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All Outputs (125)

Evaluating the performance resilience of serverless applications using chaos engineering (2024)
Conference Proceeding
Zayed, A., & Al-Said Ahmad, A. (in press). Evaluating the performance resilience of serverless applications using chaos engineering.

This study explores the use of chaos engineering in evaluating the performance and resilience of serverless applications, which are built as complex distributed systems subject to different types of failures and errors. By intentionally injecting con... Read More about Evaluating the performance resilience of serverless applications using chaos engineering.

VidSearch: Privacy-by-Design Video Search and Retrieval System for Large-Scale CCTV Data (2023)
Conference Proceeding
Tahir, M., Qiao, Y., Kanwal, N., Lee, B., & Asghar, M. N. (2023). VidSearch: Privacy-by-Design Video Search and Retrieval System for Large-Scale CCTV Data. . https://doi.org/10.1109/icmla58977.2023.00329

The surge in surveillance camera deployment in the era of Big Data and the Internet of Things (IoT) has emphasized the paramount importance of safeguarding the privacy of individuals, objects, and locations they record. Therefore, this paper proposes... Read More about VidSearch: Privacy-by-Design Video Search and Retrieval System for Large-Scale CCTV Data.

The Content Quality of Crowdsourced Knowledge on Stack Overflow- A Systematic Mapping Study (2023)
Conference Proceeding
Shahrour, G., Quincey, E. D., & Lal, S. (2023). The Content Quality of Crowdsourced Knowledge on Stack Overflow- A Systematic Mapping Study. . https://doi.org/10.1145/3625007.3627729

Community Question Answering (CQA) forums such as Stack Overflow (SO) are a form of crowdsourced knowledge for software engineers who seek solutions to development and programming challenges. While such a forum provides valuable support to engineers,... Read More about The Content Quality of Crowdsourced Knowledge on Stack Overflow- A Systematic Mapping Study.

Deep Neural Networks Based Multiclass Animal Detection and Classification in Drone Imagery (2023)
Conference Proceeding
Chen, C., Edirisinghe, E., Leonce, A., Simkins, G., Khafaga, T., Sher Shah, M., & Yahya, U. (2023). Deep Neural Networks Based Multiclass Animal Detection and Classification in Drone Imagery. In 2023 International Symposium on Networks, Computers and Communications (ISNCC). https://doi.org/10.1109/isncc58260.2023.10323685

There is a growing interest among the research community in the search for possible technology-driven strategies for the conservation of the much-needed, historically rich and culturally important, desert life. In this work, we investigate the use of... Read More about Deep Neural Networks Based Multiclass Animal Detection and Classification in Drone Imagery.

Deep Neural Network Based Automatic Litter Detection in Desert Areas Using Unmanned Aerial Vehicle Imagery (2023)
Conference Proceeding
Wang, G., Leonce, A., Hacid, H., & Edirisinghe, E. (2023). Deep Neural Network Based Automatic Litter Detection in Desert Areas Using Unmanned Aerial Vehicle Imagery. In 2023 International Symposium on Networks, Computers and Communications (ISNCC). https://doi.org/10.1109/isncc58260.2023.10323960

The United Arab Emirates (UAE) values its relationship with the desert, considering it a crucial part of its heritage and culture. However, the desert faces environmental challenges due to the improper disposal of garbage by visitors and the dumping... Read More about Deep Neural Network Based Automatic Litter Detection in Desert Areas Using Unmanned Aerial Vehicle Imagery.

Virtual and Augmented Reality Interfaces for 3D Mesopotamian Environments and Artefacts – A Survey (2023)
Conference Proceeding
Rhodes, R., & Woolley, S. (2023). Virtual and Augmented Reality Interfaces for 3D Mesopotamian Environments and Artefacts – A Survey. . https://doi.org/10.14236/ewic/BCSHCI2023.8

This paper surveys twenty years of published works and implementations of virtual reality (VR), augmented reality (AR) and 3D repositories relevant to ancient Mesopotamia. Results are sorted according to type, relevance to cuneiform, evaluation, an... Read More about Virtual and Augmented Reality Interfaces for 3D Mesopotamian Environments and Artefacts – A Survey.

Quantifying Device Usefulness - How Useful is an Obsolete Device? (2023)
Conference Proceeding
Goodwin, C., Woolley, S., de Quincey, E., & Collins, T. (2023). Quantifying Device Usefulness - How Useful is an Obsolete Device?. In Human-Computer Interaction – INTERACT 2023 (90-99). https://doi.org/10.1007/978-3-031-42293-5_8

Obsolete devices add to the rising levels of electronic waste, a major environmental concern, and a contributing factor to climate change. In recent years, device manufacturers have established environmental commitments and launched initiatives such... Read More about Quantifying Device Usefulness - How Useful is an Obsolete Device?.

