Contents Overview 1 Introduction to Backing Up and Restoring Data 2 Preparing to Back Up Data 3 Backing Up Data 9 Restoring Data 16 Lab A: Backing Up and Restoring Data 21 Best Practices 32 Review 33 This course is a prerelease course and is based on Microsoft Windows 2000 Beta 3 software. Content in the final release of the course may be different than the content included in this prerelease version. All labs in the course are to be completed using the Beta 3 version of Microsoft Windows 2000 Advanced Server. Module 10: Backing Up and Restoring Data Information in this document is subject to change without notice. The names of companies, products, people, characters, and/or data mentioned herein are fictitious and are in no way intended to represent any real individual, company, product, or event, unless otherwise noted. Complying with all applicable copyright laws is the responsibility of the user. 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The names of companies, products, people, characters, and/or data mentioned herein are fictitious and are in no way intended to represent any real individual, company, product, or event, unless otherwise noted. Other product and company names mentioned herein may be the trademarks of their respective owners. Project Lead/Senior Instructional Designer: Red Johnston Instructional Designers: Tom de Rose (S&T OnSite), Meera Krishna (NIIT (USA) Inc.) Program Manager: Jim Cochran (Volt Computer) Lab Simulations Developers: David Carlile (ArtSource), Tammy Stockton (Write Stuff) Technical Contributor: Kim Ralls Graphic Artist: Julie Stone (Independent Contractor) Editing Manager: Tina Tsiakalis Editors: Wendy Cleary (S&T OnSite), Diana George (S&T OnSite) Online Program Manager: Nikki McCormick Online Support: Tammy Stockton (Write Stuff) Compact Disc Testing: ST Labs Production Support: Rob Heiret, Ismael Marrero, Mary Gutierrez (Wasser) Manufacturing Manager: Bo Galford Manufacturing Support: Mimi Dukes (S&T OnSite) Lead Project Manager, Development Services: Elaine Nuerenberg Lead Product Manager: Sandy Alto Group Product Manager: Robert Stewart Module 10: Backing Up and Restoring Data iii Introduction This module provides students with the knowledge and skills that are necessary to back up and restore data. The module discusses tools that students will use to back up data, the different backup types, and planning issues involved in backing up data. The module then discusses the procedure of backing up data by using the Microsoft ® Windows ® 2000 Backup utility and the various options that students can set for a backup job. Finally, the module covers the procedure to restore data from a backup and the options that can be set for a restore job. There is one lab in this The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-5, 2014 ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy DOOR RECOGNITION IN CLUTTERED BUILDING INTERIORS USING IMAGERY AND LIDAR DATA L Díaz-Vilariđo a, *, J Martínez-Sáncheza, S Lagüelaa, J Armestoa, K Khoshelhamb a Applied Geotechnologies Research Group, University of Vigo, ETSE Minas, 36310 Vigo (Spain)- (lucia, joaquin.martinez, susiminas, julia)@uvigo.es b Faculty of Geo-Information Science and Earth Observation, University of Twente, P.O Box 217, Enschede 7514 AE, The Netherlands (k.khoshelham)@utwente.nl Commission V KEY WORDS: 3D modelling, feature extraction, imagery, terrestrial laser scanning ABSTRACT: Building indoors reconstruction is an active research topic due to the importance of the wide range of applications to which they can be subjected, from architecture and furniture design, to movies and video games editing, or even crime scene investigation Among the constructive elements defining the inside of a building, doors are important entities in applications like routing and navigation, and their automated recognition is advantageous e.g in case of large multi-storey buildings with many office rooms The inherent complexity of the automation of the recognition process is increased by the presence of clutter and occlusions, difficult to avoid in indoor scenes In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors using information acquired in the form of point clouds and images The methodology goes in depth with door detection and labelling as either opened, closed or furniture (false positive) INTRODUCTION In the last decade, 3D building reconstruction has been a research of interest due to the increasing demand of realistic and accurate building models, which are requested as an input source for a variety of purposes The representation of building interiors can support a wide range of applications in many fields, from architectural planning, to lighting analysis, crime scene investigation or indoor navigation Specifically, openings of the building (windows and door) are of primary interest due to their unchanging nature within the building, especially when compared to furniture, as well as their influence of natural illumination and emergency route planning, among others The geometric detail and the semantic content of existing indoor models strongly depend on the application area for which models are created In this way, Building Information Modelling (BIM) provide highly detailed 3D indoor models that support a large amount of semantic data; while the representation of building interiors in GIS is limited in both geometry and semantic contents This is