All Outputs (2072)

Fast Probabilistic Consensus with Weighted Votes (2020)
Presentation / Conference Contribution
Müller, S., Penzkofer, A., Ku´smierz, B., Camargo, D., & Buchanan, W. J. (2020, November). Fast Probabilistic Consensus with Weighted Votes. Presented at FTC 2020 - Future Technologies Conference 2020, Vancouver, Canada

The fast probabilistic consensus (FPC) is a voting consensus protocol that is robust and efficient in Byzantine infrastructure. We propose an adaption of the FPC to a setting where the voting power is proportional to the nodes reputations. We model t... Read More about Fast Probabilistic Consensus with Weighted Votes.

PlanCurves: An Interface for End-Users to Visualise Multi-Agent Temporal Plans (2020)
Presentation / Conference Contribution
Le Bras, P., Carreno, Y., Lindsay, A., Petrick, R. P. A., & Chantler, M. J. (2020, October). PlanCurves: An Interface for End-Users to Visualise Multi-Agent Temporal Plans. Presented at ICAPS 2020 Workshop on Knowledge Engineering for Planning and Scheduling (KEPS 2020), Nancy, France [Online]

In operational contexts, there is a growing need to make automatically generated plans available for assessment, verification, and accountability purposes, in order to evaluate the risks associated with such plans prior to their execution. However, t... Read More about PlanCurves: An Interface for End-Users to Visualise Multi-Agent Temporal Plans.

Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System. (2020)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., & Hussain, A. (2020, October). Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System. Presented at Interspeech 2020, Shanghai, China

In this paper, we present VIsual Speech In real nOisy eNvironments (VISION), a first of its kind audio-visual (AV) corpus comprising 2500 utterances from 209 speakers, recorded in real noisy environments including social gatherings, streets, cafeteri... Read More about Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System..

Research Impact Value and Library and Information Science (RIVAL): development, implementation and outcomes of a Scottish network for LIS researchers and practitioners (2020)
Presentation / Conference Contribution
Hall, H., & Ryan, B. (2020, October). Research Impact Value and Library and Information Science (RIVAL): development, implementation and outcomes of a Scottish network for LIS researchers and practitioners. Presented at 83rd Annual Meeting of the Association for Information Science and Technology (ASIS&T)

The research-practice gap in Library and Information Science (LIS) is well documented, especially in respect of the difficulties of translating research into practice, and resultant lost opportunities. While many researchers attempt to explain this r... Read More about Research Impact Value and Library and Information Science (RIVAL): development, implementation and outcomes of a Scottish network for LIS researchers and practitioners.

Corralling Culture as a Concept in LIS Research (2020)
Presentation / Conference Contribution
Salzano, R., Hall, H., & Webster, G. (2020, October). Corralling Culture as a Concept in LIS Research. Presented at 83rd Annual Meeting of the Association of Information Science and Technology, Virtual

Individuals’ cultural backgrounds influence their use of societal resources, including libraries. A literature search and review was completed on the treatment of culture in library and information science (LIS) in the body of work on information beh... Read More about Corralling Culture as a Concept in LIS Research.

Attribute-Based Symmetric Searchable Encryption (2020)
Presentation / Conference Contribution
Dang, H., Ullah, A., Bakas, A., & Michalas, A. (2020). Attribute-Based Symmetric Searchable Encryption. In Applied Cryptography and Network Security Workshops (318-336). https://doi.org/10.1007/978-3-030-61638-0_18

Symmetric Searchable Encryption (SSE) is an encryption technique that allows users to search directly on their outsourced encrypted data while preserving the privacy of both the files and the queries. Unfortunately, majority of the SSE schemes allows... Read More about Attribute-Based Symmetric Searchable Encryption.

A New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network (2020)
Presentation / Conference Contribution
Thomson, C., Wadhaj, I., Al-Dubai, A., & Tan, Z. (2020, April). A New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network. Presented at IEEE 6th World Forum on Internet of Things, New Orleans, Louisiana, USA

The issue of energy holes, or hotspots, in wireless sensor networks is well referenced. As is the proposed mobilisa-tion of the sink node in order to combat this. However, as the sink node shall still pass some nodes more closely and frequently than... Read More about A New Mobility Aware Duty Cycling and Dynamic Preambling Algorithm for Wireless Sensor Network.