Visual Attention Assisted Games (2023)
Conference Proceeding
Mandal, B., Puhan, N. B., & Homi Anil, V. (2023). Visual Attention Assisted Games. In 2023 IEEE Conference on Games (CoG). https://doi.org/10.1109/cog57401.2023.10333186

In this work, we propose a committee of attention models developed for improving the deep reinforcement learning frequently used for games. The game environment is manifested with spatial and temporal attention mechanisms so as to focus on important... Read More about Visual Attention Assisted Games.

Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction (2023)
Conference Proceeding
Goparaju, S. U., Biju, R., M, P., MC, B., Gangadharan, D., Mandal, B., & C, P. (2023). Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction. . https://doi.org/10.1109/vtc2023-spring57618.2023.10200600

Traffic flow prediction has been regarded as a critical problem in intelligent transportation systems. An accurate prediction can help mitigate congestion and other societal problems while facilitating safer, cost and time-efficient travel. However,... Read More about Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction.

A Restricted Parametrized Model for Interval-Valued Regression (2023)
Conference Proceeding
Ying, J., Kabir, S., & Wagner, C. (2023). A Restricted Parametrized Model for Interval-Valued Regression. . https://doi.org/10.1109/fuzz52849.2023.10309686

This paper explores the parameter generation of the existing ‘Parametrized Model’ (PM) as the state-of-the-art linear interval-valued regression model, highlighting that its strong performance may arise from unexpected behavior. Focusing on the appro... Read More about A Restricted Parametrized Model for Interval-Valued Regression.

Asymptotic Theory of Generalised Rayleigh Beams and the Dynamic Coupling (2023)
Conference Proceeding
Nieves, M. J., & Movchan, A. B. (2023). Asymptotic Theory of Generalised Rayleigh Beams and the Dynamic Coupling. . https://doi.org/10.1007/978-3-031-24141-3_11

We consider the effective dynamic response of an infinite asymmetric structure formed from a beam attached to a periodic array of resonators. The array couples the axial and flexural motions of the beam. We develop a point-wise description of the res... Read More about Asymptotic Theory of Generalised Rayleigh Beams and the Dynamic Coupling.

Industrial Internet of Things Security Modelling using Ontological Methods (2023)
Conference Proceeding
Aslam Jarwar, M., Watson, J., Ani, U., & Chalmers, S. (2023). Industrial Internet of Things Security Modelling using Ontological Methods. In IoT '22: Proceedings of the 12th International Conference on the Internet of Things (163 - 170). https://doi.org/10.1145/3567445.3571103

The Industrial Internet of Things (IIoT) trend presents many significant benefits for improving industrial operations. However, its emergence from the convergence of legacy Industrial Control Systems (ICS) and Information and Communication Technologi... Read More about Industrial Internet of Things Security Modelling using Ontological Methods.

Deep Neural Network Based Attention Model for Structural Component Recognition (2023)
Conference Proceeding
Sarangi, S., & Mandal, B. (2023). Deep Neural Network Based Attention Model for Structural Component Recognition. . https://doi.org/10.5220/0011688400003417

The recognition of structural components from images/videos is a highly complex task because of the appearance of huge components and their extended existence alongside, which are relatively small components. The latter is frequently overestimated or... Read More about Deep Neural Network Based Attention Model for Structural Component Recognition.

Visualization of Interval Regression for Facilitating Data and Model Insight (2022)
Conference Proceeding
Kabir, S., & Wagner, C. (2022). Visualization of Interval Regression for Facilitating Data and Model Insight. . https://doi.org/10.1109/fuzz-ieee55066.2022.9882717

With growing significance of interval-valued data, interest in artificial intelligence methods tailored to this data type is similarly increasing across a range of application domains. Here, regression, i.e., the modelling of the association between... Read More about Visualization of Interval Regression for Facilitating Data and Model Insight.

A Performance Evaluation Approach for n-tier Cloud-Based Software Services (2022)
Conference Proceeding
Al-Said Ahmad, A., & Alzboon, G. (2022). A Performance Evaluation Approach for n-tier Cloud-Based Software Services. . https://doi.org/10.1145/3555962.3555968

Cloud computing and cloud testing are vast fields that have attracted significant attention recently. In addition, the need to find an approach for measuring cloud-based applications' effectiveness has also increased. In this work, we introduced an a... Read More about A Performance Evaluation Approach for n-tier Cloud-Based Software Services.

Connected Virtual Experiences for Small and Less Visible Museum Artefacts (2022)
Conference Proceeding
Rhodes, R., & Woolley, S. (2022). Connected Virtual Experiences for Small and Less Visible Museum Artefacts. . https://doi.org/10.14236/ewic/hci2022.60

This paper summarises a programme of research motivated by the challenge of achieving engaging 3D virtual experiences for small heritage artefacts, the sorts of artefacts that mare difficult to display and may be easily overlooked in museum settings.... Read More about Connected Virtual Experiences for Small and Less Visible Museum Artefacts.