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Patterns are crucial for efficiently scheduling microservice workflow applications to containers in cloud computing scenarios. However, it is challenging to learn patterns of microservice workflows because of their complex precedence constrained structures provided by users with more lightweighted, diversified, and personalized services. In this paper, we propose a graph neural network is designed to identify patterns within a set of microservice workflows by mining the common substructures of workflows. Based on the learned patterns, a pattern-based scheduling algorithm framework is developed for microservice workflows with soft deadline constraints to minimize the average tardiness. A sorting strategy is introduced based on urgency and pattern coverage rate. For simplification of the task sorting process, the pattern-based task sorting algorithm (PB-TS) is devised. Furthermore, a resource selection phase is incorporated to the pattern-based resource selection algorithm (PB-RS) to minimize the candidate resource space. Experimental results demonstrate the proposed method is much efficient as compared to three classical algorithms. [ABSTRACT FROM AUTHOR]

Belief and plausibility functions based on evidence theory (ET) have been widely used in managing uncertainty. Various generalizations of ET to fuzzy sets (FSs) have been reported in the literature, but no generalization of ET to q-rung orthopair fuzzy sets (q-ROFSs) has been made yet. Therefore, this paper proposes a novel, simple, and intuitive approach to distance and similarity measures for q-ROFSs based on belief and plausibility functions within the framework of ET. This research addresses a significant research gap by introducing a comprehensive framework for handling uncertainty in q-ROFSs using ET. Furthermore, it acknowledges the limitations inherent in the current state of research, notably the absence of generalizations of ET to q-ROFSs and the challenges in extending belief and plausibility measures to certain aggregation operators and other generalizations including Hesitant fuzzy sets, Bipolar fuzzy sets, Fuzzy soft sets etc. Our contribution lies in the proposal of a novel approach to distance and similarity measures for q-ROFSs under ET, utilizing Orthopairian belief and plausibility intervals (OBPIs). We establish new similarity measures within the generalized ET framework and demonstrate the reasonability of our method through useful numerical examples. Additionally, we construct Orthopairian belief and plausibility GRA (OBP-GRA) for managing daily life complex issues, particularly in multicriteria decision-making scenarios. Numerical simulations and results confirm the usability and practical applicability of our proposed method in the framework of ET. [ABSTRACT FROM AUTHOR]

This paper considers the use of machine learning for diagnosis of diseases that is based on the analysis of a complete gene expression profile. This distinguishes our study from other approaches that require a preliminary step of finding a limited number of relevant genes (tens or hundreds of genes). We conducted experiments with complete genetic expression profiles (20 531 genes) that we obtained after processing transcriptomes of 801 patients with known oncologic diagnoses (oncology of the lung, kidneys, breast, prostate, and colon). Using the indextron (instant learning index system) for a new purpose, i.e., for complete expression profile processing, provided diagnostic accuracy that is 99.75% in agreement with the results of histological verification. [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]

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]

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]

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]

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]

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]

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]

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]

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]

Raman spectroscopy can characterize the rich molecular vibration information of soybean milk powder samples, but difficulties arise in its direct use for sample classification and identification. Therefore, it is urgent to develop an intelligent identification technology based on Raman spectroscopy. For brand identification of soybean milk powder, this work investigates and discusses a variety of spectral processing technologies including wavelet denoising, normalization, principal component analysis, and the results show that appropriate spectral processing can improve the recognition accuracy of the random forest algorithm. Under the optimal conditions (db2 wavelet, normalization, principal component analysis, 30 decision trees), the best recognition effect of soybean milk brand identification can be achieved. The Raman spectral signal acquisition time of each sample is 40 s, and the spectra pretreatment and identification operation time only takes a few minutes. The analytical approach established in this paper has the advantages of convenient and fast Raman spectra acquisition, fast and accurate model identification. [ABSTRACT FROM AUTHOR]

