Reviewerly/exHarmony huggingface dataset:
Secure Face and Liveness Detection with Criminal Identification for Security Systems

The advancement of computer vision, machine learning, and image processing techniques has opened new avenues for enhancing security systems. In this research work focuses on developing a robust and secure framework for face and liveness detection with criminal identification, specifically designed for security systems. Machine learning algorithms and image processing techniques are employed for accurate face detection and liveness verification. Advanced facial recognition methods are utilized for criminal identification. The framework incorporates ML technology to ensure data integrity and identification techniques for security system. Experimental evaluations demonstrate the system's effectiveness in detecting faces, verifying liveness, and identifying potential criminals. The proposed framework has the potential to enhance security systems, providing reliable and secure face and liveness detection for improved safety and security.&#x0D; The accuracy of the algorithm is 94.30 percent. The accuracy of the model is satisfactory even after the results are acquired by combining our rules inwritten by humans with conventional machine learning classification algorithms. Still, there is scope for improving and accurately classifying the attack precisely.
Feature based-Learning with Data Increasing for video Recommendation and Computing

Image content analysis is crucial for determining the reliability of a link between two videos. Video characteristics are increasingly being used in image and video representation as custom pre-trained picture and video convolutional neural networks become generally available. People also have limited access to video editing tools for a variety of reasons, such as ownership and privacy concerns. You don't need to go back to the source video data to use the refined features again. An affine transformation, for instance, can be used to map a well-studied function onto an unfamiliar domain. To do this, we use a unique triplet failure in conjunction with the re-learning strategy. We propose a contemporary data augmentation method that may be applied to functionality on various frames for videos as an alternative to employing specific motion data. Extensive testing on the well-known Hulu content-based Video Relevance challenge demonstrates the process's efficacy and provides solid evidence of state-of-the-art performance.
Expressivity of Tweets on Social Issues Using Aspect Based Text Classification

Social discussions about crime on Twitter and open forums aims to understand the barriers that hinder people from expressing their concerns or aligning with popular opinions. A curated dataset spanning three months in 2023 was collected, covering categories like crimes and Gender Equality and Violence Against Women.&#x0D; The study employs aspect-based sentiment analysis to classify sentiment polarity in tweets, utilising a comprehensive framework involving three text feature classification stages. The initial stage analyses individual words, phrases, and tweet patterns to classify text features based on specific linguistic elements. In the subsequent step, semantic relations explore a better understanding of the core sentiment and infer relationships between different text keywords. This stage enhances the analysis by considering the meaning and contextual nuances of the language used in the tweets. The final stage incorporates transformer-based models for effective multilabel classification to view the diversity present in the dataset. The study's quantitative analysis reveals that the Ensemble learning approach demonstrates an impressive precision measure of 93%. By integrating the three stages of text feature classification, the study enhances the accuracy and comprehensiveness of sentiment analysis in social discussions about crime on Twitter.
Towards the Exploration of Task and Workflow Scheduling Methods and Mechanisms in Cloud Computing Environment

Cloud computing sets a domain and application-specific distributed environment to distribute the services and resources among users. There are numerous heterogeneous VMs available in the environment to handle user requests. The user requests are defined with a specific deadline. The scheduling methods are defined to set up the order of request execution in the cloud environment. The scheduling methods in a cloud environment are divided into two main categories called Task and Workflow Scheduling. This paper, is a study of work performed on task and workflow scheduling. Various feature processing, constraints-restricted, and priority-driven methods are discussed in this research. The paper also discussed various optimization methods to improve scheduling performance and reliability in the cloud environment. Various constraints and performance parameters are discussed in this research.
Detect and Evaluate Visual Pollution on Street Imagery Taken of a Moving Vehicle

Visual pollution is a growing problem in urban areas. It is important for environmental management to identify, formalize, measure and evaluate visual pollution. This paper presents a study on the development of an automated system for visual pollution classification using street images taken from a moving vehicle. The proposed system uses convolutional neural networks to classify different types of visual pollutants such as graffiti, faded signage, potholes, litter, construction zones, broken signage, poor street lighting, poor billboards, road sand, sidewalk clutter, and unmaintained facades.In this study, we utilized a large dataset of raw sensor camera inputs gathered from a fleet of multiple vehicles in a specific geographical area. Our aim was to develop convolutional neural networks that simulate human learning to classify visual pollutants from these images. The successful implementation of this system would be a significant contribution to the development of urban planning and the strengthening of communities worldwide. Additionally, it could lead to the creation of a "visual pollution score/index" for urban areas, which could serve as a new metric for urban environmental management. Our findings, which we present in this paper, will be a valuable addition to the academic community and the field of computer vision for environmental management applications.
Implementing Vertical Drainage to Reduce Flood Risk at Settlement Area in Medan

