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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Wei Li Meng Qin Kai Lei ICNLAB School of Electronics and Computer Engineering (SECE) Peking University Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Community detection is a fundamental step in social network analysis. In this study, we focus on the hybrid identification of overlapping communities with network topology (e.g., user interaction) and edge-induced content (e.g., user messages). Conventional hybrid methods tend to combine topology and node-induced content to explore disjoint communities, but fail to integrate edge-induced content and to derive overlapping communities. Moreover, although several semantic community detection approaches have been developed, which can generate the semantic description for each community besides the partition result, they can only incorporate the word-level content to derived coarse-grained descriptions. We propose a novel Semantic Link Community Detection (SLCD) model based on non-negative matrix factorization (NMF). It adopts the perspective of link communities to integrate topology and edge-induced content, where the partitioned link communities can be further transformed into an overlapping node-induced membership. Moreover, SLCD incorporates both the word-level and sentence-level content while considering the diversity of user messages. Hence, it can also derive strongly interpretable and multi-view community descriptions simultaneously when community partition is finished. SLCD's superior performance and powerful application ability over other state-of-the-art competitors are further demonstrated in our experiments on a series of real social networks.

关键词： Semantics Network topology Topology Social networking (online) image edge detection Task analysis Cultural differences

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A Cloud Environment for Ubiquitous Medical image Reconstruction

A Cloud Environment for Ubiquitous Medical Image Reconstruct...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Estefania Serrano Javier Garcia-Blas Jesus Carretero Computer Architecture and Technology Group University Carlos III of Madrid Leganes Spain

ISBN: (纸本)9781728111421;9781728111414

The new needs that have appeared in recent years around the medical image reconstruction field have pushed a greater investment on computing and storage platforms in order to keep up with the increased requirements in performance and quality. To reduce the cost of purchasing and managing these resources novel ubiquituos services have arisen. In this work we describe and evaluate a cloud environment for ubiquitous medical image reconstruction based on Amazon EC2 and a heterogeneous Apache Spark platform. We show an architecture composed by a client application, a front-end server and an isolated cluster per application for the uninterrupted execution of the reconstruction service. We present an execution and cost model for the prediction and configuration of the cloud environment based on performance and monetary costs. The evaluation shows the best configurations in terms of execution time for different types of instances and tuning parameters. Based on these results we compute a Pareto front to study the best solutions for the configuration of the cloud environment with the objective of minimizing both execution time and costs.

关键词： Cloud computing Graphics processing units image reconstruction Task analysis Biomedical imaging Sparks Computer architecture

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DEEP image CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDDING WITH INCEPTION-LIKE BLOCK

DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDD...

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IEEE International conference on image processing

作者： Qiang Wang Jiaqing Xu Rongchun Li Peng Qiao Ke Yang Shijie Li Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China

image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. These methods take a two-stage approach, feature learning and clustering, sequentially or jointly. We observe that these works usually focus on the combination of reconstruction loss and clustering loss, relatively little work has focused on improving the learning representation of the neural network for clustering. In this paper, we propose a deep convolutional embedded clustering algorithm with inception-like block (DCECI). Specifically, an inception-like block with different type of convolution filters are introduced in the symmetric deep convolutional network to preserve the local structure of convolution layers. We simultaneously minimize the reconstruction loss of the convolutional autoencoders with inception-like block and the clustering loss. Experimental results on multiple image datasets exhibit the promising performance of our proposed algorithm compared with other competitive methods.

关键词： Convolutional codes Convolution Clustering algorithms image reconstruction Decoding Clustering methods Task analysis

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dMazeRunner: Executing Perfectly Nested Loops on Dataflow Accelerators

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2019年第5期18卷 70-70页

作者： Dave, Shail Kim, Youngbin Avancha, Sasikanth Lee, Kyoungwoo Shrivastava, Aviral Arizona State Univ Compiler Microarchitecture Lab Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA Yonsei Univ Seoul South Korea Intel Labs Parallel Comp Lab Bangalore Karnataka India

