Sirui Li

Question answering over temporal knowledge graphs (TKGs) is crucial for understanding evolving facts and relationships, yet its development is hindered by limited datasets and difficulties in generating custom QA pairs. We propose a novel categorization framework based on timeline-context relationships, along with TimelineKGQA, a universal temporal QA generator applicable to any TKGs. The code is available at: https://github.com/PascalSun/TimelineKGQA as an open source Python package.

Current state-of-the-art Named Entity Recognition (NER) typically involves fine-tuning transformer-based models like BERT or RoBERTa with annotated datasets, posing challenges in annotation cost, model robustness, and data privacy. An emerging approach uses pre-trained Large Language Models (LLMs) such as ChatGPT to extract entities directly with a few or zero examples, achieving performance comparable to fine-tuned models. However, reliance on the close-source commercial LLMs raises cost and privacy concerns. In this work, we investigate open-source LLMs like Llama2 for NER on local consumer-grade GPUs, aiming to significantly reduce costs compared to cloud solutions while ensuring data security. Experimental results demonstrate competitive NER performance, achieving F1 85.37% on the CoNLL03 dataset and can also be generalised to specific domains, such as scientific texts.

Multimodal conversational agents are highly desirable because they offer natural and human-like interaction. However, there is a lack of comprehensive end-to-end solutions to support collaborative development and benchmark-ing. While proprietary systems like GPT-4o and Gemini demonstrating impressive integration of audio, video, and text with response times of 200-250ms, challenges remain in balancing latency, accuracy, cost, and data privacy. To better understand and quantify these issues, we developed OpenOmni, an open-source, end-to-end pipeline benchmarking tool that integrates advanced technologies such as Speech-to-Text, Emotion Detection, Retrieval Augmented Generation, Large Language Models , along with the ability to integrate cus-tomized models. OpenOmni supports local and cloud deployment, ensuring data privacy and supporting latency and accuracy bench-marking. This flexible framework allows researchers to customize the pipeline, focus-ing on real bottlenecks and facilitating rapid proof-of-concept development. OpenOmni can significantly enhance applications like indoor assistance for visually impaired individuals, advancing human-computer interaction. Our demonstration video is available https://www. youtube.com/watch?v=zaSiT3clWqY, demo is available via https://openomni.ai4wa. com, code is available via https://github. com/AI4WA/OpenOmniFramework.

This paper introduces a novel method for the registration of color fundus photographs, featuring a new descriptor named Haar-Optimized Local Binary Descriptor (HOLBD). HOLBD is a fast-to-compute and match descriptor, highly optimized to uniquely describe retinal bifurcation and crossover points, which are crucial landmarks for fundus image registration. It utilizes four patterns reminiscent of Haar basis functions, optimized to define these bifurcation and crossover points. These patterns perform pixel intensity tests to form a 340-bit binary vector. Before computing the HOLBD descriptor, the overall image orientation and scaling factors are estimated, and images are normalized, making HOLBD robust against rotation and scaling. Experiments were conducted on both publicly available and private retinal image registration datasets, comprising a total of 484 retinal images (i.e., 242 pairs). The proposed method was compared with state-of-the-art techniques, including Generalized Dual-Bootstrap Iterative Closest Point, Hernandez-Matas et al., Saha et al., and Chen et al.'s methods. Results show that the proposed method outperforms the best performing method. On private dataset, the proposed method achieves 1-3% higher accuracy than the best-performing method for error thresholds up to 15 pixels. It significantly outperforms other methods by 4-30% for error thresholds up to 10 pixels. On the public dataset, the proposed method marginally outperforms the best reported method. It significantly outperforms GDP ICP, Hernandez-Matas et al., and Chen et al. by a margin of 10-40%.

The transmission of African swine fever (ASF) could be influenced by temperature and rainfall, particularly through the transmission of wild boars. Australia's ASF risk assessment capabilities can be further enhanced by analyzing the impact of temperature and precipitation on ASF. As there are currently no cases of ASF in Australia, this study utilized Poland's ASF-wild boar cases between 2018 and 2021 to establish a risk assessment model for Australia. Two methods were adopted to model the risk by analyzing the correlation between the number of ASF-wild boar cases, and the temperature and rainfall. The two methods used were linear regression and fuzzy inference systems. The aim is to develop a risk assessment analysis that can estimate the seasonal risk of ASF in Australia. The results from the two models showed that there is a significant relationship between the number of cases and the changes in the temperature, but has shown no prominent association with the amount of rainfall. To the best of our knowledge, this is the first model that conducts a seasonal assessment of ASF risk in Australia. The proposed technique used in modelling the Australia’s risk assessment is leading and can handle the incompleteness of data, making this a novel approach that can be used to build models for other countries or regions and also for different infectious diseases.