SICX1020 THEORY of ROBOTICS and AUTOMATION (Common to EIE, E&C, ECE, ETCE, EEE & BIOMED)
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Assembly Automation with Evolutionary Nanorobots and Sensor-Based Control Applied to Nanomedicine
IEEE Transactions on Nanotechnology, Vol. 2, no. 2, June 2003 82 Assembly Automation with Evolutionary Nanorobots and Sensor-Based Control applied to Nanomedicine Adriano Cavalcanti, Member, IEEE Germany only the Federal Ministry of Education and Research Abstract—The author presents a new approach within has announced 50 million Euros to be invested in the years advanced graphics simulations for the problem of nano-assembly 2002-2006 in research and development on nanotechnology automation and its application for medicine. The problem under [21]. More specifically the firm DisplaySearch predicts rapid study concentrates its main focus on nanorobot control design for assembly manipulation and the use of evolutionary competitive market growth from US$ 84 million today to $ 1.6 Billion in agents as a suitable way to warranty the robustness on the 2007 [20]. A first series of commercially nanoproducts has proposed model. Thereby the presented paper summarizes as well been announced also as foreseeable for 2007, and to reach this distinct aspects of some techniques required to achieve a goal of build organic electronics, firms are forming successful nano-planning system design and its simulation collaborations and alliances that bring together new visualization in real time. nanoproducts through the joint effort from companies such as IBM, Motorola, Philips Electronics, PARC, Xerox, Hewlett Index Terms—Biomedical computing, control systems, genetic algorithms, mobile robots, nanotechnology, virtual reality. Packard, Dow Chemical, Bell Laboratories, Intel Corp., just to quote a few ones [13], [20]. Building patterns and manipulating atoms with the use of I.INTRODUCTION Scanning Probe Microscope (SPM) such as Atomic Force he presented paper describe the design and simulation of Microscopy and Scanning Tunneling Microscopy has been Ta mobile nanorobot in atomic scales to perform used with satisfactory success as a promising approach for the biomolecular assembly manipulation for nanomedicine [12]. -
Artificial Intelligence, Automation, and Work
Artificial Intelligence, Automation, and Work The Economics of Artifi cial Intelligence National Bureau of Economic Research Conference Report The Economics of Artifi cial Intelligence: An Agenda Edited by Ajay Agrawal, Joshua Gans, and Avi Goldfarb The University of Chicago Press Chicago and London The University of Chicago Press, Chicago 60637 The University of Chicago Press, Ltd., London © 2019 by the National Bureau of Economic Research, Inc. All rights reserved. No part of this book may be used or reproduced in any manner whatsoever without written permission, except in the case of brief quotations in critical articles and reviews. For more information, contact the University of Chicago Press, 1427 E. 60th St., Chicago, IL 60637. Published 2019 Printed in the United States of America 28 27 26 25 24 23 22 21 20 19 1 2 3 4 5 ISBN-13: 978-0-226-61333-8 (cloth) ISBN-13: 978-0-226-61347-5 (e-book) DOI: https:// doi .org / 10 .7208 / chicago / 9780226613475 .001 .0001 Library of Congress Cataloging-in-Publication Data Names: Agrawal, Ajay, editor. | Gans, Joshua, 1968– editor. | Goldfarb, Avi, editor. Title: The economics of artifi cial intelligence : an agenda / Ajay Agrawal, Joshua Gans, and Avi Goldfarb, editors. Other titles: National Bureau of Economic Research conference report. Description: Chicago ; London : The University of Chicago Press, 2019. | Series: National Bureau of Economic Research conference report | Includes bibliographical references and index. Identifi ers: LCCN 2018037552 | ISBN 9780226613338 (cloth : alk. paper) | ISBN 9780226613475 (ebook) Subjects: LCSH: Artifi cial intelligence—Economic aspects. Classifi cation: LCC TA347.A78 E365 2019 | DDC 338.4/ 70063—dc23 LC record available at https:// lccn .loc .gov / 2018037552 ♾ This paper meets the requirements of ANSI/ NISO Z39.48-1992 (Permanence of Paper). -
Detecting Automation of Twitter Accounts: Are You a Human, Bot, Or Cyborg?
