Reviewerly/exHarmony huggingface dataset:
An Adaptive Consumer Centric Management Model For Central Bank Digital Currency With Special Reference To Coimbatore District

A revolutionary turning point in the financial services industry has been reached with the introduction of Central Bank Digital Currency (CBDC). This research proposes an adaptable Consumer-Centric Management Model (CCMM) designed for the distinct financial environment of Coimbatore District, with the potential to transform digital transactions and consumer interaction. The model uses insights from customer experiences and preferences, with a focus on consumer protection, privacy, and security. To collect both quantitative and qualitative data for the study, a mixed-method approach is employed, which involves conducting stakeholder interviews and surveys. The Coimbatore District's financial institutions, central banks, and regulatory bodies can incorporate CBDC in a way that puts the interests of their customers first by following the guidelines provided by the CCMM. It covers stakeholder cooperation, risk management, consumer awareness, and regulatory compliance.
The Study of Fundamental Concepts of Agent and Multi-agent Systems

The concept of n intelligent agent is a concept that is born from the area of artificial intelligence; in fact, a commonly-accepted definition relates the discipline of artificial intelligence with the analysis and design of autonomous entities capable of exhibitin intelligent behavior. From that perspective, it is assumed that an intelligent agent must be able to perceive its environment, reason about how to achieve its objectives, act towards achieving them through the application of some principle of rationality, and interact with other intelligent agents, being artificial or human [1]. Multi-agent systems are a particular case of a distributed system, and its particularity lies in the fact that the components of the system are autonomous and selfish, seeking to satisfy their own objectives. In addition, these systems also stand out for being open systems without a centralized design [2]. One main reason for the great interest and attention that multi-agent systems have received is that they are seen as an enabling technology for complex applications that require distributed and parallel processing of data and operate autonomously in complex and dynamic domains.
Breast cancer prediction using K Nearest Neighbors, Support Vector Machine Techniques

Breast cancer is the trickiest and sneakiest diseases that modern science is aware of. It is one of the most important causes of death for women worldwide. We introduce the SVM (Support Vector Machine) and KNN (K Nearest Neighbors), which are the machine learning algorithms for breast disease diagnosis by training its attributes, and we present an original prediction of breast cancer. The suggested system employs 10-fold cross validation to produce accurate results. The UCI machine learning warehouse was used to obtain the Wisconsin breast melanoma diagnosis data set. The accurateness, sensitiveness, specificity, false detection rate, false exclusion rate, and Matthews' correlation coefficient are used to evaluate the presentation of the suggested system.
Adversarial Regularized Class Incremental Autoencoder Technique with Convolution Neural Network for Secure Clustering of Short Text in the High Dimensional Data

High Dimensional data Clustering with differential privacy has been gained significant attention recently in the large scale distributed cloud data center to cluster the high dimensional data with increased security to the data outsourced. Especially deep learning architecture based on deep adversarial regularized hierarchical autoencoder structure provides efficient clustering solution to high dimensional data containing non linear long text. However it provides class imbalance problem in terms of over-fitting and under-fitting issues with high computation complexity. To mitigate those challenges, class incremental deep adversarial regularized multi-view autoencoder technique is proposed in this work. Initially, missing value prediction using factor analysis and dimensionality reduction and data normalization is carried out to high dimensional data using principle component analysis. Principle component analysis technique eliminates the reconstruction errors occurring due to short text in the dimension space to project the sparse matrix. Sparse matrix is used to the select the discriminative features using non linear discriminant analysis. Selected feature is employed to the convolution neural network to process the selected features to establish the cluster using fully connected layer with activation function and softmax layer. Convolution Neural Network is capable of clustering structurally similar attributes and its information containing the normalized short text with increased classes. Proposed model achieves minimized intra cluster similarity and inter cluster variation by computing the data affinity of new representation. Further autoencoder model is to encode cluster structure containing attribute information by reconstructing the adjacency matrix. Decoder model retrieves or suggest the data records on basis of the attribute similarity or structure similarity of the record attribute or user attribute of the high dimensional data. Detailed experimental analysis has been performed on benchmarks datasets to compute the proposed model performance with conventional approaches using cross fold validation. The performance outcome represents that proposed architecture can produce good accuracy and effectiveness on high dimensional data containing the short-text.
A New form of Art Through Paper Messy Arts

