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Hyper-Pairing network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation 22nd

Hyper-Pairing Network for Multi-phase Pancreatic Ductal Aden...

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10th international Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI)

作者： Zhou, Yuyin Li, Yingwei Zhang, Zhishuai Wang, Yan Wang, Angtian Fishman, Elliot K. Yuille, Alan L. Park, Seyoun Johns Hopkins Univ Baltimore MD 21218 USA Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China Johns Hopkins Univ Sch Med Baltimore MD 21287 USA

ISBN: (纸本)9783030322458;9783030322441

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of pancreatic disease. In this study, we aim at enhancing PDAC automatic segmentation by integrating multi-phase information (i.e., arterial phase and venous phase). To this end, we present Hyper-Pairing network (HPN), a 3D fully convolution neural network which effectively integrates information from different phases. the proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange. Additionally, a pairing loss is added to encourage the commonality between high-level feature representations of different phases. Compared to prior arts which use single phase data, HPN reports a significant improvement up to 7.73% (from 56.21% to 63.94%) in terms of DSC.

关键词： Textures

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A low cost method for determining the entire network path bandwidths of indirect high-performance computer networks 16

A low cost method for determining the entire network path ba...

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16th IEEE international Symposium on parallel and Distributed Processing with Applications, 17th IEEE international conference on Ubiquitous computing and Communications, 8th IEEE international conference on Big Data and Cloud computing, 11th IEEE international conference on Social computing and networking and 8th IEEE international conference on Sustainable computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018

作者： Yan, Baicheng Zhou, Yi Xiao, Limin Yang, Zhang Wu, Hongyu Wei, Bing School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Applied Physics and Computational Mathematics Beijing100094 China

ISBN: (纸本)9781728111414

Bandwidth is an important performance metric for high-performance computer network (HINET). How to determine large-scale HINET path bandwidths quickly and precisely is a highly pivotal challenge for parallel application performance analysis and prediction, optimizing end-to-end data transport performance, network management, etc. However, it is hard to determine the entire network point-to-point bandwidths of HINET since it often costs amounts of measurement time. Existing path bandwidth measurement methods focus on improving the accuracy of single path bandwidth measurement, however, lack considerations for reducing the entire network path bandwidths measurement times. To address this issue, we present an efficient method to generate the full-scale point-to-point bandwidth measurement plan for rapidly obtaining each path bandwidth in HINET. For evaluating, a source routing Fat-tree simulator and comprehensive experiments were conducted for different application scenarios. Both experimental and analysis showed that our method can reduce the measurement times significantly. In particular, compared to the exhaustive method, the measurement times of our method are one in 185 for the entire path bandwidth fault and can save 95% measurement times in 1024 nodes network with synthetic traffic. © 2018 IEEE.

关键词： Bandwidth

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Proceedings - 16th IEEE international Symposium on parallel and Distributed Processing with Applications, 17th IEEE international conference on Ubiquitous computing and Communications, 8th IEEE international conference on Big Data and Cloud computing, 11th IEEE international conference on Social computing and networking and 8th IEEE international conference on Sustainable computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018

Proceedings - 16th IEEE International Symposium on Parallel ...

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ISBN: (纸本)9781728111414

the proceedings contain 161 papers. the topics discussed include: deep learning for phishing detection;a partition matching method for optimal attack path analysis;an energy and robustness adjustable optimization method of file distribution services;deriving the political affinity of twitter users from their followers;on the usability of big (social) data;re-running large-scale parallel programs using two nodes;predicting hacker adoption on darkweb forums using sequential rule mining;an on-the-fly scheduling strategy for distributed stream processing platform;deadlock-free adaptive routing based on the repetitive turn model for 3D network-on-chip;and radix: enabling high-throughput georeferencing for phenotype monitoring over voluminous observational data.

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Testbeds for the Implementation of 5G in the European Union: the Innovative Case of the 5G-DRIVE Project 15th

Testbeds for the Implementation of 5G in the European Union:...

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15th ifip WG 12.5 conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Chochliouros, Ioannis P. Spiliopoulou, Anastasia S. Kostopoulos, Alexandros Vasilaki, Eirini Herzog, Uwe Dardamanis, Athanassios Chen, Tao Ladid, Latif Agapiou, Marinos Hellen Telecommun Org OTE SA 99 Kifissias Ave Athens 15124 Greece EURESCOM GmbH Wieblinger Weg 19-64 D-69123 Heidelberg Germany SmartNet SA 2 Lakonias St Athens 17342 Greece VTT Tech Res Ctr Finland Kaitovayla 1 Oulu 90571 Finland Univ Luxembourg 6 Ave Fonte L-4364 Esch Sur Alzette Luxembourg Univ Athens 6 Panepistimiopolis Ilissia 15784 Greece

