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Relay navigation strategy study on intelligent drive on urban roads

The Journal of China Universities of Posts and Telecommunications 2016年第2期23卷 79-90页

作者： Gao Hongbo Zhang Xinyu An Lifeng Liu Yuchao Li Deyi State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China Information Technology Center Tsinghua University Beijing 100083 China Department of Computer Science and Technology Tsinghua University Beijing 100083 China The 61th Institute of Electronic System Engineering Beijing 100039 China

In order to solve navigation problem of intelligent vehicle driving on urban roads and to achieve the navigation in intersection area, intersection transition area and section area. The relay navigation strategy and algorithm can solve the navigation problem of intelligent vehicle driving in typical urban roads such as intersection area, intersection transition area and section area, realizing seamless handover among different typical areas. Bezier curve function model was introduced to different typical areas, which solved the self-adaption recognition problem in different typical areas and revised positional accuracy with the help of cloud computing positioning service. In order to explain the strategy implement, an instance based on the strategy was adopted. Instance analysis indicates that as for the navigation problem in intersection area, intersection transition area and section area, if the relay navigation strategy is utilized, the self-adaption recognition problem in different typical areas can be handled. Based on the relay navigation strategy, the drive of intelligent vehicle on urban roads can effectively solve the self-adaption recognition problem in different typical areas in urban and further solve driving problems of intelligent vehicle of the same category in urban roads.

关键词： intelligent vehicle intelligent drive urban roads relay navigation strategy

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SHACL4P: SHACL constraints validation within Protégé ontology editor

SHACL4P: SHACL constraints validation within Protégé ontol...

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International Conference on Data and software engineering (ICODSE)

作者： Fajar J. Ekaputra Xiashuo Lin Christian Doppler Laboratory of Software Engineering Integration for Flexible Automation System Institute of Software Technology and Information System Vienna TU Wien Austria

ISBN: (纸本)9781509056729

Recently, Semantic Web Technologies (SWT) have been introduced and adopted to address the problem of enterprise data integration (e.g., to solve the problem of terms and concepts heterogeneity within large organizations). One of the challenges of adopting SWT for enterprise data integration is to provide the means to define and validate structural constraints over Resource Description Framework (RDF) graphs. This is difficult since RDF graph axioms behave like implications instead of structural constraints. SWT researchers and practitioners have proposed several solutions to address this challenge (e.g., SPIN and Shape Expression). However, to the best of our knowledge, none of them provide an integrated solution within open source ontology editors (e.g., Protégé). We identified this absence of the integrated solution and developed SHACL4P, a Protégé plugin for defining and validating Shapes Constraint Language (SHACL), the upcoming W3C standard for constraint validation within Protégé ontology editor.

关键词： OWL Biological system modeling Ontologies Tools

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The MR neuroimaging protocol for the Accelerating Medicines Partnership® Schizophrenia Program

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Schizophrenia 2025年第1期11卷 1-14页

