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The selection of papers on a specified topic requires not only analysis of the vocabulary of every paper on the topic of interest to an end user for relevance, but also taking into account the ultimate goal of the user as such (i.e., what exactly is the problem for which this selection is made to find a solution?). The formation of such a selection on the basis of the occurrence of words in the text of the analyzed paper from the abstracts of papers selected by an expert makes it possible to take into account not only the terms, but also the narration language, which is important, in particular, for the preparation of e-learning courses. The submitted paper is devoted to the relevant problem of estimating the quality of the selection thus formed and the explanation of the solutions obtained. For a document added to the selection, it is proposed to estimate the sentences incorporated into it cumulatively by the share of words with nonzero TF measure values (term frequency), the length of a sentence, and the sum of nonzero values for the term frequency of words incorporated into it. The sentences selected by the criterion of a maximum for the mentioned parameters are used to construct an essay, which is further evaluated for originality with respect to each abstract from the collection formed by an expert and for semantic connectedness in the sentences incorporated into it (with the use of neural network models of the BERT family). Based on the totality of these two estimations, a conclusion on the significance is made for each abstract selected by an expert when including the paper analyzed into the selection. In this case, the generated essay is also used to explain the obtained solution. [ABSTRACT FROM AUTHOR]

The publication presents an introductory paper to the Special issue of the international journal Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications of the Russian Academy of Sciences. The main scientific results of the 8th International Workshop "Image Mining: Theory and Applications," held on August 21, 2022, Montreal, Canada, are presented. Historical information is given on this series of international workshops, and their significant role in the development of the theory and practice of automation of image analysis, pattern recognition, and artificial intelligence is emphasized. The list of papers of the Special issue of PRIA, prepared based on the invited and regular papers selected and recommended for publication by the Program Committee of the IMTA-VIII-2022, is presented. [ABSTRACT FROM AUTHOR]

The paper considers the problem of numerical estimation of the closeness of a topical text to the most rational linguistic variant (i.e. semantic pattern or sense standard) of the description of the knowledge fragment it represents without paraphrasing. This problem is relevant when implementing targeted selection of text information by the maximum of the useful semantic component with respect to the tasks solved by the user. Examples of practical applications may include selection of papers for scientific publishing and design of training courses and educational portals. In the suggested solution, the basis of the estimate of the closeness of the text to the semantic pattern is the splitting of the words of each of its phrases into classes by the TF-IDF metric value relative to texts of a corpus preformed by an expert. Abstracts of scientific papers together with their titles are analyzed. The suggested numerical estimate of closeness to the sense standard makes it possible to rank articles by the significance of the described fragments of knowledge regarding a given subject area and by non-redundancy of the description itself. Here, the semantic images of the texts closest to the semantic pattern specify the words with the highest TF-IDF values, which, when placed next to each other in the linear series of a phrase, are, most probably, semantically related and form key combinations with words whose mentioned metric is close to average. To classify word combinations as key ones, the interpretation of the TF-IDF metric, estimating the number of simultaneous occurrences of all words in the analyzed combination into phrases of the individual document, is introduced. [ABSTRACT FROM AUTHOR]

The paper considers and analyzes the main scientific results of the 15th International Conference on Pattern Recognition and Information Processing (PRIP-2021), Minsk, Republic of Belarus, September 2021. The history of this series of conferences is outlined and its significant role in the development of the theory and practice of image analysis, pattern recognition, and artificial intelligence is indicated. A list of articles in the special issue prepared on the basis of reports selected by the PRIP-2021 program committee is provided. [ABSTRACT FROM AUTHOR]

In recent years, Deep Neural Networks (DNN) have emerged as a practical method for image recognition. The raw data, which contain sensitive information, are generally exploited within the training process. However, when the training process is outsourced to a third-party organization, the raw data should be desensitized before being transferred to protect sensitive information. Although masks are widely applied to hide important sensitive information, preventing inpainting masked images is critical, which may restore the sensitive information. The corresponding models should be adjusted for the masked images to reduce the degradation of the performance for recognition or classification tasks due to the desensitization of images. In this paper, we propose a mask-based image desensitization approach while supporting recognition. This approach consists of a mask generation algorithm and a model adjustment method. We propose exploiting an interpretation algorithm to maintain critical information for the recognition task in the mask generation algorithm. In addition, we propose a feature selection masknet as the model adjustment method to improve the performance based on the masked images. Extensive experimentation results based on multiple image datasets reveal significant advantages (up to 9.34% in terms of accuracy) of our approach for image desensitization while supporting recognition. [ABSTRACT FROM AUTHOR]

Brief historical background on the establishment and activities of the Institute of Control Sciences (IPU RAS). The paper presents key findings of the Institute (obtained mainly in the 20th century) in the field of pattern recognition and of related analysis of complex data. It focuses on four areas of research including (a) the method of potential functions, (b) the theory of learning and self-learning systems, (c) the generalized portrait method and recovery of dependences based on empirical data, and (d) automatic classification methods and expert classification analysis. Relations between these areas are studied. The pioneers in the field are named (M.A. Aizerman, E.M. Braverman, L.I. Rozonoer, Ya.Z. Tsypkin, V.N. Vapnik, A.Ya. Chervonenkis, I.B. Muchnik, and A.A. Dorofeyuk among others) and brief biographical notes on the life and scientific work of these scientists are presented. The follow-ups of the results thus obtained are shown. The bibliography of publications by the Institute's researchers in leading journals of Russia on pattern recognition problems and related complex data analysis tasks is provided. [ABSTRACT FROM AUTHOR]

This paper contains information about the creation, personnel, and main areas of scientific, scientific-organizational, and educational activities, as well as the main results obtained at the scientific school of the Honored Scientist of the Russian Federation, Doctor of Technical Sciences, Professor Yu.G. Vasin. [ABSTRACT FROM AUTHOR]

This paper provides a short survey of the main direction of activity of the Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences (FRC CSC RAS) in the areas concerning the topics of the journal Pattern Recognition and Image Analysis. The FRC CSC RAS was founded in December 2014 through the reorganization of the Institute of Informatics Problems of the Russian Academy of Sciences (IIP RAS) (by including the Institute for System Analysis of the Russian Academy of Sciences (ISA RAS) and the Dorodnycin Computing Center of the Russian Academy of Sciences (DCC RAS)). Section 1 reviews the activity of these institutes before their merger. Section 2 studies the work performed after 2015. [ABSTRACT FROM AUTHOR]

