Machine Learning Testing: Survey, Landscapes and Horizons
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INSTITUTE for COMPUTATIONAL and BIOLOGICAL LEARNING (ICBL)
ICBL (HANSON, GLUCK, VAPNIK) 1 August 20th 2003 Governor's Commission on Jobs & Economic Growth INSTITUTE FOR COMPUTATIONAL and BIOLOGICAL LEARNING (ICBL) PRIMARY PARTICIPANTS: Stephen José Hanson, Psych. Rutgers-Newark; Cog. Sci. Rutgers-NB, Info. Sci., NJIT; Advanced Imaging Center, UMDNJ (Principal Investigator and Co-Director) Mark A. Gluck, CMBN Rutgers-Newark (co-Principal Investigator and Co-Director) Vladimir Vapnik, NEC (co-Principal Investigator and Co-Director) We also include participants and contact personnel for other participating institutions including Rutgers NB-Piscataway, Rutgers-Camden, UMDNJ, NJIT, Princeton, NEC, ATT, Siemens Corporate Research, and Merck Pharmaceuticals (See attachments that follow this document.) PRIMARY OBJECTIVE: The Institute for Computational and Biological Learning (ICBL) will be an interdisciplinary center for research and training in the learning sciences, encompassing computational, biological,and psychological studies of systems that adapt, extract patterns from high complexity data and in general improve from experience. Core programs include learning theory, neuroinformatics, computational neuroscience of learning and memory, neural-network models, machine learning, human computer interaction and financial/economic modeling. The learning sciences is an emerging interdisciplinary technology, recently recognized by the National Science Foundation (NSF) as a national priority. The NSF announced this year a major multi-disciplinary initiative which will fund approximately five new national Learning Sciences Centers with budgets of $3M to $5M a year for up to 10 years, for a total commitment of up to $50,000,000 per center. As envisioned by NSF, these centers will be “large-scale, long-term Centers that will extend the frontiers of knowledge on learning and create the intellectual, organizational, and physical infrastructure needed for the long-term advancement of learning research. -
A Priori Knowledge from Non-Examples
A priori Knowledge from Non-Examples Diplomarbeit im Fach Bioinformatik vorgelegt am 29. März 2007 von Fabian Sinz Fakultät für Kognitions- und Informationswissenschaften Wilhelm-Schickard-Institut für Informatik der Universität Tübingen in Zusammenarbeit mit Max-Planck-Institut für biologische Kybernetik Tübingen Betreuer: Prof. Dr. Bernhard Schölkopf (MPI für biologische Kybernetik) Prof. Dr. Andreas Schilling (WSI für Informatik) Prof. Dr. Hanspeter Mallot (Biologische Fakultät) 1 Erklärung Die folgende Diplomarbeit wurde ausschließlich von mir (Fabian Sinz) erstellt. Es wurden keine weiteren Hilfmittel oder Quellen außer den Angegebenen benutzt. Tübingen, den 29. März 2007, Fabian Sinz Contents 1 Introduction 4 1.1 Foreword . 4 1.2 Notation . 5 1.3 Machine Learning . 7 1.3.1 Machine Learning . 7 1.3.2 Philosophy of Science in a Nutshell - About the Im- possibility to learn without Assumptions . 11 1.3.3 General Assumptions in Bayesian and Frequentist Learning . 14 1.3.4 Implicit prior Knowledge via additional Data . 17 1.4 Mathematical Tools . 18 1.4.1 Reproducing Kernel Hilbert Spaces . 18 1.5 Regularisation . 22 1.5.1 General regularisation . 22 1.5.2 Data-dependent Regularisation . 27 2 The Universum Algorithm 32 2.1 The Universum Idea . 32 2.1.1 VC Theory and Structural Risk Minimisation (SRM) in a Nutshell . 32 2.1.2 The original Universum Idea by Vapnik . 35 2.2 Implementing the Universum into SVMs . 37 2.2.1 Support Vector Machine Implementations of the Uni- versum . 38 2.2.1.1 Hinge Loss Universum . 38 2.2.1.2 Least Squares Universum . 46 2.2.1.3 Multiclass Universum . -
System Safety Engineering: Back to the Future
