Improving Classroom Engagement using Enhanced Teaching Methods

Third level student engagement in the classroom can be difficult for a number of reasons. Putting the content aside, factors include the duration of the class, size of the class, and time of day. Introducing classroom activities can be seen to improve student engagement and to reinforce key components. Teaching a technical discipline possess additional challenges in that the requirement to use technology in the classroom may not be feasible due to available building services. However, many students now possess mobile technology which allows them to participate in simple short classroom quizzes. The classroom quiz provides an opportunity to open discussions regarding question specifics. In addition to this it can be shown that improving participation in the classroom can improve motivation and performance in a subject overall. This paper will assess the performance of students studying a networking module whilst undertaking year 3 of an honours degree in computing

Towards A Framework for Privacy-Preserving Pedestrian Analysis

The design of pedestrian-friendly infrastructures plays a crucial role in creating sustainable transportation in urban environments. Analyzing pedestrian behaviour in response to existing infrastructure is pivotal to planning, maintaining, and creating more pedestrian-friendly facilities. Many approaches have been proposed to extract such behaviour by applying deep learning models to video data. Video data, however, includes an broad spectrum of privacy-sensitive information about individuals, such as their location at a given time or who they are with. Most of the existing models use privacy-invasive methodologies to track, detect, and analyse individual or group pedestrian behaviour patterns. As a step towards privacy-preserving pedestrian analysis, this paper introduces a framework to anonymize all pedestrians before analyzing their behaviors. The proposed framework leverages recent developments in 3D wireframe reconstruction and digital in-painting to represent pedestrians with quantitative wireframes by removing their images while preserving pose, shape, and background scene context. To evaluate the proposed framework, a generic metric is introduced for each of privacy and utility. Experimental evaluation on widely-used datasets shows that the proposed framework outperforms traditional and state-of-the-art image filtering approaches by generating best privacy utility trade-off

MULTI-MODAL SELF-SUPERVISED REPRESENTATION LEARNING FOR EARTH OBSERVATION

Self-Supervised learning (SSL) has reduced the performance gap between supervised and unsupervised learning, due to its ability to learn invariant representations. This is a boon to the domains like Earth Observation (EO), where labelled data availability is scarce but unlabelled data is freely available. While Transfer Learning from generic RGB pre-trained models is still common-place in EO, we argue that, it is essential to have good EO domain specific pre-trained model in order to use with downstream tasks with limited labelled data. Hence, we explored the applicability of SSL with multi-modal satellite imagery for downstream tasks. For this we utilised the state-of-art SSL architectures i.e. BYOL and SimSiam to train on EO data. Also to obtain better invariant representations, we considered multi-spectral (MS) images and synthetic aperture radar (SAR) images as separate augmented views of an image to maximise their similarity. Our work shows that by learning single channel representations through non-contrastive learning, our approach can outperform ImageNet pre-trained models significantly on a scene classification task. We further explored the usefulness of a momentum encoder by comparing the two architectures i.e. BYOL and SimSiam but did not identify a significant improvement in performance between the models

Volunteered and crowdsourced geographic information: the OpenStreetMap project

Advancements in technology over the last two decades have changed how spatial data are created and used. In particular, in the last decade, volunteered geographic information (VGI), i.e., the crowdsourcing of geographic information, has revolutionized the spatial domain by shifting the map-making process from the hands of experts to those of any willing contributor. Started in 2004, OpenStreetMap (OSM) is the pinnacle of VGI due to the large number of volunteers involved and the volume of spatial data generated. While the original objective of OSM was to create a free map of the world, its uses have shown how the potential of such an initiative goes well beyond map-making: ranging from projects such as the Humanitarian OpenStreetMap (HOT) project, that understands itself as a bridge between the OSM community and humanitarian responders, to collaborative projects such as Mapillary, where citizens take street-level images and the system aims to automate mapping. A common trend among these projects using OSM is the fact that the community dynamic tends to create spin-off projects. Currently, we see a drive towards projects that support sustainability goals using OSM. We discuss some such applications and highlight challenges posed by this new paradigm. We also explore the most promising future uses of this increasingly popular participatory phenomenon

Self-Supervised Learning for Invariant Representations From Multi-Spectral and SAR Images

