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2023 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Sawadogo, Zakaria Dembele, Jean-Marie Mendy, Gervais Ouya, Samuel Gaston Berger University Laboratory of Numerical Analysis and Computer Science Dakar Senegal Cheikh Anta Diop University Computer Science Telecommunications and Applications Dakar Senegal

ISBN: (纸本)9798350322972

The prevalence of cyber security threats, such as the Android Zero-day vulnerability, is becoming increasingly worrisome. With the widespread use of Android-powered mobile devices, attackers are leveraging zero-day vulnerabilities to infect Android software at an alarming rate. Detecting zero-day vulnerabilities in Android applications is particularly challenging due to their unpatched and undiscovered nature, resulting in a lack of reference points for identification. In response to this issue, we propose a novel and effective system called Zero-Vuln, which is designed to classify and identify zero-day Android malware. Zero-Vuln leverages deep learning and zero-shot learning techniques, as well as established data-sets, to identify previously unknown malware. Our approach achieves a remarkable performance of 83% accuracy, as well as high precision and recall, and represents a significant contribution to the field of cyber security. © 2023 IEEE.

关键词： Zero-day attack

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Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models

Modeling stochastic eye tracking data: A comparison of quant...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Bhandari, Shailendra Lencastre, Pedro Lind, Pedro Department of Computer Science OsloMet - Oslo Metropolitan University OsloN-0130 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo0164 Norway

ISBN: (纸本)9798400704956

We explore the use of quantum generative adversarial networks QGANs for modeling eye movement velocity data. We assess whether the advanced computational capabilities of QGANs can enhance the modeling of complex stochastic distribution beyond the traditional mathematical models, particularly the Markov model. The findings indicate that while QGANs demonstrate potential in approximating complex distributions, the Markov model consistently outperforms in accurately replicating the real data distribution. This comparison underlines the challenges and avenues for refinement in time series data generation using quantum computing techniques. It emphasizes the need for further optimization of quantum models to better align with real-world data characteristics. © 2024 is held by the owner/author(s).

关键词： Generative adversarial networks

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A Non-Parametric Identification Scheme of SISO-LTI Systems Using Blaschke-Products∗ 32

A Non-Parametric Identification Scheme of SISO-LTI Systems U...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Ungvari, Gergo Soumelidis, Alexandros Dozsa, Tamas Eötvös Loránd University Faculty of Informatics Department of Numerical Analysis Budapest Hungary HUN-REN Institute for Computer Science and Control Systems and Control Laboratory Budapest Hungary

ISBN: (纸本)9798350395440

In this manuscript, a novel algorithm is presented for the identification of single input single output linear time invariant (SISO-LTI) systems. The proposed method is able to find poles of the transfer function describing the system without any assumptions on the system order. The results presented in this paper generalize earlier identification schemes based on expansions of the transfer function using generalized rational orthogonal basis functions (GOBFs). These previous methods depended on a convergent sequence constructed from the coefficients of the orthogonal GOBF expansion. The first contribution of this study is showing that a wider family of functions may be used to construct similar pole finding algorithms. The aforementioned previous approaches can be interpreted as special cases of the results presented here. In addition, using this generalization a new pole identification scheme is proposed using finite Blaschke-products. Through experiments, the practical utility of this scheme is verified when compared to earlier variations of the pole finding scheme. © 2024 IEEE.

关键词： Poles

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Evaluating Automatic Annotation Techniques for Fine-Tuning Large Language Models in Financial Sentiment analysis 4

Evaluating Automatic Annotation Techniques for Fine-Tuning L...

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4th International Conference on Sentiment analysis and Deep Learning, ICSADL 2025

作者： Youb, Ibtissam Hamlich, Mohamed Ventura, Sebastian Al-Obeidat, Feras University of Cordoba Department of Computer Science and Numerical Analysis Cordoba Spain University of Hassan Ii Ccps Laboratory Ensam Department of Electronic Casablanca Morocco College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331523923

Accurate sentiment analysis in financial contexts is crucial for market analysis and investment decisions. However, manually annotating large datasets for training models is time-consuming and costly. This study evaluates two automatic an-notation tools, VADER and TextBlob, for their effectiveness in generating labels for training large language models (LLMs) like BERT and GPT-2 in financial sentiment analysis. We used a large dataset of stock market-related tweets, including a manually annotated subset for benchmarking. The remaining tweets were automatically labeled using VADER and TextBlob. We trained BERT and GPT-2 models with these sentiment labels and compared their performance against the benchmark dataset. Our findings show that models trained with VADER annotations had a higher correlation with human-labeled data (62% accuracy) compared to those trained with TextBlob annotations (48% accuracy). These results suggest that VADER is more suitable for automatic annotation in financial sentiment analysis, providing more accurate and reliable sentiment labels, which can significantly improve the efficiency of training large language models for financial applications. © 2025 IEEE.

