UML Profile for Communicating Systems a New UML Profile for the Specification and Description of Internet Communication and Signaling Protocols
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Magicdraw Manual.Book
USER’S MANUAL version 10.0 No Magic, Inc. October, 2005 CONTENTS 1 CONTENTS 2 2INTRODUCING MAGICDRAW 2-10 MagicDraw Editions and Features 2-11 MagicDraw Personal edition 2-11 MagicDraw Standard edition 2-11 MagicDraw Professional editions 2-11 MagicDraw Enterprise edition 2-12 MagicDraw Reader Edition 2-12 MagicDraw Community Edition 2-12 MagicDraw Documentation and Support 2-12 Help 2-13 User’s Guides 2-13 Other Documentation 2-14 Support 2-15 FAQ 2-15 Newsgroups 2-15 E-mail 2-15 Bug Reports 2-15 3GETTING STARTED 3-18 Installing and Running 3-18 System requirements 3-18 Java Virtual Machine (JVM) 3-18 Operating system - dependent issues 3-19 Installation procedure 3-19 Windows 2000/9x/NT/XP 3-19 Unix 3-20 MAC OS X 3-20 All other platforms instructions (no install version) 3-20 Updating 3-20 Auto-Check for Updates dialog box 3-21 structure of menus and toolbars 3-21 Setting Personal Preferences 3-22 Environment Options 3-22 General pane 3-23 Diagram pane 3-26 Browser pane 3-28 MagicDraw User’s Manual Contents Teamwork pane 3-29 Floating pane 3-30 CVS pane 3-31 Update pane 3-32 HTTP Proxy pane 3-33 Keyboard pane 3-34 Plugins pane 3-35 Resources pane 3-36 Path Variables Pane 3-37 Launcher Pane 3-38 Look and Feel: Controlling Interface 3-38 JIDE and Multiple Windows interface styles 3-40 4WORKING WITH PROJECTS 4-42 Creating New Project 4-42 Creating new project from already created template 4-43 Importing project 4-44 Saving and Exporting 4-44 Exporting project as template 4-45 Saving diagram as image 4-46 Autosave 4-48 Loading 4-48 Project Options. -
An Optimal Control Approach for Determiniation of the Heat Loss Coefficient in an Ics Solar Domesticater W Heating System
University of Central Florida STARS Electronic Theses and Dissertations, 2004-2019 2010 An Optimal Control Approach For Determiniation Of The Heat Loss Coefficient In An Ics Solar Domesticater W Heating System Camilo Gil University of Central Florida Part of the Electrical and Electronics Commons Find similar works at: https://stars.library.ucf.edu/etd University of Central Florida Libraries http://library.ucf.edu This Doctoral Dissertation (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS. For more information, please contact [email protected]. STARS Citation Gil, Camilo, "An Optimal Control Approach For Determiniation Of The Heat Loss Coefficient In An Ics Solar Domestic Water Heating System" (2010). Electronic Theses and Dissertations, 2004-2019. 4297. https://stars.library.ucf.edu/etd/4297 AN OPTIMAL CONTROL APPROACH FOR DETERMINATION OF THE HEAT LOSS COEFFICIENT IN AN ICS SOLAR DOMESTIC WATER HEATING SYSTEM by CAMILO GIL B.S. Pontificia Universidad Javeriana, 2001 M.S. University of New Mexico, 2005 A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Electrical and Computer Engineering in the College of Engineering and Computer Science at the University of Central Florida Orlando, Florida Summer Term 2010 Major Professor: Marwan Simaan ©2010 Camilo Gil ii ABSTRACT Water heating in a typical home in the U.S. accounts for a significant portion (between 14% and 25%) of the total home’s annual energy consumption. The objective of considerably reducing the home’s energy consumption from the utilities calls for the use of onsite renewable energy systems. -
UML Summary 1
UML Summary 1 The UML Summary provides an introduction to the UML, discussing its motivation and history. Contents 1.1 Overview 1-3 1.2 Primary Artifacts of the UML 1-3 1.3 Motivation to Define the UML 1-4 1.4 Goals of the UML 1-5 1.5 Scope of the UML 1-7 1.6 UML - Past, Present, and Future 1-11 UML V1.3 alpha R5 March 1999 1-1 1 UML Summary 1-2 UML V1.3 alpha R5 March 1999 1.1 Overview 1UML Summary 1.1 Overview The Unified Modeling Language (UML) is a language for specifying, visualizing, constructing, and documenting the artifacts of software systems, as well as for business modeling and other non-software systems. The UML represents a collection of the best engineering practices that have proven successful in the modeling of large and complex systems. 1.2 Primary Artifacts of the UML What are the primary artifacts of the UML? This can be answered from two different perspectives: the UML definition itself and how it is used to produce project artifacts. 1.2.1 UML-defining Artifacts To aid the understanding of the artifacts that constitute the Unified Modeling Language itself, this document consists of the UML Semantics, UML Notation Guide, and UML Extensions chapters. 1.2.2 Development Project Artifacts The choice of what models and diagrams one creates has a profound influence upon how a problem is attacked and how a corresponding solution is shaped. Abstraction, the focus on relevant details while ignoring others, is a key to learning and communicating. -
A Hybrid Static Type Inference Framework with Neural
