Recently, network representation learning has been widely used to mine and analyze network characteristics, and it is also applied to blockchain, but most of the embedding methods in blockchain ...ignore the heterogeneity of network, so it is difficult to accurately describe the characteristics of the transaction. As smart society evolves, Ethereum makes smart contracts reality, while the mine of transaction characteristics appearing on the Ethereum platform is scarce; thus, there is an urgent need to mine Ethereum from contract and transfer. In this article, we propose a heterogeneous network representation learning method to mine implicit information inside Ethereum transactions. Specifically, we construct an Ethereum transaction network by collecting transaction data from normal and phishing Ethereum accounts. Then, we propose a walk strategy that combines timestamps and transaction amounts to represent the information that occurs at the time of a transaction. To mine the types of nodes and edges, we use a heterogeneous network representation learning method to map the transaction network to a low-dimensional space. Finally, we improve the accuracy of the embedding results in the node classification task, which has important implications for Ethereum mining as well as identity recognition.
Remote sensing image segmentation has large uncertainty related to the heterogeneity of similar objects and complex spectrum in satellite images, causing the traditional segmentation methods to be ...greatly limited. Existing semantic segmentation methods represented by deep learning have made breakthrough progress. However, traditional deep learning methods, such as deep convolution neural network, are a completely deterministic model, which cannot describe the uncertainty of remote sensing image well. To solve this problem, a new deep neural network combined with fuzzy logic units is proposed in this paper, called RSFCNN (Remote Sensing image segmentation with Fuzzy Convolutional Neural Network). The network integrates convolution units and fuzzy logic units. Convolution units are used to extract discriminant features with different proportions, thus providing comprehensive information for pixel-level remote sensing image segmentation. Fuzzy logic units are used to deal with various uncertainties and provide more reliable segmentation results, and each unit handles the feature maps at a particular image scale by Gaussian blur function. Experiments were carried out on two data sets, and the results showed that RSFCNN has higher segmentation accuracy and better performance than state-of-the-art algorithms.
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While scanning or shooting a document, factors like ink density and paper transparency may cause the content from the reverse side to become visible through the paper, resulting in a digital image ...with a ‘seen-through’ phenomenon, which will affect practical applications. In addition, document images can be affected by random factors during the imaging process, such as differences in the performance of camera equipment and variations in the physical properties of the document itself. These random factors increase the noise of the document image and may cause the seen-through phenomena to become more complex and diverse. To tackle this issue, we propose the Fuzzy Diffusion Model (FDM), which combines fuzzy logic with diffusion models. It effectively models complex seen-through effects and handles uncertainties in document images. Specifically, we gradually degrade the original image with mean-reverting stochastic differential equation(SDE) to transform it into seen-through mean state with fixed Gaussian noise version. Following this, fuzzy operations are introduced into the noise network. Which helps the model better learn noise and data distributions by reasoning about the affiliation relationship of each pixel point through fuzzy logic. Eventually, in the reverse process, the low-quality image is gradually restored by simulating the corresponding reverse-time SDE. Extensive quantitative and qualitative experiments conducted on various datasets demonstrate that the proposed method significantly removes the seen-through effects and achieves good results under several metrics. The proposed FDM effectively solves the seen-through effects of document images and obtains better visual quality.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Semantic segmentation is a fundamental but meaningful task in the remote sensing image understanding community. Great progress has been made in optical sensor photography technology, which poses an ...opportunity and a challenge for remote sensing image segmentation task. But, in fact, a longstanding and intractable problem is that many hard pixels in special position, i.e. the prevalent intra-class noise and a poor boundary delineation, is difficult to classify due to their inherent uncertainty. In this paper, we comprehensively consider the characteristics of deep learning and introduce traditional pattern recognition methods to drive structure learning, which can leverage the corresponding fuzzy logic model to alleviate the aforementioned problem in remote sensing images. Specifically, this paper designs a multiscale bidirectional fuzzy-driven learning network (MBFNet), which takes advantage of both deep learning and fuzzy logic to effectively alleviate the inherent uncertainty of these hard pixels. The structure of convolutional neural networks driven by fuzzy systems also provides a new modelling paradigm for solving the uncertain problem in remote sensing images. Meanwhile, multiscale techniques and bidirectional fusion are introduced to enhance feature aggregation and avoid the potential adverse effects of fuzzy systems, respectively. Experimental results on two datasets demonstrate qualitatively and quantitatively that the proposed MBFNet is competitive.
