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Wifi-Based Human Activity Recognition Using Attention-Based Bilstm, Amany Elkelany, Robert J. Ross, Susan Mckeever
Wifi-Based Human Activity Recognition Using Attention-Based Bilstm, Amany Elkelany, Robert J. Ross, Susan Mckeever
Conference papers
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 …
Detecting Patches On Road Pavement Images Acquired With 3d Laser Sensors Using Object Detection And Deep Learning, Ibrahim Hassan Syed, Dympna O'Sullivan, Susan Mckeever, David Power, Ray Mcgowan, Kieran Feighan
Detecting Patches On Road Pavement Images Acquired With 3d Laser Sensors Using Object Detection And Deep Learning, Ibrahim Hassan Syed, Dympna O'Sullivan, Susan Mckeever, David Power, Ray Mcgowan, Kieran Feighan
Academic Posters Collection
Regular pavement inspections are key to good road maintenance and road defect corrections. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of different defects using 3D lasers. However, such systems still require manual involvement to complete the detection of pavement defects. This work proposes an automatic patch detection system using an object detection technique. Results show that the object detection model can successfully detect patches inside LCMS images and suggest that the proposed approach could be integrated into the existing pavement inspection systems.
Explaining Deep Learning Time Series Classification Models Using A Decision Tree, Ephrem T. Mekonnen, Pierpaolo Dondio, Luca Longo
Explaining Deep Learning Time Series Classification Models Using A Decision Tree, Ephrem T. Mekonnen, Pierpaolo Dondio, Luca Longo
Academic Posters Collection
This preliminary study proposes a new post hoc method to explain deep learning-based time series classification models using a decision tree. Our approach generates a decision tree graph or rulesets as an explanation, improving interpretability compared to saliency map-based methods. The method involves two phases: training and evaluating the deep learning-based time series classification model and extracting prototypical events from the evaluation set to train the decision tree classifier. We conducted experiments on artificial and real datasets, evaluating the explanations based on accuracy, fidelity, number of nodes, and depth. Our preliminary findings suggest that our post-hoc method improves the interpretability …
An Investigation Of The Reconstruction Capacity Of Stacked Convolutional Autoencoders For Log-Mel-Spectrograms, Anastasia Natsiou, Luca Longo, Seán O'Leary
An Investigation Of The Reconstruction Capacity Of Stacked Convolutional Autoencoders For Log-Mel-Spectrograms, Anastasia Natsiou, Luca Longo, Seán O'Leary
Conference Papers
In audio processing applications, the generation of expressive sounds based on high-level representations demonstrates a high demand. These representations can be used to manipulate the timbre and influence the synthesis of creative instrumental notes. Modern algorithms, such as neural networks, have inspired the development of expressive synthesizers based on musical instrument timbre compression. Unsupervised deep learning methods can achieve audio compression by training the network to learn a mapping from waveforms or spectrograms to low-dimensional representations. This study investigates the use of stacked convolutional autoencoders for the compression of time-frequency audio representations for a variety of instruments for a single …
Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy
Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy
Dissertations
Deepfake classification has seen some impressive results lately, with the experimentation of various deep learning methodologies, researchers were able to design some state-of-the art techniques. This study attempts to use an existing technology “Transformers” in the field of Natural Language Processing (NLP) which has been a de-facto standard in text processing for the purposes of Computer Vision. Transformers use a mechanism called “self-attention”, which is different from CNN and LSTM. This study uses a novel technique that considers images as 16x16 words (Dosovitskiy et al., 2021) to train a deep neural network with “self-attention” blocks to detect deepfakes. It creates …
Detecting Road Intersections Automatically From Satellite Images Using A Deep Learning Approach, Fatmaelzahraa Eltaher, Luis Miralles-Pechuán, Jane Courtney, Susan Mckeever
Detecting Road Intersections Automatically From Satellite Images Using A Deep Learning Approach, Fatmaelzahraa Eltaher, Luis Miralles-Pechuán, Jane Courtney, Susan Mckeever
Datasets
Automatic detection of road intersections is an important task in various domains such as navigation, route planning, traffic prediction, and road network extraction. Road intersections range from simple three-way T-junctions (degree 3) to complex large-scale junctions with many branches. The location of intersections and their complexity is an important consideration in route planning, such as the requirement to avoid complex intersections on pedestrian journeys. This is relevant to vulnerable road users such as People with Blindness or Visually Impairment (PBVI) or children. Route planning applications, however, do not give information about the location or complexity of intersections as this information …
