Reviewerly/exHarmony huggingface dataset:
A Fast Blind Scheme for Color Image Authentication

Today, information security has become a stumbling block in the development of useful multimedia applications. Since advanced applications contain sensitive data, attackers prioritized data authentication. This situation makes the transmission of sensitive and confidential data over Internet a prime target for theft and manipulation. Watermarking is a reliable and promising solution to safeguard sensitive data and ensure its authenticity. In this paper, an innovative blind image watermarking technique is presented for personal private data’s validity and secrecy. This technique consists of two major processes, embedding and extracting. The first consists of achieving the watermark embedding on DFT coefficients using A-star algorithm with a chaotic sequence. The second one extracts the watermark using the same embedding steps that succeed without any original or watermarked image. The proposed technique has been tested against a variety of attacks to show its reliability. Experimental results show that the proposed technique maintains strong robustness while producing good image quality.
Extracting Gradual Periodic Patterns from Transactional Databases: Application to Felonies and Misdemeanors Data

In recent years, discovering interesting periodic frequent patterns from transactional databases has become an attractive task for many researchers. It consists in finding patterns that are not only frequent in a database but also periodic, i.e., their successive occurrences are sufficiently close to each others. In this paper we propose a new methodology that analyses the combination of the results of two algorithms for mining periodic patterns in order to distinguish different levels of pattern’s periodicity and hence define a kind of gradual periodicity. This idea is then applied to a database of felonies and misdemeanours that occurred in the wilaya of Bordj Bou Arreridj in Algeria, within the years 2020-2021. The obtained results allow one to associate to each area of the wilaya its dangerousness level. This may be very helpful for police authorities in taking useful decisions.
Heuristic approaches for hiding sensitive frequent itemsets in uncertain databases

Pattern mining is a core objective within data mining, involving the detection of frequent itemsets (collections of values) within databases. This process serves to extract valuable insights from the data, facilitating informed decision-making. In recent times, a range of algorithms has emerged to adapt this task to the realm of uncertain data.This has a wide range of applications, such as analyzing medical data to identify correlated symptoms based on their existential probabilities. However, pattern mining algorithms also pose the risk of revealing sensitive information. As far as we know, there is no existing solution that addresses the inadvertent disclosure of sensitive itemsets when pattern mining algorithms are employed on uncertain databases. This paper addresses this problem by proposing three heuristic approaches, which ensure that sensitive expected frequent itemsets are not discovered while minimizing undesirable side effects for other itemsets. The first heuristic approach (called aggregate) deletes some transactions from an uncertain database. The second approach (named disaggregate) removes some selected items from each transaction. And the third approach (called the hybrid approach) combines the two previous approaches, and deletes some selected items from some selected transactions. An experimental evaluation is presented to assess and compare the effectiveness of the three approaches.
Towards Deep Supervised Features Learning: Item-to-Knowledge Mapping in Educational Data Using Convolutional Neural Networks

Item to knowledge mapping is a very useful technique in educational data mining that assesses to improve the curriculum system and the order learners should perceive new skills. The wide range of item to knowledge mapping workflows consists of the use of the item-item similarity methods that calculate a certain similarity degree between items, in order to regroup items in highly correlated clusters. Although those methods may skip some information that weaken the performance of clustering phase and eventually assign items to not their right clusters. Therefore, in this paper we propose an extra phase in item to knowledge mapping , that learns new features representation from the item-item similarity matrix using supervised feature learning based on deep convolutional neural network and k-means clustering. The proposed model was tested on two different educational data sets, and compared to direct clustering using the item-item similarity matrix . The obtained results shows a very interesting results in terms of higher clustering performance and especially item on clusters distribution in which more than 30% of items changed their initial clusters.
SoftFlow: Automated HW-SW Confidentiality Verification for Embedded Processors

Despite its ever-increasing impact, security is not considered as a design objective in commercial electronic design automation (EDA) tools. This results in vulnerabilities being overlooked during the software-hardware design process. Specifically, vulnerabilities that allow leakage of sensitive data might stay unnoticed by standard testing, as the leakage itself might not result in evident functional changes. Therefore, EDA tools are needed to elaborate the confidentiality of sensitive data during the design process. However, state-of-the-art implementations either solely consider the hardware or restrict the expressiveness of the security properties that must be proven. Consequently, more proficient tools are required to assist in the software and hardware design. To address this issue, we propose SoftFlow, an EDA tool that allows determining whether a given software exploits existing leakage paths in hardware. Based on our analysis, the leakage paths can be retained if proven not to be exploited by software. This is desirable if the removal significantly impacts the design’s performance or functionality, or if the path cannot be removed as the chip is already manufactured. We demonstrate the feasibility of SoftFlow by identifying vulnerabilities in OpenSSL cryptographic C programs, and redesigning them to avoid leakage of cryptographic keys in a RISC-V architecture.
Evolution of Image Captioning Models: An Overview