作者： Harms, Michael P. Cho, Kang-Ik K. Anticevic, Alan Bolo, Nicolas R. Bouix, Sylvain Campbell, Dylan Cannon, Tyrone D. Cecchi, Guillermo Goncalves, Mathias Haidar, Anastasia Hughes, Dylan E. Izyurov, Igor John, Omar Kapur, Tina Kim, Nicholas Kotler, Elana Kubicki, Marek Kuperman, Joshua M. Laulette, Kristen Lindberg, Ulrich Markiewicz, Christopher Ning, Lipeng Poldrack, Russell A. Rathi, Yogesh Romo, Paul A. Tamayo, Zailyn Wannan, Cassandra Wickham, Alana Yassin, Walid Zhou, Juan Helen Addington, Jean Alameda, Luis Arango, Celso Breitborde, Nicholas J. K. Broome, Matthew R. Cadenhead, Kristin S. Calkins, Monica E. Chen, Eric Yu Hai Choi, Jimmy Conus, Philippe Corcoran, Cheryl M. Cornblatt, Barbara A. Diaz-Caneja, Covadonga M. Ellman, Lauren M. Fusar-Poli, Paolo Gaspar, Pablo A. Gerber, Carla Glenthøj, Louise Birkedal Horton, Leslie E. Hui, Christy Lai Ming Kambeitz, Joseph Kambeitz-Ilankovic, Lana Keshavan, Matcheri S. Kim, Sung-Wan Koutsouleris, Nikolaos Kwon, Jun Soo Langbein, Kerstin Mamah, Daniel Mathalon, Daniel H. Mittal, Vijay A. Nordentoft, Merete Pearlson, Godfrey D. Perez, Jesus Perkins, Diana O. Powers, Albert R. Rogers, Jack Sabb, Fred W. Schiffman, Jason Shah, Jai L. Silverstein, Steven M. Smesny, Stefan Stone, William S. Strauss, Gregory P. Thompson, Judy L. Upthegrove, Rachel Verma, Swapna K. Wang, Jijun Wolf, Daniel H. Kahn, Rene S. Kane, John M. McGorry, Patrick D. Nelson, Barnaby Woods, Scott W. Shenton, Martha E. Wood, Stephen J. Bearden, Carrie E. Pasternak, Ofer Department of Psychiatry Washington University School of Medicine St. Louis MO United States Department of Psychiatry Brigham and Women’s Hospital Harvard Medical School Boston MA United States Department of Psychiatry Yale University School of Medicine New Haven CT United States Department of Psychiatry Beth Israel Deaconess Medical Center Harvard Medical School Boston MA United States Department of Software Engineering and Information Technology École de technologie supérieure Montréal QC Canada Department of Psychology Yale University New Haven CT United States T.J. Watson Research Laboratory IBM Research Yorktown Heights NY United States Department of Psychology Stanford University Stanford CA United States Department of Psychology University of California Los Angeles CA United States Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany Massachusetts General Hospital Harvard Medical School Boston MA United States Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston MA United States Department of Radiology University of California San Diego CA United States Department of Clinical Physiology and Nuclear Medicine Copenhagen University Hospital - Rigshospitalet Glostrup Denmark Seaman Family MR Research Centre Calgary AB Canada Orygen Parkville VIC Australia Centre for Sleep and Cognition and Centre for Translational MR Research Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Department of Psychiatry Hotchkiss Brain Institute University of Calgary Calgary AB Canada General Psychiatry Service Treatment and Early Intervention in Psychosis Program Lausanne University Hospital and University of Lausanne Lausanne Switzerland Department of Child and Adolescent Psychiatry Institute of Psychiatry and Mental Health Hospital General Universitario Gregorio Marañón Instituto de Salud Carlos III School of Medicine Universidad Complutense Madrid Spain

Neuroimaging with MRI has been a frequent component of studies of individuals at clinical high risk (CHR) for developing psychosis, with goals of understanding potential brain regions and systems impacted in the CHR state and identifying prognostic or predictive biomarkers that can enhance our ability to forecast clinical outcomes. To date, most studies involving MRI in CHR are likely not sufficiently powered to generate robust and generalizable neuroimaging results. Here, we describe the prospective, advanced, and modern neuroimaging protocol that was implemented in a complex multi-site, multi-vendor environment, as part of the large-scale Accelerating Medicines Partnership® Schizophrenia Program (AMP® SCZ), including the rationale for various choices. This protocol includes T1- and T2-weighted structural scans, resting-state fMRI, and diffusion-weighted imaging collected at two time points, approximately 2 months apart. We also present preliminary variance component analyses of several measures, such as signal- and contrast-to-noise ratio (SNR/CNR) and spatial smoothness, to provide quantitative data on the relative percentages of participant, site, and platform (i.e., scanner model) variance. Site-related variance is generally small (typically <10%). For the SNR/CNR measures from the structural and fMRI scans, participant variance is the largest component (as desired;40–76%). However, for SNR/CNR in the diffusion scans, there is substantial platform-related variance (>55%) due to differences in the diffusion imaging hardware capabilities of the different scanners. Also, spatial smoothness generally has a large platform-related variance due to inherent, difficult to control, differences between vendors in their acquisitions and reconstructions. These results illustrate some of the factors that will need to be considered in analyses of the AMP SCZ neuroimaging data, which will be the largest CHR cohort to date. Watch Dr. Harms discuss this article at https://vimeo.

