Modular And Incremental Global Model Management With Extended Generalized Discrimination Networks

Complex projects developed under the model-driven engineering paradigm nowadays often involve several interrelated models, which are automatically processed via a multitude of model operations. Modular and incremental construction and execution of such networks of models and model operations are required to accommodate efficient development with potentially large-scale models. The underlying problem is also called Global Model Management. In this report, we propose an approach to modular and incremental Global Model Management via an extension to the existing technique of Generalized Discrimination Networks (GDNs). In addition to further generalizing the notion of query operations employed in GDNs, we adapt the previously query-only mechanism to operations with side effects to integrate model transformation and model synchronization. We provide incremental algorithms for the execution of the resulting extended Generalized Discrimination Networks (eGDNs), as well as a prototypical implementation for a number of example eGDN operations. Based on this prototypical implementation, we experiment with an application scenario from the software development domain to empirically evaluate our approach with respect to scalability and conceptually demonstrate its applicability in a typical scenario. Initial results confirm that the presented approach can indeed be employed to realize efficient Global Model Management in the considered scenario.

Like conventional software projects, projects in model-driven software engineering require adequate management of multiple versions of development artifacts, importantly allowing living with temporary inconsistencies. In the case of model-driven software engineering, employed versioning approaches also have to handle situations where different artifacts, that is, different models, are linked via automatic model transformations. In this report, we propose a technique for jointly handling the transformation of multiple versions of a source model into corresponding versions of a target model, which enables the use of a more compact representation that may afford improved execution time of both the transformation and further analysis operations. Our approach is based on the well-known formalism of triple graph grammars and a previously introduced encoding of model version histories called multi-version models. In addition to showing the correctness of our approach with respect to the standard semantics of triple graph grammars, we conduct an empirical evaluation that demonstrates the potential benefit regarding execution time performance.

The interwoven futures of humanity and our planet are under threat. Urgent action, taken together, is needed to change course and reimagine our futures.

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Cardiovascular disease (CVD), once thought to be confined primarily to industrialized nations, has emerged as a major health threat in developing countries. Cardiovascular disease now accounts for nearly 30 percent of deaths in low and middle income countries each year, and is accompanied by significant economic repercussions. Yet most governments, global health institutions, and development agencies have largely overlooked CVD as they have invested in health in developing countries. Recognizing the gap between the compelling evidence of the global CVD burden and the investment needed to prevent and control CVD, the National Heart, Lung, and Blood Institute (NHLBI) turned to the IOM for advice on how to catalyze change. In this report, the IOM recommends that the NHLBI, development agencies, nongovernmental organizations, and governments work toward two essential goals: creating environments that promote heart healthy lifestyle choices and help reduce the risk of chronic diseases, and building public health infrastructure and health systems with the capacity to implement programs that will effectively detect and reduce risk and manage CVD. To meet these goals, the IOM recommends several steps, including improving cooperation and collaboration; implementing effective and feasible strategies; and informing efforts through research and health surveillance. Without better efforts to promote cardiovascular health, global health as a whole will be undermined.

Digital violence continues to increase, especially during times of crisis. Racism, bullying, ageism, sexism, child pornography, cybercrime, and digital tracking raise critical social and digital security issues that have lasting effects. Digital violence can cause children to be dragged into crime, create social isolation for the elderly, generate inter-communal conflicts, and increase cyber warfare. A closer study of digital violence and its effects is necessary to develop lasting solutions. The Handbook of Research on Digital Violence and Discrimination Studies introduces the current best practices, laboratory methods, policies, and protocols surrounding international digital violence and discrimination. Covering a range of topics such as abuse and harassment, this major reference work is ideal for researchers, academicians, policymakers, practitioners, professionals, instructors, and students.

This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages.