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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

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.

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Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction

arXiv

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arXiv 2025年

作者： Gijón, Alfonso Eiraudo, Simone Manjavacas, Antonio Schiera, Daniele Salvatore Molina-Solana, Miguel Gómez-Romero, Juan Department of Computer Science and Artificial Intelligence Universidad de Granada Granada18071 Spain Energy Center Lab Politecnico di Torino Torino10138 Italy Deparment of Informatics and Numerical Analysis Universidad de Córdoba Córdoba14014 Spain

The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations and ensure effective maintenance through early fault detection systems. While traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed, they often fail to capture the complex, non-linear relationships between other input variables and the resulting power output. Data-driven machine learning methods present a promising avenue for improving wind turbine modeling by leveraging large datasets, enhancing prediction accuracy but often at the cost of interpretability. In this study, we propose a hybrid semi-parametric model that combines the strengths of both approaches, applied to a dataset from a wind farm with four turbines. The model integrates a physics-inspired submodel, providing a reasonable approximation of power generation, with a non-parametric submodel that predicts the residuals. This non-parametric submodel is trained on a broader range of variables to account for phenomena not captured by the physics-based component. The hybrid model achieves a 37% improvement in prediction accuracy over the physics-based model. To enhance interpretability, SHAP values are used to analyze the influence of input features on the residual submodel’s output. Additionally, prediction uncertainties are quantified using a conformalized quantile regression method. The combination of these techniques, alongside the physics grounding of the parametric submodel, provides a flexible, accurate, and reliable framework. Ultimately, this study opens the door for evaluating the impact of unmodeled variables on wind turbine power generation, offering a basis for potential optimization. © 2025, CC BY.

关键词： Windmill

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How quantum and evolutionary algorithms can help each other: two examples

arXiv

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arXiv 2024年

作者： Bhandari, Shailendra Nichele, Stefano Denysov, Sergiy Lind, Pedro G. Department of Computer Science OsloMet - Oslo Metropolitan University P.O. Box 4 St. Olavs plass OsloN-0130 Norway OsloMet Artificial Intelligence Lab Pilestredet 52 OsloN-0166 Norway NordSTAR - Nordic Center for Sustainable and Trustworthy AI Research Pilestredet 52 OsloN-0166 Norway Department of Computer Science and Communication Østfold University College B R A Veien 4 HaldenN-1757 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo0164 Norway

We investigate the potential of bio-inspired evolutionary algorithms for designing quantum circuits with specific goals, focusing on two particular tasks. The first one is motivated by the ideas of Artificial Life that are used to reproduce stochastic cellular automata with given rules. We test the robustness of quantum implementations of the cellular automata for different numbers of quantum gates The second task deals with the sampling of quantum circuits that generate highly entangled quantum states, which constitute an important resource for quantum computing. In particular, an evolutionary algorithm is employed to optimize circuits with respect to a fitness function defined with the Mayer-Wallach entanglement measure. We demonstrate that, by balancing the mutation rate between exploration and exploitation, we can find entangling quantum circuits for up to five qubits. We also discuss the trade-off between the number of gates in quantum circuits and the computational costs of finding the gate arrangements leading to a strongly entangled state. Our findings provide additional insight into the trade-off between the complexity of a circuit and its performance, which is an important factor in the design of quantum circuits. © 2024, CC BY.

关键词： Quantum computers

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The dynamical law behind eye movements: distinguishing between Lévy and intermittent strategies

arXiv

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arXiv 2025年

作者： Lencastre, Pedro Bystryk, Yurii Yazidi, Anis Denisov, Sergey Lind, Pedro G. Department of Computer Science OsloMet – Oslo Metropolitan University OsloN-0130 Norway AI Lab – OsloMet Artificial Intelligence Lab OsloN-0166 Norway Institute of Applied Physics National Academy of Sciences of Ukraine Sumy40000 Ukraine Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo0164 Norway