due to the fact that, 3D GIS models are aimed at urban, global and large-scale purposes Manual generation of a building model is a time-consuming process that requires expert knowledge (Tang, et al, 2010; Gonzalez-Aguilera et al, 2012) Reconstruction tools based on the interpretation of measured data such as LiDAR Data and Images are frequently used for the automatic interpretation and reconstruction of building geometry One of the key challenges to the automation of the reconstruction of building interiors is the presence of clutter and occlusions, caused by furniture and other objects To be useful, modelling algorithms should be functional in unmodified environments since it is not practical to remove the furniture and objects of an indoor scene prior data acquisition Their specific challenges are, on the one hand, occluding objects that can block the visibility of the structural building surfaces (i.e walls, ceilings or floors) causing absence of measured data; on the other hand, they can be erroneously interpreted as parts of the model itself For example, a large bookshelf or cupboard can be confused with a door because their size and shape can be similar; or the frame of a picture can also be not clearly distinguishable from a window Despite the presence of clutter and occlusions in building interiors, some approaches have dealt successfully with the reconstruction of structural elements of indoor scenes from imagery and/or point cloud data Some methods based on datadriven approaches are presented by (Valero et al, 2012; DíazVilariđo et al 2014) and prior knowledge is used by (Budroni and Boehm, 2010; Becker et al, 2003) Regarding openings, as windows can be modelled also from outside, most literature about windows reconstruction is focused on facades because they present lower occlusion levels and repetitive patterns Nevertheless, several methods have been developed to extract windows from indoor environments Adán et al, (2011) and Previtali et al (2014) detect openings in indoor scenes by analysing data density and classifying lowdensity areas as openings, thus limiting the scope to low-density windows and doorways Demisse et al, ...Guiding an HPSG Parser using Semantic and Pragmatic Expectations Jim Skon Computer and Information Science Department The Ohio State University Columbus, OH 43210, USA Internet: skon@ cis.ohio-state.edu Abstract 1 Efficient natural language generation has been successfully demonstrated using highly compiled knowledge about speech acts and their related social actions. A design and prototype implementation of a parser which utilizes this same pragmatic knowledge to efficiently guide parsing is presented. Such guidance is shown to prune the search space and thus avoid needless processing of pragmatically unlikely constituent structures. INTRODUCTION The use of purely syntactic knowledge during the parse phase of natural language understanding yields considerable local ambiguity (consideration of impossible subeonstituents) as well global ambiguity (construction of syntactically valid parses not applicable to the socio-pragmatic context). This research investigates bringing socio-pragmatic knowledge to bear during the parse, while maintaining a domain independent grammar and parser. The particular technique explored uses knowledge about the pragmatic context to order the consideration of proposed parse constituents, thus guiding the parser to consider the best (wrt the expectations) solutions first. Such a search may be classified as a best- first search. The theoretical models used to represent the pragmatic knowledge in this study are based on Halliday's Systemic Grammar and a model of the pragmatics of conversation. The model used to represent the syntax and domain independent semantic knowledge is HPSG - Head-driven Phrase Structure Grammar. BACKGROUND Patten, Geis and Becker (1992) demonstrate the application of knowledge compilation to achieve the rapid generation of natural language. Their mechanism is based on Halliday's systemic networks, and on Geis' theory of the pragmatics of conversation. A model of conversation using principled compilation of pragmatic knowledge and other linguistic knowledge is used to permit the application of pragmatic inference without expensive computation. A pragmatic component is used to model social action, including speech acts, and utilize conventions of us.g involving such features of context such as politeness, ~e~gister, and stylistic features. These politeness features are critiqd}l to the account of indirect speech acts. This pragmatic knovCledge is compiled into course-grained knowledge in the form of a classification hierarchy. A planner component uses knowledge about conditions which need to be satisfied (discourse goals) to produce a set of pragmatic features which characterize a desired utterance. These features are mapped into the systemic l Research Funded by The Ohio State Center for Cognitive Science and The Ohio State Departments of Computer and Information Science and Linguistics grammar (using compiled knowledge) which is then used to realize the actual utterance. The syntactic/semantic component used in this study is a parser based on the HPSG (Head Driven Phrase Structure Grammar) theory of grammar (Pollard and Sag, 1992). HPSG models all linguistic constituents in terms of part/a/ information structures called feature structures. Linguistic signs incorporate