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International Workshop on Enterprise Distributed Object computing, EDOCW

作者： Kan Ngamakeur Sira Yongchareon Veronica Liesaputra Chengfei Liu Jian Yu Department of Computing Unitec Institute of Technology New Zealand Department of Computer Science and Software Engineering Swinburne University of Technology Australia School of Engineering Auckland University of Technology New Zealand

Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.

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Image segmentation using membrane computing: A literature survey 11th

Image segmentation using membrane computing: A literature su...

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11th International Conference on Bio-inspired computing – Theories and Applications, BIC-TA 2016

作者： Yahya, Rafaa I. Shamsuddin, Siti Mariyam Yahya, Salah I. Hasan, Shafatnnur Al-Salibi, Bisan Al-Khafaji, Ghada Faculty of Computing Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Computer College of Science University of Al-Mustansiriyah Baghdad Iraq UTM Big Data Center Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Software Engineering Koya University Iraq School of Computer Sciences Universiti Sains Malaysia USM GelugorPenang11800 Malaysia Department of Computer Science College of Science Baghdad University Baghdad Iraq

ISBN: (纸本)9789811036101

Membrane computing, a recent branch of natural computing, has been gaining momentum attention in the last few decades due to its massive parallelism and efficient computation. Many researchers in the field of membrane computing have proposed sophisticated techniques inspired by cell biology for computer science applications, especially when they considered cell organization in tissues, organs, and most recently, from the organization of neurons. The interdisciplinary applications of membrane computing include, but not limited to computer science, biology, biomedicine, bioinformatics and several other fields such as mathematics, artificial intelligence, automation, economics, to name but a few. Their applications are pertaining to computer graphics, approximate optimization, cryptography, parallel computing and image processing. Hence, in this paper we present an up to date comprehensive literature review pertaining to the application of membrane computing in the area of digital image analysis, especially image segmentation, comprehensively and systematically. We thoroughly investigate the recent advancement in the field of image segmentation using membrane system. Furthermore, we highlight the merits and demerits of various software tools and methods. Finally, we suggest some intuitive future directions in light of the current limitations. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Image segmentation

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Multi-agent Reinforcement Learning for Service Composition

Multi-agent Reinforcement Learning for Service Composition

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IEEE International Conference on Services computing (SCC)

作者： yu lei Philip S. Yu Inner Mongolia Engineering Lab of Cloud Computing and Service Software Inner Mongolia University Hohhot China Department of Computer Science University of Illinois at Chicago Chicago USA

This paper investigates the multi-agent cooperation problems in Web services domain. For Pareto-optimal Nash equilibrium, reinforcement learning algorithms are used to solve the coordination problem in cooperative environments. Most previous works study the deterministic gain of a state. However, in practical service environments, the gain may be nondeterministic due to unstable Quality of Service (QoS). To avoid local optimal solution, we use an improved update function. The experimental results show that proposed reinforcement learning algorithm outperforms other learning methods.

关键词： Learning (artificial intelligence) Games Web services Nash equilibrium Quality of service Multi-agent systems

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Visual Orientation Inhomogeneity Based Convolutional Neural Networks

Visual Orientation Inhomogeneity Based Convolutional Neural ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Sheng-Hua Zhong Jiaxin Wu Yingying Zhu Peiqi Liu Jianmin Jiang Yan Liu College of Computer Science and Software Engineering Shenzhen University Shenzhen P.R. China Department of Computer Science The University of Texas at San Antonio San Antonio TX US College of Computer Science and Software Engineering Shenzhen University Department of Computing The Hong Kong Polytechnic University Hong Kong P.R. China

ISBN: (纸本)9781509044603

The details of oriented visual stimuli are better resolved when they are horizontal or vertical rather than oblique. This "oblique effect" has been researched and confirmed in numerous research studies, including behavioral studies and neurophysiological and neuroimaging findings. Although the "oblique effect" has influence in many fields, little research integrated it into computational models. In this paper, we try to explore this inhomogeneity of visual orientation based on Convolutional neural networks (CNNs) in image recognition. We validate that visual orientation inhomogeneity CNNs can achieve comparable performance with higher computational efficiency on various datasets. We can also get the conclusion that, compared with the cardinal information, oblique information is indeed less useful in natural color image recognition. Through the exploration of the proposed model on image recognition, we gain more understanding of the inhomogeneity of visual orientation. It also illuminates a wide range of opportunities for integrating the inhomogeneity of visual orientation with other computational models.

关键词： Visualization Nonhomogeneous media Convolution Computational modeling Image recognition Standards Kernel

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A separability-entanglement classifier via machine learning

arXiv

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arXiv 2017年

作者： Lu, Sirui Huang, Shilin Li, Keren Li, Jun Chen, Jianxin Lu, Dawei Ji, Zhengfeng Shen, Yi Zhou, Duanlu Zeng, Bei Department of Physics Tsinghua University Beijing100084 China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Beijing Computational Science Research Center Beijing100193 China Department of Mathematics & Statistics University of Guelph GuelphONN1G 2W1 Canada Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD United States Department of Physics Southern University of Science and Technology Shenzhen518055 China Centre for Quantum Computation & Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Statistics and Actuarial Science University of Waterloo WaterlooONN2L 3G1 Canada Institute of Physics Chinese Academy of Sciences Beijing100190 China

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing, which is known to be an NP-hard problem in general. Despite the proposed many methods such as the positive partial transpose (PPT) criterion and the k-symmetric extendibility criterion to tackle this problem in practice, none of them enables a general, effective solution to the problem even for small dimensions. Explicitly, separable states form a high-dimensional convex set, which exhibits a vastly complicated structure. In this work, we build a new separability-entanglement classifier underpinned by machine learning techniques. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum entanglement via the machine learning approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum entanglement

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Test Set Diameter: Quantifying the Diversity of Sets of Test Cases

Test Set Diameter: Quantifying the Diversity of Sets of Test...

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International Conference on software Testing, Verification, and Validation, ICST

作者： Robert Feldt Simon Poulding David Clark Shin Yoo Software Engineering Research Lab Blekinge Institute of Technology Karlskrona Sweden Department of Computer Science University College London London UK School of Computing KAIST Daejeon Republic of Korea

ISBN: (纸本)9781509018284

A common and natural intuition among software testers is that test cases need to differ if a software system is to be tested properly and its quality ensured. Consequently, much research has gone into formulating distance measures for how test cases, their inputs and/or their outputs differ. However, common to these proposals is that they are data type specific and/or calculate the diversity only between pairs of test inputs, traces or outputs. We propose a new metric to measure the diversity of sets of tests: the test set diameter (TSDm). It extends our earlier, pairwise test diversity metrics based on recent advances in information theory regarding the calculation of the normalized compression distance (NCD) for multisets. A key advantage is that TSDm is a universal measure of diversity and so can be applied to any test set regardless of data type of the test inputs (and, moreover, to other test-related data such as execution traces). But this universality comes at the cost of greater computational effort compared to competing approaches. Our experiments on four different systems show that the test set diameter can help select test sets with higher structural and fault coverage than random selection even when only applied to test inputs. This can enable early test design and selection, prior to even having a software system to test, and complement other types of test automation and analysis. We argue that this quantification of test set diversity creates a number of opportunities to better understand software quality and provides practical ways to increase it.

关键词： Measurement Complexity theory software testing Electronic mail software systems

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Deep convolutional neural networks for detecting secondary structures in protein density maps from cryo-electron microscopy

Deep convolutional neural networks for detecting secondary s...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rongjian Li Dong Si Tao Zeng Shuiwang Ji Jing He Department of Computer Science Old Dominion University Norfolk Virginia Division of Computing and Software Systems University of Washington Bothell Bothell WA School of Electrical Engineering and Computer Science Washington State University Pullman WA

ISBN: (纸本)9781509016129

The detection of secondary structure of proteins using three dimensional (3D) cryo-electron microscopy (cryo-EM) images is still a challenging task when the spatial resolution of cryo-EM images is at medium level (5-10Å). Prior researches focused on the usage of local features that may not capture the global information of image objects. In this study, we propose to use deep learning methods to extract high representative global features and then automatically detect secondary structures of proteins. In particular, we build a convolutional neural network (CNN) classifier that predicts the probability of label for every individual voxel in 3D cryo-EM image with respect to the secondary structure elements of proteins such as α-helix, β-sheet and background. To effectively incorporate the 3D spatial information in protein structures, we propose to perform 3D convolutions in the convolutional layers of CNNs. We show that the proposed CNN classifier can outperform existing SVM method on identifying the secondary structure elements of proteins from 3D cryo-EM medium resolution images.