The orthogonal moments have recently achieved outstanding predictive performance and become an indispensable tool in a wide range of imaging and pattern recognition applications, including image reconstruction, image classification and object detection. We present in this paper, a new set of orthogonal functions, called "Orthogonal helmet functions." Using these functions we introduce three new sets of orthogonal moments and their invariants to scaling, rotation and translation for image representation and recognition, named, respectively, "the orthogonal helmet-Fourier moments" for the gray-level images, the multi-channel orthogonal helmet-Fourier moments and the quaternion orthogonal helmet-Fourier moments (QHFMs) for the color images. We introduce a series of experimental tests in image analysis and pattern recognition to validate the theoretical framework of our approach. The performance of these feature vectors is compared with the existing orthogonal invariant moments. The results of the comparative study show the efficiency and the superiority of our three orthogonal invariant moments. Thanks to our orthogonal moments QHFMs, we were able to lift the image recognition quality with rate can reach 2.06 %. [ABSTRACT FROM AUTHOR]

In general, medical pattern is a collection of characters formed using the characters such as 'a', 'c', 'g' and 't'. The length of the pattern varies from one disease to another disease, and the pattern also seems to be different for different patients. Identifying an unusual pattern from the information design is troublesome a decade ago. In this paper, with a specific end goal to enhance the precision of the anomalous distinguishing pattern, an Artificial Immune System (AIS) is framed. In the present study, AIS is used to obtain the abnormal pattern by learning the characteristics of the entire data set. Due to the powerful and adaptive nature of AIS, detecting and identifying abnormal pattern is more accurate. This proposed idea is implemented in MATLAB software and experimented on DNA/RNA dataset and the performance is verified. [ABSTRACT FROM AUTHOR]

Due to its capability of machining hard-to-cut materials as well as its high machining efficiency as compared with conventional electrical discharge machining (EDM) processes, fast electrical discharge drilling (fast EDM drilling) is widely applied in industries such as mold and die as well as aerospace component manufacturing. The breakout detection is an essential technique for hole completion judgment and back-strike prevention. This paper presents a novel method, called classification of machining state graphs (CMSG), for online detection of breakout events. A machining state graph (MSG) is formed by the recent changing patterns of feature signals, which would change abruptly when breakout happens. Then, the detection problem is solved by classification of real-time MSGs. In this paper, the feature signals were selected to be normal discharge ratio, short circuit ratio, and servo feedrate of the tool electrode. The signals were preprocessed in order to improve the detection accuracy and reduce the decision lag. A classification model was built to classify MSGs. To simplify the modeling process and improve the generalization ability of the detector, a pattern recognition (PR) algorithm was adopted as the core algorithm for classification. The classification model of the detector was acquired through offline training and loaded on the start-up of the control system for online detection. Performance judgment criteria were proposed and experimental results proved the high performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper develops a prognostic technique called the logical analysis of survival curves (LASC). This technique is used to learn the degradation process of any physical asset, and consequently to predict its failure time (T). It combines the reliability information that is obtained from a classical Kaplan–Meier non-parametric curve to that obtained from online measurements of multiple sensed signals of degradation. An analysis of these signals by the machine learning technique, logical analysis of data (LAD), is performed to exploit the instantaneous knowledge about the state of degradation of the asset studied. The experimental results of the predictions of failure times for cutting tools are reported. The results show that LASC prognostic results are better than the results obtained by well-known machine learning techniques. Other advantages of the proposed techniques are also discussed. [ABSTRACT FROM AUTHOR]

Star identification is the foundation of star trackers, which are used to precisely determine the attitude of spacecraft. In this paper, we propose a novel star identification approach based on spectral graph matching. In the proposed approach, we construct a feature called the neighbor graph for each main star, transforming the star identification to the problem of finding the most similar neighbor graph. Then the rough search and graph matching are cooperated to form a dynamic search framework to solve the problem. In the rough search stage, the total edge weight in the minimum spanning tree of the neighbor graph is selected as an indicator, then the k-vector range search is applied for reducing the search scale. Spectral graph matching is utilized to achieve global matching, identifying all stars in the neighbor circle with good noise-tolerance ability. Extensive simulation experiments under the position noise, lost-star noise, and fake-star noise show that our approach achieves higher accuracy (mostly over 99%) and better robustness results compared with other baseline algorithms in most cases. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new time-shared twin memristor crossbar for pattern-recognition applications. By sharing two memristor arrays at different time, the number of memristor arrays can be reduced by half, saving the crossbar area by half, too. To implement the time-shared twin memristor crossbar, we also propose CMOS time-shared subtractor circuit, in this paper. The operation of the time-shared twin memristor crossbar is verified using 3 × 3 memristor array which is made of aluminum film and carbon fiber. Here, the crossbar array is programmed to store three different patterns. When we apply three different input vectors to the array, we can verify that the input vectors are well recognized by the proposed crossbar. Moreover, the proposed crossbar is tested for the recognition of complicated gray-scale images. Here, 10 images with 32 × 32 pixels are applied to the proposed crossbar. The simulation result verifies that the input images are recognized well by the proposed crossbar, even though the noise level of each image is varied from −10 to +10 dB. [ABSTRACT FROM AUTHOR]

Performance optimization in speaker recognition is a challenging task in the field of vocal based human-computer interaction. Many researches have shown that deep learning Neural Network methods have the best performance in comparison with other classifiers. However, those methods with many parameters require a lot of tunings in order to optimize the performance in different supervised learning tasks. In this paper, we show that picking a good combination of parameters can significantly improve the performance of Stochastic Gradient Descent deep learning Neural Network method in automatic speaker recognition even in a noisy environment. Parameters that are analyzed are learning rate, hidden and input layer dropout rate. [ABSTRACT FROM AUTHOR]

Traditional biometric systems are subjected to several attacks. If the original database of human biometrics is compromised, the biometric data is lost forever. This paper presents a sophisticated implementation of one of the watermarking algorithms in the field of cancelable speaker identification. A watermark strength factor is used to control the level of intended distortion created in the speech signal prior to the identification process. The practical simulation of the proposed system proves that it is possible through a watermarking algorithm to distort speech signals intentionally to generate cancelable speech templates. [ABSTRACT FROM AUTHOR]

A key issue in scientific visualization is the transfer function (TF) for direct volume rendering (DVR). The TF serves as a tool for translating data values into color and opacity, to visualize the relevant structures present in the volumetric data studied. An adequate transfer function should have a non-complicated interactive strategy for new users or even experts. Furthermore, it has to achieve high-quality and not time-consuming visualization. In this paper, we propose a novel image-centric method for the real-time generation of transfer functions. The method is based on incremental classification. This incremental classification-based approach is theoretically faster than that using batch classification. The method does not require users to manipulate complex widgets. We present a simple user interface adapted to the incremental learning process. Thus, this interface made it possible for the user to interact with a series of 2D images, precise the cluster, and identify some voxels. The whole volume is incrementally classified and the rendering result is shown to the user as selected voxels are added. The TF is generated by assigning the optical properties to clusters using harmonic colors. We further introduce a novel incremental classifier, namely incremental discriminant-based support vector machine(IDSVM), that can learn through time. The IDSVM was used in the classification stage of the proposed image-centric method. To evaluate the IDSVM, an extensive comparison of the model with other state-of-the-art incremental and batch classifiers on 12 real-world datasets and four other famous large datasets, namely MNIST-full, MNIST-test, USPS, and Fashion-MNIST, has been carried out. Using the area under curve, it has been found that the IDSVM outperforms the other classifiers. Furthermore, to evaluate the proposed image-centric method, we made use of several benchmark datasets. Qualitative results and a detailed user survey demonstrate the effectiveness of the proposed method and the positive effect of the incrementality in visual and interaction time performance. [ABSTRACT FROM AUTHOR]

Machine Learning as network attack detection is one of the popular methods researched. Signature based network attack detection is no longer convinced the efficiency in the diversified intrusions (Limmer and Dressler in 17th ACM Conference on Computer and Communication Security, ). Moreover, as the various Zero-day attacks, non notified attacks cannot be detected (Wu and Banzhaf in Appl Soft Comput 10(1):1-35, ). This paper suggests an effective update method of data set on Machine Learning to detect non notified attacks. In addition, this paper compares and verifies the effects of Machine Learning Detection with updated data set to the former methods. [ABSTRACT FROM AUTHOR]

Data representation plays a crucial role in semi-supervised learning. This paper proposes a framework for semi-supervised data representation. It introduces a flexible nonlinear embedding model that integrates graph-based data convolutions. The proposed approach exploits structured data in order to estimate a nonlinear data representation as well as a linear transformation, enabling an inductive semi-supervised model. The introduced approach exploits data graphs at two different levels. First, it integrates manifold regularization that is encoded by the graph itself. Second, it optimizes a flexible linear transformation that maps the convolved data samples to their nonlinear representations. These convolved data are generated by the joint use of the graph and data. The proposed semi-supervised model overcomes some challenges related to some samples distributions in the original spaces. The proposed Graph Convolution based Semi-supervised Embedding (GCSE) provides flexible models which can improve both the data representation and the final performance of the learning model. Experiments are run on six image datasets for comparing the proposed approach with several state-of-art semi-supervised methods. These results show the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) is a kind of valuable bioelectric signal and very potential in the field of human–machine interaction. Ideal interactions require sEMG based patterns recognition not only with high accuracy but also with good rapidity. However, too much real-time feature-related computation will greatly slow down the interaction, especially for multichannel sEMG. To decrease the feature-related time consumption, the paper formulates the feature reduction as an optimization problem, and develops a double-phases particle swarm optimization (PSO) with hybrid coding to solve the problem. In the research, the initial feature data set with 31 kinds of feature is built firstly based on eight subjects' 16 channels forearm sEMG signals, then PSO is introduced to conduct the feature reduction of 31 × 16 dimensions through the feature and channel optimization in double phases. During the optimization, two improved k-nearest neighbor (KNN) methods such as weighted representation based KNN (WRKNN) and weighted local mean representation based KNN (WLMRKNN) are introduced to classify the gestures, and the classification accuracy is used to evaluate the particles of PSO. Experimental results and comparison analysis show that PSO based feature reduction methods outperform genetic algorithm (GA), ant colony optimization (ACO) and principal component analysis (PCA) based feature reduction methods. With the optimized feature data subset by PSO, WRKNN and WLMRKNN are superior to KNN, quadratic discriminant analysis (QDA), and naive bayes (NB) greatly. The proposed method can be applied in the pattern recognition of high dimensional sEMG with multichannel or high-density channels for the purpose of rapidity and without a decline of accuracy in real-time control. Further, it can be used to reduce the economic cost of the personalized customization equipment through the optimal channels for any subjects in the future. [ABSTRACT FROM AUTHOR]

Structural information is very important for improving the classification performance of classifiers. In order to increase the generalization performance of support vector machine (SVM) directly, several kinds of structured SVMs have been proposed. These algorithms with structural information only simply embed the global structural information or the local within-class information into SVM model. Thus, they sometimes are not suitable for real-world problems. To overcome the drawbacks, we firstly propose a novel SVM with local structural information (LSI-SVM) in this paper. In the LSI-SVM, the K-nearest neighbor (KNN) method is adopted. Applying the KNN method, the farthest neighbors set intra-class and the nearest neighbors set inter-class of the overall samples are obtained. It is more reasonable to maximize the margin between the nearest neighbors and the farthest neighbors. Both the global and local data structures are added into the optimization problem, making the LSI-SVM can fully utilize the underlying structural information and yield better performance. Furthermore, for nonlinear classification, the reproducing kernel Hilbert space theory is introduced and the kernel-based LSI-SVM is generated. What's more, in order to accelerate the training speed of LSI-SVM, a stochastic gradient LSI-SVM (LSI-SVM +) is constructed using the stochastic gradient descent (SGD) solver. Lastly, experimental results on regular-scale datasets, steel surface defects datasets, ORL face dataset and large-scale datasets demonstrate that both LSI-SVM and LSI-SVM + outperform other state-of-the-art algorithms on accuracy. In the meanwhile, our LSI-SVM + has high efficiency. [ABSTRACT FROM AUTHOR]

Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3) this paper adopts the Gauss Elimination, one of the on-the-shelf techniques, to generate a basis of the original feature space, which is stable and efficient. [ABSTRACT FROM AUTHOR]

Images recognition and classification require an extraction technique of feature vectors of these images. These vectors must be invariant to the three geometric transformations: rotation, translation and scaling. Several authors used the theory of orthogonal moments to extract the feature vectors of images. Jacobi moments are orthogonal moments, which have been widely applied in imaging and pattern recognition. However, the invariance to rotation of Cartesian Jacobi moments is very difficult to obtain. In this paper, we obtain at first a set of transformed orthogonal Jacobi polynomials, called "Adapted Jacobi polynomials". Based on these polynomials, a set of orthogonal moments is presented, named adapted Jacobi moments (AJMs). These moments are orthogonal on the rectangle 0 , N × 0 , M , where N × M is the size of the described image. We also provide a new series of feature vectors of images based on adapted Jacobi orthogonal invariants moments, which are a linear combination of geometric moment invariants, where the latest ones are invariant under rotation, translation and scaling of the described image. Based on k-NN algorithm, we apply a new 2D image classification system. We introduce a set of experimental tests in pattern recognition. The obtained results express the efficiency of our method. The performance of these feature vectors is compared with someones extracted from Hu, Legendre and Tchebichef invariant moments using three different 2D image databases: MPEG7-CE shape database, Columbia Object Image Library (COIL-20) database and ORL database. The results of the comparative study show the performance and superiority of our orthogonal invariant moments. [ABSTRACT FROM AUTHOR]

Orthogonal moments and their invariants to geometric transformations for gray-scale images are widely used in many pattern recognition and image processing applications. In this paper, we propose a new set of orthogonal polynomials called adapted Gegenbauer–Chebyshev polynomials (AGC). This new set is used as a basic function to define the orthogonal adapted Gegenbauer–Chebyshev moments (AGCMs). The rotation, scaling, and translation invariant property of (AGCMs) is derived and analyzed. We provide a novel series of feature vectors of images based on the adapted Gegenbauer–Chebyshev orthogonal moments invariants (AGCMIs). We practice a novel image classification system using the proposed feature vectors and the fuzzy k-means classifier. A series of experiments is performed to validate this new set of orthogonal moments and compare its performance with the existing orthogonal moments as Legendre invariants moments, the Gegenbauer and Tchebichef invariant moments using three different image databases: the MPEG7-CE Shape database, the Columbia Object Image Library (COIL-20) database and the ORL-faces database. The obtained results ensure the superiority of the proposed AGCMs over all existing moments in representation and recognition of the images. [ABSTRACT FROM AUTHOR]

Inspired by quaternion algebra and the idea of fractional-order transformation, we propose a new set of quaternion fractional-order generalized Laguerre orthogonal moments (QFr-GLMs) based on fractional-order generalized Laguerre polynomials. Firstly, the proposed QFr-GLMs are directly constructed in Cartesian coordinate space, avoiding the need for conversion between Cartesian and polar coordinates; therefore, they are better image descriptors than circularly orthogonal moments constructed in polar coordinates. Moreover, unlike the latest Zernike moments based on quaternion and fractional-order transformations, which extract only the global features from color images, our proposed QFr-GLMs can extract both the global and local color features. This paper also derives a new set of invariant color-image descriptors by QFr-GLMs, enabling geometric-invariant pattern recognition in color images. Finally, the performances of our proposed QFr-GLMs and moment invariants were evaluated in simulation experiments of correlated color images. Both theoretical analysis and experimental results demonstrate the value of the proposed QFr-GLMs and their geometric invariants in the representation and recognition of color images. [ABSTRACT FROM AUTHOR]

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The automatic creation of a repository of the building's floor plan helps a lot to the architects to reuse them. The basic approach is to extract and recognize texts, symbols or graphics to retrieve the information of the floor plan from the images. This paper proposes a floor plan information retrieval algorithm. The proposed algorithm is based on shape extraction and room identification. α -shape is used for finding an accurate shape. From the detected shapes, actual areas of rooms are calculated. Later, a regression model-based binary room classification model is proposed to classify them into room-type, i.e., bedroom, drawing room, kitchen, and non-room-type, i.e., parking porch, bathroom, study room and prayer room. The proposed model is tested on the CVC-FP dataset with an average room detection accuracy of 85.71% and room recognition accuracy of 88%. [ABSTRACT FROM AUTHOR]

In statistical process monitoring, data mining algorithms are applied for control chart pattern recognition (CCPR) not only to detect but also to identify abnormal patterns associated with assignable causes. Recently, the use of support vector machines (SVMs) has achieved remarkable results in statistical process control applications due to its excellent generalization performance. Although there is a lot of research that highlights the superiority of support vector machine over other algorithms used in systems based on data mining control, there are no studies that analyze the design elements of a SVM classifier. Consequently, a comprehensive review, classification and an analysis of information found in the literature are presented in this paper regarding to the SVM classifier. The aim of this research is to provide researchers a starting point to potentiate the performance of the SVM classifier for assuring the best possible classification and improving the detection efficiency. Sixty-one research articles from 2001 to 2015 are critically analyzed based on their methodology following a classification scheme derived from the support vector classification framework. The analysis showed that the feature extraction and selection play a crucial role on the performance of classifiers. Studies revealed that using extracted features or a combination of raw data and feature extraction achieves better classification performances than using only raw data as an input. On the other hand, there is an ample gap to extend the research on internal structure configurations under various types of kernels, multi-classification, and ensemble approaches. Finally, this paper evidences that the application of nature inspired algorithms for kernel parameter selection in auto-correlated SVM-based process monitoring systems remains unexplored. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced polymer (CFRP) is an important composite material. It has many applications in aerospace and automotive fields. The little information available about the machining process of this material, specifically when routing process is considered, makes the process control quite difficult. In this paper, we propose a new process control technique and we apply it to the routing process for that important material. The measured machining conditions are used to evaluate the quality and the geometric profile of the machined part. The machining conditions, whether controllable or uncontrollable are used to control part accuracy and its quality. We present a pattern-based machine learning approach in order to detect the characteristic patterns, and use them to control the quality of a machined part at specific range. The approach is called logical analysis of data (LAD). LAD finds the characteristic patterns which lead to conforming products and those that lead to nonconforming products. As an example, LAD is used for online control of a simulated routing process of CFRP. We introduce the LAD technique, we apply it to the high speed routing of woven carbon fiber reinforced epoxy, and we compare the accuracy of LAD to that of an artificial neural network, since the latter is the most known machine learning technique. By using experimental results, we show how LAD is used to control the routing process by tuning autonomously the routing conditions. We conclude with a discussion of the potential use of LAD in manufacturing. [ABSTRACT FROM AUTHOR]

The systems based on image processing have numerous applications in the domain of motion control of robots and autonomous vehicles. The current paper is oriented to the solution of the problem that precedes the implementation of automatic avoidance of the on-road obstacles-how to detect them, to track in the sequence of images, and to recognize which of them are stationary, incoming, or outgoing from the camera. The overall algorithm of obstacle classification presented in this paper consists of three basic phases: (1) image segmentation in order to extract the pixels belonging to the image of a road and the objects over it; (2) extraction of characteristic points inside the area of the obstacle, their description and tracking in following frames; and (3) estimation of distances between the camera, the obstacles and their rates of change (relative velocities). The verifications of particular steps of the proposed algorithm are illustrated using real road-traffic images, while the overall algorithm is tested using both synthesized sequences of images and the ones acquired in real driving. [ABSTRACT FROM AUTHOR]

The detection of multi-oriented objects is a difficult pattern recognition problem. In this paper, we propose to evaluate the performance of different families of descriptors for the classification of galaxy morphologies. We investigate the performance of the Hu moments, Flusser moments, Zernike moments, Fourier–Mellin moments, and ring projection techniques based on 1D moment and the Fourier transform. We consider two main datasets for the performance evaluation. The first dataset is an artificial dataset based on representative templates from 11 types of galaxies, which are evaluated with different transformations (noise, smoothing), alone or combined. The evaluation is based on image retrieval performance to estimate the robustness of the rotation invariant descriptors with this type of images. The second dataset is composed of real images extracted from the Galaxy Zoo 2 project. The binary classification of elliptical and spiral galaxies is achieved with pre-processing steps including morphological filtering and a Laplacian pyramid. For the binary classification, we compare the different set of features with Support Vector Machines, Extreme Learning Machine, and different types of linear discriminant analysis techniques. The results support the conclusion that the proposed framework for the binary classification of elliptical and spiral galaxies provides an area under the receiver operating characteristic curve reaching 99.54%, proving the robustness of the approach for helping astronomers to study galaxies. [ABSTRACT FROM AUTHOR]

