Tech3Lab

Our continued reliance on email communications ensures that this type of data remains a major source of evidence during a digital investigation. Due to the many applications and data types, there is no standard email format. Therefore, much current work in the forensic investigation of emails has focused on data extraction. This paper focuses on the investigatory process and posits a model for social network discovery for use in digital investigations. This model is applied to the widely used Enron email corpus to demonstrate its applicability.

Contemporary heritage institutions model installations and artefacts around a passive receivership where content is consumed but not influenced by visitors. Increasingly, heritage institutions are incorporating ubiquitous technologies to provide visitors with experiences that not only transfer knowledge but also entertain. This poses the challenge of how to incorporate technologies into exhibits to make them more approachable and memorable, whilst preserving cultural salience. We present work towards an adaptive interface which responds to a museum visitor's level of interest, in order to deliver a personalised experience through adaptive curation within a cultural heritage installation. The interface is realised through the use of psychophysiological measures, physiological computing and a machine learning algorithm. We present studies which serve to illustrate how entertainment, education, aesthetic experience and immersion, identified as four factors of visitor experience, can be operationalised through a psychological construct of -interest‖. Two studies are reported which take a subject-dependent experimental approach to record and classify psychophysiological signals using mobile physiological sensors and a machine learning algorithm. The results show that it is possible to reliably infer a state of

This paper presents a psychophysiological construct of interest as a knowledge emotion and illustrates the importance of interest detection in a cultural heritage context. The objective of this work is to measure and classify psychophysiological reactivity in response to cultural heritage material presented as visual and audio. We present a data processing and classification framework for the classification of interest. Two studies are reported, adopting a subject-dependent approach to classify psychophysiological signals using mobile physiological sensors, and the support vector machine learning algorithm. The results show that it is possible to reliably infer a state of interest from cultural heritage material using psychophysiological feature data and a machine learning approach, informing future work for the development of a real-time physiological computing system for use within an adaptive cultural heritage experience designed to adapt the provision of information in order to sustain the interest of the visitor.

This paper presents the psychological construct interest as a knowledge emotion and illustrates the importance of interest in a cultural heritage context. Contemporary heritage institutions model installations and artifacts around a passive receivership where content is consumed but not influenced by visitors. We present work towards an adaptive interface that uses psychophysiological measures, physiological computing and a machine learning algorithm, designed to respond to the level of interest of the visitor, in order to deliver a personalised experience within cultural heritage institutions. Two studies are reported which take a subject-dependent experimental approach to record and classify psychophysiological signals using mobile physiological sensors and a machine learning algorithm. The results show that it is possible to reliably infer a state of interest from cultural heritage material, informing future work for the development of a real-time physiological computing system for use within an adaptive cultural heritage experience.

This paper presents the psychological construct interest as a knowledge emotion and illustrates the importance of interest in a cultural heritage context. Contemporary heritage institutions model installations and artifacts around a passive receivership where content is consumed but not influenced by visitors. We present work towards an adaptive interface that uses psychophysiological measures, physiological computing and a machine learning algorithm, designed to respond to the level of interest of the visitor, in order to deliver a personalised experience within cultural heritage institutions. Two studies are reported which take a subject-dependent experimental approach to record and classify psychophysiological signals using mobile physiological sensors and a machine learning algorithm. The results show that it is possible to reliably infer a state of interest from cultural heritage material, informing future work for the development of a real-time physiological computing system f...

Contemporary heritage institutions model installations and artefacts around a passive receivership where content is consumed but not influenced by visitors. Increasingly, heritage institutions are incorporating ubiquitous technologies to provide visitors with experiences that not only transfer knowledge but also entertain. This poses the challenge of how to incorporate technologies into exhibits to make them more approachable and memorable, whilst preserving cultural salience. We present work towards an adaptive interface which responds to a museum visitor's level of interest, in order to deliver a personalised experience through adaptive curation within a cultural heritage installation. The interface is realised through the use of psychophysiological measures, physiological computing and a machine learning algorithm. We present studies which serve to illustrate how entertainment, education, aesthetic experience and immersion, identified as four factors of visitor experience, can be operationalised through a psychological construct of "interest". Two studies are reported which take a subject-dependent experimental approach to record and classify psychophysiological signals using mobile physiological sensors and a machine learning algorithm. The results show that it is possible to reliably infer a state of