Authenticity in multimedia information retrieval system is one such parameter which is of utmost in demand. One of such parameters is the biometric based authentication system, as seen in case of human the system proves its importance in almost each and every field. But in case of animals it becomes quite complex to recognize them using unique biometric parameter. This paper presents a modified 10 layered DenseNet deep learning framework which is implemented for the identification of Humpback whale, in an online challenge conducted by the kaggle, using biometric parameter. The proposed dense model minimizes the traditional limitation of vanishing gradient problem and outperforms as compared with other methods exists in the literature. This paper also proposed an hand free flow algorithm for ROI segmentation. In addition, the proposed methodology also uses CLAHE as a preprocessing method and focused on the feature re-usability with an optimized number of parameters used for recognition. During the experimentation, validation of the proposed model is found satisfactory with an accuracy of 93.21%. The test set results are provided by the kaggle with an AUC-ROC result over public and private scoreboard with an accuracy of 94.47% and 92.54% respectively. The comparative result with other deep methodologies suggests that the highest accuracy gained is 95.55% by Se-ResNet50 by some other learderboard and proposed model is found at second place. [ABSTRACT FROM AUTHOR]

In order to effectively identify the pattern of personalized adaptive learning in online education and improve the recommendation satisfaction of personalized learning resources in online education platform, this paper studies the pattern recognition method of personalized adaptive learning in online education. The learning behavior pattern data in the online education platform are mined, preprocessed, clustered and made correlation analysis, and the obtained data are used to construct the learner's personalized adaptive learning characteristics model; on this basis, the framework of learning pattern recognition model is constructed to recognize the personalized adaptive learning pattern from four aspects: cognitive level, learning style, interactive behavior pattern characteristics and online social learning characteristics. The experimental test results show that this method can effectively identify the personalized adaptive learning patterns of learners, including interactive learning behavior patterns and online social learning patterns. The personalized learning resources recommended by the online education platform according to the identification results of this paper have obtained the learners' satisfaction score of a high level at 93.27%. [ABSTRACT FROM AUTHOR]

This paper proposes a gait intention detection (GID) method based on motion information from a single leg. This method detects three gait states: stair ascending (SA), stair descending (SD), and level walking (LW). The GID is a highly important factor for the control of gait assistive devices. If the GID is limited to the motion information from a single leg, analyzing the gait motions becomes more challenging, owing to the absence of information regarding the phase difference between both legs. To address this challenging, previous GID methods have used fusion data from several sensors. In this paper, only three inertial measurement units are used for proposed single leg GID method. To analyze gait motions using these limited sensors, the divergent component of motion (DCM) of the user is computed. The DCM is a physical quantity incorporating the linear position and linear velocity of the center of mass; these can be computed from the kinematic data of a support leg. In each of the three gait states SA, SD, and LW, the DCM shows different patterns. To classify and detect these different patterns, a pattern recognition method based on an artificial neural network algorithm is used. The GID performance of the proposed method was experimentally evaluated, and it showed 99% success rate for the SA, SD, and LW states. [ABSTRACT FROM AUTHOR]

With the development of the big data era, the manufacturing industry is focusing on integrating the technologies related to the evolving intelligent industry. Intelligent manufacturing is the latest form of advanced manufacturing development. In order to pay attention to the design and evaluation status of manufacturing system, we understand the connotation of manufacturing system from the perspective of actual manufacturing production and sales. In this paper, the evaluation model and algorithm of Intelligent Manufacturing System Based on pattern recognition and big data are proposed. In the process of studying the information transmission of intelligent manufacturing system, the evaluation hierarchy algorithm of intelligent control system is improved with reference to several modeling methods. Finally, an intelligent manufacturing system suitable for processing big data is designed. In the experiment in workshop A, with the help of computer simulation of artificial intelligent activities, the intelligent manufacturing system analyzes the processing and production data, obtains the dynamic requirements of products in the machining workshop, and provides judgment basis for the construction of dynamic units in enterprises. The experimental results show that after the change of the production organization model using the seasonal change forecasting method, the introduction of the smart manufacturing system, the sales production of this shop has been increasing, and in the 20th month has increased seven times more than the initial, while the overall situation of the predicted sales and actual sales and costs, the superiority of using the smart manufacturing evaluation model is derived. [ABSTRACT FROM AUTHOR]

Sign language is a non-verbal communication tool used by the deaf. A robust sign language recognition framework is needed to develop Human–Robot Interaction (HRI) platforms that are able to interact with humans via sign language. Iranian sign language (ISL) is composed of both static postures and dynamic gestures of the hand and fingers. In this paper, we present a robust framework using a Deep Neural Network (DNN) to recognize dynamic ISL gestures captured by motion capture gloves in Real-Time. To this end, first, a dataset of fifteen ISL classes was collected in time series; then, this dataset was virtually augmented and pre-processed using the "state-image" method to produce a unique collection of images, each image corresponding to a specific set of sequential data representing a class. Next, by implementing a continuous Genetic algorithm, an optimal deep neural network with the minimum number of weights (trainable parameters) and the maximum overall accuracy was found. Finally, the dataset was fed to the DNN to train the model. The results showed that the optimization process was successful at finding a DNN structure highly suitable for this application, with 99.7% accuracy on the verification (test) data. Then, after implementing the module in a robotic architecture, an HRI experiment was conducted to assess the system's performance in real-time applications. Preliminary statistical analysis on the standard UTAUT model for eight participants showed that the system can recognize ISL signs quickly and accurately during human–robot interaction. The proposed methodology can be used for other sign languages as no specific characteristics of ISL were used in the preprocessing or training stage. [ABSTRACT FROM AUTHOR]

In this paper, we present a new approach for defining a dual proximity measure for intuitionistic fuzzy sets. The new approach utilizes an extended form of the restricted equivalence functions and receives their values as a two tuple. The two values in the two tuple are due to the dual character of the proximity measure under consideration. In fact, the computation of proximity between two intuitionistic fuzzy sets simultaneously provides the value of similarity as well as non-similarity between the intuitionistic fuzzy sets. So, the novel approach proposed in this paper provides a comprehensive information theoretic evaluation of intuitionistic fuzzy sets. Further, we investigate the application of the novel measure in pattern recognition and clustering analysis. We also contrast the performance of the proposed measure with the state-of-art in view of the structured linguistic variables, pattern classification and clustering analysis. The comparative analysis shows that the newly introduced two-valued proximity measure performs better in certain circumstances and encompasses the dualism of the human cognition. [ABSTRACT FROM AUTHOR]

