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In order to overcome the efficiency issues of single chain technology in blockchain, a new paradigm directed acyclic graphs is flourishing. This article focused on the non independent task scheduling problem considering cost weights in blockchain directed acyclic graphs, and constructed a mathematical model for task scheduling in blockchain DAG, and proposed a new task scheduling algorithm based on improved distribution estimation whale to solve this problem. The new algorithm introduced spatial sampling and statistical learning of EDA in the WOA algorithm to predict the optimal search area, thereby generating excellent new individuals, making the new algorithm have stronger global search ability and faster convergence speed. Finally, through program simulation, it compared the performance of multiple algorithms in terms of convergence speed and global optimization ability. The experiments show that IEWOA has significant advantages over traditional WOA and EDA in the above parameter performance. In addition, comparing with the improved genetic algorithm FPGA, IEWOA also has certain advantages in parameter performance. [ABSTRACT FROM AUTHOR]

Abnormal network traffic detection is a key cybersecurity technology that assists in identifying and preventing malicious network attacks. Existing methods for detecting abnormal network traffic typically rely on complex machine learning models and a large amount of labeled data. Consequently, these methods are challenging to apply to different scenarios without retraining the model and cannot effectively handle large-scale, ongoing network attacks in real-time. To address these issues, this paper proposes a classification method based on a network flow energy model. It utilizes a reverse statistical physics model to learn target traffic features in the network, allowing it to be based on macroscopic real observations or real data without the need for manual labeling. Subsequently, the paper combines the concept of the energy model to construct a network traffic recognition model. This model judges whether a sample conforms to the main statistical distribution. Specifically, the method describes individual behavior characteristics and interaction features between traffic packets through the local field and coupling field in the energy model. By combining these two features, the method calculates the sample' s energy. If the energy is below a threshold, the sample aligns with the main distribution, indicating normal data; otherwise, it is considered abnormal data. As this method does not rely on manual labeling, it can adapt to various network environments without the need for repetitive training. This addresses current issues in traffic abnormality detection methods, which struggle to adapt to different scenarios and require extensive labeling. To evaluate the effectiveness of this method, the paper validates it using the Kitsune-2018 and CTU-13 datasets. Experimental results demonstrate that the proposed method achieves good classification performance and overall effectiveness in network traffic classification tasks. This further indicates its accuracy in performing network flow classification tasks and its adaptability to changing scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute 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.)

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology 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.)

In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropriate statistical methods is one of the key factors affecting the quality of research results. This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics, difference analysis, consistency test and multivariate statistical analysis, as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals, and summarizes the relevance and application prospects between medical statistical methods and machine learning methods. This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department 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.)

Copyright of Commerce & Management Quarterly is the property of Chinese Association of Business & Management Technology 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.)

Copyright of Chinese Journal of Inorganic Analytical Chemistry / Zhongguo Wuji Fenxi Huaxue is the property of Beijing Research Institute of Mining & Metallurgy Technology Group 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.)

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute 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.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering 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.)

In cancer research, high-throughput sequencing techniques have yielded a large amount of complex heterogeneous data. Although several deep learning or statistical methods have been applied to integrate such data, there is a lack of work on how to integrate multi-omics data more efficiently. Therefore, a deep learning-based multi-omics data integration method named deep autoencoder for multi-omics integration (DAEMI) is proposed. It utilizes a deep learning algorithm named autoencoder, which uses a bottleneck layer in the network to get a compressed knowledge representation of the original input. Unlike previous deep learning integration studies, this method finds more subtypes with significant survival differences. DAEMI does not rely on survival data for compressed features selection. It uses all features obtained from the bottleneck layer and uses K-means to identify cancer subtypes. By comparing with high-order path elucidated similarity (HOPES), Similarity network fusion (SNF), iClusterPlus, and moCluster on 4 cancer datasets and simulate dataset, DAEMI performs better than other methods. Functional analysis revealed neurodegenerative diseases and mitochondrial dysfunction may share some pathways with cancer. [ABSTRACT FROM AUTHOR]

Copyright of China Sciencepaper is the property of China Sciencepaper 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.)