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, VOL. 9, NO. X, XXXXXXX 2012 1 Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? Zi Chu, Steven Gianvecchio, Haining Wang, Senior Member, IEEE, and Sushil Jajodia, Senior Member, IEEE Abstract—Twitter is a new web application playing dual roles of online social networking and microblogging. Users communicate with each other by publishing text-based posts. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots, which appear to be a double-edged sword to Twitter. Legitimate bots generate a large amount of benign tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. More interestingly, in the middle between human and bot, there has emerged cyborg referred to either bot-assisted human or human-assisted bot. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human, bot, and cyborg accounts on Twitter. We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement results, we propose a classification system that includes the following four parts: 1) an entropy-based component, 2) a spam detection component, 3) an account properties component, and 4) a decision maker. It uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot, or cyborg. Our experimental evaluation demonstrates the efficacy of the proposed classification system. -
Design and Implementation of Home Automation System
DESIGN AND IMPLEMENTATION OF HOME AUTOMATION SYSTEM USING RASPBERRY PI A Project Presented to the Faculty of California State Polytechnic University, Pomona In Partial Fulfilment of the Requirements for the Degree Master of Science in Computer Science By Irwin Soni 2018 SIGNATURE PAGE PROJECT: DESIGN AND IMPLEMENTATION OF HOME AUTOMATION SYSTEM USING RASPBERRY PI AUTHOR: Irwin Soni DATE SUBMITTED: Spring 2018 Computer Science Department Dr. Yu Sun Project Committee Chair Computer Science Dr. Sampath Jayarathna Computer Science ii ACKNOWLEDGEMENT First and foremost, I would like to thank God for blessing me with such amazing people who have been there to support me in all that I have achieved. To my parents, who have filled my life with immense love, happiness and shaping me into the person I have become today. I would like to thank Dr. Yu Sun, my project advisor, for his selfless support and enlightening guidance. Working with Dr. Sun was such a valuable experience of learning computer science and life at the same time. I would also like to thank Dr. Sampath Jayarathna for his review, suggestions and encouragement. I would also like to thank my classmates for their companionship and friendship. iii ABSTRACT Home automation system achieved great popularity in the last decades as it increases the comfort and quality of life. Smartphone applications are used to control and monitor the home appliances using different types of communication techniques. As mobile devices continue to grow in popularity and functionality, the demand for advanced ubiquitous mobile applications in our daily lives also increases. The paper deals with the design and implementation of a flexible and low-cost Home Automation System for various mobile devices that leverages mobile technology to provide essential functionalities to our homes and associated control operations. -
Software Testing
Software Testing PURPOSE OF TESTING CONTENTS I. Software Testing Background II. Software Error Case Studies 1. Disney Lion King 2. Intel Pentium Floating Point Division Bug 3. NASA Mars Polar Lander 4. Patriot Missile Defense System 5. Y2K Bug III. What is Bug? 1. Terms for Software Failure 2. Software Bug: A Formal Definition 3. Why do Bugs occur? and cost of bug. 4. What exactly does a Software Tester do? 5. What makes a good Software Tester? IV. Software Development Process 1. Product Components 2. What Effort Goes into a Software Product? 3. What parts make up a Software Product? 4. Software Project Staff V. Software Development Lifecycle Models 1. Big Bang Model 2. Code and Fix Model 3. Waterfall Model 4. Spiral Model VI. The Realities of Software Testing VII. Software Testing Terms and Definition 1. Precision and Accuracy 2. Verification and Validation 3. Quality Assurance and Quality Control Anuradha Bhatia Software Testing I. Software Testing Background 1. Software is a set of instructions to perform some task. 2. Software is used in many applications of the real world. 3. Some of the examples are Application software, such as word processors, firmware in an embedded system, middleware, which controls and co-ordinates distributed systems, system software such as operating systems, video games, and websites. 4. All of these applications need to run without any error and provide a quality service to the user of the application. 5. The software has to be tested for its accurate and correct working. Software Testing: Testing can be defined in simple words as “Performing Verification and Validation of the Software Product” for its correctness and accuracy of working. -
Magma: a Ground-Truth Fuzzing Benchmark
