In the past decade, Peer-to-Peer (P2P) Systems achieved great successes. Its fascinating characteristics, such as decentralized control, self-governance, fault tolerance and load balancing, make it the default infrast...
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Optical Coherence Tomography(OCT)is very important in medicine and provide useful diagnostic *** retinal layer thicknesses plays a vital role in pathophysiologic factors of many ocular *** the existing retinal layer s...
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Optical Coherence Tomography(OCT)is very important in medicine and provide useful diagnostic *** retinal layer thicknesses plays a vital role in pathophysiologic factors of many ocular *** the existing retinal layer segmentation approaches,learning or deep learning-based methods belong to the ***,most of these techniques rely on manual-marked layers and the performances are limited due to the image *** order to overcome this limitation,we build a framework based on gray value curve matching,which uses depth learning to match the curve for semi-automatic segmentation of retinal layers from *** depth convolution network learns the column correspondence in the OCT image *** whole OCT image participates in the depth convolution neural network operation,compares the gray value of each column,and matches the gray value sequence of the transformation column and the next *** this algorithm,when a boundary point is manually specified,we can accurately segment the boundary between retinal *** experimental results obtained from a 54-subjects database of both normal healthy eyes and affected eyes demonstrate the superior performances of our approach.
Cloud-oriented service mashup can aggregate many services to provide personalized services for end-users on demand. However, how to securely aggregate mashup services becomes a bottleneck of hampering the development ...
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Cloud-oriented service mashup can aggregate many services to provide personalized services for end-users on demand. However, how to securely aggregate mashup services becomes a bottleneck of hampering the development of cloud computing. In this paper, we present a secure cloud service mashup framework called SMEF to address this problem. In SMEF, we employ security entropy to measure the unascertained security degree of service mashup. The nonfunctional criteria of SMEF are aggregated as a single criterion by defining a utility function. Then the relatively optimal mashup services are selected to meet the user requirements. Finally, we have implemented a simulation of SMEF and conducted extensive experiments using simulations of different sizes of services and security factors. Experimental results show the feasibility and efficiency of the SMEF service mashup framework.
Multispectral imaging (MSI) technique is often used to capture imagesof the fundus by illuminating it with different wavelengths of light. However,these images are taken at different points in time such that eyeball m...
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Multispectral imaging (MSI) technique is often used to capture imagesof the fundus by illuminating it with different wavelengths of light. However,these images are taken at different points in time such that eyeball movementscan cause misalignment between consecutive images. The multispectral imagesequence reveals important information in the form of retinal and choroidal bloodvessel maps, which can help ophthalmologists to analyze the morphology of theseblood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deeplearning framework called “Adversarial Segmentation and Registration Nets”(ASRNet) for the simultaneous estimation of the blood vessel segmentation andthe registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills theblood vessel segmentation task, and (ii) A registration module R that estimatesthe spatial correspondence of an image pair. Based on the segmention-drivenregistration network, we train the segmentation network using a semi-supervisedadversarial learning strategy. Our experimental results show that the proposedASRNet can achieve state-of-the-art accuracy in segmentation and registrationtasks performed with real MSI datasets.
As an important part of searching result presentation, query-biased document snippet generation has become a popular method of search engines that makes the result list more informative to users. Generating a single s...
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As an important part of searching result presentation, query-biased document snippet generation has become a popular method of search engines that makes the result list more informative to users. Generating a single snippet is a lightweight task. However, it will be a heavy workload to generate multiple snippets of multiple documents as the search engines need to process large amount of queries per second, and each result list usually contains several snippets. To deal with this heavy workload, we propose a new high-performance snippet generation approach based on CPU-GPU hybrid system. Our main contribution of this paper is to present a parallel processing stream for large-scale snippet generation tasks using GPU. We adopt a sliding document segmentation method in our approach which costs more computing resources but can avoid the common defect that the high relevant fragment may be cut off. The experimental results show that our approach gains a speedup of nearly 6 times in average process time compared with the baseline approach-Highlighter.
Detecting human in still images is one of the most challenging object detection problems. In this paper we apply the scale theory to human detection. By integrating Gaussian Pyramids multi-scale object representation ...