Dataflow accelerators feature simplicity, programmability, and energy-efficiency and are visualized as a promising architecture for accelerating perfectly nested loops that dominate several important applications, including image and media processing and deep learning. Although numerous accelerator designs are being proposed, how to discover the most efficient way to execute the perfectly nested loop of an application onto computational and memory resources of a given dataflow accelerator (execution method) remains an essential and yet unsolved challenge. In this paper, we propose dMazeRunner - to efficiently and accurately explore the vast space of the different ways to spatiotemporally execute a perfectly nested loop on dataflow accelerators (execution methods). The novelty of dMazeRunner framework is in: i) a holistic representation of the loop nests, that can succinctly capture the various execution methods, ii) accurate energy and performance models that explicitly capture the computation and communication patterns, data movement, and data buffering of the different execution methods, and iii) drastic pruning of the vast search space by discarding invalid solutions and the solutions that lead to the same cost. Our experiments on various convolution layers (perfectly nested loops) of popular deep learning applications demonstrate that the solutions discovered by dMazeRunner are on average 9.16x better in Energy-Delay-Product (EDP) and 5.83x better in execution time, as compared to prior approaches. With additional pruning heuristics, dMazeRunner reduces the search time from days to seconds with a mere 2.56% increase in EDP, as compared to the optimal solution.

关键词： Coarse-grained reconfigurable array dataflow deep neural networks loop optimization energy-efficiency systolic arrays mapping analytical model design space exploration

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Segmentation of Calcification and Brain Hemorrhage with Midline Detection 15

Segmentation of Calcification and Brain Hemorrhage with Midl...

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15th IEEE International Symposium on parallel and distributed processing with Applications (ISPA) / 16th IEEE International conference on Ubiquitous Computing and Communications (IUCC)

作者： Arif, Muhammad Wang, Guojun Guangzhou Univ Sch Comp Sci & Educ Software Dept Comp Sci Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781538637906

A considerable amount of deformities are prone to be neglected by the radiotherapists besides acquisition of excessive and false positive rates. In this article, a process has been developed representing the selective parameters with Fuzzy C-Means and ANFIS (Adaptive Neuro-fuzzy inference). As it is a grave concern to differentiate the hemorrhage and calcification on the basis of selective factors. The desirable outcome and useful information are most likely to be delivered by the fuzzy clustering segmentation methods. The abnormal tissues in the brain are accurately identified by our proposed method. ANFIS can be referred to as the extension of the ANN family and it displays excellent learning skills and estimating competences, so it is believed to be a highly productive tool through which ambiguities in any system can be efficiently managed. The medical discipline is realizing the benefits of this system, such as the identification of hemorrhage and calcification. Our method is beneficial to image fusion techniques whose applications rely on the source information of local images. Results show that our method is superior then the other traditional methods in terms of quantitative image segmentation performance parameters.

关键词： Brain images Calcification Hemorrhage Fuzzy C-mean Segmentation ANFIS Classification Detection

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A Deep Learning Approach for Sentiment Analysis in Spanish Tweets 27th

A Deep Learning Approach for Sentiment Analysis in Spanish T...

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27th International conference on Artificial Neural Networks (ICANN)

作者： Vizcarra, Gerson Mauricio, Antoni Mauricio, Leonidas Univ Catolica San Pablo Res & Innovat Ctr Comp Sci Arequipa Peru Univ Nacl Ingn Artificial Intelligence Image Proc & Robot Lab Dept Mech Engn Bldg AOff A1-221210 Tupac Amaru Ave Lima Peru

ISBN: (纸本)9783030014247

Sentiment Analysis at Document Level is a well-known problem in Natural Language processing (NLP), being considered as a reference in NLP, over which new architectures and models are tested in order to compare metrics that are also referents in other issues. This problem has been solved in good enough terms for English language, but its metrics are still quite low in other languages. In addition, architectures which are successful in a language do not necessarily works in another. In the case of Spanish, data quantity and quality become a problem during data preparation and architecture design, due to the few labeled data available including not-textual elements (like emoticons or expressions). This work presents an approach to solve the sentiment analysis problem in Spanish tweets and compares it with the state of art. To do so, a preprocessing algorithm is performed based on interpretation of colloquial expressions and emoticons, and trivial words elimination. Processed sentences turn into matrices using the 3 most successful methods of word embeddings (GloVe, FastText and Word2Vec), then the 3 matrices merge into a 3-channels matrix which is used to feed our CNN-based model. The proposed architecture uses parallel convolution layers as k-grams, by this way the value of each word and their contexts are weighted, to predict the sentiment polarity among 4 possible classes. After several tests, the optimal tuple which improves the accuracy were <1, 2>. Finally, our model presents % 61.58 and % 71.14 of accuracy in InterTASS and General Corpus respectively.

