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ECNet: An Efficient and Context-Aware network for Street Scene Parsing 9

ECNet: An Efficient and Context-Aware Network for Street Sce...

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9th International conference on Parallel architectures, Algorithms and programming (PAAP)

作者： Jiang, Bin Tu, Wenxuan Yang, Chao Xiao, Yi Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781538694039

Semantic segmentation for scene parsing in traffic not only needs to be precise, but also needs to be efficient for further applications in self-driving system. Most existing approaches employ a heavyweight structure as base network and mutil-scale module to enhance the context information, which often suffers from problems of modeling inefficiency and contextual information missing, respectively. We propose an Efficient and Context-Aware network (ECNet) based on DenseNet121 and a novel mutil-scale mechanism. Firstly, we stack dense blocks with hybird dilation rates to extract feature maps, which is much more efficient in parameter computation and model storage. Secondly, we present an Enhanced Atrous Spatial Pyramid Pooling (EASPP) module which allows it to enlarge receptive field and obtain more discriminative features compared with Atrous Spatial Pyramid Pooling (ASPP). Additionally, our Decomposed Residual Block (DRB) module adopts decomposed convolution layer without increasing too much computational burden in order to boost the spatial information, which beneficially narrows the gap in semantic levels and spatial resolution. Experimental results on CityScapes and Camvid datasets show that our method is not only efficient but also performs more accurate for street scene understanding.

关键词： traffic scene deep learning image semantic segmentation dilated convolution feature fusion

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G-thinker: A Distributed Framework for Mining Subgraphs in a Big Graph

G-thinker: A Distributed Framework for Mining Subgraphs in a...

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International conference on Data Engineering

作者： Da Yan Guimu Guo Md Mashiur Rahman Chowdhury M. Tamer Özsu Wei-Shinn Ku John C. S. Lui University of Alabama at Birmingham University of Waterloo Auburn University The Chinese University of Hong Kong

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Mining from a big graph those subgraphs that satisfy certain conditions is useful in many applications such as community detection and subgraph matching. These problems have a high time complexity, but existing systems to scale them are all IO-bound in execution. We propose the first truly CPU-bound distributed framework called G-thinker that adopts a user-friendly subgraph-centric vertex-pulling API for writing distributed subgraph mining algorithms. To utilize all CPU cores of a cluster, G-thinker features (1) a highly-concurrent vertex cache for parallel task access and (2) a lightweight task scheduling approach that ensures high task throughput. These designs well overlap communication with computation to minimize the CPU idle time. Extensive experiments demonstrate that G-thinker achieves orders of magnitude speedup compared even with the fastest existing subgraph-centric system, and it scales well to much larger and denser real network data. G-thinker is open-sourced at http://***/gthinker with detailed documentation.

关键词： Task analysis Data mining Time complexity programming Scheduling Throughput Social network services

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19th International conference on Computational Science and Its Applications, ICCSA 2019

19th International Conference on Computational Science and I...

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19th International conference on Computational Science and Its Applications, ICCSA 2019

ISBN: (纸本)9783030243074

The proceedings contain 103 papers. The special focus in this conference is on Computational Science and Its Applications. The topics include: Integrating the Scrum Framework and Lean Six Sigma;hardware and Software System for Hydric Estimation and Crop Irrigation Scheduling;electronic Medical Information Encryption Using Modified Blowfish Algorithm;a Deep Convolutional Encoder-Decoder Architecture for Retinal Blood Vessels Segmentation;context Data Preprocessing for Context-Aware Smartphone Authentication;extending and Instantiating a Software Reference Architecture for IoT-Based Healthcare Applications;artificial Intelligence Techniques for Electrical Load Forecasting in Smart and Connected Communities;computational Investigation of Consistency and Performance of the Biochemical network of the Malaria Parasite, Plasmodium falciparum;A Systematic Mapping Study on Software architectures Description Based on ISO/IEC/ieee 42010:2011;investigating Evolution in open Source Software;development of an Alumni Feedback System for Curriculum Improvement in Building Technology Courses;e-Maintenance Framework for Strategic Asset Management in Tertiary Institutions;scenes Segmentation in Self-driving Car Navigation System Using Neural network Models with Attention;evolutionary and Adaptive Elderly Care Ecosystem;software Engineering Methodologies for the Evaluation and Monitoring of Projects of Higher Education Students;a Web Framework for Online Peer Tutoring Application in a Smart Campus;use of Blockchain Smart Contracts in Software Engineering: A Systematic Mapping;SPLide: An Integrated Development Environment Supporting Software Product Lines;Analysis of Noise Perturbation on Neural network Based ECG Classification;ArchCaMO - A Maturity Model for Software Architecture Description Based on ISO/IEC/ieee 42010:2011;Multi-class Classification of Impulse and Non-impulse Sounds Using Deep Convolutional Neural network (DCNN);parallel Symbiotic Organisms Search Algorithm.

