Reviewerly/exHarmony huggingface dataset:
Interpolative AMBTC based Reversible Data Hiding In Encrypted Images using Rhombus Mean

This paper proposes an interpolative absolute moment block truncation coding (IAMBTC) based reversible data hiding method in encrypted images (RDHEI) using rhombus mean. The proposed method aims to increase the embedding efficacy by reserving more room before encryption, for embedding the confidential data. For this, the cover image is divided into a checkerboard pattern - grey cells and white cells. Grey cells are calculated using rhombus mean to group the same intensity pixels. Next, the IAMBTC is employed first on the grey cells and difference errors are stored. Afterward, the white cells are compressed using I <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AMBTC</inf> and their difference is also stored. The complete difference information is then compressed using Huffman coding to condense its size and make bigger rooms for embedding. Experimental results demonstrate that the suggested method outperforms existing related methods in terms of hiding efficiency.
RGB-D Dataset: The Impact of Yoga and Gym Exercise for Human Activity Recognition

Gesture technology is the future technology because it is widely used in different fields like computer game and sports, e-commerce, assistant system and Human robot interaction. Body gesture one of the most important recognition gesture for navigation automation cars, surveillance, 3D-animations, health sector and so many. Therefore, in this paper two types of datasets Gym dataset and yoga dataset are created for body gesture recognition. Gym dataset contains total body exercises RGB images 1992 of 1080 x 608 pixels with two subjects and two classes and same in yoga dataset total RGB images are 3012 of 2336 x 1080 pixels with one subject and four classes. All datasets are captured by camera at different views which has special configuration. This dataset is free to use for only experiment purpose.
A Paradigm Shift towards Computer Vision

In today’s world, machine learning, artificial intelligence, IoT, deep learning and several other techniques have become the need of the moment. One such division of artificial intelligence is computer vision. The main goal of computer vision development is to create paradigms for extracting data and information from images. It has various applications in the fields of industry, agriculture, automations, healthcare, e-commerce and much more. The study examines the most recent events and conceptual frameworks governing the progress of computer vision, with a focus on pattern recognition and image processing, using a variety of applications from the t field. This article attempts to discuss the most current results and applications in computer vision.
Layerwise Adversarial Learning for Image Steganography

Image steganography is a subfield of pattern recognition. It involves hiding secret data in a cover image and extracting the secret data from the stego image (described as a container image) when needed. Existing image steganography methods based on Deep Neural Networks (DNN) usually have a strong embedding capacity, but the appearance of container images is easily altered by visual watermarks of secret data. One of the reasons for this is that, during the end-to-end training process of their Hiding Network, the location information of the visual watermarks has changed. In this paper, we proposed a layerwise adversarial training method to solve the constraint. Specifically, unlike other methods, we added a single-layer subnetwork and a discriminator behind each layer to capture their representational power. The representational power serves two purposes: first, it can update the weights of each layer which alleviates memory requirements; second, it can update the weights of the same discriminator which guarantees that the location information of the visual watermarks remains unchanged. Experiments on two datasets show that the proposed method significantly outperforms the most advanced methods.
Explaining and Visualizing Embeddings of One-Dimensional Convolutional Models in Human Activity Recognition Tasks

Human Activity Recognition (HAR) is a complex problem in deep learning, and One-Dimensional Convolutional Neural Networks (1D CNNs) have emerged as a popular approach for addressing it. These networks efficiently learn features from data that can be utilized to classify human activities with high performance. However, understanding and explaining the features learned by these networks remains a challenge. This paper presents a novel eXplainable Artificial Intelligence (XAI) method for generating visual explanations of features learned by one-dimensional CNNs in its training process, utilizing t-Distributed Stochastic Neighbor Embedding (t-SNE). By applying this method, we provide insights into the decision-making process through visualizing the information obtained from the model's deepest layer before classification. Our results demonstrate that the learned features from one dataset can be applied to differentiate human activities in other datasets. Our trained networks achieved high performance on two public databases, with 0.98 accuracy on the SHO dataset and 0.93 accuracy on the HAPT dataset. The visualization method proposed in this work offers a powerful means to detect bias issues or explain incorrect predictions. This work introduces a new type of XAI application, enhancing the reliability and practicality of CNN models in real-world scenarios.
An Improved CCF Detector to Handle the Problem of Class Imbalance with Outlier Normalization Using IQR Method

