arXiv subjects

Sadok Ben Yahia

Publications and source records attributed to Sadok Ben Yahia.

At least 19 recordsLinked to original sources

arXiv2026

QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution

Recent advancements in Single-Image Super-Resolution (SISR) using deep learning have significantly improved image restoration quality. However, the high computational cost of processing high-resolution images due to the large number of parameters in classical models, along with the scalability challenges of quantum algorithms for image processing, remains a major obstacle. In this paper, we propose the Quantum Image Enhancement Transformer for Super-Resolution (QUIET-SR), a hybrid framework that extends the Swin transformer architecture with a novel shifted quantum window attention mechanism, built upon variational quantum neural networks. QUIET-SR effectively captures complex residual mappings between low-resolution and high-resolution images, leveraging quantum attention mechanisms to enhance feature extraction and image restoration while requiring a minimal number of qubits, making it suitable for the Noisy Intermediate-Scale Quantum (NISQ) era. We evaluate our framework in MNIST (30.24 PSNR, 0.989 SSIM), FashionMNIST (29.76 PSNR, 0.976 SSIM) and the MedMNIST dataset collection, demonstrating that QUIET-SR achieves PSNR and SSIM scores comparable to state-of-the-art methods while using fewer parameters. Our efficient batching strategy directly enables massive parallelization on multiple QPU's paving the way for practical quantum-enhanced image super-resolution through coordinated QPU-GPU quantum supercomputing.

quant-ph↗

arXiv2025

QAS-QTNs: Curriculum Reinforcement Learning-Driven Quantum Architecture Search for Quantum Tensor Networks

Quantum Architecture Search (QAS) is an emerging field aimed at automating the design of quantum circuits for optimal performance. This paper introduces a novel QAS framework employing hybrid quantum reinforcement learning with quantum curriculum learning strategies, enabling learning agents to tackle increasingly complex quantum circuit design tasks. We benchmark four state-of-the-art classical reinforcement learning algorithms (A2C, PPO, DDQN, TD3) against their quantum-enhanced counterparts (QA2C, QPPO, QDDQN, QTD3) for optimizing variational quantum circuits (VQCs). Our approach progressively increases circuit depth and gate complexity during training, leveraging parameterized quantum circuits as function approximations. To improve learning efficiency and stability, all algorithms, both classical and quantum, are augmented with Prioritized Experience Replay (PER). Experimental results show that quantum-enhanced RL significantly outperforms classical methods. In a 2-qubit environment, PERQDDQN achieves a success probability of 0.46 with ~3,000 optimal successes, surpassing classical PERDDQN (0.42, ~2,400). In the more complex 3-qubit setting, PERQDDQN and PERQTD3 reach success probabilities of ~0.47, with optimal success counts of ~3,800 and ~3,600, respectively, outperforming their classical counterparts. Additionally, we apply our QAS-QTN approach to a classification problem, where the optimized quantum circuit achieves an accuracy of 90.33\%, outperforming quantum models consisting of random ansatz. This hybrid classical-quantum approach leads to faster convergence and more efficient quantum circuit designs, demonstrating its potential for advancing automated quantum architecture search.

quant-ph↗

arXiv2025

MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption

The integration of fully homomorphic encryption (FHE) in federated learning (FL) has led to significant advances in data privacy. However, during the aggregation phase, it often results in performance degradation of the aggregated model, hindering the development of robust representational generalization. In this work, we propose a novel multimodal quantum federated learning framework that utilizes quantum computing to counteract the performance drop resulting from FHE. For the first time in FL, our framework combines a multimodal quantum mixture of experts (MQMoE) model with FHE, incorporating multimodal datasets for enriched representation and task-specific learning. Our MQMoE framework enhances performance on multimodal datasets and combined genomics and brain MRI scans, especially for underrepresented categories. Our results also demonstrate that the quantum-enhanced approach mitigates the performance degradation associated with FHE and improves classification accuracy across diverse datasets, validating the potential of quantum interventions in enhancing privacy in FL.

quant-ph↗

arXiv2024

Enhancing the conformal predictability of context-aware recommendation systems by using Deep Autoencoders

In the field of Recommender Systems (RS), neural collaborative filtering represents a significant milestone by combining matrix factorization and deep neural networks to achieve promising results. Traditional methods like matrix factorization often rely on linear models, limiting their capability to capture complex interactions between users, items, and contexts. This limitation becomes particularly evident with high-dimensional datasets due to their inability to capture relationships among users, items, and contextual factors. Unsupervised learning and dimension reduction tasks utilize autoencoders, neural network-based models renowned for their capacity to encode and decode data. Autoencoders learn latent representations of inputs, reducing dataset size while capturing complex patterns and features. In this paper, we introduce a framework that combines neural contextual matrix factorization with autoencoders to predict user ratings for items. We provide a comprehensive overview of the framework's design and implementation. To evaluate its performance, we conduct experiments on various real-world datasets and compare the results against state-of-the-art approaches. We also extend the concept of conformal prediction to prediction rating and introduce a Conformal Prediction Rating (CPR). For RS, we define the nonconformity score, a key concept of conformal prediction, and demonstrate that it satisfies the exchangeability property.

cs.IR↗

arXiv2024

Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML

The widespread deployment of products powered by machine learning models is raising concerns around data privacy and information security worldwide. To address this issue, Federated Learning was first proposed as a privacy-preserving alternative to conventional methods that allow multiple learning clients to share model knowledge without disclosing private data. A complementary approach known as Fully Homomorphic Encryption (FHE) is a quantum-safe cryptographic system that enables operations to be performed on encrypted weights. However, implementing mechanisms such as these in practice often comes with significant computational overhead and can expose potential security threats. Novel computing paradigms, such as analog, quantum, and specialized digital hardware, present opportunities for implementing privacy-preserving machine learning systems while enhancing security and mitigating performance loss. This work instantiates these ideas by applying the FHE scheme to a Federated Learning Neural Network architecture that integrates both classical and quantum layers.

quant-ph↗

arXiv2024

AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modeling

The growing computational demands of artificial intelligence (AI) in addressing climate change raise significant concerns about inefficiencies and environmental impact, as highlighted by the Jevons paradox. We propose an attention-enhanced quantum physics-informed neural networks model (AQ-PINNs) to tackle these challenges. This approach integrates quantum computing techniques into physics-informed neural networks (PINNs) for climate modeling, aiming to enhance predictive accuracy in fluid dynamics governed by the Navier-Stokes equations while reducing the computational burden and carbon footprint. By harnessing variational quantum multi-head self-attention mechanisms, our AQ-PINNs achieve a 51.51% reduction in model parameters compared to classical multi-head self-attention methods while maintaining comparable convergence and loss. It also employs quantum tensor networks to enhance representational capacity, which can lead to more efficient gradient computations and reduced susceptibility to barren plateaus. Our AQ-PINNs represent a crucial step towards more sustainable and effective climate modeling solutions.

quant-ph↗

arXiv2024

QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction

Financial market prediction and optimal trading strategy development remain challenging due to market complexity and volatility. Our research in quantum finance and reinforcement learning for decision-making demonstrates the approach of quantum-classical hybrid algorithms to tackling real-world financial challenges. In this respect, we corroborate the concept with rigorous backtesting and validate the framework's performance under realistic market conditions, by including fixed transaction cost per trade. This paper introduces a Quantum Attention Deep Q-Network (QADQN) approach to address these challenges through quantum-enhanced reinforcement learning. Our QADQN architecture uses a variational quantum circuit inside a traditional deep Q-learning framework to take advantage of possible quantum advantages in decision-making. We gauge the QADQN agent's performance on historical data from major market indices, including the S&P 500. We evaluate the agent's learning process by examining its reward accumulation and the effectiveness of its experience replay mechanism. Our empirical results demonstrate the QADQN's superior performance, achieving better risk-adjusted returns with Sortino ratios of 1.28 and 1.19 for non-overlapping and overlapping test periods respectively, indicating effective downside risk management.