This article presents a state-of-the-art review of image captioning methodologies developed in the past five years. Image captioning, which aims to generate text describing the visual content of an image, has gained increasing interest in the field of artificial intelligence. The article analyzes how image captioning methods have evolved, transitioning from traditional machine learning-based approaches to deep learning-based methods that have become dominant due to their effectiveness in efficiently extracting image features. The article also explores two main approaches to image captioning: dense (region-based) captioning and whole-scene captioning. It discusses approaches based on visual space and multimodal space for text generation. Furthermore, the article examines reinforcement learning methods, semantic enhancements, the use of self-attention transformer models, and pretrained models such as the Generative Pre-trained Transformer (GPT) that have improved the performance of image captioning. Finally, the article highlights various applications of this technique, including human-machine interaction, biomedicine, automatic medical prescription, children’s education, industrial quality control, traffic data analysis, and assistive technologies for visually impaired individuals.
Building Segmentation in Satellite Images using U-net to Identify Non-Technical Losses

Energy losses incur a significant challenge for utility companies annually, with non-technical losses reaching to R$ 6.3 billion in 2022. This financial burden not only impacts the bottom line but also emphasizes the pressing need for innovative solutions. Unfortunately, the current methods employed for detection are not only costly but also time-consuming. With regard to this scenario, this article aims to present the use of the U-net architecture for identifying buildings through satellite images, introducing a new approach to the network structure, originally used for biomedical image segmentation. In order to evaluate the model, we conduct systematic parameter variations. By analyzing the resulting metrics and visually inspecting the outcomes, the article highlights the significant progress achieved in building detection using U-net architecture. This proposition holds the potential to yield substantial advantages for future applications, as they can leverage the methodology outlined here to attain superior results within their respective domains. Subsequently, the segmented images will be used for analysis and classification regarding the probability of the existence of illegal connections, offering a promising pathway for enhancing loss prevention strategies within the energy sector.
Semantic Interoperability in Electronic Health Record System Based on Blockchain Technology: a Survey

The Electronic Health Record (EHR) represents a patient's medical information and treatment history in a digital format. Patients’ data are heterogeneous, divided, and distributed among healthcare organizations. Thus, these data help enhance the quality of healthcare services and improve diagnosis efficiency. Designing an EHR system requires many considerations, such as security, privacy, scalability, integrity, information confidentiality, and interoperability. Healthcare parties involved can internally exchange patient data, but there is an issue in sharing, interpreting, and processing it semantically with other healthcare providers. In this context, blockchain enables the sharing of clinic data in a robust, transparent, and interoperable way between different healthcare providers. It provides secure and scalable sharing of the patient’s health data, expediting coordinated care. This paper provides a systematic review focused on semantic interoperability in EHR systems based on blockchain technology. The objective is to identify the current challenges, solutions, and limitations related to this topic.
Real-Time Masked Face Recognition for Logging System with Health and Temperature Monitoring

The COVID-19 pandemic has greatly affected the country, particularly in executing health and safety protocols. In response, the researchers designed and proposed a project which aims to implement a real-time masked face recognition for logging system with health and temperature monitoring. This includes image processing, machine learning model training, masked and unmasked face detection and recognition, facemask classification, body temperature checking, health declaration form verification for health status validation, health data integration, and log entry recording. The project implementation involved MSI-Z97M System Unit, Logitech HD Pro Webcam C920, FLIR Lepton 3.5 with PureThermalV2 smart I/O board, Dual-display Monitor, FaceNet with Single Shot MultiBox Detector training model, and Microsoft Office 365 applications. The results showed that the researchers successfully implemented the system and achieved the objectives of the study. This research offers a promising solution for real-time tracking and management of health protocols, amidst the ongoing concern brought by the pandemic.
Comparative Analysis of ARIMA, LSTM, and XGBoost for Very Short-Term Photovoltaic Forecasting

