Practical Lab Work

A pre-defined lab exercise done in the weekly lab session

Individual Assignments

A generic programming assignment that gives leeway for student to customise the assignment with their own designs and features

Recognition Situations Using Extended Dempster-Shafer Theory

Weiser’s [111] vision of pervasive computing describes a world where technology seamlessly integrates into the environment, automatically responding to peoples’ needs. Underpinning this vision is the ability of systems to automatically track the situation of a person. The task of situation recognition is critical and complex: noisy and unreliable sensor data, dynamic situations, unpredictable human behaviour and changes in the environment all contribute to the complexity. No single recognition technique is suitable in all environments. Factors such as availability of training data, ability to deal with uncertain information and transparency to the user will determine which technique to use in any particular environment. In this thesis, we propose the use of Dempster-Shafer theory as a theoretically sound basis for situation recognition - an approach that can reason with uncertainty, but which does not rely on training data. We use existing operations from Dempster-Shafer theory and create new operations to establish an evidence decision network. The network is used to generate and assess situation beliefs based on processed sensor data for an environment. We also define two specific extensions to Dempster-Shafer theory to enhance the knowledge that can be used for reasoning: 1) temporal knowledge about situation time patterns 2) quality of evidence sources (sensors) into the reasoning process. To validate the feasibility of our approach, this thesis creates evidence decision networks for two real-world data sets: a smart home data set and an officebased data set. We analyse situation recognition accuracy for each of the data sets, using the evidence decision networks with temporal/quality extensions. We also compare the evidence decision networks against two learning techniques: Naïve Bayes and J48 Decision Tree

Increasing Gender Balance Across Academic Staffing in Computer Science - case study

As at 2019, Technological University Dublin* Computer Science is the top university in Ireland in terms of gender balance of female academic staff in computer science schools. In an academic team of approximately 55 full-time equivalents, 36% of our academic staff are female, 50% of our senior academic leadership team (2 of 4) are female and 75% of our School Executive are female (3 of 4), including a female Head of School. This is as a result of our seven year SUCCESS programme which had a four strand approach: Source, Career, Environment and Support. The Source strand explicitly encouraged females to apply for each recruitment drive; Career focused on female career and skills development initiatives; Environment created a female-friendly culture and reputation, both within the School, across our organisation and across the third level sector in Ireland and Support addressed practical supports for the specific difficulties experienced by female staff. As a result we have had 0% turnover in female staff in the past five years (in contrast to 10% male staff turnover). We will continue to work across these four strands to preserve our pipeline of female staff and ensure their success over the coming years in an academic and ICT sector that remains challenging for females

Presenting a Hybrid Processing Mining Framework for Automated Simulation Model Generation

Recent advances in information technology systems have enabled organizations to store tremendous amounts of business process data. Process mining offers a range of algorithms and methods to analyze and extract metadata for these processes. This paper presents a novel approach to the hybridization of process mining techniques with business process modelling and simulation methods. We present a generic automated end-to-end simulation framework that produces unbiased simulation models using system event logs. A conceptual model and various meta-data are derived from the logs and used to generate the simulation model. We demonstrate the efficacy of our framework using a business process event log, achieving reduction in waiting times using resource reallocation. The intrinsic idea behind our framework is to enable managers to develop simulation models for their business in a simple way using actual business process event logs and to support the investigation of possible scenarios to improve their business performance

Increasing Gender Balance Across Academic Staffing in Computer Science - case study

As at 2019, Technological University Dublin* Computer Science is the top university in Ireland in terms of gender balance of female academic staff in computer science schools. In an academic team of approximately 55 full-time equivalents, 36% of our academic staff are female, 50% of our senior academic leadership team (2 of 4) are female and 75% of our School Executive are female (3 of 4), including a female Head of School. This is as a result of our seven year SUCCESS programme which had a four strand approach: Source, Career, Environment and Support. The Source strand explicitly encouraged females to apply for each recruitment drive; Career focused on female career and skills development initiatives; Environment created a female-friendly culture and reputation, both within the School, across our organisation and across the third level sector in Ireland and Support addressed practical supports for the specific difficulties experienced by female staff. As a result we have had 0% turnover in female staff in the past five years (in contrast to 10% male staff turnover). We will continue to work across these four strands to preserve our pipeline of female staff and ensure their success over the coming years in an academic and ICT sector that remains challenging for females

An audio processing pipeline for acquiring diagnostic quality heart sounds via mobile phone

Recently, heart sound signals captured using mobile phones have been employed to develop data-driven heart disease detection systems. Such signals are generally captured in person by trained clinicians who can determine if the recorded heart sounds are of diagnosable quality. However, mobile phones have the potential to support heart health diagnostics, even where access to trained medical professionals is limited. To adopt mobile phones as self-diagnostic tools for the masses, we would need to have a mechanism to automatically establish that heart sounds recorded by non-expert users in uncontrolled conditions have the required quality for diagnostic purposes. This paper proposes a quality assessment and enhancement pipeline for heart sounds captured using mobile phones. The pipeline analyzes a heart sound and determines if it has the required quality for diagnostic tasks. Also, in cases where the quality of the captured signal is below the required threshold, the pipeline can improve the quality by applying quality enhancement algorithms. Using this pipeline, we can also provide feedback to users regarding the cause of low-quality signal capture and guide them towards a successful one. We conducted a survey of a group of thirteen clinicians with auscultation skills and experience. The results of this survey were used to inform and validate the proposed quality assessment and enhancement pipeline. We observed a high level of agreement between the survey results and fundamental design decisions within the proposed pipeline. Also, the results indicate that the proposed pipeline can reduce our dependency on trained clinicians for capture of diagnosable heart sounds

Moving Targets: Addressing Concept Drift in Supervised Models for Hacker Communication Detection

Abstract—In this paper, we are investigating the presence of concept drift in machine learning models for detection of hacker communications posted in social media and hacker forums. The supervised models in this experiment are analysed in terms of performance over time by different sources of data (Surface web and Deep web). Additionally, to simulate real-world situations, these models are evaluated using time-stamped messages from our datasets, posted over time on social media platforms. We have found that models applied to hacker forums (deep web) presents an accuracy deterioration in less than a 1-year period, whereas models applied to Twitter (surface web) have not shown a decrease in accuracy for the same period of time. The problem is alleviated by retraining the model with new instances (and applying weights) in order to reduce the effects of concept drift. While our results indicated that performance degradation due to concept drift is avoided by 50% relabelling, which is challenging in real-world scenarios, our work paves the way to more targeted concept drift solutions to reduce the re-training tasks. Index Terms—Cyber Security, Machine Learning, Concept Drift, Hacker Communication, Software Vulnerabilitie

Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy

With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the Society of Automotive Engineers, to specific drone tasks in order to create a clear definition of autonomy when applied to drones. A top–down examination of research work in the area is conducted, focusing on drone navigation tasks, in order to understand the extent of research activity in each area. Autonomy levels are cross-checked against the drone navigation tasks addressed in each work to provide a framework for understanding the trajectory of current research. This work serves as a guide to research in drone autonomy with a particular focus on Deep Learning-based solutions, indicating key works and areas of opportunity for development of this area in the future

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