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Optimal Control in a Mathematical Model for the Occurrence of Electronic Cheating in Certification Exams;Baccalaureate Exams in Morocco as an Example

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Operations Research Forum 2024年第4期5卷 104页

作者： El Mansouri, Abdelbar Khajji, Bouchaib Labzai, Abderrahim Belam, Mohamed Laboratory LMACS Department of Mathematics and Computer Science Khouribga Polydisciplinary Faculty Sultan Moulay Slimane University Beni Mellal Morocco Laboratory of Analysis Modeling and Simulation Department of Mathematics and Computer Science Faculty of Sciences Ben M’Sik Hassan II University of Casablanca Casablanca Morocco

Researchers across a range of fields have raised concerns about the serious rise in the phenomenon of school cheating among pupils and students, which has a negative impact on the quality of education and training. This is because cheating has gone from being an unacceptable act to being a” legitimate right” for a large group of students. Here, we offer a model using mathematics to attempt to understand how this scandalous occurrence spreads among Moroccan students, particularly those in their second year of baccalaureate, and we also examine the effectiveness of some of the suggested solutions. Our method leverages Pontryagin’s maximum principle to identify the best control strategies, and we then apply iterative methods to solve the system that results. Lastly, we validate our theoretical analysis with numerical simulations conducted using MATLAB. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： 34H05 34K28 34K35 49J25 93C15 Academic performance Cheating practices Objective functional Pontryagin’s maximum principle School cheating

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First Competition on Presentation Attack Detection on ID Card 18

First Competition on Presentation Attack Detection on ID Car...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Tapia, Juan E. Damer, Naser Busch, Christoph Espin, Juan M. Barrachina, Javier Rocamora, Alvaro S. Ocvirk, Krištof Alessio, Leon Batagelj, Borut Patwardhan, Sushrut Ramachandra, Raghavendra Mudgalgundurao, Raghavendra Raja, Kiran Schulz, Daniel Aravena, Carlos Da/sec-Biometrics and Internet Security Research Germany Fraunhofer Institute for Computer Graphics Research Igd Darmstadt Germany Tu Darmstadt Department of Computer Science Darmstadt Germany Facephi Company Spain University of Ljubljana Faculty of Computer and Information Science Ljubljana Slovenia Gjøvik Norway Santiago Chile

ISBN: (纸本)9798350364132

This paper summarises the Competition on Presentation Attack Detection on ID Cards (PAD-IDCard) held at the 2024 International Joint Conference on Biometrics (IJCB 2024). The competition attracted a total of ten registered teams, both from academia and industry. In the end, the participating teams submitted five valid submissions, with eight models to be evaluated by the organisers. The competition presented an independent assessment of current state-of-the-art algorithms. Today, no independent evaluation on cross-dataset is available;therefore, this work determined the state-of-the-art on ID cards. To reach this goal, a sequestered test set and baseline algorithms were used to evaluate and compare all the proposals. The sequestered test dataset contains ID cards from four different countries. In summary, a team that chose to be "Anonymous"reached the best average ranking results of 74.80%, followed very closely by the "IDVC"team with 77.65%. © 2024 IEEE.

关键词： Biometrics

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A Security Model of Multihospital FHIR Database Authorization Based on Secret Sharing and Blockchain

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IEEE Internet of Things Journal 2024年第6期11卷 10325-10335页

作者： Yang, Ching-Nung Li, Peng Cheng, Hsiang-Han Kuo, Hsin-Chuan Lu, Ming-Chan Xiong, Lizhi National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan North China Electric Power University Department of Mathematics and Physics Hebei Key Laboratory of Physics and Energy Technology Baoding071003 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing210044 China

Secret sharing (SS) is a threshold technology that shares a secret value by generating and distributing n shares in the way that a set of any k shares can recover the secret. On the other hand, blockchain is a decentralized system establishing a secure data structure for storing data in the form of a hash chain. Combining SS and blockchain is definitely a promising paradigm in the data security field. Recently, the fast healthcare interoperability resources (FHIRs) standard is defined for medical data exchange across various medical institutions. In this article, we combine SS, ElGamal's encryption/decryption, and blockchain to propose a multihospital FHIR database authorization (MFDA). To achieve a secure MFDA, the group-based SS (GSS) and the notion using GSS on exponent (referred to as EGSS) are proposed. The secrets of GSS and EGSS have the discrete logarithm problem (DLP) relation, on which the secrets can be used as ElGamal's private key and public key, respectively. Also, their shares will have DLP relation. By uploading public shares of EGSS on blockchain and using private shares of GSS in a private network, we can recover ElGamal's public key from a deployed smart contract on blockchain and recover ElGamal's private key from a qualified set. Meantime, when recovering private key, the private shares of GSS can be authenticated by public shares on blockchain to check tampering and corrupted of private shares during the communication in a private network. Theorems, examples, and experiments demonstrate that the proposed MFDA has efficient and secure database authorization. © 2014 IEEE.

