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Digital watermarking technology and watermark attack methods are mutually reinforcing and complementary. Currently, traditional watermark attack methods are relatively mature, but these traditional attack methods will inevitably damage the visual quality of original images (OIs). Therefore, this paper proposes a covert attack method called residual dense based imperceptible watermark attack network (RD-IWAN). First, this paper designs a watermark attack residual dense network (WARDN) based on the residual dense network (RDN), which can effectively remove the watermark information in the middle and low frequency features of the watermarked image (WMI). Second, to improve the attack ability of the network, this paper innovatively proposes a progressive preprocessing method based on the information enhancement preprocessing method. Concurrently, to ensure the imperceptibility of this watermark attack method, a comprehensive loss function that combines the perceptual loss and mean square error loss (MSE) of OI and attacked watermarked image (AWMI) is designed in this study. Finally, attack experiments are designed and performed on watermarks with different embedding strengths and sizes. Experimental results show that, compared to traditional attack methods, the watermark attack method proposed in this paper exhibits stronger attack ability and higher imperceptibility. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs), including convolutional neural networks (CNNs) and residual networks (ResNets) models, are able to learn abstract representations from the input data by considering a deep hierarchy of layers that perform advanced feature extraction. The combination of these models with visual attention techniques can assist with the identification of the most representative parts of the data from a visual standpoint, obtained through more detailed filtering of the features extracted by the operational layers of the network. This is of significant interest for analyzing remotely sensed hyperspectral images (HSIs), characterized by their very high spectral dimensionality. However, few efforts have been conducted in the literature in order to adapt visual attention methods to remotely sensed HSI data analysis. In this paper, we introduce a new visual attention-driven technique for the HSI classification. Specifically, we incorporate attention mechanisms to a ResNet in order to better characterize the spectral–spatial information contained in the data. Our newly proposed method calculates a mask that is applied to the features obtained by the network in order to identify the most desirable ones for classification purposes. Our experiments, conducted using four widely used HSI data sets, reveal that the proposed deep attention model provides competitive advantages in terms of classification accuracy when compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Copyright of Radio Electronics, Computer Science, Control is the property of Zaporizhzhia National Technical University 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.)

There has been a resurgence of research in the design of deep architecture models and learning algorithms, i.e., methods that rely on the extraction of a multilayer representation of the data. Often referred to as deep learning, this topic of research has been building on and contributing to many different research topics, such as neural networks, graphical models, feature learning, unsupervised learning, optimization, pattern recognition, and signal processing. Deep learning is also motivated and inspired by neuroscience and has had a tremendous impact on various applications such as computer vision, speech recognition, and natural language processing. The clearly multidisciplinary nature of deep learning led to a call for papers for a special issue dedicated to learning deep architectures. [ABSTRACT FROM PUBLISHER]

Classifying bank accounts by using transaction data is encouraging in cracking down on illegal financial activities. However, few research simultaneously use heterogenous features, which are embedded in the time series data. In this paper, a two route convolution neural network TRHD-CNN model, fed with two types of heterogeneous feature matrices, is proposed for classifying the bank accounts. TRHD-CNN adopts divide and conquer strategy to extract characteristics from two types of data source independently. The strategy is proved able in mining complementary classification characteristics. We firstly transfer the original log data into a directed and dynamic transaction network. On the basis of that, two feature generation methods are devised for extracting information from local topological structure and time series transaction respectively. A DirectedWalk method is developed in this paper to learning the network vector of vertices used for embedding the neighbor relationship of bank account. The extensive experimental results, conducted on a real bank transaction dataset that contains illegal pyramid selling accounts, show the significant advantage of TRHD-CNN over the existing methods. TRHD-CNN can provide recall scores up to 5.15% higher than competing methods. In addition, the two-route architecture of TRHD-CNN is easy to extend to multi-route scenarios and other fields. [ABSTRACT FROM AUTHOR]

Fault diagnosis plays an important role in modern industry. With the development of smart manufacturing, the data-driven fault diagnosis becomes hot. However, traditional methods have two shortcomings: 1) their performances depend on the good design of handcrafted features of data, but it is difficult to predesign these features and 2) they work well under a general assumption: the training data and testing data should be drawn from the same distribution, but this assumption fails in many engineering applications. Since deep learning (DL) can extract the hierarchical representation features of raw data, and transfer learning provides a good way to perform a learning task on the different but related distribution datasets, deep transfer learning (DTL) has been developed for fault diagnosis. In this paper, a new DTL method is proposed. It uses a three-layer sparse auto-encoder to extract the features of raw data, and applies the maximum mean discrepancy term to minimizing the discrepancy penalty between the features from training data and testing data. The proposed DTL is tested on the famous motor bearing dataset from the Case Western Reserve University. The results show a good improvement, and DTL achieves higher prediction accuracies on most experiments than DL. The prediction accuracy of DTL, which is as high as 99.82%, is better than the results of other algorithms, including deep belief network, sparse filter, artificial neural network, support vector machine and some other traditional methods. What is more, two additional analytical experiments are conducted. The results show that a good unlabeled third dataset may be helpful to DTL, and a good linear relationship between the final prediction accuracies and their standard deviations have been observed. [ABSTRACT FROM AUTHOR]

