Article
Automation & Control Systems
Jason J. Ford, Jasmin James, Timothy L. Molloy
Summary: This paper discusses the quickest detection problem for hidden Markov models (HMMs) in a Bayesian setting. An augmented HMM representation is constructed, and a dynamic programming approach is applied to prove that Shiryaev's rule is an optimal solution. The augmented representation highlights the problem's fundamental information structure and suggests possible relaxations to more exotic change event priors not found in existing literature. Additionally, an efficient computational method for implementing the optimal solution is presented.
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
Psychology, Mathematical
Janet H. Hsiao, Hui Lan, Yueyuan Zheng, Antoni B. Chan
Summary: The study combined EMHMM with co-clustering to identify explorative and focused eye-movement patterns among Asian participants in tasks with stimuli featuring different layouts. The findings showed that similarity to the explorative pattern predicted better foreground object recognition performance, while similarity to the focused pattern was associated with improved feature integration in the flanker task. This approach offers quantitative assessments on eye-movement patterns and can be applied to various real-life visual tasks, enhancing the utility of eye tracking in studying cognitive behavior.
BEHAVIOR RESEARCH METHODS
(2021)
Article
Chemistry, Analytical
Xu Zhang, Ting Wu, Qiuhua Zheng, Liang Zhai, Haizhong Hu, Weihao Yin, Yingpei Zeng, Chuanhui Cheng
Summary: This study proposes a pre-training method for multi-step attack detection models based on semantic similarity, improving the detection accuracy compared to other methods.
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
Geochemistry & Geophysics
Kareth M. Leon-Lopez, Florian Mouret, Henry Arguello, Jean-Yves Tourneret
Summary: This article introduces a framework for anomaly detection, localization, and classification in crop monitoring using satellite remote sensing data. The method utilizes hidden Markov models to detect and localize outliers at a parcel level, followed by classification using a supervised classifier. Experimental results on synthetic and real data show better detection rates and the ability to localize and characterize anomalies compared to standard methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
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
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
Physics, Multidisciplinary
Ozgur Danisman, Umay Uzunoglu Kocer
Summary: Hidden Markov models are commonly used for modeling probabilistic structures with latent variables. They assume that observation symbols are conditionally independent and identically distributed, but in practice, this assumption may not always hold. The proposed model introduces a first-order Markov dependency between the current pair of hidden state-emitted observation symbol and the previous pair, which can better capture possible dependencies in real-life scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Psychology, Multidisciplinary
Miles Tallon, Mark W. Greenlee, Ernst Wagner, Katrin Rakoczy, Ulrich Frick
Summary: The study reveals that artistic expertise has a significant impact on latent class membership probability in visual search, while Visual Literacy (VL) experts and non-experts do not significantly differ in task time and number of targets found. However, experts show greater precision in fixating specific regions compared to non-experts during the visual search process.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Biochemical Research Methods
Srilakshmi Pattabiraman, Tandy Warnow
Summary: Profile Hidden Markov Models (HMMs) are graphical models that generate finite length sequences from a distribution, widely used in bioinformatics. The construction of profile HMMs is a statistical estimation problem, and it is unknown whether they are statistically identifiable.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Automation & Control Systems
Rahul Singh, Qinsheng Zhang, Yongxin Chen
Summary: In this paper, an algorithm is proposed for estimating the parameters of a time-homogeneous hidden Markov model (HMM) from aggregate observations. The algorithm is built upon the expectation-maximization algorithm and the aggregate inference algorithm, and it exhibits convergence guarantees for both discrete and continuous observations. When the population size is 1, the algorithm is equivalent to the standard Baum-Welch learning algorithm.
Article
Computer Science, Artificial Intelligence
Isidoros Perikos, Spyridon Kardakis, Ioannis Hatzilygeroudis
Summary: The article introduces a novel, interpretable HMM-based method for recognizing sentiments in text, which has been tested under various architectures, training methods, orders, and ensembles, showing competitive performance and outperforming traditional HMM methods.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Management
Maria Luz Gamiz, Nikolaos Limnios, Maria Del Carmen Segovia-Garcia
Summary: This paper discusses the use of hidden Markov models in reliability engineering, where the state of the system is not directly observable. The paper investigates the maximum-likelihood estimation of system reliability and the effectiveness of preventive maintenance strategies. Extensive simulation studies are conducted to evaluate the finite sample performance of the methodology.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Chemistry, Analytical
Hsing-Hao Lee, Zih-Ling Chen, Su-Ling Yeh, Janet Huiwen Hsiao, An-Yeu (Andy) Wu
Summary: Mind-wandering significantly impacts learning efficiency in the digital age. Using a wearable eye-tracker, this study demonstrated that distinct eye movement patterns are associated with different attentional states, providing a novel approach to study attention.