&#x0D; With the increasing number of residents in a city, there will be excessive exploitation of nature, uncontrolled land use changes and a decrease in the carrying capacity of the environment. These various results have a potential to increase the frequency and severity of flood disasters. Flooding is a problem for the town because it leads in losses of property and lives, the emergence of health issues, damage to different infrastructure facilities and infrastructure, among other things. Although the government has been developing a horizontal drainage system for years, it has yet to decrease urban flooding. Consequently, a new technique is required to overcome flooding, specifically the use of vertical drainage. In an attempt to improve the community's living conditions, this activity's goal is to reduce the risk of flooding in Kelurahan Pasar Merah Timur settlement area. The advantages of this program include an improvement in the community's quality of life as a result of less intense flooding following the installation of vertical drainage. According to the test, 2 liters of water can be drained into the aquifer channel every second by the vertical drainage. So it can be assumed that if there is a puddle of water that cannot be drained by horizontal drainage of 4000 liters or 4 m3, it will take 34 minutes to deliver the water into the aquifer layer.&#x0D;
Visibility Enhancement in Turbid Water Based on Polarization and CLAHE

Abstract Underwater visual distance is usually limited due to absorption and scattering of light, particularly in turbid water scenes. Currently, most underwater optical image processing algorithms are developed for the post-processing of static single-frame images taken in shallow water environments to achieve clear, color restoration images. Moreover, in turbid water scenes without ambient light, relying solely on post-processing algorithms to obtain ideal image quality can be challenging. In this study, we seek for underwater image quality recovery solution that can be applied during underwater vehicle operations. Polarization technology is used to remove the veiling light, which is the main cause of image degradation. The optical devices are fitted by polarizing filters through simple modifications. Additionally, a contrast limited adaptive histogram equalization (CLAHE) algorithm is employed to improve contrast and modify the color cast of images caused by direct signal attenuation. The proposed solution is tested and evaluated on underwater images acquired in laboratory experiments and the results are compared against other underwater post-processing methods. The study expects this approach will enhance the clarity and visual range of underwater targets, thereby improving target recognition in underwater operations.
Automated Personalized Big Data Model to Promote Traditional Culture with Aesthetic Education

Big data can make significant contributions to the field of aesthetic education in universities. By analyzing large amounts of data, researchers can gain insights into student engagement with artistic content and better understand how students learn and appreciate the arts. Aesthetic education is a field of study that focuses on the cultivation of aesthetic sensibility and appreciation, as well as the development of skills in various forms of artistic expression. Aesthetic education in universities is that it helps to develop students’ emotional intelligence and empathy. Hence, in this paper constructed the automated framework model based on big data is constructed for Aesthetic education in universities. The constructed model is termed the Mamdani Fuzzy Set Optimization (MFsO) for the personalized automated model. The student information associated with aesthetic education in universities is processed with MFsO model. The MFsO model uses the fuzzy set rules for the personalized comments to the students for the promotion of tradition among students. The model uses the Flemingo Optimization model for the computation of the effective features in the big data for the generation of rules. The automated model uses the deep learning architecture model for the data transmission to the students. The comparative analysis stated that the proposed MFsO model performance is effective compared with the conventional techniques for the personalized automated system design.
Feature Extraction Techniques in Medical Imaging: A Systematic Review

With the surge in the development of various applications in the field of Computer Vision and Digital Image Processing, a significant amount of medical pictures are being produced. Thus, the patient-specific scan pictures represent the boundless volume of data that requires careful organization and supervision to assist clinical decision support systems. Now that retrieval, classification, segmentation, and other procedures have been completed, these devices assist doctors to uncover serious illnesses including skin conditions, tumors, and cancer. This imaging largely depends on characteristics to detect the afflicted region and perform the diagnosis visually. The authors of this paper present an overview of numerous feature extraction approaches used to extract features from medical images obtained via different modalities, but only used a handful of these techniques for this job and provided the findings.
Membership-Grade Based Prototype Rectification for Fine-Grained Few-Shot Classification

