limited adaptive enhancement and the dataset is balanced using augmentation.Two lightweight efficient Convolution Neural Network (CNN) models MobileNetV2 and Neural search Architecture Network Mobile (NasNetMobile) are trained, and feature vectors are extracted.The Whale Optimization Algorithm (WOA) is utilized to remove irrelevant features from these vectors.Finally, the optimized features are serially fused to pass them to the classifier.A comprehensive set of experiments were carried out for the evaluation of real-time image datasets FER-2013, MMA, and CK+ to report performance based on various metrics.Accuracy results show that the proposed model has achieved 82.5% accuracy and performed better in comparison to the state-of-the-art classification techniques in terms of accuracy.We would like to highlight that the proposed technique has achieved better accuracy by using 2.8 times lesser number of features.
Dataset of Large Gathering Images for Person Identification and Tracking

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi, Madinah, Saudi Arabia.This dataset consists of raw and processed images reflecting a highly challenging and unconstraint environment.The methodology for building the dataset consists of four core phases; that include acquisition of videos, extraction of frames, localization of face regions, and cropping and resizing of detected face regions.The raw images in the dataset consist of a total of 4613 frames obtained from video sequences.The processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented data.The dataset further consists of 8 images corresponding to each of the 250 subjects (persons) for a total of 2000 images.It portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality (resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors, it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research purposes.We have also gathered and displayed records of the presence of subjects who appear in presented frames; in a temporal context.This can also be used as a temporal benchmark for tracking, finding persons, activity monitoring, and crowd counting in large crowd scenarios.
An Efficient Long Short-Term Memory Model for Digital Cross-Language Summarization

The rise of social networking enables the development of multilingual Internet-accessible digital documents in several languages.The digital document needs to be evaluated physically through the Cross-Language Text Summarization (CLTS) involved in the disparate and generation of the source documents.Cross-language document processing is involved in the generation of documents from disparate language sources toward targeted documents.The digital documents need to be processed with the contextual semantic data with the decoding scheme.This paper presented a multilingual crosslanguage processing of the documents with the abstractive and summarising of the documents.The proposed model is represented as the Hidden Markov Model LSTM Reinforcement Learning (HMM lstm RL).First, the developed model uses the Hidden Markov model for the computation of keywords in the cross-language words for the clustering.In the second stage, bi-directional long-short-term memory networks are used for key word extraction in the cross-language process.Finally, the proposed HMM lstm RL uses the voting concept in reinforcement learning for the identification and extraction of the keywords.The performance of the proposed HMM lstm RL is 2% better than that of the conventional bi-direction LSTM model.
Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Generally, conventional methods for anomaly detection rely on clustering, proximity, or classification.With the massive growth in surveillance videos, outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient.This research explores the structure of Graph neural networks (GNNs) that generalize deep learning frameworks to graph-structured data.Every node in the graph structure is labeled and anomalies, represented by unlabeled nodes, are predicted by performing random walks on the node-based graph structures.Due to their strong learning abilities, GNNs gained popularity in various domains such as natural language processing, social network analytics and healthcare.Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies.The Graph-based deep learning networks are designed to predict unknown objects and outliers.In our case, they detect unusual objects in the form of malicious nodes.The edges between nodes represent a relationship of nodes among each other.In case of anomaly, such as the bike rider in Pedestrians data, the rider node has a negative value for the edge and it is identified as an anomaly.The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome.Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities, which shows a huge potential in automatically monitoring surveillance videos.Performing autonomous monitoring of CCTV, crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places.The suggested GNN model improves accuracy by 4% for the Pedestrian 5418 CMC, 2023, vol.74, no.3 2 dataset and 12% for the Pedestrian 1 dataset compared to a few state-ofthe-art techniques.
A Novel 2D Hyperchaotic with a Complex Dynamic Behavior for Color Image Encryption

The generation method of the key stream and the structure of the algorithm determine the security of the cryptosystem.The classical chaotic map has simple dynamic behavior and few control parameters, so it is not suitable for modern cryptography.In this paper, we design a new 2D hyperchaotic system called 2D simple structure and complex dynamic behavior map (2D-SSCDB).The 2D-SSCDB has a simple structure but has complex dynamic behavior.The Lyapunov exponent verifies that the 2D-SSCDB has hyperchaotic behavior, and the parameter space in the hyperchaotic state is extensive and continuous.Trajectory analysis and some randomness tests verify that the 2D-SSCDB can generate random sequences with good performance.Next, to verify the excellent performance of the 2D-SSCDB, we use the 2D-SSCDB to generate a keystream for color image encryption.In the encryption algorithm, the encryption algorithm scrambles and diffuses simultaneously, increasing the cryptographic system's security.The horizontal correlation, vertical correlation, and diagonal correlation of ciphertext are -0.0004,-0.0004 and 0.0007, respectively.The average information entropy of the ciphertext is 7.9993.In addition, the designed encryption algorithm reduces the correlation between the three channels of the color image.Security analysis shows that the color image encryption algorithm designed using 2D-SSCDB has good security, can resist standard attack methods, and has high efficiency.
Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

The news ticker is a common feature of many different news networks that display headlines and other information.News ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory authorities.In this paper, we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news channel.The primary emphasis of this research is on ticker recognition methods and storage schemes.To that end, the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification method.The proposed learning architecture considers the grouping of homogeneousshaped classes.This incorporates linguistic taxonomy in a unified manner to address the imbalance in data distribution which leads to individual biases.Furthermore, experiments with a novel Arabic News Ticker (Al-ENT) dataset that provides accurate character-level and character components-level labeling to evaluate the effectiveness of the suggested approach.The proposed method attains 96.5%, outperforming the current state-of-the-art technique by 8.5%.The study reveals that our strategy improves the performance of lowrepresentation correlated character classes.