Study of application of space telescope science operations software for SIRTF use

The design and development of the Space Telescope Science Operations Ground System (ST SOGS) was evaluated to compile a history of lessons learned that would benefit NASA's Space Infrared Telescope Facility (SIRTF). Forty-nine specific recommendations resulted and were categorized as follows: (1) requirements: a discussion of the content, timeliness and proper allocation of the system and segment requirements and the resulting impact on SOGS development; (2) science instruments: a consideration of the impact of the Science Instrument design and data streams on SOGS software; and (3) contract phasing: an analysis of the impact of beginning the various ST program segments at different times. Approximately half of the software design and source code might be useable for SIRTF. Transportability of this software requires, at minimum, a compatible DEC VAX-based architecture and VMS operating system, system support software similar to that developed for SOGS, and continued evolution of the SIRTF operations concept and requirements such that they remain compatible with ST SOGS operation

Big Data for All: Privacy and User Control in the Age of Analytics

We live in an age of “big data.” Data have become the raw material of production, a new source for immense economic and social value. Advances in data mining and analytics and the massive increase in computing power and data storage capacity have expanded by orders of magnitude the scope of information available for businesses and government. Data are now available for analysis in raw form, escaping the confines of structured databases and enhancing researchers’ abilities to identify correlations and conceive of new, unanticipated uses for existing information. In addition, the increasing number of people, devices, and sensors that are now connected by digital networks has revolutionized the ability to generate, communicate, share, and access data. Data creates enormous value for the world economy, driving innovation, productivity, efficiency, and growth. At the same time, the “data deluge” presents privacy concerns which could stir a regulatory backlash dampening the data economy and stifling innovation. In order to craft a balance between beneficial uses of data and individual privacy, policymakers must address some of the most fundamental concepts of privacy law, including the definition of “personally identifiable information,” the role of individual control, and the principles of data minimization and purpose limitation. This article emphasizes the importance of providing individuals with access to their data in usable format. This will let individuals share the wealth created by their information and incentivize developers to offer user-side features and applications harnessing the value of big data. Where individual access to data is impracticable, data are likely to be de-identified to an extent sufficient to diminish privacy concerns. In addition, since in a big data world it is often not the data but rather the inferences drawn from them that give cause for concern, organizations should be required to disclose their decisional criteria

Development Of A Slide-Rolling Contact Fatigue Tester

Contact fatigue failure is a common problem experienced in many applications such as bearings, gears and railway tracks. In recent years, research companies have developed finishing processes which aim to improve a component\u27s contact fatigue life. One such process has been patented by REM Chemicals in Texas. Preliminary contact fatigue tests have shown that this superfinishing process could potentially improve a component’s contact fatigue life by 300%. Before this technology can move from the laboratory to an industrial platform, more tests are needed to verify the claim. To the best of the author’s knowledge, no standard process or test machine exists to assess the fatigue endurance of superfinished contacting surfaces. The objective of this thesis is to discuss the completion and verification of a sliding-rolling contact fatigue (S-RCF) test rig. A majority of the development and fabrication was performed by a group of students at the University of North Dakota before May 2007. After this, completion of the tester was finished by the graduate student of this thesis. Unlike other contact fatigue testers built for specific purposes, the proposed tester allows for more flexible testing parameters such as any combination of slide-roll ratio between the surfaces, any operating speed and dry or lubricated testing. For failure detection, the proposed tester is equipped with a state-of-the-art eddy current crack detection system. However, it can be easily modified to use ultrasonic or vibration based crack detection devices. The eddy current crack detection system can also be used to monitor and investigate crack growth for different materials, levels of superfinish and operating conditions. Three preliminary tests on a common gear material (A1S1 8620 steel) were performed mechanical limits as well as the software performance of the tester. Two of the 8620 specimens exhibited signs of failure during testing, while one specimen was suspended after running for several days. While the last specimen was being tested, a shaft broke and further testing could not be performed. The tester failure was caused due to poor manufacturing of the shafts and a slight misalignment of the rollers. During the three tests, the various systems of the S-RCF tester were left running for several days and only minor problems were found. These problems were either inconsequential or fixed. Several Matlab scripts were developed and modified to control the tester and analyze the data online and offline. The S-RCF tester has a few components that need to be fixed before further testing can be performed. Two options exist to continue testing. The first option is to repair the current S-RCF tester and continue testing. The second option is to design a second generation machine with several upgrades and improvements. Either option will provide the University of North Dakota\u27s Engineered Surfaces Center the ability to test and compare the fatigue endurance for different surface finishes