System Safety Engineering: Back To The Future Nancy G. Leveson Aeronautics and Astronautics Massachusetts Institute of Technology c Copyright by the author June 2002. All rights reserved. Copying without fee is permitted provided that the copies are not made or distributed for direct commercial advantage and provided that credit to the source is given. Abstracting with credit is permitted. i We pretend that technology, our technology, is something of a life force, a will, and a thrust of its own, on which we can blame all, with which we can explain all, and in the end by means of which we can excuse ourselves. — T. Cuyler Young ManinNature DEDICATION: To all the great engineers who taught me system safety engineering, particularly Grady Lee who believed in me, and to C.O. Miller who started us all down this path. Also to Jens Rasmussen, whose pioneering work in Europe on applying systems thinking to engineering for safety, in parallel with the system safety movement in the United States, started a revolution. ACKNOWLEDGEMENT: The research that resulted in this book was partially supported by research grants from the NSF ITR program, the NASA Ames Design For Safety (Engineering for Complex Systems) program, the NASA Human-Centered Computing, and the NASA Langley System Archi- tecture Program (Dave Eckhart). program. Preface I began my adventure in system safety after completing graduate studies in computer science and joining the faculty of a computer science department. In the first week at my new job, I received a call from Marion Moon, a system safety engineer at what was then Ground Systems Division of Hughes Aircraft Company. -
Manuscript Instructions/Template
INCOSE Working Group Addresses System and Software Interfaces Sarah Sheard, Ph.D. Rita Creel CMU Software Engineering Institute CMU Software Engineering Institute (412) 268-7612 (703) 247-1378 [email protected] [email protected] John Cadigan Joseph Marvin Prime Solutions Group, Inc. Prime Solutions Group, Inc. (623) 853-0829 (623) 853-0829 [email protected] [email protected] Leung Chim Michael E. Pafford Defence Science & Technology Group Johns Hopkins University +61 (0) 8 7389 7908 (301) 935-5280 [email protected] [email protected] Copyright © 2018 by the authors. Published and used by INCOSE with permission. Abstract. In the 21st century, when any sophisticated system has significant software content, it is increasingly critical to articulate and improve the interface between systems engineering and software engineering, i.e., the relationships between systems and software engineering technical and management processes, products, tools, and outcomes. Although systems engineers and software engineers perform similar activities and use similar processes, their primary responsibilities and concerns differ. Systems engineers focus on the global aspects of a system. Their responsibilities span the lifecycle and involve ensuring the various elements of a system—e.g., hardware, software, firmware, engineering environments, and operational environments—work together to deliver capability. Software engineers also have responsibilities that span the lifecycle, but their focus is on activities to ensure the software satisfies software-relevant system requirements and constraints. Software engineers must maintain sufficient knowledge of the non-software elements of the systems that will execute their software, as well as the systems their software must interface with. -
Infrastructure (Resilience-Oriented) Modelling Language: I®ML
Infrastructure (Resilience-oriented) Modelling Language: I®ML A proposal for modelling infrastructures and their interconnections Andrés Silva, Roberto Filippini EUR 24727 EN - 2011 The mission of the JRC-IPSC is to provide research results and to support EU policy-makers in their effort towards global security and towards protection of European citizens from accidents, deliberate attacks, fraud and illegal actions against EU policies. European Commission Joint Research Centre Institute for the Protection and Security of the Citizen Contact information Address: TP 210, EC JRC Ispra, Ispra (Va) Italy E-mail: [email protected] Tel.: +39 0332 789936 Fax: http://ipsc.jrc.ec.europa.eu/ http://www.jrc.ec.europa.eu/ Legal Notice Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. Europe Direct is a service to help you find answers to your questions about the European Union Freephone number (*): 00 800 6 7 8 9 10 11 (*) Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed. A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server http://europa.eu/ JRC 63302 EUR 24727 EN ISBN 978-92-79-19324-8 ISSN 1018-5593 doi:10.2788/54708 Luxembourg: Publications Office of the European Union © European Union, 2011 Reproduction is authorised provided the source is acknowledged Printed in Italy Infrastructure (Resilience-oriented) Modelling Language: I®ML A proposal for modelling infrastructures and their connections Andrés Silva1 Roberto Filippini Universidad Politécnica de Madrid JRC of the European Commission Abstract The modelling of critical infrastructures (CIs) is an important issue that needs to be properly addressed, for several reasons. -
