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With the rapid development of Internet of Things technology, vehicle-based entertainment systems are developing towards a more natural, faster, and automated direction. To achieve automatic control of vehicle-based music, based on machine vision, the music automation control method is designed by recognizing human gestures. The improved gesture motion trajectory point localization method, and enhanced recognition method based on the HMM are proposed to control vehicle-based music. The research results indicated that the method proposed in this research had a recognition rate of 95.3% for non-similar gestures. The recognition rate for similar gestures reached 94.56%. This indicates that the designed method can accurately and automatically control in car music equipment It is expected that this research can provide some reference value for the current research on vehicle-based music automated control. [ABSTRACT FROM AUTHOR]

The Metaverse, envisioned as the next-generation Internet, will be constructed via twining a practical world in a virtual form, wherein Meterverse service providers (MSPs) are required to collect massive data from Meterverse users (MUs). In this regard, a critical demand exists for MSPs to motivate MUs to contribute computing resources and data while preserving user privacy. Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, can support distributed intensive computation in the Metaverse. In this work, we first investigate minting the machine learning models into NFT with FL assistance (referred to as FL-NFT), such that MUs as stakeholders can control the ownership and share the economic value of user-generated content (UGC). Specifically, MUs are encouraged to establish a decentralized autonomous organization (i.e., MU-DAO) to aggregate local models and mint FL-NFT. MUs and MSPs optimize the strategies by formulating an imperfect information Stackelberg game to trade off the cost and benefit. We apply the backward induction to derive the equilibrium solution. Then, we construct a privacy-preserving multi-winner sealed-bid auction mechanism (PMS-AM), in which the Hidden Markov Model assists MSPs in choosing rational bidding strategies according to historical bids, and the double auction mechanism determines the winners and price of FL-NFT. Finally, the numerical results based on theoretical analysis and simulations demonstrate that the proposed PMS-AM can increase the quality of FL-NFT and achieve the economic properties of incentive mechanisms such as individual rationality and incentive compatibility. [ABSTRACT FROM AUTHOR]

Abstract This paper highlights the need for intelligent analysis of students’ behavioral states in physical education tasks. The hand-ring inertial data is used to identify students’ motion sequence states. First, statistical feature extraction is performed based on the acceleration and angular velocity data collected from the bracelet. After completing the filtering and noise reduction of the data, we perform feature extraction by Back Propagation Neural Network (BPNN) and use the sliding window method for analysis. Finally, the classification capability of the model sequence is enhanced by the Hidden Markov Model (HMM). The experimental results indicate that the classification accuracy of student action sequences in physical education exceeds 96% after optimization by the HMM method. This provides intelligent means and new ideas for future student state recognition in physical education and teaching reform.

The impact of climate on construction site safety varies significantly depending on subcontractor types due to the diverse nature of workplaces and work methods. This study introduces a novel approach by categorizing construction work according to subcontractor types and assessing accident risk probabilistically through the Physiologically Equivalent Temperature (PET), an outdoor thermal comfort index. Additionally, a Hidden Markov Model (HMM)-based clustering methodology was proposed to classify new groups using PET and accident probability. This study proceeded in the following sequence: (i) collection and classification of data, (ii) PET calculation, (iii) calculation of accident probability, and (iv) clustering and Pearson correlation coefficient analysis. As a result of clustering, each group was classified according to the workplace. Groups 2 and 3 demonstrated a strong positive correlation between accident probability and PET, with correlation coefficients of 0.837 and 0.772, while Group 1 exhibited a moderately positive correlation of 0.474. This study quantitatively evaluated the impact of climate on workers for each subcontractor type using PET, an outdoor thermal comfort index for construction work, and accident probability, resulting in the identification of new groups. The findings of this study may serve as novel benchmarks for safety management in construction worker safety based on PET. [ABSTRACT FROM AUTHOR]

Ball bearings are one of the most critical components of rotating machines. They ensure shaft support and friction reduction, thus their malfunctioning directly affects the machine's performance. As a consequence, it is necessary to monitor the health conditions of such a component to avoid major degradations which could permanently damage the entire machine. In this context, HMS (Health Monitoring Systems) and PHM (Prognosis and Health Monitoring) methodologies propose a wide range of algorithms for bearing diagnosis and prognosis. The present article proposes an end-to-end PHM approach for ball bearing RUL (Remaining Useful Life) estimation. The proposed methodology is composed of three main steps: HI (Health Indicator) construction, bearing diagnosis and RUL estimation. The HI is obtained by processing non-stationary vibration data with the MODWPT (Maximum Overlap Discrete Wavelet Packet Transform). After that, a degradation profile is defined and coupled with crack initiation and crack propagation fatigue models. Lastly, a MB-HMM (Hidden Markov Model) is trained to capture the bearing degradation dynamics. This latter model is used to estimate the current degradation state as well as the RUL. The obtained results show good RUL prediction capabilities. In particular, the fatigue models allowed a reduction of the ML (Machine Learning) model size, improving the algorithms training phase. [ABSTRACT FROM AUTHOR]