关键词： Tweets

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Pole Identification using discrete Laguerre expansion and variable projection

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IFAC-PapersOnLine 2023年第2期56卷 3994-3999页

作者： Tamás Dózsa Mátyás Szabari Alexandros Soumelidis Péter Kovács Institute for Computer Science and Control Systems and Control Laboratory Eötvös Loránd University Department of Numerical Analysis Eötvös Loránd University Faculty of Science Institute for Computer Science and Control Systems and Control Laboratory Eötvös Loránd University Department of Numerical Analysis

We propose a novel algorithm for identifying the poles of transfer functions describing SISO-LTI (single input single output, linear time invariant) systems. Our Identification method works in the frequency domain and consists of two parts. In the first part, we extend a discrete Laguerre expansion based method with an automatic parameter selection scheme. This allows us to find an initial estimate of the poles of SISO-LTI transfer functions without the need for human intuition. Then, in the second part, we propose a novel optimization problem to improve our initial estimates. The proposed optimization aims to reduce the least squared error of a parameterized model, which can be interpreted as an orthogonal projection of the system's frequency response onto a subspace spanned by Generalized Orthogonal Rational Basis functions (GOBFs). We solve the corresponding nonlinear optimization task using gradient based methods, where we can analytically calculate the gradient of the error functional. Through robust numerical experiments, we investigate the behavior of the developed methods and show that they work even in scenarios, when the transfer function has a high number of poles.

关键词： Frequency domain identification Recursive identification Linear systems Variable Projection Generalized Orthogonal Basis Functions, Malmquist-Takenaka functions

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Zero-Vuln: Using deep learning and zero-shot learning techniques to detect zero-day Android malware

Zero-Vuln: Using deep learning and zero-shot learning techni...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Zakaria Sawadogo Jean-Marie Dembele Gervais Mendy Samuel Ouya Laboratory of Numerical Analysis and Computer Science Gaston Berger University Dakar Senegal Lab of Numerical Analysis and Computer Science Gaston Berger University Saint Louis Senegal Computer Science Telecommunications and Applications Cheikh Anta Diop University Dakar Senegal

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Android Malware Detection: Feature Update Using Incremental Learning Approach: Further Investigation of UFILA

Android Malware Detection: Feature Update Using Incremental ...

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International Conference on Advanced Communication Technology (ICACT)

作者： Zakaria Sawadogo Jean-Marie Dembele Gervais Mendy Samuel Ouya LITA(Laboratory of Computer Science Telecommunications and Applications) Cheikh Anta Diop University of Dakar Senegal LANI(Laboratory of Numerical Analysis and Computer Science) Gaston Berger University Senegal

Several kinds of mobile applications are available on platforms offering various services to users. Both malware and good applications are found in software repositories, which is a major cybersecurity problem. To address this problem, machine learning approaches have been proposed in the literature for the detection of malware in general and malicious Android applications in particular. But obfuscation techniques are used by some developers to hide malicious applications, which implies the need to update Android malware detection models. In the literature few works are focused on updating features. Our contribution is therefore an incremental learning approach capable of detecting Android malware by taking into account both static and dynamic features. We propose through the UFILA approach an updated feature set for Android malware detection and classification by adding new features. We evaluated 13 classification algorithms and selected the four most effective algorithms to implement our approach. The results obtained by our approach outperform several malware detection approaches in the literature. Our approach allows to detect malicious android applications based on static and dynamic features with feature updating. It also allows to efficiently classify the different classes.

关键词： Measurement Heuristic algorithms Machine learning Feature extraction Malware Communications technology Classification algorithms

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Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models

arXiv

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arXiv 2024年

作者： Bhandari, Shailendra Lencastre, Pedro Lind, Pedro Department of Computer Science OsloMet – Oslo Metropolitan University OsloN-0130 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo0164 Norway

关键词： Generative adversarial networks

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DREAMS: A python framework to train deep learning models with model card reporting for medical and health applications

arXiv

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arXiv 2024年

作者： Khadka, Rabindra Lind, Pedro G. Yazidi, Anis Belhadi, Asma Department of Computer Science OsloMet – Oslo Metropolitan University OsloN-0130 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing OsloN-0164 Norway

Electroencephalography (EEG) data provides a non-invasive method for researchers and clinicians to observe brain activity in real time. The integration of deep learning techniques with EEG data has significantly improved the ability to identify meaningful patterns, leading to valuable insights for both clinical and research purposes. However, most of the frameworks so far, designed for EEG data analysis, are either too focused on pre-processing or in deep learning methods per, making their use for both clinician and developer communities problematic. Moreover, critical issues such as ethical considerations, biases, uncertainties, and the limitations inherent in AI models for EEG data analysis are frequently overlooked, posing challenges to the responsible implementation of these technologies. In this paper, we introduce a comprehensive deep learning framework tailored for EEG data processing, model training and report generation. While constructed in way to be adapted and developed further by AI developers, it enables to report, through model cards, the outcome and specific information of use for both developers and clinicians. In this way, we discuss how this framework can, in the future, provide clinical researchers and developers with the tools needed to create transparent and accountable AI models for EEG data analysis and diagnosis. © 2024, CC BY.

关键词： Electroencephalography

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A Non-Parametric Identification Scheme of SISO-LTI Systems Using Blaschke-Products*

A Non-Parametric Identification Scheme of SISO-LTI Systems U...

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Mediterranean Conference on Control and Automation (MED)

作者： Gergő Ungvári Alexandros Soumelidis Tamás Dózsa Department of Numerical Analysis Faculty of Informatics Eötvös Loránd University Budapest Hungary Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

关键词： Automation Transfer functions

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