Text detection is widely applied in the automatic driving and cross-modal image retrieval fields. This technique is also an important pre-procedure in optical character-based text recognition tasks. At present, text detection in complex natural scenes remains a challenging topic. Because text distribution and orientation are varied in different scenes and domains, there is still room for improvement in existing computer vision-based text detection methods. To complicate matters, natural scene texts, such as those in guideposts and shop signs, always contain words in different languages. Even characters are missing from some natural scene texts. These circumstances present more difficulties for feature extraction and feature description, thereby weakening the detectability of existing computer vision and image processing methods. In this context, text detection applications in natural scenes were summarized in this paper, the classical and newly presented techniques were reviewed, and the research progress and status were analyzed. First, the definitions of natural scene text detection and associated concepts were provided based on an analysis of the main characteristics of this problem. In addition, the classic natural scene text detection technologies, such as connected component analysis-based methods and sliding detection window-based methods, were introduced comprehensively. These methods were also compared and discussed. Furthermore, common deep learning models for scene text detection of the past decade were also reviewed. We divided these models into two main categories: region proposal-based models and segmentation-based models. Accordingly, the typical detection and semantic segmentation frameworks, including Faster R-CNN, SSD, Mask R-CNN, FCN, and FCIS, were integrated in the deep learning methods reviewed in this section. Moreover, hybrid algorithms that use region proposal ideas and segmentation strategies were also analyzed. As a supplement, several end-to-end text recognition strategies that can automatically identify characters in natural scenes were elucidated. Finally, possible research directions and prospects in this field were analyzed and discussed.

“Big data” is always collected from different resources that have different data structures. With the rapid development of information technologies, current precious data resources are characteristic of multimodes. As a result, based on classical machine learning strategies, multi-modal learning has become a valuable research topic, enabling computers to process and understand “big data”. The cognitive processes of humans involve perception through different sense organs. Signals from eyes, ears, the nose, and hands (tactile sense) constitute a person’s understanding of a special scene or the world as a whole. It reasonable to believe that multi-modal methods involving a higher ability to process complex heterogeneous data can further promote the progress of information technologies. The concepts of multimodality stemmed from psychology and pedagogy from hundreds of years ago and have been popular in computer science during the past decade. In contrast to the concept of “media”, a “mode” is a more fine-grained concept that is associated with a typical data source or data form. The effective utilization of multi-modal data can aid a computer understand a specific environment in a more holistic way. In this context, we first introduced the definition and main tasks of multi-modal learning. Based on this information, the mechanism and origin of multi-modal machine learning were then briefly introduced. Subsequently, statistical learning methods and deep learning methods for multi-modal tasks were comprehensively summarized. We also introduced the main styles of data fusion in multi-modal perception tasks, including feature representation, shared mapping, and co-training. Additionally, novel adversarial learning strategies for cross-modal matching or generation were reviewed. The main methods for multi-modal learning were outlined in this paper with a focus on future research issues in this field.

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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.)

In order to know the current research status of medical text representation learning in time, this paper made systematic and comprehensive review of the existing research. First of all, this paper summarized the mainstream technology and representative achievements of medical text representation learning from the methods based on statistical learning, knowledge representation and graph representation. Then, this paper proposed and used quantitative and qualitative quality evaluation system to systematically sort out and summarize the quality evaluation methods of medical text representation learning. Finally, this paper looked forward to the research trend of medical text representation learning. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering 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.)

Accurate and timely winter wheat yield estimation has significant effect to grain markets and policy. Most crop estimation methods can be divided into two categories, one is based on the crop model and the other is the statistical learning method. For statistical learning methods with recent advances in deep learning, convolutional neural network (CNN) have become state-of-the-art algorithms. can extract the depth-dependent features of crop growth. However, the pivotal challenge is to combine remote sensing images with CNN. In this paper, we employ the method of histogram dimensionality reduction and time series fusion to generate the input layer. The experiment firstly performed projection transformation, splicing, mask, fusion, and clipping for 6 different MODIS images in the research area from 2006 to 2016, and then generated 21 600 fusion images of 12 bands (surface reflectance data of 7 different wavelengths in MOD09A1, surface temperature of day and night in MYD11A2, NDVI and EVI in MOD13A1, and FPAR in MOD15A2H). Then, the sensitivity range of winter wheat growth in each band is divided into 36 sections, and the histogram statistics are used to reduce the dimension to generate a vector of length 36, so the remote sensing image generates a matrix of 36×36×12 in the 228-day growing season. The corresponding time and regional statistics are applied as the output layer to construct a complete sample. The yield estimation sample database of 12 indices in the winter wheat region of north China (60 prefecture-level cities) from 2006 to 2016 was constructed, and the training set and verification set were divided into 10:1 for the training and evaluation of yield estimation model. Finally, the neural network structure is designed according to the sample, which consists of the input layer, 7 convolution layers (c1-c7), 7 activation layers, 7 batch normalization layers, 3 dropout layers, 2 full connection layers, and output layer. The number of c1-c7 convolution kernels is 64, 64, 128, 128, 256, 256, 256, the convolution kernel size is 3×3 dpi, and the sliding step length is 2, 1, 2, 2, 2, 1 and 2 respectively, 1 zero paddings per convolutional layer. At the same time, batch normalization and Relu function activation are performed on each convolutional layer, and the Dropout layer is used in the fully connected layer. The results show that: 1) The root-mean-square error (RMSE) and coefficient of determination (R²) of the convolutional neural network model on the training set are 183.82 kg/hm² and 0.98 respectively. In the validation set, RMSE and R² are 689.72 kg/hm² and 0.71. 2) With the same neural network structure, the average RMSE of the estimated samples from 2006 to 2016 trained as validation sets for 11 models was 772.03 kg/hm². The error of the yield estimation model was the largest in 2007 and the smallest in 2012, and the RMSE was 920.45 kg/hm² and 632.08 kg/hm² respectively. Crop estimation algorithm based on CNN has high robustness and precision; 3) The accuracy analysis of prediction yield at the municipal level of different provinces in three temporal points of 2007, 2012 and 2016 indicates that the model has higher accuracy in most areas of the northern winter wheat region, especially, RMSE of Hebei and Shandong provinces is approximately 500 kg/hm². The result shows that CNN is well applied to the estimation of winter wheat production. This is a great thought of remote sensing combined with the deep learning algorithm. This method can be used to estimate yield by remote sensing in different scales and regions. Compared with the traditional method, this “start-to-end” learning method has the advantage of synergy and can obtain the optimal estimation model relative to the whole area. Meanwhile, As data accumulates, the estimation accuracy will be continuously improved, and it has a good application prospect in the national agricultural production forecast. [ABSTRACT FROM AUTHOR]

