Academic conferences serve as a useful platform for researchers and educators to share their work and ideas. Upon the paper acceptance, authors need to register their papers, pay registration fees, and present their work at conferences. International conferences often involve multiple parties of users and cross-border payments with multiple channels that incur additional service charges. Conference organisers and reviewers typically contribute voluntarily without any monetary rewards. The emergence of Web 3.0 technology, leveraging the decentralized and secure nature of blockchain, presents an opportunity for this domain. This research creates a decentralized academic conference management system using Web 3.0 (ConfMan Web 3.0) and Rust programming language that simplifies cross-border payments on different channels by using cryptocurrencies instead of fiat currency, thereby reducing service charges. It aims to provide rewards, recognition of contributions, and consolidated historical records to all conference contributors, including authors, reviewers, programme chairs, and organising committees. The Solana blockchain is used to store conference- related data, and a web application is developed for ConfMan Web 3.0. Various testings are conducted to evaluate its performance. The findings highlight the potential of Web 3.0 technology in transforming the academic conference management landscape.

Research outcomes have been reported to various industrial applications of Artificial Intelligence generated content (AIGC). This paper explores AI assisted creative production of storyboard and game characters in the game industry, by leveraging Stable Diffusion 1.5 coupled with Control Nets (SD-1.5+CN) for image generations and Generative Pretrained Transformer (GPT) of OpenAI for narrative generations. Experiment evaluations are conducted for visual appeal and narrative flows of the generated contents using SketchBoard. The results illustrate SketchBoard with weighted prompts achieving a higher Contrastive Language-Image Pre-Training (CLIP) score than the baseline models. In addition to reducing the time and effort required for manual creation, it obtains strong alignments between the generated images and provided text prompts. User study is conducted where the user feedback indicates a high level of satisfaction, with 92 % of participants expressing enj oyment and contentment with SketchBoard. Its ability to generate visually appealing storyboard frames with coherent narratives offers opportunities for efficient storyboard creation, paving the way for future advancements in automated storyboard generation.

The Non-Line-of-Sight (NLOS) phenomenon can hinder signal propagation and significantly reduce the accuracy of UWB for indoor positioning and navigation. The Channel Impulse Response (CIR) sequence generated during UWB ranging is widely used for channel identification. However, existing deep learning algorithms struggle to balance the local and global features of the CIR sequence effectively. To address this, this paper constructs a Bidirectional Encoder Representations from Transformers (BERT) channel identification model using the self-attention mechanism to improve the NLOS identification. The identification Accuracy, LOS recall, and F2 scores in multiple scenarios are 96.65%, 97.13%, and 0.9703 respectively. Comparing to state-of-art algorithms such as LS-SVM, CNN, and LSTM, our algorithm outperformed by 17.9%, 11.86%, and 10.80% respectively. For NLOS ranging errors, a fine-grained classification model is constructed with error correction model based on BERT. In multiple scenarios, the average NLOS ranging error is reduced by 41.97% and outperforms LS-SVM, CNN, and LSTM by 34.04%, 31.99%, and 16.81% respectively. In the overall positioning experiment, our algorithm achieves better performance than the existing algorithms by 32.13%.

In computing science (CS) education, software programming and software engineering subjects are parts of core CS subjects where students learn various programming languages and software development life cycle (SDLC). With the aim of equipping students with the necessary project skills and experiences essential for workplaces, Institutes of Higher Learning (IHL) have increasingly adopted the educational practice of bringing real-world team-based projects in the curriculum. Such software projects commonly support the use of Agile process practices, which place more emphasis on communications and teamwork among students in a team, stakeholders and industry customers. This could be manageable for small scale projects where student teams are in their learning journey with active and continuous communications with customers, while developing the necessary cohesion within the team to facilitate incremental deliverables of their software products. However, teamwork remains a bottleneck that is difficult to be consistently realized among members in group projects. It is not easy to motivate and measure all team members working towards the common goals or complete project tasks on time. As such, this paper proposes the Weekly Team Assessment (WTA) framework to continuously encourage teamwork throughout the software project development duration. We also propose the settings of dedicated leadership and constructive feedback iteratively in the WTA framework. The proposed methods have been applied and evaluated through the experiments in two software engineering modules, Professional Software Development and Team Project, involving 50 sophomore CS students who are divided into control group and experimental group equally. Experiment results are analyzed and discussed in this paper. Positive outcomes are observed from the experiment where about 72% of participants appreciate the proposed methods in improving their teamwork in group projects. The degree of effectiveness of the proposed methods is further evaluated from three aspects in the experiment.

One of the challenges in developing autonomous vehicles (AV) is the collection of suitable real environment data for the training and evaluation of machine learning algorithms for autonomous vehicles. Such environment data collection via various sensors mounted on AV is big data in nature which require massive time and money investment and in some specific scenarios could pose a significant danger to human lives. This necessitates the virtual scenarios simulator to simulate the real environment by generating big data images from a virtual fisheye lens that can mimic the field of view and radial distortion of commercial available camera lens of any manufacturer and model. In this paper, we proposed the novelty of developing a fisheye lens with distortion system to generate big data scenarios images to train and test imaged based sensing functions and to evaluate scenarios according to EuroNCAP standards. A total of 10,123 RGB, depth and segmentation images of varying road scenarios were generated by proposed system in approximately 14 hours as compared to existing methods of 20 hours, achieving 42.86% improvement.

This paper presents a novel tracking localization scheme that requires only a single dominant non-line-of-sight (NLOS) path and its corresponding scattering point. The position of the scattering point arising out of the dominant one bounce NLOS scattering path can be estimated using the proposed two-step elliptical Lagrange constrained optimization method if the initial position of the mobile device (MD) can be estimated or when the MD transits from line-of-sight (LOS) to a NLOS region. With the dominant NLOS path and corresponding scattering point, the subsequent trajectory of the MD can be estimated. Simulation and empirical data through channel measurement experimentation in two different multipath environments have shown that our proposed NLOS localization scheme surpassed existing NLOS localization schemes by significant margins, especially at high TOA and AOA noise due to, e.g., low bandwidth and low signal-to-noise ratio (SNR).

This paper presents a comprehensive Non Line of Sight (NLOS) localization scheme and a least square estimator that leverages on the bi-directional estimation of the Angle of Arrival (AOA) and Time of Arrival (TOA) of signals exchanged between mobile and reference devices. The proposed localization scheme requires two or more signal paths which can be either Line of Sight (LOS) signals or Non-line of Sight (NLOS) multipath signals that undergo one bound scattering. Our multipath selection scheme is shown to be able to discard multiple bound scattering paths with a high degree accuracy. We used empirical data obtained through experimentation in a real environment to analyse the performance of our proposed localization scheme, and to compare it to the existing methods. The results of this experiment show that the proposed localization scheme that just uses two signal paths, is able to outperform the existing localization schemes in both LOS and extreme NLOS situations where all reference devices are in NLOS with the mobile device. This localization approach is very useful in multipath environments where it may not always be possible to have at least three reference devices in LOS with the mobile device.