In this study, we introduce an experimental framework for Moroccan dialect speech recognition under various additive noise conditions using the open-source tool PocketSphinx. We curated a corpus comprising the ten most commonly used greetings in the Moroccan dialect, extracted from telephone conversations. This corpus was recorded with 60 speakers (30 males and 30 females). Each speaker articulated each expression three times in natural and noisy conditions. Feature extraction utilized Mel Scale Cepstral Coefficients (MFCC), and acoustic modeling, based on monophony, was implemented using Hidden Markov Models (HMM). While automatic speech recognition systems demonstrate commendable performance in noise-free conditions, their efficacy noticeably diminishes in the presence of noise. [ABSTRACT FROM AUTHOR]

Single-crystal diamond tools occupy an important position in the field of optical processing as the basis and key to advanced optical manufacturing technology, such as grating manufacturing and optical mirror-turning processing. Single-crystal diamond tools have become the cornerstone of the development of related industries. This paper takes a single-crystal diamond arc tool as the research object. Sound signal analysis technology and vibration signal analysis technology are comprehensively applied to the online orientation identification of a single-crystal diamond tool in the indexing grinding process. The online orientation method of the tool is explored, the sound signal and the vibration signal are taken as the characteristic signals, and a wavelet algorithm (WT) is used to reduce the noise of the vibration signal and sound signal. The kurtosis of the sound signal and the kurtosis and skewness of the vibration signal in the high-order statistics strongly related to the grinding direction of a single-crystal diamond are used as the characteristic parameters, and the online direction recognition model of the tool is established using the Hidden Markov Method (HMM). The above characteristic parameters are used as model input for multi-information fusion. The mapping relationship between the characteristic parameters of the characteristic signal and the crystal orientation of the single-crystal diamond crystal face is obtained, and then the online orientation method of the single-crystal diamond arc tool in the process of indexing grinding is formed. The effectiveness of the method is verified by experiments, and effective orientation information is provided for research on the positioning control strategy of the tool grinding process to ensure the efficiency of grinding and improve the manufacturing level of the tool. [ABSTRACT FROM AUTHOR]

This paper highlights the need for intelligent analysis of students' behavioral states in physical education tasks. The hand-ring inertial data is used to identify students' motion sequence states. First, statistical feature extraction is performed based on the acceleration and angular velocity data collected from the bracelet. After completing the filtering and noise reduction of the data, we perform feature extraction by Back Propagation Neural Network (BPNN) and use the sliding window method for analysis. Finally, the classification capability of the model sequence is enhanced by the Hidden Markov Model (HMM). The experimental results indicate that the classification accuracy of student action sequences in physical education exceeds 96% after optimization by the HMM method. This provides intelligent means and new ideas for future student state recognition in physical education and teaching reform. [ABSTRACT FROM AUTHOR]

The conversion funnel is a model describing the stages consumers go through in their journey toward a purchase. This journey often lasts several days to weeks and can include multiple visits to a seller’s website. A large body of literature has focused on using observable search patterns to identify consumers’ hidden purchasing stages and to estimate their likelihood of conversion. We propose a novel set of measures to better reveal the consumer’s hidden stage in the funnel. These measures are based on the diversity of the searches that a customer engages in while browsing an e-commerce website, and they include not only the number of different products that are searched for, but also measures that rely on unobserved similarities among products, captured in a product network (in which products are assumed to be “similar” if they are frequently co-searched). We operationalize and evaluate our proposed measures using a large-scale dataset from a medium-sized tourism website used for comparing and booking flights. We estimate a hidden Markov model to show that our proposed diversity measures are associated with progress in the funnel and consumers’ conversion likelihood. Specifically, we show that consumers go through different distinguishable stages (states) in their journey, characterized by different values of our proposed diversity measures. To demonstrate the managerial and business implications of our theory, we show that incorporating search-diversity measures into a baseline prediction model significantly improves the model’s performance in predicting purchase likelihood and churn. [ABSTRACT FROM AUTHOR]

Abstract: The study presented in this paper applies hidden Markov modeling (HMM) to uncover the recurring patterns within a neural activation dataset collected while designers engaged in a design concept generation task. HMM uses a probabilistic approach that describes data (here, fMRI neuroimaging data) as a dynamic sequence of discrete states. Without prior assumptions on the fMRI data's temporal and spatial properties, HMM enables an automatic inference on states in neurocognitive activation data that are highly likely to occur in concept generation. The states with a higher likelihood of occupancy show more activation in the brain regions from the executive control network, the default mode network, and the middle temporal cortex. Different activation patterns and transfers are associated with these states, linking to varying cognitive functions, for example, semantic processing, memory retrieval, executive control, and visual processing, that characterize possible transitions in cognition related to concept generation. HMM offers new insights into cognitive dynamics in design by uncovering the temporal and spatial patterns in neurocognition related to concept generation. Future research can explore new avenues of data analysis methods to investigate design neurocognition and provide a more detailed description of cognitive dynamics in design.