关键词： Blockchain

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How transformations of representation for input data can affect the properties of induced decision reducts and rules 27

How transformations of representation for input data can aff...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Stanczyk, Urszula Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Decision reducts and rules belong to forms used for the representation of knowledge learnt from input data while using a rough set approach in the exploration stage. As with any patterns that capture properties of data, the size is considered as the most important indicator of their quality, as a more general and concise description allows for improved understanding and simplified interpretation. The size of a reduct is given by its cardinality, and for a rule it is a number of included conditions, both referred to as lengths. These lengths undergo transformations along with data transformations, so such processing as discretisation leads to changed characteristics of existing patterns and through them also reducts and rules. The paper presents research with contributions in the observations focused on the influence of supervised and unsupervised discretisation on properties of attributes, induced decision reducts, and rules, offering a deeper insight into relations among them. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Data exploration decision reduct decision rule discretisation knowledge representation rough set theory transformation

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Arabic Multiclass Emotional Analysis Using a Bidirectional GRU-LSTM Deep Model on Twitter 4

Arabic Multiclass Emotional Analysis Using a Bidirectional G...

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4th International Conference on Innovative Research in Applied science, Engineering and Technology, IRASET 2024

作者： Elfaik, Hanane Nfaoui, El Habib Chouaib Doukkali University Laroseri Laboratory Faculty Of Sciences Department Of Computer Science El Jadida Morocco Sidi Mohamed Ben Abdellah University Lisac Laboratory Faculty Of Sciences Dhar El Mahraz Department Of Computer Science Fez Morocco

ISBN: (纸本)9798350309508

Recognizing emotional states within Arabic tweets is a challenging task due to the inherent complexity of the Arabic language. This complexity encompasses aspects such as ambiguity, agglutination, dialectal variations, and morphological richness. Additionally, user-generated data frequently include misspelled words, abbreviations, acronyms, slang, informal language, and non-standard punctuation, further complicating the task. In our research, we have implemented an efficient multiclass emotional recognition classifier. To achieve this, we proposed a Bidirectional GRU-LSTM deep learning model. This model is designed to capture high-quality global features within the input tweet. Our experimental evaluation, conducted using a benchmark dataset, has produced highly promising results and significant performance improvements. © 2024 IEEE.

关键词： Emotion Recognition

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A memory-efficient neural ordinary differential equation framework based on high-level adjoint differentiation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1110-1120页

作者： Zhang, Hong Zhao, Wenjun Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States Department of Mathematics Brown University ProvidenceRI02906 United States

Abstract-Neural ordinary differential equations (neural ODEs) have emerged as a novel network architecture that bridges dynamical systems and deep learning. However, the gradient obtained with the continuous adjointmethod in the vanilla neuralODEis not *** approaches suffer either froman excessive memory requirement due to deep computational graphs or from limited choices for the time integration scheme, hampering their application to large-scale complex dynamical systems. To achieve accurate gradients without compromising memory efficiency and flexibility, we present a new neural ODE framework, PNODE, based on high-level discrete adjoint algorithmic differentiation. By leveraging discrete adjoint time integrators and advanced checkpointing strategies tailored for these integrators, PNODE can provide a balance between memory and computational costs, while computing the gradients consistently and accurately. In this article, we provide an open-source implementation based on PyTorch and PETSc, one of the most commonly used portable, scalable scientific computing libraries. We demonstrate the performance through extensive numerical experiments on image classification and continuous normalizing flow *** show that PNODE achieves the highest memory efficiency when compared with other reverse-Accurate methods. On the image classification problems, PNODE is up to two times faster than the vanilla neural ODE and up to 2.3 times faster than the best existing reverse-Accurate method. We also show that PNODE enables the use of the implicit time integration methods that are needed for stiff dynamical systems. Impact Statement-Neural ODEs not only are important for classic machine learning applications as an emerging deep learning architecture but also are increasingly important in scientific machine learning where differential equations and artificial intelligence are more ***, the challenges and constraints in the gradient calculation,

关键词： Dynamical systems

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Enhancing News Credibility Detection in Arabic: A Study of Attentional Bidirectional LSTM 3

Enhancing News Credibility Detection in Arabic: A Study of A...