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Detecting human in still images is one of the most challenging object detection problems. In this paper we apply the scale theory to human detection. By integrating Gaussian Pyramids multi-scale object representation approach we present a Learned Multi-scale Mid-level Feature (LMMF) based human detection algorithm. Firstly multiscale low-level features are extracted by Gaussian Pyramid decomposition and gradient computation. Then LMMFs are learned from multi-scale low-level features using AdaBoost algorithm. The final human/non-human decision is made by classification on the LMMFs. Using LMMF descriptors, our method attempts to harvest more information than using uni-scale feature descriptors. Experiments on INRIA person dataset demonstrate that our method outperforms the previous state of the art detector.
Spatiograms were generalization of histograms, which can harvest spatial information of images. The similarity measure is important when applying spatiograms to various computer vision problems such as tracking and im...
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Spatiograms were generalization of histograms, which can harvest spatial information of images. The similarity measure is important when applying spatiograms to various computer vision problems such as tracking and image retrieval. The original proposed measures use Mahalanobis distance of coordinate mean to measure spatial information in spatiograms. However, spatial information which is described by spatiograms does not lie on vector space. Measures for vector space such as Mahalanobis distance are not effective measures for them. In this paper, We model spatial information as Gaussian approximation of coordinate distributions. Then we parameterize them as a Lie group. Based on Lie group theory, we analyze function space structure of Gaussian pdfs (probability density function) and propose an effective spatiogram similarity measure. We test our measure in object tracking scenarios. Experiments show better tracking results compared with previously proposed measures.
Serverless computing is emerging as a promising paradigm to manage compute in Edge-Cloud continuum. However, distributing and balancing the computational load (serverless functions) across the continuum remains a sign...
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ISBN:
(纸本)9798400708541
Serverless computing is emerging as a promising paradigm to manage compute in Edge-Cloud continuum. However, distributing and balancing the computational load (serverless functions) across the continuum remains a significant challenge. In this paper, we introduce AttentionFunc – a novel framework for decentralized and efficient function offloading and computation balancing in the Edge-Cloud continuum. The AttentionFunc framework strives to introduce a fully decentralized decision-making model that accounts for the multi-objective nature of serverless workflows, the limitations of shared resources in the Edge-Cloud environment, and the dynamic behaviors such as resource contentions or cooperations among serverless functions. In addition, AttentionFunc incorporates an innovative multi-agent offloading model based on the Markov Decision Process (MDP), designed to minimize functions’ execution time and costs. The application of MDP allows the framework to efficiently address these issues using deep reinforcement learning approaches, with an aim to significantly improve function completion latency. Furthermore, AttentionFunc pioneers an attention-based optimization mechanism for multi-agent deep reinforcement learning. This mechanism permits DRL agents to reach a consensus with minimal coordination information, leading to substantial reductions in communication and computation overhead. We evaluate AttentionFunc and compare it against select relevant state-of-the-art approaches. Our experiments and simulations show that AttentionFunc outperforms state-of-the-art approaches in terms of 1) the completion latency (up to 44.2% reduction), 2) the function success rate (up to 43.3% increase). Additionally, we provide the results of many experiments with different MEC scenarios to highlight the components of our approach that influence the results. We conclude that our approach reduces the low-latency challenge faced by most offloading models and improves the successful comple
In this paper, a fast and robust video copy detection scheme is proposed, which is suitable for the DCT-coded video sequences. To address the efficiency and effectiveness issue, we extract the video signature directly...
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In this paper, a fast and robust video copy detection scheme is proposed, which is suitable for the DCT-coded video sequences. To address the efficiency and effectiveness issue, we extract the video signature directly from the compressed domain. The video sequence clusters are constructed with a fixed length. Each cluster consists of several fictional key-frames. For each key-frame, some low-middle frequency full DCT coefficients are obtained directly from block DCT coefficients, and their ordinal measure is computed and acts as video signature. A rotation compensational strategy is further employed to resist the rotation attacks. The experimental results show that the proposed scheme can be resilient to various types of video transformations, including scaling, rotation, speed change, text insertion, and subsequence insertion/deletion etc.. The most important thing is that the proposed approach not only handles geometric distortion perfectly, but also reduces the computation costs substantially.
Accurate network traffic prediction of base station cell is very vital for the expansion and reduction of wireless devices in base station cell. The burst and uncertainty of base station cell network traffic makes the...
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