IS2018 Book of Abstract
th Annual INCOSE 28 international symposium Washington, DC, USA July 7 - 12, 2018 Delivering Systems in the Age of Globalization Status as of May 15 th , 2018 Book of Abstract Table of contents keynotes ............................................................................................................................................................................... p. 7 keynotes#Keynote#2: The Big Shift: Innovation and Systems Engineering ................................................................ p. 7 Papers .................................................................................................................................................................................. p. 8 Papers#107: A Framework for Concept and its Testing on Patents ............................................................................ p. 8 Papers#75: A Framework for Testability Analysis from System Architecture Perspective .......................................... p. 9 Papers#128: A Framework for Understanding Systems Principles and Methods .......................................................p. 10 Papers#31: A fresh look at Systems Engineering - what is it, how should it work? .....................................................p. 11 Papers#35: A Hybrid Liver-Candidate Transportation System to Improve Accessibility and Extend Organ Life in L ..p. 12 Papers#55: A Novel “Resilience Viewpoint” to aid in Engineering Resilience in Systems of Systems (SoS) .............p. 13 Papers#97: A successful use of systems approaches in -
United States Patent 19 11 Patent Number: 5,640,492 Cortes Et Al
US005640492A United States Patent 19 11 Patent Number: 5,640,492 Cortes et al. 45 Date of Patent: Jun. 17, 1997 54). SOFT MARGIN CLASSIFIER B.E. Boser, I. Goyon, and V.N. Vapnik, “A Training Algo rithm. For Optimal Margin Classifiers”. Proceedings Of The 75 Inventors: Corinna Cortes, New York, N.Y.: 4-th Workshop of Computational Learning Theory, vol. 4, Vladimir Vapnik, Middletown, N.J. San Mateo, CA, Morgan Kaufman, 1992. 73) Assignee: Lucent Technologies Inc., Murray Hill, Y. Le Cun, B. Boser, J. Denker, D. Henderson, R. Howard, N.J. W. Hubbard, and L. Jackel, "Handwritten Digit Recognition with a Back-Propagation Network', (D. Touretzky, Ed.), Advances in Neural Information Processing Systems, vol. 2, 21 Appl. No.: 268.361 Morgan Kaufman, 1990. 22 Filed: Jun. 30, 1994 A.N. Referes et al., “Stock Ranking: Neural Networks Vs. (51] Int. Cl. ................ G06E 1/00; G06E 3100 Multiple Linear Regression':, IEEE International Conf. on 52 U.S. Cl. .................................. 395/23 Neural Networks, vol. 3, San Francisco, CA, IEEE, pp. 58) Field of Search ............................................ 395/23 1419-1426, 1993. M. Rebollo et al., “AMixed Integer Programming Approach 56) References Cited to Multi-Spectral Image Classification”, Pattern Recogni U.S. PATENT DOCUMENTS tion, vol. 9, No. 1, Jan. 1977, pp. 47-51, 54-55, and 57. 4,120,049 10/1978 Thaler et al. ........................... 365/230 V. Uebele et al., "Extracting Fuzzy Rules From Pattern 4,122,443 10/1978 Thaler et al. ....... 340/.46.3 Classification Neural Networks”, Proceedings From 1993 5,040,214 8/1991 Grossberg et al. ....................... 381/43 International Conference on Systems, Man and Cybernetics, 5,214,716 5/1993 Refregier et al.. -
Model for Safety Case Modeling and Documentation