quant-ph↗

arXiv2023

A Random Walk Approach for Simulation-Based Continuous Dynamic Traffic Assignment

This paper presents a new simulation-based approach to address the stochastic Dynamic Traffic Assignment (DTA) problem, focusing on large congested networks and dynamic settings. The proposed methodology incorporates a random walk model inspired by the theoretical concept of the \textit{equivalent impedance} method, specifically designed to overcome the limitations of traditional Multinomial Logit (MNL) models in handling overlapping routes and scaling issues. By iteratively contracting non-overlapping subnetworks into virtual links and computing equivalent virtual travel costs, the route choice decision-making process is shifted to intersections, enabling a more accurate representation of travelers' choices as traffic conditions evolve and allowing more accurate performance under fine-grained temporal segmentation. The approach leverages Directed Acyclic Graphs (DAGs) structure to efficiently find all routes between two nodes, thus obviating the need for route enumeration, which is intractable in general networks. While with the calculation approach of downstream node choice probabilities, all available routes in the network can be selected with non-zero probability. To evaluate the effectiveness of the proposed method, experiments are conducted on two synthetic networks under congested demand scenarios using Simulation of Urban MObility (SUMO), an open-source microscopic traffic simulation software. The results demonstrate the method's robustness, faster convergence, and realistic trip distribution compared to traditional route assignment methods, making it an ideal proposal for real-time or resource-intensive applications such as microscopic demand calibration.

cs.MA↗

arXiv2023

Uncertainty in Automated Ontology Matching: Lessons Learned from an Empirical Experimentation

Data integration is considered a classic research field and a pressing need within the information science community. Ontologies play a critical role in such a process by providing well-consolidated support to link and semantically integrate datasets via interoperability. This paper approaches data integration from an application perspective, looking at techniques based on ontology matching. An ontology-based process may only be considered adequate by assuming manual matching of different sources of information. However, since the approach becomes unrealistic once the system scales up, automation of the matching process becomes a compelling need. Therefore, we have conducted experiments on actual data with the support of existing tools for automatic ontology matching from the scientific community. Even considering a relatively simple case study (i.e., the spatio-temporal alignment of global indicators), outcomes clearly show significant uncertainty resulting from errors and inaccuracies along the automated matching process. More concretely, this paper aims to test on real-world data a bottom-up knowledge-building approach, discuss the lessons learned from the experimental results of the case study, and draw conclusions about uncertainty and uncertainty management in an automated ontology matching process. While the most common evaluation metrics clearly demonstrate the unreliability of fully automated matching solutions, properly designed semi-supervised approaches seem to be mature for a more generalized application.

cs.AI↗

arXiv2023

Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings

Knowledge representation (KR) is vital in designing symbolic notations to represent real-world facts and facilitate automated decision-making tasks. Knowledge graphs (KGs) have emerged so far as a popular form of KR, offering a contextual and human-like representation of knowledge. In international economics, KGs have proven valuable in capturing complex interactions between commodities, companies, and countries. By putting the gravity model, which is a common economic framework, into the process of building KGs, important factors that affect trade relationships can be taken into account, making it possible to predict international trade patterns. This paper proposes an approach that leverages Knowledge Graph embeddings for modeling international trade, focusing on link prediction using embeddings. Thus, valuable insights are offered to policymakers, businesses, and economists, enabling them to anticipate the effects of changes in the international trade system. Moreover, the integration of traditional machine learning methods with KG embeddings, such as decision trees and graph neural networks are also explored. The research findings demonstrate the potential for improving prediction accuracy and provide insights into embedding explainability in knowledge representation. The paper also presents a comprehensive analysis of the influence of embedding methods on other intelligent algorithms.