Foraging is a complex spatio-temporal process which is often described with stochastic models. Two particular ones, Lévy walks (LWs) and intermittent search (IS), became popular in this context. Researchers from the two communities, each advocating for either Lévy or intermittent approach, independently analyzed foraging patterns and reported agreement between empirical data and the model they used. We resolve this Lévy-intermittent dichotomy for eye-gaze trajectories collected in a series of experiments designed to stimulate free foraging for visual information. By combining analytical results, statistical quantifiers, and basic machine learning techniques, we devise a method to score the performance of the models when they are used to approximate an individual gaze trajectory. Our analysis indicates that the intermittent search model consistently yields higher scores and thus approximates the majority of the eye-gaze trajectories better. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic models

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Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity

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Scientific reports 2025年第1期15卷 19929页

作者： Shailendra Bhandari Pedro Lencastre Rujeena Mathema Alexander Szorkovszky Anis Yazidi Pedro G Lind Department of Computer Science OsloMet - Oslo Metropolitan University P.O. Box 4 St. Olavs plass N-0130 Oslo Norway. shailendra.bhandari@oslomet.no. OsloMet Artificial Intelligence Lab Pilestredet 52 N-0166 Oslo Norway. shailendra.bhandari@oslomet.no. Department of Computer Science OsloMet - Oslo Metropolitan University P.O. Box 4 St. Olavs plass N-0130 Oslo Norway. OsloMet Artificial Intelligence Lab Pilestredet 52 N-0166 Oslo Norway. Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo 0164 Norway. Department of Informatics University of Oslo Oslo Norway. School of Economics Innovation and Technology Kristiania University of Applied Sciences Kirkegata 24-26 N-0153 Oslo Norway.

Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models like Markov models often fail to capture the complex temporal dependencies and distributional nuance inherent in eye gaze trajectories data. This study introduces a Generative Adversarial Network (GAN) framework employing Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) generators and discriminators to generate high-fidelity synthetic eye gaze velocity trajectories. We conducted a comprehensive evaluation of four GAN architectures: CNN-CNN, LSTM-CNN, CNN-LSTM, and LSTM-LSTM-trained under two conditions: using only adversarial loss ([Formula: see text]) and using a weighted combination of adversarial and spectral losses. Our findings reveal that the LSTM-CNN architecture trained with this new loss function exhibits the closest alignment to the real data distribution, effectively capturing both the distribution tails and the intricate temporal dependencies. The inclusion of spectral regularization significantly enhances the GANs' ability to replicate the spectral characteristics of eye gaze movements, leading to a more stable learning process and improved data fidelity. Comparative analysis with a Hidden Markov Model (HMM) optimized to four hidden states further highlights the advantages of the LSTM-CNN GAN. Statistical metrics show that the HMM-generated data significantly diverges from the real data in terms of mean, standard deviation, skewness, and kurtosis. In contrast, the LSTM-CNN model closely matches the real data across these statistics, affirming its capacity to model the complexity of eye gaze dynamics effectively. These results position the spectrally regularized LSTM-CNN GAN as a robust tool for generating synthetic eye gaze velocity data with high fidelity. Its ability to accurately replicate both the distributional and temporal properties of real data holds

关键词： Eye-gaze trajectories Generative Adversarial Networks Hidden Markov Models Stochastic Processes

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Procurer usability requirements: Negotiations in contract development 02

Procurer usability requirements: Negotiations in contract de...

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2nd Nordic Conference on Human-computer Interaction, NordiCHI '02

作者： Artman, Henrik Interaction and Presentation Lab. Centre for User-Oriented Design Numerical Analysis and Computer Science Royal Institute of Technology 100 44 Stockholm Sweden

ISBN: (纸本)1581136161

This article describes a case study that examined one procurer's reasoning about and work with usability-related issues as well as the contractor's response to those requirements. The aim of this study was to examine the procurer's power to direct the system development process according to user-centred principles and indeed to point out its responsibility to use that power. The study elucidated the procurer's and the contractor's differing views of usability. The results suggest that the project leaders from the two organisations examined in this study had differing views of usability and that both approached usability more from a business perspective than from a user perspective. Furthermore, we found that the procurer valued user-centred activities less for their results than for the opportunity they gave to come in contact with the user's point of view and then to visualize the requirements concretely. We conclude this article with an analysis of some contradictions within and between the two organisations from a socio-cultural point of view. We suggest some mundane but nevertheless important requirements that procurers should think of when contracting consultants. © 2002 ACM.