The accurate classification of psychophysiological data is an important determinant of the quality when interacting with a physiological computing system. Previous research has focused on classification accuracy of psychophysiological data in purely mathematical terms but little is known about how accuracy metrics relate to users' perceptions of accuracy during real-time interaction. A group of 14 participants watched a series of movie trailers and were asked to subjectively indicate their level of interest in a binary high/low fashion. Psychophysiological data (EEG, ECG and SCL) were used to create a binary classification of interest via a Support Vector Machine (SVM) algorithm. After a period of training, participants received real-time feedback from the classification algorithm and perceptions of accuracy were assessed. The purpose of the study was to compare mathematical classification accuracy with the perceived accuracy of the system as experienced by the users. Results indicated that perceived accuracy was subject to a number of psychological biases resulting from expectations, entrainment and development of trust. The F1 score was generally a significant predictor of perceived accuracy.

The contemporary heritage institution visitor model is built around passive receivership where content is consumed but not influenced by the visitor. This paper presents work in progress towards an adaptive interface designed to respond to the level of interest of the visitor, in order to deliver a personalised experience within cultural heritage institutions. A subjectdependent experimental approach was taken to record and classify physiological signals using mobile physiological sensors and a machine learning algorithm. The results show a high classification rate using this approach, informing future work for the development of a real-time physiological computing component for use within an adaptive cultural heritage experience.

The continued reliance on email communications ensures that it remains a major source of evidence during a digital investigation. Emails comprise both structured and unstructured data. Structured data provides qualitative information to the forensics examiner and is typically viewed through existing tools. Unstructured data is more complex as it comprises information associated with social networks, such as relationships within the network, identification of key actors and power relations, and there are currently no standardised tools for its forensic analysis. This paper posits a framework for the forensic investigation of email data. In particular, it focuses on the triage and analysis of unstructured data to identify key actors and relationships within an email network. This paper demonstrates the applicability of the approach by applying relevant stages of the framework to the Enron email corpus. The paper illustrates the advantage of triaging this data to identify (and discount...

Arguably, automation is fast transforming many enterprise business processes, transforming operational jobs into monitoring tasks. Consequently, the ability to sustain attention during extended periods of monitoring is becoming a critical skill. This manuscript presents a Brain-Computer Interface (BCI) prototype which seeks to combat decrements in sustained attention during monitoring tasks within an enterprise system. A brain-computer interface is a system which uses physiological signals output by the user as an input. The goal is to better understand human responses while performing tasks involving decision and monitoring cycles, finding ways to improve performance and decrease on-task error. Decision readiness and the ability to synthesize complex and abundant information in a brief period during critical events has never been more important. Closed-loop control and motivational control theory were synthesized to provide the basis from which a framework for a prototype was devel...

Explainable artificial intelligence aims to bring transparency to artificial intelligence (AI) systems by translating, simplifying, and visualizing its decisions. While society remains skeptical about AI systems, studies show that transparent and explainable AI systems can help improve the Human-AI trust relationship. This manuscript presents two studies that assess three AI decision visualization attribution models that manipulate morphological clarity (MC) and two information presentation-order methods to determine each visualization’s impact on the Human-AI trust relationship through increased confidence and cognitive fit (CF). The first study, N = 206 (Avg. age = 37.87 ± 10.51, Male = 123), utilized information presentation methods and visualizations delivered through an online experiment to explore trust in AI by asking participants to complete a visual decision-making task. The second study, N = 19 (24.9 ± 8.3 years old, Male = 10), utilized eye-tracking technology and the sam...

Based on our many training experiences, this paper provides instructors with guidelines to create a learning environment where learners have a reduced risk of making mistakes, to provide scaffolds that help learn- ers build self confidence, and to help learners reflect on their mistakes, speculate on root causes, and intelligently design alternative solutions. Instructors trained with this approach report signifi- cant improvements in student evaluation, learner motivation, attendance, and engagement as well as increased learner competence with the technology. These guidelines can be transferred to a variety of other educational fields in which trainers are migrating towards a more active educational approach. http://informingscience.org/jite/documents/Vol10/JITEv10p037-051Leger914.pdf

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