Compacted graphite iron (CGI), having a specific graphite form with a large matrix contact surface, is a unique casting material. This type of cast iron tends to favor direct ferritization and is characterized by a complex of very interesting properties. Intelligent computing tools such as artificial neural networks (ANNs) are used as predictive modeling tools, allowing their users to forecast the microstructure of the tested cast iron at the level of computer simulation. This paper presents the process of the development of a metamodel for the selection of a neural network appropriate for a specific chemical composition. Predefined models for the specific composition have better precision, and the initial selection provides the user with automation of reasoning and prediction. Automation of the prediction is based on the rules obtained from the decision tree, which classifies the type of microstructure. In turn, the type of microstructure was obtained by clustering objects of different chemical composition. The authors propose modeling the prediction of the volume fraction of phases in the CGI microstructure in a three-step procedure. In the first phase, k-means, unsupervised segmentation techniques were used to determine the metamodel (DT), which in the second phase enables the selection of the appropriate ANN submodel (third phase). [ABSTRACT FROM AUTHOR]

The paper considers discovering new and potentially useful information from large amounts of data that actualizes the role of developing data mining tools for complex socio-economic processes and systems based on the principles of the digital economy and their processing using network applications. The stages of data mining for complex socio-economic processes and systems were outlined. The algorithm of data mining was considered. It is determined that the previously used stages of data mining, which were limited to the model-building process, can be extended through the use of more powerful computer technology and the emergence of free access to large amounts of multidimensional data. The available stages of data mining for complex socio-economic processes and systems include the processes of facilitating data preparation, evaluation, and visualization of models, as well as indepth learning. The data mining tools for complex socio-economic processes and systems in the context of technological progress and following the big data paradigm were identified. The data processing cycle has been investigated; this process consists of a series of steps starting with the input of raw data and ending with the output of useful information. The knowledge obtained at the data processing stage is the basis for creating models of complex socio-economic processes and systems. Two types of models (descriptive and predictive) that could be created in the data mining process were outlined. Algorithms for estimating and analyzing data for modeling complex socio-economic processes and systems in accordance with the pre-set task were determined. The efficiency of introducing neural networks and deep learning methods used in data mining was analyzed. It was determined that they would allow effective analysis and use of the existing large data sets for operational human resources management and strategic planning of complex socio-economic processes and systems. [ABSTRACT FROM AUTHOR]

This paper presents a way to introduce data science to college (business) students. To this end, data mining using neural networks may be practiced. After briefly clarifying the main differences between data science and data mining, the work with neural networks is examined in detail. The examination deals with the features and affordances of this work, as well as its expected challenges with possible reasons. The paper ends with a number of implications for practice, teacher education, and research. [ABSTRACT FROM AUTHOR]

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, subsequent tasks that involve inference, reasoning, and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, it is naturally desirable to tightly integrate deep learning and Bayesian models within a principled probabilistic framework, which we call Bayesian deep learning. In this unified framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in return, the feedback from the inference process is able to enhance the perception of text or images. This paper proposes a general framework for Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this paper, we also discuss the relationship and differences between Bayesian deep learning and other related topics such as the Bayesian treatment of neural networks. [ABSTRACT FROM PUBLISHER]

Deep learning, especially a discriminative model for image denoising, has shown great potential in removing complex spectral–spatial noise in hyperspectral images (HSI). For HSI denoising, it is crucial to extract more context information around each pixel and to predict each pixel according to the surrounding context. Therefore, the effective receptive field plays an important role when performing denoising task. Generally, an HSI denoising model can achieve better performance by reserving the correlation of adjacent spectral bands and extracting more pixel features in the spatial domain. In this paper, 3-D atrous denoising convolution neural network (3DADCNN) is proposed for HSI. The model extracts feature maps along both spatial and spectral dimensions and enlarges the receptive field without significantly increasing the number of network parameters. Simultaneously, the multibranch and multiscale structure is utilized to reduce training difficulty, lessen overfitting risk, and preserve details in texture. The proposed model can be applied to the corrupted image with a mixed type of photon and thermal noise. Experimental results of the quantitative and qualitative evaluation show that 3DADCNN outperforms state-of-the-art HSI denoising methods. [ABSTRACT FROM AUTHOR]