Abstract Objective The superfamily of protein kinases features a common Protein Kinase-like (PKL) three-dimensional fold. Proteins with PKL structure can also possess enzymatic activities other than protein phosphorylation, such as AMPylation or glutamylation. PKL proteins play a vital role in the world of living organisms, contributing to the survival of pathogenic bacteria inside host cells, as well as being involved in carcinogenesis and neurological diseases in humans. The superfamily of PKL proteins is constantly growing. Therefore, it is crucial to gather new information about PKL families. Results To this end, the KINtaro database ( http://bioinfo.sggw.edu.pl/kintaro/ ) has been created as a resource for collecting and sharing such information. KINtaro combines protein sequence information and additional annotations for more than 70 PKL families, including 32 families not associated with PKL superfamily in established protein domain databases. KINtaro is searchable by keywords and by protein sequence and provides family descriptions, sequences, sequence alignments, HMM models, 3D structure models, experimental structures with PKL domain annotations and sequence logos with catalytic residue annotations.

Abstract Background Lytic polysaccharide monooxygenases (LPMOs) catalyzing the oxidative cleavage of different types of polysaccharides have potential to be used in various industries. However, AA13 family LPMOs which specifically catalyze starch substrates have relatively less members than AA9 and AA10 families to limit their application range. Amylase has been used in enzymatic desizing treatment of cotton fabric for semicentury which urgently need for new assistant enzymes to improve reaction efficiency and reduce cost so as to promote their application in the textile industry. Results A total of 380 unannotated new genes which probably encode AA13 family LPMOs were discovered by the Hidden Markov model scanning in this study. Ten of them have been successfully heterologous overexpressed. AlLPMO13 with the highest activity has been purified and determined its optimum pH and temperature as pH 5.0 and 50 °C. It also showed various oxidative activities on different substrates (modified corn starch > amylose > amylopectin > corn starch). The results of enzymatic textile desizing application showed that the best combination of amylase (5 g/L), AlLPMO13 (5 mg/L), and H2O2 (3 g/L) made the desizing level and the capillary effects increased by 3 grades and more than 20%, respectively, compared with the results treated by only amylase. Conclusion The Hidden Markov model constructed basing on 34 AA13 family LPMOs was proved to be a valid bioinformatics tool for discovering novel starch-active LPMOs. The novel enzyme AlLPMO13 has strong development potential in the enzymatic textile industry both concerning on economy and on application effect.

In this paper, we deal with the estimation of two widely used risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES) in a cryptocurrency context. To face the presence of regime switching in the cryptocurrency volatilities and the dynamic interconnection between them, we propose a Monte Carlo-based approach using heteroskedastic factor analysis and hidden Markov models (HMM) combined with a structured variational Expectation-Maximization (EM) learning approach. This composite approach allows the construction of a diversified portfolio and determines an optimal allocation strategy making it possible to minimize the conditional risk of the portfolio and maximize the return. The out-ofsample prediction experiments show that the composite factorial HMM approach performs better, in terms of prediction accuracy, than some other baseline methods presented in the literature. Moreover, our results show that the proposed methodology provides the best performing crypto-asset allocation strategies and it is also clearly superior to the existing methods in VaR and ES predictions. [ABSTRACT FROM AUTHOR]

Digital speech processing applications including automatic speech recognition (ASR), speaker recognition, speech translation, and others, essentially require large volumes of speech data for training and testing purposes. Although there are available speech corpora, speech data for speakers suffering speech disorders are hardly available for many languages including Arabic language. Consequently, developing digital speech processing applications that target the entire society becomes hard due to the unavailability of speech corpora that contain sufficient speakers' variations including healthy and disordered speech. This research presents our work towards developing a Modern Standard Arabic (MSA) speech corpus for speakers suffering distortion and substitution articulation disorders. The speech corpus was recorded by 40 (20 male and 20 female) Jordanian speakers who suffer either distortion or/and substitution articulation disorders. This speech corpus can be used for various applications including ASR, speech and hearing, and others. Part of this speech corpus is used for developing and evaluating an ASR for MSA using the Carnegie Mellon University (CMU) Pocketsphinx tools based on Mel-Frequency Cepstral Coefficients (MFCC) and Hidden Markov Model (HMM) techniques. Furthermore, Linear Discriminant Analysis (LDA) and Maximum Likelihood Linear Transform (MLLT) optimization techniques were applied. Using three different testing data sets, this work obtained 98.38% and 1.76% average word recognition correctness rate (WRCR) and average Word Error Rate (WER), respectively, for speaker-dependent and text-independent. For speaker-independent and text-dependent, this work obtained 99.37% and 0.68% average WRCR and average WER, respectively, whereas for speaker-independent and text-independent this work obtained 96.53% and 4.00% average WRCR and average WER, respectively. [ABSTRACT FROM AUTHOR]

