WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Developing Learning Analytics for Epistemic Commitments in a Collaborative Information Seeking Environment

Learning analytics sits at the confluence of learning, information, and computer sciences. Using a distinctive account of learning analytics as a form of assessment, I first argue for its potential in pedagogically motivated learning design, suggesting a particular construct – epistemic cognition in literacy contexts – to probe using learning analytics. I argue for a recasting of epistemic cognition as ‘epistemic commitments’ in collaborative information tasks drawing a novel alignment between information seeking and multiple document processing (MDP) models, with empirical and theoretical grounding given for a focus on collaboration and dialogue in such activities. Thus, epistemic commitments are seen in the ways students seek, select, and integrate claims from multiple sources, and the ways in which their collaborative dialogue is brought to bear in this activity. Accordingly, the empirical element of the thesis develops two pedagogically grounded literacy based tasks: a MDP task, in which pre-selected documents were provided to students; and a collaborative information seeking task (CIS), in which students could search the web. These tasks were deployed at scale (n > 500) and involved writing an evaluative review, followed by a pedagogically supported peer assessment task. Assessment outcomes were analysed in the context of a new epistemic commitments-oriented set of trace data, and psychometric data regarding the participants’ epistemic cognition. Demonstrating the value of the methodological and conceptual approach taken, qualitative analyses indicate clear epistemic activity, and stark differences in behaviour between groups, the complexity of which is challenging to model computationally. Despite this complexity, quantitative analyses indicate that up to 30% of variance in output scores can be modelled using behavioural indicators. The explanatory potential of behaviourally-oriented models of epistemic commitments grounded in tool-interaction and collaborative dialogue is demonstrated. The thesis provides an exemplification of theoretically positioned analytic development, drawing on interdisciplinary literatures in addressing complex learning contexts

Digital life stories: Semi-automatic (auto)biographies within lifelog collections

Our life stories enable us to reflect upon and share our personal histories. Through emerging digital technologies the possibility of collecting life experiences digitally is increasingly feasible; consequently so is the potential to create a digital counterpart to our personal narratives. In this work, lifelogging tools are used to collect digital artifacts continuously and passively throughout our day. These include images, documents, emails and webpages accessed; texts messages and mobile activity. This range of data when brought together is known as a lifelog. Given the complexity, volume and multimodal nature of such collections, it is clear that there are significant challenges to be addressed in order to achieve coherent and meaningful digital narratives of our events from our life histories. This work investigates the construction of personal digital narratives from lifelog collections. It examines the underlying questions, issues and challenges relating to construction of personal digital narratives from lifelogs. Fundamentally, it addresses how to organize and transform data sampled from an individual’s day-to-day activities into a coherent narrative account. This enquiry is enabled by three 20-month long-term lifelogs collected by participants and produces a narrative system which enables the semi-automatic construction of digital stories from lifelog content. Inspired by probative studies conducted into current practices of curation, from which a set of fundamental requirements are established, this solution employs a 2-dimensional spatial framework for storytelling. It delivers integrated support for the structuring of lifelog content and its distillation into storyform through information retrieval approaches. We describe and contribute flexible algorithmic approaches to achieve both. Finally, this research inquiry yields qualitative and quantitative insights into such digital narratives and their generation, composition and construction. The opportunities for such personal narrative accounts to enable recollection, reminiscence and reflection with the collection owners are established and its benefit in sharing past personal experience experiences is outlined. Finally, in a novel investigation with motivated third parties we demonstrate the opportunities such narrative accounts may have beyond the scope of the collection owner in: personal, societal and cultural explorations, artistic endeavours and as a generational heirloom

On smart and natural language technology support of strategy work

This research explores how natural language processing of text, which is in electronic format, might be exploited to benefit strategic business planning in companies. It entails considering devices for making sense of huge, complex and dynamic data for decision making in a complex and dynamic world. It entails grasping managers' true requirements. Text that is in electronic format abounds. The Internet is continuing its phenomenal growth. Strategic decision-makers in companies struggle with making sense, keeping up with and taking advantage of the emerging world. Theoretical and technological advances in many fields of inquiry, from strategy to information technology, give rise to new promises for computer support of strategic managerial work. Management of knowledge and computer processing of natural language format text are among these. After surveying the field of strategy for its requirements and the state of art some alternative solutions are considered. A partial solution that was actually built is described together with experiments performed with it. Feedback solicited and obtained from managers with help of the concrete existence of the partial solution is then examined and analysed thoroughly. Ideas related to the use of systems based on language and knowledge technology have been developed and many issues identified. This together with managerial feedback forms a base for creating strategy support systems in the future.reviewe

The development of a model of information seeking behaviour of students in higher education when using internet search engines.

