Reviewerly/exHarmony huggingface dataset:
Hiding Information in images using LSBSteganography technique

Technology advancements and the availability of high -speed internet have made data dissemination around the globe simple and cost-effective, causing individuals to be concerned about their privacy. Data transformation is becoming more common and is being used in more organisations. So, in order to ensure data security and prevent data loss and unau-thorised access, we created an image steganographic technique. Steganography is taken from the Greek word steganos, which means “covered text.” The study of discrete communication is known as steganography. This study describes a ridge analysis approach to finding secret messages encrypted in images. The information is encrypted and decrypted using a variety of techniques. Least Significant Bit (LSB) is one of the most well-known approaches.
Supervised and Unsupervised Prediction Application of Machine Learning

Machine learning field is introduced at the level of concept. Ideas such as supervised and unsupervised as well as regression are explained. The trade-off between bias, diversity, and the complexity of the model is discussed as the primary study guide concept. The different types of models that can be produced by machine learning are introduced as a neural network (feed and repetitive), vector support machine, random forest, map maps, and Bayesian network. Model training is discussed next with its main ideas for dividing the database into sets of training, testing, and validation and performing cross-validation. In this chapter, we begin by reviewing the basics of machine learning such as feature testing, unregulated and supervised learning and classification types. Then, we identify the main problems in designing a machine learning test and evaluating its performance. Finally, we introduce supervised learning methods. Modeling the quality of the model is handled next to the important role of the site specialist in keeping the project real. The paper concludes with some practical tips on how to create a machine learning project.
Classification of White Blood Cells Empowered with Auto Encoder and CNN

Differential counting of white blood cells (WBCs) is a well-established clinical practice for assessing a patient's immune system state. Information about our health state may be gained by determining the amount and type of white blood cells (WBCs). The quantity of white blood cells (WBCs) may be used to identify disorders such as leukemia, AIDS, autoimmune diseases, immunological deficiencies, and blood diseases. Convolutional Neural Networks (CNNs), in particular, have a tremendous impact on the medical industry, where a large quantity of pictures must be processed and studied. Images and objects may be categorized and identified using ACNN in this research. Input is provided in the form of raw pixels, and the algorithm outputs an indication of how likely it is that the pixels fall into one of many different categories. Convolution and pooling are added to each layer to minimize the parameter magnitude by a significant amount. The proposed approach will take images automatically from data set and reduce the size of images with auto approach for working faster. It is time-consuming and exhausting to manually locate, identify, and count the many WBC subclasses. Accuracy in classification and counting is directly related to the skill and knowledge of the workers
Using Cycling 3D Chaotic Map and DNA Sequences for Introducing a Novel Algorithm for Color Image Encryption

Today, social communication through the Internet has become more popular and has become a crucial part of our daily life. Naturally, sending and receiving various data through the Internet has also grown a lot. Keeping important data secure in transit has become a challenge for individuals and even organizations. Therefore, the trinity of confidentiality, integrity, and availability will be essential, and encryption will definitely be one of the best solutions to this problem. Of course, for image data, it will not be possible to use conventional encryption methods for various reasons, such as the redundancy of image data, the strong correlation of adj acent pixels, and the large volume of image data. Therefore, special methods were developed for image encryption. Among the prevalent methods for image encryption is the use of DNA sequences as well as chaos signals. In this paper, a cycling 3D chaotic map and DNA sequences are used to present a new method for color image encryption. Several experimental analyses were performed on the proposed method, and the results proved that the presented method is secure and efficient.
Enhancing the Performance Accuracy of Student Monitoring System by Face Detection and Tracking in Classroom Environment using RNN over MTCNN Algorithms

To enhance the accuracy of face recognition in a classroom environment using Recurrent Neural Network (RNN). The study consists of 2 groups i.e Recurrent Neural Network and Multi Cascaded Convolutional Neural Network (MTCNN) with sample size of 10 for each group. Gpower software is used to determine sample size with power value 0.8 and alpha is 0.05. Proposed model has an improved accuracy of 95.44% in facial detection and tracking than existing model of 91.73% with significance value of 0.97 (p¿0.05). End result of proposed model Recurrent neural network provides better accuracy when compared with Multi cascaded convolutional neural network.
Shadow Detection and Disparity Processing Based on Binocular Vision