Few-shot fine-grained classification aims to recognize novel fine-grained categories with the help of a few examples. Under the impact of the low inter-class and high intra-class differences properties of fine-grained datasets, the prototype-based approach, which originally performed well in general FS classification, could not achieve the expected results. In this paper, we propose a transductive method consisting of a feature mapping module and a prototype rectification module. Specifically, the feature mapping module removes redundant attributes from the feature space to enhance the inter-class difference. The prototype rectification module assigns a pseudo-label for each query sample according to the membership-grade between the query samples and the prototypes and uses them to update the prototypes. Experiments on multiple popular fine-grained benchmark datasets and few-shot general classification datasets demonstrate the effectiveness of our approach.
PSML: Prototype-Based OSSL Framework for Multi-information Mining

Openset semi-supervised learning (OSSL) has been mainly used in recent years to address the negative impact of out-of-distribution (OOD) unlabeled data on semi-supervised learning. However, current openset semi-supervised learning approaches directly discard the identified ood data, while ignoring the positive impact of ood data similar to in-distribution (ID) data on semi-supervised learning. And the method based on learnable parameters is prone to overfitting ood unlabeled data. Therefore, we propose a prototype-based OSSL framework for multi-information mining (PSML) to better mine ood unlabeled data and improve the classification performance of ID data. Specifically, we detect the multi-level information density of unlabeled data by a learnable OOD detector and a non-learnable prototype similarity detector. Then we perform different degrees of prototype based information mining on the data with different information densities (ID data, similar data, and dissimilar data). As a result, PSML is able to perform better information mining in ood unlabeled data, and thus it achieves a significant performance improvement relative to previous work, as demonstrated in experiments on multiple benchmark datasets.
Multi-level Association Based 3D Multiple-Object Tracking Framework for Self-driving Cars

The process of fusing Camera and Light Detection and Ranging (LiDAR) data in real-time is acknowledged to be essential step in many applications, including automation industry, robotics and autonom-ous vehicles. The integration of data from these two sensors is effective, especially in case of autonomous cars, because it enables the detection of objects at both close and far distances as well as the depth. The combination of these features with a successful fusion approach helps in consistent and reliable perception of the environment because both sensors may capture several environmental properties at once. In the 3D Multi-Object Tracking (MOT) community, using 3D bounding boxes to find an object has become increasingly common over time. Nevertheless, occlusion are hard to handle in crowded scenes since ground-truth bounding boxes frequently overlap. In a recent research, many 3D MOT have worked and focused on tracking accuracy by computing feature extractors and complex cost function. However, some 3D MOT have focused on computational speed. The research gap here is the trade-off between speed and accuracy, and also to handle occlusion scenario. The paper proposes a simple and quick LiDAR-Camera sensors fusion-based 3D Multiple-Object Tracking method for Self-driving cars and the tracking results are demonstrated with consecutive frames of image sequence.
Implementation of the QR Code system in the Medical Malacology Collection of the René Rachou Institute, Oswaldo Cruz Foundation

The Medical Malacology Collection (Fiocruz-CMM) was inaugurated in 1993 and is located at the René Rachou Institute in Fiocruz (Belo Horizonte, MG, Brazil). The collection has about 16,000 limnic mollusks of medical and veterinary importance. Information about each specimen is stored in an electronic Microsoft Excel spreadsheet and is available on the Centro de Referência em Informação Ambiental (CRIA) webpage. A QR Code system was implemented for convenience: it can be printed in reduced format with flexible representation on different surfaces and has a low cost of implantation. A script in PHP language was developed to load the spreadsheet in XLS format using an open-license library, PHP-ExcelReader. A PHP script was created to read and process this spreadsheet line by line and generate individual HTML pages, which were formatted with the Bootstrap tool. Then, the PHP QR code was used to create the images of the QR codes corresponding to each specimen. The QR codes were printed and affixed to the lids of the bottles containing each specimen. Using a device with a camera and internet access, the QR code redirects to the file with the information for each mollusk. The use of QR codes at Fiocruz-CMM facilitates the organization of the collection and allows quick and easy access to information about each specimen.
The microstructural representation and fractal nature intepolation analysis of feldspar