3D object recognition and pattern recognition are active and fast-growing research areas in the field of computer vision. It is mandatory to define the pattern class, feature extraction, design classifiers, clustering, and selection of test datasets and evaluate performance for any pattern recognition system. The pattern recognition system recognizes the object, so it is required to extract the features in such a way that it will be suitable for a particular recognition method. Features can be retrieved either locally or globally. The object recognition technique is divided into two parts: the local feature extraction method and the global feature extraction method. Many researchers have done admirable work in the field of local and global feature extraction. Local feature-based techniques are more suitable for the real-world scene. The Global feature-based methods are more suitable for retrieving the 3D model & identifying the object's shape when the object's geometric structure is fragile. A lot of research has been done on pattern recognition in the last 50 years. Still, no single technique can be used for all types of applications, such as bioinformatics, data mining, speech recognition, remote sensing, multimedia applications, text detection, localization, etc. The main agenda of this paper is to summarize the 3D object recognition methodologies. This paper provides a complete study of 3D object recognition based on local and global feature-based methods and different techniques of pattern recognition. We have tried to summarize the results of different technologies and the future scope of this paper's particular technique. We enlisted the accessible online 3D database and their attributes, evaluation parameters of the 3D datasets. This paper will immensely help the researchers to Identify the research gap and limitations in pattern recognition and object recognition so that the researchers will be motivated to do something new in this field. [ABSTRACT FROM AUTHOR]

A massive amount of textual data now exists in digital repositories in the form of research articles, news articles, reviews, Wikipedia articles, and books, etc. Text clustering is a fundamental data mining technique to perform categorization, topic extraction, and information retrieval. Textual datasets, especially which contain a large number of documents are sparse and have high dimensionality. Hence, traditional clustering techniques such as K-means, Agglomerative clustering, and DBSCAN cannot perform well. In this paper, a clustering technique especially suitable to large text datasets is proposed that overcome these limitations. The proposed technique is based on word embeddings derived from a recent deep learning model named "Bidirectional Encoders Representations using Transformers". The proposed technique is named as WEClustering. The proposed technique deals with the problem of high dimensionality in an effective manner, hence, more accurate clusters are formed. The technique is validated on several datasets of varying sizes and its performance is compared with other widely used and state of the art clustering techniques. The experimental comparison shows that the proposed clustering technique gives a significant improvement over other techniques as measured by metrics such Purity and Adjusted Rand Index. [ABSTRACT FROM AUTHOR]

Understanding the glassy nature of neural networks is pivotal both for theoretical and computational advances in Machine Learning and Theoretical Artificial Intelligence. Keeping the focus on dense associative Hebbian neural networks (i.e. Hopfield networks with polynomial interactions of even degree P > 2 ), the purpose of this paper is twofold: at first we develop rigorous mathematical approaches to address properly a statistical mechanical picture of the phenomenon of replica symmetry breaking (RSB) in these networks, then—deepening results stemmed via these routes—we aim to inspect the glassiness that they hide. In particular, regarding the methodology, we provide two techniques: the former (closer to mathematical physics in spirit) is an adaptation of the transport PDE to this case, while the latter (more probabilistic in its nature) is an extension of Guerra’s interpolation breakthrough. Beyond coherence among the results, either in replica symmetric and in the one-step replica symmetry breaking level of description, we prove the Gardner’s picture (heuristically achieved through the replica trick) and we identify the maximal storage capacity by a ground-state analysis in the Baldi-Venkatesh high-storage regime. In the second part of the paper we investigate the glassy structure of these networks: at difference with the replica symmetric scenario (RS), RSB actually stabilizes the spin-glass phase. We report huge differences w.r.t. the standard pairwise Hopfield limit: in particular, it is known that it is possible to express the free energy of the Hopfield neural network (and, in a cascade fashion, all its properties) as a linear combination of the free energies of a hard spin glass (i.e. the Sherrington–Kirkpatrick model) and a soft spin glass (the Gaussian or ”spherical” model). While this continues to hold also in the first step of RSB for the Hopfield model, this is no longer true when interactions are more than pairwise (whatever the level of description, RS or RSB). For dense networks solely the free energy of the hard spin glass survives. As the Sherrington–Kirkpatrick spin glass is full-RSB (i.e. Parisi theory holds for that model), while the Gaussian spin-glass is replica symmetric, these different representation theorems prove a huge diversity in the underlying glassiness of associative neural networks. [ABSTRACT FROM AUTHOR]

Illumination preprocessing is an important ingredient for handling lighting variation face recognition challenge. Nonetheless, existing methods are usually designed to be independent of the face recognition methods and the interaction between them is not yet well explored. In this paper, we formulate the face image illumination preprocessing and recognition into a unified sparse representation framework and propose a novel joint reflectance field estimation and sparse representation (JRSR) method for face recognition under extreme lighting conditions. The proposed method separates the identify factor and the interfered illumination of a query sample simultaneously by one nonconvex sparse optimizing model. We also present an efficient approximation algorithm to solve JRSR in this paper. Evaluation on several face databases and the experimental results of face recognition with illumination variation clearly demonstrate the advantages of our proposed JRSR algorithm in illumination preprocessing efficiency and recognition accuracy. [ABSTRACT FROM AUTHOR]

Semantic segmentation involves extracting meaningful information from images or input from a video or recording frames. It is the way to perform the extraction by checking pixels by pixel using a classification approach. It gives us more accurate and fine details from the data we need for further evaluation. Formerly, we had a few techniques based on some unsupervised learning perspectives or some conventional ways to do some image processing tasks. With the advent of time, techniques are improving, and we now have more improved and efficient methods for segmentation. Image segmentation is slightly simpler than semantic segmentation because of the technical perspective as semantic segmentation is pixels based. After that, the detected part based on the label will be masked and refer to the masked objects based on the classes we have defined with a relevant class name and the designated color. In this paper, we have reviewed almost all the supervised and unsupervised learning algorithms from scratch to advanced and more efficient algorithms that have been done for semantic segmentation. As far as deep learning is concerned, we have many techniques already developed until now. We have studied around 120 papers in this research area. We have concluded how deep learning is helping in solving the critical issues of semantic segmentation and gives us more efficient results. We have reviewed and comprehensively studied different surveys on semantic segmentation, specifically using deep learning. [ABSTRACT FROM AUTHOR]