simultaneous representation of phonological, syntactic, and semantic attributes of grammatical constituents. HPSG is a lexiealized theory, with the lexical definitions, rather then phrase structure rules, specifying most configurational constraints. Control (such as subcategorization, for example) is asserted by the use of HPSG constraints - partially Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pages 913–920, Sydney, July 2006. c 2006 Association for Computational Linguistics Boosting Statistical Word Alignment Using Labeled and Unlabeled Data Hua Wu Haifeng Wang Zhanyi Liu Toshiba (China) Research and Development Center 5/F., Tower W2, Oriental Plaza, No.1, East Chang An Ave., Dong Cheng District Beijing, 100738, China {wuhua, wanghaifeng, liuzhanyi}@rdc.toshiba.com.cn Abstract This paper proposes a semi-supervised boosting approach to improve statistical word alignment with limited labeled data and large amounts of unlabeled data. The proposed approach modifies the super- vised boosting algorithm to a semi- supervised learning algorithm by incor- porating the unlabeled data. In this algo- rithm, we build a word aligner by using both the labeled data and the unlabeled data. Then we build a pseudo reference set for the unlabeled data, and calculate the error rate of each word aligner using only the labeled data. Based on this semi- supervised boosting algorithm, we inves- tigate two boosting methods for word alignment. In addition, we improve the word alignment results by combining the results of the two semi-supervised boost- ing methods. Experimental results on word alignment indicate that semi- supervised boosting achieves relative er- ror reductions of 28.29% and 19.52% as compared with supervised boosting and unsupervised boosting, respectively. 1 Introduction Word alignment was first proposed as an inter- mediate result of statistical machine translation (Brown et al., 1993). In recent years, many re- searchers build alignment links with bilingual corpora (Wu, 1997; Och and Ney, 2003; Cherry and Lin, 2003; Wu et al., 2005; Zhang and Gildea, 2005). These methods unsupervisedly train the alignment models with unlabeled data. A question about word alignment is whether we can further improve the performances of the word aligners with available data and available alignment models. One possible solution is to use the boosting method (Freund and Schapire, 1996), which is one of the ensemble methods (Dietterich, 2000). The underlying idea of boost- ing is to combine simple "rules" to form an en- semble such that the performance of the single ensemble is improved. The AdaBoost (Adaptive Boosting) algorithm by Freund and Schapire (1996) was developed for supervised learning. When it is applied to word alignment, it should solve the problem of building a reference set for the unlabeled data. Wu and Wang (2005) devel- oped an unsupervised AdaBoost algorithm by automatically building a pseudo reference set for the unlabeled data to improve alignment results. In fact, large amounts of unlabeled data are available without difficulty, while labeled data is costly to obtain. However, labeled data is valu- able to improve performance of learners. Conse- quently, semi-supervised learning, which com- bines both labeled and unlabeled data, has been applied to some NLP tasks such as word sense disambiguation (Yarowsky, 1995; Pham et al., 2005), classification (Blum and Mitchell, 1998; Thorsten, 1999), clustering (Basu et al., 2004), named entity classification (Collins and For More Information Visit RAND at www.rand.org Explore RAND Project AIR FORCE View document details Support RAND Purchase this document Browse Reports & Bookstore Make a charitable contribution Limited Electronic Distribution Rights is document and trademark(s) contained herein are protected by law as indicated in a notice appearing later in this work. is electronic representation of RAND intellectual property is provided for non- commercial use only. Unauthorized posting of RAND electronic documents to a non-RAND website is prohibited. RAND electronic documents are protected under copyright law. Permission is required from RAND to reproduce, or reuse in another form, any of our research documents for commercial use. For information on reprint and linking permissions, please see RAND Permissions. Skip all front matter: Jump to Page 16 e RAND Corporation is a nonprot institution that helps improve policy and decisionmaking through research and analysis. is electronic document was made available from www.rand.org as a public service of the RAND Corporation. CHILDREN AND FAMILIES EDUCATION AND THE ARTS ENERGY AND ENVIRONMENT HEALTH AND HEALTH CARE INFRASTRUCTURE AND TRANSPORTATION INTERNATIONAL AFFAIRS LAW AND BUSINESS NATIONAL SECURITY POPULATION AND AGING PUBLIC SAFETY SCIENCE AND TECHNOLOGY TERRORISM AND HOMELAND SECURITY This product is part of the RAND Corporation technical report series. Reports may include research findings on a specific topic that is limited in scope; present discussions of the methodology employed in research; provide literature reviews, survey instru- ments, modeling exercises, guidelines for practitioners and research professionals, and supporting documentation; or deliver preliminary findings. All RAND reports un- dergo rigorous peer review to ensure that they meet high standards for research quality and objectivity. TECHNICAL REPORT Expendable Missiles