Article
Engineering, Environmental
Paolo Maranzano, Philipp Otto, Alessandro Fasso
Summary: This paper proposes a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). The algorithm simultaneously selects the relevant spline basis functions and regressors for modeling the fixed effects, automatically shrinking irrelevant functional coefficients or the entire function for an irrelevant regressor. It is based on an adaptive LASSO penalty function with weights obtained from unpenalized f-HDGM maximum likelihood estimators.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Biochemistry & Molecular Biology
Jesper Svedberg, Vladimir Shchur, Solomon Reinman, Rasmus Nielsen, Russell Corbett-Detig
Summary: Adaptive introgression, the flow of adaptive genetic variation between species or populations, has attracted significant interest and has been implicated in cases of adaptation, but methods for identifying it from population genomic data are lacking. Ancestry_HMM-S is a hidden Markov model-based method for identifying genes undergoing adaptive introgression and quantifying selection strength, showing good performance on moderately sized data sets. It was applied to an admixed Drosophila melanogaster population from South Africa, identifying loci with signatures of adaptive introgression and providing powerful insights into genetic consequences of admixture across diverse populations.
MOLECULAR BIOLOGY AND EVOLUTION
(2021)
Article
Chemistry, Multidisciplinary
Norah Abanmi, Heba Kurdi, Mai Alzamel
Summary: The prevalence of malware attacks targeting IoT systems has raised concerns and emphasized the need for effective detection and defense mechanisms. However, detecting malware, especially those with new or unknown behaviors, is challenging. The main issue lies in its ability to hide, making it difficult to detect. Moreover, limited information on malware families restricts the availability of big data for analysis. This paper introduces a new Profile Hidden Markov Model (PHMM) for app analysis and classification in Android systems, while also dynamically identifying suspicious calls to reduce the risk of code execution. The experimental results demonstrate that the proposed Dynamic IoT malware Detection in Android Systems using PHMM (DIP) outperforms eight rival malware detection frameworks, achieving up to 96.3% accuracy at a 5% False Positive Rate (FP rate), 3% False Negative Rate (FN rate), and 94.9% F-measure.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Carlos Puerto-Santana, Pedro Larranaga, Concha Bielza
Summary: This article introduces asymmetric hidden Markov models with feature saliencies, which are capable of simultaneously determining relevant variables/features and probabilistic relationships between variables during their learning phase. Comparing with other approaches, the proposed models have better or equal fitness and provide further data insights.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Elizabeth Fons, Paula Dawson, Jeffrey Yau, Xiao-jun Zeng, John Keane
Summary: The financial crisis of 2008 triggered interest in more transparent, rules-based portfolio construction strategies, with smart beta strategies becoming a trend among institutional investors. Researchers have utilized Hidden Markov Models (HMMs) to build a dynamic asset allocation system to manage short-term risk and proposed a smart beta allocation system based on the FSHMM algorithm, showing significant improvement in risk-adjusted returns.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Carlos Puerto-Santana, Pedro Larranaga, Concha Bielza
Summary: In a real-life process evolving over time, the relationship between relevant variables may change. Asymmetric hidden Markov models provide a dynamic framework where different inference models can be used for each state of the process. This paper modifies recent asymmetric hidden Markov models to incorporate an asymmetric autoregressive component for continuous variables, allowing the model to choose the optimal order of autoregression. The paper also demonstrates the adaptation of inference, hidden states decoding, and parameter learning for the proposed model. Experimental results with synthetic and real data showcase the capabilities of this new model.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Automation & Control Systems
Yu Zhang, Zhuo Jin, Jiaqin Wei, George Yin
Summary: This paper studies closed-loop equilibrium strategies for mean-variance portfolio selection problems in a hidden Markov model with dynamic attention behavior. The investor's attention to news is introduced as a control of the accuracy of the news signal process. Equilibrium strategies are found by numerically solving an extended HJB equation using the Markov chain approximation method, and an iterative algorithm is constructed with established convergence. Numerical examples are provided to illustrate the results.
Article
Engineering, Industrial
Chaoqun Duan, Yifan Li, Huayan Pu, Jun Luo
Summary: This paper proposes an adaptive monitoring scheme for predicting faults of systems with hidden degradation processes. A hidden Markov model is used to describe the degradation process, and an expectation maximization algorithm is applied to estimate the model parameters. An adaptive Bayesian control scheme is also developed to monitor the potential risk of the system.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Mathematics, Interdisciplinary Applications
Abdessatar Souissi, El Gheteb Soueidi
Summary: This paper aims to expand on previous research on quantum hidden Markov processes by introducing the concept of entangled hidden Markov processes. These are hidden Markov processes in which the hidden processes themselves are entangled Markov processes. The paper provides an explicit expression for the joint expectation of these processes and demonstrates that the approach also applies to the classical case.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Computer Science, Information Systems
William J. Hulme, Glen P. Martin, Matthew Sperrin, Alexander J. Casson, Sandra Bucci, Shon Lewis, Niels Peek
Summary: Wearable and mobile technology offer new opportunities for remote health management by balancing the frequency and quantity of information collected with the user or technology burden. Adaptive sampling helps to maintain high sampling frequency when necessary, while effectively managing the trade-off.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Jose M. Moyano, Sebastian Ventura
Summary: An auto-adaptive algorithm based on Grammar-Guided Genetic Programming is proposed in this paper to generate ensembles of multi-label classifiers based on projections of k labels. The algorithm outperforms existing methods, can handle different values of k, and reduces the number of hyper-parameters to tune.