In recent years, advanced persistent threat (APT) has become the chief threat to enterprise users.The traditional security protection methods, such as signature-based detection and perimeter protection, are insufficient in dealing with APT.Therefore, the status of network anomaly behavior detection method was described.The technology roadmap and system architecture of abnormal behavior detection based on statistical learning were introduced.The feature extract method and statistical modeling methods were proposed.The characteristic of abnormal behavior detection based on big data was concluded and the direction of future research was proposed.

Currently the mainstream methods for Chinese word segmentation exploit statistical machine learning models. These methods usually require manual-annotated segmented sentences as training corpus, yet have neglected the annotated large-scale lexicon resources which have been built before, where these resources can be highly valuable when cross-domain evaluation is conducted, as the gold-standard sentence-level annotations arerare. Recently, the integration of lexicon formation into word segmentation models has gained increasing interest. As a whole, the integration methods can be classified into two categories:one being based on character-based models that cast word segmentation problem as sequence labeling, and the other being based on word-basedmodels that use beam-search to decode. This paper compared these two models, and combined them. Experimental results on benchmark datasetsshow that lexicon information can be more fully explored after combination, and finally the combined model can achieve better performances with both in-and cross-domain settings. [ABSTRACT FROM AUTHOR]

Aiming at the computational complexity of the online prediction of short time traffic flow, this paper proposed a least square support vector machine online short term traffic flow prediction method based on sliding time window. The method simplified the solving process of Lagrange multiplier in the process of online learning, and controlled the new samples to join and remove old samples by the movement of the sliding time window of the training data set. Furthermore, the method could use a few linear operations to update the Lagrange multiplier, and then completed the online updating of the prediction model.The test results indicate that, compared with the existing algorithms, the proposed algorithm can reduce the average time of online model updating about 62. 64% under the conditions of guaranteed the accuracy of the prediction, and it is an effective online short traffic flow prediction method. [ABSTRACT FROM AUTHOR]

Due to the weakness of complex multi-target detection using some saliency detection algorithms in remote sensing images, this paper proposed an extraction algorithm based on saliency detection and superpixel segmentation. Firstly, it used graph-based visual saliency (GBVS) method to extract some high saliency regions correctly, then applied simple linear iterative clustering (SLIC) method to segment these saliency regions into some superpixels, and meanwhile amended part of the edge superpixels,and as the training sample. Secondly, by computing the statistical feature parameters of the training sample and constructing a reasonable statistic, it established a saliency objects extraction threshold range. Finally, it used the threshold range to extract the salient objects from the whole superpixels successfully. Compared with other saliency regions detection methods,experimental results show that the proposed algorithm has more higher precision and recall values and can detect the saliency objects more efficient, and can be better applied to extract remote sensing saliency information from the complex multitarget remote sensing images. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Copyright of Acta Automatica Sinica is the property of Chinese Academy of Sciences, Institute of Automation 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.)

According to ihe essential feature of object-oriented image segmentation method, this paper explores a minimum span tree (MST) based image segmentation method. We define an edge weight based optimal criterion (merging predicate) which based on statistical learning theory (Sl.T). a scale control parameter is used to control the segmentation scale. Kxperiments based on the high resolution UAV images show that the proposed merging predicate can keep the integrity of the objects and do well on preventing over segmentation. It also proves its efficiency in segmenting the rich texture images while can get good boundary of the object. [ABSTRACT FROM AUTHOR]