Objective: The superfamily of protein kinases features a common Protein Kinase-like (PKL) three-dimensional fold. Proteins with PKL structure can also possess enzymatic activities other than protein phosphorylation, such as AMPylation or glutamylation. PKL proteins play a vital role in the world of living organisms, contributing to the survival of pathogenic bacteria inside host cells, as well as being involved in carcinogenesis and neurological diseases in humans. The superfamily of PKL proteins is constantly growing. Therefore, it is crucial to gather new information about PKL families. Results: To this end, the KINtaro database (http://bioinfo.sggw.edu.pl/kintaro/) has been created as a resource for collecting and sharing such information. KINtaro combines protein sequence information and additional annotations for more than 70 PKL families, including 32 families not associated with PKL superfamily in established protein domain databases. KINtaro is searchable by keywords and by protein sequence and provides family descriptions, sequences, sequence alignments, HMM models, 3D structure models, experimental structures with PKL domain annotations and sequence logos with catalytic residue annotations. [ABSTRACT FROM AUTHOR]

Packet loss concealment (PLC) techniques are utilized to improve the quality of Voice over IP (VoIP) communications by reconstructing missing speech packets. Hidden Markov Model (HMM)-based PLC methods have shown a significant improvement over traditional methods by tracking the statistical evolution of speech signals. However, these methods may result in perceptually disturbing artifacts that arise from the structure of the HMM model. In this study, we introduce HMM-based PLC methods and investigate the impact of Markovian assumptions on their performance, specifically pitch fluctuation and gain mismatching. We then propose a new PLC method implemented on the G.722.2 codec, which incorporates the HMM and Decision Tree (DT) architecture. Our proposed architecture avoids the HMM's emissions dependencies assumption by using a DT layer, resulting in more accurate speech packet regeneration and natural transitions between the synthesized and concealed speech signals. The proposed method uses HMM and DT to track the statistical evolution of speech signals and accurately predict/estimate lost speech packets by exploiting the surrounding received speech packets. The proposed method is evaluated using mathematical proofs, objective, and subjective metrics, with results showing a considerable enhancement in speech quality compared to conventional PLC methods, achieving a Perceptual Evaluation of Speech Quality (PESQ) score higher than 3 at a 20% packet loss ratio. [ABSTRACT FROM AUTHOR]

Speech is one of the communication processes of humans. One of the important features of speech is to convey the inner feelings of the person to the listener. When a speech is expressed by the speaker, this speech also contains the feelings of the person, which leads to the creation of thoughts and behaviors appropriate to oneself. Speech Emotion Recognition (SER) is a very important issue in the field of human–machine interaction. The expansion of the use of computers and its impact on today's life has caused this mutual cooperation between man and machine to be widely investigated and researched. In this article, SER in English and Persian has been examined. Frequency time characteristics such as Mel- Frequency Cepstral Coefficient (MFCC), Linear Predictive Coding and Predictive Linear Perceptual (PLP) are extracted from the data as feature vectors, then they are combined with each other and a selection of suitable features from them. Also, Principal components analysis (PCA) is used to reduce dimensions and eliminate redundancy while retaining most of the intrinsic information content of the pattern. Then, each emotional state was classified using the Gaussian Mixtures Model (GMM) and Hidden Markov Model (HMM) technique. Combining the MFCC + PLP properties, PCA features, and HMM classification with a precision of 88.85% and a runtime of 0.3 s produces the average diagnostic rate in the English database; similarly, the PLP properties, PCA features, and HMM classification with a precision of 90.21% and a runtime of 0.4 s produce the average diagnostic rate in the Persian database. Based on the combination of features and classifications, the experimental results demonstrated that the suggested approach can attain a high level of stable detection performance for every emotional state. [ABSTRACT FROM AUTHOR]

Background: Lytic polysaccharide monooxygenases (LPMOs) catalyzing the oxidative cleavage of different types of polysaccharides have potential to be used in various industries. However, AA13 family LPMOs which specifically catalyze starch substrates have relatively less members than AA9 and AA10 families to limit their application range. Amylase has been used in enzymatic desizing treatment of cotton fabric for semicentury which urgently need for new assistant enzymes to improve reaction efficiency and reduce cost so as to promote their application in the textile industry. Results: A total of 380 unannotated new genes which probably encode AA13 family LPMOs were discovered by the Hidden Markov model scanning in this study. Ten of them have been successfully heterologous overexpressed. AlLPMO13 with the highest activity has been purified and determined its optimum pH and temperature as pH 5.0 and 50 °C. It also showed various oxidative activities on different substrates (modified corn starch > amylose > amylopectin > corn starch). The results of enzymatic textile desizing application showed that the best combination of amylase (5 g/L), AlLPMO13 (5 mg/L), and H2O2 (3 g/L) made the desizing level and the capillary effects increased by 3 grades and more than 20%, respectively, compared with the results treated by only amylase. Conclusion: The Hidden Markov model constructed basing on 34 AA13 family LPMOs was proved to be a valid bioinformatics tool for discovering novel starch-active LPMOs. The novel enzyme AlLPMO13 has strong development potential in the enzymatic textile industry both concerning on economy and on application effect. [ABSTRACT FROM AUTHOR]