E-commerce has increased online credit card usage nowadays. Similarly, credit card transactions have increased for physical sales and purchases. This has increased the risk of credit card fraud (CCF) and made payment networks more vulnerable. Therefore, there is a need to develop a precise CCF detector to control such online fraud. Previously, many studies have been presented on CCF detection and gave good results and performance. However, these solutions still lack performance, and most of them have ignored the outlier problem before applying feature selection and oversampling techniques to give solutions for classification. The class imbalance problem is most prominent in available datasets of credit card transactions. Therefore, the proposed study applies preprocessing to clean the feature set at first. Then, outliers are detected and normalized using the IQR method. This outlier normalizes data fed to the Shapiro method for feature ranking and the 20 most prominent features are selected. This selected feature set is then fed to the SMOTEN oversampling method, which increases the minority class instances and equalizes the positive and negative instances. Next, this cleaned feature set is then fed to five ML classifiers, and four different splits of holdout validation are applied. There are two experiments conducted in which, firstly, the original data are fed to five ML classifiers and the holdout validation technique is used, in which the AUC reaches a maximum of 0.971. In Experiment 2, outliers are normalized, features are selected using the Shapiro method, and oversampling is performed using the SMOTEN method. This normalized and processed feature set is fed to five ML classifiers via holdout validation methods. The experimental results show a 1.00 AUC compared with state-of-the-art studies, which proves that the proposed study achieves better results using this specific framework.
Digital Currency and Blockchain Technology in the Financial World

A distributed ledger, called blockchain, keeps track of every detail about a digital asset. Using this technology, users can utilize the benefits of digital currency. Digital currency has a massive effect on the banking sector. It would be wise to be informed of the technologies that might seriously disrupt the Indian banking sector's operations because it plays a crucial part in the economy and contributes considerably to the nation's gross domestic product. The introduction of this technology in the banking industry significantly reduces fraud resulting from mistakes and identity theft. The specifics of blockchain technology and how it affects the Indian banking industry are discussed in this chapter. It also provides details about various types of digital currency, highlighting their advantages and disadvantages. Much emphasis will be on the importance of blockchain technologies in the future of digital currency. The research's conclusions were strengthened and condensed using SWOT analysis.
A <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>k</mml:mi></mml:math> nearest neighbour ensemble via extended neighbourhood rule and feature subsets

kNN based ensemble methods minimise the effect of outliers by identifying a set of data points in the given feature space that are nearest to an unseen observation in order to predict its response by using majority voting. The ordinary ensembles based on kNN find out the k nearest observations in a region (bounded by a sphere) based on a predefined value of k. This scenario, however, might not work in situations where the test observation follows the pattern of the closest data points with the same class that lie on a certain path not contained in the given sphere. This paper proposes a k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. This new ensemble method is applied on 20 benchmark datasets and compared with other classical methods, including kNN based models, in terms of classification accuracy, kappa and Brier score as performance metrics. Boxplots are also utilised to illustrate the difference in the results given by the proposed and other state-of-the-art methods. The proposed method outperformed the considered classical methods in the majority of cases. The proposed method is further assessed through a detailed simulation study.
Slide deep reinforcement learning networks: Application for left ventricle segmentation

Automatic segmentation of the left ventricle (LV) in four-chamber view images is critical for computer-aided cardiac disease diagnosis. The complex structure of the cardiac image and the encoder-decoder networks may cause coarse segmentation results. High-accuracy LV segmentation is still a challenge with existing automatic LV segmentation methods. In this paper, we propose a slide deep reinforcement learning segmentation network for pixelwise LV segmentation. The main architecture of the slide reinforcement learning networks consists of a slider item combined state, a group of morphology transforming actions and an agent network. The specifically designed reinforcement learning state comprises an image item and a slider item, which contains both original image information and network act information. The reinforcement learning actions proposed in this paper enable accurate and fast formulation of the binary segment result for each frame by controlling the length and location of the slider. Additionally, the confidence branch proposed in our experiment provides a continuous frame series environment, and the identification algorithm avoids losing the segmentation target. The segmentation result reveals that the proposed method outperforms FCN, SegNet, U-Net and TransUnet. The IoU improved by 23.01%, 15.4%, 11.24% and 6.9%. Additionally, we demonstrate how the proposed method can be used as a semisupervised method, which is more convenient for the image annotation process.
Marine Predators Algorithm for Sizing Optimization of Truss Structures with Continuous Variables