The mechanism of L2-norm loss can be explained from the perspective of maximizing margin and minimizing margin variance, which is equivalent to the idea of margin distribution. Recent studies suggest that the margin distribution is more vital for the generalization performance of models. Inspired by the idea, a nonparallel support vector machine (NPSVM) with L2-norm loss is proposed in this paper, termed as L2-NPSVM. We first reconstruct a L2-norm loss for NPSVM. The L2-norm loss can make full use of the margin distribution information in the training samples. Based on the L2-norm loss, we formulate the L2-NPSVM algorithm. L2-NPSVM is a more reliable training model with stronger generalization ability. Since L2-norm loss is used to measure the empirical risk in the objective function, L2-NPSVM can also reduce the impact of outliers. Furthermore, due to the L2-norm loss, the dual coordinate descent (DCD) method can be applied to linear and nonlinear L2-NPSVM. Under the premise of high accuracy, DCD can accelerate the solving process. At last, we conduct a series of comparative experiments on UCI datasets, noise-added UCI datasets and real-world datasets, which demonstrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Printed fabrics often have rich colors and variable patterns in different sizes and shapes, which make it difficult to achieve accurate pattern recognition and color measurement using traditional spectrophotometers and digital cameras. This paper develops a grid-based density peaks clustering (GDPC) algorithm to automatically recognize patterns and extract colors of multispectral images of printed fabrics. The multispectral images captured by a self-developed multispectral imaging system is firstly converted into color images in CIELAB color space and three principal components are calculated by applying principal component analysis to reduce the dimensions of the multispectral images. During the multispectral image processing, the noise pixels are removed by calculating the local stability of each pixel, and then the remaining stable pixels are clustered using proposed GDPC algorithm based on three CIELAB color channels and three principal components. Compared with widely-used color clustering algorithms, the proposed GDPC algorithm can recognize the color patterns from more intricate multispectral printed fabric images with higher accuracy and less computational time. [ABSTRACT FROM AUTHOR]

Similarity measures are very effective and meaningful tool used for evaluating the closeness between any two attributes which are very important and valuable to manage awkward and complex information in real-life problems. Therefore, for better handing of fuzzy information in real life, Ullah et al. (Complex Intell Syst 6(1): 15–27, 2020) recently introduced the concept of complex Pythagorean fuzzy set (CPyFS) and also described valuable and dominant measures, called various types of distance measures (DisMs) based on CPyFSs. The theory of CPyFS is the essential modification of Pythagorean fuzzy set to handle awkward and complicated in real-life problems. Keeping the advantages of the CPyFS, in this paper, we first construct an example to illustrate that a DisM proposed by Ullah et al. does not satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Then, combining the 3D Hamming distance with the Hausdorff distance, we propose a new DisM for CPyFSs, which is proved to satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Moreover, similarly to some DisMs for intuitionistic fuzzy sets, we present some other new complex Pythagorean fuzzy DisMs. Finally, we apply our proposed DisMs to a building material recognition problem and a medical diagnosis problem to illustrate the effectiveness of our DisMs. Finally, we aim to compare the proposed work with some existing measures is to enhance the worth of the derived measures. [ABSTRACT FROM AUTHOR]

This paper presents an approach for identifying fingerprints through the extraction of geometric and statistical features of characteristic Minutiae. The proposed approach is in accordance with statistical features to extract important points from the skeleton of a fingerprint's image. Through the addition of geometric features as a kind of preprocessing to this approach, the images are divided into distinct regions. In this approach, statistical parameters like min, max, mean and standard deviation are applied in order to compute the general abstract of the features. Another achievement in this article is the presentation of a similarity measure in identification tools which is only used for methods based on matching patterns. The Optimized version of the proposed method achieved near zero EER percentage in some of the datasets. [ABSTRACT FROM AUTHOR]

Broad neural networks provide an alternative way of deep learning with a novel flatted structure, which employ a non-iterative training mechanism and exhibit high efficiency in various recognition tasks. However, such broad networks unavoidably suffer from unstable performance due to the double random mappings in the generation of feature representations and the consequent uncertainty. The existing research often neglect to explore the effect of the quality of the broaden feature representations, which is crucial for the model performance. This paper presents a progressively-enhanced framework taking broad network as basic learners (PE-BL) to address the existing issues. The basic broad learners in PE-BL are trained in sequence, and the core manipulation is to modify the primitive hidden feature representations of the current learner through the nonlinear transformation of the prediction from the previous one, so the resulting broaden representations become more discriminative. Further, PE-BL is adapted to the scenarios where only a single broad learner is employed. Finally, extensive comparative experiments on some benchmark datasets and Electroencephalogram (EEG)-based emotion recognition task verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

A speaker identification framework that combines both high- and low-level features, for state-of-the-art variability analysis and classification is proposed in this paper. The proposed framework introduces a workable solution that targets robust speaker variability classification using speech samples recorded in suboptimal conditions. A translated Ibibio (New Benue-Congo, Nigeria) version of "The Tiger and the Mouse"—a prosodically balanced corpus that demonstrates the prosody of read-aloud English was used in this study with speech samples obtained from 50 participants (25 males and 25 females). Identity-vectors (i-vectors) or low dimensional signal patterns were extracted and used as baselines for investigating speakers' variability patterns across various classifiers (Decision Tree: DT, Support Vector Machine: SVM, k-Nearest Neighbour: k-NN, and Deep Neural Network: DNN) and kernels. Treatment of the baselines with high level features (speech duration, F0, intensity) was also experimented for word, syllable, and phoneme units. Results obtained revealed that DTs and some variants of SVM gave high classification accuracies (above 70%). Hence, the hypothesis of universal Gaussianity appears inexact, as the linear predictor that is optimal in the mean square error sense may not hold for Ibibio. Further treatments of the baselines with Linear Discriminant Analysis (LDA) and Cosine Distant Scoring (CDS) yielded very poor classification results, except for the k-NN and Gaussian SVM classifiers which performed well for the LDA treated baselines. [ABSTRACT FROM AUTHOR]