Shri B. Mohan Negi Ji the pride of uttarakhand was a great person. who is known as the pride of uttarakhand, due to his glorious work and fame, he was been honoured with the God Vibhuti Samman of uttarakhand : from have a new look to the pictures of which the paper mache art.&#x0D; Arts and crafts are a profound expression of the emotional life of the people anywhere. A close and living relationship ties land to the people to the craft and culture. This is particularly true of uttarakhand.&#x0D; Here changes economic, social and religious during many centuries are reflected in our arts and crafts. The creative urge of our crafts people have thus varied at different periods.&#x0D; Uttarakhand is not only home to the vast cultural and ethnic diversity but also the myriad arts and crafts that have been carefully nurtured for the centuries.&#x0D; A variety of motifs, techniques and craft flourished in the land as the people from regions flocked through the beautiful place and many of the skilled craftsmen desided to settle amidst its charming abundance of natural beauty with time, these arts ave gained even more distinctiveness and paper mache, wood carving hand knotted carpets and let of other traditional crafts.In Uttarakhand one meets with all those arts and crafts which are in most cities, such as stone polishing, stone cutting, window cutting etc.
Complete Oriented Deviation Texture Loss Based GAN (CODT_GAN) for MRI Image Denoising

The use of denoising is crucial in MRI applications for diagnosing medical conditions. MRI pictures frequently include undesirednoises that might impair the accuracy of a pathological diagnosis. Deep learning networks have recently been used to generate a large number of models for MRI denoising with excellent results. However, the majority of them are incapable of providing clients with additional perceptual features of the image without information loss. In this paper, a novel structural and texture loss approach named Complete Oriented Deviated Texture Loss (CODTL) based GAN (CODT_GAN) was proposed to complete the image denoising task. Initially, a GAN network was used to produce images to resemble original images as closely as feasible. Then the GAN generated image is fed into the proposed CODTL operator to estimate textural loss. This proposed approach incorporates structural loss and textural loss, which has been used to fill in the gaps left by GAN network denoising of MRI images. The effectiveness of the proposed work is evaluated by BraTS’17 dataset. The results of the experiments show that the suggested approach is more reliable and performs better than the traditional methods in terms of both the degree of denoising and retention of the anatomical structures, textural details, and defined contrast.
Evaluation of Responsive With Big Data Technologies

The volume of produced digital data &amp; everyday usage is increasing as a result of rising man-machine interaction, process automation, and falling hardware and software costs.&#x0D; The vast amount of digital information produced per second worldwide in unstructured, structured, and semi-structured formats is referred to as big data in this context.&#x0D; Big data analytics is a new topic that has inspired researchers all over the world to create, develop, and implement a variety of mechanisms, technologies, architectures, and areas for evaluating the enormous amount of data created every day. Traditional database management systems struggle to make sense of the massive amounts of data that make up "big data." The paper describes a few analyses, including word count, sentiment analysis, and responsive analysis.&#x0D; Responsiveness is crucial for improving the system, identifying vulnerabilities, and ensuring that tasks are distributed fairly. Responsive is important data that can be used for making wise decisions.&#x0D; Responsive is significant for both strengths and weaknesses, not just when it identifies flaws. If the responsive analysis is performed incorrectly, the analysis's conclusion will likewise be incorrect. As a result, the system as a whole will be inaccurate because the pattern that was discovered will also be incorrect. We will utilize the Map-Reduce framework to create this suggested system for responsive analysis, and Hadoop will be used for storage.
SmishSMS- The Latest Detection of SMS Phishing Trends

Banking frauds is an emergency issue of concern in today’s busy schedule of the people around the world which has left many banking organizations bankrupt, and also has led many customers to a deadly torment. It is a cyber-security attack, where each day the fraudsters are inventing newer techniques which uses Short Message Service (SMS) to steal the credentials of mobile users. This paper has recklessly examined the e-banking frauds, the different methods for detection of e-banking frauds, control of banking frauds and the challenges associated with the detection and control of e-banking frauds. This paper focuses on Comparative analysis with different machine-learning techniques and the methods of the features extracted and classified. The paper also shows the further scope of study to boom the efficiency of the system.
Optimizing the Application of Hybrid Cryptographic Methods for Secure File Storage and Retrieval

&#x0D; &#x0D; &#x0D; &#x0D; In the modern digital era, the protection of sensitive data from unauthorized access is of utmost importance. A key concern in this regard is secure file storage. To address this issue, hybrid cryptography offers a viable solution by combining the strengths of various encryption techniques in both a vertical and horizontal manner. In hybrid cryptography, the original symmetric key is encrypted with a different symmetric key, while the actual data is encrypted using yet another symmetric key. This approach allows for quick and efficient encryption and decryption of the data, while also providing the added security of multiple encryption methods for authentication. By employing hybrid cryptography, organizations can ensure the secure storage of their critical data, safeguarding it against unauthorized access and potential data breaches.&#x0D; &#x0D; &#x0D; &#x0D;
Role of Organizational Culture on Relationship between Knowledge Management and Teacher's Performance for National Security