Explaining Deep Learning Models For Tabular Data Using Layer-Wise Relevance Propagation, Ihsan Ullah, Andre Rios, Vaibhov Gala, Susan Mckeever
Explaining Deep Learning Models For Tabular Data Using Layer-Wise Relevance Propagation, Ihsan Ullah, Andre Rios, Vaibhov Gala, Susan Mckeever
Articles
Trust and credibility in machine learning models are bolstered by the ability of a model to explain its decisions. While explainability of deep learning models is a well-known challenge, a further challenge is clarity of the explanation itself for relevant stakeholders of the model. Layer-wise Relevance Propagation (LRP), an established explainability technique developed for deep models in computer vision, provides intuitive human-readable heat maps of input images. We present the novel application of LRP with tabular datasets containing mixed data (categorical and numerical) using a deep neural network (1D-CNN), for Credit Card Fraud detection and Telecom Customer Churn prediction use …
Flying Free: A Research Overview Of Deep Learning In Drone Navigation Autonomy, Thomas Lee, Susan Mckeever, Jane Courtney
Flying Free: A Research Overview Of Deep Learning In Drone Navigation Autonomy, Thomas Lee, Susan Mckeever, Jane Courtney
Articles
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 …
Wider Vision: Enriching Convolutional Neural Networks Via Alignment To External Knowledge Bases, Xuehao Liu, Sarah Jane Delany, Susan Mckeever
Wider Vision: Enriching Convolutional Neural Networks Via Alignment To External Knowledge Bases, Xuehao Liu, Sarah Jane Delany, Susan Mckeever
Conference papers
Deep learning models suffer from opaqueness. For Convolutional Neural Networks (CNNs), current research strategies for explaining models focus on the target classes within the associated training dataset. As a result, the understanding of hidden feature map activations is limited by the discriminative knowledge gleaned during training. The aim of our work is to explain and expand CNNs models via the mirroring or alignment of the network to an external knowledge base. This will allow us to give a semantic context or label for each visual feature. Using the resultant aligned embedding space, we can match CNN feature activations to nodes …
Evaluating The Performance Of Transformer Architecture Over Attention Architecture On Image Captioning, Deepti Balasubramaniam
Evaluating The Performance Of Transformer Architecture Over Attention Architecture On Image Captioning, Deepti Balasubramaniam
Dissertations
Over the last few decades computer vision and Natural Language processing has shown tremendous improvement in different tasks such as image captioning, video captioning, machine translation etc using deep learning models. However, there were not much researches related to image captioning based on transformers and how it outperforms other models that were implemented for image captioning. In this study will be designing a simple encoder-decoder model, attention model and transformer model for image captioning using Flickr8K dataset where will be discussing about the hyperparameters of the model, type of pre-trained model used and how long the model has been trained. …
Language-Driven Region Pointer Advancement For Controllable Image Captioning, Annika Lindh, Robert J. Ross, John D. Kelleher
Language-Driven Region Pointer Advancement For Controllable Image Captioning, Annika Lindh, Robert J. Ross, John D. Kelleher
Conference papers
Controllable Image Captioning is a recent sub-field in the multi-modal task of Image Captioning wherein constraints are placed on which regions in an image should be described in the generated natural language caption. This puts a stronger focus on producing more detailed descriptions, and opens the door for more end-user control over results. A vital component of the Controllable Image Captioning architecture is the mechanism that decides the timing of attending to each region through the advancement of a region pointer. In this paper, we propose a novel method for predicting the timing of region pointer advancement by treating the …
Transformer Neural Networks For Automated Story Generation, Kemal Araz
Transformer Neural Networks For Automated Story Generation, Kemal Araz
Dissertations
Towards the last two-decade Artificial Intelligence (AI) proved its use on tasks such as image recognition, natural language processing, automated driving. As discussed in the Moore’s law the computational power increased rapidly over the few decades (Moore, 1965) and made it possible to use the techniques which were computationally expensive. These techniques include Deep Learning (DL) changed the field of AI and outperformed other models in a lot of fields some of which mentioned above. However, in natural language generation especially for creative tasks that needs the artificial intelligent models to have not only a precise understanding of the given …
A U-Net Deep Learning Framework For High Performance Vessel Segmentation In Paitents With Cerebrovascular Disease, Michelle Livne, Jana Rieger, Orhun Utku Aydin, Abdel Aziz Taha, Ela Maria Akay, Tabea Kossen, Jan Sobesky, John D. Kelleher, Kristian Hildebrand, Dietmar Frey, Vince I. Madai