In order to solve the problem of the large disparity errors in shadows, this paper proposes a sound framework for shadow detection and disparity processing. Firstly, the shadow boundary is obtained by segmenting the road image; secondly, the disparity is recalculated by template matching with a larger sliding window for the shadow boundary; then, the disparity with lower confidence in the original disparity is removed; finally, the blank value is filled by interpolating the disparity of the surrounding non-shadowed part. It is verified that the method proposed in this paper can effectively remove the disparities in the shaded part that cause the wrong height, and the height at the shaded part is significantly reduced after the treatment by this paper.
Cloud-based Data Extraction From Mobile Devices: A Preliminary Analysis

Most people nowadays are always occupied with their mobile devices. Mobile devices were utilized not just as a means of communication but also to launch applications installed to aid routine activities. The deployment of cloud storage applications to access cloud storage accounts is one of the most popular uses. Consumers have rapidly adopted cloud storage services from mobile devices, often available over the internet. However, this situation allows criminals to engage in illegal acts such as child pornography and violence. Since cloud services are virtual, non-local, and primarily proprietary technologies, digital forensics investigators encounter a significant challenge in resolving such issues. For investigators, data from mobile devices is vital. It will be stored on the mobile device and provide an overview of the user's cloud storage contents. This study focuses on Android devices and employs the MediaFire and Sync.com applications along with 20 data sets in various formats as samples. The mobile device will then be extracted using MSAB XRY under various circumstances (after the process of file modification, data cleanup, cache clearance, and application removal). The study contributes to the design of a data extraction framework guided by NIST 800–101 rev 1 and SWGDE Best Practices, as well as the documentation of the extraction procedure, as a step in ensuring that the data extraction process adheres to digital forensics standards. This research is projected to offer a fundamental reference for conducting digital forensic investigations of mobile cloud storage services.
ASSESSING EDUCATIONAL ATTAINMENTS OF PRIMARY SCHOOL LEARNERS: COMPETENCE EXTREMES

Introduction. This study analyses learning standards and regulatory documents, which are regarded as a basis for providing quality educational activities in primary schools. Subjected to analysis are different levels of pedagogical activities reflected in competence characteristics, which are determined by the state normative documents for primary schools. The results of the conducted analysis lead to a rightful conclusion about definite contradictions in understanding and interpreting their essence, which might be extrapolated to the author's elucidations of competence guidelines for students’ learning outcomes. Despite the fact that some logical and conceptual inconsistencies in the content of the State Standards of Primary Education have been eliminated of late, its current version cannot be considered ultimate and conclusive, as it requires further elaboration and improvement. The purpose of this article is to identify and expose conceptual inconsistencies in the understanding and exposition of competences, provided by the State Standards of Primary Education and suggested in contemporary pedagogical research. Results. In the study it is rationalized that competence extremes in defining and interpreting possible attainments of primary school students lead to inconsistencies in the interpretation of compulsory learning outcomes. Moreover, it is argued that competence-based assessment of primary school students' academic attainments is unjustified. Conclusion. The premise is advanced that in the current educational situation there is a necessity to pursue exploring methodological opportunities in order to avoid redundancy in goal setting and interpreting primary school students' academic attainments. With this in mind, it deems plausible to identify and expose efficacious means of achieving the established learning outcomes in the form of a definite scope of knowledge, habits, skills and abilities. The acquisition of the aforementioned attainments should correspond to conventional and content objectives of learning. Furthermore, it is emphasized that there is also a need to develop functional diagnostic tools that would elucidate students’ value orientations ​​and specify the strategies of enhancing their general and content mental performance.
Morphology Without Borders: Clause-Level Morphology

Abstract Morphological tasks use large multi-lingual datasets that organize words into inflection tables, which then serve as training and evaluation data for various tasks. However, a closer inspection of these data reveals profound cross-linguistic inconsistencies, which arise from the lack of a clear linguistic and operational definition of what is a word, and which severely impair the universality of the derived tasks. To overcome this deficiency, we propose to view morphology as a clause-level phenomenon, rather than word-level. It is anchored in a fixed yet inclusive set of features, that encapsulates all functions realized in a saturated clause. We deliver MightyMorph, a novel dataset for clause-level morphology covering 4 typologically different languages: English, German, Turkish, and Hebrew. We use this dataset to derive 3 clause-level morphological tasks: inflection, reinflection and analysis. Our experiments show that the clause-level tasks are substantially harder than the respective word-level tasks, while having comparable complexity across languages. Furthermore, redefining morphology to the clause-level provides a neat interface with contextualized language models (LMs) and allows assessing the morphological knowledge encoded in these models and their usability for morphological tasks. Taken together, this work opens up new horizons in the study of computational morphology, leaving ample space for studying neural morphology cross-linguistically.
Understanding Dark Web: A Systematic Literature Review