Defocus blur detection is an important and challenging task in computer vision and digital imaging fields. Previous work on defocus blur detection has put a lot of effort into designing local sharpness metric maps. This paper presents a simple yet effective method to automatically obtain the local metric map for defocus blur detection, which based on the feature learning of multiple convolutional neural networks (ConvNets). The ConvNets automatically learn the most locally relevant features at the super-pixel level of the image in a supervised manner. By extracting convolution kernels from the trained neural network structures and processing it with principal component analysis, we can automatically obtain the local sharpness metric by reshaping the principal component vector. Meanwhile, an effective iterative updating mechanism is proposed to refine the defocus blur detection result from coarse to fine by exploiting the intrinsic peculiarity of the hyperbolic tangent function. The experimental results demonstrate that our proposed method consistently performed better than the previous state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Big data classification is very slow when using traditional machine learning classifiers, particularly when using a lazy and slow-by-nature classifier such as the k-nearest neighbors algorithm (KNN). This paper proposes a new approach which is based on sorting the feature vectors of training data in a binary search tree to accelerate big data classification using the KNN approach. This is done using two methods, both of which utilize two local points to sort the examples based on their similarity to these local points. The first method chooses the local points based on their similarity to the global extreme points, while the second method chooses the local points randomly. The results of various experiments conducted on different big datasets show reasonable accuracy rates compared to state-of-the-art methods and the KNN classifier itself. More importantly, they show the high classification speed of both methods. This strong trait can be used to further improve the accuracy of the proposed methods. [ABSTRACT FROM AUTHOR]

The paper discusses methods of data mining for prediction of air pollution. Two tasks in such a problem are important: generation and selection of the prognostic features, and the final prognostic system of the pollution for the next day. An advanced set of features, created on the basis of the atmospheric parameters, is proposed. This set is subject to analysis and selection of the most important features from the prediction point of view. Two methods of feature selection are compared. One applies a genetic algorithm (a global approach), and the other--a linear method of stepwise fit (a locally optimized approach). On the basis of such analysis, two sets of the most predictive features are selected. These sets take part in prediction of the atmospheric pollutants PM10, SO2, NO2 and O3. Two approaches to prediction are compared. In the first one, the features selected are directly applied to the random forest (RF), which forms an ensemble of decision trees. In the second case, intermediate predictors built on the basis of neural networks (the multilayer perceptron, the radial basis function and the support vector machine) are used. They create an ensemble integrated into the final prognosis. The paper shows that preselection of the most important features, cooperating with an ensemble of predictors, allows increasing the forecasting accuracy of atmospheric pollution in a significant way. [ABSTRACT FROM AUTHOR]

Distant supervision is widely used to extract relational facts with automatically labeled datasets to reduce high cost of human annotation. However, current distantly supervised methods suffer from the common problems of word-level and sentence-level noises, which come from a large proportion of irrelevant words in a sentence and inaccurate relation labels for numerous sentences. The problems lead to unacceptable precision in relation extraction and are critical for the success of using distant supervision. In this paper, we propose a novel and robust neural approach to deal with both problems by reducing influences of the multi-granularity noises. Three levels of noises from word, sentence until knowledge type are carefully considered in this work. We first initiate a question-answering based relation extractor (QARE) to remove noisy words in a sentence. Then we use multi-focus multi-instance learning (MMIL) to alleviate the effects of sentence-level noise by utilizing wrongly labeled sentences properly. Finally, to enhance our method against all the noises, we initialize parameters in our method with a priori knowledge learned from the relevant task of entity type classification by transfer learning. Extensive experiments on both existing benchmark and an improved larger dataset demonstrate that our proposed approach remarkably achieves new state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Segmentation in banking for the business client market is traditionally based on size measured in terms of income and the number of employees, and on statistical clustering methods (e.g. hierarchical clustering, k-means). The goal of the paper is to demonstrate that self-organizing maps (SOM) effectively extend the pool of possible criteria for segmentation of the business client market with more relevant criteria, including behavioral, demographic, personal, operational, situational, and cross-selling products. In order to attain the goal of the paper, the dataset on business clients of several banks in Croatia, which, besides size, incorporates a number of different criteria, is analyzed using the SOM-Ward clustering algorithm of Viscovery SOMine software. The SOM-Ward algorithm extracted three segments that differ with respect to the attributes of foreign trade operations (import/export), annual income, origin of capital, important bank selection criteria, views on the loan selection and the industry. The analyzed segments can be used by banks for deciding on the direction of further marketing activities. [ABSTRACT FROM AUTHOR]