The safety and security of any nation depend on its organizational culture, knowledge management and teachers' performance. Good organizational culture leads to good performance, likewise, knowledge management helps in improving good organizational culture which in general can lead to safety and security in any State. This paper explores the role of organisational culture in the relationship between knowledge management and teacher's performance for national security. The study has two research objectives and two research hypotheses. The study adopted a quantitative research design. The population was 7986 out of which 370 were selected. The sample was selected using a multi-stage sampling technique. The data was collected via a questionnaire. Survey questionnaires were distributed and analyzed using Smart PLS (4.0) statistical software. The findings revealed a positive relationship between knowledge management and teacher performance. It further highlights the importance of effective knowledge acquisition, sharing, storage, and application processes. Equally, the moderating role of organizational culture indicates that a strong and supportive culture enhances the impact of knowledge management on teachers’ performance. The study recommends among others that; government and educational administrators in Sokoto State should model cultural values and take responsibility for nurturing the desired culture. The Ministry of Education should put in place policies promoting effective teacher management and engagement, which are critical for developing a positive image for any educational organisation and creating a strong organisational culture.
Impact of Phishing Activity on Youth: A Study in Srikakulam District of Andhra Pradesh.

During Pandemic, there was a significant raise in online purchases that enabled the hackers to launch a series of fake logistic attacks, posing as delivery firms like UPS or DHL Similar patterns were seen occurring during the Christmas, Sankranti/Pongal holidays every year. Keeping these facts in view, the present study focused on phishing attacks on festive changes. The qualitative study developed semi structured interview schedule and adopted snow ball sampling technique to collect data. Data was analyzed using manual content analysis. The study reveals that the Phishing attacks increased during festive season due to excessive time and online access to shopping.
DIDACTICS OF DISTANCE LEARNING AND TRAINING

The aim of the article is to systematize knowledge about distance learning and training, characteristics of the subject and basic concepts of didactics, an attempt to identify the theoretical and practical roots of the changes introduced in didactics forced by technical and technological development, to show the essence and types of distance learning and training, the principles and methods of distance education and to present the advantages and disadvantages of distance education.
Penerapan Building Information Modeling (BIM) dalam Perencanaan Rumah Susun Kementerian Sosial BBRSPDF Kota Surakarta 6 Lantai

Construction planning and management is carried out as an attempts to find out various aspects that will be use in facilitating the construction, so it is following the applicable requirements, building functions, and economy. In construction projects, problems are often found at the planning stage or during construction, such as clashes between design elements or between schedules which can cause delays in project construction, large amounts of waste generated after construction, cost overruns during construction, and other problems caused by a lack of accuracy and poor coordination that can disrupt the productivity of construction projects. As technology develops, Building Information Modeling (BIM) is use as a solution to overcome various problems in the construction sector with a system that can collect and manage important data in the construction planning process that is integrated into accurate 3D modeling. In this final project, Building Information Modeling (BIM) will be used to plan the 6-floor Flats of the Ministry of Social Affairs BBRSPDF Surakarta City. Outputs be generated can be in the form of superstructure and substructure plans, namely the design of foundation structures, columns, beams, and plates, 2D Detail Engineering Design (DED), 3D modeling, and construction management plans in the form of Budget Plans, and scheduling.
Comparative analysis of automated breast volume scanner (ABVS) combined with conventional hand-held ultrasound and mammography in female breast cancer detection

This study aimed to compare the diagnostic value of an automated breast volume scanner (ABVS) combined with conventional hand-held ultrasound and mammography in detecting female breast cancer. Early detection is vital in improving patient outcomes for this prevalent disease.Seventy-eight suspicious breast lesions from 60 patients were examined between August 2019 and July 2020. Each patient underwent ABVS, conventional hand-held ultrasound, and mammography. Diagnostic values, including coincidence rate, sensitivity, specificity, positive predictive value, and negative predictive value, were calculated using histopathology results as the "gold standard."Histopathology confirmed 55 malignant (70.51%) and 23 benign lesions (29.48%). ABVS combined with conventional hand-held ultrasound identified 56 malignant (52 confirmed, 4 benign) and 22 benign nodules (3 confirmed, 19 benign). Mammography detected 48 malignant (45 confirmed, 3 benign) and 30 benign nodules (10 confirmed, 20 benign). ABVS combined with conventional hand-held ultrasound had a sensitivity of 94.5%, specificity of 82.6%, positive predictive value of 92.9%, and negative predictive value of 86.4%. Mammography showed a sensitivity of 81.8%, specificity of 87.0%, positive predictive value of 93.8%, and negative predictive value of 66.7%.ABVS combined with conventional hand-held ultrasound showed high diagnostic value in detecting female breast cancer. The "convergence sign" in the coronal section played a significant role. It slightly outperformed mammography and offered advantages in terms of cost, convenience, comfort, and absence of radiation. Further promotion and implementation are supported.
PERSPECTIVE ON BARRIERS TO E-LEARNING: INSIGHT FROM INDONESIAN PARENTS