Various domains generate continuous data in the form of multivariate time series (MTS), including electricity and stock trading. It is difficult to maintain a low variance in forecasting error while forecasting multi-step MTS. Deep learning models can forecast large and complex multivariate time series with high accuracy, but simulating the hyperparameters for a model to achieve high accuracy is time-consuming. Figuring out the appropriate hyperparameters becomes increasingly stagnant and resource-intensive as the dataset size increases. In this study, we propose a distributed random parameter optimization model to deal with short-term multivariate time series forecasting problems. It utilizes the deep neural network for forecasting in synchronization with the hyperparameter tuning optimization in a distributed manner to exploit the resources and speed up the computation. The proposed model has been validated against a real-world multivariate dataset of electricity consumption and weather conditions for the Canadian province of Ontario from 2010 to 2019. Extensive experiments on the Ontario region demonstrate that the proposed model is scalable and outperforms traditional models such as vector autoregression(VAR), Multilayer Perceptron(MLP), Hidden Markov Model (HMM), and Long Short Term Memory (LSTM). Furthermore, the model outperformed all others in terms of accuracy on a benchmark Spanish electricity dataset. [ABSTRACT FROM AUTHOR]

Polyethylene terephthalate (PET) is a widely used synthetic polymer and known to contaminate marine and terrestrial ecosystems. Only few PET‐active microorganisms and enzymes (PETases) are currently known, and it is debated whether degradation activity for PET originates from promiscuous enzymes with broad substrate spectra that primarily act on natural polymers or other bulky substrates, or whether microorganisms evolved their genetic makeup to accepting PET as a carbon source. Here, we present a predicted diene lactone hydrolase designated PET40, which acts on a broad spectrum of substrates, including PET. It is the first esterase with activity on PET from a GC‐rich Gram‐positive Amycolatopsis species belonging to the Pseudonocardiaceae (Actinobacteria). It is highly conserved within the genera Amycolatopsis and Streptomyces. PET40 was identified by sequence‐based metagenome search using a PETase‐specific hidden Markov model. Besides acting on PET, PET40 has a versatile substrate spectrum, hydrolyzing δ‐lactones, β‐lactam antibiotics, the polyester‐polyurethane Impranil® DLN, and various para‐nitrophenyl ester substrates. Molecular docking suggests that the PET degradative activity is likely a result of the promiscuity of PET40, as potential binding modes were found for substrates encompassing mono(2‐hydroxyethyl) terephthalate, bis(2‐hydroxyethyl) terephthalate, and a PET trimer. We also solved the crystal structure of the inactive PET40 variant S178A to 1.60 Å resolution. PET40 is active throughout a wide pH (pH 4–10) and temperature range (4–65 °C) and remarkably stable in the presence of 5% SDS, making it a promising enzyme as a starting point for further investigations and optimization approaches. [ABSTRACT FROM AUTHOR]

Understanding the behavior of the human postural system has become a very attractive topic for many researchers. This system plays a crucial role in maintaining balance during both stationary and moving states. Parkinson's disease (PD) is a prevalent degenerative movement disorder that significantly impacts human stability, leading to falls and injuries. This research introduces an innovative approach that utilizes a hidden Markov model (HMM) to distinguish healthy individuals and those with PD. Interestingly, this methodology employs raw data obtained from stabilometric signals without any preprocessing. The dataset used for this study comprises 60 subjects divided into healthy and PD patients. Impressively, the proposed method achieves an accuracy rate of up to 98% in effectively differentiating healthy subjects from those with PD. [ABSTRACT FROM AUTHOR]

Recently, artificial intelligence and data science have witnessed dramatic progress and rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). Consequently, new end-to-end Recurrent Neural Network (RNN) toolkits were developed with higher speed and accuracy that can often achieve a Word Error Rate (WER) below 10%. These toolkits can nowadays be deployed, for instance, within aircraft cockpits and Air Traffic Control (ATC) systems in order to identify aircraft and display recognized voice messages related to flight data, especially for airports not equipped with radar. Hence, the performance of air traffic controllers and pilots can ultimately be improved by reducing workload and stress and enforcing safety standards. Our experiment conducted at Tangier's International Airport ATC aimed to build an ASR model that is able to recognize aircraft call signs in a fast and accurate way. The acoustic and linguistic models were trained on the Ibn Battouta Speech Corpus (IBSC), resulting in an unprecedented speech dataset with approved transcription that includes real weather aerodrome observation data and flight information with a call sign captured by an ADS-B receiver. All of these data were synchronized with voice recordings in a structured format. We calculated the WER to evaluate the model's accuracy and compared different methods of dataset training for model building and adaptation. Despite the high interference in the VHF radio communication channel and fast-speaking conditions that increased the WER level to 20%, our standalone and low-cost ASR system with a trained RNN model, supported by the Deep Speech toolkit, was able to achieve call sign detection rate scores up to 96% in air traffic controller messages and 90% in pilot messages while displaying related flight information from ADS-B data using the Fuzzy string-matching algorithm. [ABSTRACT FROM AUTHOR]

Purpose: To compare photoreceptor density automated quantification in eyes with subretinal drusenoid deposits (SDD) and healthy controls using Heidelberg Spectralis High Magnification Module (HMM) imaging. Methods: Twelve eyes of 6 patients with intermediate AMD, presenting with SDD were included, as well as twelve eyes of healthy controls. Individual dot SDD within the central 30° retina were examined with infrared confocal laser ophthalmoscopy, HMM, and spectral-domain optical coherence tomography (SD-OCT). Photoreceptor density analysis was performed on the best-quality image using the ImageJ Foci Picker plugin, after the removal of SDD from the HMM image. Correlations were made between the HMM quantified photoreceptor density, SD-OCT characteristics, stage, and number of SDD. Results: Mean age was 75.17 ± 2.51 years in the SDD group (3 males, 3 females) versus 73.17 ± 3.15 years in the healthy control group (p = 0.2). Defects in the overlying ellipsoid zone were present on SD-OCT in 8/12 (66.66%) eyes. The mean ± standard deviation foci detected (i.e., cone photoreceptors) was 7123.75 ± 3683.32 foci/mm2 in the SDD group versus 13,253 ± 3331.00 foci/mm2 in the healthy control group (p = 0.0003). The number of SDD was associated with a reduction in foci density, p = 0.0055, r = − 0.7622. Conclusion: The decreased cone density in eyes with SDD may correlate with a decrease in retinal function in intermediate AMD eyes independent of neovascular complications or outer retinal pigment epithelial atrophy. [ABSTRACT FROM AUTHOR]

Due to the increase in technology and the evolution of computers, speech recognition technology plays a vital role in our daily lives. Communicating with the computer through speech is more feasible than communicating through text. Speech recognition is the process of conversion of human speech to readable text to perform various operations. India has the most amazing diverse, multilingual community. Currently, there are 22 recognized languages with 12 scripts in the constitution. On the other hand, 1900+ dialects are divided into four language groups. This literature review aims to comprehensively analyse the automatic speech recognition system based on its history, types, application and problems in existing systems. Furthermore, the state-of-art speech recognition approaches are examined to lead a comparative study. As a result of an extensive literature review, future research directions are highlighted in terms of practical and relevant methods. [ABSTRACT FROM AUTHOR]

The study explored the performance of vowel recognition using an acoustic model built on Audio Fingerprint techniques [1]. The research compares the performance of Support Vector Machines (SVMs), Hidden Markov Models (HMMs), Artificial Neural Networks (ANNs) and k-Nearest Neighbours (k-NN) classifiers in the recognition of isolated and within-word vowels and investigates the importance of different types of acoustic speech features in this process. Temporal, spectral, cepstral, formant, LPC and perceptual features of speech were examined. Importance of features was tested using a random forest classifier. Vowel classification was tested at three confidence levels for feature importance: 90%, 95% and 99%. Two author databases consisting of a total of 1,200 samples from 20 speakers, recorded under household conditions, were used. The classifiers were evaluated by confusion matrix, accuracy, precision, sensitivity and F1 score. A segmentation of words into speech sounds was carried out using a tool based on BiLSTM recurrent neural networks and the BIC criterion. Three most important features were determined: power spectral density, spectral cut-off, and Power-Normalised Cepstral Coefficients. In the isolated vowel recognition task, the SVM classifier was the most effective with a feature significance confidence level of 95% obtaining accuracy = 81%, precision = 81%, sensitivity = 81%, F1 score = 80%. In the task of recognising a vowel within a word, it was verified if the algorithm detected the presence of vowels in the correct segment and if it recognised the correct vowel within it. The best results were obtained by the k-NN classifier (statistical confidence level of feature importance of 99.9%). However, these results were low, correct recognition of the vowel in the word: A, E, U: 20%, I, O: 7%, Y: 23%. This indicates strong influence of the neighbourhood of other speech sounds in speech on the acoustic model of vowels and their recognition. [ABSTRACT FROM AUTHOR]