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Combining Model Refinement and Test Generation for Conformance Testing of the ieee PHD Protocol Using Abstract State Machines 31st

Combining Model Refinement and Test Generation for Conforman...

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31st IFIP WG 6.1International conference on Testing Software and Systems (ICTSS)

作者： Bombarda, Andrea Bonfanti, Silvia Gargantini, Angelo Radavelli, Marco Duan, Feng Lei, Yu Univ Bergamo Dept Management Informat & Prod Engn Bergamo Italy Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

ISBN: (纸本)9783030312800;9783030312794

In this paper we propose a new approach to conformance testing based on Abstract State Machine (ASM) model refinement. It consists in generating test sequences from ASM models and checking the conformance between code and models in multiple iterations. This process is applied at different models, starting from the more abstract model to the one that is very close to the code. The process consists of the following steps: (1) model the system as an Abstract State Machine, (2) generate test sequences based on the ASM model, (3) compute the code coverage using generated tests, (4) if the coverage is low refine the Abstract State Machine and return to step 2. We have applied the proposed approach to Antidote, an open-source implementation of ieee 11073-20601 Personal Health Device (PHD) protocol which allows personal healthcare devices to exchange data with other devices such as small computers and smartphones.

关键词： Personal computers

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INetCEP: In-network Complex Event Processing for Information-Centric networking

INetCEP: In-Network Complex Event Processing for Information...

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ACM/ieee Symposium on architectures for networking and Communications Systems (ANCS)

作者： Manisha Luthra Boris Koldehofe Jonas Höchst Patrick Lampe Ali Haider Rizvi Ralf Kundel Bernd Freisleben Technical University of Darmstadt Germany University of Marburg Germany

Emerging network architectures like Information-Centric networking (ICN)offer simplicity in the data plane by addressing named data. Such flexibility opens up the possibility to move data processing inside network elements for high-performance computation, known as in-network processing. However, existing ICN architectures are limited in terms of (i)in-network processing and (ii)data plane programming abstractions. Such architectures can benefit from Complex Event Processing (CEP), an in-network processing paradigm to efficiently process data inside the data plane. Yet, it is extremely challenging to integrate CEP because the current communication model of ICN is limited to consumer-initiated interaction that comes with significant overhead in number of requests to process continuous data streams. In contrast, a change to producer-initiated interaction, as favored by CEP, imposes severe limitations for request-reply interactions. In this paper, we propose an in-network CEP architecture, INETCEP that supports unified interaction patterns (consumer- and producer-initiated). In addition, we provide a CEP query language and facilitate CEP operations while increasing the range of applications that can be supported by ICN. We provide an open source implementation and evaluation of INETCEP over an ICN architecture, Named Function networking, and two applications: energy forecasting in smart homes and a disaster scenario.

