All Outputs (242)

Quantifying the effects of increasing user choice in MAP-Elites applied to a Workforce Scheduling and Routing Problem. (2019)
Presentation / Conference Contribution
Urquhart, N., Hart, E., & Hutcheson, W. (2019, April). Quantifying the effects of increasing user choice in MAP-Elites applied to a Workforce Scheduling and Routing Problem. Presented at EvoStar2019: International Conference on the Applications of Evolutionary Computation, Leipzig

Quality-diversity algorithms such as MAP-Elites provide a means of supporting the users when finding and choosing solutions to a problem by returning a set of solutions which are diverse according to set of user-defined features. The number of soluti... Read More about Quantifying the effects of increasing user choice in MAP-Elites applied to a Workforce Scheduling and Routing Problem..

Decrypting Live SSH Traffic in Virtual Environments (2019)
Journal Article
Mclaren, P., Russell, G., Buchanan, W. J., & Tan, Z. (2019). Decrypting Live SSH Traffic in Virtual Environments. Digital Investigation, 29, 109-117. https://doi.org/10.1016/j.diin.2019.03.010

Decrypting and inspecting encrypted malicious communications may assist crime detection and prevention. Access to client or server memory enables the discovery of artefacts required for decrypting secure communications. This paper develops the MemDe-... Read More about Decrypting Live SSH Traffic in Virtual Environments.

Toward a Lightweight Intrusion Detection System for the Internet of Things (2019)
Journal Article
Jan, S. U., Ahmed, S., Shakhov, V., & Koo, I. (2019). Toward a Lightweight Intrusion Detection System for the Internet of Things. IEEE Access, 7, 42450-42471. https://doi.org/10.1109/access.2019.2907965

Integration of the Internet into the entities of the different domains of human society (such as smart homes, health care, smart grids, manufacturing processes, product supply chains, and environmental monitoring) is emerging as a new paradigm called... Read More about Toward a Lightweight Intrusion Detection System for the Internet of Things.

Memory Allocation For Seamless Media Content Presentation (2019)
Patent
Mitchell, K., Koniaris, C., & Chitalu, F. (2019). Memory Allocation For Seamless Media Content Presentation. US20190096028

A system for performing memory allocation for seamless media content presentation includes a computing platform having a CPU, a GPU having a GPU memory, and a main memory storing a memory allocation software code. The CPU executes the memory allocati... Read More about Memory Allocation For Seamless Media Content Presentation.

The efficacy of Euler diagrams and linear diagrams for visualizing set cardinality using proportions and numbers (2019)
Journal Article
Stapleton, G., Chapman, P., Rodgers, P., Touloumis, A., Blake, A., & Delaney, A. (2019). The efficacy of Euler diagrams and linear diagrams for visualizing set cardinality using proportions and numbers. PLOS ONE, 14(3), https://doi.org/10.1371/journal.pone.0211234

This paper presents the first empirical investigation that compares Euler and linear diagrams when they are used to represent set cardinality. A common approach is to use area-proportional Euler diagrams but linear diagrams can exploit length-proport... Read More about The efficacy of Euler diagrams and linear diagrams for visualizing set cardinality using proportions and numbers.

IoT Forensics: Amazon Echo as a Use Case (2019)
Journal Article
Li, S., Li, S., Choo, K. R., Sun, Q., Buchanan, W. J., & Cao, J. (2019). IoT Forensics: Amazon Echo as a Use Case. IEEE Internet of Things Journal, 1-11. https://doi.org/10.1109/jiot.2019.2906946

Internet of Things (IoT) are increasingly common in our society, and can be found in civilian settings as well as sensitive applications such as battlefields and national security. Given the potential of these devices to be targeted by attackers, the... Read More about IoT Forensics: Amazon Echo as a Use Case.

Random Occlusion Recovery for Person Re-identification (2019)
Journal Article
Wu, D., Zhang, K., Zheng, S., Hao, Y., Liu, F., Qin, X., Cheng, F., Zhao, Y., Liu, Q., Yuan, C., & Huang, D. (2019). Random Occlusion Recovery for Person Re-identification. Journal of Imaging Science and Technology, 63(3), 30405(1)-30405(9). https://doi.org/10.2352/j.imagingsci.technol.2019.63.3.030405

As a basic task of multi-camera surveillance system, person re-identification aims to re-identify a query pedestrian observed from non-overlapping multiple cameras or across different time with a single camera. Recently, deep learning-based person re... Read More about Random Occlusion Recovery for Person Re-identification.

Computational and natural language processing based studies of hadith literature: a survey (2019)
Journal Article
Azmi, A. M., Al-Qabbany, A. O., & Hussain, A. (2019). Computational and natural language processing based studies of hadith literature: a survey. Artificial Intelligence Review, 52(2), 1369-1414. https://doi.org/10.1007/s10462-019-09692-w

Hadith is one of the most celebrated resources of Classical Arabic text. The hadiths, or Prophetic traditions (tradition for short), are narrations originating from the sayings and conduct of Prophet Muhammad. For Muslims, hadiths are the second most... Read More about Computational and natural language processing based studies of hadith literature: a survey.

Designing Motion Matching for Real-World Applications: Lessons from Realistic Deployments (2019)
Presentation / Conference Contribution
Verweij, D., Esteves, A., Bakker, S., & Khan, V. (2019, March). Designing Motion Matching for Real-World Applications: Lessons from Realistic Deployments. Presented at 13th International Conference on Tangible, Embedded, and Embodied Interaction (ACM TEI), Tempe, Arizona

Amongst the variety of (multi-modal) interaction techniques that are being developed and explored, the Motion Matching paradigm provides a novel approach to selection and control. In motion matching, users interact by rhythmically moving their bodies... Read More about Designing Motion Matching for Real-World Applications: Lessons from Realistic Deployments.

Wattom: a Consumption and Grid Aware Smart Plug with Mid-air Controls (2019)
Presentation / Conference Contribution
Quintal, F., Esteves, A., Caires, F., Baptiste, V., & Mendes, P. (2019, March). Wattom: a Consumption and Grid Aware Smart Plug with Mid-air Controls. Presented at 13th International Conference on Tangible, Embedded, and Embodied Interaction (ACM TEI), Tempe, Arizona

This paper presents Wattom, a highly interactive ambient eco-feedback smart plug that aims to support a more sustainable use of electricity by being tightly coupled to users' energy-related activities. We describe three use cases of the system: using... Read More about Wattom: a Consumption and Grid Aware Smart Plug with Mid-air Controls.

Automated strategic visualisations and user confidence (2019)
Thesis
Le Bras, P. (2019). Automated strategic visualisations and user confidence. (Thesis). Heriot-Watt University. Retrieved from http://researchrepository.napier.ac.uk/Output/2884475

Data visualisations aim at providing accessible and interpretable information for people. At a strategic level, such representations can be used to stimulate decision making. We have found that users are however hesitant to exploit unfamiliar visuali... Read More about Automated strategic visualisations and user confidence.

Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations (2019)
Journal Article
Chouikhi, N., Ammar, B., Hussain, A., & Alimi, A. M. (2019). Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations. Neurocomputing, 341, 195-211. https://doi.org/10.1016/j.neucom.2019.03.012

The Multi-Layered Echo-State Network (ML-ESN) is a recently developed, highly powerful type of recurrent neural network. It has succeeded in dealing with several non-linear benchmark problems. On account of its rich dynamics, ML-ESN is exploited in t... Read More about Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations.

"So far back, I'm anonymous": Exploring Student Identity using Photovoice (2019)
Presentation / Conference Contribution
Meharg, D., Cairncross, S., & Varey, A. (2019). "So far back, I'm anonymous": Exploring Student Identity using Photovoice. In 2018 IEEE Frontiers in Education Conference (FIE). https://doi.org/10.1109/FIE.2018.8658441

This Research Full paper presents empirical work focused on the transition experiences of transfer students into computing degrees in Scotland and seeks to understand the complex formation of identity from the student perspective. Drawing on [3]'s wo... Read More about "So far back, I'm anonymous": Exploring Student Identity using Photovoice.