As one of the most important assets in the transportation of oil and gas products, subsea pipelines are susceptible to various environmental hazards, such as mechanical damage and corrosion, that can compromise their structural integrity and cause catastrophic environmental and financial damage. Autonomous underwater systems (AUS) are expected to assist offshore operations personnel and contribute to subsea pipeline inspection, maintenance, and damage detection tasks. Despite the promise of increased safety, AUS technology needs to mature, especially for image-based inspections with computer vision methods that analyze incoming images and detect potential pipeline damage through anomaly detection. Recent research addresses some of the most significant computer vision challenges for subsea environments, including visibility, color, and shape reconstruction. However, despite the high quality of subsea images, the lack of training data for reliable image analysis and the difficulty of incorporating risk-based knowledge into existing approaches continue to be significant obstacles. In this paper, we analyze industry-provided images of subsea pipelines and propose a methodology to address the challenges faced by popular computer vision methods. We focus on the difficulty posed by a lack of training data and the opportunities of creating synthetic data using risk analysis insights. We gather information on subsea pipeline anomalies, evaluate the general computer vision approaches, and generate synthetic data to compensate for the challenges that result from lacking training data, and evidence of pipeline damage in data, thereby increasing the likelihood of a more reliable AUS subsea pipeline inspection for damage detection. [ABSTRACT FROM AUTHOR]

Delamination between layers of composite greatly reduces their strength and stiffness, and thus, it is necessary to investigate the heat treatment method of 3D printed continuous fiber-reinforced composites to enhance their interlaminar properties. In this paper, 3D printed composites with different fiber volume fractions were treated at high temperature, and the damage behavior and failure mechanism were analyzed by acoustic emission technology. The dominant frequency of a single waveform was obtained by extracting energy features through wavelet packet transform, and the dominant damage mode of each acoustics emission (AE) event was analyzed and accurately classified. Then, support vector machine algorithm is performed to achieve supervised classification of AE signals. The result shows the maximum load of Specimen A-4 (5% fiber) and B-4 (15% fiber) increased by 83.58% and 86.8%, respectively, after being treated at 180 °C. It can be known that the temperature post-treatment makes the composite have a stronger ability to resist deformation. The temperature treatment will improve the mechanical properties of the composites but simultaneously generate more high-frequency AE signals. [ABSTRACT FROM AUTHOR]

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling. [ABSTRACT FROM AUTHOR]

With the vigorous development of the Internet, multimedia technology, and virtual reality technology, digital museum has been born and gradually entered the stage of rapid development. As a typical application of 3D virtual scene in the field of traditional culture protection and communication, 3D virtual museum has become an important trend in the development of museums in recent years. Three-dimensional virtual museum can maximize the development of museum functions and better play the museum's cultural communication and heritage protection and other functions. Faced with such rich cultural relic resources, how to construct 3D virtual museum quickly becomes an important research problem. Virtual reality technology integrates the latest research and development achievements of digital image graphics processing technology, computer technology, artificial intelligence, multimedia technology, sensor technology, and other information technologies, which provides powerful support and help for us to create and experience virtual world. The combination of virtual reality technology and cultural heritage protection has created a new research field. Based on this, this paper studies personalized healthcare museum exhibition system design based on the VR and deep learning-driven multimedia and multimodal sensing. The novel computational model is designed to improve the systematic performance of the VR framework, and the application scenarios are tested. Through the verification, the performance is tested. [ABSTRACT FROM AUTHOR]

The amount of high dimensional data produced by visual sensors in the smart environments and by autonomous vehicles is increasing exponentially. In order to search and model this data for real-time applications, the dimensionality of the data should be reduced. In this paper, a novel dimensionality reduction algorithm based on fuzzy rough set theory, called Centralized Binary Mapping (CBM), is proposed. The fuzzy CBM kernel is used for extracting the central elements and the memory cells from the blocks of high dimensional data. The proposed applications of CBM in this paper include hashing and generative modelling of multimedia big data. The robustness of the proposed CBM based hashing algorithm is 10% higher than comparable methods. Furthermore, based on the CBM, a novel architecture for neural networks called Deep Root Dimensional Mapping (DRDM) is proposed. The DRDM is used for generative modelling of multimedia big data using a new autonomous vehicle visual navigation dataset as well as the standard datasets. The simulation results show that the proposed DRDM converges rapidly and the perceptual quality of the outputs at the same epoch is higher than generative adversarial networks. The proposed CBM can be used as a new data structures in various pattern recognition and machine learning tasks. [ABSTRACT FROM AUTHOR]

QR codes are often placed on complex backgrounds and under unstable illumination conditions, which in fact renders their localization and decoding quite challenging. In this paper, we propose a robust system devised to ensure two main tasks: firstly, extracting QR codes from captured images regardless of their quality. Secondly, enhance the extracted QR codes textures so that they can be decoded correctly. A QR code is principally localized through three finder patterns (FPs), which are placed in its three corners, as well as a set of alignment patterns (APs). FP and AP are characterized by specific textures (alternative black and white pixel sequences), defined respectively by the following ratios: 1:1:3:1:1 and 1:1:1:1:1. The proposed system starts by segmenting the captured image using an achromatic filter. This is followed by horizontal and vertical scans, aiming at extracting the valid horizontal and vertical segments satisfying the aforementioned ratios. Afterwards, the valid segment intersection is computed to localize candidate areas containing FPs and APs. The rough extracted patterns are filtered based on Multiclass Support Vector Machine classifier combined with the histogram of Oriented Gradients. The retained patterns are then transmitted to a pattern closeness measurement algorithm to conveniently localize QR code positions. In order to evaluate the robustness and weakness of the proposed approach, a comparative study has been carried out. In this study, we compared our approach against the most relevant competitive QR code extractors, such as Zxing, Zbar, Quirc, Leadtools and Dynamsoft. Our algorithm outperforms the evoked extractors with a precision rate of 97,3% and 96% under motion blur and JPEG compression respectively. Furthermore, the execution time analysis proved that our algorithm is not computationally expensive with a maximum running time of 1092 ms at a high resolution image and high number of QR codes per image.. [ABSTRACT FROM AUTHOR]

Fungi are one of the major causes that contributed to plant diseases. There are lots of fungi species but it is estimated that only 10% have been described. There are two major approaches to identifying fungi species, morphological identification, and molecular test which need cautious clarification to make good interpretations and are time-consuming. In this paper, we propose a Machine Learning approach that involves the use of the K-Means clustering technique, and Decision Tree to highlight the observed fungi spore images taken under the microscopic view and discard background pixels to produce digital images database which later can be used for Deep Learning. [ABSTRACT FROM AUTHOR]

Automatic emotion recognition from speech signals is an important research area. Many speech emotion recognition (SER) methods have been proposed, among which ensemble learning is an effective way to recognize speech emotion. However, the ability and diversity of the base classifier are not carefully considered in the case of limited available speech emotion samples. To overcome the above problem, this paper proposes a new diversity subspace generation based on feature selection (DSGFS) for SER. In DSGFS, a constrained problem is cleverly designed, which can iteratively select many diversity and strong subspaces, so the classification ability and the diversity of the corresponding base classifiers can be ensured. As a result, more features can be extracted from the data, an ensemble classifier framework with strong base classifiers can be automatically generated, and the number of base classifiers can be smaller. The proposed models offered SER weighted average recall of 87.24%, 64.58%, 69.10%, 53.50% on the EmoDB, SAVEE, RAVDESS, CASIA datasets with speaker independent, respectively, which validate the proposed approach in terms of the performance of speech emotion recognition. [ABSTRACT FROM AUTHOR]