关键词： Convolutional neural network (CNN) Sentiment analysis Spanish tweets

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6th International conference on Big Data Analytics, BDA 2018

6th International Conference on Big Data Analytics, BDA 2018

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6th International conference on Big Data Analytics, BDA 2018

ISBN: (纸本)9783030047795

The proceedings contain 29 papers. The special focus in this conference is on Big Data Analytics. The topics include: Automated credibility assessment of web page based on genre;cbI: Improving credibility of user-generated content on facebook;a parallel approach to detect communities in evolving networks;modeling sparse and evolving data;polystore data management systems for managing scientific data-sets in big data archives;MPP SQL query optimization with RTCG;big data analytics framework for spatial data;an ingestion based analytics framework for complex event processing engine in internet of things;An energy-efficient greedy mapreduce scheduler for heterogeneous hadoop YARN cluster;blockchain-powered big data analytics platform;Analysis of narcolepsy based on single-channel EEG signals;formal methods, artificial intelligence, big-data analytics, and knowledge engineering in medical care to reduce disease burden and health disparities;*** based soil N-P-K prediction model for soil and crop specific data: A predictive modelling approach;deep neural network based image captioning;oversample based large scale support vector machine for online class imbalance problem;using crowd sourced data for music mood classification;applying big data intelligence for real time machine fault prediction;PRISMO: Priority based spam detection using multi optimization;malware detection using machine learning and deep learning;spatial co-location pattern mining;humble data management to big data analytics/science: A retrospective stroll;Fusion of game theory and big data for AI applications;distributed financial calculation framework on cloud computing environment;testing concept drift detection technique on data stream;homogenous ensemble of time-series models for indian stock market.

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Red Blood Cells Segmentation: A Fully Convolutional Network Approach

Red Blood Cells Segmentation: A Fully Convolutional Network ...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Ario Sadafi Martin Radolko Iosif Serafeimidis Steffen Hadlak Arivis AG Munich Germany

ISBN: (纸本)9781728111421;9781728111414

Red blood cell segmentation in microscopic images is the first step for various clinical studies carried out on blood samples such as cell counting, cell shape identification, etc. Conventional methods while often showing a high accuracy are heavily depending on the acquisition modality. Deep learning approaches have shown to be more robust regarding such modalities and still showing a comparable accuracy. In this paper, we first investigate necessary steps to apply a specific type of deep learning methods, namely fully convolutional networks, to red blood cell segmentation. Based on data given and constraints imposed by our partners mainly regarding a high throughput of their data we then describe an exemplary application. First results show, that even with a focus on high performance a good accuracy above 90% can be reached.

关键词： image segmentation Training Shape Deep learning Red blood cells image analysis

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FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING STRATEGY

FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING ST...

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IEEE International conference on image processing

作者： Zheng Qin Zhaoning Zhang Xiaotao Chen Changjian Wang Yuxing Peng Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and accurate network for very limited computational budgets (e.g., 10-140 MFLOPs). Our key idea is applying a fast down-sampling strategy to MobileNet framework. In FD-MobileNet, we perform 32× downsampling within 12 layers, only half the layers in the original MobileNet. This design brings three advantages: (i) It remarkably reduces the computational cost. (ii) It increases the information capacity and achieves significant performance improvements. (iii) It is engineering-friendly and provides fast actual inference speed. Experiments on ILSVRC 2012 and PASCAL VOC datasets demonstrate that FD-MobileNet consistently outperforms MobileNet and achieves comparable results with ShuffleNet under different computational budgets, for instance, surpassing Mobile-Net by 5.5% on the ILSVRC 2012 top-1 accuracy and 8.3% on the VOC 2007 mAP under a complexity of 12 MFLOPs. On an ARM-based device, FD-MobileNet achieves 1.11× inference speedup over MobileNet and 1.82× over ShuffleNet under the same complexity.

关键词： Computer architecture Complexity theory Neural networks Training Hardware Computational modeling Standards

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parallelization of Synthetic Aperture Radar (SAR) image Formation Algorithm 1st

Parallelization of Synthetic Aperture Radar (SAR) Image Form...

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1st International conference on Computational Intelligence and Informatics (ICCII)

作者： Rao, P. V. R. R. Bhogendra Shashank, S. S. Def Res & Dev Lab Hyderabad Andhra Pradesh India BrahMos Aerosp Private Ltd Hyderabad Andhra Pradesh India

ISBN: (纸本)9789811024719;9789811024702

Synthetic aperture radar (SAR)-based platforms have to process increasingly large number of complex floating-point operations and have to meet hard real-time deadlines. However, real-time use of SAR is severely restricted by computation time taken for image formation. One of the classical methods of reducing this computation time to make it suitable for real-time application is multi-processing. A successful attempt has been made by the authors to develop and test a parallel algorithm for synthetic aperture radar image formation, and the results are presented in this paper.

关键词： Synthetic aperture radar SAR image formation Digital signal processing parallel computing distributed computing

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