This paper presents a comparative analysis of microstructural parameters by the powder X-ray diffraction method on a polycrystalline sample and the fractal analysis. The presented results of microstructural parameters are obtained by crystallographic programs FullProf based on the Rietveld method. The synthetic Ba/Ca-celsian sample has been used for microstructural parameters analysis. The feldspar sample has been synthesized by the thermal induction phase transformation process of ion exchange Ca-LTA zeolite with Ba cation. After the ion exchange Ba/Ca-LTA, the sample was heated at the temperature of 1300?C and the structure of Ba/Ca-feldspar was determined by the X-ray powder diffraction analysis. The X and U shape parameters that contribute to the strain have been reconstructed by fractal interpolation. Once we can reconstruct the shapes, which are quantum mechanics effects, we should provide the data for predicting desired parameters mentioned previously. We reported in this paper the successful application of this advanced and deep microstructure analysis, which confirmed original fractal copies based on the presented morphology characterization.
Iterative Deep Structural Graph Contrast Clustering for Multiview Raw Data

Multiview clustering has attracted increasing attention to automatically divide instances into various groups without manual annotations. Traditional shadow methods discover the internal structure of data, while deep multiview clustering (DMVC) utilizes neural networks with clustering-friendly data embeddings. Although both of them achieve impressive performance in practical applications, we find that the former heavily relies on the quality of raw features, while the latter ignores the structure information of data. To address the above issue, we propose a novel method termed iterative deep structural graph contrast clustering (IDSGCC) for multiview raw data consisting of topology learning (TL), representation learning (RL), and graph structure contrastive learning to achieve better performance. The TL module aims to obtain a structured global graph with constraint structural information and then guides the RL to preserve the structural information. In the RL module, graph convolutional network (GCN) takes the global structural graph and raw features as inputs to aggregate the samples of the same cluster and keep the samples of different clusters away. Unlike previous methods performing contrastive learning at the representation level of the samples, in the graph contrastive learning module, we conduct contrastive learning at the graph structure level by imposing a regularization term on the similarity matrix. The credible neighbors of the samples are constructed as positive pairs through the credible graph, and other samples are constructed as negative pairs. The three modules promote each other and finally obtain clustering-friendly embedding. Also, we set up an iterative update mechanism to update the topology to obtain a more credible topology. Impressive clustering results are obtained through the iterative mechanism. Comparative experiments on eight multiview datasets show that our model outperforms the state-of-the-art traditional and deep clustering competitors.
Cloud Computing: Solusi Untuk Information And Communication Technology

Cloud computing is one of the hottest topics that exist today, in the era of the continuous development of information and communication technology. The emergence of cloud computing technology is an effort to access resources and applications from anywhere via the internet, with this being able to overcome the limitations in utilizing the previous ICT infrastructure. Cloud computing is defined as a convenient network access model to unify the configuration of computing resources such as networks, servers, storage media, applications, and services, and can be configured quickly with minimal administrative effort or interactively. The discussion begins with a description of some opinions or points of view from previous studies on cloud computing technology, then reviewing the possible advantages and disadvantages of implementing cloud computing. Then it ends with a conclusion whether cloud computing is an ICT solution and recommendations that users can consider when implementing cloud computing.
Main Character in the Novel Cover of Night by Linda Howard: Psychological Analysis

Background: Novels are book-length fictional prose narratives, usually representing characters and actions with some degree of realism. Because of the main character in Linda Howard’s Novel Cover Night, it is important to study&#x0D; Purpose: to find out and describe the psychology of the main character in the novel Cover of Night by Linda Howard so that it helps obtain information and knowledge about the linguistic psychology of the main character, as well as developing literature. Studies related to character building.&#x0D; Method: This study uses the library method. The researcher will describe the main character of the novel. Namely Linda Howard’s novel “Cover of Night” as the primary data source. The other is about the characters in the novel. Researchers use a psychological approach to analyze the novel.&#x0D; Results and Discussion: The results of this study are that the main characters, Cate Nightingale and Calvin Harris, in this novel are portrayed positively as good characters, while Cate Nightingale is a loyal woman. She had no relationship with other men after her husband’s death. Her love for her husband made her blind and never attracted to other men. Cate is also a hardworking woman. He has to work hard to earn money to raise his children, so Calvin falls in love with Cate. Calvin Harris is a strong and mighty man with such a significant character that he makes an impression on one’s life.&#x0D; Implication: This research provides positive inspiration about Calvin’s personality as a character who has a heroic nature. This novel helps obtain information about the linguistic psychology of the main character, who is always brave in protecting someone he loves, even though he has to risk his life.