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Multi-modality stacked deep polynomial network based feature learning for Alzheimer's disease diagnosis

Multi-modality stacked deep polynomial network based feature...

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IEEE International Symposium on Biomedical Imaging

作者： Xiao Zheng Jun Shi Yan Li Xiao Liu Qi Zhang School of Communication and Information Engineering Shanghai University Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781479923519

Feature representation is the critical factor for the computer-aided Alzheimer's disease (AD) diagnosis. Deep polynomial network (DPN) is a novel deep learning algorithm, which can effectively learn feature representation from small samples. In this work, a stacked DPN (S-DPN) algorithm is proposed to further improve feature representation. We then propose a multi-modality S-DPN (MM-S-DPN) algorithm to fuse multi-modality neuroimaging data and learn more discriminative and robust feature representation for AD classification. Experiments are performed on ADNI dataset with MRI and PET images as multi-modality data. The results indicate that S-DPN is superior to DPN and stacked auto-encoder algorithms. Moreover, MM-S-DPN achieves best performance compared with single-modality S-DPN and other multi-modality feature learning based algorithms.

关键词： Classification algorithms Magnetic resonance imaging Positron emission tomography Neuroimaging Diseases Training Machine learning

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Automatic Fix for C Integer Errors by Precision Improvement

Automatic Fix for C Integer Errors by Precision Improvement

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Xi Cheng Min Zhou Xiaoyu Song Ming Gu Jiaguang Sun Tsinghua National Laboratory for Information Science and Technology (TNList) China Key Laboratory for Information System Security China School of Software Tsinghua University China Electrical and Computer Engineering Portland State University USA

Integer errors in C program may lead to serious failures and vulnerabilities. They are harbored in a wide range of programs including mature software such as Linux kernel. Code reviewing is laborious and cannot guarantee reliable fixes for errors. Addressing potential errors in the development phase is error-prone even for experts and essentially hinders developing efficiency. In this paper we propose a novel approach to automate fix for C integer errors. Our approach directly replaces original C integers with dynamic-precision integers to fix potential errors without detecting them in advance. Many errors can be fixed by precision improvement without changing the design of application. We implement a tool CIntFix to automatically fix C integer errors. CIntFix succeeds in fixing all 5414 programs in NIST's Juliet test suite from 7 weakness categories. Meanwhile, on Juliet test suite and SPEC CINT2000 benchmarks, CIntFix processes C source code at the rate of 0.157s/KLOC and the fixed programs have 18.0% slowdown on average. The results show that CIntFix is capable to fix integer errors in real-world C programs.

关键词： Semantics Encoding Kernel Security software reliability

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Studying and comparing the free e-learning platforms 4th

Studying and comparing the free e-learning platforms

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Colloquium in information Science and Technology (CIST)

作者： Mohammed Ouadoud Mohamed Yassin Chkouri Amel Nejjari Kamal Eddine El Kadiri Faculty of Sciences (FS) Research Operational and Statistic Applied (LIROSA) Tetouan Morocco UAE Laboratory of the Information System and Software Engineering (SIGL) Tetouan Morocco

ISBN: (纸本)9781509007523

E-learning platforms are constantly changing; they have transformed the everyday life of teachers and students. An e-learning platforms study, from a methodical, systemic approach that identifies the dimensions, specifications and the essential criteria for the evaluation of these platforms is necessary. In this paper, we propose an analytical study of free e-learning platforms. Four platforms have been selected according to requested criteria filter among the 600 platforms listed in the THOT CURSUS directory. Subsequently, we make a comparative study of these platforms. On the one hand, we would like to measure the degree of adequacy between technological opportunities they offer and the pedagogical presuppositions of training. On the other hand, for clarifying and supporting the users in their choice of the most suitable platform for their needs so that they can benefit to the maximum of potential offered by technologies in pedagogy, and approach the problematic of the platform choice for a better pedagogy.