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Multi-view clustering divides similar objects into the same class through using the fused multiview information. Most multi-view clustering methods obtain clustering result by only analyzing ...structure relationship among samples, ignoring the analysis of intrinsic features of each sample, while a few methods operate the original feature on the corresponding high-dimensional kernel matrices. However, noisy and redundant features of samples are inevitably mixed in original multi-view data or high-dimensional kernel matrices. To address this problem, we propose a novel multiview clustering method, which unifies structure learning and feature learning to a framework. Specifically, we obtain a consensus structure information from multiple views via sparse subspace structure learning with weight tensor nuclear norm constraint. Then our feature learning seeks projection directions to obtain data representation by data pseudo labels, which are obtained via the fused consensus structure information. Furthermore, we use the manifold regularization term to establish the relationship between data structure information and learnt data presentation. At last, the two subtasks are alternately iterated and optimized to acquire accurate structure and discriminative data presentation. Experimental results on different datasets validate the proposed method is superior to the state-of-the-art methods.
•Propose a Collaborative Structure and Feature Learning for multi-view clustering.•Propose a feature learning method with data pseudo-labels.•Propose structure learning with weighted tensor term for multiple subspace fusion.•The both learning can be iteratively boosted by using the result of the other one.
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Because of the uncertainty in remote sensing images and the ill-posedness of the problem, it is difficult for traditional unsupervised classification algorithms to create an accurate classification ...model. In contrast, pattern recognition methods based on fuzzy set theory, such as fuzzy c-means clustering, can manage the fuzziness of data effectively. Of these methods, the type-2 fuzzy c-means algorithm is better able to control uncertainty. Furthermore, semi-supervised training can use prior knowledge to deal with ill-posedness, and hence is more suitable. Therefore, we propose a novel classification method based the semi-supervised adaptive interval type-2 fuzzy c-means algorithm (SS-AIT2FCM). First, by integrating the semi-supervised approach, an evolutional fuzzy weight index m is proposed that improves the robustness and well-posedness of the model used in the clustering algorithm. This makes the algorithm suitable for remote sensing images with severe spectral aliasing, large coverage areas, and abundant features. In addition, soft constraint supervision is performed using a small number of labeled samples, which optimizes the iterative process of the algorithm and determines the optimal set of features for the data. This further reduces the ill-posedness of the model itself. The experimental data consist of three study areas: SPOT5 imagery from Big Hengqin Island, Guangdong, China, and the Summer Palace, Beijing, China, as well as TM imagery from Hengqin Island. Compared with several state-of-the-art fuzzy classification algorithms, our algorithm improves classification accuracy by more than 5% overall and obtains clearer boundaries in remote sensing images with serious mixed pixels. Moreover, it is able to suppress the phenomenon of isomorphic spectra.