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3rd International Conference on Embedded Systems and Artificial Intelligence, ESAI 2024

作者： Elfaik, Hanane Nfaoui, El Habib Chouaib Doukkali University LAROSERI Laboratory Faculty of Sciences Computer Science Department El Jadida Morocco Dhar El Mahraz Sidi Mohamed Ben Abdellah University LISAC Laboratory Faculty of Sciences Computer Science Department Fez Morocco

ISBN: (纸本)9798331522056

Detecting fake news continues to be a major challenge in the digital era, especially in languages with significant linguistic diversity such as Arabic, which is highly ambiguous, morphologically rich, and has numerous dialects. This study presents an enhanced method for detecting Arabic fake news by leveraging an Attentional Bidirectional Long Short-Term Memory (AttBiLSTM) model. Our proposed model integrates an attentional mechanism with BiLSTM to improve classification accuracy. Evaluated on the benchmark AFND Fake News dataset, our approach demonstrates significant improvements in identifying news credibility, achieving an accuracy of7 9. 2 0%. This research advances deep learning methods for fake news detection by offering an effective solution tailored to Arabic language contexts. © 2024 IEEE.

关键词： Fake detection

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LightFinder: Finding Persistent Items with Small Memory 20th

LightFinder: Finding Persistent Items with Small Memory

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Cao, Lu Xiao, Weiqiang Zhang, Weizhe Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9789819628292

Detecting persistent items in large-scale data streams efficiently and accurately is a significant challenge, particularly when working with limited memory. Current state-of-the-art methods often require substantial memory resources to maintain high detection accuracy. To address this limitation, we present LightFinder (LF), which leverages a Conservative-Update (CU) strategy to minimize hash collision errors by selectively updating the smallest active counter. Additionally, LF supports detection across varying persistence windows, enhancing detection accuracy and flexibility in skewed data streams. Our analysis reveals that LF excels in detecting persistent items with high accuracy even under stringent memory constraints. Our approach effectively estimates item persistence and maintains robust performance across various persistence windows. Notably, when operating with just 10% of the memory required by On-Off Sketch (OO), LF achieves an Average Absolute Error (AAE) nearly half that of OO (up to 1.5 times lower), and its Average Relative Error (ARE) is reduced by an order of magnitude. This demonstrates LF significantly outperforms existing methods in terms of space efficiency. Our codes are open-sourced on GitHub [1]. © IFIP International Federation for Information Processing 2025.

关键词： Data accuracy

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Multimodal Sarcasm Detection Method Using RNN and CNN 6

Multimodal Sarcasm Detection Method Using RNN and CNN

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6th International Conference on Intelligent Computing in Data sciences, ICDS 2024

作者： Azahouani, Bouchra Elfaik, Hanane Nfaoui, El Habib El Garouani, Said Sidi Mohamed Ben Abdellah University LISAC Laboratory Faculty of Sciences Dhar El Mahraz Computer Science Department Fez Morocco Chouaib Doukkali University LAROSERI Laboratory Faculty of Sciences Computer Science Department El Jadida Morocco

ISBN: (纸本)9798350351200

In the current digital environment, sarcasm is preva-lent on social media platforms, characterized by a combination of verbal and non-verbal cues, such as prosodic variations, phonetic inflections, and textual markers including lexical selection, irony, and exaggeration. Previous research has primarily focused on sarcasm detection in either audio or text data independently. This paper presents a novel deep learning approach to detect sarcasm in conversational data by integrating both textual and auditory elements. Our method employs a bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) network for text processing, capturing sequential dependencies and contextual information. For the audio component, we use a Convolutional Neural Network (CNN) to extract key features from speech. The fusion of these modalities is achieved by combining the extracted features into a composite vector, which improves the detection of sarcasm. Experimental evaluations on the MUStARD Extended dataset show that our hybrid model significantly outperforms unimodal models, achieving an F1-score of 74.67%. © 2024 IEEE.

关键词： Convolutional neural networks

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A New Approach for Quantum Phase Estimation Based Algorithms for Machine Learning 8th

A New Approach for Quantum Phase Estimation Based Algorithm...

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8th International Conference on Smart City Applications, SCA 2023

作者： Ouedrhiri, Oumayma Banouar, Oumayma El Hadaj, Salah Raghay, Said Department of Computer Science and Mathematics University of Quebec at Trois-Riviéres Trois-Riviéres Canada Faculty of Sciences and Techniques Laboratory of Computer Science Engineering and Systems Marrakesh Morocco

ISBN: (纸本)9783031543753

One of the greatest developments in computer science is undoubtedly quantum computing. It has demonstrated to give various benefits over the classical algorithms, particularly in the significant reduction of processing time, due to the parallelism and entanglement properties. One of the most crucial quantum computing algorithms is quantum phase estimation (QPE). It is called the eigenvalue finding module for unitary operators. It has helped to solve the order finding and the factoring problem, and to calculate the eigenvalues of unitary matrices and quantum sampling methods. In this paper, we study recent improved versions for the QPE procedure, their advantages and experimentation. We also propose a new approach for QPE based algorithms for machine learning (ML). These algorithms are the Harrow-Hassidim-Lloyd (HHL) algorithm for solving linear systems, the quantum singular value thresholding (QSVT) algorithm for matrix completion in recommender systems, and the quantum principal components analysis (QPCA) for data visualization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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