The phenomenon of online learning has grown at a rapid pace over the years. Despite its benefits, online learning also comes with certain drawbacks, such as a shortage of access to reliable network connectivity, digital literacy, and socializing. The current study aims to explore the perceptions of parents regarding the challenges of distance learning faced by their children in Indonesia. Questionnaires via google forms and interviews were used to diagnose the parents’ responses to handicaps that existed during distance learning. The participants comprised 30 parents whose children joined online schooling. The findings revealed that parents experienced various kinds of obstacles in their efforts to help their children learn remotely such as (1) personal barriers (2) technical barriers (3) logistical barriers, and (4) financial barriers. The trend toward online learning is projected to continue and grow in the coming years with more dialogical, synchronic, pluralist, open, and accessible to various groups.
THE PROBLEM OF PROTECTING THE INTERESTS OF CREATORS OF IDEAS IN THE RUSSIAN AND FOREIGN LEGAL SYSTEMS

Abstract: the article considers the issue of protecting the interests of the idea creators. This issue in the Russian law is primarily discussed within the framework of copyright, namely in the context of the dichotomy of form/content. The task is to identify options for protecting the idea creators with the help of different civil constructions. The author refers to the foreign experience in legal regulation. In the Anglo-American legal family, there is a way to protect the idea creators as a claims based on an implied contract for the idea disclosure (Desny claims). In the RomanGerman legal family, the idea protection can be carried out within the framework of unfair competition law, without granting a private right to the idea owner. Based on the foreign and Russian experiences, it is concluded that it is advisable to protect the interests of the idea creators with the help of other legal structures of private law, not limited to the norms of intellectual property law. The corresponding conclusion is determined by the need to balance the interests of society and the idea creators, as well as the principle of justice.
Weighted Two-Hidden-Layer Extreme Learning Machine Method with Improved Gray Wolf Optimization for Complex Data Classification

Extreme Learning Machine (ELM) is widely popular for its advantages such as fast training speed and good generalization performance. However, the randomness of hidden layer parameters in ELM leads to unstable prediction performance of the model. We propose a novel two-hidden-layer extreme learning machine (TELM) for complex data classification. Firstly, the idea of weighting is introduced into TELM, and a weighted two-hidden-layer extreme learning machine (WTELM) model is proposed to improve the classification accuracy of the model. Secondly, the convergence factor and position update formula in gray wolf optimization algorithm (GWO) are adjusted to enhance the optimization algorithm’s ability to search for optimal parameters. Finally, an improved gray wolf optimization (IGWO) is utilized to search for the optimal parameters of the WTELM model. The impact of different intelligent optimization algorithms on the model’s classification results is compared. The experimental results demonstrate that the classification accuracy of the WTELM model has been improved by about 11–18% compared to traditional ELM models. Moreover, compared to the WTELM model, the classification accuracy of the IGWO-WTELM model has improved by about 1–19%. This indicates that the method proposed in this paper is significantly superior to traditional ELM methods and their variants, improving the stability, training speed and classification accuracy of the model.
An End-to-End Video Coding Method via Adaptive Vision Transformer

Deep learning-based video coding methods have demonstrated superior performance compared to classical video coding standards in recent years. The vast majority of the existing deep video coding (DVC) networks are based on convolutional neural networks (CNNs), and their main drawback is that since CNNs are affected by the size of the receptive field, they cannot effectively handle long-range dependencies and local detail recovery. Therefore, how to better capture and process the overall structure as well as local texture information in the video coding task is the core issue. Notably, the transformer employs a self-attention mechanism that captures dependencies between any two positions in the input sequence without being constrained by distance limitations. This is an effective solution to the problem described above. In this paper, we propose end-to-end transformer-based adaptive video coding (TAVC). First, we compress the motion vector and residuals through a compression network built on the vision transformer (ViT) and design the motion compensation network based on ViT. Second, based on the requirement of video coding to adapt to different resolution inputs, we introduce a position encoding generator (PEG) as adaptive position encoding (APE) to maintain its translation invariance across different resolution video coding tasks. The experiment shows that for multiscale structural similarity index measurement (MS-SSIM) metrics, this method exhibits significant performance gaps compared to conventional engineering codecs, such as ×264, ×265, and VTM-15.2. We also achieved a good performance improvement compared to the CNN-based DVC methods. In the case of peak signal-to-noise ratio (PSNR) evaluation metrics, TAVC also achieves good performance.