Computer-Aided Design of User Interfaces V ; Proceedings of the 6th International Conference on Computer-Aided Design of User Interfaces CADUI '06 (6-8 June 2006, Bucharest, Romania)
Today, the development life cycle of 3D User Interfaces (UIs) mostly remains an art more than a principled-based approach. Several methods [1,3,7,8,9,10,11,15,17,18,19] have been introduced to decompose this life cycle into steps and sub-steps, but these methods rarely provide the design knowledge that should be typically used for achieving each step. In addition, the development life cycle is more focusing directly on the programming - sues than on the design and analysis phases. This is sometimes reinforced by the fact that available tools for 3D UIs are toolkits, interface builders, r- dering engines, etc. When there is such a development life cycle defined, it is typically structured into the following set of activities: 1. The conceptual phase is characterized by the identification of the content and interaction requests. The meta-author discusses with the interface designer to take advantage of the current interaction technology. The int- face designer receives information about the content. The result of this phase is the production of UI schemes (e. g. , written sentences, visual schemes on paper) for defining classes of interactive experiences (e. g. , class Guided tour). Conceptual schemes are produced both for the final users and the authors. The meta-author has a deep knowledge of the c- tent domain and didactic skills too. He/she communicates with the final user too, in order to focus on didactic aspects of interaction. 2.
كتب مشابهة
New challenges in software engineering ; Vol 1
Explores the key challenges shaping the future of software development, including automation, AI-driven development, security-focused engineering, resilient and autonomous architectures, business process optimization, cloud computing, microservices, high-performance distributed systems, and sustainable technologies. Software engineering is undergoing a constant transformation, driven by rapid technological advances and evolving market demands. additionally, it delves into the ethical considerations of AI, the evolution of intuitive user interfaces, and the importance of multidisciplinary collaboration.
Fundamentals of manufacturing engineering using digital visualization
Offers a guide to core principles and practices of manufacturing engineering. It covers the design of, together with technological and measurement issues for, technical systems. Locating charts and setup schemes describing different machining processes are included. Concepts of product quality, with a focus on accuracy indicators, machining accuracy, roughness, and the impact of surface quality on exploitation properties are also explained. Furthermore, key machining methods, including turning, milling, hole machining, grinding, and gear machining, are analyzed in depth, covering their principles, applications, and techniques. The book is enriched by QR codes, linking to a mobile application presenting additional information about the content, for an interactive and extended learning experience. It also uses illustrations visualized with digital tools to promote a better understanding of the concepts.
AI in drug discovery
Constitutes the refereed proceedings of the First international workshop on ai in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.
AI in banking : Practical applications and case studies
Delves into the application of AI from theory to practice, offering detailed insights into AI project design and code implementation across eleven business scenarios in four major sectors: retail banking, e-banking, bank credit, and tech operations. it provides hands-on examples of various technologies, including automatic machine learning, integrated learning, graph computation, recommendation systems, causal inference, generative adversarial networks, supervised learning, unsupervised learning, computer vision, reinforcement learning, fuzzy control, automatic control, speech recognition, semantic understanding, bayesian networks, edge computing, and more. this book stands as a rare and practical guide to AI projects in the banking industry.