cs.AI↗

arXiv2021

Challenges of Applying E-Learning in the Libyan Higher Education System

The adoption of ICT in classrooms is very important in order to improve education quality, promote effective management of knowledge, and improve delivery of knowledge in higher education. Some of the Libyan universities have already started using E-learning in classrooms, but many challenges are still hindering that adoption. This paper endeavors to find the obstacles that may face the adoption of E-learning in Libya and sketches out the possible solutions. Further, it highlights the potentials for the adoption of E-learning in the higher education system in Libya using both qualitative and quantitative approaches. Both questioner and interview have been used on a focused group to collect the data. Teachers and students at Al Asmarya Islamic University have been selected as a sample for this study. This paper reveals that the challenges hindering teachers and students from using ICT and E-learning are: the lack of knowledge about ICT and E-learning, the lack of ICT infrastructure, and the lack of financial support. However, the participants show a high level of interest in applying the ICT and E-learning in the university despite the unsuitability of the environment.

cs.CY↗

arXiv2019

Contributions to the Formalization and Extraction of Generic Bases of Association Rules

In this thesis, a detailed study shows that closed itemsets and minimal generators play a key role for concisely representing both frequent itemsets and association rules. These itemsets structure the search space into equivalence classes such that each class gathers the itemsets appearing in the same subset aka objects or transactions of the given data. In this respect, we proposed lossless reductions of the minimal generator set thanks to a new substitution-based process. Our theoretical results are extended to the association rule framework in order to reduce as much as possible the number of retained rules without information loss. We then give a thorough formal study of the related inference mechanism allowing to derive all redundant association rules, starting from the retained ones. We also lead a thorough exploration of the disjunctive search space, where itemsets are characterized by their respective disjunctive supports, instead of the conjunctive ones. This exploration is motivated by the fact that, in some applications, such information brings richer knowledge to the end-users. To obtain a redundancy free representation of the disjunctive search space, an interesting solution consists in selecting a unique element to represent itemsets covering the same set of data. Two itemsets are equivalent if their respective items cover the same set of data. In this regard, we introduced a new operator dedicated to this task. In each induced equivalence class, minimal elements are called essential itemsets, while the largest one is called disjunctive closed itemset. The introduced operator is then at the roots of new concise representations of frequent itemsets. We also exploit the disjunctive search space to derive generalized association rules. These latter rules generalize classic ones to also offer disjunction and negation connectors between items, in addition to the conjunctive one.

cs.DB↗

arXiv2018

Hybrid Microaggregation for Privacy-Preserving Data Mining

k-Anonymity by microaggregation is one of the most commonly used anonymization techniques. This success is owe to the achievement of a worth of interest tradeoff between information loss and identity disclosure risk. However, this method may have some drawbacks. On the disclosure limitation side, there is a lack of protection against attribute disclosure. On the data utility side, dealing with a real datasets is a challenging task to achieve. Indeed, the latter are characterized by their large number of attributes and the presence of noisy data, such that outliers or, even, data with missing values. Generating an anonymous individual data useful for data mining tasks, while decreasing the influence of noisy data is a compelling task to achieve. In this paper, we introduce a new microaggregation method, called HM-PFSOM, based on fuzzy possibilistic clustering. Our proposed method operates through an hybrid manner. This means that the anonymization process is applied per block of similar data. Thus, we can help to decrease the information loss during the anonymization process. The HMPFSOM approach proposes to study the distribution of confidential attributes within each sub-dataset. Then, according to the latter distribution, the privacy parameter k is determined, in such a way to preserve the diversity of confidential attributes within the anonymized microdata. This allows to decrease the disclosure risk of confidential information.

cs.CR↗

arXiv2015

Towards Unveiling the Ontology Key Features Altering Reasoner Performances

Reasoning with ontologies is one of the core fields of research in Description Logics. A variety of efficient reasoner with highly optimized algorithms have been developed to allow inference tasks on expressive ontology languages such as OWL(DL). However, reasoner reported computing times have exceeded and sometimes fall behind the expected theoretical values. From an empirical perspective, it is not yet well understood, which particular aspects in the ontology are reasoner performance degrading factors. In this paper, we conducted an investigation about state of art works that attempted to portray potential correlation between reasoner empirical behaviour and particular ontological features. These works were analysed and then broken down into categories. Further, we proposed a set of ontology features covering a broad range of structural and syntactic ontology characteristics. We claim that these features are good indicators of the ontology hardness level against reasoning tasks.