vs. Reusable Platform Costs and Historical Data Thomas Hamilton PROJECT AIR FORCE Prepared for the United States Air Force Approved for public release; distribution unlimited The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND’s publications do not necessarily reflect the opinions of its research clients and sponsors. R ® is a registered trademark. © Copyright 2012 RAND Corporation Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Copies may not be duplicated for commercial purposes. Unauthorized posting of RAND documents to a non-RAND website is prohibited. RAND documents are protected under copyright law. For information on reprint and linking permissions, please visit the RAND permissions page (http://www.rand.org/publications/ permissions.html). Published 2012 by the RAND Corporation 1776 Main Street, P.O. Box 2138, Santa Monica, CA 90407-2138 1200 South Hayes Street, Arlington, VA 22202-5050 4570 Fifth Avenue, Suite 600, Pittsburgh, PA 15213-2665 RAND URL: http://www.rand.org To order RAND documents or to obtain additional information, contact Distribution Services: Telephone: (310) 451-7002; Fax: (310) 451-6915; Email: order@rand.org Library of Congress Cataloging-in-Publication Data Hamilton, Thomas. Expendable missiles vs. reusable platform costs and historical data / Thomas Hamilton. p. cm. Includes bibliographical references. ISBN 978-0-8330-7455-3 (pbk. : alk. paper) 1. Air warfare—United States—Economic aspects. 2. Air-to-surface missiles—Cost effectiveness. 3. Bombing, Aerial—United States. 4. Precision guided munitions—United States. 5. United States—Armed Forces— Weapons systems-—Cost effectiveness. 6. Bombardment. I. Identifying Syntactic Role of Antecedent in Korean Relative Clause Using Corpus and Thesaurus Information Hui-Feng Li, Jong-Hyeok Lee, Geunbae Lee Department of Computer Science and Engineering Pohang University of Science and Technology San 31 Hyoja-dong, Nam-gu, Pohang 790-784, Republic of Korea hflee@madonna.postech.ac.kr, {jhlee, gblee)@postech.ac.kr Abstract This paper describes an approach to identify- ing the syntactic role of an antecedent in a Ko- rean relative clause, which is essential to struc- tural disambiguation and semantic analysis. In a learning phase, linguistic knowledge such as conceptual co-occurrence patterns and syntac- tic role distribution of antecedents is extracted from a large-scale corpus. Then, in an appli- cation phase, the extracted knowledge is ap- plied in determining the correct syntactic role of an antecedent in relative clauses. Unlike pre- vious research based on co-occurrence patterns at the lexical level, we represent co-occurrence patterns with concept types in a thesaurus. In an experiment, the proposed method showed a high accuracy rate of 90.4% in resolving am- biguitie s of syntactic role determination of an- tecedents. 1 Introduction A relative clause is the one that modifies an an- tecedent in a sentence. To determine the syn- tactic role of the antecedent in a verb argu- ment structure of relative clause is important in parsing and structural disambiguation(Li et al., 1998). While applying case frames of a verb for structural disambiguation, identifying the role of antecedent will affect the correctness of struc- tural disambiguation impressively. In this paper, we will describe a method of identifying the syntactic role of antecedents, which consists of two phases. First, in the learning phase, conceptual patterns (CPs) and syntactic role distribution of antecedents are extracted from a corpus of 6 million words, the Korean Language Information Base (KLIB). The conceptual patterns reflect the possible case restriction of a verb with concept types, while the syntactic role distribution shows the prefer- ence of syntactic role of antecedents of a verb. Second, in the application phase, the syntactic role of an antecedent is decided using CPs and the syntactic role distribution. In regards to the rest of this paper, Section 2 will review the problems and related work. Section 3 will describe a statistical approach of conceptual pattern extraction from a large corpus as knowledge for determining syntactic roles. Section 4 will describe how to identify syntactic roles using conceptual patterns and syntactic role distribution of antecedents in the corpus. Section 5 will then present an experi- mental evaluation of the method. The last sec- tion makes a conclusion with some discussion. The Yale Romanization is used to represent Ko- rean expressions. 2 Problems and Related Work In English, it is possible to recognize the syntac- tic role of antecedents by their position (trace) in relative clauses and the valency information of verbs. For example, the syntactic role of an antecedent man can be recognized as subject of the relative clause in a sentence "He is the man who ... into account as initial parameters a minimum and maximum door width (0.8 m and 1.2 m, respectively) and a minimum and maximum door height (1.8 m and 2.4 m ) As a result of the segmentation procedure,... of 360º horizontally and 80º vertically, which implies missing information from the immediate ceiling and floor on top and under it The choice of number of scanner positions and their location... expected value for an axis ( ( ) ̅ ), and and denote the variance and covariance values, respectively 3D building Envelope Orthoimages Generalized Hough Transform Door Candidates classification Eigenvalues