Visual Encodings for Networks with Multiple Edge Types (2020)
Presentation / Conference Contribution
Vogogias, T., Archambault, D. W., Bach, B., & Kennedy, J. (2020, October). Visual Encodings for Networks with Multiple Edge Types. Presented at International Conference on Advanced Visual Interfaces, Napkes, Italy

This paper reports on a formal user study on visual encodings of networks with multiple edge types in adjacency matrices. Our tasks and conditions were inspired by real problems in computational biology. We focus on encodings in adjacency matrices, s... Read More about Visual Encodings for Networks with Multiple Edge Types.

Real-time anomaly intrusion detection for a clean water supply system, utilising machine learning with novel energy-based features (2020)
Presentation / Conference Contribution
Robles-Durazno, A., Moradpoor, N., McWhinnie, J., & Russell, G. (2020, July). Real-time anomaly intrusion detection for a clean water supply system, utilising machine learning with novel energy-based features. Presented at International Joint Conference on Neural Networks (IJCNN 2020), Glasgow, UK

Industrial Control Systems have become a priority domain for cybersecurity practitioners due to the number of cyber-attacks against those systems has increased over the past few years. This paper proposes a real-time anomaly intrusion detector for a... Read More about Real-time anomaly intrusion detection for a clean water supply system, utilising machine learning with novel energy-based features.

Deep Neural Network Driven Binaural Audio Visual Speech Separation (2020)
Presentation / Conference Contribution
Gogate, M., Dashtipour, K., Bell, P., & Hussain, A. (2020, July). Deep Neural Network Driven Binaural Audio Visual Speech Separation. Presented at 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow

The central auditory pathway exploits the auditory signals and visual information sent by both ears and eyes to segregate speech from multiple competing noise sources and help disambiguate phonological ambiguity. In this study, inspired from this uni... Read More about Deep Neural Network Driven Binaural Audio Visual Speech Separation.

Federated learning with hierarchical clustering of local updates to improve training on non-IID data (2020)
Presentation / Conference Contribution
Briggs, C., Fan, Z., & Andras, P. (2020, July). Federated learning with hierarchical clustering of local updates to improve training on non-IID data. Presented at 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow

Federated learning (FL) is a well established method for performing machine learning tasks over massively distributed data. However in settings where data is distributed in a non-iid (not independent and identically distributed) fashion - as is typic... Read More about Federated learning with hierarchical clustering of local updates to improve training on non-IID data.

Impact of Content Popularity on Content Finding in NDN: Default NDN vs. Vicinity-based Enhanced NDN (2020)
Presentation / Conference Contribution
Suwannasa, A., Broadbent, M., & Mauthe, A. (2020, September). Impact of Content Popularity on Content Finding in NDN: Default NDN vs. Vicinity-based Enhanced NDN. Presented at 2020 10th International Conference on Information Science and Technology (ICIST), Bath, London, and Plymouth, UK

Named Data Networking allows a consumer to locate a desired content object by its name prefix. By using the best route strategy of the default NDN architecture, an Interest packet is forwarded along a default path indicated by the packet's name to fi... Read More about Impact of Content Popularity on Content Finding in NDN: Default NDN vs. Vicinity-based Enhanced NDN.

Sonification of an exoplanetary atmosphere (2020)
Presentation / Conference Contribution
Quinton, M., McGregor, I., & Benyon, D. (2020). Sonification of an exoplanetary atmosphere. In AM '20: Proceedings of the 15th International Conference on Audio Mostly (191-198). https://doi.org/10.1145/3411109.3411117

This study investigates the effectiveness of user design methods to create a sonification for an astronomer who analyses exoplanet meteorological data situated in habitable zones. Requirements about the astronomer’s work, the dataset and how to sonif... Read More about Sonification of an exoplanetary atmosphere.