Early fault detection technique is crucial to reduce the machine downtime and has high impact on a wide variety of industrial applications. However, early fault detection is still subject to the following challenges: 1) extracting features from incipient fault signals; 2) detecting anomalies with considering sequential data correlation; and 3) enhancing the reliability of fault alarm. In this paper, we introduce a novel deep-structured framework to solve the early fault detection problem. First, the system variation is measured with the deviation value generated by a current feature extraction model using deep neural network (DNN) and a distribution estimator based on the long short-term memory (LSTM) network. DNN has the ability of representing a complicated and intrinsic distribution for data, which is suitable for handling the early fault data masked by heavy noise, and LSTM is able to discover temporal dependencies in high-dimensional sequential data, which allows distribution estimator making use of previous context information as well as makes the distribution estimator more robust to warp along the time axis. Second, a circular indirect alarm assessment strategy is designed for collecting deviation values and confirming the fault appearance only when a specified confidence level is reached. Experimental results on the typical real-world bearing data sets demonstrate the effectiveness and the reliability of our model. [ABSTRACT FROM AUTHOR]

Due to increasing use of Online Social Networks (OSNs) applications such as Facebook, Twitter, etc., several research challenges related to security and privacy of OSN users introduced. The end users of OSNs expecting that social networks should strong enough to preserve their private data secure from the attackers. In this research work, we introduce the novel data mining based framework to protect the OSNs from various privacy violation concerns. Risk of disclosure of individual's confidential information have risen tremendously due to widening of social network and publication of its data. From security perspective privacy retaining becomes mandatory prior to service providers publish network information. Recently, preservation of privacy in data of social networks has become most challenging and concerning problem as it has caught our lives in a dramatic way. Various methods of anonymization exist that helps in retaining privacy of social networking. By developing graph and nodes degree, k-Anonymity and among all available techniques is an utmost one that assist in delivering security of information on internet. With major manipulation in editing of node techniques in this research paper, improvement of K-anonymity has been explained. With integration of same degree in one group, clusters are developed and processes are repeated untill recognition and identification of noisy data is done. For minimizing node misplacement in groups an Advanced Cuckoo Search is commenced and processed. To cross verify structure and for reducing node miss placement in groups outcome of Cuckoo Searches are combined with Feed Forward Back Propagation Neural Networks. We have computed the Information Loss and Average Path Length of proposed model. These results showed that the reduction in these parameters to a good extent compared to other implementations. These values of Information Loss and Average Path Length in case of a network with 9 nodes are obtained as 0.24 and 32.2 respectively. [ABSTRACT FROM AUTHOR]

Many real‐world data mining applications involve obtaining predictive models using imbalanced datasets. Frequently, the least common target variables present within datasets are associated with events that are highly relevant for end users. When these variables are nominal, we have a class‐imbalance problem which has been thoroughly studied within machine learning. As for regression tasks where target variables are continuous, few predictive models and evaluation techniques exist. This paper proposes a solution to these challenges. First, we introduce a cost‐sensitive learning algorithm based on a neural network trained on the minimization of a biased loss function. Results show a higher or comparable performance and convergence speed to existent techniques. Second, we develop new approaches for performance assessment of regression tasks within imbalanced domains by proposing new scalar measures, namely Geometric Mean Error (GME) and Class‐Weighted Error (CWE), as well as new graphical‐based measures, namely RECTPR, RECTNR, RECG − Mean and RECCWA curves. Unlike standard measures, our evaluation strategies are shown to be more robust to data imbalance as they reflect the performance of both rare and frequent events. [ABSTRACT FROM AUTHOR]

We study the problem of learning to rank from multiple information sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a composite ranking method while keeping a close correlation with the individual rankings simultaneously. We present a generic framework for multi-view subspace learning to rank (MvSL2R), and two novel solutions are introduced under the framework. The first solution captures information of feature mappings from within each view as well as across views using autoencoder-like networks. Novel feature embedding methods are formulated in the optimization of multi-view unsupervised and discriminant autoencoders. Moreover, we introduce an end-to-end solution to learning towards both the joint ranking objective and the individual rankings. The proposed solution enhances the joint ranking with minimum view-specific ranking loss, so that it can achieve the maximum global view agreements in a single optimization process. The proposed method is evaluated on three different ranking problems, i.e., university ranking, multi-view lingual text ranking, and image data ranking, providing superior results compared to related methods. [ABSTRACT FROM AUTHOR]

In this paper, we focus on yield analysis task where engineers identify the cause of failure from wafer failure map patterns and manufacturing histories. We organize yield analysis task into the following three stages, namely, failure map pattern monitoring, failure cause identification, and failure recurrence monitoring, and incorporate machine learning and data mining technologies into each stage to support engineers’ work. The important point is that big data analysis enables comprehensive and long-term monitoring automation. We make use of fast and scalable methods of clustering and pattern mining and realize daily comprehensive monitoring with massive manufacturing data. We also apply deep learning, which has been an innovative core technology of machine learning in recent years, to classification of wafer failure map patterns, and explore its performance in detail. Finally, these machine learning and data mining techniques are integrated into an automated monitoring system with interfaces familiar to engineers to attain large yield enhancement. [ABSTRACT FROM AUTHOR]

