Toward user oriented semantic geographical information systems

User Oriented Geographical Information Systems, a recent adaptation of classical GIS concepts to everyday usage, are becoming more and more present in the web landscape. Recent developments show the need of adding higher semantic levels to the existing frameworks, to improve their usage, as well as to ease scalability. We point out limits of actual examples, related to handling heterogeneous data, scalability issues, and expressiveness, and suggest a framework for building a Semantic User Oriented GIS. Notably this framework aims to address the peculiarities of the geographical space domain, and to offer a cognitively sound interface to the user

Mining usage data for adaptive personalisation of smartphone based help-on-demand services

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile computing devices and their applications that encompass context aware components are becoming increasingly more prevalent. The context-awareness of these types of applications typically focuses on the services offered. In this paper we describe a framework that supports the monitoring and analysis of mobile application usage patterns with the goal of updating user models for adaptive services and user interface personalisation. This paper focuses on two aspects of the framework. The first is the modelling and storage of the usage data. The second focuses on the data mining component of the framework, outlining the five different capabilities of the adaptation in addition to the algorithms used. The proposed framework has been evaluated through specific case studies, with the results attained demonstrating the effectiveness of the data mining capabilities and in particular the adaptation of the User Interface. The accuracy and efficiency of the algorithms used are also evaluated with three users. The results of the evaluation show that the aims of the data mining component were achieved with the personalisation and adaptation of content and user interface, respectively

Learning from Interaction: User Interface Adaptation using Reinforcement Learning

The continuous adaptation of software systems to meet the evolving needs of users is very important for enhancing user experience (UX). User interface (UI) adaptation, which involves adjusting the layout, navigation, and content presentation based on user preferences and contextual conditions, plays an important role in achieving this goal. However, suggesting the right adaptation at the right time and in the right place remains a challenge in order to make it valuable for the end-user. To tackle this challenge, machine learning approaches could be used. In particular, we are using Reinforcement Learning (RL) due to its ability to learn from interaction with the users. In this approach, the feedback is very important and the use of physiological data could be benefitial to obtain objective insights into how users are reacting to the different adaptations. Thus, in this PhD thesis, we propose an RL-based UI adaptation framework that uses physiological data. The framework aims to learn from user interactions and make informed adaptations to improve UX. To this end, our research aims to answer the following questions: Does the use of an RL-based approach improve UX? How effective is RL in guiding UI adaptation? and Can physiological data support UI adaptation for enhancing UX? The evaluation plan involves conducting user studies to evaluate answer these questions. The empirical evaluation will provide a strong empirical foundation for building, evaluating, and improving the proposed adaptation framework. The expected contributions of this research include the development of a novel framework for intelligent Adaptive UIs, insights into the effectiveness of RL algorithms in guiding UI adaptation, the integration of physiological data as objective measures of UX, and empirical validation of the proposed framework's impact on UX

A FRAMEWORK FOR INTELLIGENT VOICE-ENABLED E-EDUCATION SYSTEMS

Although the Internet has received significant attention in recent years, voice is still the most convenient and natural way of communicating between human to human or human to computer. In voice applications, users may have different needs which will require the ability of the system to reason, make decisions, be flexible and adapt to requests during interaction. These needs have placed new requirements in voice application development such as use of advanced models, techniques and methodologies which take into account the needs of different users and environments. The ability of a system to behave close to human reasoning is often mentioned as one of the major requirements for the development of voice applications. In this paper, we present a framework for an intelligent voice-enabled e-Education application and an adaptation of the framework for the development of a prototype Course Registration and Examination (CourseRegExamOnline) module. This study is a preliminary report of an ongoing e-Education project containing the following modules: enrollment, course registration and examination, enquiries/information, messaging/collaboration, e-Learning and library. The CourseRegExamOnline module was developed using VoiceXML for the voice user interface(VUI), PHP for the web user interface (WUI), Apache as the middle-ware and MySQL database as back-end. The system would offer dual access modes using the VUI and WUI. The framework would serve as a reference model for developing voice-based e-Education applications. The e-Education system when fully developed would meet the needs of students who are normal users and those with certain forms of disabilities such as visual impairment, repetitive strain injury (RSI), etc, that make reading and writing difficult

A Framework for Computational Design and Adaptation of Extended Reality User Interfaces

To facilitate high quality interaction during the regular use of computing systems, it is essential that the user interface (UI) deliver content and components in an appropriate manner. Although extended reality (XR) is emerging as a new computing platform, we still have a limited understanding of how best to design and present interactive content to users in such immersive environments. Adaptive UIs offer a promising approach for optimal presentation in XR as the user's environment, tasks, capabilities, and preferences vary under changing context. In this position paper, we present a design framework for adapting various characteristics of content presented in XR. We frame these as five considerations that need to be taken into account for adaptive XR UIs: What?, How Much?, Where?, How?, and When?. With this framework, we review literature on UI design and adaptation to reflect on approaches that have been adopted or developed in the past towards identifying current gaps and challenges, and opportunities for applying such approaches in XR. Using our framework, future work could identify and develop novel computational approaches for achieving successful adaptive user interfaces in such immersive environments.Comment: 5 pages, CHI 2023 Workshop on The Future of Computational Approaches for Understanding and Adapting User Interface

A user-centred personalised e-learning system

The paper proposes a framework for understanding the factors that affect usability of e-learning. The framework can be applied to the development of (1) a formative usability evaluation method for e-learning systems and (2) personalisation rules for e-learning systems interface. The formative usability evaluation method is intended for the evaluation of e-learning systems during its development stages, from screen-based prototypes to near completion. The evaluation criteria will be customisable depending on contingent criteria such as user characteristics and e-learning system characteristics. A web-based prototype will be developed to allow the convenient implementation of the methodology. The personalisation rules for e-learning system is intended for the automatic adaptation of e-learning systems' interface to different users' preferences in order to maximise its usability and learnability for individual users

Public Sound Objects : a shared environment for networked music practice on the Web

The Public Sound Objects (PSOs) project consists of the development of a networked musical system, which is an experimental framework to implement and test new concepts for online music communication. The PSOs project approaches the idea of collaborative musical performances over the Internet by aiming to go beyond the concept of using computer networks as a channel to connect performing spaces. This is achieved by exploring the internet’s shared nature in order to provide a public musical space where anonymous users can meet and be found performing in collective sonic art pieces. The system itself is an interface-decoupled musical instrument, in which a remote user interface and a sound processing engine reside with different hosts in an extreme scenario where a user can access the synthesizer from any place in the world using the World Wide Web. Specific software features were implemented in order to reduce the disruptive effects of network latency, such as dynamic adaptation of the musical tempo to communication latency measured in real time and consistent sound panning with the object’s behavior at the graphical user interface

Adaptive data communication interface: a user-centric visual data interpretation framework

In this position paper, we present ideas about creating a next generation framework towards an adaptive interface for data communication and visualisation systems. Our objective is to develop a system that accepts large data sets as inputs and provides user-centric, meaningful visual information to assist owners to make sense of their data collection. The proposed framework comprises four stages: (i) the knowledge base compilation, where we search and collect existing state-of-the-art visualisation techniques per domain and user preferences; (ii) the development of the learning and inference system, where we apply artificial intelligence techniques to learn, predict and recommend new graphic interpretations (iii) results evaluation; and (iv) reinforcement and adaptation, where valid outputs are stored in our knowledge base and the system is iteratively tuned to address new demands. These stages, as well as our overall vision, limitations and possible challenges are introduced in this article. We also discuss further extensions of this framework for other knowledge discovery tasks