HiTyper: A Hybrid Static Type Inference Framework with Neural Prediction Yun Peng Zongjie Li Cuiyun Gao∗ The Chinese University of Hong Kong Harbin Institute of Technology Harbin Institute of Technology Hong Kong, China Shenzhen, China Shenzhen, China [email protected] [email protected] [email protected] Bowei Gao David Lo Michael Lyu Harbin Institute of Technology Singapore Management University The Chinese University of Hong Kong Shenzhen, China Singapore Hong Kong, China [email protected] [email protected] [email protected] ABSTRACT also supports type annotations in the Python Enhancement Pro- Type inference for dynamic programming languages is an impor- posals (PEP) [21, 22, 39, 43]. tant yet challenging task. By leveraging the natural language in- Type prediction is a popular task performed by most attempts. formation of existing human annotations, deep neural networks Traditional static type inference techniques [4, 9, 14, 17, 36] and outperform other traditional techniques and become the state-of- type inference tools such as Pytype [34], Pysonar2 [33], and Pyre the-art (SOTA) in this task. However, they are facing some new Infer [31] can predict sound results for the variables with enough challenges, such as fixed type set, type drift, type correctness, and static constraints, e.g., a = 1, but are unable to handle the vari- composite type prediction. ables with few static constraints, e.g. most function arguments. To mitigate the challenges, in this paper, we propose a hybrid On the other hand, dynamic type inference techniques [3, 37] and type inference framework named HiTyper, which integrates static type checkers simulate the workflow of functions and solve types inference into deep learning (DL) models for more accurate type according to input cases and typing rules. -
EB GUIDE Documentation Version 6.1.0.101778 EB GUIDE Documentation
EB GUIDE documentation Version 6.1.0.101778 EB GUIDE documentation Elektrobit Automotive GmbH Am Wolfsmantel 46 D-91058 Erlangen GERMANY Phone: +49 9131 7701-0 Fax: +49 9131 7701-6333 http://www.elektrobit.com Legal notice Confidential and proprietary information. ALL RIGHTS RESERVED. No part of this publication may be copied in any form, by photocopy, microfilm, retrieval system, or by any other means now known or hereafter invented without the prior written permission of Elektrobit Automotive GmbH. ProOSEK®, tresos®, and street director® are registered trademarks of Elektrobit Automotive GmbH. All brand names, trademarks and registered trademarks are property of their rightful owners and are used only for description. Copyright 2015, Elektrobit Automotive GmbH. Page 2 of 324 EB GUIDE documentation Table of Contents 1. About this documentation ................................................................................................................ 15 1.1. Target audiences of the user documentation ......................................................................... 15 1.1.1. Modelers .................................................................................................................. 15 1.1.2. System integrators .................................................................................................... 16 1.1.3. Application developers ............................................................................................... 16 1.1.4. Extension developers ............................................................................................... -
Lecture 2: Variables and Primitive Data Types
Lecture 2: Variables and Primitive Data Types MIT-AITI Kenya 2005 1 In this lecture, you will learn… • What a variable is – Types of variables – Naming of variables – Variable assignment • What a primitive data type is • Other data types (ex. String) MIT-Africa Internet Technology Initiative 2 ©2005 What is a Variable? • In basic algebra, variables are symbols that can represent values in formulas. • For example the variable x in the formula f(x)=x2+2 can represent any number value. • Similarly, variables in computer program are symbols for arbitrary data. MIT-Africa Internet Technology Initiative 3 ©2005 A Variable Analogy • Think of variables as an empty box that you can put values in. • We can label the box with a name like “Box X” and re-use it many times. • Can perform tasks on the box without caring about what’s inside: – “Move Box X to Shelf A” – “Put item Z in box” – “Open Box X” – “Remove contents from Box X” MIT-Africa Internet Technology Initiative 4 ©2005 Variables Types in Java • Variables in Java have a type. • The type defines what kinds of values a variable is allowed to store. • Think of a variable’s type as the size or shape of the empty box. • The variable x in f(x)=x2+2 is implicitly a number. • If x is a symbol representing the word “Fish”, the formula doesn’t make sense. MIT-Africa Internet Technology Initiative 5 ©2005 Java Types • Integer Types: – int: Most numbers you’ll deal with. – long: Big integers; science, finance, computing. – short: Small integers. -
ACDT: Architected Composite Data Types Trading-In Unfettered Data Access for Improved Execution
