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This document provides guidance on academic writing for a final year project in artificial intelligence. It discusses the importance of an exact, clear, and compact writing style with references throughout. It outlines the writing process, including reading literature, collecting sources, and the typical structure of an introduction, background, related work, application, evaluation, and conclusions. It also notes some reference styles and class-based writing tasks.
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This document provides an overview of smart learning environments (SLEs) and discusses key concepts around designing SLEs that promote equity, diversity, and inclusion (EDI). SLEs are physical learning spaces enhanced with digital technologies like sensors and devices. The document outlines several models for SLEs, including centralized and distributed models, and directions for research to focus more on learner experience. It also discusses applying EDI concepts to the design of AI interfaces for SLEs and how AI can be used to create personalized, accessible, and culturally relevant learning experiences. The document proposes activities for groups to discuss scenarios for implementing smart learning environments that address EDI challenges and propose solutions. It concludes by outlining principles for the design of smart classrooms and
ICS 2208 Lecture Slide Notes for Topic 6
Lecture Slides for ICS2208 - Textual Interfaces - Use of Natural Language Processing Techniques and Generative AI.
ICS2208 Lecture4 Intelligent Interface Agents.pdf
This document provides an overview of intelligent user interfaces and interface agents. It discusses agent models and characteristics of agents in complex systems. Environment properties and task environments in AI that affect agent decision making are described. The document also covers intelligent agent models and examples. It discusses how interface agents learn and perceive their environment, including through machine learning algorithms. Challenges of using reinforcement learning for interface agents are also outlined.
ICS2208 Lecture3 2023-2024 - Model Based User Interfaces
Model-based user interface (MBUI) development uses models to capture world knowledge about users, tasks, and systems to develop user interfaces. These models include user models, task models, and system models. MBUIs have benefits like independence, rapid development, flexibility, and automation compared to traditional manual coding approaches. Challenges include complexity, limitations of automatic generation, and integrating adaptation. Example frameworks for MBUI development include Cameleon Reference Framework and MARIA. The future of MBUIs may integrate machine learning techniques like predictive user modeling and natural language processing to develop more adaptive and user-centered intelligent interfaces.
ICS2208 Lecture 2 Slides Interfaces_.pdf
This document provides an overview of intelligent user interfaces and user interface design. It discusses key topics such as:
- User interface design models including design models, user models, mental models, and implementation models.
- The user interface design process which includes interface task analysis, design, construction, and validation.
- User interface design evaluation which is an iterative process of building prototypes, getting user feedback, and making modifications to improve the design.
- User-centric characteristics of smart environments which should be embedded, context-aware, personalized, adaptive, anticipatory, unobtrusive, and non-invasive.
ICS Lecture 11 - Intelligent Interfaces 2023
This document provides an overview of affective computing for intelligent interfaces. It discusses affective computing, which focuses on developing systems that can recognize, interpret, process, and simulate human emotions. Benefits include personalized interactions, improved decision making, and enhanced patient care. Challenges include sensing emotions, affect modeling, ethics, and adapting to individual differences. It also describes methods for emotion recognition, including facial expression analysis using CNNs/SVMs, voice tone analysis using DNNs, and physiological signal processing using wearable sensors. Examples of affective computing applications in healthcare, retail, education, and more are discussed. Finally, students are tasked with outlining an emotion-aware interface for a specific application.
ICS3211_lecture 09_2023.pdf
This document provides an overview of a week 9 course on design for immersive realities. The course will include a guest talk on identities and representation in immersive reality, a VR design case study on emergency response in cardiac arrest, and design discussions. Students will learn about VR interface design elements, identities and representation in immersive realities, and analyze a case study VR game for emergency cardiac arrest response training. They will provide feedback and insights on interface design considerations for VR learning applications.
ICS3211_lecture 08_2023.pdf
This document discusses multimodal interfaces. It defines multimodal interfaces as those that process two or more combined user input modes, such as speech, gestures, touch, etc. It outlines some key characteristics of multimodal interfaces including exploiting multiple human senses and providing new functionalities. The document also covers guidelines for designing multimodal interfaces, such as supporting flexibility and adaptivity. Example application scenarios for multimodal interfaces in healthcare robots, education systems, and smart homes are also presented.
ICS3211_lecture_week72023.pdf
This document summarizes a university lecture on intelligent interfaces and automatic speech recognition. It discusses adaptive user interfaces and design guidelines, as well as trends, applications, and challenges of ASR systems. The lecture covers stimulus-response compatibility principles for interface design, applications of ASR in telecommunications, healthcare, and aviation, and challenges of ASR like varying accents and noise.
ICS3211_lecture_week62023.pdf
This document provides an overview of the topics to be covered in Week 6 of an Intelligent Interfaces course, including trends in IUIs, designing adaptive and speech-based interfaces, and automatic speech recognition. The key learning outcomes are to identify future IUI trends, evaluate interface designs, and understand recent advances and challenges in ASR. The lecture will cover emerging IUI technologies, design guidelines for IUIs, and an activity on ASR trends, applications, and challenges.
