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A Facial Morphology-Guided Feature Selection Method For Spontaneous Expression Recognition

Procedia computer science 2024年 246卷 3044-3053页

作者： Ones Sidhom Haythem Ghazouani Walid Barhoumi Abdellah Chehri Université de Tunis El Manar Institut Supérieur d’Informatique Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA) LR16ES06 Laboratoire de recherche en Informatique Modélisation et Traitement de l’Information et de la Connaissance (LIMTIC) 2 Rue Abou Rayhane Bayrouni Ariana 2080 Tunisia Université de Carthage Ecole Nationale d’Ingénieurs de Carthage 45 Rue des Entrepreneurs Tunis-Carthage 2035 Tunisia Department of Mathematics and Computer Science Royal Military College of Canada Girouard Building Room 320

Facial Expression Recognition (FER) is a crucial aspect in various domains, given its significance in understanding human emotions. However, designing efficient FER systems entails addressing challenges in feature extraction and selection. While previous studies have primarily focused on static feature selection methods, these approaches often struggle with spontaneous expressions due to the unique facial characteristics of each individual. To address this challenge, we implemented a Facial Morphology-Guided Feature Selection Method that combines texture features using Local Binary Pattern histograms and geometric features employing linear and eccentricity features. Subsequently, we employ Recursive Feature Elimination (RFE) and Binarized Genetic Algorithm (BGA) algorithms for feature selection, combining their outputs to identify the optimal subset of features tailored for each face. Experimental validation using the CK+ and DISFA datasets demonstrates the effectiveness of our approach in enhancing facial expression recognition accuracy.

关键词： Facial expression recognition Feature extraction Feature selection Recursive feature elimination Binarized genetic algorithm Facial morphology-guided feature selection

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DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning

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Protein science 2023年第10期32卷 e4758页

作者： Yao, Lantian Zhang, Yuntian Li, Wenshuo Chung, Chia-Ru Guan, Jiahui Zhang, Wenyang Chiang, Ying-Chih Lee, Tzong-Yi Kobilka Institute of Innovative Drug Discovery School of Medicine The Chinese University of Hong Kong Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China School of Medicine The Chinese University of Hong Kong Shenzhen China Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan

Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for inducing resistance. In this study, we developed a deep learning-based framework called DeepAFP to efficiently identify AFPs. DeepAFP fully leverages and mines composition information, evolutionary information, and physicochemical properties of peptides by employing combined kernels from multiple branches of convolutional neural network with bi-directional long short-term memory layers. In addition, DeepAFP integrates a transfer learning strategy to obtain efficient representations of peptides for improving model performance. DeepAFP demonstrates strong predictive ability on carefully curated datasets, yielding an accuracy of 93.29% and an F1-score of 93.45% on the DeepAFP-Main dataset. The experimental results show that DeepAFP outperforms existing AFP prediction tools, achieving state-of-the-art performance. Finally, we provide a downloadable AFP prediction tool to meet the demands of large-scale prediction and facilitate the usage of our framework by the public or other researchers. Our framework can accurately identify AFPs in a short time without requiring significant human and material resources, and hence can accelerate the development of AFPs as well as contribute to the treatment of fungal infections. Furthermore, our method can provide new perspectives for other biological sequence analysis tasks. © 2023 The Protein Society.

关键词： antifungal peptides deep learning drug discovery sequence analysis

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Investment Strategy of Offshore Wind Power Under Gradient Interactive Policy Mixes: A Real Options Approach

SSRN

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SSRN 2024年

作者： Yu, Xianyu Sang, Xiuzhi Chen, Yunfei Pedrycz, Witold Zhang, Tonglin Xu, Wenjing You, Luhan China College of Economics and Management Nanjing University of Aeronautics and Astronautics 29 Jiangjun Avenue Nanjing211106 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey Research Centre for Soft Energy Science Nanjing University of Aeronautics and Astronautics 29 Jiangjun Avenue Nanjing211106 China

Offshore wind power (OWP) has become one of the most important renewable energy sources in the global energy transition. The interactive influence of different policies, i.e., Feed-in tariff (FIT), R&D subsidies, fiscal and tax support (FTS) and tradable green certificates (TGC), has become a key issue to decision makers (DMs) on OWP investment. In this paper, a four-layer investment decision-making framework based on real option (RO) and Least-squares Monte Carlo simulation (LS-MCS) is developed to analyze the influence of policy interaction mixes. And then, a case study of Xiangshan County is proposed to verify the effectiveness of the proposed decision-making framework and analyze the investment strategy of OWP project. The key findings of this study are as follows: (1) the considered OWP project at Xiangshan County is shown to be delayed by 4 years or more in all the considered gradient interactive policy mixes;(2) the improvement of technological innovation ability has the least effect on reducing project cost, while R&D subsidy has the best effect, but with the increase of R&D subsidy, its promoting effect gradually weakens;(3) although China's TGC market is still in its infancy, it has great development potential to improve the returns of OWP projects. © 2024, The Authors. All rights reserved.

关键词： Decision making

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Time Delay Estimation with Fixed-time SMC Scheme for Nonlinear Dynamics

Time Delay Estimation with Fixed-time SMC Scheme for Nonline...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Saim Ahmed Ahmad Taher Azar Arezki Fekik Haoping Wang Chakib Ben Njima College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Department of Electrical Engineering University Akli Mohand Oulhadj-Bouria Rue Drissi Yahia Bouira Algeria CNRS LS2N UMR 6004 Nantes Université École Centrale Nantes Nantes France School of Automation Nanjing University of Science and Technology China University of Sousse Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Time delay estimation using fixed-time terminal sliding mode control (TDEFxSMC) for unknown nonlinear systems with uncertainty and external disturbances is presented in this study. Firstly, the notion of fixed-time terminal sliding mode control (FxSMC) is outlined and thoroughly examined. Using the FxSMC scheme, nonsingular fixed-time control, non-chatter control inputs, and high performance tracking positions have been achieved. To estimate the unknown dynamics, proposed method incorporates a TDE scheme. The Lyapunov theorem is applied to find whether the proposed system can achieve its stability in a fixed-time. The effectiveness of the suggested method is evaluated and demonstrated by presenting the results of the proposed method implemented to the nonlinear robot dynamics.

关键词： Uncertainty Automation Delay effects Estimation 2-DOF Stability analysis Nonlinear dynamical systems

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Highlighting a Common Confusion in the Computation of Capacitance of Electrochemical Energy Storage Devices

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JOURNAL OF PHYSICAL CHEMISTRY C 2021年第18期125卷 9591-9592页

作者： Allagui, Anis Elwakil, Ahmed S. Eleuch, Hichem Department of Sustainable and Renewable Energy Engineering University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Center for Advanced Materials Research Research Institute of Sciences and Engineering University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Department of Mechanical and Materials Engineering Florida International University Miami Florida 33174 United States Department of Electrical and Computer Engineering University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Nanoelectronics Integrated Systems Center (NISC) Nile University Cairo 12677 Egypt Department of Electrical and Computer Engineering University of Calgary Calgary Alberta T2N 1N4 Canada Department of Applied Physics and Astronomy University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Institute for Quantum Science and Engineering Texas A&M University College Station Texas 77843 United States

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n-ary Isolation Forest: An Experimental Comparative Analysis 19th

n-ary Isolation Forest: An Experimental Comparative Analysis

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19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020

作者： Karczmarek, Pawel Kiersztyn, Adam Pedrycz, Witold Department of Computer Science Lublin University of Technology ul. Nadbystrzycka 36B Lublin20-618 Poland Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw Poland

ISBN: (纸本)9783030615338

One of the most challenging problems of modern data mining and Computational Intelligence society has been the task of anomaly detection in large datasets, particularly containing mixed data, namely categorical, spatial, or spatio-temporal. In this study, we discuss various versions of the well-known Isolation Forest method as a efficient tool for finding outliers or anomalies. The versions are based on binary, ternary, etc. search trees. Traditional Isolation Forest is based on searching binary search trees. We build and investigate n-ary search trees and analyze their efficiency in the context of anomaly detection. © 2020, Springer Nature Switzerland AG.

关键词： Anomaly detection

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Using the Model of a Functional Rationalizer of Consumer Behavior in Recommendation systems for Managing the Transport Activity of the Urban Population 23

Using the Model of a Functional Rationalizer of Consumer Beh...

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23rd International Conference on Soft Computing and Measurements, SCM 2020

作者： Seliverstov, Yaroslav A. Seliverstov, Svytoslav A. Podoprigora, Nikolay V. Starichenkov, Alexey L. Naryshkin, Roman S. Solomenko Institute of Transport Problems Russian Academy of Sciences Institute of Computer Science and Technology Peter the Great St.Petersburg Polytechnic University Laboratory of Intelligent Transport Systems St. Petersburg Russia Saint Petersburg State University of Architecture and Civil Engineering Department of Ground Transportation Technological Machines St. Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Ship Control Systems St. Petersburg Russia Saint Petersburg State University Department of Programming Technology St. Petersburg Russia

ISBN: (纸本)9781728196923

An overview of existing urban software mobile applications of the transport and economic direction is given. A model of a functional rationalizer of consumer behavior is being built. The software model of the functional rationalizer of consumer behavior (SM FRCB) was developed for the first time. PMFRCB allows the user to expand the number of his preferred needs (occupations, activities) during the daily cycle through intelligent recommendations. Also, SM FRCB allows you to optimize needs (activities, activities) at the cost and rate of their use. The SM FRCB is made in Python_3 using the libraries matplotlib, collections, numpy, pyplot, mplot3d. The analysis of consumer behavior before and after using the functional rationalizer model is carried out. The graphical results of a structural analysis of the costs of old and new consumer behavior, analysis of consumer behavior within the boundaries of the theory of possibilities and the model of transport behavior of a resident before and after applying the functional rationalizer model are presented. The prospects of further improving the SM FRCB are considered. © 2020 IEEE.