关键词： Proteins Convolution Image resolution Three-dimensional displays Deconvolution Protein engineering Computational modeling

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Learning with only multiple instance positive bags

Learning with only multiple instance positive bags

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2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Zhigang Zhao, Zengshun Zhang, Changshui Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education China College of Electronics Communication and Physics Shandong University of Science and Technology China Automation Department Tsinghua University State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781509006199

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag - positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called 'Positive Multiple Instance' (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms. © 2016 IEEE.

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Fast RFID Polling Protocols

Fast RFID Polling Protocols

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International Conference on Parallel Processing (ICPP)

作者： Jia Liu Bin Xiao Xuan Liu Lijun Chen State Key Laboratory for Novel software Technology Nanjing University China Department of Computing The Hong Kong Polytechnic University China College - of Computer Science and Electronic Engineering Hunan University China

ISBN: (纸本)9781509028245

Polling is a widely used anti-collision protocol that interrogates RFID tags in a request-response way. In conventional polling, the reader needs to broadcast 96-bit tag IDs to separate each tag from others, leading to long interrogation delay. This paper takes the first step to design fast polling protocols by shortening the polling vector. We first propose an efficient Hash Polling Protocol (HPP) that uses hash indices rather than tag IDs as the polling vector to query each tag. The length of the polling vector is dropped from 96 bits to no more than log(n) bits (n is the number of tags). We then enhance HPP (EHPP) to make it not only more efficient but also more steady with respect to the number of tags. To avoid redundant transmissions in both HPP and EHPP, we finally propose a Tree-based Polling Protocol (TPP) that reserves the invariant portion of the polling vector while updates only the discrepancy by constructing and broadcasting a polling tree. Theoretical analysis shows that the average length of the polling vector in TPP levels off at only 3.44, 28 times less than 96-bit tag IDs. We also apply our protocols to collect tag information and simulation results demonstrate that our best protocol TPP outperforms the state-of-the-art information collection protocol.

关键词： Protocols Indexes RFID tags Broadcasting Wireless communication Delays

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Metamorphic testing: A new student engagement approach for a new software testing paradigm

Metamorphic testing: A new student engagement approach for a...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Dave Towey Tsong Yueh Chen Fei-Ching Kuo Huai Liu Zhi Quan Zhou School of Computer Science The University of Nottingham Ningbo China Ningbo Republic of China Australia-India Research Centre for Automation Software Engineering RMIT University Melbourne VIC Australia Department of Computer Science and Software Engineering Swinburne University of Technology Victoria Australia School of Computing and Information Technology University of Wollongong Wollongong NSW Australia

ISBN: (纸本)9781509055999

software testing, as part of the software quality assurance processes in software engineering (SE), is seldom cited as the most engaging part of a software engineer's training. In spite of its importance, it was often a neglected area, and only recently, with the adoption of more agile methodologies, has testing come to the fore. Testers face challenges, including the need to be able to identify when software is not behaving as expected. Metamorphic Testing (MT) first appeared twenty years ago in an effort to help testers facing the oracle problem (not having a mechanism to determine correct software behavior). Although MT is simple in concept, it has demonstrated very impressive fault-finding ability, and has recently been attracting a great deal of interest. Training students and practitioners to effectively apply MT has become an important goal, but to date, no textbook or guidelines have been made readily available. This paper, written by some of the leading voices in the MT community, is a sharing of some of the experiences, reflections and insights gained through teaching MT (and other related subjects). These experiences indicate that not only is MT effective in industry and at fault-finding, it has the potential to really engage students in creative SE classroom activities.

关键词： Education software software engineering software testing Reflection Context

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