Self-Supervised learning (SSL) has become the new state of the art in several domain classification and segmentation tasks. One popular category of SSL are distillation networks such as Bootstrap Your Own Latent (BYOL). This work proposes RS-BYOL, which builds on BYOL in the remote sensing (RS) domain where data are non-trivially different from natural RGB images. Since multi-spectral (MS) and synthetic aperture radar (SAR) sensors provide varied spectral and spatial resolution information, we utilise them as an implicit augmentation to learn invariant feature embeddings. In order to learn RS based invariant features with SSL, we trained RS-BYOL in two ways, i.e. single channel feature learning and three channel feature learning. This work explores the usefulness of single channel feature learning from random 10 MS bands of 10m-20 m resolution and VV-VH of SAR bands compared to the common notion of using three or more bands. In our linear probing evaluation, these single channel features reached a 0.92 F1 score on the EuroSAT classification task and 59.6 mIoU on the IEEE Data Fusion Contest (DFC) segmentation task for certain single bands. We also compare our results with ImageNet weights and show that the RS based SSL model outperforms the supervised ImageNet based model. We further explore the usefulness of multi-modal data compared to single modality data, and it is shown that utilising MS and SAR data allows better invariant representations to be learnt than utilising only MS data

Volunteered and Crowdsourced Geographic Information: the OpenStreetMap Project

Advancements in technology over the last two decades have changed how spatial data are created and used. In particular, in the last decade, volunteered geographic information (VGI), i.e., the crowdsourcing of geographic information, has revolutionized the spatial domain by shifting the map-making process from the hands of experts to those of any willing contributor. Started in 2004, OpenStreetMap (OSM) is the pinnacle of VGI due to the large number of volunteers involved and the volume of spatial data generated. While the original objective of OSM was to create a free map of the world, its uses have shown how the potential of such an initiative goes well beyond map-making: ranging from projects such as the Humanitarian OpenStreetMap (HOT) project, that understands itself as a bridge between the OSM community and humanitarian responders, to collaborative projects such as Mapillary, where citizens take street-level images and the system aims to automate mapping. A common trend among these projects using OSM is the fact that the community dynamic tends to create spin-off projects. Currently, we see a drive towards projects that support sustainability goals using OSM. We discuss some such applications and highlight challenges posed by this new paradigm. We also explore the most promising future uses of this increasingly popular participatory phenomenon

Comparative Study of Feature Representations for Disaster Tweet Classification

Twitter is a popular social media platform where users publicly broadcast short messages on a myriad of topics. In recent years it has enjoyed an increased usage around disaster events due to availability of information in near real time. Additionally, enhanced information representations to facilitate the classification of social media in terms of relevancy and type of information is currently a highly active research area (Ashktorab et al., 2014, Imran et al., 2014, Win et al., 2018). In this work we consider the usefulness and reliability of a range of representation models in the analysis of disaster related social media

MediaEval2019: Flood Detection in Time Sequence Satellite Images

In this work, we present a flood detection technique from time series satellite images for the City-centered satellite sequences (CCSS) task in the MediaEval 2019 competition [1]. This work utilises a three channel feature indexing technique [13] along with a VGG16 pretrained model for automatic detection of floods. We also compared our result with RGB images and a modified NDWI technique by Mishra et al, 2015 [15]. The result shows that the three channel feature indexing technique performed the best with VGG16 and is a promising approach to detect floods from time series satellite images

SMPL-Based 3D Pedestrian Pose Prediction

Modeling human motion is a long-standing problem in computer vision. The rapid development of deep learning technologies for computer vision problems resulted in increased attention in the area of pose prediction due to its vital role in a multitude of applications, for example, behavior analysis, autonomous vehicles, and visual surveillance. In 3D pedestrian pose prediction, joint-rotation-based pose representation is extensively used due to the unconstrained degree of freedom for each joint and its ability to regress the 3D statistical wireframe. However, all the existing joint-rotation-based pose prediction approaches ignore the centrality of the distinct pose parameter components and are consequently prone to suffer from error accumulation along the kinematic chain, which results in unnatural human poses. In joint-rotation-based pose prediction, Skinned Multi-Person Linear (SMPL) parameters are widely used to represent pedestrian pose. In this work, a novel SMPL-based pose prediction network is proposed to address the centrality of each SMPL component by distributing the network weights among them. Furthermore, to constrain the network to generate only plausible human poses, an adversarial training approach is employed. The effectiveness of the proposed network is evaluated using the PedX and BEHAVE datasets. The proposed approach significantly outperforms state-of-the-art methods with improved prediction accuracy and generates plausible human pose predictions

Maintaining the Identity of Dynamically Embodied Agents

Virtual agents are traditionally constrained in their embod- iment, as they are restricted to one form of body. We propose allowing them to change their embodiment in order to expand their capabili- ties. This presents users with a number of di±culties in maintaining the identity of the agents, but these can be overcome by using identity cues, certain features that remain constant across embodiment forms. This pa- per outlines an experiment that examines these identity cues, and shows that they can be used to help address this identity problem