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IAPR International Conference on Biometrics (ICB)

作者： Abhijit Das Umapada Pal Miguel A. Ferrer Michael Blumenstein Institute for Integrated and Intelligent Systems Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain School of Software University of Technology Sydney Australia

ISBN: (纸本)9781509018703

This article reports and summarizes the results of a competition on sclera segmentation and recognition benchmarking, called Sclera Segmentation and Recognition Benchmarking Competition 2016 (SSRBC 2016). It was organized in the context of the 9th IAPR International Conference on Biometrics (ICB 2016). The goal of this competition was to record the recent developments in sclera segmentation and recognition, and also to gain the attention of researchers on this subject of biometrics. In this regard, we have used a multi-angle sclera dataset (MASD version 1). It is comprised of 2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82*2) different eyes. We have prepared a manual segmentation mask of these images to create the baseline for both tasks. We have, furthermore, adopted precision and recall based statistical measures to evaluate the effectiveness of the segmentation and the ranks of the competing algorithms. The recognition accuracy measure has been employed to measure the recognition task. To summarize, twelve participants registered for the competition, and among them, three participants submitted their algorithms/ systems for the segmentation task and two their recognition algorithm. The results produced by these algorithms reflect developments in the literature of sclera segmentation and recognition, employing cutting edge segmentation techniques. Along with the algorithms of three competing teams and their results, the MASD version 1 dataset will also be freely available for research purposes from the organizer's website. The competition also demonstrates the recent interests of researchers from academia as well as industry on this subject of biometrics.

关键词： Image segmentation Clustering algorithms Iris recognition Manuals Feature extraction

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Fast and efficent multimodal eye biometrics using projective dictionary pair learning

Fast and efficent multimodal eye biometrics using projective...

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Congress on Evolutionary Computation

作者： Abhijit Das Prabir Mondal Umapada Pal Miguel Angel Ferrer Michael Blumenstein Institute for Integrated and Intelligent Systems Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain School of Software University of Technology Sydney Australia

ISBN: (纸本)9781509006243

This work proposes a projective pairwise dictionary learning-based approach for fast and efficient multimodal eye biometrics. The work uses a faster Projective pairwise Discriminative Dictionary Learning (DL) in contrast to the traditional DL which uses synthesis DL. Projective Pairwise Discriminative Dictionary (PPDD) uses a synthesis dictionary and an analysis dictionary jointly to achieve the goal of pattern representation and discrimination. As the PPDD process of DL is in contrast to the use of l 0 or l 1 -norm sparsity constraints on the representation coefficients adopted in most traditional DL, it works faster than other DL. Moreover, the blending of synthesis dictionary and an analysis dictionary also enhance the feature representation of the complex eye patterns. We employed the combination of sclera and iris traits to establish multimodal biometrics. The experimental study and analysis conducted fulfill the hypothesis we considered. In this work we employed a part of the UBIRIS version 1 dataset to conduct the experiments.

关键词： Dictionaries Iris recognition Training Time complexity Pattern recognition

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Neural Incremental Attribute Learning Based on Principal Component Analysis

Neural Incremental Attribute Learning Based on Principal Com...

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2016 IEEE International Conference on Big Data Analysis

作者： Ting Wang Wei Zhou Xiaoyan Zhu Fangzhou Liu Sheng-Uei Guan State Key Laboratory of Intelligent Technology and Systems Tsinghua University Wuxi Research Institute of Applied Technologies Tsinghua University Dept. of Computer Science University of Liverpool Dept. of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University

ISBN: (纸本)9781467395922

Feature Extraction(FE) based on Principal Component Analysis(PCA) can effectively improve classification results by reducing the interference among features. However, such a good method has not been employed in previous studies of Incremental Attribute Learning(IAL), a novel machine learning strategy, where features are gradually trained one by one in order to remove interference among features and improve classification results. This study proposed a preprocessing for neural IAL algorithm based on feature extraction with PCA. Experimental results show that this approach is not only very efficient, but also applicable for pattern classification performance improvement.

关键词： pattern Classification Feature Extraction Principal Component Analysis Neural Networks Incremental Attribute Learning

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Quantum State Tomography via Reduced Density Matrices

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Physical Review Letters 2017年第2期118卷 020401-020401页

作者： Tao Xin Dawei Lu Joel Klassen Nengkun Yu Zhengfeng Ji Jianxin Chen Xian Ma Guilu Long Bei Zeng Raymond Laflamme State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Institute for Quantum Computing University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Physics University of Guelph Guelph N1G 2W1 Ontario Canada Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia Department of Mathematics and Statistics University of Guelph Guelph N1G 2W1 Ontario Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences 100190 Beijing China Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland USA Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada Canadian Institute for Advanced Research Toronto M5G 1Z8 Ontario Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Quantum state tomography via local measurements is an efficient tool for characterizing quantum states. However, it requires that the original global state be uniquely determined (UD) by its local reduced density matrices (RDMs). In this work, we demonstrate for the first time a class of states that are UD by their RDMs under the assumption that the global state is pure, but fail to be UD in the absence of that assumption. This discovery allows us to classify quantum states according to their UD properties, with the requirement that each class be treated distinctly in the practice of simplifying quantum state tomography. Additionally, we experimentally test the feasibility and stability of performing quantum state tomography via the measurement of local RDMs for each class. These theoretical and experimental results demonstrate the advantages and possible pitfalls of quantum state tomography with local measurements.

关键词： Quantum entanglement Quantum information processing Quantum tomography Random matrix theory

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Evaluating technologies for improved airplane state awareness and prediction

Evaluating technologies for improved airplane state awarenes...

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AIAA Infotech @ Aerospace Conference, 2016

作者： Young, Steven Daniels, Taumi Evans, Emory Etherington, Timothy Shish, Kimberlee de Haag, Maarten Uijt Schuet, Stefan Kiggins, Daniel NASA Langley Research Center HamptonVA United States Millennium Engineering & Integration Company NASA Ames Research Center Moffett FieldCA United States Ohio University AthensOH United States NASA Ames Research Center Moffett FieldCA United States National Institute of Aerospace HamptonVA United States Safety-Critical Avionics Systems 1 S. Wright St United States Electromagnetics and Sensors 8 N. Dryden St United States Flight Software Systems 5 N. Dryden St United States Intelligent Systems Division Mail Stop 269-1 United States Advanced Technology Center 350 Collins Rd United States School of Electrical Engineering 337 Stocker Center United States 100 Exploration Way United States

ISBN: (数字)9781624103889

ISBN: (纸本)9781624103889

Airplane state awareness (ASA) is a pilot performance attribute derived from the more general attribute known as "situation awareness." Airplane state alludes primarily to attitude and energy state, but also infers other state variables, such as the state of automated (or autonomous) systems that can affect attitude or energy state. Effective ASA must encompass the perception of all such relevant state variables with respect to time and space, understanding the effect of the flight environment on these variables, and projecting impacts as, or when, one or more these variables change. Recognizing that loss of ASA has been a leading causal and contributing factor to recent accidents in aviation, an industry-wide team has recommended several Safety Enhancements (SEs) to resolve or mitigate the problem. Two of these (SE-207 and SE-208) call for research and development of new technology concepts that can predict energy and/or auto-flight system states, and intuitively notify or alert flight crews to future unsafe or otherwise undesired states. This paper will describe a high-fidelity flight simulation activity designed to address aspects of these two SEs by achieving four objectives: (1) raise the technology readiness level for selected new technologies via testing across a span of conditions that emulate those encountered in previous accidents or studies;(2) discover design characteristics requiring refinement as well as any previously unknown contributing factors to loss of ASA;(3) assess the usability of new technology (e.g. ease of use, usefulness, usage);and (4) advance test infrastructure capability for subsequent tests. This latter includes (1) evaluating an eye-tracking system and physiological measures for their potential to objectively validate design effectiveness and quantify attention or other behavioral issues;and (2) validating test platform performance given several modifications to create Boeing 787-like flight deck interfaces and supporting systems. Ba

关键词： Flight simulators

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Decentralized Bisimulation for Multiagent systems 15

Decentralized Bisimulation for Multiagent Systems

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International Conference on Autonomous Agents and Multiagent systems

作者： Lei Song Yuan Feng Lijun Zhang Quantum Computation & Intelligent Systems University of Technology State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences

ISBN: (纸本)9781450337694

The notion of bisimulation has been introduced as a powerful way to abstract from details of systems in the formal verification community. When applying to multiagent systems, classical bisimulations will allow one agent to make decisions based on full histories of others. Thus, as a general concept, classical bisimulations are unrealistically powerful for such systems. In this paper, we define a coarser notion of bisimulation under which an agent can only make realistic decisions based on information available to it. Our bisimulation still implies trace distribution equivalence of the systems, and moreover, it allows a compositional abstraction framework of reasoning about the systems.

