This book is a second edition, updated and expanded to explain the technologies that help us find information on the web. Search engines and web navigation tools have become ubiquitous in our day to day use of the web as an information source, a tool for commercial transactions and a social computing tool. Moreover, through the mobile web we have access to the web's services when we are on the move. This book demystifies the tools that we use when interacting with the web, and gives the reader a detailed overview of where we are and where we are going in terms of search engine and web navigation technologies.

This book constitutes the refereed proceedings of the 17th European Conference on Machine Learning, ECML 2006, held, jointly with PKDD 2006. The book presents 46 revised full papers and 36 revised short papers together with abstracts of 5 invited talks, carefully reviewed and selected from 564 papers submitted. The papers present a wealth of new results in the area and address all current issues in machine learning.

With greater organizational complexities looming on the horizon, PMI has introduced Navigating Complexity: A Practice Guide. The definitive guide expands upon the principles, tools, and techniques presented in the PMBOK&® Guide and other foundational standards, providing a streamlined approach to understanding and navigating complexity. This groundbreaking guide fills a void, providing the first published reference to help project management professionals successfully mitigate complexities and accomplish their organizational goals.

This two-volume set, IFIP AICT 726 and 727, constitutes the refereed proceedings of the 25th IFIP WG 5.5 Working Conference on Virtual Enterprise, PRO-VE 2024, held in Albi, France, during October 28–30, 2024. The 56 full papers presented in these two volumes were carefully reviewed and selected from 113 submissions. The papers presented in these two volumes are organized in the following topical sections: Part I: AI and collaboration; Human-machine collaboration; Emotions and collaborative networks; Collaborative ecosystems: Skills for resilient futures; Collaborative ecosystems: Technologies for resilient futures; Uncertainty and collaboration in supply chain; Collaborative networks as driver of innovation in organizations 5.0: Models; Collaborative networks as driver of innovation in organizations 5.0: Participation; Trust and trustworthy technologies in collaborative networks. Part II: Empowering vulnerable populations well-being through collaborative networks; Collaborative manufacturing systems in the digital era; Fostering collaborative and interoperable digital models for digital twins: Methods; Fostering collaborative and interoperable digital models for digital twins: Cases; Zero defects and zero waste strategies in industrial collaborative networks; Simulation frameworks; Collaborative decision making; Design of collaborative environments.

This two-volume set, IFIP AICT 726 and 727, constitutes the refereed proceedings of the 25th IFIP WG 5.5 Working Conference on Virtual Enterprise, PRO-VE 2024, held in Albi, France, during October 28–30, 2024. The 56 full papers presented in these two volumes were carefully reviewed and selected from 113 submissions. The papers presented in these two volumes are organized in the following topical sections: Part I: AI and collaboration; Human-machine collaboration; Emotions and collaborative networks; Collaborative ecosystems: Skills for resilient futures; Collaborative ecosystems: Technologies for resilient futures; Uncertainty and collaboration in supply chain; Collaborative networks as driver of innovation in organizations 5.0: Models; Collaborative networks as driver of innovation in organizations 5.0: Participation; Trust and trustworthy technologies in collaborative networks. Part II: Empowering vulnerable populations well-being through collaborative networks; Collaborative manufacturing systems in the digital era; Fostering collaborative and interoperable digital models for digital twins: Methods; Fostering collaborative and interoperable digital models for digital twins: Cases; Zero defects and zero waste strategies in industrial collaborative networks; Simulation frameworks; Collaborative decision making; Design of collaborative environments.

Research on gene drive systems is rapidly advancing. Many proposed applications of gene drive research aim to solve environmental and public health challenges, including the reduction of poverty and the burden of vector-borne diseases, such as malaria and dengue, which disproportionately impact low and middle income countries. However, due to their intrinsic qualities of rapid spread and irreversibility, gene drive systems raise many questions with respect to their safety relative to public and environmental health. Because gene drive systems are designed to alter the environments we share in ways that will be hard to anticipate and impossible to completely roll back, questions about the ethics surrounding use of this research are complex and will require very careful exploration. Gene Drives on the Horizon outlines the state of knowledge relative to the science, ethics, public engagement, and risk assessment as they pertain to research directions of gene drive systems and governance of the research process. This report offers principles for responsible practices of gene drive research and related applications for use by investigators, their institutions, the research funders, and regulators.

An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.