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This book constitutes the proceedings of the 7th International Conference on Mathematical Software, ICMS 2020, held in Braunschweig, Germany, in July 2020. The 48 papers included in this volume were carefully reviewed and selected from 58 submissions. The program of the 2020 meeting consisted of 20 topical sessions, each of which providing an overview of the challenges, achievements and progress in a environment of mathematical software research, development and use.
Special education encounters distinct challenges in delivering personalized and practical assistance to students with disabilities. Educators frequently require support to address the varied needs of these students, resulting in learning and development gaps. Moreover, early identification and catering to these needs can take time and effort, affecting students' long-term academic success. There is an urgent need for innovative solutions that can bridge these gaps and improve the educational experiences of students with disabilities. Transforming Special Education Through Artificial Intelligence offers a comprehensive exploration of how Artificial Intelligence (AI) can transform special education by providing personalized and individualized support for students with disabilities. Through case studies and real-life examples, we demonstrate how AI can analyze data to tailor learning experiences, and most importantly, identify learning difficulties early. This crucial aspect of AI can significantly enhance communication among stakeholders and reassure them about the potential of AI in improving educational outcomes for students with disabilities.
This book highlights recent research advances in various domains related to software ecosystems such as library reuse, collaborative development, cloud computing, open science, sentiment analysis and machine learning. A key aspect of software ecosystems is that software products belong to ever more interdependent networks of co-evolving software components. The ever-increasing importance of social coding platforms has made software ecosystems indispensable to software practitioners, in commercial as well as open-source settings. The book starts with an introductory chapter that provides a historical account of the origins of software ecosystems. It provides the necessary context about the domain of software ecosystems by highlighting its different perspectives, definitions, and representations. It also exemplifies the variety of software ecosystems that have emerged during the previous decades. The remaining book is composed of five parts: Part I contains two chapters on software ecosystem representations, Part II two chapters that focus on complementary ways and techniques of analyzing software ecosystems. Next, Part III includes two chapters that focus on aspects related to the evolution within software ecosystems, while Part IV looks at workflow automation and infrastructure-as-code ecosystems. Finally, Part V focuses on ecosystems for software modeling and for data-intensive software. This book is intended for researchers and practitioners interested in data mining, tooling, and empirical analysis of software ecosystems. The reader will appreciate chapters that cover a wide spectrum of social and technical aspects of software ecosystems, each including an overview of the state of the art. Chapter 2 The Software Heritage Open Science Ecosystem is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Zusammenfassung: Metric algebraic geometry combines concepts from algebraic geometry and differential geometry. Building on classical foundations, it offers practical tools for the 21st century. Many applied problems center around metric questions, such as optimization with respect to distances. After a short dive into 19th-century geometry of plane curves, we turn to problems expressed by polynomial equations over the real numbers. The solution sets are real algebraic varieties. Many of our metric problems arise in data science, optimization and statistics. These include minimizing Wasserstein distances in machine learning, maximum likelihood estimation, computing curvature, or minimizing the Euclidean distance to a variety. This book addresses a wide audience of researchers and students and can be used for a one-semester course at the graduate level. The key prerequisite is a solid foundation in undergraduate mathematics, especially in algebra and geometry. This is an open access book
The first book to intervene in debates on computation in the digital humanities Bringing together leading experts from across North America and Europe, Computational Humanities redirects debates around computation and humanities digital scholarship from dualistic arguments to nuanced discourse centered around theories of knowledge and power. This volume is organized around four questions: Why or why not pursue computational humanities? How do we engage in computational humanities? What can we study using these methods? Who are the stakeholders? Recent advances in technologies for image and sound processing have expanded computational approaches to cultural forms beyond text, and new forms of data, from listservs and code repositories to tweets and other social media content, have enlivened debates about what counts as digital humanities scholarship. Providing case studies of collaborations between humanities-centered and computation-centered researchers, this volume highlights both opportunities and frictions, showing that data and computation are as much about power, prestige, and precarity as they are about p-values. Contributors: Mark Algee-Hewitt, Stanford U; David Bamman, U of California, Berkeley; Kaspar Beelen, U of London; Peter Bell, Philipps U of Marburg; Tobias Blanke, U of Amsterdam; Julia Damerow, Arizona State U; Quinn Dombrowski, Stanford U; Crystal Nicole Eddins, U of Pittsburgh; Abraham Gibson, U of Texas at San Antonio; Tassie Gniady; Crystal Hall, Bowdoin College; Vanessa M. Holden, U of Kentucky; David Kloster, Indiana U; Manfred D. Laubichler, Arizona State U; Katherine McDonough, Lancaster U; Barbara McGillivray, King’s College London; Megan Meredith-Lobay, Simon Fraser U; Federico Nanni, Alan Turing Institute; Fabian Offert, U of California, Santa Barbara; Hannah Ringler, Illinois Institute of Technology; Roopika Risam, Dartmouth College; Joshua D. Rothman, U of Alabama; Benjamin M. Schmidt; Lisa Tagliaferri, Rutgers U; Jeffrey Tharsen, U of Chicago; Marieke van Erp, Royal Netherlands Academy of Arts and Sciences; Lee Zickel, Case Western Reserve U.