关键词： Bisimulation Multiagent systems Decentralized Multi-agent systems Decentralized Agents

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Estimating diffusion networks: recovery conditions, sample complexity & soft-thresholding algorithm

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Manuel Gomez-Rodriguez Le Song Hadi Daneshmand Google MPI for Intelligent Systems Tübingen Germany College of Computing MPI for Software Systems Kaiserslautern Germany College of Computing Georgia Institute of Technology Atlanta GA Computer Science Department Zürich Switzerland

Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called cascades. Can we recover the hidden network structures from these observed cascades? What kind of cascades and how many cascades do we need? Are there some network structures which are more difficult than others to recover? Can we design efficient inference algorithms with provable guarantees?Despite the increasing availability of cascade data and methods for inferring networks from these data, a thorough theoretical understanding of the above questions remains largely unexplored in the literature. In this paper, we investigate the network structure inference problem for a general family of continuous-time diffusion models using an l1- regularized likelihood maximization framework. We show that, as long as the cascade sampling process satisfies a natural incoherence condition, our framework can recover the correct network structure with high probability if we observe O(d3logN) cascades, where d is the maximum number of parents of a node and N is the total number of nodes. Moreover, we develop a simple and efficient soft-thresholding network inference algorithm which demonstrate the match between our theoretical prediction and empirical results. In practice, this new algorithm also outperforms other alternatives in terms of the accuracy of recovering hidden diffusion networks.

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HGCUDF: Hierarchical Grid Clustering Using Data Field

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Chinese Journal of Electronics 2014年第1期23卷 37-42页

作者： WANG Shuliang FAN Jinghua FANG Meng YUAN Hanning School of Software Beijing Institute of Technology International School of Software Wuhan University Centre for Quantum Computation and Intelligent Systems University of Technology SydneyBroadway NSW 2007 Australia

A new clustering algorithm of Hierarchical grid clustering using data field(HGCUDF) is proposed. Under the distributed characteristics of data points on objects, the hierarchical grids divide and conquer the large datasets in their hierarchical subsets, which reduces the scope in search of the clustering centers, and minifies the area of data space for generating data field. The compared experiments show that HGCUDF computes the grids rather than retrieves all data from database, for improving the efficiency.

关键词： Hierarchical grid clustering using data field(HGCUDF) Data field Spatial clustering

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Selected challenges of software evolution for automated production systems

Selected challenges of software evolution for automated prod...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： B. Vogel-Heuser S. Feldmann J. Folmer J. Ladiges A. Fay S. Lity M. Tichy M. Kowal I. Schaefer C. Haubeck W. Lamersdorf T. Kehrer S. Getir M. Ulbrich V. Klebanov B. Beckert Institute of Automation and Information Systems Technische Universität München Germany Institute of Software Engineering and Automotive Informatics University of Braunschweig Germany Automation Technology Institute Helmut Schmidt University Germany Distributed Systems and Information Systems Universität Hamburg Germany Institute for Programming and Reactive Systems Technische Universität Braunschweig Germany Software Engineering Group University of Siegen Germany Software Engineering Division University of Gothenburg Chalmers Sweden Reliable Software Systems University of Stuttgart Germany Application-oriented Formal Verification Karlsruhe Institute of Technology Germany

Automated machines and plants are operated for some decades and undergo an everlasting evolution during this time. In this paper, we present three related open evolution challenges focusing on software evolution in the domain of automated production systems, i.e. evolution and co-evolution of (interdisciplinary) engineering models and code, quality assurance as well as variant and version management during evolution.

关键词： software Unified modeling language Automation Hardware Adaptation models Solid modeling Testing

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Workflow scheduling in grid based on bacterial foraging optimization 1

Workflow scheduling in grid based on bacterial foraging opti...

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1st International Workshop on Process-Aware systems, PAS 2014

作者： Yao, Feng Ge, Jidong Li, Chuanyi Ge, Yuhang Hu, Haiyang Zhou, Yu Hu, Hao Luo, Bin State Key Laboratory for Novel Software Technology Software Institute Nanjing University Nanjing210093 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Nanjing University of Science and Technology Nanjing210094 China School of Computer Hangzhou Dianzi University Hangzhou310018 China College of Computer Science Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9783662461693

Optimal assignment of a workflow application in heterogeneous computing system is NP-complete in general case. We proposed the algorithm based on bacterial foraging optimization technique for Grid resource scheduling. This algorithm aims at minimizing the makespan of workflow application. To show the advantage of this algorithm, we made comparison with ant colony optimization and particle swarm optimization. The experiment shows that this bacterial foraging optimization algorithm is better than the other two algorithms in minimizing the makespan. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Grid computing

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