Mysticetes’ produce distinctive vocalisations which are used for echolocation, communication, and other marine functions. These cryptic vocalisations are studied by marine scientist to determine the behavioural patterns and movement of this suborder of cetaceans within their ecosystem. In practice, these vocalisations are gathered using passive acoustic monitoring over days, weeks, months, and even years. Therefore, it is complex to study these sounds using traditional visual inspection techniques because the gathered datasets are huge. Machine learning (ML) tools such as Gaussian mixture models (GMMs), support vector machines (SVMs), and hidden Markov models (HMMs) have been adopted in recent times to proffer analytic solutions to automatically detect and study these cryptic vocalisations. Notwithstanding, the feature extraction techniques employed play a vital role in determining the performance of these ML tools. In most cases, the performance of the feature extraction technique is directly proportional to the performance of the ML tools. Thus, the method of linear discriminant analysis (LDA) is introduced in this article as a feature extraction technique that can be adapted with the HMMs (LDA-HMM) to seamlessly detect the vocalisations of Mysticetes. The performance of the proposed LDA-HMM detector is compared with other recent detectors for Mysticetes’ vocalisations in the literature using two different species: Humpback whale songs and Bryde’s whale pulses. Experimental results show that the developed LDA-HMM detector is a performance-efficient alternative in comparison to the recent detection techniques studied in this article. Besides, the LDA-HMM detector offers less computational time complexity; as such, it is more suitable for real-time applications.

Abstract Background In canonical protein translation, ribosomes initiate translation at a specific start codon, maintain a single reading frame throughout elongation, and terminate at the first in-frame stop codon. However, ribosomal behavior can deviate at each of these steps, sometimes in a programmed manner. Certain mRNAs contain sequence and structural elements that cause ribosomes to begin translation at alternative start codons, shift reading frame, read through stop codons, or reinitiate on the same mRNA. These processes represent important translational control mechanisms that can allow an mRNA to encode multiple functional protein products or regulate protein expression. The prevalence of these events remains uncertain, due to the difficulty of systematic detection. Results We have developed a computational model to infer non-canonical translation events from ribosome profiling data. Conclusion ORFeus identifies known examples of alternative open reading frames and recoding events across different organisms and enables transcriptome-wide searches for novel events.

Abstract Background Basecalling long DNA sequences is a crucial step in nanopore-based DNA sequencing protocols. In recent years, the CTC-RNN model has become the leading basecalling model, supplanting preceding hidden Markov models (HMMs) that relied on pre-segmenting ion current measurements. However, the CTC-RNN model operates independently of prior biological and physical insights. Results We present a novel basecaller named Lokatt: explicit duration Markov model and residual-LSTM network. It leverages an explicit duration HMM (EDHMM) designed to model the nanopore sequencing processes. Trained on a newly generated library with methylation-free Ecoli samples and MinION R9.4.1 chemistry, the Lokatt basecaller achieves basecalling performances with a median single read identity score of 0.930, a genome coverage ratio of 99.750%, on par with existing state-of-the-art structure when trained on the same datasets. Conclusion Our research underlines the potential of incorporating prior knowledge into the basecalling processes, particularly through integrating HMMs and recurrent neural networks. The Lokatt basecaller showcases the efficacy of a hybrid approach, emphasizing its capacity to achieve high-quality basecalling performance while accommodating the nuances of nanopore sequencing. These outcomes pave the way for advanced basecalling methodologies, with potential implications for enhancing the accuracy and efficiency of nanopore-based DNA sequencing protocols.

Background: In canonical protein translation, ribosomes initiate translation at a specific start codon, maintain a single reading frame throughout elongation, and terminate at the first in-frame stop codon. However, ribosomal behavior can deviate at each of these steps, sometimes in a programmed manner. Certain mRNAs contain sequence and structural elements that cause ribosomes to begin translation at alternative start codons, shift reading frame, read through stop codons, or reinitiate on the same mRNA. These processes represent important translational control mechanisms that can allow an mRNA to encode multiple functional protein products or regulate protein expression. The prevalence of these events remains uncertain, due to the difficulty of systematic detection. Results: We have developed a computational model to infer non-canonical translation events from ribosome profiling data. Conclusion: ORFeus identifies known examples of alternative open reading frames and recoding events across different organisms and enables transcriptome-wide searches for novel events. [ABSTRACT FROM AUTHOR]

Identifying protein sequences with common ancestry is a core task in bioinformatics and evolutionary biology. However, methods for inferring and aligning such sequences in annotated genomes have not kept pace with the increasing scale and complexity of the available data. Thus, in this work, we implemented several improvements to the traditional methodology that more fully leverage the redundancy of closely related genomes and the organization of their annotations. Two highlights include the application of the more flexible k-clique percolation algorithm for identifying clusters of orthologous proteins and the development of a novel technique for removing poorly supported regions of alignments with a phylogenetic hidden Markov model (phylo-HMM). In making the latter, we wrote a fully documented Python package Homomorph that implements standard HMM algorithms and created a set of tutorials to promote its use by a wide audience. We applied the resulting pipeline to a set of 33 annotated Drosophila genomes, generating 22,813 orthologous groups and 8,566 high-quality alignments. [ABSTRACT FROM AUTHOR]