Magma: A Ground-Truth Fuzzing Benchmark Ahmad Hazimeh Mathias Payer EPFL EPFL ABSTRACT fuzzer is evaluated against a set of target programs. These target pro- High scalability and low running costs have made fuzz testing grams can be sourced from a benchmark suite—such as the Cyber the de-facto standard for discovering software bugs. Fuzzing tech- Grand Challenge [9], LAVA-M[12], the Juliet Test Suite [30], and niques are constantly being improved in a race to build the ultimate Google’s Fuzzer Test Suite [17], oss-fuzz [2], and FuzzBench [16]— bug-finding tool. However, while fuzzing excels at finding bugs, or from a set of real-world programs [34]. Performance metrics— comparing fuzzer performance is challenging due to the lack of including coverage profiles, crash counts, and bug counts—are then metrics and benchmarks. Crash count, the most common perfor- collected during the evaluation. Unfortunately, while such metrics mance metric, is inaccurate due to imperfections in de-duplication can provide insight into a fuzzer’s performance, they are often methods and heuristics. Moreover, the lack of a unified set of targets insufficient to compare different fuzzers. Moreover, the lackofa results in ad hoc evaluations that inhibit fair comparison. unified set of target programs makes for unfounded comparisons. We tackle these problems by developing Magma, a ground-truth Most fuzzer evaluations consider crash count a reasonable metric fuzzer evaluation framework enabling uniform evaluations and for assessing fuzzer performance. However, crash count is often comparison. By introducing real bugs into real software, Magma inflated [25], even after attempts at de-duplication (e.g., via coverage allows for a realistic evaluation of fuzzers against a broad set of profiles or stack hashes), highlighting the need for more accurate targets. -
Robots, Automation, and Employment: Where We Are
WORKING PAPER SERIES ROBOTS, AUTOMATION, AND EMPLOYMENT: WHERE WE ARE Lukas Wolters PhD Candidate MIT Political Science [email protected] MIT Work of the Future Working Paper 05-2020 Date: May 26, 2020 400 Main Street, E19-733, Cambridge, MA 02139 Robots, Automation, and Employment: Where We Are∗ Lukas Woltersy May 26, 2020 Introduction “Robots will destroy our jobs – and we’re not ready for it” titled The Guardian in early 2017.1 Headlines like this have become more and more common over the past couple of years, with newspapers and media outlets reporting that “the robots are coming! And they are going to take all our jobs,“2 asserting that “sometime in the next 40 years, robots are going to take your job,”3 and that “robots may steal as many as 800 million jobs in the next 13 years,”4 and proclaiming gloomy headlines such as “automation threatening 25% of jobs in the US,”5 and “robots to replace up to 20 million factory jobs by 2030.”6 While idea that technology can render human labor obsolete is not new, and concerns about technological unemployment go back at least to Keynes (1930; p. 3) who in 1930 wrote about potential unemployment “due to our discovery of means of economizing the use of labor outrunning the pace at which we can find new uses for labor,” the recent proliferation of reports warning about the potential effect of new technologies, particularly advances in machine learning and robotics, on employment stands out in terms of the number of jobs allegedly under threat of replacement by machines and obsolescence. -
History of Robotics: Timeline
History of Robotics: Timeline This history of robotics is intertwined with the histories of technology, science and the basic principle of progress. Technology used in computing, electricity, even pneumatics and hydraulics can all be considered a part of the history of robotics. The timeline presented is therefore far from complete. Robotics currently represents one of mankind’s greatest accomplishments and is the single greatest attempt of mankind to produce an artificial, sentient being. It is only in recent years that manufacturers are making robotics increasingly available and attainable to the general public. The focus of this timeline is to provide the reader with a general overview of robotics (with a focus more on mobile robots) and to give an appreciation for the inventors and innovators in this field who have helped robotics to become what it is today. RobotShop Distribution Inc., 2008 www.robotshop.ca www.robotshop.us Greek Times Some historians affirm that Talos, a giant creature written about in ancient greek literature, was a creature (either a man or a bull) made of bronze, given by Zeus to Europa. [6] According to one version of the myths he was created in Sardinia by Hephaestus on Zeus' command, who gave him to the Cretan king Minos. In another version Talos came to Crete with Zeus to watch over his love Europa, and Minos received him as a gift from her. There are suppositions that his name Talos in the old Cretan language meant the "Sun" and that Zeus was known in Crete by the similar name of Zeus Tallaios. -
Managing Defects in an Agile Environment
Managing Defects in an Agile environment Auteur Ron Eringa Managing Defects in an Agile environment Introduction Injecting passion, Agility Teams often struggle with answering the following and quality into your question: “How to manage our Defects in an organisation. Agile environment?”. They start using Scrum as a framework for developing their software and while implementing, they experience trouble on how to deal with the Defects they find/cause along the way. Scrum is a framework that does not explicitly tell you how to handle Defects. The strait forward answer is to treat your Defects as Product Backlog Items that should be added to the Product Backlog. When the priority is set high enough by the Product Owner, they will be picked up by the Development Team in the next Sprint. The application of this is a little bit more difficult and hence should be explained in more detail. RON ERINGA 1. What is a defect? AGILE COACH Wikipedia: “A software bug (or defect) is an error, flaw, After being graduated from the Fontys failure, or fault in a computer program or system that University in Eindhoven, I worked as a Software produces an incorrect or unexpected result, or causes Engineer/Designer for ten years. Although I it to behave in unintended ways. Most bugs arise have always enjoyed technics, helping people from mistakes and errors made by people in either a and organizations are my passion. Especially program’s source code or its design, or in frameworks to deliver better quality together. When people and operating systems used by such programs, and focus towards a common goal, interaction is a few are caused by compilers producing incorrect increasing and energy is released. -
