Large language models (LLMs) have demonstrated remarkable capabilities across various natural language processing tasks. This research introduces a novel “Prompting-in-a-Series” algorithm, termed psychology-informed content embeddings for personality recognition (PICEPR), featuring two pipelines: 1) contents; and 2) embeddings. The approach demonstrates how a modularised decoder-only LLM can summarize or generate content, which can aid in classifying or enhancing personality recognition functions as a personality feature extractor and a generator for personality-rich content. We conducted various experiments to provide evidence to justify the rationale behind the PICEPR algorithm. Meanwhile, we also explored closed-source models such asgpt4ofrom OpenAI andgeminifrom Google, along with open-source models such asmistralfrom Mistral AI, to compare the quality of the generated content. The PICEPR algorithm has achieved a new state-of-the-art performance for personality recognition by 5–15% improvement. The work repository and models’ weight can be found at:https://research.jingjietan.com/?q=PICEPR.

The widespread adoption of Unmanned Aerial Vehicles (UAVs) or drones has introduced significant security and privacy challenges, particularly concerning unauthorized drone activities near sensitive areas. To address these concerns, we propose a novel drone detection model, WRN-YOLO, which integrates the Wide Residual Network (WRN) architecture with the You Only Look Once (YOLO) object detection framework. This integration enhances feature extraction capabilities, leading to improved detection accuracy. Through comprehensive ablation studies, we have identified the optimal YOLO variant that synergizes with our backbone modifications, ensuring superior performance in diverse scenarios. Recognizing the complexities of real-world environments, we have also developed a synthetic dataset designed to train our WRN-YOLO. This dataset encompasses a variety of challenging conditions, including intricate backgrounds and the presence of confounding elements, to robustly assess the model's efficacy. Experimental results demonstrate that our method significantly outperforms existing models in accurately detecting drones amidst complex scenes, offering a promising solution for real-time UAV threat mitigation. The proposed approach ranked Top 3 in the 8th WOSDETC Drone-vs-Bird Detection Challenge. Our source code and synthetic dataset are publicly available at https://github.com/yjwong1999/IJCNN2025-DvB.

This study proposes a registration framework to register 2D echocardiography images with cardiac CT volume. The registration realizes the fusion of CT and echocardiography with the aim to aid the diagnosis of cardiac diseases. The image registration framework consists of two major steps: temporal and spatial registration. Temporal registration utilizes the ECG data to identify frames at similar cardiac phase as the CT volume. Spatial registration is an intensity-based normalized mutual information (NMI) method applied with pattern search optimization algorithm to produce interpolated cardiac CT image that matches the echocardiography image. Our proposed registration method has been applied on the short axis "Mercedes Benz" sign view of the aortic valve. The accuracy of our fully automated registration method were 0.81 ± 0.08 and 1.30 ± 0.13 mm in terms of Dice coefficient and Hausdorff distance. This accuracy is comparable to gold standard manual registration by expert. There was no significant difference in aortic annulus diameter measurement between the automatically and manually registered CT images. Without the use of optical tracking, we have shown the applicability of this technique for effective registration of echocardiography with cardiac CT volume.