As people get older, living at home can expose them to potentially dangerous situations when performing everyday actions or simple tasks due to physical, sensory or cognitive limitations. This could compromise the residents' health, a risk that in many cases could be reduced by early detection of the incidents. The present work focuses on the development of a system capable of detecting in real time the main activities of daily life that one or several people can perform at the same time inside their home. The proposed approach corresponds to an unsupervised learning method, which has a number of advantages, such as facilitating future replication or improving control and knowledge of the internal workings of the system. The final objective of this system is to facilitate the implementation of this method in a larger number of homes. The system is able to analyse the events provided by a network of non-intrusive sensors and the locations of the residents inside the home through a Bluetooth beacon network. The method is built upon an accurate combination of two hidden Markov models: one providing the rooms in which the residents are located and the other providing the activity the residents are carrying out. The method has been tested with the data provided by the public database SDHAR-HOME, providing accuracy results ranging from 86.78% to 91.68%. The approach presents an improvement over existing unsupervised learning methods as it is replicable for multiple users at the same time. [ABSTRACT FROM AUTHOR]

Dynamic functional connectivity (DFC) analysis has been widely applied to functional magnetic resonance imaging (fMRI) data to reveal the time-varying functional interactions between brain regions. Although the sliding window (SW) method is popular for DFC analysis, the selection of window length is hard, and the temporal resolution is limited by the window length. The hidden Markov model (HMM) without the limitation of window length has been proven to be able to estimate time-varying brain states from fMRI data. However, HMM tends to be overfitted in DFC analysis of fMRI data because of the high spatial dimension and the limited sample size of fMRI data. In this study, we proposed an alternating HMM (aHMM) method that used the functional connectivity estimation of SW to initialize the covariance matrix of HMM and adopted an alternating HMM procedure to reduce the number of parameters during each optimization. The simulated and real fMRI resting data from the Human Connectome Projects showed that aHMM produced better robustness to noise, parameter number and sample size in DFC estimation than SW and HMM. For the real fMRI resting data of cerebral small vessel disease (CSVD), results of aHMM revealed that amnesia and mild cognitive impairment (aMCI) caused the CSVD with aMCI (CSVD-aMCI) group tended to spend more time on the brain state with overall weak connections and less time on the state with overall strong connections than the CSVD-controls. Moreover, CSVD-aMCI showed significantly lower connectivity amplitude and higher connectivity fluctuation than CSVD-control. In contrast, HMM did not detect intergroup differences of the connectivity amplitude and fluctuations and SW did not detect intergroup differences of connectivity fluctuations and fraction of time. The results further indicated that aHMM outperformed HMM and SW in detecting inter-group differences of temporal properties of DFC and connectivity fluctuations. [ABSTRACT FROM AUTHOR]

With the deepening of the reform of civic education in colleges and universities, the traditional mode of civic education has gradually revealed its limitations. The integration of red cultural resources into the ideological education of colleges and universities has become a more common method of ideological education. According to the attributes of red cultural resources in civic education, the study combines the HMM model, BiLSTM model, and BiLSTM-CRF model to construct a red cultural resource base for civic education courses in colleges and universities so as to dynamically integrate red cultural resources in civic education in colleges and universities, analyze the textual features of the red cultural resources in the research methods in the college and university education resource base, and explore the teaching of civic education in colleges and universities. The top five high-frequency words in the mainstream research methods are “history” (540), “comparison” (538), “literature” (486), “system” (334), “discipline” (245), and the most frequent words in the empirical research method are “case” (67), “investigation “(26), “interview” (23), “culture” (15), and “qualitative research” (18). The degree of understanding of the content of Yan’an red cultural resources by students of colleges and universities in Shaanxi Province is Yan’an spirit (14.65%), celebrity heroes’ deeds (26.19%), red literary writings (3.59%), red attractions (35.95%), the course of the revolutionary struggle (25.16%), deeds of poverty alleviation (19.54%), and no understanding (8.57%).

With the rapid development of science and technology, the use of artificial intelligence in the field of higher education is becoming more and more common. The use of intelligent speech recognition technology in English teaching in colleges and universities is on the rise. The research utilizes speech recognition logic to create a hybrid CTC/Attention model for teaching spoken English in colleges and universities. Then we compare and analyze the performance of artificial speech recognition models, such as the HMM model and CTC/Attention model, and explore the effects of teaching English speaking in colleges and universities using this model. In terms of the overall recognition rate, with the increase of the test vocabulary, the recognition rates of both the artificial speech recognition model and the HMM model decrease, while the CTC/Attention model shows an increasing trend. Except for intonation, the CTC/Attention model has the best recognition rate performance across all dimensions. Through the college-speaking teaching experiment based on the CTC/Attention model, the students’ total English speaking performance, accuracy, fluency, and complexity were improved. The Sig values of total speaking achievement, accuracy, fluency, and complexity are less than 0.05. College English-speaking teaching with intelligent speech recognition has a significant positive impact on the improvement of student’s English-speaking achievement, as indicated.