Photovoltaic (PV) forecasting is required to optimize grid management and boost renewable energy integration. This paper presents a comparative analysis of three forecast models, autoregressive integrated moving average (ARIMA), eXtreme Gradient Boosting (XGBoost), and long short-term memory (LSTM) for very short-term PV forecasting applied to two real datasets. The main objective is to evaluate the complexity, performance, and accuracy of these algorithms in predicting very short-term PV power based only on the historical PV time series data. Furthermore, an additional study incorporates weather and calendar features for the LSTM model to examine their impact on forecasting quality. The performance of the models is evaluated in terms of root mean squared error (RMSE) and mean absolute error (MAE). Feature selection and hyperparameter optimization are also considered. The results highlight the strengths and limitations of each forecasting model in accurately capturing very short-term PV variations and show that XGBoost had the most accurate forecasting metrics for both datasets for univariate models. The research outcomes provide valuable insights for energy system operators, grid managers, and renewable energy stakeholders to make informed decisions regarding grid stability and efficient integration of PV generation.
Toward an Eﬃcient in-Network Caching Using Federated Learning

In today’s digital age, the Internet has experienced remarkable growth, accompanied by an exponential increase in both the diversity of available content and the number of users. Consequently, the demand for server resources and the volume of server requests have surged significantly. This places a significant strain on servers, diminishing their ability to handle user demands effectively. To alleviate this issue, caching is employed to store frequently requested content in memory that is closer to users. However, determining which content should be cached poses a challenge. Eﬃcient cache management plays a vital role in enhancing data access speed and overall eﬃciency. This challenge has been extensively studied and applied in the context of federated learning engineering, where effective cache management techniques are crucial for optimizing the performance of distributed machine learning models. By addressing cache management challenges, researchers aim to improve scalability, eﬃciency, and overall system performance, ultimately enhancing the effectiveness of federated learning methodologies. In this paper, we conducted a study on enhancing network caching eﬃciency by implementing federated learning. Our study involved the creation of different users, each of whom was assigned different databases with the same purpose (e.g., movies). The main aim is to identify the most popular content using artificial neural networks and cache them for each user, thus improving delivery services within the network by bringing these contents closer to the respective users.
Dhati: a Fine-tuned Large Language Model for evaluating Subjectivity in Arabic Textual Data

Despite being a linguistically rich and morphologically complex language, Arabic remains an under-resourced language. The scarcity of large annotated datasets creates a challenge to providing accurate tools for many natural language processing (NLP) tasks such as subjectivity and sentiment analysis. The efficacy of text classification has been significantly enhanced for various languages, including English and French, due to the notable progress made in deep learning (DL) and Transformers, leading to the development of large language models. The aforementioned models have undergone pre-training using extensive datasets, followed by a process of fine-tuning for targeted downstream tasks. In this paper, we provide a tool, which we call "Dhati", for the evaluation of subjectivity in Arabic textual data by fine-tuning a large language model (XLM-RoBERTa) on the Arabic Sentiment Tweets Dataset (ASTD). Then, for comparison purposes, we provide a parallel approach, where we translate the Arabic text into English and use two existing fine-tuned models. The findings indicate that the Dhati model has superior performance compared to the parallel approach, as it achieves an accuracy rate of 82% in the Arabic subjectivity classification task using the ASTD benchmark.
Comparative Analysis of Stand-alone Artificial Intelligence for 3D Automated Breast Ultrasound System (ABUS) and Standard Clinical Practice with Radiologists in Breast Cancer Screening

The purpose of this study is to develop an artificial intelligence (AI)-driven screening system designed for 3D automated breast ultrasound (3D ABUS) to enhance the screening process for breast cancer. Additionally, we evaluated the system's performance by comparing risk-score assessments from the AI system and the standard clinical practice by radiologists (combination of mammography and handheld ultrasound) using a forward testing dataset of 50 patients. For the methodology, a set of 90 patients' data was used as the training dataset. The system combines Faster R-CNN with ResNet-50 for detection, the U-net model for segmentation, and a radiomics method with machine learning algorithms for breast lesion classification. The three models were first tested using 20 patients' data. The system demonstrated high precision and recall rates for detection (0.741 and 0.869 respectively), a Dice Similarity Coefficient (DSC) score of 0.793 for segmentation, and high precision (0.949) and recall (0.919) for the classification model. In forward testing with a dataset from 50 patients, the system achieved a sensitivity of 83.34%, specificity of 60.00%, and accurately identified all biopsy-proven malignancies. This proposed system could have a significant impact by increasing the volume of breast cancer screening due to its cost-effectiveness, time efficiency, and reduced dependency on user input, thereby benefiting clinical use.
Unsupervised Learning-Based Attention-Guided Network for 3D Deformable Medical Image Registration