A U-Net Deep Learning Framework For High Performance Vessel Segmentation In Paitents With Cerebrovascular Disease, Michelle Livne, Jana Rieger, Orhun Utku Aydin, Abdel Aziz Taha, Ela Maria Akay, Tabea Kossen, Jan Sobesky, John D. Kelleher, Kristian Hildebrand, Dietmar Frey, Vince I. Madai
Articles
Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method—the U-net—is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We …
Multi-Sensory Deep Learning Architectures For Slam Dunk Scene Classification, Paul Minogue
Multi-Sensory Deep Learning Architectures For Slam Dunk Scene Classification, Paul Minogue
Dissertations
Basketball teams at all levels of the game invest a considerable amount of time and effort into collecting, segmenting, and analysing footage from their upcoming opponents previous games. This analysis helps teams identify and exploit the potential weaknesses of their opponents and is commonly cited as one of the key elements required to achieve success in the modern game. The growing importance of this type of analysis has prompted research into the application of computer vision and audio classification techniques to help teams classify scoring sequences and key events using game footage. However, this research tends to focus on classifying …
A Comparison Of Classical Versus Deep Learning Techniques For Abusive Content Detection On Social Media Sites, Hao Che, Susan Mckeever, Sarah Jane Delany
A Comparison Of Classical Versus Deep Learning Techniques For Abusive Content Detection On Social Media Sites, Hao Che, Susan Mckeever, Sarah Jane Delany
Conference papers
The automated detection of abusive content on social media websites faces a variety of challenges including imbalanced training sets, the identification of an appropriate feature representation and the selection of optimal classifiers. Classifiers such as support vector machines (SVM), combined with bag of words or ngram feature representation, have traditionally dominated in text classification for decades. With the recent emergence of deep learning and word embeddings, an increasing number of researchers have started to focus on deep neural networks. In this paper, our aim is to explore cutting-edge techniques in automated abusive content detection. We use two deep learning approaches: …
Back To The Future: Logic And Machine Learning, Simon Dobnik, John D. Kelleher
Back To The Future: Logic And Machine Learning, Simon Dobnik, John D. Kelleher
Conference papers
In this paper we argue that since the beginning of the natural language processing or computational linguistics there has been a strong connection between logic and machine learning. First of all, there is something logical about language or linguistic about logic. Secondly, we argue that rather than distinguishing between logic and machine learning, a more useful distinction is between top-down approaches and data-driven approaches. Examining some recent approaches in deep learning we argue that they incorporate both properties and this is the reason for their very successful adoption to solve several problems within language technology.
Tackling The Interleaving Problem In Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher
Tackling The Interleaving Problem In Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher
Conference papers
Activity discovery (AD) is the unsupervised process of discovering activities in data produced from streaming sensor networks that are recording the actions of human subjects. One major challenge for AD systems is interleaving, the tendency for people to carry out multiple activities at a time a parallel. Following on from our previous work, we continue to investigate AD in interleaved datasets, with a view towards progressing the state-of-the-art for AD.
Review Of Trends In Health Social Media Analysis, Liliya Akhtyamova, Mikhail Alexandrov, John Cardiff
Review Of Trends In Health Social Media Analysis, Liliya Akhtyamova, Mikhail Alexandrov, John Cardiff
Conference Papers
This paper surveys recent publications (2008-2017) on using social media data to study public health. The survey describes the main topics being discussed in forums and presents short information about methods and tools used for analysis health social media. We put especial attention on adverse drug reaction detection problem (ADR).
Towards A Deep Learning-Based Activity Discovery System, Eoin Rogers, John D. Kelleher, Robert J. Ross
Towards A Deep Learning-Based Activity Discovery System, Eoin Rogers, John D. Kelleher, Robert J. Ross
Conference papers
Activity discovery is a challenging machine learning problem where we seek to uncover new or altered behavioural patterns in sensor data. In this paper we motivate and introduce a novel approach to activity discovery based on modern deep learning techniques. We hypothesise that our proposed approach can deal with interleaved datasets in a more intelligent manner than most existing AD methods. We also build upon prior work building hierarchies of activities that capture the inherent ag- gregate nature of complex activities and show how this could plausibly be adapted to work with the deep learning technique we present. Finally, we …