ISBN: (纸本)9783030199098;9783030199081

An essential part of the actual EU policy towards promoting and validating 5G applications and of related solutions is via the establishment of an explicit plan and of a detailed roadmap for trials, tests and experimental activities though dedicated testbeds, in parallel with the current research and development activities coming from the 5G-PPP framework. the present paper discusses the fundamental role of the proposed trials' initiatives within the broader European framework for the establishment and the promotion of 5G and also analyses the corresponding streams as indispensable parts of the 5G-PPP context, aiming to support innovation and growth. In addition, as part of the broader initiative for trial actions we identify the case of the 5G-DRIVE project that aims to realise 5G deployment scenarios (i.e., enhanced Mobile Broadband and Vehicle-to-Everything communications), between the EU and China, by discussing the fundamental features of the respective trials sites.

关键词： 5G 5G Action Plan (5GAP) 5G Public Private Partnership (5G-PPP) Horizon 2020 Enhanced Mobile Broadband (eMBB) Experimental platforms Mobile edge computing (MEC) network slicing Pilot trials Testing and validation facilities Vehicle-to-Everything (V2X)

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Constrained Big Data Mining in an Edge computing Environment 4th

Constrained Big Data Mining in an Edge Computing Environment

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4th international conference on Big Data Applications and Services ( (BigDAS)

作者： Leung, Carson K. Deng, Deyu Hoi, Calvin S. H. Lee, Wookey Univ Manitoba Winnipeg MB Canada Inha Univ Incheon South Korea

ISBN: (纸本)9789811306952;9789811306945

High volumes of wide varieties of valuable data of different veracity can be easily generated or collected at a high velocity from various big data applications and services. A rich source of these big data is the Internet of things (IoT), which can be viewed as a network of sensors, mobile devices, wearable devices, and other "things" that are capable to operate within the existing Internet infrastructure. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge-in terms of sets of frequently co-occurring items-embedded in these big data. Existing frequent pattern mining algorithms mostly run serially on a single local computer or in distributed and parallel environments on computer clusters, grids, or clouds. Many of these algorithms return large numbers of frequent patterns, of which only some may be of interest to the user. In this paper, we present a constrained big data mining algorithm that (i) focuses the mining to those frequent patterns that are interested to the users and (ii) runs in an edge computing environment, in which computation is performed at edges of the computing network.

关键词： Big data Big data applications and services Data analytics Data mining Data science Edge computing Frequent patterns Knowledge discovery in databases

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Exploring Semantic Relationships for Image Captioning without parallel Data 19

Exploring Semantic Relationships for Image Captioning withou...

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19th IEEE international conference on Data Mining (ICDM)

作者： Liu, Fenglin Gao, Meng Zhang, Tianhao Zou, Yuexian Peking Univ Sch ECE ADSPLAB Shenzhen Peoples R China Beijing Univ Posts & Telecommun Sch ICE Beijing Peoples R China Beijing Foreign Studies Univ Beijing Peoples R China

ISBN: (纸本)9781728146034

Recently, image captioning has aroused great interest in both academic and industrial worlds. Most existing systems are built upon large-scale datasets consisting of image-sentence pairs, which, however, are time-consuming to construct. In addition, even for the most advanced image captioning systems, it is still difficult to realize deep image understanding. In this work, we achieve unpaired image captioning by bridging the vision and the language domains with high-level semantic information. the motivation stems from the fact that the semantic concepts with the same modality can be extracted from both images and descriptions. To further improve the quality of captions generated by the model, we propose the Semantic Relationship Explorer, which explores the relationships between semantic concepts for better understanding of the image. Extensive experiments on MSCOCO dataset show that we can generate desirable captions without paired datasets. Furthermore, the proposed approach boosts five strong baselines under the paired setting, where the most significant improvement in CIDEr score reaches 8%, demonstrating that it is effective and generalizes well to a wide range of models.

关键词： vision and language unpaired training data image captioning deep neural network

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parallel Processing of Computed Tomography Images 39th

Parallel Processing of Computed Tomography Images

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39th international conference on Information Systems Architecture and Technology (ISAT)

作者： Polap, Dawid Wozniak, Marcin Silesian Tech Univ Inst Math Kaszubska 23 PL-44100 Gliwice Poland

ISBN: (纸本)9783319999968;9783319999951

Medical research is not only expensive but also time-consuming, what can be seen in the queues, and then after the waiting time for the analysis of the effects obtained from tests. In the case of computed tomography examinations, the end result is a series of the described images of the examined object's shape. the description is made on the careful observation of the results. In this work, we propose a solution that allows to select images that are suspicious. this type of technique reduces the amount of data that needs to be analyzed and thus reduces the waiting time for the patient. the idea is based on a three-stage data processing. In the first one, key-points are located as features of found elements, in the second, images are constructed containing found areas of images, and in the third, the classifier assesses whether the image should be analyzed in terms of diseases. the method has been described and tested on a large CT dataset, and the results are widely discussed.