Medical image registration has long been an integrated part of both medical imaging researches and clinical practice. Recently, deep learning-based convolutional neural networks are increasingly utilized instead of traditional registration methods thanks to its convenience, accuracy and speed. However, the potential of attention mechanism in medical image registration has mostly been neglected as the deformation field is inferred directly from raw feature maps. Therefore, it is challenging when the fixed and moving image contain significantly different anatomical information, such as the shape, location and contrast uniformity of the organ of interest. This study proposes an unsupervised learning-based convolutional neural network with the integration of attention modules for better refinement and concentration of feature maps. The auxiliary attention guidance is expected to improve the quality of the displacement field, and consequently lead to the enhancement of the registration performance. We conduct extensive experiments on three public liver datasets and compare to several well-known registration methods to verify the performance of the proposed method. Experimental results show that the proper utilization of attention mechanism improve the accuracy of the 3D deformation medical image registration.
Enhancing Security and Authentication in IoT-based Energy Internet using Post-Quantum Blockchain

The Energy Internet (EI) has revolutionized the generation, distribution, and consumption of energy by leveraging the Internet of Things (IoT). However, it faces significant security challenges, particularly in light of advancements in quantum computing. To address these challenges, this paper proposes a combined approach of post-quantum cryptography and blockchain technology to enhance security and authentication in the EI. The proposed solution aims to ensure the confidentiality, integrity, and availability of critical information, thereby strengthening the resilience of the energy grid. We validate the security of the proposed protocol using BAN logic. Furthermore, simulations conducted with the Proverif security tool demonstrate the protocol’s effectiveness against various threats. The research presented in this paper contributes to the development of a secure and sustainable future for the Energy Internet.
FONN: Federated Optimization with Nys-Newton

Federated optimization or federated learning (FL) involves optimization of the global model or the server model by minimizing the global loss function which is weighted average of all the local loss functions. The optimization of the global model requires faster convergence to reduce the number of communication rounds or global iterations which is one of the major challenge in federated optimization. This paper propose FONN which handles this communication overhead in federated optimization by utilizing Nys-Newton, while updating local models. As compared to existing state-of-the-art FL algorithms, SCAFFOLD, GIANT and DONE, utilization of Nys-Newton leads to better convergence and reduction in communication rounds or global iterations while achieving a desired performance from the global model which may be observed from the experimental results on various heterogeneously partitioned datasets.
Comparative intelligent Navigation of Pioneer Mobile Robot using laser and camera sensors

The work presented in this paper is focused on an autonomous navigation simulation of a Pioneer mobile robot. The objective is to ensure that wherever the set goal is, the robot will always find its way while avoiding any obstacle in its path using at first the data of a laser sensor and at second a camera, and at the end a comparative study between the two kinds of navigation is given.
Enhancing Spam Email Classification using Multilayer Perceptron: Performance Analysis and Comparative Evaluation

With the rapid increase in spam email volume and the associated productivity and security risks, effective spam email classification mechanisms are crucial in ensuring smooth communication systems. After several studies on spam email classification, the outcome demonstrated that Multilayer Perceptron (MLP) outperforms SVM and Decision Trees, making it the chosen model for further research in this paper, as it presented a study on spam email classification using a multilayer perceptron (MLP) model. The proposed MLP-based approach demonstrates strong performance in distinguishing between legitimate and spam emails. A comprehensive evaluation is conducted using a dataset of 5,172 emails, resulting in an accuracy rate of 94.40% on the testing set. The analysis of the results reveals interesting patterns and trends in the data, providing valuable insights into the effectiveness of the MLP model. Furthermore, a comparison with some state-of-the-art approaches showcases the superiority of the MLP model in terms of accuracy, F1 score, precision, recall, and MSE. The results of this study aid in the creation of reliable and precise spam email classification systems, increasing productivity and defending communication channels against spam threats.
A Pareto-Optimal Privacy-Accuracy Settlement For Differentially Private Image Classification

Effectively training differentially private models in machine learning requires optimizing the hyper-parameters while ensuring privacy and maintaining accuracy. This research addresses this challenge by analyzing hyper-parameter tuning results and employs the Pareto frontier approach to identify optimal trade-offs and architectures for private learning. The findings enhance understanding of privacy considerations and inform the development of effective training methodologies and the decision-making process for practical applications.