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Obstacles and opportunities in implementing large-scale agile project management: Re-positioning activity theory as an analytical tool (2022)
Thesis
Chita, P. S. Obstacles and opportunities in implementing large-scale agile project management: Re-positioning activity theory as an analytical tool. (Thesis). Edinburgh Napier University. http://researchrepository.napier.ac.uk/Output/2913134

A key challenge facing organisations adopting large-scale Agile delivery methods is that of quickly and effectively learning new ways of working. This thesis posits that fundamental historical, cultural and behavioural aspects will affect the transit... Read More about Obstacles and opportunities in implementing large-scale agile project management: Re-positioning activity theory as an analytical tool.

A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars (2022)
Journal Article
Yang, Z., Wu, H., Liu, Q., Liu, X., Zhang, Y., & Cao, X. (2023). A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars. ISA Transactions, 132, 155-166. https://doi.org/10.1016/j.isatra.2022.06.046

In recent years, the number of weather-related disasters significantly increases across the world. As a typical example, short-range extreme precipitation can cause severe flooding and other secondary disasters, which therefore requires accurate pred... Read More about A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars.

CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets (2022)
Journal Article
Yang, Z., Liu, Q., Wu, H., Liu, X., & Zhang, Y. (2023). CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets. Computer Modeling in Engineering and Sciences, 135(1), 45-64. https://doi.org/10.32604/cmes.2022.022045

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain. Recent relevant research activities have shown their conc... Read More about CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets.

A Fast Inference Framework for Medical Image Semantic Segmentation Tasks Using Deep Learning Framework (2022)
Book Chapter
Yang, S., & Li, Y. (2022). A Fast Inference Framework for Medical Image Semantic Segmentation Tasks Using Deep Learning Framework. In C. H. Chen (Ed.), Computational Intelligence and Image Processing in Medical Applications (157-174). World Scientific Publishing. https://doi.org/10.1142/9789811257452_0010

Deep neural network powered semantic segmentation implementation has great advantages of providing accurate object detection using pixel-based classification; however, when this technique is applied within resource-constrained platforms, such as mobi... Read More about A Fast Inference Framework for Medical Image Semantic Segmentation Tasks Using Deep Learning Framework.

Building Towards Automated Cyberbullying Detection: A Comparative Analysis (2022)
Journal Article
Al Harigy, L. M., Al Nuaim, H. A., Moradpoor, N., & Tan, Z. (2022). Building Towards Automated Cyberbullying Detection: A Comparative Analysis. Computational Intelligence and Neuroscience, 2022, Article 4794227. https://doi.org/10.1155/2022/4794227

The increased use of social media between digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, it’s this anonymity feature which gives users freedom of speech and allows them to cond... Read More about Building Towards Automated Cyberbullying Detection: A Comparative Analysis.

A novel flow-vector generation approach for malicious traffic detection (2022)
Journal Article
Hou, J., Liu, F., Lu, H., Tan, Z., Zhuang, X., & Tian, Z. (2022). A novel flow-vector generation approach for malicious traffic detection. Journal of Parallel and Distributed Computing, 169, 72-86. https://doi.org/10.1016/j.jpdc.2022.06.004

Malicious traffic detection is one of the most important parts of cyber security. The approaches of using the flow as the detection object are recognized as effective. Benefiting from the development of deep learning techniques, raw traffic can be di... Read More about A novel flow-vector generation approach for malicious traffic detection.

A Deep Learning-Based Semantic Segmentation Architecture for Autonomous Driving Applications (2022)
Journal Article
Masood, S., Ahmed, F., Alsuhibany, S. A., Ghadi, Y. Y., Siyal, M. Y., Kumar, H., Khan, K., & Ahmad, J. (2022). A Deep Learning-Based Semantic Segmentation Architecture for Autonomous Driving Applications. Wireless Communications and Mobile Computing, 2022, Article 8684138. https://doi.org/10.1155/2022/8684138

In recent years, the development of smart transportation has accelerated research on semantic segmentation as it is one of the most important problems in this area. A large receptive field has always been the center of focus when designing convolutio... Read More about A Deep Learning-Based Semantic Segmentation Architecture for Autonomous Driving Applications.

Near-Data Prediction Based Speculative Optimization in a Distribution Environment (2022)
Journal Article
Liu, Q., Wu, X., Liu, X., Zhang, Y., & Hu, Y. (2022). Near-Data Prediction Based Speculative Optimization in a Distribution Environment. Mobile Networks and Applications, 27(6), 2339-2347. https://doi.org/10.1007/s11036-021-01793-7

Hadoop is an open source from Apache with a distributed file system and MapReduce distributed computing framework. The current Apache 2.0 license agreement supports on-demand payment by consumers for cloud platform services, helping users leverage th... Read More about Near-Data Prediction Based Speculative Optimization in a Distribution Environment.

An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation (2022)
Journal Article
Rashid, J., Kanwal, S., Wasif Nisar, M., Kim, J., & Hussain, A. (2023). An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation. Computer Systems Science and Engineering, 44(2), 1309-1324. https://doi.org/10.32604/csse.2023.026018

In project management, effective cost estimation is one of the most crucial activities to efficiently manage resources by predicting the required cost to fulfill a given task. However, finding the best estimation results in software development is ch... Read More about An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation.