ACDT: Architected Composite Data Types Trading-in Unfettered Data Access for Improved Execution Andres Marquez∗, Joseph Manzano∗, Shuaiwen Leon Song∗, Benoˆıt Meistery Sunil Shresthaz, Thomas St. Johnz and Guang Gaoz ∗Pacific Northwest National Laboratory fandres.marquez,joseph.manzano,[email protected] yReservoir Labs [email protected] zUniversity of Delaware fshrestha,stjohn,[email protected] Abstract— reduction approaches associated with improved data locality, obtained through optimized data and computation distribution. With Exascale performance and its challenges in mind, one ubiquitous concern among architects is energy efficiency. In the SW-stack we foresee the runtime system to have a Petascale systems projected to Exascale systems are unsustainable particular important role to contribute to the solution of the at current power consumption rates. One major contributor power challenge. It is here where the massive concurrency is to system-wide power consumption is the number of memory managed and where judicious data layouts [11] and data move- operations leading to data movement and management techniques ments are orchestrated. With that in mind, we set to investigate applied by the runtime system. To address this problem, we on how to improve efficiency of a massively multithreaded present the concept of the Architected Composite Data Types adaptive runtime system in managing and moving data, and the (ACDT) framework. The framework is made aware of data trade-offs an improved data management efficiency requires. composites, assigning them a specific layout, transformations Specifically, in the context of run-time system (RTS), we and operators. Data manipulation overhead is amortized over a explore the power efficiency potential that data compression larger number of elements and program performance and power efficiency can be significantly improved. -
UML 2 Toolkit, Penker Has Also Collaborated with Hans- Erik Eriksson on Business Modeling with UML: Business Practices at Work
UML™ 2 Toolkit Hans-Erik Eriksson Magnus Penker Brian Lyons David Fado UML™ 2 Toolkit UML™ 2 Toolkit Hans-Erik Eriksson Magnus Penker Brian Lyons David Fado Publisher: Joe Wikert Executive Editor: Bob Elliott Development Editor: Kevin Kent Editorial Manager: Kathryn Malm Production Editor: Pamela Hanley Permissions Editors: Carmen Krikorian, Laura Moss Media Development Specialist: Travis Silvers Text Design & Composition: Wiley Composition Services Copyright 2004 by Hans-Erik Eriksson, Magnus Penker, Brian Lyons, and David Fado. All rights reserved. Published by Wiley Publishing, Inc., Indianapolis, Indiana Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rose- wood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8700. Requests to the Pub- lisher for permission should be addressed to the Legal Department, Wiley Publishing, Inc., 10475 Crosspoint Blvd., Indianapolis, IN 46256, (317) 572-3447, fax (317) 572-4447, E-mail: [email protected]. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. -
Source Code Auditing: Day 2
Source Code Auditing: Day 2 Penetration Testing & Vulnerability Analysis Brandon Edwards [email protected] Data Types Continued Data Type Signedness Remember, by default all data types are signed unless specifically declared otherwise But many functions which accept size arguments take unsigned values What is the difference of the types below? char y; unsigned char x; x = 255; y = -1; 3 Data Type Signedness These types are the same size (8-bits) char y; unsigned char x; 4 Data Type Signedness A large value in the unsigned type (highest bit set) is a negative value in the signed type char y; unsigned char x; 5 Data Type Bugs Same concept applies to 16 and 32 bit data types What are the implications of mixing signed & unsigned types ? #define MAXSOCKBUF 4096 int readNetworkData(int sock) { char buf[MAXSOCKBUF]; int length; read(sock, (char *)&length, 4); if (length < MAXSOCKBUF) { read(sock, buf, length); } } 6 Data Type Signedness The check is between two signed values… #define MAXSOCKBUF 4096 if (length < MAXSOCKBUF) So if length is negative (highest bit / signed bit set), it will evaluate as less than MAXSOCKBUF But the read() function takes only unsigned values for it’s size Remember, the highest bit (or signed bit is set), and the compiler implicitly converts the length to unsigned for read() 7 Data Type Signedness So what if length is -1 (or 0xFFFFFFFF in hex)? #define MAXSOCKBUF 4096 if (length < MAXSOCKBUF) { read(sock, buf, length); } When the length check is performed, it is asking if -1 is less than 4096 When the length is passed to read, it is converted to unsigned and becomes the unsigned equivalent of -1, which for 32bits is 4294967295 8 Data Type Bugs Variation in data type sizes can also introduce bugs Remember the primitive data type sizes? (x86): An integer type is 32bits A short type is 16bits A char type is 8 bits Sometimes code is written without considering differences between these. -
Plantuml Language Reference Guide (Version 1.2021.2)