Learning the language of apps

To explore the functionality of an app, automated test generators systematically identify and interact with its user interface (UI) elements. A key challenge is to synthesize inputs which effectively and efficiently cover app behavior. To do so, a test generator has to choose which elements to interact with but, which interactions to do on each element and which input values to type. In summary, to better test apps, a test generator should know the app's language, that is, the language of its graphical interactions and the language of its textual inputs. In this work, we show how a test generator can learn the language of apps and how this knowledge is modeled to create tests. We demonstrate how to learn the language of the graphical input prior to testing by combining machine learning and static analysis, and how to refine this knowledge during testing using reinforcement learning. In our experiments, statically learned models resulted in 50\% less ineffective actions an average increase in test (code) coverage of 19%, while refining these through reinforcement learning resulted in an additional test (code) coverage of up to 20%. We learn the language of textual inputs, by identifying the semantics of input fields in the UI and querying the web for real-world values. In our experiments, real-world values increase test (code) coverage ~10%; Finally, we show how to use context-free grammars to integrate both languages into a single representation (UI grammar), giving back control to the user. This representation can then be: mined from existing tests, associated to the app source code, and used to produce new tests. 82% test cases produced by fuzzing our UI grammar can reach a UI element within the app and 70% of them can reach a specific code location.Automatisierte Testgeneratoren identifizieren systematisch Elemente der Benutzeroberfläche und interagieren mit ihnen, um die Funktionalität einer App zu erkunden. Eine wichtige Herausforderung besteht darin, Eingaben zu synthetisieren, die das App-Verhalten effektiv und effizient abdecken. Dazu muss ein Testgenerator auswählen, mit welchen Elementen interagiert werden soll, welche Interaktionen jedoch für jedes Element ausgeführt werden sollen und welche Eingabewerte eingegeben werden sollen. Um Apps besser testen zu können, sollte ein Testgenerator die Sprache der App kennen, dh die Sprache ihrer grafischen Interaktionen und die Sprache ihrer Texteingaben. In dieser Arbeit zeigen wir, wie ein Testgenerator die Sprache von Apps lernen kann und wie dieses Wissen modelliert wird, um Tests zu erstellen. Wir zeigen, wie die Sprache der grafischen Eingabe lernen vor dem Testen durch maschinelles Lernen und statische Analyse kombiniert und wie dieses Wissen weiter verfeinern beim Testen Verstärkung Lernen verwenden. In unseren Experimenten führten statisch erlernte Modelle zu 50% weniger ineffektiven Aktionen, was einer durchschnittlichen Erhöhung der Testabdeckung (Code) von 19% entspricht, während die Verfeinerung dieser durch verstärkendes Lernen zu einer zusätzlichen Testabdeckung (Code) von bis zu 20% führte. Wir lernen die Sprache der Texteingaben, indem wir die Semantik der Eingabefelder in der Benutzeroberfläche identifizieren und das Web nach realen Werten abfragen. In unseren Experimenten erhöhen reale Werte die Testabdeckung (Code) um ca. 10%; Schließlich zeigen wir, wie kontextfreien Grammatiken verwenden beide Sprachen in einer einzigen Darstellung (UI Grammatik) zu integrieren, wieder die Kontrolle an den Benutzer zu geben. Diese Darstellung kann dann: aus vorhandenen Tests gewonnen, dem App-Quellcode zugeordnet und zur Erstellung neuer Tests verwendet werden. 82% Testfälle, die durch Fuzzing unserer UI-Grammatik erstellt wurden, können ein UI-Element in der App erreichen, und 70% von ihnen können einen bestimmten Code-Speicherort erreichen

AiREAS: Sustainocracy for a Healthy City

This book describes the coming about and first results of the AiREAS "healthy city" cooperative in the city of Eindhoven and Province of North Brabant in the Netherlands. AiREAS is an initiative focused on the multidisciplinary co-creation of healthy cities using the core human value of human health and air quality as guiding principle for profound regional innovation. The unique group process that followed uses the complexity of the city of Eindhoven as living lab. It is an anthropology based initiative that invites directly to the same table of core innovative responsibility the local government, innovative business partners, scientific insights and research, and civilian participation. The first phase is described here in which the consortium decided to want to make the invisible of air pollution and human exposure visible for the integral innovative participation of all city's core pillars (policy, education, infrastructure, culture and entrepreneurship). The experience is unique in the world and proceeding now with more phases in Eindhoven and the role out of the same working format in other cities. This Brief is made available to inspire the world to address together the most complex issues of our current era: pollution, climate and core human values

Public Innovation and Digital Transformation

Public innovation and digitalization are reshaping organizations and society in various ways and within multiple fields, as innovations are essential in transforming our world and addressing global sustainability and development challenges. This book addresses the fascinating relationship of these two contemporary topics and explores the role of digital transformation in promoting public innovation. This edited collection includes examples of innovations that emerge suddenly, practices for processing innovations, and the requirements for transformation from innovation to the "new normal". Acknowledging that public innovation refers to the development and realization of new and creative ideas that challenge conventional wisdom and disrupt the established practices within a specific context, expert contributions from international scholars explore and illustrate the various activities that are happening in the world of multiple digitalization opportunities. The content covers public administration, technical and business management, human, social, and future sciences, paying attention to the interaction between public and private sectors to utilize digitalization in order to facilitate public innovation. This timely book will be of interest to researchers, academics and students in the fields of technology and innovation management, as well as knowledge management, public service management and administration.fi=vertaisarvioitu|en=peerReviewed

Public Innovation and Digital Transformation

Public innovation and digitalization are reshaping organizations and society in various ways and within multiple fields, as innovations are essential in transforming our world and addressing global sustainability and development challenges. This book addresses the fascinating relationship of these two contemporary topics and explores the role of digital transformation in promoting public innovation. This edited collection includes examples of innovations that emerge suddenly, practices for processing innovations, and the requirements for transformation from innovation to the ""new normal"". Acknowledging that public innovation refers to the development and realization of new and creative ideas that challenge conventional wisdom and disrupt the established practices within a specific context, expert contributions from international scholars explore and illustrate the various activities that are happening in the world of multiple digitalization opportunities. The content covers public administration, technical and business management, human, social, and future sciences, paying attention to the interaction between public and private sectors to utilize digitalization in order to facilitate public innovation. This timely book will be of interest to researchers, academics and students in the fields of technology and innovation management, as well as knowledge management, public service management and administration