The problem of distributed representation learning is one in which multiple sources of information $X_1,\ldots, X_K$ X 1 ,... , X K are processed separately so as to learn as much information as possible about some ground truth $Y$ Y . We investigate this problem from information-theoretic grounds, through a generalization of Tishby's centralized Information Bottleneck (IB) method to the distributed setting. Specifically, $K$ K encoders, $K \geq 2$ K ≥ 2 , compress their observations $X_1,\ldots, X_K$ X 1 ,... , X K separately in a manner such that, collectively, the produced representations preserve as much information as possible about $Y$ Y . We study both discrete memoryless (DM) and memoryless vector Gaussian data models. For the discrete model, we establish a single-letter characterization of the optimal tradeoff between complexity (or rate) and relevance (or information) for a class of memoryless sources (the observations $X_1,\ldots, X_K$ X 1 ,... , X K being conditionally independent given $Y$ Y ). For the vector Gaussian model, we provide an explicit characterization of the optimal complexity-relevance tradeoff. Furthermore, we develop a variational bound on the complexity-relevance tradeoff which generalizes the evidence lower bound (ELBO) to the distributed setting. We also provide two algorithms that allow to compute this bound: i) a Blahut-Arimoto type iterative algorithm which enables to compute optimal complexity-relevance encoding mappings by iterating over a set of self-consistent equations, and ii) a variational inference type algorithm in which the encoding mappings are parametrized by neural networks and the bound approximated by Markov sampling and optimized with stochastic gradient descent. Numerical results on synthetic and real datasets are provided to support the efficiency of the approaches and algorithms developed in this paper. [ABSTRACT FROM AUTHOR]

Extracting valuable information from large sets of diverse meteorological data is a time-intensive process. Machine-learning methods can help to improve both speed and accuracy of this process. Specifically, deep-learning image-segmentation models using the U-Net structure perform faster and can identify areas that are missed by more restrictive approaches, such as expert hand-labeling and a priori heuristic methods. This paper discusses four different state-of-the-art U-Net models designed for detection of tropical and extratropical cyclone regions of interest (ROI) from two separate input sources: total precipitable water output from the Global Forecast System (GFS) model and water vapor radiance images from the Geostationary Operational Environmental Satellite (GOES). These models are referred to as International Best Track Archive for Climate Stewardship (IBTrACS)-GFS, Heuristic-GFS, IBTrACS-GOES, and Heuristic-GOES. All four U-Nets are fast information extraction tools and perform with an ROI detection accuracy ranging from 80% to 99%. These are additionally evaluated with the Dice and Tversky intersection-over-union (IoU) metrics, having Dice coefficient scores ranging from 0.51 to 0.76 and Tversky coefficients ranging from 0.56 to 0.74. The extratropical cyclone U-Net model performed 3 times as fast as the comparable heuristic model used to detect the same ROI. The U-Nets were specifically selected for their capabilities in detecting cyclone ROI beyond the scope of the training labels. These machine-learning models identified more ambiguous and active ROI missed by the heuristic model and hand-labeling methods that are commonly used in generating real-time weather alerts, having a potentially direct impact on public safety. [ABSTRACT FROM AUTHOR]

Cellular signaling data have become increasingly indispensable in analyzing residents' travel characteristic. Especially with the enhancement of positioning quality in 4G-LTE and 5G wireless communication systems, it is expected that the identification accuracy of fine-grained travel modes will achieve an optimal level. However, due to data privacy issues, the empirical evaluation of the performance of different identification methods is not yet sufficient. This paper builds a travel mode identification model that utilizes the gated recurrent unit (GRU) neural network. With 24 features as input, this method can identify four traffic modes, including walking, bicycle, car, and bus. Moreover, in cooperation with the operator, we organized an experiment collecting cellular signaling data, as well as the corresponding GPS data. Using the collected dataset as ground-truth data, the performance of the method presented in this paper and other popular methods is verified and compared. The results indicate that the GRU-based method has a better performance, with a precision, recall, and F score of 90.5%. Taking F score as an example, the outcome of the GRU-based method is about 6% to 7% higher than methods based on other machine learning algorithms. Considering the identification accuracy and model training time comprehensively, the method suggested in this paper outperforms the other three deep learning-based methods, namely, recurrent neural network (RNN), long short-term memory network (LSTM), and bidirectional long short-term memory network (Bi-LSTM). This study may provide some insights for the application and development of cellular signaling-based travel information collection technology for residents in the future., Author(s): Yanchen Wang [1]; Fei Yang (corresponding author) [1]; Li He [2]; Haode Liu [3]; Li Tan [4]; Cheng Wang [4] 1. Introduction Understanding travel behaviors is important for urban [...]