关键词： CT images Image processing Convolutional neural network

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Lower Bound on network Diameter for Distributed Function Computation 6th

Lower Bound on Network Diameter for Distributed Function Com...

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6th international conference on Future Data and Security Engineering (FDSE)

作者： Dai, H. K. Toulouse, M. Oklahoma State Univ Comp Sci Dept Stillwater OK 74078 USA Vietnamese German Univ Comp Sci Dept Binh Duong New City Vietnam

ISBN: (纸本)9783030356538;9783030356521

parallel and distributed computing network-systems are modeled as graphs with vertices representing compute elements and adjacency-edges capturing their uni- or bi-directional communication. Distributed function computation covers a wide spectrum of major applications, such as quantized consensus and collaborative hypothesis testing, in distributed systems. Distributed computation over a networksystem proceeds in a sequence of time-steps in which vertices update and/or exchange their values based on the underlying algorithm constrained by the time-(in)variant network-topology. For finite convergence of distributed information dissemination and function computation in the model, we study lower bounds on the number of time-steps for vertices to receive (initial) vertex-values of all vertices regardless of underlying protocol or algorithmics in time-invariant networks via the notion of vertex-eccentricity in a graph-theoretic framework. We prove a lower bound on the maximum vertex-eccentricity in terms of graph-order and -size in a strongly connected directed graph, and demonstrate its optimality via an explicitly constructed family of strongly connected directed graphs.

关键词： Distributed function computation Linear iterative schemes Information dissemination Finite convergence Vertex-eccentricity

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An Efficient and Secure Authentication Scheme using Markov Chain for Wireless Sensor networks 8

An Efficient and Secure Authentication Scheme using Markov C...

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IEEE 8th international Advance computing conference (IACC)

作者： Singh, Deepti Kumar, Bijendra Singh, Samayveer Chand, Satish Netaji Subhas Inst Technol Dept Comp Engn New Delhi India Bennett Univ Dept Comp Sci Engn Greater Noida India Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India

ISBN: (纸本)9781538666784

Due to varied applications of wireless sensor networks (WSNs), data is required by any user when and wherever they require. Usually, base station gathers all information from the sensor and sends it periodically to the user. But for real-time application, mutual authentication among the communicating nodes is required. During user authentication base station checks that the user is authorized to gather the collected information from the sensor node through an insecure channel. In this paper, we propose an efficient authentication scheme which provides anonymity of user in WSNs that uses Markov chain. the Markov chain is a stochastic process that can be used for a system in which it follows a chain of linked events but next event depends only on the current state of the system. Stationary limit distribution of matrix is created by the base station to help the user to keep their password and identity safe. the security analysis verifies that the proposed scheme is safe against various attacks like forgery, parallel session attacks, user impersonation, etc.

关键词： Wireless sensor network authentication smart card session key

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3DFPN-HS²: 3D Feature Pyramid network Based High Sensitivity and Specificity Pulmonary Nodule Detection 22nd

3DFPN-HS<SUP>2</SUP>: 3D Feature Pyramid Network Based High ...

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10th international Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI)

作者： Liu, Jingya Cao, Liangliang Akin, Oguz Tian, Yingli CUNY New York NY 10031 USA UMass CICS Amherst MA 01002 USA Google AI New York NY 10011 USA Mem Sloan Kettering Canc Ctr New York NY 10065 USA

ISBN: (纸本)9783030322267;9783030322250

Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which limited the automatic diagnosis in routine clinical practice. In this paper, we propose a novel pulmonary nodule detection framework based on a 3D Feature Pyramid network (3DFPN) to improve the sensitivity of nodule detection by employing multi-scale features to increase the resolution of nodules, as well as a parallel top-down path to transit the high-level semantic features to complement low-level general features. Furthermore, a High Sensitivity and Specificity (HS2) network is introduced to eliminate the falsely detected nodule candidates by tracking the appearance changes in continuous CT slices of each nodule candidate. the proposed framework is evaluated on the public Lung Nodule Analysis (LUNA16) challenge dataset. Our method is able to accurately detect lung nodules at high sensitivity and specificity and achieves 90.4% sensitivity with 1/8 false positive per scan which outperforms the state-of-the-art results 15.6%.

关键词： Lung nodule detection False positive reduction CT Deep learning

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