关键词： Electronic learning Training Usability Licenses Standards

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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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A More Accurate Model for Finding Tutorial Segments Explaining APIs

A More Accurate Model for Finding Tutorial Segments Explaini...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： He Jiang Jingxuan Zhang Xiaochen Li Zhilei Ren David Lo State Key Laboratory of Software Engineering Wuhan University Wuhan China Service Software of Liaoning Province Key Laboratory for Ubiquitous Network Dalian China School of Software Dalian University of Technology Dalian China School of Information System Singapore Management University Singapore

Developers prefer to utilize third-party libraries when they implement some functionalities and Application Programming Interfaces (APIs) are frequently used by them. Facing an unfamiliar API, developers tend to consult tutorials as learning resources. Unfortunately, the segments explaining a specific API scatter across tutorials. Hence, it remains a challenging issue to find the relevant segments. In this study, we propose a more accurate model to find the exact tutorial fragments explaining APIs. This new model consists of a text classifier with domain specific features. More specifically, we discover two important indicators to complement traditional text based features, namely co-occurrence APIs and knowledge based API extensions. In addition, we incorporate Word2Vec, a semantic similarity metric to enhance the new model. Extensive experiments over two publicly available tutorial datasets show that our new model could find up to 90% fragments explaining APIs and improve the state-of-the-art model by up to 30% in terms of F-measure.

关键词： Tutorials Androids Humanoid robots Programming Feature extraction Semantics Animation

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Generalized interface automata with multicast synchronization

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Frontiers of Computer Science 2015年第1期9卷 1-14页

作者： Fei HE Xiaoyu SONG Ming GU Jiaguang SUN Tsinghua National Laboratory for Information Science and Technology (TNList) Tsinghua University Beijing 100084 China Key Laboratory for Information System Security Ministry of Education Beijing 100084 China School of Software Tsinghua University Beijing 100084 China Department of Electrical & Computer Engineering Portland State University Oregon 97207 USA

Interface automata are one of the prominent formalisms for specifying interface behaviors of componentbased systems. However, only one-to-one communication is allowed in the composition of interface automata. This paper presents multicast interface automata which generalize the classic interface automata and accommodate multicast communication mechanism. The multicast interface automata endorse both bottom-up and top-down design methodologies. Theoretical results on compatibility and refinement are established for incremental design and independent implementability.

关键词： interface automata multicast communication,component interaction verification

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Reusable planning and scheduling tool for SPPSS: Public Operation Service 14th

Reusable planning and scheduling tool for SPPSS: Public Oper...

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14th International Conference on Space Operations, SpaceOps 2016

作者： Li, Dalin Gu, Yanfeng Bai, Meng Yang, Binbin Wu, Hai Yan Harbin Institute of Technology Harbin150001 China National Space Science Center CAS Beijing100190 China Beihang University Beijing100191 China Key Laboratory of Integrated Avionics and Information Technology for Complex Aerospace System No.1 Nanertiao Zhonguancun Haidian district Beijing China School of Electronic and Information Engineering No.92 Xidazhijie Nangang district Harbin China Satellite Mission Operation Laboratory No.1 Nanertiao Zhonguancun Haidian district Beijing China School of Software No.37 Xueyuanlu Haidian district Beijing China

ISBN: (数字)9781624104268

ISBN: (纸本)9781624104268

The Strategic Pioneer Program on Space Science (SPPSS) was approved by Chinese Academy of Sciences on January 2011. The first satellite in the program, Dark Matter Particle Explorer, was launched on December 2015. Public Operation Service system is designed to provide scheduling and planning services for all the satellite missions related to SPPSS. The system requirements are analyzed, and the layout of it is detailed introduced in this paper. Then the performance of the system is concluded at the end. © 2016, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Scheduling

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