This paper is focused on the real-time characterization of weld in high speed high-end drawn metallic wires using encircling coil eddy current sensor. Emphasis was laid on the evaluation of weld-length and its span of stability with a special reference to signal pattern recognition through the improvised non-linear regression tool. A 3-D CIVA simulation electromagnetic testing module was implemented to evaluate the theoretical expected response of the system. The qualitative and quantitative analysis of welds was studied using signal processing of EC data. The percentage deviations in weld-span between simulated and experimental results were found to be around 1.6% and 12.5% at welding location of 1st and 8th pass respectively. This demonstrates the non- uniformity of the weld on relatively smaller diameter wire (8th pass) as compared to the larger diameter wire (1st pass). A good agreement was observed between the theoretical and experimental results. From the applications perspective, these findings indicate the importance on achieving higher efficiency combined with reliable quality control. On-line implementation of the proposed method satisfies both the requirements; detection accuracy and detection speed. [ABSTRACT FROM AUTHOR]

It is of great economic benefit to realize the automatic welding of 90° welds, and difficulty of automatic welding of 90° welds is the machine identification of the corner points of 90° welds. The math model of welding-torch pose was built, and the identification algorithms of wire extension, welding deviations, and tilt angles of welding torch have been studied respectively. In this paper, three pattern recognition algorithms are studied, and they are used to identify the corner points of 90° welds based on variation characteristics of wire extension, welding deviations, and tilt angles of welding torch, respectively. At the same time, accuracy values of three recognition algorithms have been researched respectively. On this basis, the recognition method of the corner points of 90° welds based on multi-sensor signal fusion technology has been studied in three-dimensional directions, and the calculation method for the total accuracy of corner point identification has been analyzed. At last, the corner point recognition experiments of 90° welding seam were carried out, and the change curves of wire extension, welding deviations, and tilt angles have been analyzed respectively. The welding results indicated that the corner point of 90° welding seam could be identified stably, accurately, and quickly by using the methods in this article. [ABSTRACT FROM AUTHOR]

In this paper, an online measurement and error compensation system for curve grinding based on pattern recognition was presented and verified by experiments. The measurement system organization and its principle of operation were introduced in detail. The work piece and grinding wheel image were sampled at certain positions to avoid spark influence. In order to increase system resolution, images were sampled only at local areas of the work piece and grinding wheel. A discrimination technology based on a circular tolerance zone was proposed which can solve the problem of local image edge comparison. For image de-noising, a local threshold algorithm was applied to determine new wavelet coefficients. Furthermore, a two-step edge detection method was used to realize sub-pixel precision. Finally, a series of experiments were carried out to examine the detection precision of the image measurement system and its influencing factors. From experiments, it can be said that the proposed method in this paper is effective, and its detection precision is much better than traditional methods. [ABSTRACT FROM AUTHOR]

In recent past, there has been a steep increase in the use of online platforms for the search of desired products. Real estate industry is no exception and has started initiating rent/sale of houses through online platforms. In this paper, we propose a deep neural network framework to facilitate automatic search of homes based on their floor plans. The salient features of this framework are that the query can be either an image (existing floor plan) or a sketch through a sketch pad interface. Our proposed framework automatically determines the type of query (image or sketch) and retrieves similar floor plan images from the database. The critical contributions of our proposed approach are: (1) a novel unified floor plan retrieval framework using multimodal query, i.e., an intuitive and convenient sketch query mode as well as a query by example mode ; (2) a conjunction of autoencoder, Cyclic GAN and CNN for the task of domain mapping and floor plan image retrieval. We have reported results of extensive experimentation and comparison with baseline results to establish the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Pythagorean fuzzy sets (PFSs), characterized by membership degrees and non-membership degrees, are a more effective and flexible way than intuitionistic fuzzy sets (IFSs) to capture indeterminacy. In this paper, some new diverse types of similarity measures, overcoming the blemishes of the existing similarity measures, for PFSs with multiple parameters are studied, along with their detailed proofs. The various desirable properties among the developed similarity measures and distance measures have also been derived. A comparison between the proposed and the existing similarity measures has been performed in terms of the division by zero problem, unsatisfied similarity axiom conditions, and counter-intuitive cases for showing their effectiveness and feasibility. The initiated similarity measures have been illustrated with case studies of pattern recognition, along with the effect of the different parameters on the ordering and classification of the patterns. [ABSTRACT FROM AUTHOR]