•Selecting fuzzy weight index m based on evolution theory.•Introduce semi-supervised approach with fuzzy distance metrics.•Soft constraint supervision optimizes the iterative process.•SS-AIT2FCM is suitable for remote sensing images with severe spectral aliasing.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract only Introduction: Cryptogenic strokes account for one third of all strokes. Atrial fibrillation has been described as a leading cause in this population but occurrence of atrial ...fibrillation and imaging charecteristics amongst recurrent strokes in this population have not been characterized well. Methods: We reviewed electronic medical charts of a cohort of cryptogenic patients from February 2014 to September 2017 who underwent REVEAL LINQ insertable cardiac monitor. All patients met ESUS( Embolic Stroke of Unknown Source) criteria prior to implantation and were enrolled in the atrial fibrillation clinic with structured remote follow-up. All newly diagnosed atrial fibrillation was confirmed by electrophysiologists and recurrent strokes were confirmed by vascular neurologist after reviewing presentation, electronic medical record and brain imaging studies. Results: Atrial fibrillation was detected in 108 out of a total of 429 patients ( 25%) . Time to atrial fibrillation detection was average 79.8 days with a range of 13-430 days . All but one patient were started on anticoagulation. Within the entire cohort 55 patients had recurrent strokes with 7 of them having more than 1 recurrence.. 15 recurrent strokes occurred in patients in whom atrial fibrillation was detected during monitoring and 40 in patients without atrial fibrillation detected . Imaging pattern of recurrent strokes in patients with atrial fibrillation was cortical in 81.8% with 10 patients showing multiple infarcts and subcortical in 18.2% patients with one showing multiple infarcts. Strokes in patients without atrial fibrillation were cortical in location in 60% with 10 being multiple and subcortical in 40% with 3 patient having multiple infarcts.4 patients who did not have atrial fibrillation had more than 1 recurrent stroke and 3 patients who were detected to have atrial fibrillation had more than 1 recurrent stroke Conclusion majority of recurrent strokes in cryptogenic population did not have atrial fibrillation( 72.7%) Incidence of recurrent stroke was similar in patients with atrial fibrillation (13.8%) detected versus (12.4% %)not detected group . Imaging patterns showed more cortical strokes in A. fib patients ( 80%) and more subcortical strokes in patients without A. fib( 40%)
Abstract only Introduction: Paroxysmal afib is the most common cause of cryptogenic strokes. Long-term insertable cardiac monitor (ICM) has been increasingly used for ongoing evaluation of afib in ...these patients. Due to financial affordability, it is important to identify appropriate candidates for its routine use. Methods: We reviewed EMRs of a cohort of cryptogenic stroke patients from February 2014 to May 2017 who received REVEAL LINQ ICMs. All patients met ESUS criteria prior to insertion and were enrolled in remote follow-up. At least one year monitoring was conducted if no afib was recorded. Patient demographics, stroke characteristics and risk factors were compared between patients with and without afib. Results: Among total 348 patients enrolled, 99 (28.5%) were found to have afib with median time to afib detection of 128 days. For patients without afib, the median length of follow up was 566 days. Patients with afib were significantly older (mean ± SD, 73.0 ± 9.42 vs. 64.4 ± 11.5, p<0.00001) and their left atrium size (mm) were significantly larger (37.9 ± 7.26 vs. 35.4 ± 5.81, p<0.005) than those patients without afib. Non-afib patients had statistically more frequent association with ongoing smoking or LDL ≥ 120 comparing with afib patients (p<0.05). Although not statistically significant, there was tendency that posterior circulation only strokes occurred less in afib group (p=0.08). However, intra- and extracranial atherosclerosis were comparable between afib and non-afib groups. Within the afib group, afib was detected within 30 days in 25 patients with median time to detection of 14 days. These patients had significant higher rate of intracranial stenosis (p=0.01) comparing with those patients with afib detected beyond 30 days. Conclusion: Paroxysmal afib related strokes account for about one-fourth of cryptogenic strokes in our cohort. Long term ICM may benefit more in older patients with enlarged left atrium and lack of risk factors such as smoking and high LDL. It likely has low yield in patients with posterior infarcts only. Noninvasive ambulatory ECG monitoring for 30 days may be considered first in patients with intracranial stenosis.
An improved Fuzzy C-Means (FCM) algorithm, which is called Reliability-based Spatial context Fuzzy C-Means (RSFCM), is proposed for image segmentation in this paper. Aiming to improve the robustness ...and accuracy of the clustering algorithm, RSFCM integrates neighborhood correlation model with the reliability measurement to describe the spatial relationship of the target. It can make up for the shortcomings of the known FCM algorithm which is sensitive to noise. Furthermore, RSFCM algorithm preserves details of the image by balancing the insensitivity of noise and the reduction of edge blur using a new fuzzy measure indicator. Experimental data consisting of a synthetic image, a brain Magnetic Resonance (MR) image, a remote sensing image, and a traffic sign image are used to test the algorithm’s performance. Compared with the traditional fuzzy C-means algorithm, RSFCM algorithm can effectively reduce noise interference, and has better robustness. In comparison with state-of-the-art fuzzy C-means algorithm, RSFCM algorithm could improve pixel separability, suppress heterogeneity of intra-class objects effectively, and it is more suitable for image segmentation.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