cs.AI↗

arXiv2014

Learning to Match for Multi-criteria Document Relevance

In light of the tremendous amount of data produced by social media, a large body of research have revisited the relevance estimation of the users' generated content. Most of the studies have stressed the multidimensional nature of relevance and proved the effectiveness of combining the different criteria that it embodies. Traditional relevance estimates combination methods are often based on linear combination schemes. However, despite being effective, those aggregation mechanisms are not effective in real-life applications since they heavily rely on the non-realistic independence property of the relevance dimensions. In this paper, we propose to tackle this issue through the design of a novel fuzzy-based document ranking model. We also propose an automated methodology to capture the importance of relevance dimensions, as well as information about their interaction. This model, based on the Choquet Integral, allows to optimize the aggregated documents relevance scores using any target information retrieval relevance metric. Experiments within the TREC Microblog task and a social personalized information retrieval task highlighted that our model significantly outperforms a wide range of state-of-the-art aggregation operators, as well as a representative learning to rank methods.

cs.IR↗

arXiv2014

Nouvelle représentation concise exacte des motifs corrélés rares : Application à la détection d'intrusions

Correlated rare pattern mining is an interesting issue in Data mining. In this respect, the set of correlated rare patterns w.r.t. to the bond correlation measure was studied in a recent work, in which the RCPR concise exact representation of the set of correlated rare patterns was proposed. However, none algorithm was proposed in order to mine this representation and none experiment was carried out to evaluate it. In this paper, we introduce the new RcprMiner algorithm allowing an efficient extraction of RCPR. We also present the IsRCP algorithm allowing the query of the RCPR representation in addition to the RCPRegeneration algorithm allowing the regeneration of the whole set RCP of rare correlated patterns starting from this representation. The carried out experiments highlight interesting compactness rates offered by RCPR. The effectiveness of the proposed classification method, based on generic rare correlated association rules derived from RCPR, has also been proved in the context of intrusion detection.

cs.DB↗

arXiv2013

Efficient construction of the lattice of frequent closed patterns and simultaneous extraction of generic bases of rules

In the last few years, the amount of collected data, in various computer science applications, has grown considerably. These large volumes of data need to be analyzed in order to extract useful hidden knowledge. This work focuses on association rule extraction. This technique is one of the most popular in data mining. Nevertheless, the number of extracted association rules is often very high, and many of them are redundant. In this paper, we propose a new algorithm, called PRINCE. Its main feature is the construction of a partially ordered structure for extracting subsets of association rules, called generic bases. Without loss of information these subsets form representation of the whole association rule set. To reduce the cost of such a construction, the partially ordered structure is built thanks to the minimal generators associated to frequent closed patterns. The closed ones are simultaneously derived with generic bases thanks to a simple bottom-up traversal of the obtained structure. The experimentations we carried out in benchmark and "worst case" contexts showed the efficiency of the proposed algorithm, compared to algorithms like CLOSE, A-CLOSE and TITANIC.

cs.DS↗

arXiv2013

Towards a semantic and statistical selection of association rules

The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. Association rules selection is a classical topic to address this issue, yet, new innovated approaches are required in order to provide help to decision makers. Hence, many interesting- ness measures have been defined to statistically evaluate and filter the association rules. However, these measures present two major problems. On the one hand, they do not allow eliminating irrelevant rules, on the other hand, their abun- dance leads to the heterogeneity of the evaluation results which leads to confusion in decision making. In this paper, we propose a two-winged approach to select statistically in- teresting and semantically incomparable rules. Our statis- tical selection helps discovering interesting association rules without favoring or excluding any measure. The semantic comparability helps to decide if the considered association rules are semantically related i.e comparable. The outcomes of our experiments on real datasets show promising results in terms of reduction in the number of rules.

cs.DB↗