The use of a convolutional neural network with transfer learning is a strategy that defines high-level features, commonly explored to study patterns in medical images. These features can be analyzed via different methods in order to design hybrid models with more useful and accurate solutions for clinical practice. In this paper, a computational scheme is presented to define hybrid models through deep features by transfer learning, selection by ranking and a robust ensemble classifier with five algorithms. The obtained models were applied to classify histological images from breast, colorectal and liver tissue. The strategy developed here allows knowing important results and conditions to improve models of computer-aided diagnosis, even exploring classic CNN models. The features were defined using layers from the AlexNet and ResNet-50 architectures. The attributes were organized into subsets of the most relevant features and submitted to a k-fold cross-validation process. The best hybrid models were obtained with deep features from the ResNet-50 network, using distinct layers (activation_48_relu and avg_pool) and a maximum of 35 descriptors. These hybrid models provided 98.00% and 99.32% of accuracy values, with emphasis on histological images of breast cancer, indicating the best solution among those available in the specialized Literature. Also, these models provided more relevant results for classifying UCSB and LG datasets than regularized techniques and CNN architectures, exploring data augmentation or not. The computational scheme with detailed information regarding the main hybrid models is a relevant contribution to the community interested in the study of machine learning techniques for pattern recognition. [ABSTRACT FROM AUTHOR]

The negative pulsed downlink communication system is used to send surface control commands to the downhole rotary steering tool at thousands of meters, which is a significant part of the current rotary steering technology. At present, the transmission efficiency of negative pulsed downlink communication is very low, and only simple control commands can be transmitted in a few minutes, which limits the development of rotary steering technology with complex control functions. To improve the transmission rate of the downlink system, the downlink pulse width needs to be shortened. However, due to the influence of signal transmission characteristics, the waveform of a narrow pulse width signal will be severely distorted, which increases the difficulty of decoding the downlink signal. Therefore, a decoding method based on pattern recognition for negative pulsed downlink signal with narrow pulse width is proposed in this paper, which establishes a Euclidean distance matrix model between similar characteristic signal segments on the rising or falling edge of the downlink signal, the pulse coding timing of among the signal segment with each rising or falling edge is analyzed, the decoding and recognition of the downlink instruction are achieved, which solves the problem of large timing deviation in decoding the downlink signal with a current threshold method. The experimental results show that the method proposed in this paper can achieve accurate decoding of the 6 s pulse width downlink signal. Compared with the threshold method, it can be seen that the decoding accuracy of the method proposed in this paper can be greatly improved, and the smaller the signal pulse width, the more significant the advantage. [ABSTRACT FROM AUTHOR]

In the field of pattern recognition, collected data always include some additional information which are usually termed as privileged information. The privileged information is latent information belonging to the training samples, which can be easily ignored. The privileged information can help to build a better classifier for classification. In this paper, we try to construct a robust nonparallel support vector machine (NPSVM) model under the privileged information learning (LUPI) setting, termed as R-NPSVM+. On the one hand, we introduce the privileged information into NPSVM so as to build a model for classification. In the training process, both the privileged information and "usual" samples are used to train the model, which can enhance the accuracy. On the other hand, due to the ε-insensitive loss and hinge loss, NPSVM is sensitive to noise or outliers. Hence, we use two robust loss functions in R-NPSVM+ model, which can further ensure the robustness of the model. In addition, we use the Lagrange multiplier method and the dual coordinate descent (DCD) algorithm to optimize the proposed objective function, respectively. Lastly, to evaluate the performance of R-NPSVM+, we conduct a series of experiments. Experimental results confirm that compared with other classical SVM-type algorithms, our R-NPSVM+ can produce a better performance, especially when the samples are corrupted by noise and outliers. [ABSTRACT FROM AUTHOR]

The classification of data stream susceptible to the concept drift phenomenon has been a field of intensive research for many years. One of the dominant strategies of the proposed solutions is the application of classifier ensembles with the member classifiers validated on their actual prediction quality. This paper is a proposal of a new ensemble method – Covariance-signature Concept Selector – which, like state-of-the-art solutions, uses both the model accumulation paradigm and the detection of changes in the data posterior probability, but in the integrated procedure. However, instead of ensemble fusion, it performs a static classifier selection, where model similarity assessment to the currently processed data chunk serves as a concept selector. The proposed method was subjected to a series of computer experiments assessing its temporal complexity and efficiency in classifying streams with synthetic and real concepts. The conducted experimental analysis allows concluding the advantage of this proposal over state-of-the-art methods in the identified pool of problems and high potential in practical applications. [ABSTRACT FROM AUTHOR]

Precision livestock farming consists of technological tools and techniques to improve livestock management. Proper detection and classification of jaw movement (JM) events are indispensable for the estimation of dry matter intake, detection of health problems, and flag the onset of estrus, among other information. The analysis of acoustic signals is one of the most accepted ways to monitor the feeding behavior of free-grazing cattle. Different acoustic methods have been developed for recognizing JM-events in recent years. However, their operation is limited to off-line analysis on a personal computer. The lack of on-line acoustic monitoring systems is associated with the challenging operation requirements (low-power consumption, autonomy, portability, robustness and non-intrusive on the animal). In this paper, a fixed-point variant of the chew-bite energy-based algorithm is presented. This algorithm is implemented on a new low-power audio processor system for real-time recognition of JM-events. The system includes a Nanocontroller processor, which is always-on and detects JM-events; and a second transport-triggered architecture (TTA) based processor, which is mainly in power-down and classifies JM-events. The results demonstrate that the proposed fixed-point JM-events recognizer achieves a recognition rate of 91.4% and 90.2% in noiseless and noisy conditions, respectively. The recognition rate increases by 6.1% regarding a previous reference on-line system. Moreover, the proposed audio processor system chip consumes 4 μW on average, i.e., only 2.3% of the power of an always-on TTA-based processor system for the same audio sequence. An exemplary implementation of the proposed system in a 65 nm low-leakage CMOS technology is given. [ABSTRACT FROM AUTHOR]