Web evolution and Web 2.0 social media tools facilitate communication and support the online economy. On the other hand, these tools are actively used by extremist, terrorist and criminal groups. These malicious groups use these new communication channels, such as forums, blogs and social networks, to spread their ideologies, recruit new members, market their malicious goods and raise their funds. They rely on anonymous communication methods that are provided by the new Web. This malicious part of the web is called the “dark web”. Dark web analysis became an active research area in the last few decades, and multiple research studies were conducted in order to understand our enemy and plan for counteract. We have conducted a systematic literature review to identify the state-of-art and open research areas in dark web analysis. We have filtered the available research papers in order to obtain the most relevant work. This filtration yielded 28 studies out of 370. Our systematic review is based on four main factors: the research trends used to analyze dark web, the employed analysis techniques, the analyzed artifacts, and the accuracy and confidence of the available work. Our review results have shown that most of the dark web research relies on content analysis. Also, the results have shown that forum threads are the most analyzed artifacts. Also, the most significant observation is the lack of applying any accuracy metrics or validation techniques by most of the relevant studies. As a result, researchers are advised to consider using acceptance metrics and validation techniques in their future work in order to guarantee the confidence of their study results. In addition, our review has identified some open research areas in dark web analysis which can be considered for future research work.
Machine Learning Based Prediction of Stock Exchange on NASDAQ 100: A Twitter Mining Approach

Twitter is a popular social media network. Its impact on our daily life is increasing day by day. Twitter is not only used for current affairs but also for showing its significance in corporate culture. The motivation for this research is to elaborate on the importance of user tweets on famous companies around the world by analyzing the user tweets according to their sentiments, i.e., optimistic (positive), harmful (negative) and disinterested (neutral). This analysis is an effort to show the corelation between user tweets and the variation in the stock market share prices of companies worldwide. The dataset was taken from the NASDAQ 100, the famous source of stock price data from world-famous companies. After data extraction, a sample from the entire dataset was used to obtain a minimum but optimal data subset. One-Hot Encoding technique has been used for features reduction to reduce the useless data and make the data more flexible to use. For modelling, the SVM machine learning algorithm is used with different kernels. Furthermore, cross-validation is leveraged to evaluate and verify the model's accuracy and stability. The maximum 92% accuracy was achieved using SVM with Linear kernel, whereas RBF kernel accuracy was 62%, which was low compared to the former one.)
FWC: Fitting Weight Compression Method for Reducing Communication Traffic for Federated Learning

Federated learning enables local nodes to train a global model together by uploading only training updates to the parameter server without exchanging private data. However, as the complexity of the federated learning task increases, the communication volume of the training process becomes extremely large, hence the huge communication traffic becomes a serious bottleneck in current federated learning application. Existing methods reduce communication overhead from two aspects, the number of communications and the traffic per communication. But these methods usually lead to more consumption of computing resources or a decrease in model accuracy. To handle these problems, this paper proposes a data fitting based weight compression algorithm, FWC, which includes four sequential stages: sparsification, polynomial fitting, encoding, reconstruction and two mechanism: warm-up and accumulation. In particular, the warm-up mechanism can well address the problem of slow convergence in early training period. Experimental results on models with different scales show that FWC is able to provide more than 600x traffic compression at the cost of only millisecond-level computational time cost and less than 1% accuracy loss.
ReactJS For Trading Applications

This research paper discusses about why React JS is the most preferable tool for developing trading applications. Trading applications have frequent changes in their data. The data involved in trading websites is huge and it should display the changes on the screen much faster so that when the cost of a stock price from a particular company increases or decreases, the user is able to buy the latest share. React JS is one of the most popular libraries of JavaScript developed by Facebook. A practical example has been implemented to show the practical use of these libraries. The concepts discussed in this research paper discuss in detail the advantages of using React JS. One of them is about its cross-platform development. It can be used to build web apps, android apps and even iOS apps. The cross platform syncing helps to save both money and time in the long run. The case study of the trading web App consists of in-depth analysis of the latest library versions available till date for creating forms, navigation bars and adding validations. We will be discussing the frontend framework for the development framework. Git also plays an important role as one of the tools for development of the product. The research discusses the various tools and languages that were used to build the website like HTML5, CSS, Bootstrap 5, ReactJS, JavaScript, Redux, React-redux and many libraries in react. Along with react, there are findings about the languages used for designing the user interface. There is comparison between the functionalities available in the old version compared with the new version. One of such examples include React MUI (Material User Interface) which has undeniably made a developer's life easier. It has many predefined switch tabs and even exceeds Bootstrap in ease of implementation in grids. React Mui grids are very easy to use to develop an interactive user interface. Research consists quite a lot of things that might not fit into this research paper but efforts have been put so that all the learnings can be summed up without leaving any stone unturned.
Group-individual multi-mode cooperative teaching-learning-based optimization algorithm and an industrial engineering application

