Reviewerly/exHarmony huggingface dataset:
A Multi-Cloud and Zero-Trust based Approach for Secure and Redundant Data Storage

As cloud services gain in popularity, users around the world are increasingly using them in their daily lives to store and process their critical and sensitive data, such as ecommerce services, healthcare information, government data and more. The growing importance of cloud services makes cloud storage systems an excellent target for cybercrime and protecting them is an absolute must. The aim of this paper is to propose a new zero-trust based model for data storage in a multi-cloud environment. The proposed model considers the security and availability constraints of multi-cloud environments while respecting the zero-trust principle. Our model addresses growing security challenges as it requires users and services to verify their identity credentials before accessing corporate cloud resources. In this way, access to critical resources becomes more secure. Our model is adapted to modern cloud storage systems and makes user data more secure, in line with the famous quote “Never trust, always verify”.
A robust intrusion detection model based on a combination of PCA-GM and trunced LDA

The ongoing development of digital networks led to a notable surge in attacks and intrusions. The identification of these threats is crucial for security, and Intrusion Detection Systems (IDS) were developed to address this need. However, IDS face several practical problems, including a high false alarm rate and a low detection rate, resulting from the manipulation of outlier-filled traffic data. In this study, we propose a novel technique that combines two reduction-dimension methods, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA), to improve IDS performance. Our suggested technique exhibits improved outcomes when compared to conventional PCA using NSL-KDD as a simulation database.
Recent Advances in Data Intensive Applications: Survey

This survey article explores recent advancements in data transfers, coflow scheduling, and reducer placement techniques for optimizing the performance of data-intensive applications in computer clusters. These techniques address the challenges of managing data transfers, optimizing resource allocation, and minimizing Coflow Completion Time (CCT). The surveyed research papers present innovative approaches such as intelligent data transfer scheduling, network-aware algorithms, near-optimal heuristics, and leveraging inter-flow relationships. These techniques aim to improve job completion time, enhance resource utilization, and minimize interference in datacenter networks. By summarizing these advancements, this survey article provides a comprehensive overview of the latest research in the field. The findings highlight the significance of these techniques in improving the performance and efficiency of data-intensive applications in computer clusters, and also identifies open challenges and future directions, stimulating further research and development in this area.
A Parallel Quantum Feature Encoding Scheme for Effective Classical Data Classification in Quantum Convolutional Neural Networks

Quantum machine learning is one of the most exciting new avenues in the world of artificial intelligence, especially because of the enormous computational power of quantum computers and the promise of the development of near error-free quantum computers in the not-so-distant future. For quantum algorithms to be used in real-life applications, quantum computers must be able to work with classical data. One of the key steps in quantum algorithms dealing with classical data is the encoding of classical data points to quantum states, which can then be processed by quantum gates. It is known that the type of encoding technique that works best for a particular network is dependent on the dataset being used. In this paper, a new parallel structure is proposed utilizing two encoding techniques, namely amplitude encoding and angle encoding, for effective classical data classification via quantum neural network. The paper further proposes a maximally expressible and entangled ansatz used to design a simple Quantum Convolutional Neural Network (QCNN) with only 32 parameters, that is used in the latter stages of the network and is kept the same across all encoding instances so that a comparison between the different encoding methods is possible. Extensive experimentation is carried out on two publicly available image datasets, namely MNIST and Fashion MNIST. The results show that the proposed method achieves better results than any of the encoding techniques deployed alone for binary classification.
Evaluating the Effectiveness of Capsule Neural Network in Toxic Comment Classification Using Pre-Trained BERT Embeddings

Large language models (LLMs) have attracted considerable interest in the fields of natural language understanding (NLU) and natural language generation (NLG) since their introduction. In contrast, the legacy of Capsule Neural Networks (CapsNet) appears to have been largely forgotten amidst all of this excitement. This project's objective is to reignite interest in CapsNet by reopening the previously closed studies and conducting a new research into CapsNet's potential. We present a study where CapsNet is used to classify toxic text by leveraging pre-trained BERT embed dings (bert-base-uncased) on a large multilingual dataset. In this experiment, CapsNet was tasked with categorizing toxic text. By comparing the performance of CapsNet to that of other architectures, such as DistilBERT, Vanilla Neural Networks (VNN), and Convolutional Neural Networks (CNN), we were able to achieve an accuracy of 90.44 %. This result highlights the benefits of CapsNet over text data and suggests new ways to enhance their performance so that it is comparable to DistilBERT and other reduced architectures.
Computational modeling of a 119 kWp photovoltaic system