This paper presents an extension of the community question-answering (CQA) system we developed previously. This graph-based system method that builds ranked answers for related questions using nKullback–Leibler (KL) divergence. The process of extracting answers to questions in this work involves; question core, building question query, query-based answer extraction (QBAE), pattern-based answer extraction (PBAE), and combined answer extraction. The source data for this work were existing data from ResearchGate, a socio-academic networking website that provides researchers the platform to collaborate, ask questions, and offer answers to questions. The performance for answer extraction for 2786 questions shows that when 80% of patterns and keywords were considered, QBAE and PBAE extracted 2765 and 2766 correct answers, respectively, while the QBAE + PBAE method extracted 2782 correct answers. Also, when 90% of patterns and keywords were utilized, QBAE and PBAE extracted 2782 and 2784 correct answers, whereas the QBAE + PBAE method extracted 2786 correct answers. Our method was able to identify 229 questions without answers. Finally, the evaluation of our model reveals high-performance accuracy and precision. [ABSTRACT FROM AUTHOR]

This paper represents a summary of studies done by the Russian Artificial Intelligence Research Institute of the Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, in the area of pattern recognition for various applications. They are based on the general recognition problem statement by Yu.I. Zhuravlev combined with various models, metrics, and solution methods. Invariant moments, stable cognitive images, image spectra, and other features have been used as informative parameters. Multimodal problem solving that relies on simultaneous use of text query and image analysis is considered. A model is proposed, which combines the semiotic approach represented by a sign-based world model with vector symbolic architectures. Specific examples are used to demonstrate the effectiveness of the proposed approaches and recognition methods [ABSTRACT FROM AUTHOR]

Surface roughness is a major factor controlling the shear strength of discontinuities. Various methods have so far been presented for determining surface roughness based on 2D profiles. These methods use statistical, fractal, geostatistical, directional, and spectral features of the surface profiles most of which were established through the relationship between the features and Barton standard profiles. To overcome some of the shortcomings of the standard JRC profiles, this paper presents a new method based on the investigation of 2D profiles within “pattern recognition” framework for rock surface recognition. In this way, more than 9000 profiles were gathered from 84 natural rock samples for the calculation of the features, and the joint roughness coefficient was back-calculated from test results. A representative feature vector and profile of a surface were defined, and two methods of principal component analysis (PCA) and liner discriminant analysis (LDA) were applied in order to prepare inputs for the rock surface classification. Then, minimum mean distance (MMD) and K-nearest neighbors (KNN) were used for the classification. The results show that the latter provides lower classification error (44.4% when a level difference between the predicted and back-calculated classes is accepted) due to reducing the effect of dissimilar samples. Comparison of the results with those of single-variable equations that are based on statistical (δ and Z 2 ) and directional parameters θ max ∗ 1 + C with 53.57%, 52.38%, and 48.8% errors, respectively, proves that the proposed procedure is an efficient tool for the estimation of JRC. Furthermore, based on the results obtained from the pattern recognition analysis, 10 natural and artificial reference profiles attained based on a fully quantified approach were proposed as a replacement for the current JRC profiles. [ABSTRACT FROM AUTHOR]

Color images usually have large storage sizes as they are composed of three planes in the raw image format represented with the red, green, and blue components. Traditional color image compression schemes can be used to save the storage size of the color images. Unfortunately, most of these schemes are lossy in nature, which affects the details of color images. This paper presents a different treatment to the color image storage problem depending on the original color image formation process. In the color image formation process, not all the red, green, and blue components of the color images are acquired, simultaneously. Only, one component at each pixel position is acquired and Color Filter Array (CFA) interpolation is used to estimate the other two components using interpolation algorithms like Minimized-Laplacian Residual Interpolation (MLRI) and Linear Interpolation with Laplacian Second Order Correction (LILSOC). We adopt a similar strategy in this paper for reducing the storage sizes of color images by 66.67% of their original sizes. The sensitivity of the pattern recognition process to the proposed color image storage and interpolation strategy is studied in this paper. A cepstral feature extraction algorithm is adopted in this paper for extracting features from the interpolated patterns for further classification. Moreover, two types of classifiers are considered and compared in this paper for the pattern recognition: Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs). Simulation results reveal the success of the proposed strategy for color image storage and interpolation in obtaining high-quality color images in addition to the high Recognition Rates (RR) of color patterns after interpolation. This success encourages the use of the proposed color image storage strategy in storing large volumes of color databases. [ABSTRACT FROM AUTHOR]

Methods for face verification works reasonably well on face images with standardized (frontal) face positions and good spatial resolution. However such methods have significant challenges on poor resolution images, poor lighting conditions and not standard (frontal) face positions. In this paper, we survey the capability of existing face restoration and verification methods, with the aim of understanding how useful face restoration methods are for face verification. We propose a qualitative and quantitative comparison benchmark, and apply it on eight methods for face restoration and six methods for face verification, on several real-world low-quality images from a surveillance context, and outline observed advantages and limitations. Experiments shows that each restoration method can affect each face verification method differently, with fewer than the half of face restoration methods helping face verification. Interestingly, some face restoration methods with less good qualitative evaluation helped face verification the most. Experiments also show that face verification works less good if the resolution decreases. [ABSTRACT FROM AUTHOR]

The implementation of Deep Learning (DL) Prediction techniques for Human Age Prediction (HAP) has been widely researched and studied to prevent, treat, and extend life expectancy. While most algorithms rely on facial images, MRI scans, and DNA methylation for training and testing, they are seldom implemented due to a lack of significant validation and evaluation in real-world scenarios, low performance, and technical challenges. To address these issues, this paper proposes the Data, Classification Technique, Prediction, and View (DCPV) taxonomy, which outlines the primary components required to implement and validate a deep learning model for predicting human age. By providing a common baseline for end-users and researchers, this taxonomy offers a clearer view of the constituents of deep learning prediction approaches, enabling the development of similar systems in the health domain. In contrast to existing machine learning methods, the proposed taxonomy emphasizes the value of deep learning practices based on performance, accuracy, and efficiency in predicting human age. To validate the DCPV taxonomy, the study examines 31 state-of-the-art research journal articles within the HAP system domain, assessing the taxonomy's performance, accuracy, robustness, and model comparisons. [ABSTRACT FROM AUTHOR]

