The adoption of multimedia learning in mainstream education often faces challenges, including high production costs, high technical skill requirements, and the time-consuming process of producing educational videos. In this paper, a framework is presented to simplify the production of short educational videos by using Multimodal Large Language Models (MLLMs), Retrieval-Augmented Generation (RAG), and virtual avatar synthesis. First, RAG is used to retrieve specific curriculum materials, allowing Large Language Models (LLMs) to create a video script from the knowledge database. Second, MLLMs are used to generate context-aware images from the script. Finally, a virtual avatar synthesis workflow is used to include a virtual presenter in the final video. A Human-in-the-Loop verification process is included after each step of the production process, in order to maintain editorial control and ensure pedagogical quality. Overall, this framework provides a scalable solution for producing engaging micro-learning content and also offers the potential to extend interactive virtual tutoring systems in the future.

As ultra-wideband (UWB) becomes more widely available in smartphones (e.g., over 1 billion active iPhones) and other handheld devices, new opportunities emerge for Internet of Things applications and more accurate UWB-based indoor positioning. However, existing research is limited and does not address issues such as the compatibility of UWB anchors with smartphones and the orientation dependence of UWB devices. To address these issues, this paper first presents a custom UWB anchor design that is compatible with UWB-equipped iPhones. Second, unlike traditional positioning methods that require at least three anchors in range, this paper presents positioning use cases with one and two anchors. We provide a formal proof that if one follows the direction estimated with a UWB anchor and the relative-angle error is under 60°, one still moves closer to the anchor. We also empirically demonstrate that sub-metre-level accuracy can be achieved even with two anchors. Third, the paper evaluates the accuracy of multilateration and our positioning vector framework for direction inference for different numbers of sensors in range and multilateration time intervals. This can provide a channel-impulse-response-free solution to UWB phase ambiguity in smartphones and may be especially useful for the visually impaired. The experimental results show that multilateration with higher numbers of anchors in range yields better accuracy (up to 80%). Finally, we provide comprehensive experimental results on distance and relative-angle accuracy in a large indoor environment using iPhones and DWM3001CDK-based sensors that have not been previously studied.

Embedded SD NAND flash memory is becoming a common way to store data on embedded devices. Managing reliability and optimising efficiency must happen throughout the product's life. This study proposes an integrated optimisation methodology that encompasses essential phases from production to operational use. During the production stage, we present a hierarchical AI-based method for detecting and managing defects. This method improves the reliability of factory-shipped devices by adding machine learning models to mass manufacturing tools. This process facilitates the early detection of defective blocks, pages, and columns. At runtime, we offer a lightweight, FAT-aware garbage-collection optimisation for the commonly used FAT/exFAT file systems. This approach reads the static FAT metadata layout to find and separate hot-cold data cluster chains on the controller side. This reduces write amplification caused by “hot-cold data mixing” and extends the device's lifespan. Furthermore, a dynamic ECC monitoring and error-recovery system is implemented to proactively address bit errors, thereby enhancing data reliability. The experiments' findings show that both approaches improve reliability in the initial phase and prolong lifespan during the operational phase. This framework delivers a comprehensive solution that spans from “innate quality” to “postnatal maintenance” for embedded storage systems with limited resources.

This article proposes a low-cost framework to convert traditional 2D educational videos into panoramic content to support immersive learning. Due to high production costs, long development cycles, and a limited supply of educational content, the application of immersive learning in large-scale teaching scenarios remains limited. This study explores the process of converting standard educational videos into immersive panoramic learning content, aiming to improve the temporal consistency and structural stability of the generated results. The process combines the source video, reference frames, and mask information to enable the effective reuse of existing 2D video resources. The current research mainly focuses on system architecture design, conversion pipeline implementation, and preliminary technical verification. This framework provides a practical way to transform large-scale traditional video resources into scalable immersive learning content, offering a feasible path for educational applications on spatial computing devices such as Apple Vision Pro.

UWB is becoming increasingly more available to the general public as part of consumer devices such as smartphones and smartwatches. This opens opportunities for new indoor positioning paradigms because UWB in consumer devices not only supports ranging but also AoA (Angle of Arrival) estimation, meaning dependence on additional positioning infrastructure can be reduced. Since this is a recent development, however, not much research has been conducted on evaluating UWB performance in these consumer devices and how to improve it for better indoor positioning accuracy. To contribute to this research gap, this paper is the first to propose a machine learning solution to AoA accuracy improvement in UWB-equipped iPhones when communicating with DWM3001CDK sensors while in motion. The distinguishing feature of our solution is that, unlike previous works, it uses AoA measurements for training instead of raw CIR (Channel Impulse Response) data, meaning the anchors do not need to be attached to a computer for data collection, which makes the installation of anchors more convenient. In addition, our solution combines machine learning with a collaborative approach based on our positioning vector framework, which further improves AoA error. We compiled a training dataset based on real UWB measurements collected in a large indoor environment. Extensive experiments were conducted to evaluate different machine learning models, and our results show that machine learning can improve the 90th percentile AoA error from about 60 degrees to 11 degrees and thus improve the average direction estimation accuracy to 96.85%.

Metaverse-enabled learning is an immersive educational method blending virtual elements and real-world environments. This paper adopts a 6C model-based framework to analyze how inclusiveness, interactivity, and adaptability can be improved in learning environments through metaverse-enabled learning. Vision Pro is used as the core platform of the framework to incorporate several technologies, such as eye-tracking, spatial interaction, and virtual avatars. With these advanced technologies, the framework creates an accessible and engaging learning environment. A case study of a student competition demonstrates the framework’s practical application in developing Vision Pro applications. The 6C model-based framework guides the design and enhancement of these applications to promote a transformative and inclusive metaverse-enabled educational ecosystem.

Globally, the number of falls among the elderly is rising, particularly among those 60 and older. An important contributing element to these falls is the fact that elderly people who live alone are not regularly supervised. A significant number of claims are filed for injuries caused by falls in the elderly, and at times these falls result in fatalities. Therefore, well-founded, and practical e-health technologies are critical for elder care, particularly for individuals who live alone. One of the emerging and rapid-growing technologies like Artificial Intelligence would be an excellent companion for them to continuously monitor their health condition and prevent falls. This review paper compares various research, surveys, studies, and experiments conducted on elderly fall prevention utilizing Artificial Intelligence and other technologies such as Internet of Things (IoT), Sensor, Radio Detection and Ranging (RADAR), Infrared Radiation (IR) and Hardware technologies. It has been identified that in real time and long-term monitoring without human intervention, AI-IoT technology will be the best solution for fall prevention in older adults.

This research proposes a dynamic resource allocation method for vehicle-to-everything (V2X) communications in the sixth generation (6G) cellular networks. Cellular V2X (C-V2X) communications empower advanced applications but at the same time bring unprecedented challenges in how to fully utilize the limited physical-layer resources, given the fact that most of the applications require both ultra low latency, high-data rate and high reliability. Resource allocation plays a pivotal role to satisfy such requirements as well as guarantee Quality of Service (QoS). Based on this observation, a novel fuzzy-logic-assisted $Q$ learning (FAQ) model is proposed to intelligently and dynamically allocate resources by taking advantage of the centralized allocation mode. The proposed FAQ model reuses the resources to maximize the network throughput while minimizing the interference caused by concurrent transmissions. The fuzzy-logic module expedites the learning and improves the performance of the $Q$ -learning. A mathematical model is developed to analyze the network throughput considering the interference. To evaluate the performance, a system model for V2X communications is built for urban areas, where various V2X services are deployed in the network. Simulation results show that the proposed FAQ algorithm can significantly outperform deep reinforcement learning, $Q$ -learning and other advanced allocation strategies regarding the convergence speed and the network throughput.

Computer-supported collaborative learning aims to use information technologies to support collaborative knowledge construction by practising the relevant pedagogical approaches, especially in the distance learning setting. The enabling technologies are fast advancing, and the need for solutions during the COVID-19 global pandemic led to the emergence of the Edu-Metaverse, which is conceptualised as a collection of networked virtual worlds (i.e., the Metaverse) for learning. There is a great necessity to investigate how these more recent enabling technologies can support collaborative learning. This empirical study aims to collect both quantitative and qualitative results to fill the knowledge gaps. Specifically, 20 undergraduate students (three females and 17 males) taking the Game Design and Development course voluntarily participated in this study. The participants used three representative collaboration platforms (i.e., AltSpace, Gather, and ZOOM) in our laboratory for discussing three course-specific topics, simulating the undertaking of collaborative learning tasks in the distance learning setting. The results suggest that the participants were more engaged in the learning activities using the Metaverse platforms that offer avatar-mediated communications and collaborations (i.e., AltSpace and Gather). These platforms also gave the participants a stronger sense of co-presence and belonging to the learning community. Potential improvements to the usability and the participants' feedback are also discussed in the paper. We hope the results can contribute to the fast-growing use of the Metaverse enabling technologies for educational purposes.

This article introduces the Computing for Application, Research, Entrepreneurship, and Service (CARES) model for supporting computing education and the Content, Community, Communication, and Collaboration (4C) framework for teaching effectively in a hybrid classroom.

Open educational resources (OERs) can provide useful online materials to facilitate teaching and learning. It is desirable to provide a focused list for the global computing education community. In this short paper, we present the top 10 computer science OERs based on voting, as organized by the MERLOT Computer Science Editorial Board. These OERs cover various important computing topics, including general computing topics, programming, algorithms, machine learning etc. They provide good references for instructors to complement their lectures and strengthen student computing backgrounds.

As an extension to a previous paper, a 4C model-based framework is presented in this short paper for hybrid teaching. The framework comprises three layers: model layer, platform layer and activity layer together with other supporting functions. Various methods are presented based on this framework, including 2D metaverse-based teaching, 3D metaverse-based teaching, teaching using a 360-degree camera with virtual reality support, meeting using a hybrid meeting camera and attending lectures using a telepresence robot. Furthermore, as a supporting function, online student eye movement can be analyzed using eye trackers. The aforementioned methods can provide valuable insights and also useful ideas for further research.

Despite its high ranging accuracy and secure peer-to-peer ranging, UWB (ultrawide-band) has not been as widely adopted for indoor positioning as other radio technologies, such as WiFi and BLE (Bluetooth Low Energy), due to their high availability. However, UWB chips have recently started to be embedded in consumer devices like smartphones and can often support AoA (Angle of Arrival) estimation, which can aid in the positioning process. To contribute to the emerging research on UWB performance in different chips, this paper presents experimental results on UWB distance and relative angle estimation accuracy over time in UWB-equipped iPhones and a DWM3001CDK chip. iPhones were found to generally display better ranging performance, which declined in mobile scenarios. The UWB developments open new avenues of research for UWB- based collaborative indoor positioning that can reduce dependence on infrastructure, so this paper also proposes a novel dynamic positioning vector framework for mobile UWB-equipped (ultrawide-band) devices, along with new collaborative positioning methods based on the framework. Simulations on the framework's efficacy showed that the new methods can increase positioning coverage to 80% and halve positioning error.

Searchable encryption is a technique that can support operations on encrypted data directly. However, searchable encryption is still vulnerable to attacks that exploit the leakages from encrypted query results. This paper presents an effective multi-server searchable encryption scheme to prevent volume and access pattern leakages. To hide the volume leakage of a keyword, a new index construction is proposed to compress multiple results into one index. To prevent the attacker from observing the access pattern of injected records, the update and search phases are executed in batches, such that the server can only retrieve multiple numbers of fixed volumes. To reduce the co-occurrence leakage, we propose our index distribution algorithm. Both records and queries are dispatched among cloud servers such that the attacker cannot recover the trapdoor values by only observing one cloud server. We use the minimum s−t cut algorithm to find the optimal assignment strategy that can diminish the query response time and the information disclosure at the same time. We formally analyze the security strengths and conduct evaluations. The experimental results indicate that our designs can strike a good balance between security and efficiency.

The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students’ particulars such as their academic background, current study and student activity records, their attended higher education institution’s expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students’ generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students’ activity and academic records in the case higher education institution to find out three major types of patterns between the students’ participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paper.

