Artificial Intelligence and International Conflict in Cyberspace

This edited volume explores how artificial intelligence (AI) is transforming international conflict in cyberspace. Over the past three decades, cyberspace developed into a crucial frontier and issue of international conflict. However, scholarly work on the relationship between AI and conflict in cyberspace has been produced along somewhat rigid disciplinary boundaries and an even more rigid sociotechnical divide – wherein technical and social scholarship are seldomly brought into a conversation. This is the first volume to address these themes through a comprehensive and cross-disciplinary approach. With the intent of exploring the question ‘what is at stake with the use of automation in international conflict in cyberspace through AI?’, the chapters in the volume focus on three broad themes, namely: (1) technical and operational, (2) strategic and geopolitical and (3) normative and legal. These also constitute the three parts in which the chapters of this volume are organised, although these thematic sections should not be considered as an analytical or a disciplinary demarcation

Regulatory Theory: Foundations and applications

This volume introduces readers to regulatory theory. Aimed at practitioners, postgraduate students and those interested in regulation as a cross-cutting theme in the social sciences, Regulatory Theory includes chapters on the social-psychological foundations of regulation as well as theories of regulation such as responsive regulation, smart regulation and nodal governance. It explores the key themes of compliance, legal pluralism, meta-regulation, the rule of law, risk, accountability, globalisation and regulatory capitalism. The environment, crime, health, human rights, investment, migration and tax are among the fields of regulation considered in this ground-breaking book. Each chapter introduces the reader to key concepts and ideas and contains suggestions for further reading. The contributors, who either are or have been connected to the Regulatory Institutions Network (RegNet) at The Australian National University, include John Braithwaite, Valerie Braithwaite, Peter Grabosky, Neil Gunningham, Fiona Haines, Terry Halliday, David Levi-Faur, Christine Parker, Colin Scott and Clifford Shearing

An Acceptable Cloud Computing Model for Public Sectors

Cloud computing enables information technology (IT) leaders to shift from passive business support to active value creators. However, social economic-communication barriers inhibit individual users from strategic use of the cloud. Grounded in the theory of technology acceptance, the purpose of this multiple case study was to explore strategies IT leaders in public sector organizations implement to utilize cloud computing. The participants included nine IT leaders from public sector organizations in Texas, USA. Data were collected using semi-structured interviews, field notes, and publicly available artifacts documents. Data were analyzed using thematic analysis: five themes emerged (a) user-centric and data-driven cloud model; (b) multi-cloud, (c) visibility, (d) integrations, and (e) innovation and agility due to cloud. A key recommendation is for IT leaders to strategize for individual user behavior through the top-down approach. The implications for positive social change include the potential to improve civic services, civic engagement, collaborations between the public and government, policymaking, and added socioeconomic value

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Beyond Quantity: Research with Subsymbolic AI

How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.Comment: Published in Foundations and Trends in Machine Learning Vol 4 Issue 1. See: https://www.nowpublishers.com/article/Details/MAL-08

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution