Human-robot interaction (HRI) enables intuitive and intelligent collaboration between humans and robots in real-world environments. This paper introduces a humanoid robot prototype designed as a flexible testbed for developing and integrating artificial intelligence (AI) modules in HRI tasks. The system features a 12 degree-of-freedom (DOFs) dual-arm mechanism and a 2 DOFs head with an expressive LCD screen to express facial emotions. All hardware components are controlled by a custom-designed controller board with real-time AI processing supported by an onboard Jetson module. The system incorporates three AI modules: (1) gesture recognition using MediaPipe Pose and an LSTM classifier, (2) object detection with YOLO and 3D localization, and (3) voice-command processing through speech recognition and large language model(LLM)-based semantic parsing. The platform is validated through experiments on positioning accuracy, with results showing average manipulation errors of approximately 1.83 cm. To demonstrate its versatility, experimental results show over 90
Sydney Brenner, Nobel Laureate in Physiology, once said, “Progress in science depends on new techniques, new discoveries, and new ideas, probably in that order.” A paradigm-shifting technology of our time is artificial intelligence (AI), which offers new ways to accelerate scientific discovery. This talk will explore how AI is transforming science by automating discovery workflows—representing and leveraging scientific artefacts and knowledge, predicting properties and phenomena, optimising and generating new designs, and reasoning about competing hypotheses. We will also discuss the emerging concept of AI co-scientists: autonomous agents that emulate the scientific discovery process by combining creative exploration with systematic reasoning. These agents collectively propose hypotheses, design experiments, analyse results, and communicate findings while collaborating with human scientists. Specific examples will be drawn from molecular space exploration, drug–protein interactions, chemical synthesis, crystal structure prediction, and property prediction for advanced materials such as superconductors and alloys.
The advent of 6G networks promises unprecedented advancements in connectivity, low latency, and massive machine-type communication, setting the stage for a future where artificial intelligence (AI) will play a pivotal role in network management, automation, and optimization. However, the integration of AI into 6G networks also introduces a wide range of security challenges that need to be addressed to ensure the safe and reliable operation of these next-generation networks. This talk will explore the evolving security landscape of AI-driven 6G networks, focusing on potential threats, vulnerabilities, and mitigation strategies. Key topics include the risks of AI-powered attacks, such as adversarial machine learning, data poisoning, and model inference attacks, as well as vulnerabilities in network slicing, edge computing, and Internet of Things (IoT) devices that are expected to proliferate in 6G environments. The seminar will also discuss the importance of trust, privacy, and security in AI-based decision-making processes.
The integration of digital twins into smart manufacturing has emerged as a transformative approach, revolutionizing the way factories operate, monitor, and optimize performance. In Malaysia's push toward Industry 4.0 and its upcoming transition to Industry 5.0, digital twinenhanced communication architectures play a critical role in ensuring real-time data synchronization, predictive analytics, and intelligent automation. This paper presents a comprehensive investigation into the design, implementation, and optimization of digital twin-augmented communication infrastructures tailored to Malaysia's smart manufacturing context. Leveraging edge computing, industrial IoT, 5G, and semantic interoperability standards, we propose a multilayered communication framework that addresses latency, data reliability, and system scalability challenges. Our research combines simulation modeling, industrial testbeds, and stakeholder interviews across key industrial corridors in Selangor and Penang. Findings indicate that digital twinenabled communication systems improve real-time process visibility by 58%, reduce downtime by 35%, and enhance predictive maintenance accuracy by 62% compared to conventional setups. Through architectural modeling, latency analysis, and digital twin synchronization protocols, the study demonstrates the viability of the proposed framework in small-to-medium enterprises (SMEs) and multinational manufacturing plants alike. The results underscore the necessity of standardized digital twin interfaces (e.g., OPC UA, MQTT, and ISO 23247), edge-driven latency mitigation strategies, and cloud-twin data fusion mechanisms to ensure full-loop control and scalability. This work contributes a technically validated, policy-aligned, and future-proof architecture that supports Malaysia's Smart Manufacturing Blueprint (2021-2030) and aligns with the MyDigital roadmap.
This paper presents Parameter-Space Distillation (PSD), a dataset distillation approach that compresses datasets by embedding their knowledge into the parameters of a pretrained generative model, rather than optimizing synthetic pixels as in traditional methods. This parameter-centric design improves flexibility across distillation ratios and network architectures while enabling faster and more stable training. Instead of storing fixed synthetic images, PSD dynamically generates diverse training samples and minimizes prediction discrepancies between real and generated data across multiple architectures. Experiments on CIFAR10, SVHN ($32 \times 32$), and the higher-resolution Imagenette dataset ($128 \times 128$) show that PSD achieves higher accuracy with fewer distilled samples and generalizes better across networks. These results highlight the effectiveness of learning dataset information directly in the parameter space of generative models. Full experimental details are provided in Section 4.