With rising demand for indoor location-based services (LBS) such as location-based marketing, mobile navigation, etc., there has been considerable interest in indoor positioning methods as well as their security and privacy. Current survey papers on indoor positioning methods mainly focus on positioning accuracy, whereas discussion on security and privacy considerations is limited. While there are survey papers on the security/privacy of LBS, they mainly focus on the services rather than the positioning methods. On the other hand, various survey papers on Internet of Things security/privacy mostly address device and system security. To fill the gap and complement the aforementioned survey papers, we conduct a systematic and comprehensive survey on indoor positioning security and privacy, focusing on the positioning methods. In particular, we provide the following contributions. First, based on general search (using the systematic PRISMA approach) and specific search, we study related papers published in recent years with the aim of addressing three research questions. Second, to facilitate the survey and study, we categorise the positioning methods into non-collaborative methods (i.e., proximity-based, geometric and fingerprinting methods), collaborative methods (i.e., mobile proximity-based and mobile geometric methods) and others (combining multiple technologies/methods). Third, for each method, we give an overview of the method and discuss its security and privacy issues. Last but not least, we highlight some future research directions and work on indoor positioning security and privacy. In particular, there is a need to conduct more research on collaborative positioning methods, including their security and privacy issues.

This article presents a 3E (Enrich, Extend, and Elevate) model for the effective use of open educational resources (OERs) as well as 10 selected OERs for computer science to enhance teaching/learning.

Generic competence (GC) development is an integral part of higher education to provide holistic education and enhance student career development. It also plays a critical role in complementing the curriculum. Many tertiary institutions provide various GC development activities (GCDA). Moreover, institutions strongly need to further understand student participation, especially its relationship to student backgrounds, activity profiles, and academic results. With the fast advancement of educational technologies and data mining, data analytics (DA) in formal learning and online education has been widely explored. However, there has been little work on student behavior in GCDA. To fill this gap and to provide new contributions, we conduct a comprehensive study to investigate the interrelationship of GCDA participation and academic performance before and after higher education with significant and representative data (over 10 000 records) across three years. Hypotheses are formulated and validated, and the findings are triangulated with machine learning (ML) and DA. With supervised learning, the predictors of academic performance and GCDA participation are formulated, and the features to enhance predictions are analyzed. We develop predictors using novel approaches of genetic algorithms and Stacking in ML. The impacts of the breadth and depth of involvement are also studied. Results indicate that involvement in GCDA positively impacts student academic results. Our novel approaches give improvements in predicting student participation. Our holistic studies covering hypothesis validation, data analysis, and ML provide valuable insights into GCDA development.

Indoor positioning has attracted considerable interest in both the industry and academic communities because of its wide range of applications, such as asset tracking, healthcare and context-aware services like targeted advertisements. While there are many indoor localisation methods, each has its advantages and disadvantages, taking into consideration various factors such as the effect of the indoor environment, ease of implementation, computational cost, positioning accuracy, etc. In other words, no single solution can cater for all different situations. Although many survey papers have been published on indoor positioning, new techniques and methods are proposed every year, so it is important to stay abreast of its latest developments. In addition, each survey has its own classification for indoor positioning systems without a common scheme. Inspired by the well-known OSI model and TCP/IP model, it would be desirable to develop a systematic framework for studying indoor positioning systems. In this paper, we make this new contribution by introducing a systemic survey framework based on a six-layer model to give a comprehensive survey of indoor positioning systems, namely: device layer, communication layer, network layer, data layer, method layer and application layer. Complementing the previous survey papers, this paper provides a survey of the latest research works on indoor positioning based on the six-layer model. Our emphasis is on systematic categorisation, machine learning-based enhancements, collaborative localisation and COVID-19-related applications. The six-layer model should provide a useful framework and new insights for the research community.

With the advent of generative artificial intelligence (GenAI), there is a strong need to revisit the grading or assessment mechanism. In this paper, we present a 3R framework to facilitate the grading of GenAI-based assignments. Basically, there are three essential components: Report, Revise and Reflect. Students should report on how they use GenAI tool(s). They should also revise its output by providing their own input or contributions. Last but not least, they should provide a learning reflection. We also present a 3R rubric for evaluation purposes and propose a GPT formula for determining an effective grade. For illustration purposes, we discuss two cases, covering essay assignments and programming assignments. Furthermore, to evaluate the 3R framework from the student perspective, we present and discuss student survey results. The 3R framework can provide the basis for further research study as well.