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network Optimization as a Service with Net2Plan 28

Network Optimization as a Service with Net2Plan

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28th European conference on networks and Communications, EuCNC 2019

作者： Garrich, Miquel Martinez, Cesar San Nicolas Muro, Francisco-Javier Moreno Delgado, Maria-Victoria Bueno Marino, Pablo Pavon Universidad Politécnica de Cartagena Cartagena30202 Spain E-lighthouse Networks Solutions Calle Ángel S/n Cartagena30202 Spain

ISBN: (纸本)9781728105468

Carrier-grade telecommunication infrastructure must support an ever-increasing traffic volume and dynamicity in operationally-complex networks characterized by multiple domains, multiple technologies and equipment from multiple vendors. At the same time, the advent of Software-Defined networking (SDN) and network Function Virtualization (NFV) technologies create transport ecosystems with unprecedented network control and resource dynamicity capabilities. In this context, network optimization is essential to efficiently employ network resources, relying whenever possible on automated procedures and exploiting open application programming interfaces (APIs) inherent of SDN/NFV ecosystems. Automated network optimization procedures on top of network controllers promote a client layer of third-party applications that enable optimization-as-a-service (OaaS) business opportunities. In this paper, we present a network OaaS framework built as an extension of the Net2Plan open-source network planning tool. The proposed framework is based on a classical client-server architecture, and through a REST-based API, holders of network infrastructures (i.e. client role) may request the resolution of a network resource allocation problem through the execution of an algorithmic technique. Thus, third-party players can focus on the development of optimization algorithms (i.e. server role) while potentially providing high-performance computing (HPC) capabilities for their execution. Through the paper, we detail the workflow of the proposed OaaS, illustrate its usage with several use-cases and discuss its applicability in different scenarios. © 2019 ieee.

关键词： Software defined networking

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Multi-document Cohesion network Analysis: Automated Prediction of Inferencing across Multiple Documents

Multi-document Cohesion Network Analysis: Automated Predicti...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Bogdan Nicula Cecile A. Perret Mihai Dascalu Danielle S. McNamara University Politehnica of Bucharest Bucharest Romania Arizona State University Tempe USA

ISBN: (数字)9781728192284

ISBN: (纸本)9781728185361

open-ended comprehension questions are a common type of assessment used to evaluate how well students understand one of multiple documents. Our aim is to use natural language processing (NLP) to infer the level and type of inferencing within readers' answers to comprehension questions using the linguistic and semantic features within their responses. Our taxonomy considers three types of responses to comprehension questions from students ( N=146) who read four documents: a) textbase responses (i.e., information required for the answer is present in a contiguous short sequence of text); b) single-document inference responses (i.e., requiring information from multiple text segments in a single document); and c) multi-document inference responses (i.e., information spanning multiple documents is required). The classification task was approached in two ways. First, we extracted features from students' answers to the comprehension questions using linguistic and semantic indices related to textual complexity and an extended Cohesion network Analysis (CNA) graph to assess semantic links between the answers and the reference documents. Second, we compared different Recurrent Neural networks (RNNs) architectures that rely on word embeddings to encode both answers and reference documents. Our best model based on RNNs predicts the answer type with an accuracy of 81%.

关键词： Deep learning Training Analytical models Recurrent neural networks Semantics Feature extraction Task analysis

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A Reduced-Complexity ADMM Based Decoding Algorithm for LDPC Codes 11

A Reduced-Complexity ADMM Based Decoding Algorithm for LDPC ...

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11th ieee International conference on Wireless Communications and Signal Processing (WCSP)

作者： Liang, Zhibiao Chen, Xiang Sun, Xinghua Zhai, Lijun Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China Tsinghua Univ Shenzhen RITS Res Inst Key Lab EDA Shenzhen 518075 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Guangzhou 510006 Peoples R China China Elect Technol Grp Corp Res Inst 54 Beijing 100070 Peoples R China

ISBN: (纸本)9781728135557

Alternating Direction Method of Multipliers (ADMM) is an effective method for Linear programming (LP) decoding of Low-Density Parity-Check (LDPC) codes. In the ADMM-based LDPC decoding algorithm, Euclidean projection on the check polytope is the key step. Current Euclidean projection algorithms in general require either sorting or iterative operations. However, when decoding LDPC codes with high coding rate, the sorting operation brings a high level of computation complexity. Moreover, although the current iterative projection algorithms do not involve the complex operation of sorting, its convergence speed is slow. In this paper, based on trend extrapolation, a reduced-complexity iterative check polytope projection algorithm is proposed. In the proposed algorithm, the target projection point can be found based on the variation trend of the geometric position of the projection points obtained from previous iterations. Therefore, it ensures fast convergence and does not sacrifice on bit error rate (BER) performance. The simulation results confirm that the proposed algorithm can effectively reduce the decoding complexity, especially for LDPC codes with high coding rate.