This thesis develops a model of Web information seeking behaviour of postgraduate students with a specific focus on Web search engines' use. It extends Marchionini's eight stage model of information seeking, geared towards electronic environments, to holistically encompass the physical, cognitive, affective and social dimensions of Web users' behaviour. The study recognises the uniqueness of the Web environment as a vehicle for information dissemination and retrieval, drawing on the distinction between information searching and information seeking and emphasises the importance of following user-centred holistic approaches to study information seeking behaviour. It reviews the research in the field and demonstrates that there is no comprehensive model that explains the behaviour of Web users when employing search engines for information retrieval. The methods followed to develop the study are explained with a detailed analysis of the four dimensions of information seeking (physical, cognitive affective, social). Emphasis is placed on the significance of combined methods (qualitative and quantitative) and the ways in which they can enrich the examination of human behaviour. This is concluded with a discussion of methodological issues. The study is supported by an empirical investigation, which examines the relationship between interactive information retrieval using Web search engines and human information seeking processes. This investigates the influence of cognitive elements (such as learning and problem style, and creative ability) and affective characteristics (e. g. confidence, loyalty, familiarity, ease of use), as well as the role that system experience, domain knowledge and demographics play in information seeking behaviour and in user overall satisfaction with the retrieval result. The influence of these factors is analysed by identifying users' patterns of behaviour and tactics, adopted to solve specific problems. The findings of the empirical study are incorporated into an enriched information-seeking model, encompassing use of search engines, which reveals a complex interplay between physical, cognitive, affective and social elements and that none of these characteristics can be seen in isolation when attempting to explain the complex phenomenon of information seeking behaviour. Although the model is presented in a linear fashion the dynamic, reiterative and circular character of the information seeking process is explained through an emphasis on transition patterns between the different stages. The research concludes with a discussion of problems encountered by Web information seekers which provides detailed analysis of the reasons why users express satisfaction or dissatisfaction with the results of Web searching, areas in which Web search engines can be improved and issues related to the need for students to be given additional training and support are identified. These include planning and organising information, recognising different dimensions of information intents and needs, emphasising the importance of variety in Web information seeking, promoting effective formulation of queries and ranking, reducing overload of information and assisting effective selection of Web sites and critical examination of results

Characterising semantically coherent classes of text through feature discovery

There is a growing need to provide support for social scientists and humanities scholars to gather and “engage” with very large datasets of free text, to perform very bespoke analyses. method52 is a text analysis platform built for this purpose (Wibberley et al., 2014), and forms a foundation that this thesis builds upon. A central part of method52 and its methodologies is a classifier training component based on dualist (Settles, 2011), and the general process of data engagement with method52 is determined to constitute a continuous cycle of characterising semantically coherent sub-collections, classes, of the text. Two broad methodologies exist for supporting this type of engagement process: (1) a top-down approach wherein concepts and their relationships are explicitly modelled for reasoning, and (2) a more surface-level, bottom-up approach, which entails the use of key terms (surface features) to characterise data. Following the second of these approaches, this thesis examines ways of better supporting this type of data engagement to more effectively support the needs of social scientists and humanities scholars in engaging with text data. The classifier component provides an active learning training environment emphasising the labelling of individual features. However, it can be difficult to interpret and incorporate prior knowledge of features. The process of feature discovery based on the current classifier model does not always produce useful results. And understanding the data well enough to produce successful classifiers is timeconsuming. A new method for discovering features in a corpus is introduced, and feature discovery methods are explored to resolve these issues. When collecting social media data, documents are often obtained by querying an API with a set of key phrases. Therefore, the set of possible classes characterising the data is defined by these basic surface features. It is difficult to know exactly which terms must searched for, and the usefulness of terms can change over time as new discussions and vocabulary emerge. Building on the feature discovery techniques, a framework is presented in this thesis for streaming data with an automatically adapting query to deal with these issues