Exploring women's motivations to study computer science (2019)
Presentation / Conference Contribution
Smith, S., Sobolewska, E., Bhardwaj, J., & Fabian, K. (2019). Exploring women's motivations to study computer science. In Proceedings of the Frontiers in Education 2018 Conference. https://doi.org/10.1109/FIE.2018.8658768

This paper presents a study exploring women's decisions, influencers and early experiences of computing to better understand how women's motivations and prior experience affect their decision to study computer science (CS). The emergence of a gender... Read More about Exploring women's motivations to study computer science.

SmartEdge: An end-to-end encryption framework for an edge-enabled smart city application. (2019)
Journal Article
Jan, M. A., Zhang, W., Usman, M., Tan, Z., Khan, F., & Luo, E. (2019). SmartEdge: An end-to-end encryption framework for an edge-enabled smart city application. Journal of Network and Computer Applications, 137, 1-10. https://doi.org/10.1016/j.jnca.2019.02.023

The Internet of Things (IoT) has the potential to transform communities around the globe into smart cities. The massive deployment of sensor-embedded devices in the smart cities generates voluminous amounts of data that need to be stored and processe... Read More about SmartEdge: An end-to-end encryption framework for an edge-enabled smart city application..

Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks (2019)
Journal Article
Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. https://doi.org/10.1109/access.2019.2902371

High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning, a prominent method in artificial intelligence, to design an energy-preserv... Read More about Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks.

Microgrids As A Service for Rural Electrification in Sub-Saharan Africa. (2019)
Journal Article
Liu, Q., Kamoto, K. M., & Liu, X. (2020). Microgrids As A Service for Rural Electrification in Sub-Saharan Africa. Computers, Materials & Continua, 63(3), 1249-1261. https://doi.org/10.32604/cmc.2020.05598

The majority of the population on the African continent is unable to access basic electricity services, this despite the abundance of renewable energy sources (RESs). The inability to adequately tap into these RESs has led to the continued dependence... Read More about Microgrids As A Service for Rural Electrification in Sub-Saharan Africa..

A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning (2019)
Presentation / Conference Contribution
Babaagba, K. O., & Adesanya, S. O. (2019, March). A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning. Presented at ICEIT 2019: 2019 8th International Conference on Educational and Information Technology, Cambridge, UK

In this paper, the effect of feature selection in malware detection using machine learning techniques is studied. We employ supervised and unsupervised machine learning algorithms with and without feature selection. These include both classification... Read More about A Study on the Effect of Feature Selection on Malware Analysis using Machine Learning.

Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF (2019)
Journal Article
Ma, F., Gao, F., Sun, J., Zhou, H., & Hussain, A. (2019). Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF. Remote Sensing, 11(5), Article 512. https://doi.org/10.3390/rs11050512

Synthetic aperture radar (SAR) image segmentation aims at generating homogeneous regions from a pixel-based image and is the basis of image interpretation. However, most of the existing segmentation methods usually neglect the appearance and spatial... Read More about Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF.

The Moderating Effect of Education and Experience on Students’ Use of Learning Management Systems in Saudi Higher Education (2019)
Presentation / Conference Contribution
Binyamin, S., Rutter, M. J., & Smith, S. (2019). The Moderating Effect of Education and Experience on Students’ Use of Learning Management Systems in Saudi Higher Education. . https://doi.org/10.1145/3318396.3318428

Based on the technology acceptance model, this research investigated the variables that affect students’ use of LMS in Saudi public universities. The study also examined the moderating impact of education and experience on the students’ behavior towa... Read More about The Moderating Effect of Education and Experience on Students’ Use of Learning Management Systems in Saudi Higher Education.