Shahida Sulaiman
Universiti Teknologi Malaysia, Malaysia

Shahida Sulaiman (IEEE Senior Member) is an associate professor at Faculty of Computing, Universiti Teknologi Malaysia (UTM) since 2011. Formerly, she had served at Universiti Sains Malaysia (USM) for seven years. She holds a Ph.D. degree in Computer Science and M.Sc. degree in Computer Science – Real Time Software Engineering. Her main research interests include software design, knowledge management, and education informatics. She has published and co-authored numerous technical papers mainly in software engineering area, has been the editors for a number of journals, book chapters and conference proceedings, besides serving as reviewers and technical committee members. She has a good linkage with the software industry that made her receive the Industry Involvement Award 2015 at Citra Karisma, UTM. With the strong support from fellow researchers at USM and UTM, she founded Malaysian Software Engineering Interest Group (MySEIG) in 2005 that organised the 1st Malaysian Software Engineering Conference (MySEC 2005) and its series, organised the 16th Asia-Pacific Software Engineering Conference (APSEC 2009) for the first time in Malaysia and the 1st Software Engineering Postgraduates Workshop (SEPoW 2009). She was the Organising Chair of the 26th APSEC 2019 that was hosted for the second time in Malaysia. For the recognition of her community work in education informatics, she received the IEEE Malaysia Communications Society and Vehicular Technology Society Joint Chapter Award: Best Social Activity 2016 on the effort to expose mobile learning among rural learners under the Centre for Advancement in Rural Education Informatics (iCARE) in collaboration with a rural agency. She received the Outstanding Service Award 2020 for the second time of her service with UTM and Community Service Award 2021, also recognised by UTM. Based on her efforts in the community service related to science and technology, she was selected as an IEEE STEM Champion for 2022/2023. Recently, she has received the IEEE Computer Society Diversity and Inclusion (D&I) grant 2023 to promote Computer Science among rural learners.

(Online Talk)

Speech Title: Software Engineering Research and Practice: Case Study of a Rural Community Project

Abstract: Software engineering research focuses on improving the processes in software development. The practice for improvement could directly impact software developers and software engineers, while some could indirectly impact to those targeted communities such as the underserved group. Hence, it is beneficial when software engineering researchers innovate solutions for the community to measure its indirect impact. In this speech, an example of how a software engineering research outcome could be applied and extended for the benefit of a rural community will be elaborated through a case study in the educational domain. The case study is a rural community project recognised as Centre for Advancement in Rural Education Informatics (iCARE) that has more than a decade of strategic partnership between Universiti Teknologi Malaysia (UTM) as a public university and Southeast Johor Development Authority or KEJORA, one of rural agencies in Malaysia. It concludes that the research and practice in the case study could deliver positive impact to various stakeholders in the Quadruple Helix that include government authorities, academia, industry, and citizens.

Paul Craig
Xi'an Jiaotong-Liverpool University, China

Dr. Paul Craig has been a researcher working in the area of Information visualization and bioinformatics for over 15 years. He completed his PhD, studying the use of Animated Information Visualization for the Exploratory analysis of microarray time-course data, with Professor Jessie Kennedy at Edinburgh Napier University, and has since worked on research projects for the US National Science Foundation, Scottish enterprise, the European Community, the Mexican Conacyt and the Suzhou Local Government in the areas of taxonomy, bioinformatics and intelligent user interfaces. He is currently working on a various projects in the areas of information visualisation including multi-device interfaces, IV for drone navigation and mobile interfaces for antibiotic pollution detection.

(Online Talk) Speech Title: Interactive Animated Mobile Information Visualisation

Abstract: While the potential of mobile information visualization is widely recognized, there is still relatively little research in this area and few practical guidelines for the design of mobile information visualization interfaces. Indeed, there is still a general consensus in the interface design community that mobile visualization should be limited to simple operations on smaller datasets. Information visualization research has concentrated thus-far on desktop PCs and larger displays while smaller mobile device interfaces have been largely neglected. This is in spite of their increasing popularity and widespread use for other types of application. In this talk we explore these issues describing how some of the challenges of mobile information visualisation can be overcome. We describe how we have developed a number of prototypes for interactive information visualization on mobile devices, and outline a new methodology for mobile visualization interaction design using a novel mixed-fidelity prototyping approach. It is hoped that this research can inspire a better application of information visualisation on mobile devices.


Loc Nguyen, Loc Nguyen's Academic Network, Vietnam

Loc Nguyen is an independent scholar from 2017. He holds Master degree in Computer Science from University of Science, Vietnam in 2005. He holds PhD degree in Computer Science and Education at Ho Chi Minh University of Science in 2009. His PhD dissertation was honored by World Engineering Education Forum (WEEF) and awarded by Standard Scientific Research and Essays as excellent PhD dissertation in 2014. He holds Postdoctoral degree in Computer Science from 2013, certified by Institute for Systems and Technologies of Information, Control and Communication (INSTICC) by 2015. Now he is interested in poetry, computer science, statistics, mathematics, education, and medicine. He serves as reviewer, editor, and speaker in a wide range of international journals and conferences from 2014. He is volunteer of Statistics Without Borders from 2015. He was granted as Mathematician by London Mathematical Society for Postdoctoral research in Mathematics from 2016. He is awarded as Professor by Scientific Advances and Science Publishing Group from 2016. He was awarded Doctorate of Statistical Medicine by Ho Chi Minh City Society for Reproductive Medicine (HOSREM) from 2016. He was awarded and glorified as contributive scientist by International Cross-cultural Exchange and Professional Development-Thailand (ICEPD-Thailand) from 2021 and by Eudoxia Research University USA (ERU) and Eudoxia Research Centre India (ERC) from 2022. He has published 92 papers and preprints in journals, books and conference proceedings. He is author of 5 scientific books. He is author and creator of 9 scientific and technological products.

(Online Talk) Speech Title: Adversarial Variational Autoencoders to extend and improve generative model

Abstract: Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, VAE and GAN is unified into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is a good data generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.