Telecom Companies logs customer's actions which generate a huge amount of data that can bring important findings related to customer's behavior and needs. The main characteristics of such data are the large number of features and the high sparsity that impose challenges to the analytics steps. This paper aims to explore dimensionality reduction on a real telecom dataset and evaluate customers' clustering in reduced and latent space, compared to original space in order to achieve better quality clustering results. The original dataset contains 220 features that belonging to 100,000 customers. However, dimensionality reduction is an important data preprocessing step in the data mining process specially with the presence of curse of dimensionality. In particular, the aim of data reduction techniques is to filter out irrelevant features and noisy data samples. To reduce the high dimensional data, we projected it down to a subspace using well known Principal Component Analysis (PCA) decomposition and a novel approach based on Autoencoder Neural Network, performing in this way dimensionality reduction of original data. Then K-Means Clustering is applied on both-original and reduced data set. Different internal measures were performed to evaluate clustering for different numbers of dimensions and then we evaluated how the reduction method impacts the clustering task. [ABSTRACT FROM AUTHOR]

Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas. This paper introduces two categories for multi-view representation learning: multi-view representation alignment and multi-view representation fusion. Consequently, we first review the representative methods and theories of multi-view representation learning based on the perspective of alignment, such as correlation-based alignment. Representative examples are canonical correlation analysis (CCA) and its several extensions. Then, from the perspective of representation fusion, we investigate the advancement of multi-view representation learning that ranges from generative methods including multi-modal topic learning, multi-view sparse coding, and multi-view latent space Markov networks, to neural network-based methods including multi-modal autoencoders, multi-view convolutional neural networks, and multi-modal recurrent neural networks. Further, we also investigate several important applications of multi-view representation learning. Overall, this survey aims to provide an insightful overview of theoretical foundation and state-of-the-art developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications. [ABSTRACT FROM AUTHOR]

In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, this problem is addressed in the way that a neural network algorithm named BP-MLL, i.e., Backpropagation for Multilabel Learning, is proposed. It is derived from the popular Backpropagation algorithm through employing a novel error function capturing the characteristics of multilabel learning, i.e., the labels belonging to an instance should be ranked higher than those not belonging to that instance. Applications to two real-world multilabel learning problems, i.e., functional genomics and text categorization, show that the performance of BP-MLL is superior to that of some well-established multilabel learning algorithms. [ABSTRACT FROM AUTHOR]

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the AlexNet, AlexNet-small and VGG models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data. [ABSTRACT FROM AUTHOR]

System logs play an important role in software development and system maintenance. Many system software programs continuously generate system logs during software runtimes for failure detection and diagnosis purposes. Currently, the analysis of system log data is mainly a manual process that highly depends on human knowledge and experience. This time-consuming task has become a problem because of the ever-increasing volume of log data. Existing studies have investigated machine learning and deep learning techniques to automate the failure detection task. This paper takes the deep learning approach and proposes two detection structures based on recurrent and convolutional neural networks. More importantly, this paper takes a step further by closely examining the timestamps of log data which existing studies have generally ignored. This study found that time information can be a distinguishing factor between regular and abnormal log sequences. Inspired by this observation, a novel method is proposed to integrate log timestamps in deep learning models using interpolation techniques. The evaluation results show that the log timestamps can significantly improve the performance of failure detection. Cross-comparison of the different models demonstrates that the proposed network structure can successfully utilize the timestamp information. The code is available on GitHub: https://github.com/hfyxin/Ts-models-log-data-analysis.git.

While existing watermarking attack methods can disturb the correct extraction of watermark information, the visual quality of watermarked images will be greatly damaged. Therefore, a concealed attack based on generative adversarial network and perceptual losses for robust watermarking is proposed. First, the watermarked image is utilized as the input of generative networks, and its generating target (i.e. attacked watermarked image) is the original image. Inspired by the U-Net network, the generative networks consist of encoder-decoder architecture with skip connection, which can combine the low-level and high-level information to ensure the imperceptibility of the generated image. Next, to further improve the imperceptibility of the generated image, instead of the loss function based on MSE, a perceptual loss based on feature extraction is introduced. In addition, a discriminative network is also introduced to make the appearance and distribution of generated image similar to those of the original image. The addition of the discriminative network can remove watermark information effectively. Extensive experiments are conducted to verify the feasibility of the proposed concealed attack method. Experimental and analysis results demonstrate that the proposed concealed attack method has better imperceptibility and attack ability in comparison to the existing watermarking attack methods. [ABSTRACT FROM AUTHOR]

Copyright of Iranian Journal of Information Processing & Management is the property of Iranian Information & Documentation Center (IRANDOC) 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.)

This paper describes the use of convolutional neural nets (CNN), a type of deep learning, to identify fronts in gridded data, followed by a novel postprocessing method that converts probability grids to objects. Synoptic-scale fronts are often associated with extreme weather in the midlatitudes. Predictors are 1000-mb (1 mb = 1 hPa) grids of wind velocity, temperature, specific humidity, wet-bulb potential temperature, and/or geopotential height from the North American Regional Reanalysis. Labels are human-drawn fronts from Weather Prediction Center bulletins. We present two experiments to optimize parameters of the CNN and object conversion. To evaluate our system, we compare the objects (predicted warm and cold fronts) with human-analyzed warm and cold fronts, matching fronts of the same type within a 100- or 250-km neighborhood distance. At 250 km our system obtains a probability of detection of 0.73, success ratio of 0.65 (or false-alarm rate of 0.35), and critical success index of 0.52. These values drastically outperform the baseline, which is a traditional method from numerical frontal analysis. Our system is not intended to replace human meteorologists, but to provide an objective method that can be applied consistently and easily to a large number of cases. Our system could be used, for example, to create climatologies and quantify the spread in forecast frontal properties across members of a numerical weather prediction ensemble. [ABSTRACT FROM AUTHOR]

Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. We provide a seminal review of the applications of ANN to health care organizational decision-making. We screened 3,397 articles from six databases with coverage of Health Administration, Computer Science and Business Administration. We extracted study characteristics, aim, methodology and context (including level of analysis) from 80 articles meeting inclusion criteria. Articles were published from 1997–2018 and originated from 24 countries, with a plurality of papers (26 articles) published by authors from the United States. Types of ANN used included ANN (36 articles), feed-forward networks (25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%. The majority of ANN informed decision-making at the micro level (61 articles), between patients and health care providers. Fewer ANN were deployed for intra-organizational (meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 articles) decision-making. Our review identifies key characteristics and drivers for market uptake of ANN for health care organizational decision-making to guide further adoption of this technique. [ABSTRACT FROM AUTHOR]

Effort-aware just-in-time (JIT) defect prediction is to rank source code changes based on the likelihood of detects as well as the effort to inspect such changes. Accurate defect prediction algorithms help to find more defects with limited effort. To improve the accuracy of defect prediction, in this paper, we propose a deep learning based approach for effort-aware just-in-time defect prediction. The key idea of the proposed approach is that neural network and deep learning could be exploited to select useful features for defect prediction because they have been proved excellent at selecting useful features for classification and regression. First, we preprocess ten numerical metrics of code changes, and then feed them to a neural network whose output indicates how likely the code change under test contains bugs. Second, we compute the benefit cost ratio for each code change by dividing the likelihood by its size. Finally, we rank code changes according to their benefit cost ratio. Evaluation results on a well-known data set suggest that the proposed approach outperforms the state-of-the-art approaches on each of the subject projects. It improves the average recall and popt by 15.6% and 8.1%, respectively. [ABSTRACT FROM AUTHOR]

Classification of motor imagery electroencephalograph signals is a fundamental problem in brain–computer interface (BCI) systems. We propose in this paper a classification framework based on long short-term memory (LSTM) networks. To achieve robust classification, a one dimension-aggregate approximation (1d-AX) is employed to extract effective signal representation for LSTM networks. Inspired by classical common spatial pattern, channel weighting technique is further deployed to enhance the effectiveness of the proposed classification framework. Public BCI competition data are used for the evaluation of the proposed feature extraction and classification network, whose performance is also compared with that of the state-of-the-arts approaches based on other deep networks. [ABSTRACT FROM AUTHOR]

A prediction model for methane production in a wastewater processing facility is presented. The model is built by data-mining algorithms based on industrial data collected on a daily basis. Because of many parameters available in this research, a subset of parameters is selected using importance analysis. Prediction results of methane production are presented in this paper. The model performance by different algorithms is measured with five metrics. Based on these metrics, a model built by the Adaptive Neuro-Fuzzy Inference System algorithm has provided most accurate predictions of methane production. [ABSTRACT FROM AUTHOR]

Today the tasks of complex artificial and natural objects control have come to the fore in the majority of subject domains. The efficiency and effectiveness of solving these tasks directly depends of the efficiency and effectiveness of data fusion (DF). Data fusion methods are designed to integrate data from multiple sources and transform it in order to produce more consistent, accurate, and useful information than that provided by any individual data source. Although DF has been extensively studied for a considerable period of time it is still hardly applicable in practice in the processes of the control of the real world objects with complex structure and behavior as the data produced by the objects is, in the majority of cases, heterogeneous, multimodal, and imperfect, has huge volume. To ensure proper response to the changes in the state and behavior of the controlled objects that can be caused by both internal and external influencing factors the data should be processed with high accuracy and with minimum delays. Despite the importance of the tasks of complex objects control till now there are no researches that clarify to what extent the DF problem has been solved from the perspective of its application in the processes of objects control based on the data received from the objects. In the survey we define the requirements to DF in the interests of the control of complex artificial and natural objects, consider the structure of the multilevel process of intelligent object control, identify the neural networks that can be used in the control process for data fusion. Despite the wide capabilities of the existing NN we reveal that they still do not meet all the requirements to DF for complex objects control. Based on the analysis of NN architectures, we define requirements for advanced NN architectures and discuss future research directions. To facilitate our literature analyses, we also perform conceptual exploration of collected papers with lattices of closed itemsets and implications from Formal Concept Analysis and Data Mining used for knowledge processing in similar large-scale studies. [Display omitted] • Data fusion with adaptive neural network models for intelligent control of heterogeneous objects. • Continuous neural network learning and operation. • Control of complex nonlinear artificial and natural objects. • Conceptual exploration and data mining of recent literature. • Ethical and practical implications. [ABSTRACT FROM AUTHOR]

n GEO communicational satellites, thrusts are being used for many missions such as station keeping, longitude change maneuver and actuators desaturation. These types of actuators need fuel and its estimation requires many complicated calculations. In this paper, a novel method based on availed data for previous satellites is proposed for estimation of satellite fuel mass and it does not need mathematical modeling of satellite dynamics. Two methods, least square method and artificial intelligence, are used to this aim and two mathematical model are proposed for satellite fuel mass estimation. By applying the models to several previous satellites, it is shown that the models have lower than 5% average error. The proposed method in this paper is quick and accurate and can be utilized for GEO satellites feasibility study and conceptual design

Deep learning has obtained state-of-the-art results in a variety of computer vision tasks and has also been used to solve SAR image classification problems. Deep learning algorithms typically require a large amount of training data to achieve high accuracy. In contrast, the size of SAR image datasets is often comparatively limited. Therefore, this paper proposes a novel method, deep memory convolution neural networks (M-Net), to alleviate the problem of overfitting caused by insufficient SAR image samples. Based on the convolutional neural networks (CNN), M-Net adds an information recorder to remember and store samples’ spatial features, and then it uses spatial similarity information of the recorded features to predict unknown sample labels. M-Net's use of this information recorder may cause difficulties for convergence if conventional CNN training methods were directly used to train M-Net. To overcome this problem, we propose a transfer parameter technique to train M-Net in two steps. The first step is to train a CNN, which has the same structure as the part of CNN incorporated in M-Net, to obtain initial training parameters. The second step applies the initialized parameters to M-Net and then trains the entire M-Net. This two-step training approach helps us to overcome the nonconvergence issue, and also reduces training time. We evaluate M-Net using the public benchmark MSTAR dataset, and achieve higher accuracy than several other well-known SAR image classification algorithms. [ABSTRACT FROM AUTHOR]

Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can’t satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy. [ABSTRACT FROM AUTHOR]

Compared to ground precipitation measurements, satellite-based precipitation estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of satellitebased precipitation products is still insufficient to serve many weather, climate, and hydrologic applications at high resolutions. In this paper, the authors develop a state-of-the-art deep learning framework for precipitation estimation using bispectral satellite information, infrared (IR), and water vapor (WV) channels. Specifically, a two-stage framework for precipitation estimation from bispectral information is designed, consisting of an initial rain/no-rain (R/NR) binary classification, followed by a second stage estimating the nonzero precipitation amount. In the first stage, the model aims to eliminate the large fraction of NR pixels and to delineate precipitation regions precisely. In the second stage, the model aims to estimate the pointwise precipitation amount accurately while preserving its heavily skewed distribution. Stacked denoising autoencoders (SDAEs), a commonly used deep learning method, are applied in both stages. Performance is evaluated along a number of common performance measures, including both R/NR and real-valued precipitation accuracy, and compared with an operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). For R/NR binary classification, the proposed two-stage model outperforms PERSIANN-CCS by 32.56% in the critical success index (CSI). For real-valued precipitation estimation, the two-stage model is 23.40% lower in average bias, is 44.52% lower in average mean squared error, and has a 27.21% higher correlation coefficient. Hence, the two-stage deep learning framework has the potential to serve as a more accurate and more reliable satellite-based precipitation estimation product. The authors also provide some future directions for development of satellite-based precipitation estimation products in both incorporating auxiliary information and improving retrieval algorithms. [ABSTRACT FROM AUTHOR]