An important aspect of medium and large photovoltaic systems is the ability to predict future electricity generation based on predicted meteorological data as well as the technical and structural characteristics of the system. This prediction can be enabled by applying mathematical models or artificial intelligence. This study aims to develop a reliable and valid computational model using measurements from a real 119 kWp photovoltaic plant at the Federal Institute of Espirito Santo. The model considers meteorological data from a solarimetric station located in the same area, as well as system losses in both AC and DC components. The results indicate that the simulated generation closely aligned with the actual figures, achieving an acceptable low percentage error, less than 0.5%, during the period from September to December 2022.
Benchmarking Client Selection Methods in Non-IID Federated Learning Scenarios

Federated learning (FL) is a machine learning technique aimed at collectively acquiring a shared prediction model. When applying FL, the diversity of client data distributions and hardware configurations is a challenge that must be handled. Consequently, randomly sampling clients for training a FL model may not take advantage of the local updates, leading to poor performance of the global model. In this paper, we present a benchmark study in which we evaluate different strategies for worker selection in the context of FL. These strategies can be applied in challenging cases where data exhibits Non-Independent and Identically Distributed (N-IID) characteristics across clients. We select a set of well-known strategies introduced in the literature and performed extensive evaluations according to various scenarios. The obtained results show that there is no fit for all methods for client selection, and the client selection must be selected based on specific criteria.
Exploration of Question-Answering Systems: Survey

This paper presents a comprehensive examination of the varied strategies employed in converting natural language into SPARQL queries, a critical component within Question Answering Systems (QAS). The exploration encompasses a broad spectrum of methods, encompassing rule-based, template-based, machine learning-driven, ontology-based, and hybrid approaches. By meticulously delving into the strengths and limitations of each method, the paper offers valuable insights into their practical implications. Furthermore, the provision of illustrative examples, such as PowerAqua, enriches the comprehension of these methods in real-world contexts. The paper serves to inform the selection of methods based on the specific requirements of the application and contextual constraints, thus contributing to informed decision-making in implementing QAS.
Two-Stream nnU-Net: A Novel Architecture for Precise Tumor Segmentation in Medical Imaging

This article introduces the Two-Stream nnU-Net, a novel architecture designed for automated tumor segmentation in medical imaging. Accurate tumor detection is vital for patient survival, but manual segmentation is challenging and time-consuming. This version of nnU-Net overcomes these challenges by adapting to various tasks, including preprocessing, network architecture, training, and post-processing, leveraging fixed parameters, interdependent rules, and empirical decisions to outperform traditional U-Net methods. By harnessing both structural and functional information from input images, nnU-Net independently trains two streams, combining their outputs for comprehensive segmentation. With the integration of skip connections and residual mappings, nnU-Net captures diverse features while reducing parameters, thereby enhancing convergence. A modified version of nnU-Net, featuring scalable components, demonstrates superior performance in tumor segmentation. This advancement marks a significant stride in the field of automated medical imaging, underscoring nnU-Net’s potential as a promising innovation.
Mixed Noise Suppression Using UNET and its Variants

Image denoising holds significant importance in the realm of image processing due to the potential distortions caused by environmental factors and technical problems. Consequently, it is logical to consider image denoising as a critical research domain as it aids in addressing various other image processing challenges. Although numerous techniques for image denoising have emerged in recent years, a majority of them primarily focus on restoring images afflicted by a single source of noise. In this study, the effectiveness of UNET and its variant in denoising facial images with mixed noises is examined. Furthermore, traditional filtering techniques are investigated for the purpose of comparison. The experimental results indicate the insufficiency of conventional filtering techniques in effectively mitigating mixed noise in facial images. Conversely, employing UNET-based architectures yields promising outcomes, characterized by facial images exhibiting commendable values of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Furthermore, the denoised images produced by employing the proposed residual attention UNET exhibit notable enhancements in terms of clarity and intricate details.
Sentiment Analysis: Machine Learning approaches comparison

Text serves as the predominant medium for communication across the internet, notably prevalent on social media platforms like Facebook and Instagram, discussion forums such as Reddit and Quora, as well as micro-blogging sites like Twitter and Tumblr. The extensive utilization of text has generated copious amounts of data, consequently giving rise to the emergence of a burgeoning field of study known as Natural Language Processing (NLP). Within the domain of NLP, Sentiment Analysis (SA) has garnered considerable attention. SA is focused on the extraction of user sentiments from textual content. This subfield has witnessed a surge of interest within the scientific community, leading to the publication of numerous research papers showcasing diverse approaches, methodologies, and perspectives. In this paper, we embark on a comparative analysis of some of the most frequently employed machine learning techniques within the realm of Sentiment Analysis. Our evaluation revolves around assessing the performance of each model using metrics such as accuracy and Fl-score, with a specific focus on a dataset comprised of movie reviews.
Automatic classification of emails using Deep Learning

This subject study the problem of the classification of texts. Classification (or categorization) of texts is one of the most common natural language processing (NLP) tasks. The objective of automatic classification is to automatically classify documents into categories that have been defined either beforehand by an expert, it is then a question of supervised classification or categorization, or automatically, it is then a question of unsupervised classification or even clustering. In our case, the categories are already defined, so we used supervised learning algorithms. The structure of the report consists of four chapters. The initial chapter provides an overview of data mining and text mining, which includes automatic natural language processing, definition, operation, fields of research, and applications. The second chapter discusses natural language processing (NLP) in detail, including its definition and workflow. The third chapter focuses on the fundamentals of text classification, such as learning techniques and the process of text classification. Finally, the last chapter is devoted to implementation and includes the development tools used as well as the realization of text classification.
Deep Learning Networks for Complex Activity Recognition Based on Wrist-Worn Sensor

Wearable smart devices, such as smartphones and smartwatches, offer great potential as platforms for automated human action identification. However, accurately monitoring complex human actions on these devices poses a challenge due to the presence of similarities in patterns across different actions. This occurs when distinct human actions exhibit comparable signal patterns or characteristics. The placement of motion sensors on the body plays a crucial role in detecting human behavior. Typically, wearable sensors placed at the trouser pocket or a similar location are used for this purpose. However, this positioning is not suitable for identifying actions involving manual gestures. To address this, wrist-worn motion sensors are employed to detect these specific behaviors. This study aims to investigate the effectiveness of deep learning models in accurately categorizing complex human actions using sensor data from wrist-worn devices. Nine deep learning models utilizing convolutional neural networks and recurrent neural networks were examined for their identification capabilities. The models were evaluated using the WHARF dataset, a publicly available benchmark dataset for human activity recognition. The investigation revealed that the proposed CNN-BiGRU model outperformed other deep learning models, achieving an accuracy rate of 87.20% and an Fl-score of 84.46%.
Automatic emails classification

Classification (or categorization) of texts is one of the most common natural language processing (NLP) tasks. The objective of automatic classification is to automatically classify documents into categories that have been defined either beforehand by an expert, it is then a question of supervised classification or categorization, or automatically, it is then a question of unsupervised classification or even clustering. In our case, the documents are the emails, and the categories are already defined, so we used supervised learning algorithms, specifically Naïve Bayes and Random forest in order to create a machine learning model capable of classifying the emails and giving the users access to the emails they want directly.
Proposed Sentiment Analysis of COVID-19 Tweets using Machine Learning Algorithm

Social media coverage of COVID-19 has facilitated the analysis of pandemic sentiment through the application of various Machine Learning Algorithms. Twitter is a prominent a social media platform where individuals share tweets related to COVID-19, such as the count of positive cases or deaths, inducing a surge of apprehension and anxiety gripping individuals worldwide. This presents a chance for researchers and data analysts to acquire data intended for scholarly and research objectives. Primary objective of this study is to evaluate the accuracy of sentiment analysis of people’s perceptions regarding COVID-19 related news. We gathered Twitter data by focusing on hashtag keywords related to the coronavirus, COVID-19, new case, and deaths. The data was categorized into positive or negative categories utilized machine learning algorithms, including the multilayer perceptron, naive Bayes, random forest, and support vector machine, for the classification process. The algorithms were compared for accuracy using the datasets. All of the algorithms demonstrated a high degree of accuracy in their predictions, with the support vector machine algorithm achieving the highest classification accuracy of 99%. Consequently, the findings demonstrate that 70% of the sentiments exhibited positivity, while the remaining 30% conveyed negativity.
Blockchain-based Authentication Mechanism for Edge Devices in Fog-enabled IoT Networks