Multivariate statistical process control (MSPC) can be applied for condition monitoring (CM) purposes. MSPC is implemented using a variety of techniques including neural networks (NNs). In situations when the number of process attributes is sufficiently large (e.g. 10 or more) concerns can arise with respect to training of NNs for pattern recognition. A classification method known as novelty detection (ND) can provide an effective alternative to conventional NN solutions that suffer from the above problem. Despite its great potential, ND is still unknown to the broad community of manufacturing engineers. This paper successfully demonstrates the ability of ND, using Gaussian mixture models, to notify operators of an end-milling process of the presence of faulty tool conditions. A significant achievement is that ND is used to identify abnormal time-series patterns as opposed to individual vectors of multiple simultaneous measurements related to abnormal conditions. Such patterns are found in windowed streams of signals related to 10 different process features (with an effective problem dimension of 140). The paper also investigates some of the issues related to implementation of ND for pattern recognition in condition monitoring . [ABSTRACT FROM AUTHOR]

Copyright of e & i Elektrotechnik und Informationstechnik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Online games with a variety of genres and game style have become popular. While the market for online games is growing, unauthorized program software is used and operated by some users. They damaged fair players and furthermore, bot program users have inflicted a huge loss to game service companies. Bot programs show various game play action styles and there are many users not only in Korea but also other countries, so it is not easy to trace. Also, as users of online game and smart device game which is focused recently are increasing, log data quantity are explosively increased and demands of analysis on not only bot information, but also statistical information which is necessary for game operation plan from log information. This paper examines bot program of online game, detection method and tendency of log data management technology for it. This paper introduces our method to detect game bots and manage game log data. [ABSTRACT FROM AUTHOR]

A new approach for face recognition, based on kernel principal component analysis (KPCA) and support vector machines (SVMs), is presented to improve the recognition performance of the method based on principal component analysis (PCA). This method can simultaneously be applied to solve both the over-fitting problem and the small sample problem. The KPCA method is performed on every facial image of the training set to get the core facial features of the training samples. To ensure that the loss of the image information will be as less as possible, the facial data of high-dimensional feature space is projected into low-dimensional space, and then the SVM face recognition model is established to identify the low-dimensional space facial data. Our experimental results demonstrate that the approach proposed in this paper is efficient, and the recognition accuracy of the proposed method reaches 95.4 %. [ABSTRACT FROM AUTHOR]

This paper performs a comparative study on the statistical-variation tolerance between two crossbar architectures which are the complementary and twin architectures. In this comparative study, 10 greyscale images and 26 black-and-white alphabet characters are tested using the circuit simulator to compare the recognition rate with varying statistical variation and correlation parameters. As with the simulation results of 10 greyscale image recognitions, the twin crossbar shows better recognition rate by 4 % on average than the complementary one, when the inter-array correlation = 1 and intra-array correlation = 0. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture can recognize better by 5.6 % on average than the complementary one. Similarly, when the inter-array correlation = 1 and intra-array correlation = 0, the twin architecture can recognize 26 alphabet characters better by 4.5 % on average than the complementary one. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture is better by 6 % on average than the complementary one. By summary, we can conclude that the twin crossbar is more robust than the complementary one under the same amounts of statistical variation and correlation. [ABSTRACT FROM AUTHOR]

Ensuring the durability of high-voltage (HV) cross-linked polyethylene (XLPE) cable insulation requires vigilant cleanliness maintenance during production to mitigate impurities, including oxidized parts and voids, which can compromise insulation integrity. Hence, this paper presents a MATLAB/Simulink partial discharge (PD) capacitive model of five well-known PD defects: crack, contamination, air void, microcrack, and composite, found in insulation materials HV XLPE insulation. Furthermore, this work proposes an extraordinary deep learning approach utilizing short-time Fourier transform (STFT) scalograms to represent PD signals in the time-frequency domain and train a convolutional neural network (CNN) to classify different PD defects. We focused on vital factors affecting STFT + CNN-aided pattern recognition accuracy, such as the number of network layers, convolutional kernel size, activation function, and pooling technique to optimize the network. Our study demonstrates that the proposed STFT + CNN approach outperforms traditional methods in recognizing PD patterns, especially for high signal similarity. Simulation results indicate that the STFT + CNN model achieves the highest classification accuracy of 0.9744 with minimal computation time (20 msec), making it suitable for real-time PD activity classification. [ABSTRACT FROM AUTHOR]