关键词： Alternating Direction Method of Multipliers (ADMM) Linear programming (LP) decoding Low-Density Parity-Check (LDPC) Euclidean projection check polytope

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Vision-Based Lane Detection and Lane-Marking Model Inference: A Three-step Deep Learning Approach 9

Vision-Based Lane Detection and Lane-Marking Model Inference...

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9th International conference on Parallel architectures, Algorithms and programming (PAAP)

作者： Ma, Yueen Havyarimana, Vincent Bai, Jing Xiao, Zhu Univ Toronto Fac Arts & Sci Toronto ON Canada Ecole Normale Suprieure Dept Appl Sci Bujumbura 6983 Burundi Xidian Univ Sch Artificial Intelligence Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9781538694039

In many advanced driver-assistance systems (ADAS), lane detection is often necessary. Vision-based lane detection is popular because of its cost efficiency, but it can be easily affected by illumination changes, especially abrupt ones. Moreover, since most camera systems have a very limited angle of view (AOV), a single camera ADAS can only perceive a portion of a highly curved road. This introduces another challenge to ADAS when fitting lane models. In this paper, we propose a method for lane model inference, which uses one of the two lane-markings if there is only one lane-marking can be seen;or even, using lane-marking models from previous moments if there are no lane-markings to be seen at the current moment. In addition, we also propose using deep neural networks (DNN) to reduce noise at feature extraction stage. We use two DNNs in our method: a YOLO network for detecting an removing vehicles from images;a CPN network for detecting road surfaces in order to remove noises that are not on road surfaces. We tested our method on a video in which the roads are mostly curved and the lighting conditions can change very fast. We use the distances between our lane-marking models and the ground truth to evaluate our method. We see some big improvements in scenarios where the scene suddenly becomes very bright and where the road has a very high curvature.

关键词： Lane Detection Neural network Lane-Marking Model Inference YOLO

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Accelerating Backward Convolution of Convolutional Neural networks on BWDSP 9

Accelerating Backward Convolution of Convolutional Neural Ne...

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9th International conference on Parallel architectures, Algorithms and programming (PAAP)

作者： Yang, Jiangping Wang, Gai Lu, Maohui Zheng, Qilong Univ Sci & Technol China Natl High Performance Comp Ctr Hefei Hefei Anhui Peoples R China

ISBN: (纸本)9781538694039

Convolutional neural network (CNN), a well-known deep learning architecture extended from articial neural network, has been extensively applied in many applications, which includes image recognition, text classification and robot vision, etc. In particular, quite a few inference accelerators have been proposed based on several embedded systems, such as FPGA, ASIC and DSP platforms as for their advantages of fast development round, reconfigurability and high performance, while have less attention to the backpropagation of CNN based on Edge/Embedded System. It is well known that backpropagation is very demanding on hardware resources, and the training platform should have large bandwidth and enough computing resources. That's why it is very strict with the performance requirements. As a result, we focus on the backward convolution of deep CNN in this paper, implement and optimize a deconvolutional alogrithm with loop unrolling, software-pipelined and data partition in multi-macro techniques based on a digital signal processing, combined with its(BWDSP) architecture and instruction features. The experimental results show that our method achieves a performance of 11.07GFLOPS with one core under 500MHz working clock frequency. Then we take VGGNet-19 as a case study to verify our method's effectiveness and compare the efficiency between the accelerator and CPU(Intel Celeron CPU 1005M(@1.90GHz)).Furthermore, we compare it to previous approaches in the final, it turns out that our implementation outperforms better than CPU and FPGA with equivalent computing resources and can be used to deal with scenes with continuous learning requirements.

关键词： Convolutional Neural network Deconvolution Backpropagation BWDSP

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