This paper modifies the original Teaching-Learning-based Optimization (TLBO) algorithm to present a novel Group-Individual Multi-Mode Cooperative Teaching-Learning-based Optimization (CTLBO) algorithm. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher-learner cooperation strategies in teaching and learning processes. In the preparation phase, teacher-learner interaction and teacher self-learning mechanism are applied. In the teaching phase, class-teaching and performance-based group-teaching operators are applied. In the learning phase, neighbor learning, student self-learning and team-learning strategies are mixed together to form three operators. Experiments indicate that CTLBO has significant improvement in accuracy and convergence ability compared with original TLBO in solving large scale problems and outperforms other compared variants of TLBO in literature and other 9 meta-heuristic algorithms. A large-scale industrial engineering problem—warehouse materials inventory optimization problem is taken as application case, comparison results show that CTLBO can effectively solve the large-scale real problem with 1000 decision variables, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO, revealing that CTLBO can far outperform other algorithms. CTLBO is an excellent algorithm for solving large scale complex optimization issues.
Feature selection method based on stochastic fractal search henry gas solubility optimization algorithm

In most data mining tasks, feature selection is an essential preprocessing stage. Henry’s Gas Solubility Optimization (HGSO) algorithm is a physical heuristic algorithm based on Henry’s law, which simulates the process of gas solubility in liquid with temperature. In this paper, an improved Henry’s Gas Solubility Optimization based on stochastic fractal search (SFS-HGSO) is proposed for feature selection and engineering optimization. Three stochastic fractal strategies based on Gaussian walk, Lévy flight and Brownian motion are adopted respectively, and the diffusion is based on the high-quality solutions obtained by the original algorithm. Individuals with different fitness are assigned different energies, and the number of diffusing individuals is determined according to individual energy. This strategy increases the diversity of search strategies and enhances the ability of local search. It greatly improves the shortcomings of the original HGSO position updating method is single and the convergence speed is slow. This algorithm is used to solve the problem of feature selection, and KNN classifier is used to evaluate the effectiveness of selected features. In order to verify the performance of the proposed feature selection method, 20 standard UCI benchmark datasets are used, and the performance is compared with other swarm intelligence optimization algorithms, such as WOA, HHO and HBA. The algorithm is also applied to the solution of benchmark function. Experimental results show that these three improved strategies can effectively improve the performance of HGSO algorithm, and achieve excellent results in feature selection and engineering optimization problems.
Infrastructure as Code and Software Architecture Conformance Checking

This article asks the question whether it is possible to take advantage of infrastructure as code to improve the structure and behavior of the software through realizing the deployment concerns of a system's software architecture.
Image aesthetics prediction using multiple patches preserving the original aspect ratio of contents

The spread of social networking services has created an increasing demand for selecting, editing, and generating impressive images. This trend increases the importance of evaluating image aesthetics as a fundamental function of automatic image processing. However, most existing methods for aesthetics score prediction require image rescaling for input, which can affect the prediction, especially for images with unusual aspect ratios. We propose a multi-patch method, called a multi-patch aggregation network (MPA-Net), to predict image aesthetics scores by maintaining the original aspect ratios of the contents in the images. One of our key contributions is the adoption of an equal-interval multi-patch selection approach for the prediction of the aesthetics score. The effectiveness of our strategy is shown through experiments involving the large-scale AVA dataset. Our MPA-Net outperformed the reported scores of the baseline methods and achieved a better performance in terms of the mean square error (MSE) than the state-of-the-art end-to-end continuous aesthetics score prediction methods. Most notably, MPA-Net yields a significantly lower MSE particularly for images with aspect ratios far from 1.0, indicating that MPA-Net is useful for a wide range of image aspect ratios. Moreover, MPA-Net has several benefits for training and evaluation procedures. MPA-Net meets the conditions of end-to-end learning and mini-batch learning simultaneously, and MPA-Net uses only images that do not require external information during the training or prediction stages. Thus, our easy-to-handle method improves the prediction of image aesthetics scores, outstandingly for images with extraordinary aspect ratios.