SAFE – an ITEA2 project D3.1.3 Contract number: ITEA2 – 10039 Safe Automotive soFtware architEcture (SAFE) ITEA Roadmap application domains: Major: Services, Systems & Software Creation Minor: Society ITEA Roadmap technology categories: Major: Systems Engineering & Software Engineering Minor 1: Engineering Process Support WP3 Deliverable D3.1.3: Proposal for extension of Meta- model for safety case modeling and documentation Due date of deliverable: 31/03/2013 Actual submission date: 28/03/2013 Start date of the project: 01/07/2011 Duration: 36 months Project coordinator name: Stefan Voget Organization name of lead contractor for this deliverable: fortiss Editor: Maged Khalil (fortiss GmbH) Contributors: Maged Khalil (fortiss GmbH), Eduard Metzker (Vector Informatik GmbH) Reviewers: Eduard Metzker (Vector Informatik GmbH) 2013 The SAFE Consortium 1 (50) SAFE – an ITEA2 project D3.1.3 Revision chart and history log Version Date Reason 0.1 2012-09-19 Initialization of document 0.2 2012-10-18 Input to Introduction Section 4 0.3 2012-11-07 Input to Overview on ISO Section 5 0.4 2012-11-16 Input to EAST-ADL Overview Section 7 0.5 2012-11-16 Input to Overview on ISO Section 5 0.6 2012-11-23 Input to Methodology Section 6 0.7 2012-11-29 Further Input to Methodology Section 6 0.8 2012-12-20 Editing and Input in various sections 0.9 2013-02-13 Input to SAFE Meta-model Contribution Section 8 0.10 2013-02-15 Introduction of Patterns Section 6.3 0.11 2013-03-08 Editing and Input in various sections 0.12 2013-03-24 First version ready for review 0.13 2013-03-26 Incorporation of first review comments 0.14 2013-03-27 Updated version reviewed. -
Systems Theoretic Process Analysis (STPA): a Bibliometric and Patents Analysis
ORIGINAL ARTICLE Systems Theoretic Process Analysis (STPA): a bibliometric and patents analysis Modelo teórico - Sistêmico de Análise de Processos (STPA): uma análise bibliométrica e de patentes Sarah Francisca De Souza Borges1 , Marco Antônio Fontoura De Albuquerque1 , Moacyr Machado Cardoso Junior1 , Mischel Carmen Neyra Belderrain1 , Luís Eduardo Loures Da Costa1 1Área de Gestão Tecnológica do Programa de Ciências e Tecnologias Espaciais – CTE/G, Instituto Tecnológico De Aeronáutica - ITA, São José dos Campos, SP, Brasil. E-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected] How to cite: Borges, S. F. S., Albuquerque, M. A. F., Cardoso Junior, M. M., Belderrain, M. C. N. & Costa, L. E. L. (2021). Systems Theoretic Process Analysis (STPA): a bibliometric and patents analysis. Gestão & Produção, 28(2), e5073. https://doi.org/10.1590/1806-9649-2020v28e5073 Abstract: The Systemic Theoretical Process Analysis (STPA) model is used for hazard analysis and accident prevention, based on systemic thinking and the identification of causal scenarios, created by Professor Nancy Leveson of the Institute of Technology of Massachusetts (MIT). The purpose of this article is to perform a bibliometric and patent analysis of the STPA model. Since bibliometry is an important tool in the analysis of scientific production, this method is used as a descriptive statistic, for the purposes of this study, the concepts of Goffman's Epidemic Theory were highlighted, under a mainly qualitative analysis, for a study of decline and ascent scientific method. For the bibliometric analysis, the main page of Professor Nancy Leveson was used in MIT's Web site, besides the Web of Science, Mendeley, ResearchGate, Village of Engineering and Scientific Electronic Library Online (SciELO). -
Manuscript Instructions/Template
Safety Analysis in Early Concept Development and Requirements Generation1 Nancy G. Leveson Massachusetts Institute of Technology 77 Massachusetts Ave, Cambridge MA 02139 617-258-0505 http://[email protected] [email protected] Copyright © 2018 by Nancy G. Leveson. Published and used by INCOSE with permission. Abstract. This paper shows how a new hazard analysis technique, STPA (System Theoretic Process Analysis), can be used to generate high-level safety requirements early in the concept development phase that can then assist in the design of the system architecture. These general, system-level requirements can be refined using STPA as decisions are made. The process goes hand-in-hand with design and the rest of the lifecycle as STPA can be used to provide information to assist in decision- making throughout the development and even operations phases. STPA also fits into a model-based engineering process as it works on a model of the system (which is also refined as design decisions are made) although that model is different than the architectural models usually proposed for