关键词： Smart city

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Multifractal organization of EEG signals in Multiple Sclerosis

arXiv

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arXiv 2024年

作者： Wa̢torek, Marcin Tomczyk, Wojciech Gawlowska, Magda Golonka-Afek, Natalia Zyrkowska, Aleksandra Marona, Monika Wnuk, Marcin Slowik, Agnieszka Ochab, Jeremi K. Fafrowicz, Magdalena Marek, Tadeusz Oświe̢cimka, Pawel Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Kraków31-155 Poland Institute of Theoretical Physics Jagiellonian University Lojasiewicza 11 Kraków30-348 Poland Department of Cognitive Neuroscience and Neuroergonomics Jagiellonian University Lojasiewicza 4 Kraków30-348 Poland Department of Neurology Jagiellonian University Medical College University Hospital Jakubowskiego 2 Kraków30-688 Poland Mark Kac Centre for Complex Systems Research Jagiellonian University Lojasiewicza 11 Kraków30-348 Poland Faculty of Psychology SWPS University Techników 9 Katowice40-326 Poland Complex Systems Theory Department Institute of Nuclear Physics Polish Academy of Sciences Radzikowskiego 152 Kraków31-342 Poland

Quantifying the complex/multifractal organization of the brain signals is crucial to fully understanding the brain processes and structure. In this contribution, we performed the multifractal analysis of the electroencephalographic (EEG) data obtained from a controlled multiple sclerosis (MS) study, focusing on the correlation between the degree of multifractality, disease duration, and disability level. Our results reveal a significant correspondence between the complexity of the time series and multiple sclerosis development, quantified respectively by scaling exponents and the Expanded Disability Status Scale (EDSS). Namely, for some brain regions, a well-developed multifractality and little persistence of the time series were identified in patients with a high level of disability, whereas the control group and patients with low EDSS were characterised by persistence and monofractality of the signals. The analysis of the cross-correlations between EEG signals supported these results, with the most significant differences identified for patients with EDSS > 1 and the combined group of patients with EDSS ≤ 1 and controls. No association between the multifractality and disease duration was observed, indicating that the multifractal organisation of the data is a hallmark of developing the disease. The observed complexity/multifractality of EEG signals is hypothetically a result of neuronal compensation – i.e., of optimizing neural processes in the presence of structural brain degeneration. The presented study is highly relevant due to the multifractal formalism used to quantify complexity and due to scarce resting-state EEG evidence for cortical reorganization associated with compensation. © 2024, CC BY-NC-ND.

关键词： Time series

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A Calculus for Modular Loop Acceleration and Non-Termination Proofs

arXiv

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arXiv 2021年

作者： Frohn, Florian Fuhs, Carsten Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany LuFG Informatik 2 RWTH Aachen University Aachen Germany Department of Computer Science and Information Systems Birkbeck University of London London United Kingdom

Loop acceleration can be used to prove safety, reachability, runtime bounds, and (non-)termination of programs. To this end, a variety of acceleration techniques has been proposed. However, so far all of them have been monolithic, i.e., a single loop could not be accelerated using a combination of several different acceleration techniques. In contrast, we present a calculus that allows for combining acceleration techniques in a modular way and we show how to integrate many existing acceleration techniques into our calculus. Moreover, we propose two novel acceleration techniques that can be incorporated into our calculus seamlessly. Some of these acceleration techniques apply only to non-terminating loops. Thus, combining them with our novel calculus results in a new, modular approach for proving non-termination. An empirical evaluation demonstrates the applicability of our approach, both for loop acceleration and for proving non-termination. © 2021, CC BY.

关键词： Calculations

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Robot self-recognition via facial expression sensorimotor learning

Robot self-recognition via facial expression sensorimotor le...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Zhegong Shangguan Mengyuan Ding Chuang Yu Chaona Chen Adriana Tapus Autonomous Systems and Robotics Lab/U2IS ENSTA Paris Institut Polytechnique de Paris 828 Boulevard des Maréchaux Palaiseau France National Engineering Laboratory for Visual Information Applications college of Artificial Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Cognitive Robotics Lab Department of Computer Science University of Manchester Kilburn Building Oxford Rd Manchester UK School of Psychology and Neuroscience University of Glasgow Glasgow UK

To develop robots that can show cognitive functions, we must learn from the knowledge of human cognition. Existing biological and psychological evidence suggests that self-face perception and sensorimotor learning mechanisms play a crucial role in self-recognition. However, one of the most important self-identity cues – facial information – has not been extensively studied in the robot self-recognition task. Current research on robot self-recognition primarily relies on the recognition of high-precision targets and tracking of manipulator motions, where the self-perception of facial information is not well studied. In this work, we propose a novel approach to achieve self-recognition via self-perception of facial expressions. Specifically, we developed a Conditional Generative Adversarial Network (CGAN) model using the knowledge on human cognitive and sensorimotor functions. It allows the robot to be aware of self-face (i.e., off-line model). Passing the observed visual variations in a mirror and comparing them to self-perceptive information, the robot can recognize the self through an online Bayesian learning regression. The results of our first experiment show that the robot can recognize itself in a mirror. The results from the second experiment show that our algorithm could be tricked by a similar robot with the same facial expressions, which is similar to the rubber hand illusion (RHI).

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