Modified SHARK Cipher and Duffing Map-Based Cryptosystem (2022)
Journal Article
Rabie, O., Ahmad, J., & Alghazzawi, D. (2022). Modified SHARK Cipher and Duffing Map-Based Cryptosystem. Mathematics, 10(12), Article 2034. https://doi.org/10.3390/math10122034

Recent years have seen a lot of interest in the study of chaotic structures and their accompanying cryptography frameworks. In this research, we came up with a new way to encrypt images that used the chaos and a modified block cipher named the SHARK... Read More about Modified SHARK Cipher and Duffing Map-Based Cryptosystem.

Ahead-Of-Real-Time (ART): A Methodology for Static Reduction of Worst-Case Execution Time (2022)
Presentation / Conference Contribution
Cattaneo, D., Magnani, G., Cherubin, S., & Agosta, G. (2022, June). Ahead-Of-Real-Time (ART): A Methodology for Static Reduction of Worst-Case Execution Time. Presented at NG-RES workshop, Budapest

Precision tuning is an approximate computing technique for trading precision with lower execution time, and it has been increasingly important in embedded and high-performance computing applications. In particular, embedded applications benefit from... Read More about Ahead-Of-Real-Time (ART): A Methodology for Static Reduction of Worst-Case Execution Time.

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review (2022)
Journal Article
Gulzar Ahmad, S., Iqbal, T., Javaid, A., Ullah Munir, E., Kirn, N., Jan, S. U., & Ramzan, N. (2022). Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review. Sensors, 22(12), Article 4362. https://doi.org/10.3390/s22124362

Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential... Read More about Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review.

13th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 11th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2022) (2022)
Presentation / Conference Contribution
Palumbo, F., Bispo, J., & Cherubin, S. (2022, June). 13th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 11th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2022). Presented at PARMA-DITAM workshop, Budapest, Hungary

Cyber Threats in the Healthcare Sector and Countermeasures (2022)
Book Chapter
Ahmed, M. M., Maglaras, L., & Ferrag, M. A. (2022). Cyber Threats in the Healthcare Sector and Countermeasures. In M. Khosrow-Pour (Ed.), Research Anthology on Securing Medical Systems and Records. IGI Global. https://doi.org/10.4018/978-1-6684-6311-6.ch001

Healthcare is one of the most targeted industries by cybercriminals. The healthcare sector is far behind in cybersecurity as compared to other organizations. The vulnerabilities in the system open the door for cybercriminals to exploit it and get una... Read More about Cyber Threats in the Healthcare Sector and Countermeasures.

Multi3Generation: Multi-task, Multilingual, Multi-Modal Language Generation (2022)
Presentation / Conference Contribution
Barreiro, A., de Souza, J. G., Gatt, A., Bhatt, M., Lloret, E., Erdem, A., Gkatzia, D., Moniz, H., Russo, I., Kepler, F., Calixto, I., Paprzycki, M., Portet, F., Augenstein, I., & Alhasani, M. (2022, June). Multi3Generation: Multi-task, Multilingual, Multi-Modal Language Generation. Poster presented at 23rd Annual Conference of the European Association for Machine Translation (EAMT 2022), Ghent, Belgium

This paper presents the Multitask, Multilingual, Multimodal Language Generation COST Action – Multi3Generation (CA18231), an interdisciplinary network of research groups working on different aspects of language generation. This "metapaper" will serve... Read More about Multi3Generation: Multi-task, Multilingual, Multi-Modal Language Generation.

E-Government Information Search by English-as-a Second Language Speakers: The Effects of Language Proficiency and Document Reading Level   (2022)
Journal Article
Harvey, M., & Brazier, D. (2022). E-Government Information Search by English-as-a Second Language Speakers: The Effects of Language Proficiency and Document Reading Level  . Information Processing and Management, 59(4), Article 102985. https://doi.org/10.1016/j.ipm.2022.102985

A rapid increase in the use of web-based technologies - and corresponding changes in government and local council policies - in recent years, means that many vital services are now provided solely online. While this has many potential benefits, it ca... Read More about E-Government Information Search by English-as-a Second Language Speakers: The Effects of Language Proficiency and Document Reading Level  .

Intelligent Question Answering System Based on Knowledge Graph (2022)
Presentation / Conference Contribution
Feng, X., Liu, Q., & Liu, X. (2021, December). Intelligent Question Answering System Based on Knowledge Graph. Presented at IEEE SmartCity-2021, Hainan, China

In order to build a smart city and pursue more efficient city management, various industries have introduced intelligent question answering into process management. The intelligent question answering system based on the knowledge graph is dedicated t... Read More about Intelligent Question Answering System Based on Knowledge Graph.

Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition (2022)
Journal Article
Xu, H., Jin, X., Wang, Q., Hussain, A., & Huang, K. (2022). Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition. ACM transactions on multimedia computing communications and applications, 18(2S), Article 119. https://doi.org/10.1145/3538749

Currently, many action recognition methods mostly consider the information from spatial streams. We propose a new perspective inspired by the human visual system to combine both spatial and temporal streams to measure their attention consistency. Spe... Read More about Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition.

Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: Observational Study (2022)
Journal Article
Hussain, Z., Sheikh, Z., Tahir, A., Dashtipour, K., Gogate, M., Sheikh, A., & Hussain, A. (2022). Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: Observational Study. JMIR Public Health and Surveillance, 8(5), Article e32543. https://doi.org/10.2196/32543

Background: The roll-out of vaccines for SARS-CoV-2 in the United Kingdom, started in December 2020. Uptake has been high, and there has been a subsequent reduction in infections, hospitalisations and deaths in vaccinated individuals. However, vacci... Read More about Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: Observational Study.