From knowledge to wealth : transforming Russian science and technology for a modern knowledge economy

Russia possesses a sophisticated science and technology (S&T) infrastructure (research capability, technically trained workforce, and technical research universities) which, even today, is a world leader in many fields. Despite this world class basic research capacity, Russia's exports are primarily raw materials. At a time when wealth depends to an increasing degree on knowledge, Russia does not have an effective system for converting its scientific capacity into wealth. Russia's S&T resources are isolated bureaucratically (they are deployed in the rigid hierarchical system devised in the 1920s to mobilize resources for rapid state-planned industrial development and national defense), functionally (there are few links between the supply of S&T output by research institutes and the demand for S&T by Russian or foreign enterprises), and geographically (many assets are located in formerly closed cities or isolated science/atomic cities). Overcoming these inefficiencies and adjusting the S&T system to the demands of a market economy will require a major program of institutional and sectoral reform. Part I of this paper describes the ambiguous legacy of the Soviet S&T system and the status of the Russian S&T sector after 10 years of transition. Part II describes the evolution of the Russian system of intellectual property rights protection from Soviet times to the present and argues that Russia will never develop a successful commercialization program until it clarifies the ownership of the large stock of intellectual property funded with federal budget resources. Part III outlines a comprehensive 10-point sectoral reform program to improve the efficiency of government research and development spending and link the Russian S&T system with market forces.ICT Policy and Strategies,Public Health Promotion,Scientific Research&Science Parks,Agricultural Knowledge&Information Systems,General Technology,ICT Policy and Strategies,Scientific Research&Science Parks,Science Education,Agricultural Knowledge&Information Systems,General Technology

MSIS 2016 global competency model for graduate degree programs in information systems

[Extract] This document, “MSIS 2016: Global Competency Model for Graduate Degree Programs in Information Systems”, is the latest in the series of reports that provides guidance for degree programs in the Information Systems (IS) academic discipline. MSIS 2016 is the seventh collaborative effort between ACM and AIS (following IS’97, IS 2002, and IS 2010 at the undergraduate level; MSIS 2000 and MSIS 2006 at the graduate level; and CC 2005 as an integrative document).(undefined)info:eu-repo/semantics/publishedVersio

Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines

The Value of Crowdsourcing for Complex Problems: Comparative Evidence from Software Developed By the Crowd And Professionals

Crowdsourcing is a problem solving model. In the context of complex problems, conventional theory suggests that solving complex problems is a province of professionals, that is, people with sufficient knowledge about the domain. Prior literature has indicated that the crowd, in addition to professionals, is also a great source for solving problems such as product innovation and idea generation. However, this assumption has yet to be tested. Adopting a quasi-experimental approach, this study uses a two-phase process to investigate this question. In the first phase we compare the development of a software by the crowd and professionals. In the second phase we evaluate the software developed by the crowdsourcing business model and professionals in terms of key perceived quality dimensions assessed by users of the systems. Quality is measured in terms of pragmatic quality, hedonic quality stimulation, and hedonic quality identification. Our study results suggest that there is a statistically significant difference between the software developed by a crowdsourcing business model and professionals in terms of hedonic quality stimulation and hedonic quality identification but there is no difference in terms of pragmatic quality. This research offers a first assessment of whether a crowdsourcing business model can be used to develop software with better user experience than professionallydeveloped software