model- based system engineering today. The process promotes traceability throughout the development process so decisions and designs can be changed with minimum requirements for redoing previous analyses. Finally, while this paper describes the approach with respect to safety, it can be applied to any emergent system property. Early Concept Exploration and Development Early concept development, shown at the top left of the V-model (Figure 1), is the first step in the usual system engineering process. While the label may differ in variants of the model, this stage includes such activities as stakeholder and user analysis (needs analysis), customer requirements generation, regulatory requirements review, feasibility studies, concept and tradespace exploration, and establishment of criteria for evaluation of the evolving and final design. -
Download from an Unknown Website Cannot Be Said to Be ‘Safe’ Just Because It Happens Not to Harbor a Virus
A direct path to dependable software The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Jackson, Daniel. “A direct path to dependable software.” Commun. ACM 52.4 (2009): 78-88. As Published http://dx.doi.org/10.1145/1498765.1498787 Publisher Association for Computing Machinery Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/51683 Terms of Use Attribution-Noncommercial-Share Alike 3.0 Unported Detailed Terms http://creativecommons.org/licenses/by-nc-sa/3.0/ A Direct Path To Dependable Software Daniel Jackson Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology What would it take to make software more dependable? Until now, most approaches have been indirect: some practices – processes, tools or techniques – are used that are believed to yield dependable software, and the argument for dependability rests on the extent to which the developers have adhered to them. This article argues instead that developers should produce direct evidence that the software satisfies its dependability claims. The potential advantages of this approach are greater credibility (since the ar- gument is not contingent on the effectiveness of the practices) and reduced cost (since development resources can be focused where they have the most impact). 1 Why We Need Better Evidence Is a system that never fails dependable? Not necessarily. A dependable system is one you can depend on – that is, in which you can place your reliance or trust. A rational person or organization only does this with evidence that the system’s benefits outweigh its risks. -
On the State of the Art in Machine Learning: a Personal Review
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by CiteSeerX University of Bristol DEPARTMENT OF COMPUTER SCIENCE On the state of the art in Machine Learning: a personal review Peter A. Flach December 2000 CSTR-00-018 On the state of the art in Machine Learning: a personal review Peter A. Flach December 15, 2000 Abstract This paper reviews a number of recent books related to current developments in machine learning. Some (anticipated) trends will be sketched. These include: a trend towards combining approaches that were hitherto regarded as distinct and were studied by separate research communities; a trend towards a more prominent role of representation; and a tighter integration of machine learning techniques with techniques from areas of application such as bioinformatics. The intended readership has some knowledge of what machine learning is about, but brief tuto- rial introductions to some of the more specialist research areas will also be given. 1 Introduction This paper reviews a number of books that appeared recently in my main area of exper- tise, machine learning. Following a request from the book review editors of Artificial Intelligence, I selected about a dozen books with which I either was already familiar, or which I would be happy to read in order to find out what’s new in that particular area of the field. The choice of books is therefore subjective rather than comprehensive (and also biased towards books I liked). However, it does reflect a personal view of what I think are exciting developments in machine learning, and as a secondary aim of this paper I will expand a bit on this personal view, sketching some of the current and anticipated future trends.