Drawing UML with PlantUML PlantUML Language Reference Guide (Version 1.2021.2) PlantUML is a component that allows to quickly write : • Sequence diagram • Usecase diagram • Class diagram • Object diagram • Activity diagram • Component diagram • Deployment diagram • State diagram • Timing diagram The following non-UML diagrams are also supported: • JSON Data • YAML Data • Network diagram (nwdiag) • Wireframe graphical interface • Archimate diagram • Specification and Description Language (SDL) • Ditaa diagram • Gantt diagram • MindMap diagram • Work Breakdown Structure diagram • Mathematic with AsciiMath or JLaTeXMath notation • Entity Relationship diagram Diagrams are defined using a simple and intuitive language. 1 SEQUENCE DIAGRAM 1 Sequence Diagram 1.1 Basic examples The sequence -> is used to draw a message between two participants. Participants do not have to be explicitly declared. To have a dotted arrow, you use --> It is also possible to use <- and <--. That does not change the drawing, but may improve readability. Note that this is only true for sequence diagrams, rules are different for the other diagrams. @startuml Alice -> Bob: Authentication Request Bob --> Alice: Authentication Response Alice -> Bob: Another authentication Request Alice <-- Bob: Another authentication Response @enduml 1.2 Declaring participant If the keyword participant is used to declare a participant, more control on that participant is possible. The order of declaration will be the (default) order of display. Using these other keywords to declare participants -
Sysml Distilled: a Brief Guide to the Systems Modeling Language
ptg11539604 Praise for SysML Distilled “In keeping with the outstanding tradition of Addison-Wesley’s techni- cal publications, Lenny Delligatti’s SysML Distilled does not disappoint. Lenny has done a masterful job of capturing the spirit of OMG SysML as a practical, standards-based modeling language to help systems engi- neers address growing system complexity. This book is loaded with matter-of-fact insights, starting with basic MBSE concepts to distin- guishing the subtle differences between use cases and scenarios to illu- mination on namespaces and SysML packages, and even speaks to some of the more esoteric SysML semantics such as token flows.” — Jeff Estefan, Principal Engineer, NASA’s Jet Propulsion Laboratory “The power of a modeling language, such as SysML, is that it facilitates communication not only within systems engineering but across disci- plines and across the development life cycle. Many languages have the ptg11539604 potential to increase communication, but without an effective guide, they can fall short of that objective. In SysML Distilled, Lenny Delligatti combines just the right amount of technology with a common-sense approach to utilizing SysML toward achieving that communication. Having worked in systems and software engineering across many do- mains for the last 30 years, and having taught computer languages, UML, and SysML to many organizations and within the college setting, I find Lenny’s book an invaluable resource. He presents the concepts clearly and provides useful and pragmatic examples to get you off the ground quickly and enables you to be an effective modeler.” — Thomas W. Fargnoli, Lead Member of the Engineering Staff, Lockheed Martin “This book provides an excellent introduction to SysML. -
Evaluating Variability Modeling Techniques for Supporting Cyber-Physical System Product Line Engineering
This paper will be presented at System Analysis and Modeling (SAM) Conference 2016 (http://sdl-forum.org/Events/SAM2016/index.htm) Evaluating Variability Modeling Techniques for Supporting Cyber-Physical System Product Line Engineering Safdar Aqeel Safdar 1, Tao Yue1,2, Shaukat Ali1, Hong Lu1 1Simula Research Laboratory, Oslo, Norway 2 University of Oslo, Oslo, Norway {safdar, tao, shaukat, honglu}@simula.no Abstract. Modern society is increasingly dependent on Cyber-Physical Systems (CPSs) in diverse domains such as aerospace, energy and healthcare. Employing Product Line Engineering (PLE) in CPSs is cost-effective in terms of reducing production cost, and achieving high productivity of a CPS development process as well as higher quality of produced CPSs. To apply CPS PLE in practice, one needs to first select an appropriate variability modeling technique (VMT), with which variabilities of a CPS Product Line (PL) can be specified. In this paper, we proposed a set of basic and CPS-specific variation point (VP) types and modeling requirements for proposing CPS-specific VMTs. Based on the proposed set of VP types (basic and CPS-specific) and modeling requirements, we evaluated four VMTs: Feature Modeling, Cardinality Based Feature Modeling, Common Variability Language, and SimPL (a variability modeling technique dedicated to CPS PLE), with a real-world case study. Evaluation results show that none of the selected VMTs can capture all the basic and CPS-specific VP and meet all the modeling requirements. Therefore, there is a need to extend existing techniques or propose new ones to satisfy all the requirements. Keywords: Product Line Engineering, Variability Modeling, and Cyber- Physical Systems 1 Introduction Cyber-Physical Systems (CPSs) integrate computation and physical processes and their embedded computers and networks monitor and control physical processes by often relying on closed feedback loops [1, 2].