Effect of various spatial auditory cues on the perception of threat in a first-person shooter video game (2020)
Presentation / Conference Contribution
Semionov, K., & McGregor, I. (2020, September). Effect of various spatial auditory cues on the perception of threat in a first-person shooter video game. Presented at AM'20: Audio Mostly 2020, Graz, Austria

This study interviewed game audio professionals to establish the implementation requirements for an experiment to ascertain the effect of different spatial audio localisation systems on the perception of threat in a first-person shooter. In addition,... Read More about Effect of various spatial auditory cues on the perception of threat in a first-person shooter video game.

Microtargeting or Microphishing? Phishing Unveiled (2020)
Presentation / Conference Contribution
Khursheed, B., Pitropakis, N., McKeown, S., & Lambrinoudakis, C. (2020). Microtargeting or Microphishing? Phishing Unveiled. In Trust, Privacy and Security in Digital Business (89-105). https://doi.org/10.1007/978-3-030-58986-8_7

Online advertisements delivered via social media platforms function in a similar way to phishing emails. In recent years there has been a growing awareness that political advertisements are being microtargeted and tailored to specific demographics, w... Read More about Microtargeting or Microphishing? Phishing Unveiled.

A Distributed Trust Framework for Privacy-Preserving Machine Learning (2020)
Presentation / Conference Contribution
Abramson, W., Hall, A. J., Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (2020, September). A Distributed Trust Framework for Privacy-Preserving Machine Learning. Presented at The 17th International Conference on Trust, Privacy and Security in Digital Business - TrustBus2020, Bratislava, Slovakia

When training a machine learning model, it is standard procedure for the researcher to have full knowledge of both the data and model. However, this engenders a lack of trust between data owners and data scientists. Data owners are justifiably reluct... Read More about A Distributed Trust Framework for Privacy-Preserving Machine Learning.

Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound (2020)
Presentation / Conference Contribution
Yang, S., Lemke, C., Cox, B. F., Newton, I. P., Cochran, S., & Nathke, I. (2020, September). Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound. Presented at 2020 IEEE International Ultrasonics Symposium (IUS), Las Vegas, NV, USA

With histological information on inflammation status as the ground truth, deep learning methods can be used as a classifier to distinguish different stages of bowel inflammation based on microultrasound (μUS) B-scan images. However, it is extremely t... Read More about Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound.

Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples (2020)
Presentation / Conference Contribution
Babaagba, K., Tan, Z., & Hart, E. (2020, July). Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples. Presented at The 2020 IEEE Congress on Evolutionary Computation (IEEE CEC 2020), Glasgow, UK

Detecting metamorphic malware provides a challenge to machine-learning models as trained models might not generalise to future mutant variants of the malware. To address this, we explore whether machine-learning models can be improved by augmenting t... Read More about Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples.

Props Alive: A Framework for Augmented Reality Stop Motion Animation (2020)
Presentation / Conference Contribution
Casas, L., Kosek, M., & Mitchell, K. (2017, March). Props Alive: A Framework for Augmented Reality Stop Motion Animation. Presented at 2017 IEEE 10th Workshop on Software Engineering and Architectures for Realtime Interactive Systems (SEARIS), Los Angeles, CA, USA

Stop motion animation evolved in the early days of cinema with the aim to create an illusion of movement with static puppets posed manually each frame. Current stop motion movies introduced 3D printing processes in order to acquire animations more ac... Read More about Props Alive: A Framework for Augmented Reality Stop Motion Animation.

Diagrammatic Representation and Inference: 11th International Conference, Diagrams 2020, Tallinn, Estonia, August 24–28, 2020, Proceedings (2020)
Presentation / Conference Contribution
(2020, August). Diagrammatic Representation and Inference: 11th International Conference, Diagrams 2020, Tallinn, Estonia, August 24–28, 2020, Proceedings. Presented at Diagrams: International Conference on Theory and Application of Diagrams, Tallinn, Estonia

This book constitutes the refereed proceedings of the 11th International Conference on the Theory and Application of Diagrams, Diagrams 2020, held in Tallinn, Estonia, in August 2020.* The 20 full papers and 16 short papers presented together with... Read More about Diagrammatic Representation and Inference: 11th International Conference, Diagrams 2020, Tallinn, Estonia, August 24–28, 2020, Proceedings.