关键词： Contractors

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Tracking techniques for visual servoing tasks

Tracking techniques for visual servoing tasks

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IEEE International Conference on Robotics and Automation (ICRA)

作者： D. Kragic H.I. Christensen Computer Vision and Active Perception Lab Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden Centre for Autonomous Sys Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden

Many of today's visual servoing systems rely on the use of markers on the object to provide features for control. There is thus a need for a visual system that provides control features regardless of the appearance of the object. Region based tracking is a natural approach since it does not require any special type of features. In this paper we present two different approaches to region based tracking: 1) a multi-resolution gradient based approach (using optical flow); and 2) a discrete feature based search approach. We present experiments conducted with both techniques for different types of image motions. Finally, the performance, drawbacks and limitations of used techniques are discussed.

关键词： Visual servoing Robots Tracking computer vision numerical analysis Control systems Grippers Image motion analysis Motion estimation Visual system

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A person following behaviour for a mobile robot

A person following behaviour for a mobile robot

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IEEE International Conference on Robotics and Automation (ICRA)

作者： H. Sidenbladh D. Kragic H.I. Christensen Computer Vision and Active Perception Lab Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden Centre for Autonomous Systems Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden

In this paper, a person following behaviour for a mobile robot is presented. The head of the person is located using skin colour detection. Then, a control loop is fed with the camera movements required to put the upper part of the person in the center of the image. The algorithm was tested in different rooms of a research lab. It performed well in all lighting conditions except in direct sunlight. Since the background and lighting cannot be controlled, the vision algorithm must be robust to such changes. However, since the computing power is quite limited, the algorithm must have as low complexity as possible.

关键词： Mobile robots Robot vision systems Cameras computer vision Skin Image motion analysis numerical analysis Lighting control Humans Machine vision

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EEG data Classification: Review and Taxonomy

ACM Transactions on Computing for Healthcare

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ACM Transactions on Computing for Healthcare 1000年

作者： Asma Belhadi Anis Yazidi Pedro G. Lind Youcef Djenouri Department of Computer Science OsloMet – Oslo Metropolitan University N-0130 Oslo Norway OsloMet Artificial Intelligence Lab Pilestredet 52 N-0166 Oslo Norway Department of Informatics University of Oslo 0316 Oslo Norway Department of Computer Science OsloMet – Oslo Metropolitan University N-0130 Oslo Norway School of Economics Innovation and Technology Kristiania University of Applied Sciences 0153 Oslo Norway Simula Research Laboratory Numerical Analysis and Scientific Computing 0164 Oslo Norway University of South-Eastern Norway Norwegian Research Center Oslo Norway

EEG data classification plays a pivotal role in understanding brain activity and its applications in various domains. Deep learning has emerged as a powerful paradigm for automatically learning complex patterns from raw data, eliminating the need for manual feature extraction. However, in the context of medical data, and in particular for EEG analysis, the use of deep learning approaching while having been very successful is not being included in medical diagnosis routines, yet. The aim of this survey is twofold. On one side, it provides a comprehensive overview of the current state-of-the-art in EEG data classification, with a specific focus on the use of deep learning techniques. On the other side, it also addresses the clinician community, explaining the power and trustfulness of such new approaches. The survey begins with an introduction highlighting the limitations of traditional model-based approaches and the potential of deep learning in EEG data classification. The fundamental principles and architectures of deep learning models are presented, including convolutional neural networks (CNNs), recurrent neural networks (RNNs) and Graph Convolution Neural Network (GCNNs) that have been successfully applied to EEG data classification tasks. A detailed review and analysis of existing literature on deep learning-based EEG data classification are provided, categorizing the studies based on the type of the input data, e.g., sequences, images, graphs or multi-modalities. We also discuss about the existing tools and technologies for EEG data classification and highlights the challenges and limitations associated with deep learning in EEG data classification, including limited data availability, interpretability of deep models and bias mitigation. Potential solutions and ongoing research efforts to overcome these challenges are explored, providing insights into the future directions of this field. This survey serves as a valuable resource for researchers, practitioners

关键词： EEG Data Classification Deep Learning Healthcare Applications Clinical Diseases

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