ICS3211_lecture_week52023.pdf
This document discusses a lecture on decisions, choices, and trends in intelligent user interfaces (IUIs). It covers various types of choices people make, including preferential choices about using a system and configuring applications. Factors that influence choices include goals, habits, consequences, and social influence. The lecture also discusses information processing models, the future of input devices including ubiquitous computing and sensor-based interactions, and challenges in designing adaptive and intelligent user interfaces.
ICS3211_lecture 04 2023.pdf
This document discusses a university course on intelligent interfaces. It covers various topics related to designing effective human-computer interactions, including interface evaluation methods, usability testing, and capturing user preferences. Some key learning outcomes are describing different evaluation methods, conducting a usability analysis, and understanding how user choices and preferences can inform adaptive interface design.
ICS3211_lecture 03 2023.pdf
This document provides an overview of week 2 of an intelligent interfaces course. It discusses theoretical frameworks for human-computer interaction (HCI), including information processing models. Intelligent interfaces aim to enhance flexibility, usability and power of interaction by exploiting knowledge of users, tasks and context. Theories that view HCI as information processing and as socially embedded processes are examined. Methods for evaluating information processing like signal detection and chronometry are also outlined.
ICS3211_lecture 11.pdf
This document discusses two case studies about using adaptive user interfaces (UIs) to improve healthcare systems. The first case study describes an adaptive mobile interface for healthcare monitoring that can adjust over time based on a user's needs and abilities using machine learning. The second case study presents two scenarios where a patient and doctor interact with a smart device to view health data, including scenarios where authentication is required. The document also briefly outlines several applications of medical robots, including surgical robots, rehabilitation robots, and other uses of robotics in healthcare.
FoundationsAIEthics2023.pdf
This document provides an introduction to AI ethics, including strategic pillars and enablers of AI development in Malta. It discusses the Moral Machine test for exploring ethical dilemmas around autonomous vehicles, and issues in AI like ensuring inclusive and unbiased behavior from models and data. The document proposes dividing a class into groups to debate questions around the purpose and development of AI, and defines ethics in AI as regulating technological behavior. It also includes exercises generating image captions from neural networks and recommends further reading on ethical and trustworthy AI.
ICS3211_lecture 9_2022.pdf
The document discusses various methods for testing and evaluating user interfaces, including expert review, heuristic evaluation, cognitive walkthrough, usability testing, surveys, acceptance tests, and automated evaluation. It provides details on each method, such as when they are used, how they are conducted, their benefits and limitations. The goal of evaluation and testing is to improve interfaces and ensure they meet user needs.
ICS1020CV_2022.pdf
The document discusses principles of computer vision and its applications. It is a lecture by Dr. Vanessa Camilleri from the University of Malta on computer vision fundamentals and techniques. The key topics covered include object detection methods, stages of computer vision like image acquisition and processing, and examples of computer vision applications in various domains like manufacturing, healthcare, transportation and more.
ARI5902_2022.pdf
The document discusses research topics in artificial intelligence, specifically creative computing and computational creativity. It provides an overview of the fields, including examples of algorithmic art generated by computers, as well as AI systems that collaborate with humans on art like Dall-E and Midjourney. Key research questions are posed around how AI can support and augment human creativity. The document also lists various methodologies used in computational creativity and provides a bibliography of references on the topic.
ICS2208 Lecture10
This document discusses the history and evolution of the Internet of Things (IoT) from its origins in 2005 to modern applications. It describes key characteristics of IoT including sensing, processing, connectivity, and intelligence. Examples of early IoT projects are provided that highlight challenges overcome like lack of standards and limited scalability. The document also discusses user-centric approaches to IoT design and future directions like integrating AI and ensuring privacy and user acceptance.
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ICT3909
- 1. Dr Vanessa Camilleri, November 2020 Academic Writing ICT3909 - Final Year Project in AI
- 4. Writing Style Exact, Clear & Compact
- 5. The Writing Process Read & Research • Need for references… throughout! • Source Types • Collecting Literature • Reading (!!) • References
- 7. Check out these reference styles: 1. https://apastyle.apa.org/style-grammar-guidelines/ references/examples 2. https://ieeeauthorcenter.ieee.org/wp-content/uploads/ IEEE-Reference-Guide.pdf
- 8. • Introduction: the problem • Background theory and main concepts • Related research (other existing solutions to the same or similar problems) • Your own application • Evaluation: comparison to other methods, empirical tests, or theoretical analysis • Conclusions Follow the FYP Guidelines The Thesis Structure
- 10. • Can you trust a source like Wikipedia? Why or Why not? • Find the following papers and write out their references in full: • The ARKAE Vision Project • Gesture Recognition - by Canuto dos Santos et al • Use the paper that has been uploaded in the VLE area - go through it and list your ideas: • What is the main idea? What is the main contribution? What is important for you? Class based activities Academic Writing Tasks