INFORMATION FUSION
(2022)
Article
Physics, Fluids & Plasmas
Harrison Hartle, Fragkiskos Papadopoulos, Dmitri Krioukov
Summary: This study introduces natural temporal extensions of static hidden-variable network models with stochastic dynamics of hidden variables and links, exploring the structural deviations and level of persistence compared to static models. By controlling the dynamic parameters of hidden variables and links, the equivalence between snapshots of networks in dynamic and static models under different conditions is examined. The authors also discuss qualitative resemblances between these dynamic network models and real systems, speculating on the presence of out-of-equilibrium links with respect to hidden variables in some real networks.
Article
Geochemistry & Geophysics
Zetao Wang, Gang Li, Le Yang
Summary: This letter introduces a novel method for dynamic hand gesture recognition based on micro-Doppler radar signatures. The method utilizes short-time Fourier transform to obtain time-frequency spectrograms and models them with a hidden Gauss-Markov model for recognition. Experimental results show strong generalization ability in radar gesture recognition, even in low SNR and unknown user scenarios.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2021)
Article
Automation & Control Systems
A. M. de Oliveira, O. L. Costa, M. D. Fragoso, F. Stadtmann
Summary: In this work, the design of dynamic output feedback controllers for continuous-time Markov jump linear systems is studied. The worst-case scenario of partial observation is considered, where the controller only has access to the output of a fault-detection and isolation device. Design conditions for different controllers are presented based on the switching of the detector. Mode-dependent and independent controllers are also designed, and the design of filters depending on the detector is briefly discussed. The results are given in terms of bilinear matrix inequalities, allowing for an iterative separation procedure method.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Biochemical Research Methods
Gemma Massonis, Alejandro F. Villaverde, Julio R. Banga
Summary: MotivationDynamic mechanistic modelling in systems biology has been hindered by complexity and variability, as well as uncertain and sparse experimental measurements. Ensemble modelling has been introduced to mitigate these issues, but is unreliable for predicting non-observable states. In this study, the authors present a strategy to assess and improve the reliability of model ensembles, using a diversity-enforcing technique combined with identifiability and observability analysis. They demonstrate the effectiveness of their approach with models of glucose regulation, cell division, circadian oscillations, and the JAK-STAT signalling pathway.
Article
Computer Science, Artificial Intelligence
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
NEURAL COMPUTING & APPLICATIONS
(2018)
Article
Computer Science, Artificial Intelligence
Anandarup Roy, Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
Article
Computer Science, Artificial Intelligence
M. Ali Akber Dewan, E. Granger, G. -L. Marcialis, R. Sabourin, F. Roli
PATTERN RECOGNITION
(2016)
Article
Computer Science, Artificial Intelligence
Simon Bernard, Clement Chatelain, Sebastien Adam, Robert Sabourin
PATTERN RECOGNITION
(2016)
Article
Computer Science, Artificial Intelligence
Saman Bashbaghi, Eric Granger, Robert Sabourin, Guillaume-Alexandre Bilodeau
MACHINE VISION AND APPLICATIONS
(2017)
Article
Computer Science, Artificial Intelligence
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
PATTERN RECOGNITION
(2017)
Article
Computer Science, Artificial Intelligence
Saman Bashbaghi, Eric Granger, Robert Sabourin, Guillaume-Alexandre Bilodeau
PATTERN RECOGNITION
(2017)
Article
Computer Science, Artificial Intelligence
Dayvid V. R. Oliveira, George D. C. Cavalcanti, Robert Sabourin
PATTERN RECOGNITION
(2017)
Article
Computer Science, Artificial Intelligence
Luiz G. Hafemann, Luiz S. Oliveira, Robert Sabourin
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION
(2018)
Article
Computer Science, Artificial Intelligence
Andre L. Brun, Alceu S. Britto, Luiz S. Oliveira, Fabricio Enernbreck, Robert Sabourin
PATTERN RECOGNITION
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Marcelo T. Pereira, Alceu S. Britto, Luiz S. Oliveira, Robert Sabourin
2018 IEEE 30TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Eunelson J. Silva, Alceu S. Britto, Luiz. S. Oliveira, Fabricio Enembreck, Robert Sabourin, Alessandro L. Koerich
2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Paulo R. Lisboa de Almeida, Luiz S. Oliveira, Alceu de Souza Britto, Robert Sabourin
2016 IEEE 28TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2016)
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
M. Ali Akber Dewan, E. Granger, R. Sabourin, G. -L. Marcialis, F. Roli
2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI)
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Diego Bertolini, Luiz S. Oliveira, Robert Sabourin
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2015
(2015)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)