In this study, the newly developed Marine Predators Algorithm (MPA) is formulated to minimize the weight of truss structures. MPA is a swarm-based metaheuristic algorithm inspired by the efficient foraging strategies of marine predators in oceanic environments. In order to assess the robustness of the proposed method, three normal-sized structural benchmarks (10-bar, 60-bar, and 120-bar spatial dome) and three large-scale structures (272-bar, 942-bar, and 4666-bar truss tower) were selected from the literature. Results point to the inherent strength of MPA against all state-of-the-art metaheuristic optimizers implemented so far. Moreover, for the first time in the field, a quantitative evaluation and an answer to the age-old question of the proper convergence behavior (exploration vs. exploitation balance) in the context of structural optimization is conducted. Therefore, a novel dimension-wise diversity index is adopted as a methodology to investigate each of the two schemes. It was concluded that the balance that produced the best results was about 90% exploitation and 10% exploration (on average for the entire computational process).
Cloud-Based Smart Contract Analysis in FinTech Using IoT-Integrated Federated Learning in Intrusion Detection

Data sharing is proposed because the issue of data islands hinders advancement of artificial intelligence technology in the 5G era. Sharing high-quality data has a direct impact on how well machine-learning models work, but there will always be misuse and leakage of data. The field of financial technology, or FinTech, has received a lot of attention and is growing quickly. This field has seen the introduction of new terms as a result of its ongoing expansion. One example of such terminology is “FinTech”. This term is used to describe a variety of procedures utilized frequently in the financial technology industry. This study aims to create a cloud-based intrusion detection system based on IoT federated learning architecture as well as smart contract analysis. This study proposes a novel method for detecting intrusions using a cyber-threat federated graphical authentication system and cloud-based smart contracts in FinTech data. Users are required to create a route on a world map as their credentials under this scheme. We had 120 people participate in the evaluation, 60 of whom had a background in finance or FinTech. The simulation was then carried out in Python using a variety of FinTech cyber-attack datasets for accuracy, precision, recall, F-measure, AUC (Area under the ROC Curve), trust value, scalability, and integrity. The proposed technique attained accuracy of 95%, precision of 85%, RMSE of 59%, recall of 68%, F-measure of 83%, AUC of 79%, trust value of 65%, scalability of 91%, and integrity of 83%.
Modification of Genetic Algorithm Based on Extinction Events and Migration

This article presents a genetic algorithm modification inspired by events related to great extinctions. The main objective of the modification was to minimize the number of objective function solutions until the minimum for the function was established. It was assumed that, within each step, a population should be smaller than that recommended in the applicable literature, the number of iterations should be limited, the solution area should be variable, and a great extinction event should take place following several iterations. Calculations were performed for 10 individuals within a population, 10 iterations, two generations each, with a great extinction event happening once every three iterations. The developed algorithm was presented, capable of indicating the minimum number of Eggholder and Rastrigin functions, with a higher probability than the master algorithm (default “ga” in MATLAB) at the same number of objective function solutions. An algorithm was proposed focusing on minimizing the randomization of the objective function, which may be an alternative to the surrogate model. Typically, the emphasis is on achieving as much accuracy as possible. This article presents a method for minimizing the randomization of the objective function and obtaining the highest possible accuracy. A method is presented which minimizes the disadvantages of the largest computation time and the need to generate many samples for typical genetic algorithms (GAs). Optimization results for the classic GA, GEGA, WOA, SMA, and SSA algorithms for the Eggholder and Rastrigin functions were compared. A modification of the genetic algorithm was made to obtain a global extreme with satisfactory accuracy and a sufficiently high probability, while minimizing the number of samples calculated on the basis of the objective function. The developed methodology was used to fulfill the target function for the turbine disc.
Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives

The stream mining paradigm has become increasingly popular due to the vast number of algorithms and methodologies it provides to address the current challenges of Internet of Things (IoT) and modern machine learning systems. Change detection algorithms, which focus on identifying drifts in the data distribution during the operation of a machine learning solution, are a crucial aspect of this paradigm. However, selecting the best change detection method for different types of concept drift can be challenging. This work aimed to provide a benchmark for four drift detection algorithms (EDDM, DDM, HDDMW, and HDDMA) for abrupt, gradual, and incremental drift types. To shed light on the capacity and possible trade-offs involved in selecting a concept drift algorithm, we compare their detection capability, detection time, and detection delay. The experiments were carried out using synthetic datasets, where various attributes, such as stream size, the amount of drifts, and drift duration can be controlled and manipulated on our generator of synthetic stream. Our results show that HDDMW provides the best trade-off among all performance indicators, demonstrating superior consistency in detecting abrupt drifts, but has suboptimal time consumption and a limited ability to detect incremental drifts. However, it outperforms other algorithms in detection delay for both abrupt and gradual drifts with an efficient detection performance and detection time performance.
PSYCHO-EMOTIONAL HEALTH OF MEDICAL STUDENT AT THE STAGE OF CHOOSING THE FUTURE DIRECTION OF PROFESSIONAL ACTIVITY

Abstract. The current realities exacerbate the crisis of adaptation of the individual, especially the adolescent, who is acutely experiencing an intense sense of anxiety due to the ever-increasing impact of the emotional factors, such as fear for their own safety due to the current military realities of our country. This can cause a loss of authenticity and a sense of psychological well-being. Therefore, the article analyzes in detail the concepts of “mental health”, “psychological health”, “psycho-emotional health”, identifies common and distinctive characteristics. Attention is focused on the psycho-emotional health of a student of a higher medical institution when choosing a future profession under stressful conditions. The key factors influencing the psychoemotional health of a medical student are determined. It is established that emotions play a motivating and semantic role in the choice of future professional activity, because they generate and mobilize the perception, thinking and actions of a medical student. Taking into account a number of studies, it is stated that psycho-emotional health is a guarantor of active action of the future medical specialist, aimed at realizing the need and possibility of self-realization. In addition, it is stipulated that vitality and stress resistance are of great importance in the context of understanding the psychological health of the individual at the stage of his professional development. Generally speaking, it is about the protective and compensatory parameters of personality resources throughout life, which directly or indirectly affect the components of psychological health. It is noted that each component of psycho-emotional health in this complex structural formation performs a corresponding function. Instead, it is inappropriate to consider the independent and independent role of the components. The psycho-emotional health of a student determines his ability to self-regulate behavior, activate his own vital resources, comprehend his life position, choose a future profession and assert himself in society.
Roles of Blockchain Technology in Supply Chain Capability and Flexibility

Blockchain technology is a major innovation that has swept through global supply chains recently. Blockchain technology has received immense attention in the supply chain industry due to its promising capabilities. This study was conducted to evaluate the potential capabilities of blockchain technology, which are highly relevant to the supply chain industry. To improve the understanding of the effect of blockchain on the supply chain, this research focuses on two crucial aspects of supply chain management, namely, supply chain capabilities and flexibility. The research procures measuring items for blockchain characteristics, supply chain capabilities, and flexibility through a questionnaire, the previous literature, and interviews conducted with industry experts. Through the use of statistical analysis, this study identifies the relationship between the above variables. The effect of blockchain on each variable is examined using a simple linear regression model. The findings disclose that blockchain technology has generated a notable impact on the supply chain capabilities and supply chain flexibility of firms. This makes blockchain technology highly essential for firms to generate a competitive advantage in the market and develop a new set of capabilities ahead of their competitors.
Learning Peer Recommendation Based on Weighted Heterogeneous Information Networks on Online Learning Platforms

With the development of online education, there is an urgent need to solve the problem of the low completion rate of online learning courses. Although learning peer recommendation can effectively address this problem, prior studies of learning peer-recommendation methods extract only a portion of the interaction information and fail to take into account the heterogeneity of the various types of objects (e.g., students, teachers, videos, exercises, and knowledge points). To better motivate students to complete online learning courses, we propose a novel method to recommend learning peers based on a weighted heterogeneous information network. First, we integrate the above different objects, various relationships between objects, and the attribute values to links in a weighted heterogeneous information network. Second, we propose a method for automatically generating all meaningful weighted meta-paths to extract and identify meaningful meta-paths. Finally, we use the Bayesian Personalized Ranking (BPR) optimization framework to discover the personalized weights of target students on different meaningful weighted meta-paths. We conducted experiments using three real datasets, and the experimental results demonstrate the effectiveness and interpretability of the proposed method.