The deployment of fog computing paradigms for securing IoT networks is associated with several advantages, in-cluding reduced bandwidth, latency, storage, and computational overhead at cloud servers. However, the fog layer has additional security requirements, such as the establishment of secure channels for key distribution and the overhead of repetitive device authentications. In this paper, we have addressed these issues using a permissioned blockchain-based fog network that validates the edge devices through smart contracts by establishing a mechanism for secure storage and exchange of credentials. The communication between edge devices is carried out through the M QTT protocol, and device registration is performed using a smart contract. The key pairs are generated from the secp256kl elliptic curve to ensure faster trust-based identity management and authentication of edge devices and gateways. The proposed frameworks ensures that the access to Blockchain network is given exclusively for authenticated devices The implementation of the proposed scheme is performed on private Ethereum 2.0, and the performance is evaluated in terms of node registration time, authentication time, key generation time, and throughput. The implementation results are available on GitHub(https://github.com/Aswani08/tenconresults.git).
Suspicious Activity Detection in Recorded and Live Surveillance

Video surveillance is a tiresome task to perform by a human. Anomaly events are factored by body gestures and head movements of a person. Body gestures of a crowd or a person are only considered for video surveillance when they are in a detectable range from the camera and head movements are considered when a person's face is close enough to the video surveillance, so the necessary facial features are examined. In this paper, we aim to detect abnormal activities by first calculating the facial distance to trigger the necessary modules based on the generated focal length through the camera. The abnormal activity detection module is triggered when the threshold distance is crossed and the head movement module is called when the face detected is close enough to the camera. The abnormal event notification is promptly sent to the entitled person so that necessary actions are taken.
Solving Dense Subgraph Problems Using Genetic Algorithm

In graph theory, the dense subgraph problem aims at finding the densest subgraph for a given graph. The subgraph is defined as a subset of a large graph and the density is calculated as the number of edges as per the number of vertices. Real-life graph networks are complicated and large in size; finding the dense subgraph is difficult as the number of subgraphs is numerous. It is an NP-hard problem and in most cases, it is infeasible to find an exact solution. Here we propose a genetic algorithm approach to solve it. The problem is a combinatorial optimization problem with a constraint that all solutions must be a subgraph of the given graph. Here, the crossover and mutation are designed to produce only feasible solutions. The proposed algorithm is able to give close approximation within a reasonable time.
IoT Security Based on Smart Contracts: Classification of Algorithms, Complexity, Robustness and Fields of Application

In recent years, as data processing and storage technologies have advanced, the need to ensure computer system security and information confidentiality on all types of networks has grown significantly. The emergence of the Internet of Things (IoT) has led to interconnected objects using various communication protocols through a new generation of Internet networks. The increasing number of connected objects worldwide demands improved methods and techniques to secure these networks, particularly considering the potential presence of sensitive data. Consequently, network security has become a primary focus for numerous computer science researchers, with a particular emphasis on enhancing security techniques and data confidentiality for all participants or devices involved in a network architecture. In response to these challenges, several solutions based on Blockchain technology have been developed to secure Internet of Things networks. These solutions employ smart contracts to ensure verified transaction execution while adhering to authentication, confidentiality, and data integrity rules. This article aims to explore the security solutions for the Internet of Things based on smart contracts, evaluating factors such as complexity, robustness, and fields of application.
Neighborhood Feature Extraction and Haralick Attributes for Medical Image Analysis: Application to Breast Cancer Mammography Image

This paper describes a new preprocessing method using Markovian modification to distinguish preprocessed mammograms (healthy and pathological), and these techniques are very necessary to find the direction of mammograms so that we can eliminate noise, improve image quality and obtaining more appropriate images than the original images as well as the disappearance of misplaced discrete points. Markovian image segmentation was performed to extract the region of the blocks if the blocks ended up being divided into groups of pixels that are homogeneous with the image with respect to certain criteria, based on the neighborhood system, using a relaxation approach that It is maintained by iterative conditional patterns (ICM), relying on an energy function adapted to the irregular neighborhood models of the image, and with the energy criterion of the image groups, the neighborhood system, and some cluster groups. These preprocessed mammograms are analyzed using co-occurrence matrices from which Haralick traits are extracted. The peculiarity of this approach is that it selects features from among the most selective mammograms based on the type of contact chosen for preprocessing. The most discriminating features are selected according to a supervised scheme that makes it possible to represent mammograms in a relatively small space where they can be discriminated with high reliability. To test this method and verify its validity, we use mammograms of normal and sick cases from the Reference Center for Reproductive Health in Kenitra, Morocco (CRSRKM).