In this paper, two new distance measures are introduced, which are used in various application problems in decision-making, pattern recognition, and clustering. Intuitionistic fuzzy sets are sources of information that contain both the membership and non-membership degrees of the elements in the set. As such, distance measures based on geometric concepts are sometimes misleading. Hence, six parameters are identified to construct the distance measures. These parameters are membership information dissimilarity, non-membership information dissimilarity, hesitancy information dissimilarity, product cross-information dissimilarity, maximum cross-information dissimilarity, and minimum cross-information dissimilarity. Among all the parameters, the product cross-information dissimilarity is newly introduced in this work. Compensations for the proposed distance measures are established by various counter-intuitive problems in decision-making and pattern recognition. Finally, validation of the proposed distance measures is established by diverse problems of applications in decision-making, pattern recognition, and clustering problems. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) involves the recognition of human activities using sensor data. Most of the techniques for HAR involve hand-crafted features and hence demand a good amount of human intervention. Moreover, the activity data obtained from sensors are highly imbalanced and hence demand a robust classifier design. In this paper, a novel classifier "ICGNet" is proposed for HAR, which is a hybrid of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU). The CNN block used in the proposed network derives its inspiration from the famous Inception module. It uses multiple-sized convolutional filters simultaneously over the input and thus can capture the information in the data at multiple scales. These multi-sized filters introduced at the same level in the convolution network helps to compute more abstract features for local patches of data. It also makes use of 1 × 1 convolution to pool the input across channel dimension, and the intuition behind it is that it helps the model extract the valuable information hidden across the channels. The proposed ICGNet leverages the strengths of CNN and GRU and hence can capture local features and long-term dependencies in the multivariate time series data. It is an end-to-end model for HAR that can process raw data captured from wearable sensors without using any manual feature engineering. Integrating the adaptive user interfaces, the proposed HAR system can be applied to Human-Computer Interaction (HCI) fields such as interactive games, robot learning, health monitoring, and pattern-based surveillance. The overall accuracies achieved on two benchmark datasets viz. MHEALTH and PAMAP2 are 99.25% and 97.64%, respectively. The results indicate that the proposed network outperformed the similar architectures proposed for HAR in the literature. [ABSTRACT FROM AUTHOR]

Acquisition, classification, and analysis of seismic data are crucial tasks in volcano monitoring. The large number of seismic signals that are continuously acquired during the first monitoring stage poses a huge challenge for the human experts that must classify and analyze them. Several automatic classification systems have been proposed in the literature to alleviate such an overwhelming workload, each one characterized by different levels of accuracy, computational complexity, and interpretability. Considering this last perspective, which represents one of the recent key issues in geoscience, it is possible to find many accurate methods (in terms of classification accuracy) which however represent black boxes, not permitting a clear interpretation. On the other hand, there are other approaches, such as those based on support vector machines (SVM), random forests (RF), and K-nearest neighbor (KNN), which permit the interpretation of results, rules, and models at different levels. Among these last techniques, KNN approaches for volcanic signal classification typically do not achieve the satisfactory classification results obtained with RF and SVM. One possible reason is that in this context, the KNN rule has usually been applied in its basic version, not exploiting the different advanced KNN variants that have been introduced in recent years. This paper takes one step along this direction, investigating the suitability of a number of advanced versions of the KNN rule for the problem of classifying seismic-volcanic signals. The usefulness of these rules, in comparison with the original KNN rule as well as other interpretable classifiers, is evaluated within a real-world scenario involving a five-class dataset of seismic signals acquired at the Nevado del Ruiz volcano, Colombia. The results show that the classification accuracy of basic KNN is largely improved by these advanced variants, even surpassing that obtained with other classifiers like RF and SVM. [ABSTRACT FROM AUTHOR]

Tool wear is inevitable in cutting process. If tool wear failure is not detected in time, it will lead to abnormal cutting process and affect the machining efficiency and quality seriously. The intelligent monitoring of tool wear can make the machining system perceive the real-time status of tools in advance and make early warning and decision-making, which is an effective way to ensure the efficient operation of machining and manufacturing system. By reviewing the research status of intelligent monitoring of tool wear, the key technical principles and methods of multisource-correlation signal selection, feature extraction and pattern recognition are classified. On the basis, the current application status of tool wear monitoring is discussed. In view of its shortcomings, this paper puts forward the prospect of the future, in order to provide a theoretical basis and reference for the development of tool wear intelligent monitoring technology and intelligent manufacturing industry. [ABSTRACT FROM AUTHOR]

Systematic pattern recognition as well as the corresponding description of determined patterns entail numerous challenges in the application context of high-dimensional communication data. These can cause increased effort, especially with regard to machine-based processing concerning the determination of regularities in underlying datasets. Due to the increased expansion of dimensions in multidimensional data spaces, determined patterns are no longer interpretable by humans. Taking these challenges into account, this paper investigates to what extent pre-defined communication patterns can be interpreted for the application area of high-dimensional business communication data. An analytical perspective is considered by taking into account a holistic research approach and by subsequently applying selected Machine Learning methods from Association Rule Discovery, Topic Modelling and Decision Trees with regard to the overall goal of semi-automated pattern labelling. The results show that meaningful descriptions can be derived for the interpretation of pre-defined patterns. [ABSTRACT FROM AUTHOR]

Electrocardiogram is a slow signal to acquire, and it is prone to noise. It can be inconvenient to collect large number of ECG heartbeats in order to train a reliable biometric system; hence, this issue might result in a small sample size phenomenon which occurs when the number of samples is much smaller than the number of observations to model. In this paper, we study ECG heartbeat Gaussianity and we generate synthesized data to increase the number of observations. Data synthesis, in this paper, is based on our hypothesis, which we support, that ECG heartbeats exhibit a multivariate normal distribution; therefore, one can generate ECG heartbeats from such distribution. This distribution is deviated from Gaussianity due to internal and external factors that change ECG morphology such as noise, diet, physical and psychological changes, and other factors, but we attempt to capture the underlying Gaussianity of the heartbeats. When this method was implemented for a biometric system and was examined on the University of Toronto database of 1012 subjects, an equal error rate (EER) of 6.71% was achieved in comparison to 9.35% to the same system but without data synthesis. Dimensionality reduction is widely examined in the problem of small sample size; however, our results suggest that using the proposed data synthesis outperformed several dimensionality reduction techniques by at least 3.21% in EER. With small sample size, classifier instability becomes a bigger issue and we used a parallel classifier scheme to reduce it. Each classifier in the parallel classifier is trained with the same genuine dataset but different imposter datasets. The parallel classifier has reduced predictors' true acceptance rate instability from 6.52% standard deviation to 1.94% standard deviation. [ABSTRACT FROM AUTHOR]