Home Automation Planning Guide for Individuals with Autism
HOME AUTOMATION PLANNING GUIDE FOR INDIVIDUALS WITH AUTISM WRITTEN BY A.J. PARON-WILDES WHY HOME AUTOMATION? When caring for an individual with autism, many needs must be addressed, and even the best caregiver can miss something or not see an incident coming. Home automation can offer protective solutions that support and elevate human care. The most important thing to remember when designing for individuals with autism is that they do not experience the designed environment in the same way others do. They can have increased sensitivities that evoke strong reactions to environmental stimuli. Color, lighting, sounds, and smells all can trigger extreme responses. Having an understanding that many people with autism have either hypo or hyper sensory issues will help you design an environment that can foster increased independence and confidence. Below I outline four main areas of consideration when starting a new design project or retrofitting a home to aid a person with autism. SAFETY The number-one priority for families with an autistic child is safety. Many of us do not understand the inherit dangers of our built environment and the impact it can have on those with autism. Daily challenges can range from possible elopement—kids walking out the front door—to an inadequate understanding of systems and appliances—how does the stove turn on and off? Then, as children get older, safety concerns shift. Instead of preventing the child from leaving the house, you monitor if and when they get in: Did they get off the bus and make it in the house independently? Can you see if a stranger is at the front door? Many children with autism have a hard time with speech, or have no speech, so communicating on the phone is not effective. -
Industrial Robot
1 Introduction 25 1.2 Industrial robots - definition and classification 1.2.1 Definition (ISO 8373:2012) and delimitation The annual surveys carried out by IFR focus on the collection of yearly statistics on the production, imports, exports and domestic installations/shipments of industrial robots (at least three or more axes) as described in the ISO definition given below. Figures 1.1 shows examples of robot types which are covered by this definition and hence included in the surveys. A robot which has its own control system and is not controlled by the machine should be included in the statistics, although it may be dedicated for a special machine. Other dedicated industrial robots should not be included in the statistics. If countries declare that they included dedicated industrial robots, or are suspected of doing so, this will be clearly indicated in the statistical tables. It will imply that data for those countries is not directly comparable with those of countries that strictly adhere to the definition of multipurpose industrial robots. Wafer handlers have their own control system and should be included in the statistics of industrial robots. Wafers handlers can be articulated, cartesian, cylindrical or SCARA robots. Irrespective from the type of robots they are reported in the application “cleanroom for semiconductors”. Flat panel handlers also should be included. Mainly they are articulated robots. Irrespective from the type of robots they are reported in the application “cleanroom for FPD”. Examples of dedicated industrial robots that should not be included in the international survey are: Equipment dedicated for loading/unloading of machine tools (see figure 1.3). -
A Study of Static Bug Detectors
How Many of All Bugs Do We Find? A Study of Static Bug Detectors Andrew Habib Michael Pradel [email protected] [email protected] Department of Computer Science Department of Computer Science TU Darmstadt TU Darmstadt Germany Germany ABSTRACT International Conference on Automated Software Engineering (ASE ’18), Sep- Static bug detectors are becoming increasingly popular and are tember 3–7, 2018, Montpellier, France. ACM, New York, NY, USA, 12 pages. widely used by professional software developers. While most work https://doi.org/10.1145/3238147.3238213 on bug detectors focuses on whether they find bugs at all, and on how many false positives they report in addition to legitimate 1 INTRODUCTION warnings, the inverse question is often neglected: How many of all Finding software bugs is an important but difficult task. For average real-world bugs do static bug detectors find? This paper addresses industry code, the number of bugs per 1,000 lines of code has been this question by studying the results of applying three widely used estimated to range between 0.5 and 25 [21]. Even after years of static bug detectors to an extended version of the Defects4J dataset deployment, software still contains unnoticed bugs. For example, that consists of 15 Java projects with 594 known bugs. To decide studies of the Linux kernel show that the average bug remains in which of these bugs the tools detect, we use a novel methodology the kernel for a surprisingly long period of 1.5 to 1.8 years [8, 24]. that combines an automatic analysis of warnings and bugs with a Unfortunately, a single bug can cause serious harm, even if it has manual validation of each candidate of a detected bug.