Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making. In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making

Multi-Dimensional-Personalization in mobile contexts

During the dot com era the word "personalisation” was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range / short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user’s privacy so that an approach supporting anonymous recommendations through an extended "Chinese Wall“ will be described

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Personalization platform for multimodal ubiquitous computing applications

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaWe currently live surrounded by a myriad of computing devices running multiple applications. In general, the user experience on each of those scenarios is not adapted to each user’s specific needs, without personalization and integration across scenarios. Moreover, developers usually do not have the right tools to handle that in a standard and generic way. As such, a personalization platform may provide those tools. This kind of platform should be readily available to be used by any developer. Therefore, it must be developed to be available over the Internet. With the advances in IT infrastructure, it is now possible to develop reliable and scalable services running on abstract and virtualized platforms. Those are some of the advantages of cloud computing, which offers a model of utility computing where customers are able to dynamically allocate the resources they need and are charged accordingly. This work focuses on the creation of a cloud-based personalization platform built on a previously developed generic user modeling framework. It provides user profiling and context-awareness tools to third-party developers. A public display-based application was also developed. It provides useful information to students, teachers and others in a university campus as they are detected by Bluetooth scanning. It uses the personalization platform as the basis to select the most relevant information in each situation, while a mobile application was developed to be used as an input mechanism. A user study was conducted to assess the usefulness of the application and to validate some design choices. The results were mostly positive

Critical evaluation and novel design of a non-invasive and wearable health monitoring system

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.This study is about developing a non-invasive wearable health-monitoring system. The project aims to achieve miniaturisation as much as possible, using nanotechnology. The achieved results of the project are nothing but conceptual images of a convertible watch. The system is a non-invasive health measurement system. An important part of the study is researching the automation of blood pressure measurement by means of experiments which test the effect of exterior factors on blood pressure level. These experiments have been held to improve the automation and simplicity of BP measurements to establish a 24hr BP monitoring system. This study proposed a medical sensor that is part of the watch system, and that is most compatible with the elderly people product preferences in the UK. The “sensor strip” is in cm range, integrating a number of MEMS sensors, for the non-invasive detection of certain health aspects. The health aspects are chosen according to how close they are from the “health vital signs”, which are the first measurements executed by the doctor, if a patient is to visit him. An applied QFD study showed that the most suitable measurement technology to be used in the proposed sensor strip is the infrared technology. In addition to the sensor strip, EEG health detection is added, which is the reason why the watch is convertible. MEMS sensors, MEMS memory and an embedded processor are selected, since that this project also includes minimising the size of a device where the utilization of nanotechnology is vital. The final result of the study is only a conceptual design of a product with a carefully selected subsystems. The software design of the product will not be further developed to become a physical prototype of a consumer product

Topic modelling for routine discovery from egocentric photo-streams

Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals' lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed